The adverse effects of climate change on human health are unfolding in real time. Environmental fragmentation is amplifying spillover of viruses from wildlife to humans. Increasing temperatures are expanding mosquito and tick habitats, introducing vector-borne viruses into immunologically susceptible populations. More frequent flooding is spreading water-borne viral pathogens, while prolonged droughts reduce regional capacity to prevent and respond to disease outbreaks with adequate water, sanitation, and hygiene resources. Worsening air quality and altered transmission seasons due to an increasingly volatile climate may exacerbate the impacts of respiratory viruses. Furthermore, both extreme weather events and long-term climate variation are causing the destruction of health systems and large-scale migrations, reshaping health care delivery in the face of an evolving global burden of viral disease. Because of their immunological immaturity, differences in physiology (e.g., size), dependence on caregivers, and behavioral traits, children are particularly vulnerable to climate change. This investigation into the unique pediatric viral threats posed by an increasingly inhospitable world elucidates potential avenues of targeted programming and uncovers future research questions to effect equitable, actionable change. IMPACT: A review of the effects of climate change on viral threats to pediatric health, including zoonotic, vector-borne, water-borne, and respiratory viruses, as well as distal threats related to climate-induced migration and health systems. A unique focus on viruses offers a more in-depth look at the effect of climate change on vector competence, viral particle survival, co-morbidities, and host behavior. An examination of children as a particularly vulnerable population provokes programming tailored to their unique set of vulnerabilities and encourages reflection on equitable climate adaptation frameworks.
The twenty-first century has been marked by a surge in viral epidemics and pandemics, highlighting the global health challenge posed by emerging and re-emerging pediatric viral diseases. This review article explores the complex dynamics contributing to this challenge, including climate change, globalization, socio-economic interconnectedness, geopolitical tensions, vaccine hesitancy, misinformation, and disparities in access to healthcare resources. Understanding the interactions between the environment, socioeconomics, and health is crucial for effectively addressing current and future outbreaks. This scoping review focuses on emerging and re-emerging viral infectious diseases, with an emphasis on pediatric vulnerability. It highlights the urgent need for prevention, preparedness, and response efforts, particularly in resource-limited communities disproportionately affected by climate change and spillover events. Adopting a One Health/Planetary Health approach, which integrates human, animal, and ecosystem health, can enhance equity and resilience in global communities. IMPACT: We provide a scoping review of emerging and re-emerging viral threats to global pediatric populations This review provides an update on current pediatric viral threats in the context of the COVID-19 pandemic This review aims to sensitize clinicians, epidemiologists, public health practitioners, and policy stakeholders/decision-makers to the role these viral diseases have in persistent pediatric morbidity and mortality.
Globally, wildfires have seen remarkable increase in duration and size and have become a health hazard. In addition to vegetation and habitat destruction, rapid release of smoke, dust and gaseous pollutants in the atmosphere contributes to its short and long-term detrimental effects. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has emerged as a public health concern worldwide that primarily target lungs and respiratory tract, akin to air pollutants. Studies from our lab and others have demonstrated association between air pollution and COVID-19 infection and mortality rates. However, current knowledge on the impact of wildfire-mediated sudden outburst of air pollutants on COVID-19 is limited. In this study, we examined the association of air pollutants and COVID-19 during wildfires burned during August-October 2020 in California, United States. We observed an increase in the tropospheric pollutants including aerosols (particulate matter [PM]), carbon monoxide (CO) and nitrogen dioxide (NO(2)) by approximately 150%, 100% and 20%, respectively, in 2020 compared to the 2019. Except ozone (O(3)), similar proportion of increment was noticed during the peak wildfire period (August 16 – September 15, 2020) in the ground PM(2.5), CO, and NO(2) levels at Fresno, Los Angeles, Sacramento, San Diego and San Francisco, cities with largest active wildfire area. We identified three different spikes in the concentrations of PM(2.5), and CO for the cities examined clearly suggesting wildfire-induced surge in air pollution. Fresno and Sacramento showed increment in the ground PM(2.5), CO and NO(2) levels, while San Diego recorded highest change rate in NO(2) levels. Interestingly, we observed a similar pattern of higher COVID-19 cases and mortalities in the cities with adverse air pollution caused by wildfires. These findings provide a logical rationale to strategize public health policies for future impact of COVID-19 on humans residing in geographic locations susceptible to sudden increase in local air pollution.
The climatological parameters can be different in various geographical locations. Moreover, they have possible impacts on COVID-19 incidence. Therefore, the purpose of this systematic review article was to describe the effects of climatic variables on COVID-19 pandemic in different countries. Systematic literature search was performed in Scopus, ISI Web of Science, and PubMed databases using (“Climate” OR “Climate Change” OR “Global Warming” OR “Global Climate Change” OR “Meteorological Parameters” OR “Temperature” OR “Precipitation” OR “Relative Humidity” OR “Wind Speed” OR “Sunshine” OR “Climate Extremes” OR “Weather Extremes”) AND (“COVID” OR “Coronavirus disease 2019” OR “COVID-19” OR “SARS-CoV-2” OR “Novel Coronavirus”) keywords. From 5229 articles, 424 were screened and 149 were selected for further analysis. The relationship between meteorological parameters is variable in different geographical locations. The results indicate that among the climatic indicators, the temperature is the most significant factor that influences on COVID-19 pandemic in most countries. Some studies were proved that warm and wet climates can decrease COVID-19 incidence; however, the other studies represented that warm location can be a high risk of COVID-19 incidence. It could be suggested that all climate variables such as temperature, humidity, rainfall, precipitation, solar radiation, ultraviolet index, and wind speed could cause spread of COVID-19. Thus, it is recommended that future studies will survey the role of all meteorological variables and interaction between them on COVID-19 spread in specific small areas such as cities of each country and comparison between them.
Viral respiratory infections (VRIs) cause seasonal epidemics and pandemics, with their transmission influenced by climate conditions. Despite the risks posed by novel VRIs, the relationships between climate change and VRIs remain poorly understood. In this review, we synthesized existing literature to explore the connections between changes in meteorological conditions, extreme weather events, long-term climate warming, and seasonal outbreaks, epidemics, and pandemics of VRIs from an interdisciplinary perspective. We proposed a comprehensive conceptual framework highlighting the potential biological, socioeconomic, and ecological mechanisms underlying the impact of climate change on VRIs. Our findings suggested that climate change increases the risk of VRI emergence and transmission by affecting the biology of viruses, host susceptibility, human behavior, and environmental conditions of both society and ecosystems. Further interdisciplinary research is needed to address the dual challenge of climate change and pandemics.
An important question in the context of compound disasters is the degree to which geophysical disasters amplify the transmission of infectious diseases during pandemics and how this relation-ship is influenced by the social vulnerability of affected populations. This article proposes a spatiotemporal modeling approach to understand spatially varying social, demographic and health drivers of vulnerability during pandemics co-occurring with geophysical hazards. A multilevel mixed-effects model is developed to investigate the dynamic association between census tract -level Covid-19 case count trajectories co-occurring with a hurricane and demographic, socioeconomic and health factors. A state-level analysis is conducted to identify the distinct geographical regions in which significant changes are seen in the infection count trends due to the hurricane. A subsequent region-level analysis is performed to describe, at a higher spatial resolution, the im-pact of social vulnerability on the infection count trajectories at a community level. The method provides an approach to systematically study the effects of compound hazards and distinct pat-terns of infectious disease spread during hurricanes by quantifying (1) dynamic associations between infection counts and social factors and (2) spatial heterogeneities of these associations between communities. A case study for modeling the spatiotemporal variation of social vulnerability with data from Covid-19 pandemic and Hurricane Sally in Florida is presented to illustrate the application of the approach.
Wildfires are a significant cause of exposure to ambient air pollution in the United States and other settings. Although indoor air pollution is a known contributor to tuberculosis reactivation and progression, it is unclear whether ambient pollution exposures, including wildfire smoke, similarly increase risk. Objectives: To determine whether tuberculosis diagnosis was associated with recent exposure to acute outdoor air pollution events, including those caused by wildfire smoke. Methods: We conducted a case-crossover analysis of 6,238 patients aged ⩾15 years diagnosed with active tuberculosis disease between 2014 and 2019 in 8 California counties. Using geocoded address data, we characterized individuals’ daily exposure to <2.5 μm-diameter particulate matter (PM(2.5)) during counterfactual risk periods 3-6 months before tuberculosis diagnosis (hazard period) and the same time 1 year previously (control period). We compared the frequency of residential PM(2.5) exposures exceeding 35 μg/m(3) (PM(2.5) events) overall and for wildfire-associated and nonwildfire events during individuals' hazard and control periods. Measurements and Main Results: In total, 3,139 patients experienced 1 or more PM(2.5) events during the hazard period, including 671 experiencing 1 or more wildfire-associated events. Adjusted odds of tuberculosis diagnosis increased by 5% (95% confidence interval, 3-6%) with each PM(2.5) event experienced over the 6-month observation period. Each wildfire-associated PM(2.5) event was associated with 23% (19-28%) higher odds of tuberculosis diagnosis in this time window, whereas no association was apparent for nonwildfire-associated events. Conclusions: Residential exposure to wildfire-associated ambient air pollution is associated with an increased risk of active tuberculosis diagnosis.
The atmosphere is a major route for microbial intercontinental dispersal, including harmful microorganisms, antibiotic resistance genes, and allergens, with strong implications in ecosystem functioning and global health. Long-distance dispersal is facilitated by air movement at higher altitudes in the free troposphere and is affected by anthropogenic forcing, climate change, and by the general atmospheric circulation, mainly in the intertropical convergence zone. The survival of microorganisms during atmospheric transport and their remote invasive potential are fundamental questions, but data are scarce. Extreme atmospheric conditions represent a challenge to survival that requires specific adaptive strategies, and recovery of air samples from the high altitudes relevant to study harmful microorganisms can be challenging. In this paper, we highlight the scope of the problem, identify challenges and knowledge gaps, and offer a roadmap for improved understanding of intercontinental microbial dispersal and their outcomes. Greater understanding of long-distance dispersal requires research focus on local factors that affect emissions, coupled with conditions influencing transport and survival at high altitudes, and eventual deposition at sink locations.
BACKGROUND: Legionnaires’ disease is caused by inhalation or aspiration of small water droplets contaminated with Legionella, commonly found in natural and man-made water systems and in moist soil. Over the past 5 years, notification rates of this disease have almost doubled in the European Union (EU) / European Environmental Agency (EEA), from 1.4 in 2015 to 2.2 cases per 100,000 population in 2019. Some studies show that the greater presence of the microorganism in the water network and the increase in cases of legionellosis could be related to the variations in some environmental factors, such as air temperature, which may influence the water temperature. STUDY DESIGN: Climate change is currently a prominent topic worldwide because of its significant impact on the natural environment. It is responsible for the increase in numerous waterborne pathologies. The purpose of this study was to correlate the air temperature recorded in Apulia region from January 2018 to April 2023 with the presence of Legionella in the water networks of public and private facilities and the incidence rates of legionellosis during the same period. METHODS: During the period from January 2018 to April 2023, water samples were collected from facilities involved in legionellosis cases and analyzed for Legionella. During the same period, all the cases notified to the regional epidemiological observatory (OER-Apulia) were included in this study. Statistical analyses were conducted using the Shapiro-Wilk test to determine whether the Legionella load was distributed normally, the Wilcoxon rank sum test to compare the air temperatures (average and range) of the negative and positive samples for Legionella detection, and the multivariate analysis (Poisson regression) to compare the Legionella load with the water sample temperature, average air temperature, and temperature range on the day of sampling. The Wilcoxon test for paired samples was used to compare legionellosis cases between the warmer and colder months. RESULTS: Overall, 13,044 water samples were analyzed for Legionella and 460 cases of legionellosis were notified. Legionella was isolated in 20.1% of the samples examined. The difference in the air temperature between negative samples and positive samples was statistically significant (p-value < 0.0001): on days when water samples tested positive for Legionella a higher temperature range was observed than on days when water samples tested negative (p-value = 0.004). Poisson regression showed a direct correlation between Legionella load, water temperature, and average air temperature. The incidence of legionellosis cases in warmer months was higher than in colder months (p-value = 0.03). CONCLUSIONS: Our study highlights a significant increase in the load of Legionella in the Apulian water network, and an association between warmer temperatures and legionellosis incidence. In our opinion, further investigations are needed in different contexts and territories to characterize the epidemiology of legionellosis, and to explain its extreme variability in different geographical areas and how these data may be influenced by different risk factors.
Hand, foot, and mouth disease (HFMD) is the leading Category C infectious disease affecting millions of children in China every year. In the context of global climate change, the understanding and quantification of the impact of weather factors on human health are particularly critical to the development and implementation of climate change adaptation and mitigation strategies. The aim of this study was to quantify the attributable burden of a combined bioclimatic indicator (apparent temperature) on HFMD and to identify temperature-specific sensitive populations. A total of 123,622 HFMD cases were included in the study. The non-linear relationship between apparent temperature and the incidence of HFMD was approximately M-shaped, with hot weather being more likely to be attributable than cold conditions, of which moderately hot accounting for the majority of cases (21,441, 17.34%). Taking the median apparent temperature (19.2 °C) as reference, the cold effect showed a short acute effect with the highest risk on the day of lag 0 (RR = 1.086, 95% CI: 1.024 ~ 1.152), whereas the hot effect lasted longer with the greatest risk at a lag of 7 days (RR = 1.081, 95% CI: 1.059 ~ 1.104). Subgroup analysis revealed that males, children under 3 years old, and scattered children tended to be more vulnerable to HFMD in hot weather, while females, those aged 3 ~ 5 years, and nursery children were sensitive to cold conditions. This study suggests that high temperatures have a greater impact on HFMD than low temperatures as well as lasting longer, of particular concern being moderately high temperatures rather than extreme temperatures. Early intervention takes on greater importance during cold days, while the duration of HFMD intervention must be longer during hot days.
INTRODUCTION: The associations between non-optimal ambient temperature, air pollution and SARS-CoV-2 infection and post COVID-19 condition (PCC) remain constrained in current understanding. We conducted a retrospective analysis to explore how ambient temperature affected SARS-CoV-2 infection in individuals who later developed PCC compared to those who did not. We investigated if these associations were modified by air pollution. METHODS: We conducted a bidirectional time-stratified case-crossover study among individuals who tested positive for SARS-CoV-2 between May 2021 and June 2022. We included 6302 infections, with 2850 PCC cases. We used conditional logistic regression and distributed lag non-linear models to obtain odds ratios (OR) and 95% confidence intervals (CI) for non-optimal temperatures relative to the period median temperature (10.6 °C) on lags 0 to 5. For effect modification, daily average PM(2.5) concentrations were categorized using the period median concentration (8.8 μg/m(3)). Z-tests were used to compare the results by PCC status and PM(2.5). RESULTS: Non-optimal cold temperatures increased the cumulative odds of infection (OR = 1.93; 95%CI:1.67-2.23, OR = 3.53; 95%CI:2.72-4.58, for moderate and extreme cold, respectively), with the strongest associations observed for non-PCC cases. Non-optimal heat temperatures decreased the odds of infection except for moderate heat among PCC cases (OR = 1.32; 95%CI:0.89-1.96). When PM(2.5) was >8.8 μg/m(3), the associations with cold were stronger, and moderate heat doubled the odds of infection with later development of PCC (OR = 2.18; 95%CI:1.01-4.69). When PM(2.5) was ≤8.8 μg/m(3), exposure to non-optimal temperatures reduced the odds of infection. CONCLUSION: Exposure to cold increases SARS-CoV2 risk, especially on days with moderate to high air pollution. Heated temperatures and moderate to high air pollution during infection may cause PCC. These findings stress the need for mitigation and adaptation strategies for climate change to reduce increasing trends in the frequency of weather extremes that have consequences on air pollution concentrations.
The global outbreak of COVID-19 has emerged as one of the most devastating and challenging threats to humanity. As many frontline workers are fighting against this disease, researchers are struggling to obtain a better understanding of the pathways and challenges of this pandemic. This paper evaluates the concept that the transmission of COVID-19 is intrinsically linked to temperature. Some complex nonlinear functional forms, such as the cubic function, are introduced to the empirical models to understand the interaction between temperature and the “growth” in the number of infected cases. An accurate quantitative interaction between temperature and the confirmed COVID-19 cases is obtained as log(Y) = -0.000146(temp_H)(3) + 0.007410(temp_H)(2) -0.063332 temp_H + 7.793842, where Y is the periodic growth in confirmed COVID-19 cases, and temp_H is the maximum daily temperature. This equation alone may be the first confirmed way to measure the quantitative interaction between temperature and human transmission of COVID-19. In addition, four important regions are identified in terms of maximum daily temperature (in Celsius) to understand the dynamics in the transmission of COVID-19 related to temperature. First, the transmission decreases within the range of -50 °C to 5.02 °C. Second, the transmission accelerates in the range of 5.02 °C to 16.92 °C. Essentially, this is the temperature range for an outbreak. Third, the transmission increases more slowly in the range of 16.92 °C to 28.82 °C. Within this range, the number of infections continues to grow, but at a slower pace. Finally, the transmission decreases in the range of 28.82 °C to 50 °C. Thus, according to this hypothesis, the threshold of 16.92 °C is the most critical, as the point at which the infection rate is the greatest. This result sheds light on the mechanism in the cyclicity of the ongoing COVID-19 pandemic worldwide. The implications of these results on policy issues are also discussed concerning a possible cyclical fluctuation pattern between the Northern and Southern Hemispheres.
Evidence suggests an association between air pollutant exposure and worse outcomes for respiratory viral diseases, like COVID-19. However, does breathing polluted air over many years affect the susceptibility to SARS-CoV-2 infection or severity of COVID-19 disease, and how intense are these effects? As climate change intensifies, air pollutant levels may rise, which might further affect the burden of respiratory viral diseases. We assessed the effect of increasing exposure to PM2.5 (particulate matter = 2.5 microns in diameter) on SARS-CoV-2 susceptibility or COVID-19 severity and projected the impact on infections and hospitalisations over two years. Simulations in a hypothetical, representative population show that if exposure affects severity, then hospital admissions are projected to increase by 5-10% for a one-unit exposure increase. However, if exposure affects susceptibility, then infections would increase with the potential for onward transmission and hospital admissions could increase by over 60%. Implications of this study highlight the importance of considering this potential additional health and health system burden as part of strategic planning to mitigate and respond to changing air pollution levels. It is also important to better understand at which point PM2.5 exposure affects SARS-CoV-2 infection through to COVID-19 disease progression, to enable improved protection and better support of those most vulnerable.
BACKGROUND: Tuberculosis (TB) is a severe public health problem globally. Previous studies have revealed insufficient and inconsistent associations between air pollutants, meteorological factors and TB cases. Yet few studies have examined the associations between air pollutants, meteorological factors and TB cases in Beijing. OBJECTIVE: The purpose of this study was to explore the impact of air pollutants and meteorological factors on TB in Beijing, and to provide novel insights into public health managers to formulate control strategies of TB. METHODS: Data on the daily case of TB in Beijing during 2014-2020 were obtained from Chinese tuberculosis information management system. Concurrent data on the daily PM(10), PM(2.5), SO(2), NO(2), CO and O(3), were obtained from the online publication platform of the Chinese National Environmental Monitoring Center. Daily average temperature, average wind speed, relative humidity, sunshine duration and total precipitation were collected from the China Meteorological Science Data Sharing Service System. A distributed lag non-linear model was fitted to identify the non-linear exposure-response relationship and the lag effects between air pollutions, meteorological factors and TB cases in Beijing. RESULTS: In the single-factor model, the excess risk (ER) of TB was significantly positively associated with every 10 μg/m(3) increase in NO(2) in lag 1 week (ER: 1.3%; 95% confidence interval [CI]: 0.4%, 2.3%) and every 0.1 m/s increase in average wind speed in lag 5 weeks (ER: 0.3%; 95% CI: 0.1%, 0.5%), and was negatively associated with every 10 μg/m(3) increase in O(3) in lag 1 week (ER: -1.2%; 95% CI: -1.8%, -0.5%), every 5 °C increase in average temperature (ER: -1.7%; 95% CI: -2.9%, -0.4%) and every 10% increase in average relative humidity (ER: -0.4%; 95% CI: -0.8%, -0.1%) in lag 10 weeks, respectively. In the multi-factor model, the lag effects between TB cases and air pollutants, meteorological factors were similar. The subgroup analysis suggests that the effects of NO(2), O(3), average wind speed and relative humidity on TB were greater in male or labor age subgroup, while the effect of CO was greater in the elderly. In addition, no significant associations were found between PM(2.5), SO(2,) sunshine duration and TB cases. CONCLUSION: Our findings provide a better understanding of air pollutants and meteorological factors driving tuberculosis occurrence in Beijing, which enhances the capacity of public health manager to target early warning and disease control policy-making.
BACKGROUND: In 2020, the American West faced two competing challenges: the COVID-19 pandemic and the worst wildfire season on record. Several studies have investigated the impact of wildfire smoke (WFS) on COVID-19 morbidity and mortality, but little is known about how these two public health challenges impact mortality risk for other causes. OBJECTIVES: Using a time-series design, we evaluated how daily risk of mortality due to WFS exposure differed for periods before and during the COVID-19 pandemic. METHODS: Our study included daily data for 11 counties in the Front Range region of Colorado (2010-2020). We assessed WFS exposure using data from the National Oceanic and Atmospheric Administration and used mortality counts from the Colorado Department of Public Health and Environment. We estimated the interaction between WFS and the pandemic (an indicator variable) on mortality risk using generalized additive models adjusted for year, day of week, fine particulate matter, ozone, temperature, and a smoothed term for day of year. RESULTS: WFS impacted the study area on 10% of county-days. We observed a positive association between the presence of WFS and all-cause mortality risk (incidence rate ratio (IRR) = 1.03, 95%CI: 1.01-1.04 for same-day exposures) during the period before the pandemic; however, WFS exposure during the pandemic resulted in decreased risk of all-cause mortality (IRR = 0.90, 95%CI: 0.87-0.93 for same-day exposures). DISCUSSION: We hypothesize that mitigation efforts during the first year of the pandemic, e.g., mask mandates, along with high ambient WFS levels encouraged health behaviors that reduced exposure to WFS and reduced risk of all-cause mortality. Our results suggest a need to examine how associations between WFS and mortality are impacted by pandemic-related factors and that there may be lessons from the pandemic that could be translated into health-protective policies during future wildfire events.
The Harmattan, a dry, northeasterly trade wind, transports large quantities of Saharan dust over the Sahelian region during the dry season (December-March). Studies have shown that bacterial meningitis outbreaks in Sahelian regions show hyper-endemic to endemic levels during high-dust months. We examine the (a) seasonality and intraseasonal variability of dust, climate, and meningitis and the (b) quantitative relationships between various dust proxies with meningitis lags of 0-10 weeks in Senegal from 2012 to 2017. The results show that the onset of the meningitis season occurs in February, roughly 2 months after the dusty season has begun. The meningitis season peaks at the beginning of April, when northeasterly wind speeds and particulate matter (PM) are relatively high, and the meningitis season ends near the end of June, when temperature and humidity rise and northeasterly wind speeds decline. Furthermore, we find that Senegal’s relatively high humidity year-round may help slow the transmission of the infection, contributing to a lower disease incidence than landlocked countries in the meningitis belt. Lastly, our results suggest the desert dust may have a significant impact on the onset to the peak of the meningitis season in Senegal, particularly at the 0-2 and 10-week lag, whether that be directly through biological processes or indirectly through changes in human behavior. PM and visibility, however, are not in phase with aerosol optical depth throughout the year and consequently show different relationships with meningitis. This study further exemplifies the critical need for more PM, meteorological, and meningitis measurements in West Africa to further resolve these relationships.
Prior studies of hand, foot, and mouth disease (HFMD) have often observed inconsistent results regarding meteorological factors. We propose the hypothesis that these meteorological associations vary in regions because of the heterogeneity of their geographical characteristics. We have tested this hypothesis by applying a geographical detector and Bayesian space-time hierarchy model to measure stratified spatiotemporal heterogeneity and local associations between meteorological factors and HFMD risk in five climate zones in China from January 2016 to December 2017. We found a significant spatial stratified heterogeneity in HFMD risk and climate zone explained 15% of the spatial stratified heterogeneity. Meanwhile, there was a significant temporal stratified heterogeneity of 14% as determined by meteorological factors. Average temperatures and relative humidity had a significant positive effect on HFMD in all climate zones, they were the most obvious in the southern temperate zone. In northern temperate, southern temperate, northern subtropics, middle subtropics and southern subtropics climate zone, a 1 °C rise in temperature was related to an increase of 3.99%, 13.76%, 4.38%, 3.99%, and 7.74% in HFMD, and a 1% increment in relative humidity was associated with a 1.51%, 5.40%, 2.21%, 3.44%, and 4.78% increase, respectively. These findings provide strong support for our hypotheses that HFMD incidence has a significant spatiotemporal stratified heterogeneity and different climate zones have distinct influences on the disease. These findings provide strong support for our hypotheses: HFMD incidence had significant spatiotemporal stratified heterogeneity and different climate zones had distinct influences on it. The study suggested that HFMD prevention and policy should be made according to meteorological variation in each climate zone.
Numerous studies have suggested that meteorological conditions such as temperature and absolute humidity are highly indicative of influenza outbreaks. However, the explanatory power of meteorological factors on the seasonal influenza peaks varied widely between countries at different latitudes. OBJECTIVES: We aimed to explore the modification effects of meteorological factors on the seasonal influenza peaks in multi-countries. METHODS: Data on influenza positive rate (IPR) were collected across 57 countries and data on meteorological factors were collected from ECMWF Reanalysis v5 (ERA5). We used linear regression and generalized additive models to investigate the spatiotemporal associations between meteorological conditions and influenza peaks in cold and warm seasons. RESULTS: Influenza peaks were significantly correlated with months with both lower and higher temperatures. In temperate countries, the average intensity of cold season peaks was stronger than that of warm season peaks. However, the average intensity of warm season peaks was stronfger than of cold season peaks in tropical countries. Temperature and specific humidity had synergistic effects on influenza peaks at different latitudes, stronger in temperate countries (cold season: R(2)=0.90; warm season: R(2)=0.84) and weaker in tropical countries (cold season: R(2)=0.64; warm season: R(2)=0.03). Furthermore, the effects could be divided into cold-dry and warm-humid modes. The temperature transition threshold between the two modes was 16.5-19.5 °C. During the transition from cold-dry mode to warm-humid mode, the average 2 m specific humidity increased by 2.15 times, illustrating that transporting a large amount of water vapor may compensate for the negative effect of rising temperatures on the spread of the influenza virus. CONCLUSION: Differences in the global influenza peaks were related to the synergistic influence of temperature and specific humidity. The global influenza peaks could be divided into cold-dry and warm-humid modes, and specific thresholds of meteorological conditions were needed for the transition of the two modes.
BACKGROUND: Several previous studies investigated the associations between temperature and influenza in a single city or region without a national picture. The attributable risk of influenza due to temperature and the corresponding driving factors were unclear. This study aimed to evaluate the spatial distribution characteristics of attributable risk of Influenza-like illness (ILI) caused by adverse temperatures and explore the related driving factors in the United States. METHODS: ILI, meteorological factors, and PM(2.5) of 48 states in the United States were collected during 2011-2019. The time-stratified case-crossover design with a distributed lag non-linear model was carried out to evaluate the association between temperature and ILI at the state level. The multivariate meta-analysis was performed to obtain the combined effects at the national level. The attributable fraction (AF) was calculated to assess the ILI burden ascribed to adverse temperatures. The ordinary least square model (OLS), spatial lag model (SLM), and spatial error model (SEM) were utilized to identify driving factors. RESULTS: A total of 7,716,115 ILI cases were included in this study. Overall, the temperature was negatively associated with ILI risk, and lower temperature gave rise to a higher risk of ILI. AF ascribed to adverse temperatures differed across states, from 49.44% (95% eCI: 36.47% ~ 58.68%) in Montana to 6.51% (95% eCI: -6.49% ~ 16.46%) in Wisconsin. At the national level, 29.08% (95% eCI: 27.60% ~ 30.24%) of ILI was attributable to cold. Per 10,000 dollars increase in per-capita income was associated with the increment in AF (OLS: β = -6.110, P = 0.021; SLM: β = -5.496, P = 0.022; SEM: β = -6.150, P = 0.022). CONCLUSION: The cold could enhance the risk of ILI and result in a considerable proportion of ILI disease burden. The ILI burden attributed to cold varied across states and was higher in those states with lower economic status. Targeted prevention programs should be considered to lower the burden of influenza.
Nontuberculous mycobacteria (NTM) infections are caused by environmental exposure. We describe spatial distribution of NTM infections and associations with sociodemographic factors and flooding in Missouri, USA. Our retrospective analysis of mycobacterial cultures reported to the Missouri Department of Health and Social Services surveillance system during January 1, 2008-December 31, 2019, detected geographic clusters of infection. Multilevel Poisson regression quantified small-area geographic variations and identified characteristics associated with risk for infection. Median county-level NTM infection rate was 66.33 (interquartile range 51-91)/100,000 persons. Risk of clustering was significantly higher in rural areas (rate ratio 2.82, 95% CI 1.90-4.19) and in counties with >5 floodings per year versus no flooding (rate ratio 1.38, 95% CI 1.26-1.52). Higher risk for NTM infection was associated with older age, rurality, and more flooding. Clinicians and public health professionals should be aware of increased risk for NTM infections, especially in similar environments.
Pulmonary tuberculosis (PTB) remains a serious public health problem, especially in areas of developing countries. This study aimed to explore the spatial-temporal clusters and associated risk factors of PTB in south-western China. Space-time scan statistics were used to explore the spatial and temporal distribution characteristics of PTB. We collected data on PTB, population, geographic information and possible influencing factors (average temperature, average rainfall, average altitude, planting area of crops and population density) from 11 towns in Mengzi, a prefecture-level city in China, between 1 January 2015 and 31 December 2019. A total of 901 reported PTB cases were collected in the study area and a spatial lag model was conducted to analyse the association between these variables and the PTB incidence. Kulldorff’s scan results identified two significant space-time clusters, with the most likely cluster (RR = 2.24, p < 0.001) mainly located in northeastern Mengzi involving five towns in the time frame June 2017 - November 2019. A secondary cluster (RR = 2.09, p < 0.05) was located in southern Mengzi, covering two towns and persisting from July 2017 to December 2019. The results of the spatial lag model showed that average rainfall was associated with PTB incidence. Precautions and protective measures should be strengthened in high-risk areas to avoid spread of the disease.
In Japan, major and minor bimodal seasonal patterns of varicella have been observed. To investigate the underlying mechanisms of seasonality, we evaluated the effects of the school term and temperature on the incidence of varicella in Japan. We analyzed epidemiological, demographic and climate datasets of seven prefectures in Japan. We fitted a generalized linear model to the number of varicella notifications from 2000 to 2009 and quantified the transmission rates as well as the force of infection, by prefecture. To evaluate the effect of annual variation in temperature on the rate of transmission, we assumed a threshold temperature value. In northern Japan, which has large annual temperature variations, a bimodal pattern in the epidemic curve was observed, reflecting the large deviation in average weekly temperature from the threshold value. This bimodal pattern was diminished with southward prefectures, gradually shifting to a unimodal pattern in the epidemic curve, with little temperature deviation from the threshold. The transmission rate and force of infection, considering the school term and temperature deviation from the threshold, exhibited similar seasonal patterns, with a bimodal pattern in the north and a unimodal pattern in the south. Our findings suggest the existence of preferable temperatures for varicella transmission and an interactive effect of the school term and temperature. Investigating the potential impact of temperature elevation that could reshape the epidemic pattern of varicella to become unimodal, even in the northern part of Japan, is required.
OBJECTIVE: Information about the seasonal characteristics of human immunodeficiency virus (HIV)-negative cryptococcal meningitis (CM) is quite limited. The aim of this study was to explore the seasonality and meteorological factors of HIV-negative patients with CM. METHODS: We performed a retrospective study of 469 HIV-negative CM patients admitted to the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China. Their initial onset symptoms of CM occurred from January 2011 to December 2020. The temperature, precipitation, sunlight, humidity and wind speed for the corresponding period and the associated topographic, ecological type and soil type parameters data were collected. The Poisson regression model was used to determine the meteorological factors associated with CM onset. The geographical detector method was used to detect other environmental factors associated with CM onset. RESULTS: CM onset did not showed a seasonal fluctuation, but was strongly associated with mean temperature (β = .010, p = .028) and mean relative humidity (β = -.011, p = .006). In the rainy season, only mean wind speed remained significantly associated with CM onset (β = -.108, p = .041). In the dry season, mean temperature (β = .014, p = .016), mean relative humidity (β = -.016, p = .006) and hours of sunlight (β = -.002, p = .016) were significantly associated with CM onset. Topographic, ecological type and soil type factors did not add explanatory power. CONCLUSIONS: Our findings add the knowledge about the environmental factors of HIV-negative CM. Meteorological factors, especially temperature and humidity, may be the main environmental factors affecting the onset of HIV-negative CM.
We aim to explore the seasonal influences of meteorological factors on COVID-19 era over two distinct locations in Bangladesh using a generalized linear model (GLM) and wavelet analysis. GLM model findings show that summer humidity drives COVID-19 transmission to coastal and inland locations. During the summer in the coastal area, a 1 degrees C earth’s skin temperature increase causes a 41.9% increase in COVID (95% CL 86.32%-2.54%) transmission compared to inland. Relative humidity was recorded as the highest at 73.97% (95% CL, 99.3%, and 48.63%) for the coastal region, while wind speed and precipitation reduced confirmed cases by -38.62% and -22.15%, respectively. Wavelet analysis showed that coastal meteorological parameters were more coherent with COVID-19 than inland ones. The outcomes of this study are consistent with subtropical climate regions. Seasonality and climatic similarity should address to estimate COVID-19 trends. High societal concern and strong public health measures may decrease meteorological effect on COVID-19.
Climate change has both direct and indirect effects on human health, and some populations are more vulnerable to these effects than others. Viral respiratory infections are most common illnesses in humans, with estimated 17 billion incident infections globally in 2019. Anthropogenic drivers of climate change, chiefly the emission of greenhouse gases and toxic pollutants from burning of fossil fuels, and the consequential changes in temperature, precipitation, and frequency of extreme weather events have been linked with increased susceptibility to viral respiratory infections. Air pollutants like nitrogen dioxide, particulate matter, diesel exhaust particles, and ozone have been shown to impact susceptibility and immune responses to viral infections through various mechanisms, including exaggerated or impaired innate and adaptive immune responses, disruption of the airway epithelial barrier, altered cell surface receptor expression, and impaired cytotoxic function. An estimated 90% of the world’s population is exposed to air pollution, making this a topic with high relevance to human health. This review summarizes the available epidemiologic and experimental evidence for an association between climate change, air pollution, and viral respiratory infection.
Large-scale wildfires in California, USA, are increasing in both size and frequency, with substantial health consequences. The capacity for wildfire smoke to displace microbes and cause clinically significant fungal infections is poorly understood. We aimed to determine whether exposure to wildfire smoke was associated with an increased risk of hospital admissions for systemic fungal infections. METHODS: In this population-based, retrospective study, we used hospital administrative data from 22 hospitals in California, USA, to analyse the association between wildfire smoke exposure and monthly hospital admissions for aspergillosis and coccidioidomycosis. We included hospitals that were members of the Vizient Clinical Data Base or Resource Manager during the study and excluded those that did not have complete reporting into Vizient during the study period. Smoke exposure was estimated using satellite-imaged smoke plumes in the hospital county. Incident rate ratios were calculated for all infection types 1 month and 3 months after smoke exposure. FINDINGS: Between Oct 1, 2014, and May 31, 2018, there were a median of 1638 annual admissions per hospital in the study sample. Individual patient demographics were not collected. We did not observe an association between smoke exposure and rate of hospital admission for aspergillosis. However, hospital admission for coccidioidomycosis increased by 20% (95% CI 5-38) in the month following any smoke exposure. Hospital admission increased by 2% (0-4) for every day that there had been smoke exposure in the previous month, after adjustment for temperature and temporal trend. Similar results were obtained with smoke exposure data from the 3 months before admission. INTERPRETATION: In the months following wildfire smoke exposure, California hospitals saw increased coccidioidomycosis infections. Given the projected increase in California wildfires and their expansion in endemic territories of soil-dwelling fungi, the ability for wildfire smoke to carry microbes and cause human disease warrants further research. FUNDING: None.
Compound hazards are derived from independent disasters that occur simultaneously. Since the outbreak of COVID-19, the coupling of low-probability high-impact climate events has introduced a novel form of conflicting stressors that inhibits the operation of traditional logistics developed for single-hazard emergencies. The competing goals of hindering virus contagion and expediting massive evacuation have posed unique challenges for community safety. Yet, how a community perceives associated risks has been debated. This research utilized a web-based survey to explore the relationship between residents’ perceptions of conflicting risks and emergency choices made during a historic compound event, the flooding in 2020 in Michigan, US that coincided with the pandemic. After the event, postal mail was randomly sent to 5,000 households living in the flooded area, collecting 556 responses. We developed two choice models for predicting survivors’ evacuation options and sheltering length. The impact of sociodemographic factors on perceptions of COVID-19 risks was also examined. The results revealed greater levels of concern among females, democrats, and the economically inactive population. The relationship between evacuation choice and concern about virus exposure was dependent upon the number of seniors in the household. Concern about a lack of mask enforcement particularly discouraged evacuees from extended sheltering.
In recent years, evidence of the synergistic effects of pollen and viruses on respiratory health has begun to accumulate. Pollen exposure is a known risk factor for the incidence and severity of respiratory viral infections. However, recent evidence suggests that pollen exposure may also inhibit or weaken viral infections. A comprehensive summary has not been made and a consensus on the synergistic health effects has not been reached. It is highly possible that climate change will increase the significance of pollen exposure as a cause of respiratory problems and, at the same time, affect the risk of infectious disease outbreaks. It is important to accurately assess how these two factors affect human health separately and concurrently. In this review article, for the first time, the data from previous studies are combined and reviewed and potential research gaps concerning the synergistic effects of pollen and viral exposure are identified.
In recent years, the environmental impacts of climate change have become increasingly evident. Extreme meteorological events are influenced by climate change, which also alter the magnitude and pattern of precipitations and winds. Climate change can have a particularly negative impact on respiratory health, which can lead to the emergence of asthma and allergic respiratory illnesses. Pollen is one of the main components of the atmospheric bioaerosol and is able to induce allergic symptoms in certain subjects. Climate change affects the onset, length, and severity of the pollen season, with effects on pollen allergy. Higher levels of carbon dioxide (CO2) can lead to enhanced photosynthesis and a higher pollen production in plants. Pollen grains can also interact with air pollutants and be affected by thunderstorms and other extreme events, exacerbating the insurgence of respiratory diseases such as allergic rhinitis and asthma. The consequences of climate change might also favor the spreading of pandemics, such as the COVID-19 one.
Climate change represents an unprecedented threat to humanity and will be the ultimate challenge of the 21st century. As a public health consequence, the World Health Organization estimates an additional 250,000 deaths annually by 2030, with resource-poor countries being predominantly affected. Although climate change’s direct and indirect consequences on human health are manifold and far from fully explored, a growing body of evidence demonstrates its potential to exacerbate the frequency and spread of transmissible infectious diseases. Effective, high-impact mitigation measures are critical in combating this global crisis. While vaccines and vaccination are among the most cost-effective public health interventions, they have yet to be established as a major strategy in climate change-related health effect mitigation. In this narrative review, we synthesize the available evidence on the effect of climate change on vaccine-preventable diseases. This review examines the direct effect of climate change on water-related diseases such as cholera and other enteropathogens, helminthic infections and leptospirosis. It also explores the effects of rising temperatures on vector-borne diseases like dengue, chikungunya, and malaria, as well as the impact of temperature and humidity on airborne diseases like influenza and respiratory syncytial virus infection. Recent advances in global vaccine development facilitate the use of vaccines and vaccination as a mitigation strategy in the agenda against climate change consequences. A focused evaluation of vaccine research and development, funding, and distribution related to climate change is required.
Studies have shown that climate may affect the distribution of coronavirus disease (COVID-19) and its incidence and fatality rates. Here, we applied an ensemble niche modeling approach to project the climatic suitability of COVID-19 cases in Brazil. We estimated the cumulative incidence, mortality rate, and fatality rate of COVID-19 between 2020 and 2021. Seven statistical algorithms (MAXENT, MARS, RF, FDA, CTA, GAM, and GLM) were selected to model the climate suitability for COVID-19 cases from diverse climate data, including temperature, precipitation, and humidity. The annual temperature range and precipitation seasonality showed a relatively high contribution to the models, partially explaining the distribution of COVID-19 cases in Brazil based on the climatic suitability of the territory. We observed a high probability of climatic suitability for high incidence in the North and South regions and a high probability of mortality and fatality rates in the Midwest and Southeast regions. Despite the social, viral, and human aspects regulating COVID-19 cases and death distribution, we suggest that climate may play an important role as a co-factor in the spread of cases. In Brazil, there are regions with a high probability that climatic suitability will contribute to the high incidence and fatality rates of COVID-19 in 2020 and 2021.
The history of military medicine and research is rife with examples of novel treatments and new approaches to heal and cure soldiers and others impacted by war’s devastation. In the 21st century, new threats, like climate change, are combined with traditional threats, like geopolitical conflict, to create novel challenges for our strategic interests. Extreme and inaccessible environments provide heightened risks for warfighter exposure to dangerous bacteria, viruses, and fungi, as well as exposure to toxic substances and extremes of temperature, pressure, or both providing threats to performance and eroding resilience. Back home, caring for our veterans is also a health-care priority, and the diseases of veterans increasingly overlap with the health needs of an aging society. These trends of climate change, politics, and demographics suggest performance evaluation and resilience planning and response are critical to assuring both warfighter performance and societal health. The Cleveland ecosystem, comprising several hospitals, a leading University, and one of the nation’s larger Veteran’s Health Administration systems, is ideal for incubating and understanding the response to these challenges. In this review, we explore the interconnections of collaborations between Defense agencies, particularly Air Force and Army and academic medical center-based investigators to drive responses to the national health security challenges facing the United States and the world.
BACKGROUND: Tuberculosis (TB) is a public health problem worldwide, and the influence of meteorological and air pollutants on the incidence of tuberculosis have been attracting interest from researchers. It is of great importance to use machine learning to build a prediction model of tuberculosis incidence influenced by meteorological and air pollutants for timely and applicable measures of both prevention and control. METHODS: The data of daily TB notifications, meteorological factors and air pollutants in Changde City, Hunan Province ranging from 2010 to 2021 were collected. Spearman rank correlation analysis was conducted to analyze the correlation between the daily TB notifications and the meteorological factors or air pollutants. Based on the correlation analysis results, machine learning methods, including support vector regression, random forest regression and a BP neural network model, were utilized to construct the incidence prediction model of tuberculosis. RMSE, MAE and MAPE were performed to evaluate the constructed model for selecting the best prediction model. RESULTS: (1) From the year 2010 to 2021, the overall incidence of tuberculosis in Changde City showed a downward trend. (2) The daily TB notifications was positively correlated with average temperature (r = 0.231), maximum temperature (r = 0.194), minimum temperature (r = 0.165), sunshine duration (r = 0.329), PM(2.5) (r = 0.097), PM(10) (r = 0.215) and O(3) (r = 0.084) (p < 0.05). However, there was a significant negative correlation between the daily TB notifications and mean air pressure (r = -0.119), precipitation (r = -0.063), relative humidity (r = -0.084), CO (r = -0.038) and SO(2) (r = -0.034) (p < 0.05). (3) The random forest regression model had the best fitting effect, while the BP neural network model exhibited the best prediction. (4) The validation set of the BP neural network model, including average daily temperature, sunshine hours and PM(10), showed the lowest root mean square error, mean absolute error and mean absolute percentage error, followed by support vector regression. CONCLUSIONS: The prediction trend of the BP neural network model, including average daily temperature, sunshine hours and PM(10), successfully mimics the actual incidence, and the peak incidence highly coincides with the actual aggregation time, with a high accuracy and a minimum error. Taken together, these data suggest that the BP neural network model can predict the incidence trend of tuberculosis in Changde City.
This study integrated dynamic models and statistical methods to design a novel macroanalysis approach to judge the climate impacts. First, the incidence difference across Köppen-Geiger climate regions was used to determine the four risk areas. Then, the effective influence of climate factors was proved according to the non-climate factors’ non-difference among the risk areas, multi-source non-major component data assisting the proof. It is found that cold steppe arid climates and wet temperate climates are more likely to transmit SARS-CoV-2 among human beings. Although the results verified that the global optimum temperature was around 10 °C, and the average humidity was 71%, there was evident heterogeneity among different climate risk areas. The first-grade and fourth-grade risk regions in the Northern Hemisphere and fourth-grade risk regions in the Southern Hemisphere are more sensitive to temperature. However, the third-grade risk region in the Southern Hemisphere is more sensitive to relative humidity. The Southern Hemisphere’s third-grade and fourth-grade risk regions are more sensitive to precipitation.
BACKGROUND: The number of reported cases of Legionnaires’ disease (LD) has risen markedly in Switzerland (6.5/100,000 inhabitants in 2021) and abroad over the last decade. Legionella, the causative agent of LD, are ubiquitous in the environment. Therefore, environmental changes can affect the incidence of LD, for example by increasing bacterial concentrations in the environment or by facilitating transmission. OBJECTIVES: The aim of this study is to understand the environmental determinants, in particular weather conditions, for the regional and seasonal distribution of LD in Switzerland. METHODS: We conducted a series of analyses based on the Swiss LD notification data from 2017 to 2021. First, we used a descriptive and hotspot analysis to map LD cases and identify regional clusters. Second, we applied an ecological model to identify environmental determinants on case frequency at the district level. Third, we applied a case-crossover design using distributed lag non-linear models to identify short-term associations between seven weather variables and LD occurrence. Lastly, we performed a sensitivity analysis for the case-crossover design including NO(2) levels available for the year 2019. RESULTS: Canton Ticino in southern Switzerland was identified as a hotspot in the cluster analysis, with a standardised notification rate of 14.3 cases/100,000 inhabitants (CI: 12.6, 16.0). The strongest association with LD frequency in the ecological model was found for large-scale factors such as weather and air pollution. The case-crossover study confirmed the strong association of elevated daily mean temperature (OR 2.83; CI: 1.70, 4.70) and mean daily vapour pressure (OR: 1.52, CI: 1.15, 2.01) 6-14 days before LD occurrence. DISCUSSION: Our analyses showed an influence of weather with a specific temporal pattern before the onset of LD, which may provide insights into the effect mechanism. The relationship between air pollution and LD and the interplay with weather should be further investigated.
The outbreak and prevalence of SARS-CoV-2 have severely affected social security. Physical isolation is an effective control that affects the short-term human-to-human transmission of the epidemic, although weather presents a long-term effect. Understanding the effect of weather on the outbreak allow it to be contained at the earliest possible. China is selected as the study area, and six weather factors that receive the most attention from January 20, 2020 to April 30, 2020 are selected to investigate the correlation between weather and SARS-CoV-2 to provide a theoretical basis for long-term epidemic prevention and control. The results show that (1) the average growth rate (GR) of SARS-CoV-2 in each province is logarithmically distributed with a mean value of 5.15%. The GR of the southeastern region is higher than that of the northwestern region, which is consistent with the Hu Line. (2) The specific humidity, 2-m temperature (T), ultraviolet (UV) radiation, and wind speed (WS) adversely affect the GR. By contrast, the total precipitation (TP) and surface pressure (SP) promote the GR. (3) For every 1 unit increase in UV radiation, the GR decreases by 0.30% in 11 days, and the UV radiation in China is higher than that worldwide (0.92% higher per day). Higher population aggregation and urbanization directly affect the epidemic, and weather is an indirect factor.
Background: Nowadays, pulmonary tuberculosis (TB) is still a major global cause of death. Indonesia is a country with a high burden of the disease and is ranked second as a contributor to tuberculosis in the world after India, China, the Philippines, and Pakistan [1] along with the phenomenon of deforestation [2] and global warming [3]. Forest restoration and reforestation are considered cost-effective nature-based solutions for climate change adaptation and mitigation to remove carbon dioxide from the atmosphere, provide habitat for species and balance temperatures.Methods: There is no research data on the contribution of the economic value of reforestation to reduce the incidence rate of infectious diseases especially for TB, which is very important for mitigating against the global warming. This research was conducted to determine the economic value of ecosystem services as compensation for the reforestation program. This research was carried out in Lampung Province from April to October 2021, using Landsat imagery series 2009, 2012, 2015, 2018, and 2019 to detect forest cover.Results: The study’s findings show that every 2oC increase in temperature increases the incidence of pulmonary tuberculosis by 1.5 per 10,000 population, or 3,770 cases cover class that has a significant effect on the incidence of pulmonary TB is temperature, state forests, community forests, bare land, and rice fields.Conclusions: The valuation of forest environmental services in Lampung Province with human capital through pulmonary tuberculosis medical cost approach techniques for forest mitigation costs is IDR 20.113.458.000 /year.
Air pollution and global temperature change are expected to affect infectious diseases. Air pollution usually causes inflammatory response and disrupts immune defense system, while temperature mainly exacerbates the effect of vectors on humans. Yet to date overview of systematic reviews assessing the exposure risk of air pollutants and temperature on infectious diseases is unavailable. This article aims to fill this research gap. PubMed, Embase, the Cochrane Library, Web of Science, and the Cumulative Index to Nursing and Allied Health Literature were searched. Systematic reviews and meta-analyses investigated the exposure risk of pollutants or temperature on infectious diseases were included. Two investigators screened literature, extracted data and performed the risk of bias assessments independently. A total of 23 articles met the inclusion criteria, which 3 (13%) were “low” quality and 20 (87%) were “critically low” quality. COVID-19 morbidity was associated with long-term exposure PM(2.5) (RR = 1.056 per 1 [Formula: see text], 95% CI: 1.039-1.072) and NO(2) (RR = 1.042 per 1 [Formula: see text], 95% CI: 1.017-1.068). In addition, for each 1 °C increase in temperature, the morbidity risk of dengue increased 13% (RR = 1.130 per 1 °C, 95% CI: 1.120-1.150), infectious diarrhea increased 8% (RR = 1.080 per 1 °C, 95% CI: 1.050-1.200), and hand, foot and mouth disease (HFMD) increased 5% (RR = 1.050 per 1 °C, 95% CI: 1.020-1.080). In conclusion, PM(2.5) and NO(2) increased the risk of COVID-19 and temperatures were associated with dengue, infectious diarrhoea and HFMD morbidity. Moreover, the exposure risk of temperature on COVID-19 was recommended to be further explored.
COVID-19 pandemic appeared summer surge in 2022 worldwide and this contradicts its seasonal fluctuations. Even as high temperature and intense ultraviolet radiation can inhibit viral activity, the number of new cases worldwide has increased to >78% in only 1 month since the summer of 2022 under unchanged virus mutation influence and control policies. Using the attribution analysis based on the theoretical infectious diseases model simulation, we found the mechanism of the severe COVID-19 outbreak in the summer of 2022 and identified the amplification effect of heat wave events on its magnitude. The results suggest that approximately 69.3% of COVID-19 cases this summer could have been avoided if there is no heat waves. The collision between the pandemic and the heatwave is not an accident. Climate change is leading to more frequent extreme climate events and an increasing number of infectious diseases, posing an urgent threat to human health and life. Therefore, public health authorities must quickly develop coordinated management plans to deal with the simultaneous occurrence of extreme climate events and infectious diseases.
The occurrence of flood events amid the COVID-19 pandemic represents a prominent part of the emerging multi-hazard landscape, as floods are one of the most frequent and destructive natural hazards. This spatial and temporal overlap of hydrological and epidemiological hazards translates into compounded negative effects, causing a shift in the hazard management paradigm, in which hazard interaction takes centre stage. This paper calls into question whether the river flood events that occurred during the COVID-19 pandemic in Romania and the way that they were managed had an impact on the infection with the SARS-CoV-2 virus at county scale. To this end, hazard management data concerning the flood events that were severe enough to impose the evacuation of the population were corroborated with COVID-19 confirmed cases data. A definite link between the flood events and the dynamics of COVID-19 cases registered in the selected counties is difficult to identify, but the analysis shows that all flood events were followed by various size increases in the COVID-19 confirmed cases at the end of the incubation time range. The findings are critically interpreted by providing viral load and social-related contexts, allowing a proper understanding of the interactions between concurrent hazards.
The United States of America (USA) was afflicted by extreme heat in the summer of 2021 and some states experienced a record-hot or top-10 hottest summer. Meanwhile, the United States was also one of the countries impacted most by the coronavirus disease 2019 (COVID-19) pandemic. Growing numbers of studies have revealed that meteorological factors such as temperature may influence the number of confirmed COVID-19 cases and deaths. However, the associations between temperature and COVID-19 severity differ in various study areas and periods, especially in periods of high temperatures. Here we choose 119 US counties with large counts of COVID-19 deaths during the summer of 2021 to examine the relationship between COVID-19 deaths and temperature by applying a two-stage epidemiological analytical approach. We also calculate the years of life lost (YLL) owing to COVID-19 and the corresponding values attributable to high temperature exposure. The daily mean temperature is approximately positively correlated with COVID-19 deaths nationwide, with a relative risk of 1.108 (95% confidence interval: 1.046, 1.173) in the 90th percentile of the mean temperature distribution compared with the median temperature. In addition, 0.02 YLL per COVID-19 death attributable to high temperature are estimated at the national level, and distinct spatial variability from -0.10 to 0.08 years is observed in different states. Our results provide new evidence on the relationship between high temperature and COVID-19 deaths, which might help us to understand the underlying modulation of the COVID-19 pandemic by meteorological variables and to develop epidemic policy response strategies.
Although the utility of Ecological Niche Models (ENM) and Species Distribution Models (SDM) has been demonstrated in many ecological applications, their suitability for modelling epidemics or pandemics, such as SARS-Cov-2, has been questioned. In this paper, contrary to this viewpoint, we show that ENMs and SDMs can be created that can describe the evolution of pandemics, both in space and time. As an illustrative use case, we create models for predicting confirmed cases of COVID-19, viewed as our target “species”, in Mexico through 2020 and 2021, showing that the models are predictive in both space and time. In order to achieve this, we extend a recently developed Bayesian framework for niche modelling, to include: (i) dynamic, non-equilibrium “species” distributions; (ii) a wider set of habitat variables, including behavioural, socio-economic and socio-demographic variables, as well as standard climatic variables; (iii) distinct models and associated niches for different species characteristics, showing how the niche, as deduced through presence-absence data, can differ from that deduced from abundance data. We show that the niche associated with those places with the highest abundance of cases has been highly conserved throughout the pandemic, while the inferred niche associated with presence of cases has been changing. Finally, we show how causal chains can be inferred and confounding identified by showing that behavioural and social factors are much more predictive than climate and that, further, the latter is confounded by the former.
AIM: Coronavirus is an airborne and infectious disease and it is crucial to check the impact of climatic risk factors on the transmission of COVID-19. The main objective of this study is to determine the effect of climate risk factors using Bayesian regression analysis. METHODS: Coronavirus disease 2019, due to the effect of the SARS-CoV-2 virus, has become a serious global public health issue. This disease was identified in Bangladesh on March 8, 2020, though it was initially identified in Wuhan, China. This disease is rapidly transmitted in Bangladesh due to the high population density and complex health policy setting. To meet our goal, The MCMC with Gibbs sampling is used to draw Bayesian inference, which is implemented in WinBUGS software. RESULTS: The study revealed that high temperatures reduce confirmed cases and deaths from COVID-19, but low temperatures increase confirmed cases and deaths. High temperatures have decreased the proliferation of COVID-19, reducing the virus’s survival and transmission. CONCLUSIONS: Considering only the existing scientific evidence, warm and wet climates seem to reduce the spread of COVID-19. However, more climate variables could account for explaining most of the variability in infectious disease transmission.
OBJECTIVE: Indoor mold after flooding poses health risks, including rare but serious invasive mold infections. The purpose of this study was to evaluate use of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes for mold infection and mold exposure in Houston, Texas, during the year before and the year after Hurricane Harvey. METHODS: This study used data from MarketScan, a large health insurance claims database. RESULTS: The incidence of invasive mold infections remained unchanged in the year after Hurricane Harvey; however, the incidence of diagnosis codes for mold exposure nearly doubled compared with the year before the hurricane (6.3 vs 11.0 per 100 000 enrollees, rate ratio: 1.7, 95% confidence interval 1.0-3.1). CONCLUSIONS: Diagnosis codes alone may not be sufficiently sensitive to detect changes in invasive mold infection rates within this population and time frame, demonstrating the need for more comprehensive studies.
Varicella causes a major health burden in many low- to middle-income countries located in tropical regions. Because of the lack of surveillance data, however, the epidemiology of varicella in these regions remains uncharacterized. In this study, based on an extensive dataset of weekly varicella incidence in children ≤10 during 2011-2014 in 25 municipalities, we aimed to delineate the seasonality of varicella across the diverse tropical climates of Colombia. METHODS: We used generalized additive models to estimate varicella seasonality, and we used clustering and matrix correlation methods to assess its correlation with climate. Furthermore, we developed a mathematical model to examine whether including the effect of climate on varicella transmission could reproduce the observed spatiotemporal patterns. RESULTS: Varicella seasonality was markedly bimodal, with latitudinal changes in the peaks’ timing and amplitude. This spatial gradient strongly correlated with specific humidity (Mantel statistic = 0.412, P = .001) but not temperature (Mantel statistic = 0.077, P = .225). The mathematical model reproduced the observed patterns not only in Colombia but also México, and it predicted a latitudinal gradient in Central America. CONCLUSIONS: These results demonstrate large variability in varicella seasonality across Colombia and suggest that spatiotemporal humidity fluctuations can explain the calendar of varicella epidemics in Colombia, México, and potentially in Central America.
The role of climate in the Coronavirus disease 2019 (COVID-19) transmission appears to be controversial, as reported in earlier studies. In Africa, the subject is poorly documented. In this study, over the period from January 1st, 2020 to September 31, 2022, the daily variations in cumulative confirmed cases of COVID-19 for each African country (54 countries) are modelled through time-series-based approaches and using meteorological factors as covariates. It is suggested from the findings that climate plays a role in COVID-19 transmission since at least one meteorological factor is found to be significant in 32 countries. In decreasing order, the most often occurring meteorological factors are dewpoint temperature, relative and absolute humidity, average temperature and solar radiation. Most of these factors show a lagged effect with confirmed cases (between 0 and 28 days). Also, some meteorological factors exhibit contrasting effects on COVID-19 transmission, resulting in both positive and negative association with cumulative cases, therefore highlighting the complex nature of the interplay between climate and COVID-19 transmission.
Background: Prior studies have shown that meteorological factors may be associated with increases in legion-ellosis (Legionnaire’s disease, (LD)), caused by Legionella, a globally ubiquitous bacterium found naturally in aquatic habitats, soils, and compost. The aim of this retrospective time series analysis was to examine the as-sociation between meteorological factors and air pollution parameters and the incidence of sporadic, community -acquired, laboratory confirmed LD.Methods: Daily cases of community-acquired legionellosis, meteorological and air pollution data from two urban areas, Auckland (North Island) and Christchurch (South Island) were collected from January 1, 1997 until December 31, 2020. Using Quasi-Poisson regression, associations between symptom onset and meteorological and air pollution variables were investigated using an interrupted time series analysis.Results: The two cities had different meteorological conditions and LD epidemiology and seasonal patterns of Legionella spp. LD incidence rates (per 100,000 population) were higher in Christchurch than Auckland for L. pneumophila (25.8 vs 10.8) and L.longbeachae (78.2 vs 4.9). Seasonal patterns were detected in Christchurch with a higher risk of LD during spring and summer (RR: 1.87, 95% CI: 1.42, 2.49) compared to autumn and winter months. In Auckland, the level of particulate matter 9-10 days prior to the onset date was positively associated with LD occurrence (RR: 1.02, 95% CI: 1.00, 1.04) compared to Christchurch, where Tmax recorded one day prior the onset (RR: 1.03, 95% CI: 1.00, 1.07) and sulphur dioxide 6 days prior to the onset date (RR: 1.27, 95% CI: 1.10, 1.45) were positively associated with LD occurrence. Atmospheric pressure 12 days prior (RR: 0.95, 95% CI: 0.90, 1.00) and wind speed 13 days prior (RR: 0.94, 95% CI: 0.89, 0.99) to the onset date were negatively associated with LD risk. In both cities, no association was detected between the level of precipitation and LD risk.Conclusions: Meteorological factors and air pollutants were associated with the risk of LD. However, different seasonal patterns and prevalent LD species seem to have distinct patterns of association between the two cate-gories of exposures. These findings suggest the importance of considering meteorological and air quality con-ditions in conjunction with the source of exposure and the LD species involved. They also imply potential climate change impacts on LD and benefits from reducing air pollution, though findings need to be replicated in other geographical regions.
The frequency and severity of wildfires in the Western United States have increased over recent decades, motivating hypotheses that wildfires contribute to the incidence of coccidioidomycosis, an emerging fungal disease in the Western United States with sharp increases in incidence observed since 2000. While coccidioidomycosis outbreaks have occurred among wildland firefighters clearing brush, it remains unknown whether fires are associated with an increased incidence among the general population. METHODS: We identified 19 wildfires occurring within California’s highly endemic San Joaquin Valley between 2003 and 2015. Using geolocated surveillance records, we applied a synthetic control approach to estimate the effect of each wildfire on the incidence of coccidioidomycosis among residents that lived within a hexagonal buffer of 20 km radii surrounding the fire. RESULTS: We did not detect excess cases due to wildfires in the 12 months (pooled estimated percent change in cases: 2.8%; 95% confidence interval [CI] = -29.0, 85.2), 13-24 months (7.9%; 95% CI = -27.3, 113.9), or 25-36 months (17.4%; 95% CI = -25.1, 157.1) following a wildfire. When examined individually, we detected significant increases in incidence following three of the 19 wildfires, all of which had relatively large adjacent populations, high transmission before the fire, and a burn area exceeding 5,000 acres. DISCUSSION: We find limited evidence that wildfires drive increases in coccidioidomycosis incidence among the general population. Nevertheless, our results raise concerns that large fires in regions with ongoing local transmission of Coccidioides may be associated with increases in incidence, underscoring the need for field studies examining Coccidioides spp. in soils and air pre- and post-wildfires.
Coccidioidomycosis is a fungal infection caused by Coccidioides immitis and Coccidioides posadasii. The dimorphic fungi live in the soils of arid and semi-arid regions of the western United States, as well as parts of Mexico, Central America, and South America. Incidence of disease has risen consistently in recent years, and the geographic distribution of Coccidioides spp. appears to be expanding beyond previously known areas of endemicity. Climate factors are predicted to further extend the range of environments suitable for the growth and dispersal of Coccidioides species. Most infections are asymptomatic, though a small proportion result in severe or life-threatening forms of disease. Primary pulmonary coccidioidomycosis is commonly mistaken for community-acquired pneumonia, often leading to inappropriate antibacterial treatment and unnecessary healthcare costs. Diagnosis of coccidioidomycosis is challenging and often relies on clinician suspicion to pursue laboratory testing. Advancements in diagnostic tools and antifungal therapy developments seek to improve the early detection and effective management of infection. This review will highlight recent updates and summarize the current understanding of the epidemiology, diagnosis, and treatment of coccidioidomycosis.
Background Understanding the transmission source, pattern, and mechanism of infectious diseases is essential for targeted prevention and control. Though it has been studied for many years, the detailed transmission patterns and drivers for the seasonal influenza epidemics in China remain elusive. Methods In this study, utilizing a suite of epidemiological and genetic approaches, we analyzed the updated province-level weekly influenza surveillance, sequence, climate, and demographic data between 1 April 2010 and 31 March 2018 from continental China, to characterize detailed transmission patterns and explore the potential initiating region and drivers of the seasonal influenza epidemics in China. Results An annual cycle for influenza A(H1N1)pdm09 and B and a semi-annual cycle for influenza A(H3N2) were confirmed. Overall, the seasonal influenza A(H3N2) virus caused more infection in China and dominated the summer season in the south. The summer season epidemics in southern China were likely initiated in the “Lingnan” region, which includes the three most southern provinces of Hainan, Guangxi, and Guangdong. Additionally, the regions in the south play more important seeding roles in maintaining the circulation of seasonal influenza in China. Though intense human mobility plays a role in the province-level transmission of influenza epidemics on a temporal scale, climate factors drive the spread of influenza epidemics on both the spatial and temporal scales. Conclusion The surveillance of seasonal influenza in the south, especially the “Lingnan” region in the summer, should be strengthened. More broadly, both the socioeconomic and climate factors contribute to the transmission of seasonal influenza in China. The patterns and mechanisms revealed in this study shed light on the precise forecasting, prevention, and control of seasonal influenza in China and worldwide.
This study exploits the pathway of Hurricane Laura to assess its impact on the spread of COVID-19. Using US hospital data on confirmed and suspected adult COVID-19 cases, we find average daily cases per week rose by more than 12% primarily in tropical storm-affected counties in subsequent weeks. We suspect the key mechanisms involve constraints on social distancing for two reasons. First, there is significant evidence of storm-induced mobility. Second, lower income areas endured higher growth in hospital cases during the post-hurricane period. These findings provide crucial insights for policy-makers when designing natural disaster protocols to adjust for potential respiratory viral illnesses.
Despite a substantial number of COVID-19 related research papers published, it remains unclear as to which factors are associated with the observed variation in global transmission and what are their relative levels of importance. This study applies a rigorous statistical framework to provide robust estimations of the factor effects for a global and integrated perspective on this issue. We developed a mixed effect model exploring the relative importance of potential factors driving COVID-19 transmission while incorporating spatial and temporal heterogeneity of spread. We use an integrated data set for 87 countries across six continents for model specification and fitting. The best model accounts for 70.4% of the variance in the data analyzed: 10 fixed effect factors explain 20.5% of the variance, random temporal and spatial effects account for 50% of the variance. The fixed effect factors are classified into climatic, demographic and disease control groups. The explained variance in global transmission by the three groups are 0.6%, 1.1%, and 4.4% respectively. The high proportion of variance accounted for by random effects indicated striking differences in temporal transmission trajectories and effects of population mobility among the countries. In particular, the country-specific mobility-transmission relationship turns out to be the most important factor in explaining the observed global variation of transmission in the early phase of COVID-19 pandemic. Plain Language Summary We have observed substantial variation in global transmission trajectories of COVID-19. Using statistical analysis, this study aims to investigate the factors that are associated with the observed variation in global transmission and what are their relative levels of importance. We conclude that the variation in transmission trajectories in various countries is mostly accounted for by spatiotemporal heterogeneity in transmission. In particular, disease control policies and population response to COVID-19 transmission make the largest contribution and demographic features have the least importance. Climatic factors also play a role but turn to be much less important than disease control policies. The mobility-transmission relationship is country-specific and turns out to be the most important factor in explaining the observed global variation of transmission. The complexity of COVID-19 transmission is also demonstrated through the wide range of estimated effects of population mobility on transmission between countries.
Previous research has extensively studied the seasonalities of human influenza infections and the effect of specific climatic factors in different regions. However, there is limited understanding of the influences of monsoons. This study applied generalized additive model with monthly surveillance data from mainland China to explore the influences of the East Asian Monsoon on the spatio-temporal pattern of seasonal influenza in China. The results suggested two influenza active periods in northern China and three active periods in southern China. The study found that the northerly advancement of East Asian Summer Monsoon (EASM) influences the summer influenza spatio-temporal patterns in both southern and northern China. At the interannual scale, the north-south converse effect of EASM on influenza activity is mainly due to the converse effect of EASM on humidity and precipitation. Within the annual scale, influenza activity in southern China gradually reaches its maximum during the summer exacerbated by the northerly advancement of EASM. Furthermore, the winter epidemic in China is related to the low temperature and humidity influenced by the East Asian Winter Monsoon (EAWM). Moreover, the active period in transition season is related partially to the large rapid temperature change influenced by the transition of EAWM and EASM. Despite the delayed onset and instability, the climatic condition influenced by the East Asian Monsoon is one of the potential key drivers of influenza activity.
BACKGROUND: Tuberculosis (TB) continues to pose a major public health risk in many countries. The current incidence of disease exceeds guidelines proposed by the World Health Organisation and United Nations. Whilst the relationship between climate change and TB has surfaced in recent literature, it remains neglected in global agendas. There is a need to acknowledge TB as a climate-sensitive disease to facilitate its eradication. OBJECTIVE: To review epidemiological and prediction model studies that explore how climate change may affect the risk factors for TB, as outlined in the Global Tuberculosis Report 2021: HIV infection, diabetes mellitus, undernutrition, overcrowding, poverty, and indoor air pollution. METHODS: We conducted a systematic literature search of PubMed, Embase, and Scopus databases to identify studies examining the association between climate variables and the risk factors for TB. Each study that satisfied the inclusion criteria was assessed for quality and ethics. Studies then underwent vote-counting and were categorised based on whether an association was found. RESULTS: 53 studies met inclusion criteria and were included in our review. Vote-counting revealed that two out of two studies found a positive association between the examined climate change proxy and HIV, nine out of twelve studies for diabetes, eight out of seventeen studies for undernutrition, four out of five studies for overcrowding, twelve out of fifteen studies for poverty and one out of three studies for indoor air pollution. DISCUSSION: We found evidence supporting a positive association between climate change and each of the discussed risk factors for TB, excluding indoor air pollution. Our findings suggest that climate change is likely to affect the susceptibility of individuals to TB by increasing the prevalence of its underlying risk factors, particularly in developing countries. This is an evolving field of research that requires further attention in the scientific community.
Recently, global epidemic models that use climatological factors have been proposed to explain influenza activities for both temperate and tropical regions. In this paper, these global models were extended by including interactions of climatological factors. This study was aimed to estimate the relative benefits of such interactions in explaining the global influenza epidemics. The effects of four climatological factors on laboratory-confirmed influenza cases were investigated, i.e., weekly temperature, precipitation, absolute humidity and relative humidity. It was found that countries in Europe and Australia have higher forecast skill, indicating the stronger relationship of influenza with climatological factors, than regions in other continents. The influenza activities of 47 (83%) countries can be explained with a closer match using multi-factor interactions along with original factors than only using the original factors. The temperate countries are characterized by the interaction of factors of temperature and absolute/relative humidity. In contrast, the interaction of factors of precipitation and absolute/relative humidity are dominant in tropical countries.
The 2020 COVID-19 outbreak in New South Wales (NSW), Australia, followed an unprecedented wildfire season that exposed large populations to wildfire smoke. Wildfires release particulate matter (PM), toxic gases and organic and non-organic chemicals that may be associated with increased incidence of COVID-19. This study estimated the association of wildfire smoke exposure with the incidence of COVID-19 in NSW. A Bayesian mixed-effect regression was used to estimate the association of either the average PM(10) level or the proportion of wildfire burned area as proxies of wildfire smoke exposure with COVID-19 incidence in NSW, adjusting for sociodemographic risk factors. The analysis followed an ecological design using the 129 NSW Local Government Areas (LGA) as the ecological units. A random effects model and a model including the LGA spatial distribution (spatial model) were compared. A higher proportional wildfire burned area was associated with higher COVID-19 incidence in both the random effects and spatial models after adjustment for sociodemographic factors (posterior mean = 1.32 (99% credible interval: 1.05-1.67) and 1.31 (99% credible interval: 1.03-1.65), respectively). No evidence of an association between the average PM(10) level and the COVID-19 incidence was found. LGAs in the greater Sydney and Hunter regions had the highest increase in the risk of COVID-19. This study identified wildfire smoke exposures were associated with increased risk of COVID-19 in NSW. Research on individual responses to specific wildfire airborne particles and pollutants needs to be conducted to further identify the causal links between SARS-Cov-2 infection and wildfire smoke. The identification of LGAs with the highest risk of COVID-19 associated with wildfire smoke exposure can be useful for public health prevention and or mitigation strategies.
The world faced stark challenges during the global pandemic caused by COVID-19. Large forces such as climate change, cultural ethnocentrism and racism, and increasing wealth inequality continue to ripple through communities harming community well-being. While the global pandemic caused by COVID-19 exacerbated these forces, lessons across the globe have been captured that inform the field of community well-being long-after the end of the pandemic. While many scholars have looked to political capital, financial capital, and social capital to tackle these challenges, natural capital and cultural capital have extreme relevance. However, scholarship tends to overlook the inextricable and important links between natural capital and cultural capital in community development and well-being work. These capital forms also inform contemporary understandings of sustainability and environmental justice, especially in the fields of community development and well-being. This perspective article showcases the deep connections between natural capital and social capital through literature review and community cases across the globe. Questions are posed for future research and practice tethering together cultural capital and natural capital when looking to bolster community well-being.
Texas is a geographically large state with large human and livestock populations, many farms, a long coastal region, and extreme fluctuations in weather. During the last 15 years, the state of Texas has frequently suffered disasters or catastrophes causing extensive morbidity and economic loss. These disasters often have complicated consequences requiring multi-faceted responses. Recently, an interdisciplinary network of professionals from multiple academic institutions has emerged to collaborate in protecting Texas and the USA using a One Health approach. These experts are training the next generation of scientists in biopreparedness; increasing under-standing of pathogens that cause repetitive harm; developing new therapeutics and vaccines against them; and developing novel surveillance approaches so that emerging pathogens will be detected early and thwarted before they can cause disastrous human and economic losses. These academic One Health partnerships strengthen our ability to protect human and animal health against future catastrophes that may impact the diverse ecoregions of Texas and the world.
BACKGROUND: Many studies have explored the epidemiological characteristics of influenza. However, most previous studies were conducted in a specific region without a national picture which is important to develop targeted strategies and measures on influenza control and prevention. OBJECTIVES: To explore the association between ambient temperature and incidence of influenza, to estimate the attributable risk from temperature in 30 Chinese cities with different climatic characteristics for a national picture, and to identify the vulnerable populations for national preventative policy development. METHODS: Daily meteorological and influenza incidence data from the 30 Chinese cities over the period 2016-19 were collected. We estimated the city-specific association between daily mean temperature and influenza incidence using a distributed lag non-linear model and evaluated the pooled effects using multivariate meta-analysis. The attributable fractions compared with reference temperature were calculated. Stratified analyses were performed by region, sex and age. RESULTS: Overall, an N-shape relationship between temperature and influenza incidence was found in China. The cumulative relative risk of the peak risk temperature (5.1 °C) was 2.13 (95%CI: 1.41, 3.22). And 60% (95%eCI: 54.3%, 64.3%) of influenza incidence was attributed to ambient temperature during the days with sensitive temperatures (1.6°C-14.4 °C). The ranges of sensitive temperatures and the attributable disease burden due to temperatures varied for different populations and regions. The residents in South China and the children aged ≤5 and 6-17 years had higher fractions attributable to sensitive temperatures. CONCLUSIONS: Tailored preventions targeting on most vulnerable populations and regions should be developed to reduce influenza burden from sensitive temperatures.
BACKGROUND: Influenza seasonality has been frequently studied, but its mechanisms are not clear. Urban in-situ studies have linked influenza to meteorological or pollutant stressors. Few studies have investigated rural and less polluted areas in temperate climate zones. OBJECTIVES: We examined influences of medium-term residential exposure to fine particulate matter (PM(2.5)), NO(2), SO(2), air temperature and precipitation on influenza incidence. METHODS: To obtain complete spatial coverage of Baden-Württemberg, we modeled environmental exposure from data of the Copernicus Atmosphere Monitoring Service and of the Copernicus Climate Change Service. We computed spatiotemporal aggregates to reflect quarterly mean values at post-code level. Moreover, we prepared health insurance data to yield influenza incidence between January 2010 and December 2018. We used generalized additive models, with Gaussian Markov random field smoothers for spatial input, whilst using or not using quarter as temporal input. RESULTS: In the 3.85 million cohort, 513,404 influenza cases occurred over the 9-year period, with 53.6% occurring in quarter 1 (January to March), and 10.2%, 9.4% and 26.8% in quarters 2, 3 and 4, respectively. Statistical modeling yielded highly significant effects of air temperature, precipitation, PM(2.5) and NO(2). Computation of stressor-specific gains revealed up to 3499 infections per 100,000 AOK clients per year that are attributable to lowering ambient mean air temperature from 18.71 °C to 2.01 °C. Stressor specific gains were also substantial for fine particulate matter, yielding up to 502 attributable infections per 100,000 clients per year for an increase from 7.49 μg/m(3) to 15.98 μg/m(3). CONCLUSIONS: Whilst strong statistical association of temperature with other stressors makes it difficult to distinguish between direct and mediated temperature effects, results confirm genuine effects by fine particulate matter on influenza infections for both rural and urban areas in a temperate climate. Future studies should attempt to further establish the mediating mechanisms to inform public health policies.
Problem/Condition: Coccidioidomycosis, histoplasmosis, and blastomycosis are underdiagnosed fungal diseases that often mimic bacterial or viral pneumonia and can cause disseminated disease and death. These diseases are caused by inhalation of fungal spores that have distinct geographic niches in the environment (e.g., soil or dust), and distribution is highly susceptible to climate changes such as expanding arid regions for coccidioidomycosis, the northward expansion of histoplasmosis, and areas like New York reporting cases of blastomycosis previously thought to be nonendemic. The national incidence of coccidioidomycosis, histoplasmosis, and blastomycosis is poorly characterized. Reporting Period: 2019. Description of System: The National Notifiable Diseases Surveillance System (NNDSS) tracks cases of coccidioidomycosis, a nationally notifiable condition reported to CDC by 26 states and the District of Columbia. Neither histoplasmosis nor blastomycosis is a nationally notifiable condition; however, histoplasmosis is voluntarily reported in 13 states and blastomycosis in five states. Health departments classify cases based on the definitions established by the Council of State and Territorial Epidemiologists. Results: In 2019, a total of 20,061 confirmed coccidioidomycosis, 1,124 confirmed and probable histoplasmosis, and 240 confirmed and probable blastomycosis cases were reported to CDC. Arizona and California reported 97% of coccidioidomycosis cases, and Minnesota and Wisconsin reported 75% of blastomycosis cases. Illinois reported the greatest percentage (26%) of histoplasmosis cases. All three diseases were more common among males, and the proportion for blastomycosis (70%) was substantially higher than for histoplasmosis (56%) or coccidioidomycosis (52%). Coccidioidomycosis incidence was approximately four times higher for non-Hispanic American Indian or Alaska Native (AI/AN) persons (17.3 per 100,000 population) and almost three times higher for Hispanic or Latino persons (11.2) compared with non-Hispanic White (White) persons (4.1). Histoplasmosis incidence was similar across racial and ethnic categories (range: 0.9-1.3). Blastomycosis incidence was approximately six times as high among AI/AN persons (4.5) and approximately twice as high among non-Hispanic Asian and Native Hawaiian or other Pacific Islander persons (1.6) compared with White persons (0.7). More than one half of histoplasmosis (54%) and blastomycosis (65%) patients were hospitalized, and 5% of histoplasmosis and 9% of blastomycosis patients died. States in which coccidioidomycosis is not known to be endemic had more cases in spring (March, April, and May) than during other seasons, whereas the number of cases peaked slightly in autumn (September, October, and November) for histoplasmosis and in winter (December, January, and February) for blastomycosis. Interpretation: Coccidioidomycosis, histoplasmosis, and blastomycosis are diseases occurring in geographical niches within the United States. These diseases cause substantial illness, with approximately 20,000 coccidioidomycosis cases reported in 2019. Although substantially fewer histoplasmosis and blastomycosis cases were reported, surveillance was much more limited and underdiagnosis was likely, as evidenced by high hospitalization and death rates. This suggests that persons with milder symptoms might not seek medical evaluation and the symptoms self-resolve or the illnesses are misdiagnosed as other, more common respiratory diseases. Public Health Action: Improved surveillance is necessary to better characterize coccidioidomycosis severity and to improve detection of histoplasmosis and blastomycosis. These findings might guide improvements in testing practices that enable timely diagnosis and treatment of fungal diseases. Clinicians and health care professionals should consider coccidioidomycosis, histoplasmosis, and blastomycosis in patients with community-acquired pneumonia or other acute infections of the lower respiratory tract who live in or have traveled to areas where the causative fungi are known to be present in the environment. Culturally appropriate tailored educational messages might help improve diagnosis and treatment. Public health response to these three diseases is hindered because information gathered from states’ routine surveillance does not include data on populations at risk and sources of exposure. Broader surveillance that includes expansion to other states and more detail about potential exposures and relevant host factors can describe epidemiologic trends, populations at risk, and disease prevention strategies.
Pulmonary tuberculosis (PTB) has been a major threat to global public health. The association between meteorological factors and the incidence of PTB has been widely investigated by the generalized additive model, auto-regressive integrated moving average model and the distributed lag model, etc. However, these models could not address a non-linear or lag correlation between them. In this paper, a penalized distributed lag non-linear model, as a generalized and improved one, was applied to explore the influence of meteorological factors (such as air temperature, relative humidity and wind speed) on the PTB incidence in Xinjiang from 2004 to 2019. Moreover, we firstly use a comprehensive index (apparent temperature, AT) to access the impact of multiple meteorological factors on the incidence of PTB. It was found that the relationships between air temperature, relative humidity, wind speed, AT and PTB incidence were nonlinear (showed “wave-type “, “invested U-type”, “U-type” and “wave-type”, respectively). When air temperature at the lowest value (-16.1 degrees C) could increase the risk of PTB incidence with the highest relative risk (RR = 1.63, 95% CI: 1.21-2.20). An assessment of relative humidity demonstrated an increased risk of PTB incidence between 44.5% and 71.8% with the largest relative risk (RR = 1.49, 95% CI: 1.32-1.67) occurring at 59.2%. Both high and low wind speeds increased the risk of PTB incidence, especially at the lowest wind speed 1.4 m/s (RR = 2.20, 95% CI: 1.95-2.51). In particular, the lag effects of low and high AT on PTB incidence were nonlinear. The lag effects of extreme cold AT (-18.5 degrees C, 1st percentile) on PTB incidence reached a relative risk peak (RR = 2.18, 95% CI: 2.06-2.31) at lag 1 month. Overall, it was indicated that the environment with low air temperature, suitable relative humidity and wind speed is more conducive to the transmission of PTB, and low AT is associated significantly with increased risk of PTB in Xinjiang.
BACKGROUND: Influenza epidemics pose a threat to human health. It has been reported that meteorological factors (MFs) are associated with influenza. This study aimed to explore the similarities and differences between the influences of more comprehensive MFs on influenza in cities with different economic, geographical and climatic characteristics in Fujian Province. Then, the information was used to predict the daily number of cases of influenza in various cities based on MFs to provide bases for early warning systems and outbreak prevention. METHOD: Distributed lag nonlinear models (DLNMs) were used to analyse the influence of MFs on influenza in different regions of Fujian Province from 2010 to 2021. Long short-term memory (LSTM) was used to train and model daily cases of influenza in 2010-2018, 2010-2019, and 2010-2020 based on meteorological daily values. Daily cases of influenza in 2019, 2020 and 2021 were predicted. The root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to quantify the accuracy of model predictions. RESULTS: The cumulative effect of low and high values of air pressure (PRS), air temperature (TEM), air temperature difference (TEMD) and sunshine duration (SSD) on the risk of influenza was obvious. Low (< 979 hPa), medium (983 to 987 hPa) and high (> 112 hPa) PRS were associated with a higher risk of influenza in women, children aged 0 to 12 years, and rural populations. Low (< 9 °C) and high (> 23 °C) TEM were risk factors for influenza in four cities. Wind speed (WIN) had a more significant effect on the risk of influenza in the ≥ 60-year-old group. Low (< 40%) and high (> 80%) relative humidity (RHU) in Fuzhou and Xiamen had a significant effect on influenza. When PRS was between 1005-1015 hPa, RHU > 60%, PRE was low, TEM was between 10-20 °C, and WIN was low, the interaction between different MFs and influenza was most obvious. The RMSE, MAE, MAPE, and SMAPE evaluation indices of the predictions in 2019, 2020 and 2021 were low, and the prediction accuracy was high. CONCLUSION: All eight MFs studied had an impact on influenza in four cities, but there were similarities and differences. The LSTM model, combined with these eight MFs, was highly accurate in predicting the daily cases of influenza. These MFs and prediction models could be incorporated into the influenza early warning and prediction system of each city and used as a reference to formulate prevention strategies for relevant departments.
BACKGROUND: Tuberculosis (TB) like many other infectious diseases has a strong relationship with climatic parameters. METHODS: The present study has been carried out on the newly diagnosed sputum smear-positive pulmonary TB cases reported to National TB Control Program across Pakistan from 2007 to 2020. In this study, spatial and temporal distribution of the disease was observed through detailed district wise mapping and clustered regions were also identified. Potential risk factors associated with this disease depending upon population and climatic variables, i.e. temperature and precipitation were also identified. RESULTS: Nationwide, the incidence rate of TB was observed to be rising from 7.03% to 11.91% in the years 2007-2018, which then started to decline. However, a declining trend was observed after 2018-2020. The most populous provinces, Punjab and Sindh, have reported maximum number of cases and showed a temporal association as the climatic temperature of these two provinces is higher with comparison to other provinces. Machine learning algorithms Maxent, Support Vector Machine (SVM), Environmental Distance (ED) and Climate Space Model (CSM) predict high risk of the disease with14.02%, 24.75%, 34.81% and 43.89% area, respectively. CONCLUSION: SVM has a higher significant probability of prediction in the diseased area with a 1.86 partial receiver-operating characteristics (ROC) value as compared with other models.
Small Island Developing States (SIDS) have been impacted by and responded to COVID-19 in ways that give us clues about vulnerabilities under climate change, as well as pathways to resilience. Here, we reflect on some of these experiences drawing on case study examples from the Caribbean, Pacific, and Indian Ocean SIDS, exploring how SIDS have responded to COVID-19 and considering the potential for coping mechanisms enacted for the pandemic to support long-term resilience to climate change. Island responses to the pandemic highlight both new directions, like tourist schemes that capitalize on the rise of remote working in Barbados and Mauritius, and reliance on tried and tested coping mechanisms, like bartering in Fiji. Some of the actions undertaken to respond to the pressures of the pandemic, such as visa schemes promoting “digital nomadism” and efforts to grow domestic food production, have climate resilience and equity dimensions that must be unpacked if their potential to contribute to more sustainable island futures is to be realized. Importantly, the diversity of contexts and experiences described here illustrates that there is no single “best” pathway to climate-resilient post-pandemic futures for SIDS. While the emerging rhetoric of COVID-19 recovery often speaks of “roadmaps,” we argue that the journey towards a climate-resilient COVID-19 recovery for SIDS is likely to involve detours, as solutions emerge through innovation and experiment, and knowledge-sharing across the wider SIDS community. This article is categorized under: Climate and Development > Sustainability and Human Well-Being Integrated Assessment of Climate Change > Assessing Climate Change in the Context of Other Issues
Recent evidence has shown an association between wildfire smoke and COVID-19 cases and deaths. The San Francisco Bay Area, in California (USA), experienced two major concurrent public health threats in 2020: the COVID-19 pandemic and dense smoke emitted by wildfires. This provides a unprecedented context to unravel the role of acute air pollution exposure on COVID-19 severity. A smoke product provided by the National Oceanic and Atmospheric Association Hazard Mapping System was used to identify counties exposed to heavy smoke in summer and fall of 2020. Daily COVID-19 cases and deaths for the United States were downloaded at the County-level from the CDC COVID Data Tracker. Synthetic control methods were used to estimate the causal effect of the wildfire smoke on daily COVID-19 case fatality ratios (CFRs), adjusting for population mobility. Evidence of an impact of wildfire smoke on COVID-19 CFRs was observed, with precise estimates in Alameda and San Francisco. Up to 58 (95% CI: 29, 87) additional deaths for every 1000 COVID-19 incident daily cases attributable to wildfire smoke was estimated in Alameda in early September. Findings indicated that extreme weather events such as wildfires smoke can drive increased vulnerability to infectious diseases, highlighting the need to further study these colliding crises. Understanding the environmental drivers of COVID-19 mortality can be used to protect vulnerable populations from these potentially concomitant public health threats.
The present study was conducted to assess the short-term effects of the meteorological factors on the COVID-19 mortality in Qom, Iran. The GAM with a quasi-Poisson link function was used to evaluate the impact of temperature, DTR, relative humidity, and absolute humidity on the COVID-19 mortality, controlling potential confounders such as time trend, air pollutants, and day of the week. The results showed that the risk of COVID-19 mortality was reduced, in single-day lag/multiple-day average lag, per one-unit increase in absolute humidity (percentage change in lag 0=-33.64% (95% CI (-42.44, -23.49)), and relative humidity (percentage change in lag 0=-1.87% (95% CI (-2.52, -1.22)). Also, per one-unit increase in DTR value, COVID death risk increased in single-day and multiple-day average lag. This study demonstrated a significant relationship between the four meteorological variables and the COVID-19 mortality.
BACKGROUND: Hand-foot-mouth disease (HFMD) is caused by a group of enteroviruses (EVs) and has a high incidence in children; some subtypes had high mortalities in children. The subtypes of HFMD had a different incidence across seasons. Thereby, we suspect that the infection of HFMD is varied by meteorological factors. However, studies examining serotype-specific associations between meteorological factors and HFMD incidence were rare. METHODS: We obtained all HFMD cases that occurred from 1 January 2010 to 31 December 2018 in Zhejiang province from the China Information System for Disease Control and Prevention (CISDCP). Daily meteorological data for Zhejiang province were provided by the China Methodological Data Sharing Service System and linked to HFMD cases based on residential addresses and dates of onset. The associations between meteorological factors and HFMDs were examined using distributed lag non-linear models (DLNMs) for each serotype. RESULTS: Overall, the incidences of all HFMD cases were increasing in study years, while the number of severe and fatality cases were decreasing. The dominant serotypes varied by study year. The association between temperature and incidence of both CVA16 and EV71 serotypes showed an inverted U shape. The risk ratio for CVA16 was increasing when temperature is 11-25°C, reaching the maximum RR at 18°C and humidity above 77% can promote the occurrence with CVA16, and temperature between 11 and 32°C with the maximum RR at 21°C and relative humidity above 77% are risk conditions of the occurrence of HFMD associated with EV71. For other enteroviruses causing HFMD, temperature above 11°C and humidity above 76% have a risk effect. CVA16, EV71, and all enteroviruses of HFMD have a maximum effect on lag day 0, and temperature is 35, 34, and 33°C respectively, while the enteroviruses of HFMD other than EV71 and CVA16 has a maximum effect when the temperature is 33°C and the lag time is 7 days. CONCLUSION: This study shows that meteorological factors have an effect on the occurrence of different HFMD serotypes. Local control strategies for public health should be taken in time to prevent and reduce the risk of HFMD while the weather is getting warmer and wetter.
BackgroundRespiratory syncytial virus (RSV) is the predominant cause of clinical pneumonia among infants and young children, often peaking during the winter months in temperate regions.AimTo describe RSV seasonality in 13 European countries and examine its association with meteorological factors.MethodsWe included weekly RSV seasonality data from 13 European countries between week 40 2010 and week 39 2019. Using local weighted regression method, we modelled weekly RSV activity with meteorological factors using data from the 2010/11 to the 2017/18 season. We predicted the weekly RSV activity of the 2018/19 season across 41 European countries and validated our prediction using empirical data.ResultsAll countries had annual wintertime RSV seasons with a longitudinal gradient in RSV onset (Pearson’s correlation coefficient, r = 0.71, 95% CI: 0.60 to 0.80). The RSV season started 3.8 weeks later (95% CI: -0.5 to 8.0) in countries in the eastern vs western parts of Europe, and the duration ranged from 8-18 weeks across seasons and countries. Lower temperature and higher relative humidity were associated with higher RSV activity, with a 14-day lag time. Through external validation, the prediction error in RSV season onset was -2.4 ± 3.2 weeks. Similar longitudinal gradients in RSV onset were predicted by our model for the 2018/19 season (r = 0.45, 95% CI: 0.16 to 0.66).ConclusionMeteorological factors, such as temperature and relative humidity, could be used for early warning of RSV season onset. Our findings may inform healthcare services planning and optimisation of RSV immunisation strategies in Europe.
The 2030 Sustainable Development Goals (SDGs) offer a blueprint for global peace and prosperity, while conserving natural ecosystems and resources for the planet. However, factors such as climate-induced weather extremes and other High-Impact Low-Probability (HILP) events on their own can devastate lives and livelihoods. When a pandemic affects us, as COVID-19 has, any concurrent hazards interacting with it highlight additional challenges to disaster and emergency management worldwide. Such amplified effects contribute to greater societal and environmental risks, with cross-cutting impacts and exposing inequities. Hence, understanding how a pandemic affects the management of concurrent hazards and HILP is vital in disaster risk reduction practice. This study reviews the contemporary literature and utilizes data from the Emergency Events Database (EM-DAT) to unpack how multiple extreme events have interacted with the coronavirus pandemic and affected the progress in achieving the SDGs. This study is especially urgent, given the multidimensional societal impacts of the COVID-19 pandemic amidst climate change. Results indicate that mainstreaming risk management into development planning can mitigate the adverse effects of disasters. Successes in addressing compound risks have helped us understand the value of new technologies, such as the use of drones and robots to limit human exposure. Enhancing data collection efforts to enable inclusive sentinel systems can improve surveillance and effective response to future risk challenges. Stay-at-home policies put in place during the pandemic for virus containment have highlighted the need to holistically consider the built environment and socio-economic exigencies when addressing the pandemic’s physical and mental health impacts, and could also aid in the context of increasing climate-induced extreme events. As we have seen, such policies, services, and technologies, along with good nutrition, can significantly help safeguard health and well-being in pandemic times, especially when simultaneously faced with ubiquitous climate-induced extreme events. In the final decade of SDG actions, these measures may help in efforts to “Leave No One Behind”, enhance human-environment relations, and propel society to embrace sustainable policies and lifestyles that facilitate building back better in a post-pandemic world. Concerted actions that directly target the compounding effects of different interacting hazards should be a critical priority of the Sendai Framework by 2030.
We study how seasonal climate affects influenza-pneumonia (I-P) mortality using monthly health and climate data over the past 20 years, reduced to mean annual cycle and statistically correlated. Results show that I-P deaths are inversely related to temperature, humidity, and net solar radiation in the United States, South Africa, and Puerto Rico (r < -0.93) via transmission and immune system response. The I-P mortality is 3-10 times as high in winter as in summer, with sharp transitions in autumn and spring. Public health management can rely on seasonal climate-induced fluctuations of I-P mortality to promote healthy lifestyle choices and guide efforts to mitigate epidemic impacts.
There is a growing need to (1) better understand spaces in which human-animal interactions occur in ways that increase the risk of emerging infectious disease (EID), and (2) identify the opportunities for mitigating EID risk available to urban planning. Peri-urban areas-which are typically under-governed, undergoing significant environmental change and highly susceptible to zoonotic disease transfer-are especially important in this regard. In this research note, we briefly explore how climate change is contributing to both peri-urbanization and EID risk. First, climate change is linked to the displacement of people and other species into peri-urban areas, thereby increasing opportunities for zoonotic disease transfer. Second, whether coastal or inland, peri-urban space, characterized by low resources and inadequate services, is also typically vulnerable to mounting climate impacts including severe weather events, sea level rise, flooding, erosion, drought, salinization and heat waves that create socio-ecological conditions amenable to EID outbreaks. These relationships are particularly alarming given that peri-urban environments abut urban areas creating numerous pathways for the movement of EIDs into larger populations. In this research note, we briefly explore these relationships and illustrate them with a causal loop diagram of climate change-peri-urban displacement-EID interactions based on field work in Malawi. We conclude by emphasizing the need for improved EID risk management and suggest that bringing together the environmental expertise of the conservation community with that of planners through a more convivial urbanism that draws on the concept of working landscape conservation might be a beneficial approach.
There were 18 183 889 cases of hand, foot and mouth disease (HFMD) reported in mainland China from 2008 to 2017. It is important to control and prevent the disease by monitoring spatial-temporal pattern effectively. We described the spatial-temporal pattern and influencing factors of HFMD in China to provide information and provide implements for preventing the disease. The HFMD data of China is retrieved from National Center for Disease Control and Prevention according to month. Descriptive analysis was conducted to evaluate the epidemic features. Spatial autocorrelation analysis is performed to explore the spatial-temporal pattern by Moran index for the overall distribution and GETIS-ord index for the cold and hot spots of HFMD. Multiscale geographically weighted regression is employed to analyse influencing factors of HFMD. The results show that: (sic)HFMD has a dramatic increase and become a major national infectious disease in recent years, with an average growth rate of 134.34 per 100 thousandth. (sic) the population characteristics with two peak ages of 0-5 and 25-30; There were two incidence peak occurring in April to July, and in November, the incidence is highest in May and the lowest in February. (sic) There were higher HFMD incidence rate in southeastern areas with double peaks than that in northwestern areas with a single peak. (sic) The main meteorological factors like humidity, precipitation, temperature mainly affect the seasonal variation of HFMD, while the main socio-economic factors like the number of beds per thousand and urbanization rate affect the interannual variation and spatial differentiation of HFMD. HFMD incidence rate had an increasing trend in southern areas. There was a dominant heterogeneity at the period of incidence and diffusion. The natural and economic factors were associated with the epidemic of HFMD. Thus, prevention and control measures should be implemented to reduce the incidence and mortality depending on its characteristics in different provinces.
BACKGROUND: The COVID-19 pandemic and climate change are both significant and pressing global challenges, posing threats to public health and wellbeing. Young people are particularly vulnerable to the distress both crises can cause, but understanding of the varied psychological responses to both issues is poor. We aimed to investigate these responses and their links with mental health conditions and feelings of agency. METHODS: We conducted an online survey between Aug 5 and Oct 26, 2020, targeting a diverse sample of young people (aged 16-24 years, n=530) in the UK. The survey was distributed using a combination of a survey panel (panel sample) and direct approaches to youth groups and schools who shared the survey with young people in their networks (community sample). We collected data on respondents’ psychological responses to both climate change and the COVID-19 pandemic, their sense of agency to respond to each crisis, and the range of impacts on their lives. We also collected demographics data and screened for mental health and wellbeing indicators. We used non-parametric tests for most statistical comparisons. For paired samples, we used Wilcoxon’s signed-rank test, and used Mann-Whitney U-tests or Kruskal-Wallis tests for two or more independent samples. Summed scale scores were considered as interval-level data and analysed with Student’s t tests and ANOVAs. Effect sizes are reported as Cohen’s d and partial eta-squared (η·(2)(p)), respectively. FINDINGS: After excluding 18 suspected bots and 94 incomplete responses, 530 responses were retained for analysis. Of the 518 respondents who provided demographic data, 63% were female, 71·4% were White, and the mean family affluence score was 8·22 (SD 2·29). Most participants (n=343; 70%) did not report a history of diagnosis or treatment for a mental health disorder, but mental health scores indicated a common experience of (relatively mild) symptoms of anxiety, depression, and stress. Although UK youth reported more life disruption and concern for their future due to the COVID-19 pandemic, climate change was associated with significantly greater distress overall, particularly for individuals with low levels of generalised anxiety. The COVID-19 pandemic was more associated with feelings of anxiety, isolation, disconnection, and frustration; distress around loss and grief; and effects on quality of life. Climate change was more likely to evoke emotions such as interest and engagement, guilt, shame, anger, and disgust. The greater distress attributed to climate change overall was due, in particular, to higher levels of guilt, sense of personal responsibility, and greater distress triggered by upsetting media coverage. Agency to address climate change was associated with greater climate distress, but pandemic-related distress and agency were unrelated. INTERPRETATION: The COVID-19 pandemic and climate change are affecting the wellbeing of UK young people in distinct ways, with implications for health service, policy, and research responses. There is a need for mental health practitioners, policy makers, and other societal actors to account for the complex relationship between climate agency, distress, and mental wellbeing in young people. FUNDING: Imperial College London.
The COVID-19 pandemic caused strict regulations to lower transmission rates. Industries were shut down, people were in lockdown, and travel was curtailed. Restrictions were in effect for an enough period for people’s behaviour to change. For example, online meetings rather than needing to travel. This opens the possibility for alterations to the perception that it is possible to commit to effective climate change actions. A Q methodology study was conducted to analyse how 33 university environmental students across the United Arab Emirates perceive the importance of prioritising climate change actions post-pandemic. Statistical analysis yielded four discourses. The first emphasises the need to learn lessons about climate sustainability and sustain them post-pandemic. The second, more pessimistic but advocates preventing a return to pre-pandemic norms by implementing post-pandemic climate change regulations. The third expects economic recovery to take priority over reducing emissions. The fourth raises opportunities and challenges for environmental sustainability post-COVID-19.
Global changes in response to human encroachment into natural habitats and carbon emissions are driving the biodiversity extinction crisis and increasing disease emergence risk. Host distributions are one critical component to identify areas at risk of viral spillover, and bats act as reservoirs of diverse viruses. We developed a reproducible ecological niche modelling pipeline for bat hosts of SARS-like viruses (subgenus Sarbecovirus), given that several closely related viruses have been discovered and sarbecovirus-host interactions have gained attention since SARS-CoV-2 emergence. We assessed sampling biases and modelled current distributions of bats based on climate and landscape relationships and project future scenarios for host hotspots. The most important predictors of species distributions were temperature seasonality and cave availability. We identified concentrated host hotspots in Myanmar and projected range contractions for most species by 2100. Our projections indicate hotspots will shift east in Southeast Asia in locations greater than 2°C hotter in a fossil-fuelled development future. Hotspot shifts have implications for conservation and public health, as loss of population connectivity can lead to local extinctions, and remaining hotspots may concentrate near human populations.
Many people do not make choices that minimize risk in the face of health and environmental threats. Using pre-registered analyses, we tested whether a risk communication that primed perceptions about health-protective preparation and behavior of close social contacts promoted protection views and protective behaviors. From December 10-24, 2020, we fielded a 2 (threat vignette: wildfire or COVID-19) x 3 (social contact prime: control, inaction, or action) experiment to a representative sample of 1,108 California residents facing increased COVID-19 cases/deaths, who had recently experienced the most destructive wildfire season in California history. Outcome variables were protection views and protective behavior (i.e., information seeking). Across threat conditions, stronger social norms, efficacy, and worry predicted greater protection views and some protective behaviors. Priming social-contact action resulted in greater COVID-19 information-seeking compared to the control. In the wildfire smoke condition, priming social contact action and inaction increased perceived protective behavior social norms compared to the control; social norms partially mediated the relationships of priming with protection views and protective behaviors; and having existing mask supplies enhanced the relationship between priming inaction and greater protection views compared to priming action or the control. Findings highlight the importance of social influence for health protection views and protective behaviors. Communications enhancing social norms that are sensitive to resource contexts may help promote protective behaviors.
Tuberculosis (TB) is one of the world’s deadliest infectious disease killers today, and despite China’s increasing efforts to prevent and control TB, the TB epidemic is still very serious. In the context of the COVID-19 pandemic, if reliable forecasts of TB epidemic trends can be made, they can help policymakers with early warning and contribute to the prevention and control of TB. In this study, we collected monthly reports of pulmonary tuberculosis (PTB) in Guiyang, China, from January 1, 2010 to December 31, 2020, and monthly meteorological data for the same period, and used LASSO regression to screen four meteorological factors that had an influence on the monthly reports of PTB in Guiyang, including sunshine hours, relative humidity, average atmospheric pressure, and annual highest temperature, of which relative humidity (6-month lag) and average atmospheric pressure (7-month lag) have a lagging effect with the number of TB reports in Guiyang. Based on these data, we constructed ARIMA, Holt-Winters (additive and multiplicative), ARIMAX (with meteorological factors), LSTM, and multivariable LSTM (with meteorological factors). We found that the addition of meteorological factors significantly improved the performance of the time series prediction model, which, after comprehensive consideration, included the ARIMAX (1,1,1) (0,1,2)(12) model with a lag of 7 months at the average atmospheric pressure, outperforms the other models in terms of both fit (RMSE = 37.570, MAPE = 10.164%, MAE = 28.511) and forecast sensitivity (RMSE = 20.724, MAPE = 6.901%, MAE = 17.306), so the ARIMAX (1,1,1) (0,1,2)(12) model with a lag of 7 months can be used as a predictor tool for predicting the number of monthly reports of PTB in Guiyang, China.
Coccidioidomycosis (Valley fever) is a disease caused by the fungal pathogens Coccidioides immitis and Coccidioides posadasii that are endemic to the southwestern United States and parts of Mexico and South America. Throughout the range where the pathogens are endemic, there are seasonal patterns of infection rates that are associated with certain climatic variables. Previous studies that looked at annual and monthly relationships of coccidioidomycosis and climate suggest that infection numbers are linked with precipitation and temperature fluctuations; however, these analytic methods may miss important nonlinear, nonmonotonic seasonal relationships between the response (Valley fever cases) and explanatory variables (climate) influencing disease outbreaks. To improve our current knowledge and to retest relationships, we used case data from three counties of high endemicity in southern Arizona paired with climate data to construct a generalized additive statistical model that explores which meteorological parameters are most useful in predicting Valley fever incidence throughout the year. We then use our model to forecast the pattern of Valley fever cases by month. Our model shows that maximum monthly temperature, average PM10, and total precipitation 1 month prior to reported cases (lagged model) were all significant in predicting Valley fever cases. Our model fits Valley fever case data in the region of endemicity of southern Arizona and captures the seasonal relationships that predict when the public is at higher risk of being infected. This study builds on and retests relationships described by previous studies regarding climate variables that are important for predicting risk of infection and understanding this fungal pathogen. IMPORTANCE The inhalation of environmental infectious propagules from the fungal pathogens Coccidioides immitis and Coccidioides posadasii by susceptible mammals can result in coccidioidomycosis (Valley fever). Arizona is known to be a region where the pathogen is hyperendemic, and reported cases are increasing throughout the western United States. Coccidioides spp. are naturally occurring fungi in arid soils. Little is known about ecological factors that influence the growth of these fungi, and a higher environmental burden may result in increases in human exposure and therefore case rates. By examining case and climate data from Arizona and using generalized additive statistical models, we were able to examine the relationship between disease outbreaks and climatic variables and predict seasonal time points of increased infection risk.
COVID-19 can be characterized as an outcome of degraded planetary health drivers in complex systems and has wide-reaching implications in social, economic and environmental realms. To understand the drivers of planetary health that have influences of emergence and spread of COVID-19 and their implications for sustainability systems thinking and a narrative literature review are deployed. In particular, sixteen planetary health drivers are identified, i.e., population growth, climate change, agricultural intensification, urbanization, land use and land cover change, deforestation, biodiversity loss, globalization, wildlife trade, wet markets, non-planetary health diet, antimicrobial resistance, air pollution, water stress, poverty and weak governance. The implications of COVID-19 for planetary health are grouped in six categories: social, economic, environmental, technological, political, and public health. The implications for planetary health are then judged to see the impacts with respect to sustainable development goals (SDGs). The paper indicates that sustainable development goals are being hampered due to the planetary health implications of COVID-19.
The associations between ambient temperature and influenza-like illness (ILI) have been investigated in previous studies. However, they have inconsistent results. The purpose of this study was to estimate the effect of ambient temperature on ILI in Shandong Province, China. METHODS: Weekly ILI surveillance and meteorological data over 2014-2017 of the Shandong Province were collected from the Shandong Center for Disease Control and Prevention and the China Meteorological Data Service Center, respectively. A distributed lag non-linear model was adopted to estimate the city-specific temperature-ILI relationships, which were used to pool the regional-level and provincial-level estimates through a multivariate meta-analysis. RESULTS: There were 911,743 ILI cases reported in the study area between 2014 and 2017. The risk of ILI increased with decreasing weekly ambient temperature at the provincial level, and the effect was statistically significant when the temperature was <-1.5°C (RR = 1.24, 95% CI: 1.00-1.54). We found that the relationship between temperature and ILI showed an L-shaped curve at the regional level, except for Southern Shandong (S-shaped). The risk of ILI was influenced by cold, with significant lags from 2.5 to 3 weeks, and no significant effect of heat on ILI was found. CONCLUSION: Our findings confirm that low temperatures significantly increased the risk of ILI in the study area. In addition, the cold effect of ambient temperature may cause more risk of ILI than the hot effect. The findings have significant implications for developing strategies to control ILI and respond to climate change.
Intersectoral collaborations are an integral component of the prevention and control of diseases in a complex health system. On the one hand, One Health (OH) is promoting the establishment of intersectoral collaborations for prevention at the human-animal-environment interface. On the other hand, operationalising OH can only be realized through intersectoral collaborations. This work contributes to broadening the knowledge of the process for operationalising OH by analysing the governance structures behind different initiatives that tackle health problems at the human-animal-environment interface. The cases taken as examples for the analysis are the control and response to rabies and avian influenza under “classical OH”, and the management of floods and droughts for insights into “extended OH”. Data from Ghana and India were collected and compared to identify the key elements that enable ISC for OH. Despite the case studies being heterogeneous in terms of their geographic, economic, social, cultural, and historical contexts, strong similarities were identified on how intersectoral collaborations in OH were initiated, managed, and taken to scale. The actions documented for rabies prevention and control were historically based on one sector being the leader and implementer of activities, while avian influenza management relied more on intersectoral collaborations with clearly defined sectoral responsibilities. The management of the impact of flood and droughts on health provided a good example of intersectoral collaborations achieved by sectoral integration; however, the human health component was only involved in the response stage in the case of Ghana, while for India, there were broader schemes of intersectoral collaborations for prevention, adaptation, and response concerning climate change and disaster.
Inconsistencies were discovered in the findings regarding the effects of meteorological factors on tuberculosis (TB). This study conducted a systematic review of published studies on the relationship between TB and meteorological factors and used a meta-analysis to investigate the pooled effects in order to provide evidence for future research and policymakers. The literature search was completed by August 3rd, 2021, using three databases: PubMed, Web of Science and Embase. Relative risks (RRs) in included studies were extracted and all effect estimates were combined together using meta-analysis. Subgroup analyses were carried out based on the resolution of exposure time, regional climate, and national income level. A total of eight studies were included after screening for inclusion and exclusion criteria. Our results show that TB risk was positively correlated with precipitation (RR = 1.32, 95% CI: 1.14, 1.51), while temperature (RR = 1.15, 95% CI: 1.00, 1.32), humidity (RR = 1.05, 95% CI: 0.99, 1.10), air pressure (RR = 0.89, 95% CI: 0.69, 1.14) and sunshine duration (RR = 0.95, 95% CI: 0.80, 1.13) all had no statistically significant correlation. Subgroup analysis shows that quarterly measure resolution, low and middle Human Development Index (HDI) level and subtropical climate increase TB risk not only in precipitation, but also in temperature and humidity. Moreover, less heterogeneity was observed in “high and extremely high” HDI areas and subtropical areas than that in other subgroups (I(2) = 0%). Precipitation, a subtropical climate, and a low HDI level are all positive influence factors to tuberculosis. Therefore, residents and public health managers should take precautionary measures ahead of time, especially in extreme weather conditions.
The aim of this study is to simulate the impact of mineral dust emissions from the Sahel-Saharan zone on temperature extremes over the Sahel. To achieve this goal, we performed two numerical simulations: one with the standard version of the regional climate model RegCM4 (no dust run) and another one with the same version of this model incorporating a dust module (dust run). The difference between both versions of the model allowed to isolate the impacts of mineral dust emissions on temperature extremes. The results show that the accumulation of mineral dust into the atmosphere leads to a decrease of the frequency of warm days, very warm days, and warm nights over the Sahel. This decrease is higher during the MAM (March-April-May) and JJA (June-July-August) periods especially in the northern and western parts of the Sahel. The impact of the mineral dust emissions is also manifested by a decrease of the frequency of tropical nights especially during MAM in the northern Sahel. When considering the warm spells, mineral particles tend to weaken them especially in MAM and JJA in the northern Sahel. To estimate the potential impacts of the mineral dust accumulation on heat stress, the heat index and the humidex are used. The analysis of the heat index shows that the dust impact is to reduce the health risks particularly in the northern Sahel during the MAM period, in the western Sahel during JJA, and in the southern and the northeastern parts of the Sahel during the SON (September-October-November) period. As for the humidex, it is characterized by a decrease especially in the northern Sahel for all seasons. This reduction of the occurrence of thermal extremes may have a positive effect on the energy demand for cooling and on global health. However, the accumulation of dust particles in the atmosphere may also increase the meningitis incidence and prevalence.
Since the outbreak of COVID-19, its effects on different aspects of life have been subject to much research, including food security, a domain that has been of special concern in many low-income countries. Ethiopia has been facing many challenges related to food security for decades via drought, famine, and conflict. Within this context, this case study assessed the impact of the COVID-19 pandemic on food security in Ethiopia. Results show that the ongoing pandemic has negatively impacted different regions and at-risk groups in a heterogeneous manner. This has been mainly through disruptions in the Ethiopian food value chain and the relative failure of social security programmes to address the losses generated by COVID-19. The population in the capital city, Addis Ababa, was able to maintain the same level of food security despite income losses caused by the COVID-19 pandemic. However, at-risk groups such as refugees, internally displaced persons (IDPs), and conflict affected regions were seen to suffer significantly from food insecurity exacerbated by COVID-19. Furthermore, this paper particularly emphasizes the importance of considering contextual factors other than COVID-19, such as conflicts or climate change, when discussing the state of food security in Ethiopia.
The African Sahel countries are inherently fragile, environmentally insecure and economically weak. This paper underscores the compounded impacts brought about by the COVID-19 pandemic on resource supply security and, hence, the long-term development of the region. It outlines the Sahel-specific COVID-19 scenario by firstly highlighting the underlying vulnerabilities and later linking the health sector outcomes to increased political instability and environmental insecurity, particularly the deterioration of food security. In this sense, this paper shows from a region-wide perspective how COVID-19 in the Sahel is associated with enlarged sociopolitical developmental perils. Lower remittance sent by expatriates, violent conflicts, increased cross-border terrorism and migration, discriminant mobility restrictions of people and goods, weak national healthcare infrastructures, bottlenecks in international aid, pressures on the education system and recent climate extremes are some revealing examples of aggravators of the impacts on the supply of vital resources, such as food. This paper also shows the importance of considering the close interlinks between health, food and political stability in the Sahel. There is a paramount need for more comprehensive approaches linking human health to other sectors, and for re-considering local sustainable agriculture. To avoid prolonged or recurrent humanitarian crises, the Sahel countries need to strengthen response capacities through public sector-led responses. Examples of these responses include reinforced national disaster programs for the vulnerable, support to sustainable agriculture and food markets, improved performance and communication of public sector relief, state-based cooperation, building of regional alliances and peacemaking efforts.
Objective Respiratory viruses are the most important cause of lower respiratory tract infections (LRTI) in children. Meteorological factors can influence viral outbreaks. The objective of this study was to determine the association between climate variables and respiratory virus detection. Methods Multicenter prospective 1-year surveillance was conducted among children hospitalized for LRTI in Tunisia. Nasopharyngeal aspirates were tested by direct immunofluorescence assay (DIFA) for the detection of respiratory syncytial virus (RSV); adenovirus (AdV); influenza virus (IFV) A and B; and parainfluenza virus 1, 2, and 3 (PIV1/2/3). Samples were further analyzed by reverse-transcription polymerase chain reaction for the detection of human metapneumovirus (hMPV). Monthly meteorological data were determined by consulting the National Institute of Meteorology and the World Weather Online Meteorological Company websites. Pearson’s correlation tests were used to determine the statistical association between the detection of respiratory viruses and climatic characteristics. Results Among 572 patients, 243 (42.5%) were positive for at least one virus. The most frequently detected viruses by DIFA were RSV (30.0%), followed by IFVA (3.8%), IFVB (3.5%), PIV (0.9%), and AdV (0.9%). HMPV was detected in 13 RSV-negative samples (3.3%). Dual infections were detected in seven cases (1.2%). Monthly global respiratory viruses and RSV detections correlated significantly with temperature, rainfall, cloud cover, wind speed, wind temperature, and duration of sunshine. Monthly IFV detection significantly correlated with rainfall, wind speed, wind temperature, and duration of sunshine. HMPV detection significantly correlated with temperature and wind temperature. Conclusion Respiratory viral outbreaks are clearly related to meteorological factors in Tunisia.
The Uttarakhand State, known for its Himalayan Mountains, is a territory in Northern India that is extremely vulnerable to earthquakes, landslides, and floods. Currently, due to the COVID-19 outbreak, India is facing the dual challenge of containing a pandemic and responding to natural disasters. This situation can have a negative impact on the health and the economic development of the region, leading to a long-lasting humanitarian crisis that can disrupt even more, the already overburdened health service. In addition, it can pose serious threats to the wellbeing of the population as it complicates physical distancing and other COVID-19 prevention measures. It is of utmost importance to analyse the impact of floods, landslides, and COVID-19 pandemic on the health system of the Uttarakhand State, and how these crises interact with each other.
Cyclone Amphan battered the coastal communities in the southwestern part of Bangladesh in 2020 during the COVID-19 pandemic. These coastal communities were experiencing such a situation for the first time and faced the dilemma of whether to stay at home and embrace the cyclone or be exposed to the COVID-19 virus in the cyclone shelters by evacuating. This article intends to explore individuals’ decisions regarding whether to evacuate in response to cyclone Amphan and in light of the risks of the COVID-19 pandemic. Consequently, this study investigated evacuation behaviors among the households and explored the impacts of COVID-19 during the evacuation procedures. We conducted household surveys to collect primary information and undertook 378 samples for interviews at a precision level of 0.05 in fourteen villages. Despite the utmost effort of the government, the results demonstrated that 96.6% of people in the coastal area received a cyclone evacuation order before the cyclone’s landfall, and only 42% of people followed the evacuation order. The majority of households chose to stay at home because of fear of COVID-19 exposure in the crowded shelters. Although half of the evacuees were housed in cyclone shelters, COVID-19 preventive measures were apparently not set in place. Thus, this study will assist in crafting future government policies to enhance disaster evacuation plans by providing insights from the pandemic that can inform disaster management plans in the Global South.
Background The world has been battling several vector-borne diseases since time immemorial. Socio-economic marginality, precipitation variations and human behavioral attributes play a major role in the proliferation of these diseases. Lockdown and social distancing have affected social behavioral aspects of human life and somehow impact on the spread of vector borne diseases. This article sheds light into the relationship between COVID-19 lockdown and global dengue burden with special focus on India. It also focuses on the interconnection of the COVID-19 pandemic (waves 1 and 2) and the alteration of human behavioral patterns in dengue cases. Methods We performed a systematic search using various resources from different platforms and websites, such as Medline; Pubmed; PAHO; WHO; CDC; ECDC; Epidemiology Unit Ministry of Health (Sri Lanka Government); NASA; NVBDCP from 2015 until 2021. We have included many factors, such as different geographical conditions (tropical climate, semitropic and arid conditions); GDP rate (developed nations, developing nations, and underdeveloped nations). We also categorized our data in order to conform to COVID-19 duration from 2019 to 2021. Data was extracted for the complete duration of 10 years (2012 to 2021) from various countries with different geographical region (arid region, semitropic/semiarid region and tropical region). Results There was a noticeable reduction in dengue cases in underdeveloped (70-85%), developing (50-90%), and developed nations (75%) in the years 2019 and 2021. The dengue cases drastically reduced by 55-65% with the advent of COVID-19 s wave in the year 2021 across the globe. Conclusions At present, we can conclude that COVID-19 and dengue show an inverse relationship. These preliminary, data-based observations should guide clinical practice until more data are made public and basis for further medical research.
The common cold is a leading cause of morbidity and contributes significantly to the health costs in Bhutan. The study utilized multivariate Zero-inflated Poisson regression in a Bayesian framework to identify climatic variability and spatial and temporal patterns of the common cold in Bhutan. There were 2,480,509 notifications of common cold between 2010 and 2018. Children aged < 15 years were twice (95% credible interval [CrI] 2.2, 2.5) as likely to get common cold than adults, and males were 12.4% (95 CrI 5.5%, 18.7%) less likely to get common cold than females. A 10 mm increase in rainfall lagged one month, and each 1 °C increase of maximum temperature was associated with a 5.1% (95% CrI 4.2%, 6.1%) and 2.6% (95% CrI 2.3%, 2.8%) increase in the risk of cold respectively. An increase in elevation of 100 m and 1% increase in relative humidity lagged three months were associated with a decrease in risk of common cold by 0.1% (95% CrI 0.1%, 0.2%) and 0.3% (95% CrI 0.2%, 0.3%) respectively. Seasonality and spatial heterogeneity can partly be explained by the association of common cold to climatic variables. There was statistically significant residual clustering after accounting for covariates. The finding highlights the influence of climatic variables on common cold and suggests that prioritizing control strategies for acute respiratory infection program to subdistricts and times of the year when climatic variables are associated with common cold may be an effective strategy.
INTRODUCTION: Cryptococcus gattii (C. gatti) is a rare cause of meningitis in the United States. Outbreaks in new geographic distributions in the past few decades raise concern that climate change may be contributing to a broader distribution of this pathogen. We review a case of C. gattii in a 23-year-old woman in Northern California who was diagnosed via lumbar puncture after six weeks of headache, blurred vision, and tinnitus. CASE REPORT: A 23-year-old previously healthy young woman presented to the emergency department (ED) after multiple visits to primary care, other EDs, and neurologists, for several weeks of headache, nausea, tinnitus, and blurred vision. On examination the patient was found to have a cranial nerve VI palsy (impaired abduction of the left eye) and bilateral papilledema on exam. Lumbar puncture had a significantly elevated opening pressure. Cerebrospinal fluid studies were positive for C. gattii. The patient was treated with serial lumbar punctures, followed by lumbar drain, as well as amphotericin and flucytosine. The patient had improvement in headache and neurologic symptoms and was discharged to another facility that specializes in management of this disease to undergo further treatment with immunomodulators and steroids. CONCLUSION: Fungal meningitis is uncommon in the US, particularly among immunocompetent patients. Due to climate change, C. gattii may be a new pathogen to consider. This finding raises important questions to the medical community about the way global climate change affects day to day medical care now, and how it may change in the future.
Influenza typically causes mild infection but can lead to severe outcomes for those with compromised lung health. Flooding, a seasonal problem in Iowa, can expose many Iowans to molds and allergens shown to alter lung inflammation, leading to asthma attacks and decreased viral clearance. Based on this, the hypothesis for this research was that there would be geographically specific positive associations in locations with flooding with influenza diagnosis. An ecological study was performed using influenza diagnoses and positive influenza polymerase chain reaction tests from a de-identified large private insurance database and Iowa State Hygienic Lab. After adjustment for multiple confounding factors, Poisson regression analysis resulted in a consistent 1% associated increase in influenza diagnoses per day above flood stage (95% confidence interval: 1.00-1.04). This relationship remained after removal of the 2009-2010 influenza pandemic year. There was no associated risk between flooding and influenza-like illness as a nonspecific diagnosis. Associated risks between flooding and increased influenza diagnoses were geographically specific, with the greatest risk in the most densely populated areas. This study indicates that populations who live, work, or volunteer in flooded environments should consider preventative measures to avoid environmental exposures to mitigate illness from influenza in the following year.
Big Events are periods during which abnormal large-scale events like war, economic collapse, revolts, or pandemics disrupt daily life and expectations about the future. They can lead to rapid change in health-related norms, beliefs, social networks and behavioural practices. The world is undergoing such Big Events through the interaction of COVID-19, a large economic downturn, massive social unrest in many countries, and ever-worsening effects of global climate change. Previous research, mainly on HIV/AIDS, suggests that the health effects of Big Events can be profound, but are contingent: Sometimes Big Events led to enormous outbreaks of HIV and associated diseases and conditions such as injection drug use, sex trading, and tuberculosis, but in other circumstances, Big Events did not do so. This paper discusses and presents hypotheses about pathways through which the current Big Events might lead to better or worse short and long term outcomes for various health conditions and diseases; considers how pre-existing societal conditions and changing ‘pathway’ variables can influence the impact of Big Events; discusses how to measure these pathways; and suggests ways in which research and surveillance might be conducted to improve human capacity to prevent or mitigate the effects of Big Events on human health.
Racial and ethnic minority and lower-income groups are disproportionately affected by environmental hazards and suffer worse health outcomes than other groups in the United States. Relative to whites and higher-income groups, racial-ethnic minority and lower-income Americans also frequently express greater concern about high-profile global environmental threats like climate change, but they are widely misperceived as being less concerned about these issues than white and higher-income Americans. We use new survey research to explore public perceptions of COVID-19-another global threat marked by substantial racial, ethnic, and class disparities-finding a distinct pattern of misperceptions regarding groups’ concerns. We then discuss how these misperceptions represent a unique form of social misinformation that may pose a threat to science and undermine the cooperation and trust needed to address collective problems.
This research aims to look at the link between environmental pollutants and the coronavirus disease (COVID-19) outbreak in California. To illustrate the COVID-19 outbreak, weather, and environmental pollution, we used daily confirmed cases of COVID-19 patients, average daily temperature, and air quality Index, respectively. To evaluate the data from March 1 to May 24, 2020, we used continuous wavelet transform and then applied partial wavelet coherence (PWC), wavelet transform coherence (WTC), and multiple wavelet coherence (MWC). Empirical estimates disclose a significant association between these series at different time-frequency spaces. The COVID-19 outbreak in California and average daily temperature show a negative (out phase) coherence. Similarly, the air quality index and COVID-19 also show a negative association circle during the second week of the observed period. Our findings will serve as policy implications for state and health officials and regulators to combat the COVID-19 outbreak.
INTRODUCTION: The spatiotemporal patterns of Corona Virus Disease 2019 (COVID-19) is detected in the United States, which shows temperature difference (TD) with cumulative hysteresis effect significantly changes the daily new confirmed cases after eliminating the interference of population density. METHODOLOGY: The nonlinear feature of updated cases is captured through Generalized Additive Mixed Model (GAMM) with threshold points; Exposure-response curve suggests that daily confirmed cases is changed at the different stages of TD according to the threshold points of piecewise function, which traces out the rule of updated cases under different meteorological condition. RESULTS: Our results show that the confirmed cases decreased by 0.390% (95% CI: -0.478 ~ -0.302) for increasing each one degree of TD if TD is less than 11.5°C; It will increase by 0.302% (95% CI: 0.215 ~ 0.388) for every 1°C increase in the TD (lag0-4) at the interval [11.5, 16]; Meanwhile the number of newly confirmed COVID-19 cases will increase by 0.321% (95% CI: 0.142 ~ 0.499) for every 1°C increase in the TD (lag0-4) when the TD (lag0-4) is over 16°C, and the most fluctuation occurred on Sunday. The results of the sensitivity analysis confirmed our model robust. CONCLUSIONS: In US, this interval effect of TD reminds us that it is urgent to control the spread and infection of COVID-19 when TD becomes greater in autumn and the ongoing winter.
This article considers the optimal structure of institutions that respond to existential threats such as climate change and pandemics. While science must play a central role in guiding policy responses, there are many values at stake that ought to be reflected in institutional design. There is a distinction between risk assessment, a science-driven analysis in these contexts, and risk management, in which trade-offs are considered in responding to the threats. Moreover, the nature of these threats depends on complex, uncertain and fluid scientific knowledge that requires institutions to be sensitive to communication challenges. Finally, institutions should consider collective action problems and defer or delegate to jurisdictions and institutions whose scope of mandate is appropriate. We assess the Canadian response to the COVID-19 pandemic from an institutional perspective and conclude that, amongst other things, it was insufficiently multidisciplinary, which risked marginalizing the non-public health costs of policy responses to the pandemic.
Understanding whether and how wildfires exacerbate COVID-19 outcomes is important for assessing the efficacy and design of public sector responses in an age of more frequent and simultaneous natural disasters and extreme events. Drawing on environmental and emergency management literatures, we investigate how wildfire smoke (PM(2.5)) impacted COVID-19 infections and deaths during California’s 2020 wildfire season and how public housing resources and hospital capacity moderated wildfires’ effects on COVID-19 outcomes. We also hypothesize and empirically assess the differential impact of wildfire smoke on COVID-19 infections and deaths in counties exhibiting high and low social vulnerability. To test our hypotheses concerning wildfire severity and its disproportionate impact on COVID-19 outcomes in socially vulnerable communities, we construct a county-by-day panel dataset for the period April 1 to November 30, 2020, in California, drawing on publicly available state and federal data sources. This study’s empirical results, based on panel fixed effects models, show that wildfire smoke is significantly associated with increases in COVID-19 infections and deaths. Moreover, wildfires exacerbated COVID-19 outcomes by depleting the already scarce hospital and public housing resources in local communities. Conversely, when wildfire smoke doubled, a one percent increase in the availability of hospital and public housing resources was associated with a 2 to 7 percent decline in COVID-19 infections and deaths. For California communities exhibiting high social vulnerability, the occurrence of wildfires worsened COVID-19 outcomes. Sensitivity analyses based on an alternative sample size and different measures of social vulnerability validate this study’s main findings. An implication of this study for policymakers is that communities exhibiting high social vulnerability will greatly benefit from local government policies that promote social equity in housing and healthcare before, during, and after disasters.
The impacts of wildfires on the health of children are becoming a more urgent matter as wildfires become more frequent, intense and affecting, not only forested areas, but also urban locations. It is important that medical professionals be prepared to provide information to patients and families on how to minimize the adverse health effects on children of wildfire smoke and ash from wildfires. (C) 2021 Elsevier Inc. All rights reserved.
The 2020 California wildfire season coincided with the peak of the COVID-19 pandemic affecting many counties in California, with impacts on air quality. We quantitatively analyzed the short-term effect of air pollution on COVID-19 transmission using county-level data collected during the 2020 wildfire season. Using time-series methodology, we assessed the relationship between short-term exposure to particulate matter (PM(2.5)), carbon monoxide (CO), nitrogen dioxide (NO(2)), and Air Quality Index (AQI) on confirmed cases of COVID-19 across 20 counties impacted by wildfires. Our findings indicate that PM(2.5), CO, and AQI are positively associated with confirmed COVID-19 cases. This suggests that increased air pollution could worsen the situation of a health crisis such as the COVID-19 pandemic. Health policymakers should make tailored policies to cope with situations that may increase the level of air pollution, especially during a wildfire season.
Background: Air pollution has been linked to increased susceptibility to SARS-CoV-2. Thus, it has been suggested that wildfire smoke events may exacerbate the COVID-19 pandemic. Objectives: Our goal was to examine whether wildfire smoke from the 2020 wildfires in the western United States was associated with an increased rate of SARS-CoV-2 infections in Reno, Nevada. Methods: We conducted a time-series analysis using generalized additive models to examine the relationship between the SARS-CoV-2 test positivity rate at a large regional hospital in Reno and ambient PM2.5 from 15 May to 20 Oct 2020. Results: We found that a 10 µg/m3 increase in the 7-day average PM2.5 concentration was associated with a 6.3% relative increase in the SARS-CoV-2 test positivity rate, with a 95% confidence interval (CI) of 2.5 to 10.3%. This corresponded to an estimated 17.7% (CI: 14.4-20.1%) increase in the number of cases during the time period most affected by wildfire smoke, from 16 Aug to 10 Oct. Significance: Wildfire smoke may have greatly increased the number of COVID-19 cases in Reno. Thus, our results substantiate the role of air pollution in exacerbating the pandemic and can help guide the development of public preparedness policies in areas affected by wildfire smoke, as wildfires are likely to coincide with the COVID-19 pandemic in 2021.
Individually, both droughts and pandemics cause disruptions to global food supply chains. The 21st century has seen the frequent occurrence of both natural and human disasters, including droughts and pandemics. Together their impacts can be compounded, leading to severe economic stress and malnutrition, particularly in developing countries. Understanding how droughts and pandemics interact, and identifying appropriate policies to address them together and separately, is important for maintaining a robust global food supply. Herein we assess the impacts of each of these disasters in the context of food and agriculture, and then discuss their compounded effect. We discuss the implications for policy, and suggest opportunities for future research.
BACKGROUND: In March 2020, the Louisiana Department of Health activated the Medical Monitoring Station (MMS) in downtown New Orleans. This alternative care site is designed to decompress hospitals and nursing homes overwhelmed by the coronavirus disease 2019 (COVID-19) pandemic. Given the city’s historic vulnerability to hurricanes, planning for possible tropical weather events has been a priority for MMS leadership. METHODS: The planning process incorporated input from all sectors/agencies working at the facility, to ensure consistency and cohesion. The MMS Shelter-in-Place Plan (MSIPP) was created, and a comprehensive tabletop exercise was conducted. RESULTS: Six planning topics emerged as a result of the planning process and were used to create a comprehensive plan for sheltering-in-place. These topics address hurricane preparedness for patient care, interfacility coordination, wrap-around services, medical logistics, essential staffing, and incident command during a shelter-in-place scenario. CONCLUSIONS: The MSIPP created by the MMS helped to maximize patient safety and continuity of operations during a real-world event. Select pieces of the plan were activated to meet the needs and threat level of Tropical Storm Cristobal. This experience reinforced the need for originality, scalability, and flexibility in building emergency operations plans in the midst of an unprecedented pandemic.
AIM: The purpose of this retrospective, correlational pilot study was to explore the relationship between historical weekly weather data including temperature, dew point, humidity, barometric pressure, visibility, and cloud cover compared to weekly influenza-like illness reports over a four year period. BACKGROUND: Climate and weather-related conditions may affect the viral activity and transmission of influenza, although this relationship has not been widely studied in nursing. Some research suggests that there are causal links between cold temperatures, low indoor humidity, minimal sun exposure, and influenza outbreaks. Additionally, rapid weather variability in a warming climate can increase influenza epidemic risk. METHODS: Data from a local public health district were extracted and used to correlate with weekly weather averages for the area. RESULTS: Findings showed that current influenza reports are significantly associated with temperature and visibility, both lagged two weeks. CONCLUSIONS: Though more research is needed, nurses must understand, recognize, and act upon weather and climate factors that affect the health of populations. With a greater understanding of the relationship between weather and influenza-like illness, nurses and other healthcare providers can potentially work to respond to and mitigate the consequences of weather-related illness as well as anticipate and prepare for increased flu burden. Furthermore, nurses can remain engaged in climate protective initiatives and policy development at their local community and/or organizational levels to underscore and advocate for the needs of populations and groups they serve.
The soil-borne fungal disease coccidioidomycosis (Valley fever) is prevalent across the southwestern United States (US). Previous studies have suggested that the occurrence of this infection is associated with anomalously wet or dry soil moisture states described by the “grow and blow” hypothesis. The growth of coccidioidomycosis is favored by moist conditions both at the surface and in the root zone. A statistical analysis identified two areas in Arizona and central California, with a moderate-to-high number of coccidioidomycosis cases. A Wavelet Transform Coherence (WTC) analysis between El Nino Southern Oscillation (ENSO), coccidioidomycosis cases, surface soil moisture (SSM; 0 to 5 cm) from European Space Agency-Climate Change Initiative (ESA-CCI), and shallow root zone soil moisture (RZSM; 0 to 40 cm depth) from Soil MERGE (SMERGE) was executed for twenty-four CA and AZ counties. In AZ, only SSM was modulated by ENSO. When case values were adjusted for overreporting between 2009 to 2012, a moderate but significant connection between ENSO and cases was observed at a short periodicity (2.1 years). In central CA, SSM, RZSM, and cases all had a significant link to ENSO at longer periodicities (5-to-7 years). This study provides an example of how oceanic-atmospheric teleconnections can impact human health.
Legionellosis is an infection acquired through inhalation of aerosols that are contaminated with environmental bacteria Legionella spp. The bacteria require warm temperature for proliferation in bodies of water and moist soil. The legionellosis incidence in the United States has been rising rapidly in the past two decades without a clear explanation. In the meantime, the US has recorded consecutive years of above-norm temperature since 1997 and precipitation surplus since 2008. The present study analyzed the legionellosis incidence in the US during the 20-year period of 1999 to 2018 and correlated with concurrent temperature, precipitation, solar ultraviolet B (UVB) radiation, and vehicle mileage data. The age-adjusted legionellosis incidence rates rose exponentially from 0.40/100,000 in 1999 (with 1108 cases) to 2.69/100,000 in 2018 (with 9933 cases) at a calculated annual increase of 110%. In regression analyses, the rise correlated with an increase in vehicle miles driven and with temperature and precipitation levels that have been above the 1901-2000 mean since 1997 and 2008, respectively, suggesting more road exposure to traffic-generated aerosols and promotive effects of anomalous climate. Remarkably, the regressions with cumulative anomalies of temperature and precipitation were robust (R2 ≥ 0.9145, P ≤ 4.7E-11), implying possible changes to microbial ecology in the terrestrial and aquatic environments. An interactive synergy between annual precipitation and vehicle miles was also found in multiple regressions. Meanwhile, the bactericidal UVB radiation has been decreasing, which also contributed to the rising incidence in an inverse correlation. The 2018 legionellosis incidence peak corresponded to cumulative effects of the climate anomalies, vast vehicle miles (3,240 billion miles, 15904 km per capita), record high precipitation (880.1 mm), near record low UVB radiation (7488 kJ/m2), and continued above-norm temperature (11.96°C). These effects were examined and demonstrated in California, Florida, New Jersey, Ohio, and Wisconsin, states that represent diverse incidence rates and climates. The incidence and above-norm temperature both rose most in cold Wisconsin. These results suggest that warming temperature and precipitation surplus have likely elevated the density of Legionella bacteria in the environment, and together with road exposure explain the rapidly rising incidence of legionellosis in the United States. These trends are expected to continue, warranting further research and efforts to prevent infection.
Bacteria in the air present patterns in space and time produced by different sources and environmental factors. Few studies have focused on the link between airborne pathogenic bacteria in densely populated cities, and the risk to the population’s health. Bacteria associated with particulate matter (PM) were monitored from the air of Mexico City (Mexico). We employed a metagenomic approach to characterise bacteria using the 16S rRNA gene. Airborne bacteria sampling was carried out in the north, centre, and south of Mexico City, with different urbanisation rates, during 2017. Bacteria added to the particles were sampled using high-volume PM10 samplers. To ascertain significant differences in bacterial diversity between zones and seasons, the Kruskal-Wallis, Wilcoxon tests were done on alpha diversity parameters. Sixty-three air samples were collected, and DNA was sequenced using next-generation sequencing. The results indicated that the bacterial phyla in the north and south of the city were Firmicutes, Cyanobacteria, Proteobacteria, and Actinobacteria, while in the central zone there were more Actinobacteria. There were no differences in the alpha diversity indices between the sampled areas. According to the OTUs, the richness of bacteria was higher in the central zone. Alpha diversity was higher in the rainy season than in the dry season; the Shannon index and the OTUs observed were higher in the central zone in the dry season. Pathogenic bacteria such as Kocuria, Paracoccus, and Micrococcus predominated in both seasonal times, while Staphylococcus, Corynebacterium, and Nocardioides were found during the rainy season, with a presence in the central zone. (C) Higher Education Press 2022
Elevated ambient temperatures and extreme weather events have increased the incidence of wildfires world-wide resulting in increased wood smoke particle (WSP). Epidemiologic data suggests that WSP exposure associates with exacerbations of respiratory diseases, and with increased respiratory viral infections. To assess the impact of WSP exposure on host response to viral pneumonia, we performed WSP exposures in rodents followed by infection with mouse adapted influenza (HINI-PR8). C57BL/6 male mice aged 6-8 weeks were challenged with WSP or PBS by oropharyngeal aspiration in acute (single dose) or sub-acute exposures (day 1, 3, 5, 7 and 10). Additional groups underwent sub-acute exposure followed by infection by influenza or heat-inactivated (HI) virus. Following exposures/infection, bronchoalveolar lavage (BAL) was performed to assess for total cell counts/differentials, total protein, protein carbonyls and hyaluronan. Lung tissue was assessed for viral counts by real time PCR. When compared to PBS, acute WSP exposure associated with an increase in airspace macrophages. Alternatively, sub-acute exposure resulted in a dose dependent increase in airspace neutrophils. Sub-acute WSP exposure followed by influenza infection was associated with improved respiratory viral outcomes including reduced weight loss and increased blood oxygen saturation, and decreased protein carbonyls and viral titers. Flow cytometry demonstrated dynamic changes in pulmonary macrophage and T cell subsets based on challenge with WSP and influenza. This data suggests that sub-acute WSP exposure can improve host response to acute influenza infection.
BACKGROUND: Drought is an understudied driver of infectious disease dynamics. Amidst the ongoing southwestern North American megadrought, California (USA) is having the driest multi-decadal period since 800 CE, exacerbated by anthropogenic warming. In this study, we aimed to examine the influence of drought on coccidioidomycosis, an emerging infectious disease in southwestern USA. METHODS: We analysed California census tract-level surveillance data from 2000 to 2020 using generalised additive models and distributed monthly lags on precipitation and temperature. We then developed an ensemble prediction algorithm of incident cases of coccidioidomycosis per census tract to estimate the counterfactual incidence that would have occurred in the absence of drought. FINDINGS: Between April 1, 2000, and March 31, 2020, there were 81 448 reported cases of coccidioidomycosis throughout California. An estimated 1467 excess cases of coccidioidomycosis were observed in California in the 2 years following the drought that occurred between 2007 and 2009, and an excess 2649 drought-attributable cases of coccidioidomycosis were observed in the 2 years following the drought that occurred between 2012 and 2015. These increased numbers of cases more than offset the declines in cases that occurred during drought. An IQR increase in summer temperatures was associated with 2·02 (95% CI 1·84-2·22) times higher incidence in the following autumn (September to November), and an IQR increase in precipitation in the winter was associated with 1·45 (1·36-1·55) times higher incidence in the autumn. The effect of winter precipitation was 36% (25-48) stronger when preceded by two dry, rather than average, winters. Incidence in arid counties was most sensitive to precipitation fluctuations, while incidence in wetter counties was most sensitive to temperature. INTERPRETATION: In California, multi-year cycles of dry conditions followed by a wet winter increases transmission of coccidioidomycosis, especially in historically wetter areas. With anticipated increasing frequency of drought in southwestern USA, continued expansion of coccidioidomycosis, along with more intense seasons, is expected. Our results motivate the need for heightened precautions against coccidioidomycosis in seasons that follow major droughts. FUNDING: National Institutes of Health.
From hampering the ability of water utilities to fill their reservoirs to leaving forests parched and ready to burn, drought is a unique natural hazard that impacts many human and natural systems. A great deal of research and synthesis to date has been devoted to understanding how drought conditions harm agricultural operations, leaving other drought-vulnerable sectors relatively under-served. This review aims to fill in such gaps by synthesizing literature from a diverse array of scientific fields to detail how drought impacts nonagricultural sectors of the economy: public water supply, recreation and tourism, forest resources, and public health. We focus on the Intermountain West region of the United States, where the decadal scale recurrence of severe drought provides a basis for understanding the causal linkages between drought conditions and impacts. This article is categorized under: Human Water & Value of Water Science of Water & Water Extremes.
BACKGROUND: Research on temperature and respiratory hospitalizations is lacking in the southeastern U.S. where cold weather is relatively rare. This retrospective study examined the association between cold waves and pneumonia and influenza (P&I) emergency department (ED) visits and hospitalizations in three metro-Atlanta hospitals. METHODS: We used a case-crossover design, restricting data to the cooler seasons of 2009-2019, to determine whether cold waves influenced ED visits and hospitalizations. This analysis considered effects by race/ethnicity, age, sex, and severity of comorbidities. We used generalized additive models and distributed lag non-linear models to examine these relationships over a 21-day lag period. RESULTS: The odds of a P&I ED visit approximately one week after a cold wave were increased by as much as 11%, and odds of an ED visit resulting in hospitalization increased by 8%. For ED visits on days with minimum temperatures >20 °C, there was an increase of 10-15% in relative risk (RR) for short lags (0-2 days), and a slight decrease in RR (0-5%) one week later. For minimum temperatures <0 °C, RR decreased at short lags (5-10%) before increasing (1-5%) one week later. Hospital admissions exhibited a similar, but muted, pattern. CONCLUSION: Unusually cold weather influenced ED visits and admissions in this population.
Coccidioidomycosis, or valley fever, is an infectious fungal disease currently endemic to the southwestern United States. Symptoms of valley fever range in severity from flu-like illness to severe morbidity and mortality. Warming temperatures and changes in precipitation patterns may cause the area of endemicity to expand northward throughout the western United States, putting more people at risk for contracting valley fever. This may increase the health and economic burdens from this disease. We developed an approach to describe the relationship between climate conditions and valley fever incidence using historical data and generated projections of future incidence in response to both climate change and population trends using the Climate Change Impacts and Risk Analysis (CIRA) framework developed by the U.S. Environmental Protection Agency. We also developed a method to estimate economic impacts of valley fever that is based on case counts. For our 2000-15 baseline time period, we estimated annual medical costs, lost income, and economic welfare losses for valley fever in the United States were $400,000 per case, and the annual average total cost was $3.9 billion per year. For a high greenhouse gas emission scenario and accounting for population growth, we found that total annual costs for valley fever may increase up to 164% by year 2050 and up to 380% by 2090. By the end of the twenty-first century, valley fever may cost $620,000 per case and the annual average total cost may reach $18.5 billion per year. This work contributes to the broader effort to monetize climate change-attributable damages in the United States.
Five-year retrospective analysis of respiratory viruses in children less than 18 years old at Tripler Army Medical Center and outlying clinics in Oahu. Respiratory syncytial virus and influenza A showed pronounced seasonality with peaks from September to December and December to March, respectively. Results provide a better understanding of the timing of viral preventive strategies in Oahu.
Climate change affects human health, and severe acute respiratory syndrome (SARS) incidence is one of the health impacts of climate change. This study is a retrospective cohort study. Data have been collected from the Iranian Ministry of Health and Medical Education between 17 February 2016 and17 February 2018. The Neural Network Model has been used to predict SARS infection. Based on the results of the multivariate Poisson regression and the analysis of the coexistence of the variables, the minimum daily temperature was positively associated with the risk of SARS in men and women. The risk of SARS has increased in women and men with increasing daily rainfall. According to the result, by changes in bioclimatic parameters, the number of SARS patients will be increased in cities of Iran. Our study has shown a significant relationship between SARS and the climatic variables by the type of climate and gender. The estimates suggest that hospital admissions for climate-related respiratory diseases in Iran will increase by 36% from 2020 to 2050. This study demonstrates one of the health impacts of climate change. Policymakers can control the risks of climate change by mitigation and adaptation strategists.
Objectives: Sandstorms are natural climate calamities causing severe weather changes and health prob-lems. The sandstorm allied issues are of significant apprehension worldwide, mainly in the present pan-demic. This study aims to examine the “sandstorm impact on environmental pollution particulate matter (PM2.5), carbon monoxide (CO), ozone (O3), and daily new cases and deaths due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) ” in Kuwait. Methods: The two incidences of sandstorms occurred in Kuwait, dated 13 March 2021 and 13 June 2021. The data on “PM2.5, CO, NO2, and O-3, and SARS-CoV-2 cases and deaths ” were documented three weeks before and after both incidences of the sandstorm. For the first incidence, the data was recorded from 18 February to 12 March 2021; and from 13 March to 2 April 2021. However, for the second incidence of sandstorms, data were documented from 23 May to 12 June 2021; and from 13 June to 3 July 2021. The daily “PM2.5, CO, NO2, and O-3 levels ” were recorded from “Air Quality Index-AQI, metrological web, and data on COVID-19 daily cases and deaths were recorded from the World Health Organization “. Results: After the first and second sandstorm incidence, the air contaminants PM2.5 was increased by 26.62%, CO 22.08%, and O-3 increased 18.10% compared to before the sandstorm. SARS-CoV-2 cases were markedly amplified by (21.25%), and deaths were increased by (61.32%) after the sandstorm. Conclusions: Sandstorm events increase air pollutants PM2.5, CO, and O-3 levels, and these pollutants increase the SARS-COV-2 daily cases and deaths in Kuwait. The findings have a meaningful memorandum to healthcare representatives to advise the public about the health hazards of the sandstorm and its link-age with SARS-CoV-2 cases and deaths. (C) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University.
BACKGROUND: The application of molecular diagnostics has identified enteric group adenovirus serotypes 40 and 41 as important causes of diarrhea in children. However, many aspects of the epidemiology of adenovirus 40/41 diarrhea have not been described. METHODS: We used data from the 8-site Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development Project birth cohort study to describe site- and age-specific incidence, risk factors, clinical characteristics, and seasonality. RESULTS: The incidence of adenovirus 40/41 diarrhea was substantially higher by quantitative polymerase chain reaction than enzyme immunoassay and peaked at ∼30 episodes per 100 child-years in children aged 7-15 months, with substantial variation in incidence between sites. A significant burden was also seen in children 0-6 months of age, higher than other viral etiologies with the exception of rotavirus. Children with adenovirus 40/41 diarrhea were more likely to have a fever than children with norovirus, sapovirus, and astrovirus (adjusted odds ratio [aOR], 1.62; 95% CI, 1.16-2.26) but less likely than children with rotavirus (aOR, 0.66; 95% CI, 0.49-0.91). Exclusive breastfeeding was strongly protective against adenovirus 40/41 diarrhea (hazard ratio, 0.64; 95% CI, 0.48-0.85), but no other risk factors were identified. The seasonality of adenovirus 40/41 diarrhea varied substantially between sites and did not have clear associations with seasonal variations in temperature or rainfall. CONCLUSIONS: This study supports the situation of adenovirus 40/41 as a pathogen of substantial importance, especially in infants. Fever was a distinguishing characteristic in comparison to other nonrotavirus viral etiologies, and promotion of exclusive breastfeeding may reduce the high observed burden in the first 6 months of life.
The emergence of the COVID-19 pandemic reinforced the central role of the One Health (OH) approach, as a multisectoral and multidisciplinary perspective, to tackle health threats at the human-animal-environment interface. This study assessed Brazilian preparedness and response to COVID-19 and zoonoses with a focus on the OH approach and equity dimensions. We conducted an environmental scan using a protocol developed as part of a multi-country study. The article selection process resulted in 45 documents: 79 files and 112 references on OH; 41 files and 81 references on equity. The OH and equity aspects are poorly represented in the official documents regarding the COVID-19 response, either at the federal and state levels. Brazil has a governance infrastructure that allows for the response to infectious diseases, including zoonoses, as well as the fight against antimicrobial resistance through the OH approach. However, the response to the pandemic did not fully utilize the resources of the Brazilian state, due to the lack of central coordination and articulation among the sectors involved. Brazil is considered an area of high risk for emergence of zoonoses mainly due to climate change, large-scale deforestation and urbanization, high wildlife biodiversity, wide dry frontier, and poor control of wild animals’ traffic. Therefore, encouraging existing mechanisms for collaboration across sectors and disciplines, with the inclusion of vulnerable populations, is required for making a multisectoral OH approach successful in the country.
The 2020 Atlantic hurricane season was notable for a record-setting 30 named storms while, contemporaneously, the COVID-19 pandemic was circumnavigating the globe. The active spread of COVID-19 complicated disaster preparedness and response actions to safeguard coastal and island populations from hurricane hazards. Major hurricanes Eta and Iota, the most powerful storms of the 2020 Atlantic season, made November landfalls just two weeks apart, both coming ashore along the Miskito Coast in Nicaragua’s North Caribbean Coast Autonomous Region. Eta and Iota bore the hallmarks of climate-driven storms, including rapid intensification, high peak wind speeds, and decelerating forward motion prior to landfall. Hurricane warning systems, combined with timely evacuation and sheltering procedures, minimized loss of life during hurricane impact. Yet these protective actions potentially elevated risks for COVID-19 transmission for citizens sharing congregate shelters during the storms and for survivors who were displaced post-impact due to severe damage to their homes and communities. International border closures and travel restrictions that were in force to slow the spread of COVID-19 diminished the scope, timeliness, and effectiveness of the humanitarian response for survivors of Eta and Iota. Taken together, the extreme impacts from hurricanes Eta and Iota, compounded by the ubiquitous threat of COVID-19 transmission, and the impediments to international humanitarian response associated with movement restrictions during the pandemic, acted to exacerbate harms to population health for the citizens of Nicaragua.
BACKGROUND: Respiratory Syncytial Virus (RSV) is the main cause of pediatric morbidity and mortality. The complex evolution of RSV creates a need for worldwide surveillance, which may assist in the understanding of multiple viral aspects. OBJECTIVES: This study aimed to investigate RSV features under the Brazilian Influenza Surveillance Program, evaluating the role of viral load and genetic diversity in disease severity and the influence of climatic factors in viral seasonality. METHODOLOGY: We have investigated the prevalence of RSV in children up to 3 years of age with severe acute respiratory infection (SARI) in the state of Espirito Santo (ES), Brazil, from 2016 to 2018. RT-qPCR allowed for viral detection and viral load quantification, to evaluate association with clinical features and mapping of local viral seasonality. Gene G sequencing and phylogenetic reconstruction demonstrated local genetic diversity. RESULTS: Of 632 evaluated cases, 56% were caused by RSV, with both subtypes A and B co-circulating throughout the years. A discrete inverse association between average temperature and viral circulation was observed. No correlation between viral load and disease severity was observed, but children infected with RSV-A presented a higher clinical severity score (CSS), stayed longer in the hospital, and required intensive care, and ventilatory support more frequently than those infected by RSV-B. Regarding RSV diversity, some local genetic groups were observed within the main genotypes circulation RSV-A ON1 and RSV-B BA, with strains showing modifications in the G gene amino acid chain. CONCLUSION: Local RSV studies using the Brazilian Influenza Surveillance Program are relevant as they can bring useful information to the global RSV surveillance. Understanding seasonality, virulence, and genetic diversity can aid in the development and suitability of antiviral drugs, vaccines, and assist in the administration of prophylactic strategies.
OBJECTIVE: The frequency and seasonality of viruses in tropical regions are scarcely reported. We estimated the frequency of seven respiratory viruses and assessed seasonality of respiratory syncytial virus (RSV) and influenza viruses in a tropical city. METHODS: Children (age ≤ 18 years) with acute respiratory infection were investigated in Salvador, Brazil, between July 2014 and June 2017. Respiratory viruses were searched by direct immunofluorescence and real-time polymerase chain reaction for detection of RSV, influenza A virus, influenza B virus, adenovirus (ADV) and parainfluenza viruses (PIV) 1, 2 and 3. Seasonal distribution was evaluated by Prais-Winsten regression. Due to similar distribution, influenza A and influenza B viruses were grouped to analyse seasonality. RESULTS: The study group comprised 387 cases whose median (IQR) age was 26.4 (10.5-50.1) months. Respiratory viruses were detected in 106 (27.4%) cases. RSV (n = 76; 19.6%), influenza A virus (n = 11; 2.8%), influenza B virus (n = 7; 1.8%), ADV (n = 5; 1.3%), PIV 1 (n = 5; 1.3%), PIV 3 (n = 3; 0.8%) and PIV 2 (n = 1; 0.3%) were identified. Monthly count of RSV cases demonstrated seasonal distribution (b3 = 0.626; P = 0.003). More than half (42/76 [55.3%]) of all RSV cases were detected from April to June. Monthly count of influenza cases also showed seasonal distribution (b3 = -0.264; P = 0.032). Influenza cases peaked from November to January with 44.4% (8/18) of all influenza cases. CONCLUSIONS: RSV was the most frequently detected virus. RSV and influenza viruses showed seasonal distribution. These data may be useful to plan the best time to carry out prophylaxis and to increase the number of hospital beds.
BACKGROUND: Meningitis can cause devastating epidemics and is susceptible to climate change. It is unclear how temperature variability, an indicator of climate change, is associated with meningitis incidence. METHODS: We used global meningitis incidence data along with meteorological and demographic data over 1990-2019 to identify the association between temperature variability and meningitis. We also employed future (2020-2100) climate data to predict meningitis incidence under different emission levels (SSPs: Shared Socioeconomic Pathways). RESULTS: We found that the mean temperature variability increased by almost 3 folds in the past 30 years. The largest changes occurred in Australasia, Tropical Latin America, and Central Sub-Saharan Africa. With a logarithmic unit increase in temperature variability, the overall global meningitis risk increases by 4.8 %. Australasia, Central Sub-Saharan Africa, and High-income North America are the most at-risk regions. Higher statistical differences were identified in males, children, and the elderly population. Compared to high-emission (SSP585) scenario, we predicted a median reduction of 85.8 % in meningitis incidence globally under the low-emission (SSP126) climate change scenario by 2100. CONCLUSION: Our study provides evidence for temperature variability being in association with meningitis incidence, which suggests that global actions are urgently needed to address climate change and to prevent meningitis occurrence.
Climate change impacts represent one of the most important ecological and medical issues during this century. Several fungal species will change their distribution through space and time as a response to climate changes. This will rearrange many fungal diseases throughout the world. One of the most important and very common fungi is the black mold Aspergillus niger. The COVID-19 pandemic reforms the way in which mycologists think about this fungus as an emerging healthy issue. Through this work, about one thousand records of Aspergillus niger were used to model its current and future global distribution using 19 bioclimatic variables under several climate change scenarios. Maximum entropy implemented in Maxent was chosen as the modeling tool, especially with its accuracy and reliability over the other modeling techniques. The annual mean temperature (bio 1) forms the most contributed climatological parameter to black mold distribution. The produced current distribution model came compatible with the real distribution of the species with a cosmopolitan range. The rise of temperature due to global warming will form a limitation to Aspergillus niger through several parts of its range. The generated maps of the future status of this fungus under two different RCPs for 2050 and 2070, indicate several parts that become free from black mold due to temperature limitations. The present results need more intensive future evaluation using data science and GIS, especially on a local scale including more ecological parameters other than climatological data.
Non-technical summaryHumans have the tendency to damage the natural environment in many ways. Deforestation and conversion of forests for residential, industrial development, and expansion of agricultural crops, as well as the burning of fossil fuels, are some activities that disrupt natural ecosystems and wildlife and contribute to climate change. As a result, the life cycles of pathogens and intermediate hosts (insects, rodents, mammals) as well as biodiversity are affected. Through these activities, humans meet wild animals that transmit pathogens, resulting in their infection by zoonoses and causing epidemics-pandemics, the effects of which have as their final recipient himself and his activities. Technical summaryThis article aims to highlight the two-way relationship between those human activities and the occurrence of epidemics-pandemics. We will try to elaborate this two-way relationship, through the overview of the current pandemic (origin of SARS-CoV-2, modes of transmission, clinical picture of the disease of COVID-19, influence of weather and air pollution on prevalence and mortality, pandemic effects, and treatments). They are used as primary sources, scientific articles, literature, websites, and databases (Supplementary appendix) to analyze factors involved in the occurrence and transmission of zoonotic diseases in humans (Ebola, influenza, Lyme disease, dengue fever, cholera, AIDS/HIV, SARS-CoV, MERS-CoV). The present paper concluded that humanity today faces two major challenges: controlling the COVID-19 pandemic and minimizing the risk of a new global health crisis occurring in the future. The first can be achieved through equitable access to vaccines and treatments for all people. The second needs the global community to make a great change and start protecting the natural environment and its ecosystems through the adoption of prevention policies. Summary of social mediaTwo-way relationship between human activities and epidemics highlighted, through review of the COVID-19 pandemic.
BACKGROUND: Zoonotic diseases account for more than 70% of emerging infectious diseases (EIDs). Due to their increasing incidence and impact on global health and the economy, the emergence of zoonoses is a major public health challenge. Here, we use a biogeographic approach to predict future hotspots and determine the factors influencing disease emergence. We have focused on the following three viral disease groups of concern: Filoviridae, Coronaviridae, and Henipaviruses. METHODS: We modelled presence-absence data in spatially explicit binomial and zero-inflation binomial logistic regressions with and without autoregression. Presence data were extracted from published studies for the three EID groups. Various environmental and demographical rasters were used to explain the distribution of the EIDs. True Skill Statistic and deviance parameters were used to compare the accuracy of the different models. RESULTS: For each group of viruses, we were able to identify and map areas at high risk of disease emergence based on the spatial distribution of the disease reservoirs and hosts of the three viral groups. Common influencing factors of disease emergence were climatic covariates (minimum temperature and rainfall) and human-induced land modifications. CONCLUSIONS: Using topographical, climatic, and previous disease outbreak reports, we can identify and predict future high-risk areas for disease emergence and their specific underlying human and environmental drivers. We suggest that such a predictive approach to EIDs should be carefully considered in the development of active surveillance systems for pathogen emergence and epidemics at local and global scales.
COVID-19 has revealed how challenging it is to manage global, systemic and compounding crises. Like COVID-19, climate change impacts, and maladaptive responses to them, have potential to disrupt societies at multiple scales via networks of trade, finance, mobility and communication, and to impact hardest on the most vulnerable. However, these complex systems can also facilitate resilience if managed effectively. This review aims to distil lessons related to the transboundary management of systemic risks from the COVID-19 experience, to inform climate change policy and resilience building. Evidence from diverse fields is synthesised to illustrate the nature of systemic risks and our evolving understanding of resilience. We describe research methods that aim to capture systemic complexity to inform better management practices and increase resilience to crises. Finally, we recommend specific, practical actions for improving transboundary climate risk management and resilience building. These include mapping the direct, cross-border and cross-sectoral impacts of potential climate extremes, adopting adaptive risk management strategies that embrace heterogenous decision-making and uncertainty, and taking a broader approach to resilience which elevates human wellbeing, including societal and ecological resilience.
The Lancet Countdown is an international collaboration that independently monitors the health consequences of a changing climate. Publishing updated, new, and improved indicators each year, the Lancet Countdown represents the consensus of leading researchers from 43 academic institutions and UN agencies. The 44 indicators of this report expose an unabated rise in the health impacts of climate change and the current health consequences of the delayed and inconsistent response of countries around the globe—providing a clear imperative for accelerated action that puts the health of people and planet above all else.
Background: Recent studies indicated the possible relationship between climate change, environmental pollution, and Coronavirus Disease 2019 (COVID-19) pandemic. This study reviewed the effects of air pollution, climate parameters, and lockdown on the number of cases and deaths related to COVID-19. Methods: The present review was performed to determine the effects of weather and air pollution on the number of cases and deaths related to COVID-19 during the lockdown. Articles were collected by searching the existing online databases, such as PubMed, Science Direct, and Google Scholar, with no limitations on publication dates. Afterwards, this review focused on outdoor air pollution, including PM2.5, PM10, NO2, SO2, and O-3, and weather conditions affecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/COVID-19. Results: Most reviewed investigations in the present study showed that exposure to air pollutants, particularly PM2.5 and NO2, is positively related to COVID-19 patients and mortality. Moreover, these studies showed that air pollution could be essential in transmitting COVID-19. Local meteorology plays a vital role in coronavirus spread and mortality. Temperature and humidity variables are negatively correlated with virus transmission. The evidence demonstrated that air pollution could lead to COVID-19 transmission. These results support decision-makers in curbing potential new outbreaks. Conclusions: Overall, in environmental perspective-based COVID-19 studies, efforts should be accelerated regarding effective policies for reducing human emissions, bringing about air pollution and weather change. Therefore, using clean and renewable energy sources will increase public health and environmental quality by improving global air quality.
Internists are experts in complexity, and the COVID-19 pandemic is disclosing complex and unexpected interactions between communicable and non-communicable diseases, environmental factors, and socio-economic disparities. The medicine of complexity cannot be limited to facing comorbidities and to the clinical management of multifaceted diseases. Evidence indicates how climate change, pollution, demographic unbalance, and inequalities can affect the spreading and outcomes of COVID-19 in vulnerable communities. These elements cannot be neglected, and a wide view of public health aspects by a “one-health” approach is strongly and urgently recommended. According to World Health Organization, 35% of infectious diseases involving the lower respiratory tract depend on environmental factors, and infections from SARS-Cov-2 is not an exception. Furthermore, environmental pollution generates a large burden of non-communicable diseases and disabilities, increasing the individual vulnerability to COVID-19 and the chance for the resilience of large communities worldwide. In this field, the awareness of internists must increase, as privileged healthcare providers. They need to gain a comprehensive knowledge of elements characterizing COVID-19 as part of a syndemic. This is the case when pandemic events hit vulnerable populations suffering from the increasing burden of chronic diseases, disabilities, and social and economic inequalities. Mastering the interplay of such events requires a change in overall strategy, to adequately manage not only the SARS-CoV-2 infection but also the growing burden of non-communicable diseases by a “one health” approach. In this context, experts in internal medicine have the knowledge and skills to drive this change.
COVID-19 has caused 100s of millions of infections and millions of deaths worldwide, overwhelming health and economic capacities in many countries and at multiple scales. The immediacy and magnitude of this crisis has resulted in government officials, practitioners and applied scholars turning to reflexive learning exercises to generate insights for managing the reverberating effects of this disease as well as the next inevitable pandemic. We contribute to both tasks by assessing COVID-19 as a super wicked problem denoted by four features we originally formulated to describe the climate crisis: time is running out, no central authority, those causing the problem also want to solve it, and policies irrationally discount the future (Levin et al. in Playing it forward: path dependency, progressive incrementalism, and the super wicked problem of global climate change, 2007; Levin et al. in Playing it forward: Path dependency, progressive incrementalism, and the super wicked problem of global climate change, 2009; Levin et al. in Policy Sci 45(2):123-152, 2012). Doing so leads us to identify three overarching imperatives critical for pandemic management. First, similar to requirements to address the climate crisis, policy makers must establish and maintain durable policy objectives. Second, in contrast to climate, management responses must always allow for swift changes in policy settings and calibrations given rapid and evolving knowledge about a particular disease’s epidemiology. Third, analogous to, but with swifter effects than climate, wide-ranging global efforts, if well designed, will dramatically reduce domestic costs and resource requirements by curbing the spread of the disease and/or fostering relevant knowledge for managing containment and eradication. Accomplishing these tasks requires building the analytic capacity for engaging in reflexive anticipatory policy design exercises aimed at maintaining, or building, life-saving thermostatic institutions at the global and domestic levels.
This study investigated the spatio-temporal variations in the occurrence of COVID-19 (confirmed cases and deaths) in relation to climate fluctuations in 61 countries, scattered around the world, from December 31, 2019 to May 28, 2020. Logarithm transformation of the count variable (COVID-19 cases) was used in a multiple linear regression model to predict the potential effects of weather variables on the prevalence of the disease. The study revealed strong associations (-0.510 ≤ r ≤ -0.967; 0.519 ≤ r ≤ 0.999) between climatic variables and confirmed cases of COVID-19 in majority (68.85%) of the selected countries. It showed evidences of 1 to 7-day delays in the response of the infection to changes in weather pattern. Model simulations suggested that a unit fall in temperature and humidity could increase (0.04-18.70%) the infection in 19.67% and 16.39% of the countries, respectively, while a general reduction (-0.05 to 9.40%) in infection cases was projected in 14.75% countries with a unit drop in precipitation. In conclusion, the study suggests that effective public health interventions are crucial to containing the projected upsurge in COVID-19 cases during both cold and warm seasons in the southern and northern hemispheres.
Structural and genetic differences among various viruses play a significant factor in host infectivity and vulnerability to environmental stressors. Zoonoses of viruses require several recombinations and mutations in their genetic material and among several viruses allowing them to switch hosts and infect new species. Additionally, the host genetics play a significant role in successful viral transmission among various hosts. For example, human immunodeficiency virus (HIV), Ebola virus and influenza viruses. In efficient zoonotic events, selective stresses in the host milieu-interieur are critical during viral infection of the first human host. The genetic rearrangement of the virus and the selective environmental pressure of the host immune system dominate the emergence of new viral disease outbreaks.
Evidences of an association between air pollution and Covid-19 infections are mixed and inconclusive. We conducted an ecological analysis at regional scale of long-term exposure to air-borne particle matter and spread of Covid-19 cases during the first wave of epidemics. Global air pollution and climate data were calculated from satellite earth observation data assimilated into numerical models at 10 km resolution. Main outcome was defined as the cumulative number of cases of Covid-19 in the 14 days following the date when > 10 cumulative cases were reported. Negative binomial mixed effect models were applied to estimate the associations between the outcome and long-term exposure to air pollution at the regional level (PM(10), PM(2.5)), after adjusting for relevant regional and country level covariates and spatial correlation. In total we collected 237,749 Covid-19 cases from 730 regions, 63 countries and 5 continents at May 30, 2020. A 10 μg/m(3) increase of pollution level was associated with 8.1% (95% CI 5.4%, 10.5%) and 11.5% (95% CI 7.8%, 14.9%) increases in the number of cases in a 14 days window, for PM(2.5) and PM(10) respectively. We found an association between Covid-19 cases and air pollution suggestive of a possible causal link among particulate matter levels and incidence of COVID-19.
Why have some countries done significantly better than others in fighting the Covid-19 pandemic? Had some countries been better prepared than others? This paper attempts to shed light on these questions by examining the role of climate risk and culture in explaining the cross-country variation in the Covid-19 mortality, while controlling for other potential drivers. In our analysis, we consider climate risk, readiness to climate change and individualism as main indicators reflecting the climate and culture status of individual countries. Using data from 110 countries, we find that the greater the climate risk; the lower the readiness to climate change and the more individualistic the society, the higher the pandemic mortality rate. We also present a series of sensitivity checks and show that our findings are robust to different specifications, alternative definitions of the mortality rate; and different estimation methods. One policy implication arising from our results is that countries that were better prepared for the climate emergency were also better placed to fight the pandemic. Overall, countries in which individuals look after each other and the environment, creating sustainable societies, are better able to cope with climate and public health emergencies.
BACKGROUND: Climate change based on temperature, humidity and wind can improve many characteristics of the arthropod carrier life cycle, including survival, arthropod population, pathogen communication, and the spread of infectious agents from vectors. This study aimed to find association between content of disease followed climate change we demonstrate in humans. METHODS: All the articles from 2016 to 2021 associated with global climate change and the effect of vector-borne disease were selected form databases including PubMed and the Global Biodiversity information facility database. All the articles selected for this short review were English. RESULTS: Due to the high burden of infectious diseases and the growing evidence of the possible effects of climate change on the incidence of these diseases, these climate changes can potentially be involved with the COVID-19 epidemic. We highlighted the evidence of vector-borne diseases and the possible effects of climate change on these communicable diseases. CONCLUSION: Climate change, specifically in rising temperature system is one of the world’s greatest concerns already affected pathogen-vector and host relation. Lice parasitic, fleas, mites, ticks, and mosquitos are the prime public health importance in the transmission of virus to human hosts.
Coccidioidomycosis, colloquially known as Valley Fever, is an invasive dimorphic fungal infection caused by Coccidioides immitis and C. posadasii. The fungi are found in the arid desert soils of the southwestern US, as well as in parts of Mexico and Central and South America. Acquisition is typically via inhalation of arthroconidia which become airborne after both natural (e.g., earthquakes, dust storms, and fires) and human-related events (e.g., military maneuvers, recreational activities, agriculture, and construction). The incidence of infection in increasing likely a result of both climatic and populational changes. Further, the recognized geographic distribution of Coccidioides spp. is expanding, as cases are being diagnosed in new areas (e.g., eastern Washington, Oregon, and Utah). Most coccidioidal infections are asymptomatic (60%); however, approximately one-third develop a pulmonary illness which is a leading cause of community-acquired pneumonia in highly endemic areas. Uncommonly (0.5-2% of cases), the infection disseminates to extrapulmonary locations (e.g., skin, bones/joints, and the central nervous system), and is most commonly seen among persons with cellular immunodeficiencies (e.g., transplant recipients, HIV, and pregnancy) and non-Caucasian races (especially African Americans and Filipinos). The diagnosis of coccidioidomycosis requires astute clinical suspicion and laboratory findings, including positive serology, cultures, and/or histopathology results. Treatment is warranted among persons with pneumonia who have risk factors for complicated disease and among those with extrapulmonary disease. Novel antifungals with improved fungicidal activity and rapidity of action with fewer side effects and drug interactions are needed. Preventive strategies (e.g., education regarding the disease, dust avoidance, mask wearing, including among select groups, antifungal prophylaxis, and surveillance laboratory testing) are advised for residents and travelers to endemic areas. Currently, no preventive vaccine is available. Coccidioidomycosis has been recognized for over a century, and an expanding wealth of knowledge has been gained regarding this emerging infectious disease which will be reviewed here.
Human activities have significantly impacted the environment and are changing our climate in ways that will have major consequences for ourselves, and endanger animal, plant and microbial life on Earth. Rising global temperatures and pollution have been highlighted as potential drivers for increases in infectious diseases. Although infrequently highlighted, fungi are amongst the leading causes of infectious disease mortality, resulting in more than 1.5 million deaths every year. In this review we evaluate the evidence linking anthropomorphic impacts with changing epidemiology of fungal disease. We highlight how the geographic footprint of endemic mycosis has expanded, how populations susceptible to fungal infection and fungal allergy may increase and how climate change may select for pathogenic traits and indirectly contribute to the emergence of drug resistance.
A number of studies suggest that meteorological conditions are related to the risk of Legionnaires’ disease (LD) but the findings are not consistent. A systematic review was conducted to investigate the association of weather with sporadic LD and highlight the key meteorological conditions related to this outcome. PubMed, EMBASE, The Cochrane Library and OpenGrey were searched on 26-27 March 2020 without date, language or location restrictions. Key words included “legionellosis”, “legionnaires’ disease”, combined with “meteorological conditions”, “weather”, “temperature”, “humidity”, “rain”, “ultraviolet rays”, “wind speed”, etc. Studies were excluded if they did not examine the exposure of interest, the outcome of interest and their association or if they only reported LD outbreak cases. The study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and it was registered in PROSPERO (#CRD42020168869). There were 811 articles, of which 17 were included in the review. The studies investigated different meteorological variables and most of them examined the combined effect of several variables. The most commonly examined factors were precipitation and temperature, followed by relative humidity. The studies suggested that increased precipitation, temperature and relative humidity were positively associated with the incidence of LD. There was limited evidence that higher wind speed, pressure, visibility, UV radiation and longer sunshine duration were inversely linked with the occurrence of LD. A period of increased but not very high temperatures, followed by a period of increased precipitation, favour the occurrence of LD. Increased awareness of the association of temperature and precipitation and LD occurrence among clinicians and public health professionals can improve differential diagnosis for cases of sporadic community-acquired pneumonia and at the same time contribute to improving LD surveillance.
The World Health Organization has identified vaccine hesitancy as one of its top ten global health threats for 2019. Efforts are underway to define the factors responsible for reductions in vaccine confidence. However, as global measles cases accelerated beginning in 2018, it became evident that additional factors were promoting measles re-emergence, including war, political and socio-economic collapse, shifting poverty, and vulnerability to weather events and climate change. Accordingly, we propose a Global Vaccine Risk Index (VRI) to consider these variables as a more comprehensive means to identify vulnerable nations where we might expect measles and other vaccine-preventable diseases to emerge or re-emerge. In Sub-Saharan African and Middle Eastern nations, conflict and political instability predominated as the basis for high vaccine risk scores, whereas in Southeast Asian countries, the major reasons included climate variability, current levels of measles vaccination coverage, and economic and educational disparities. In Europe, low vaccine confidence and refugee movements predominated, while in the Americas, economic disparities and vaccine confidence were important. The VRI may serve as a useful indicator and predictor for international agencies committed to childhood immunizations and might find relevance for accelerating future COVID19 vaccination programs.
RNA viruses include respiratory viruses, such as coronaviruses and influenza viruses, as well as vector-borne viruses, like dengue and West Nile virus. RNA viruses like these encounter various environments when they copy themselves and spread from cell to cell or host to host. Ex vivo differences, such as geographical location and humidity, affect their stability and transmission, while in vivo differences, such as pH and host gene expression, impact viral receptor binding, viral replication, and the host immune response against the viral infection. A critical factor affecting RNA viruses both ex vivo and in vivo, and defining the outcome of viral infections and the direction of viral evolution, is temperature. In this minireview, we discuss the impact of temperature on viral replication, stability, transmission, and adaptation, as well as the host innate immune response. Improving our understanding of how RNA viruses function, survive, and spread at different temperatures will improve our models of viral replication and transmission risk analyses.
Climate change is causing weather conditions to abruptly change and is directly impacting the health of humans. Due to climate change, there is an upsurge in conditions suitable for infectious pathogens and their carriers to survive and multiply. Infections that were eliminated decades ago are regaining their grounds among humans. Climate change is increasing the possibility of new outbreaks for these vector-borne, airborne, or waterborne infections. While adverse impacts of these outbreaks are only subject to the predictions, nevertheless, it is certain that these outbreaks will affect health status, mortality status and economy at local and international levels. However, these threats may be minimized if national and international public health departments would be willing to implement research- and evidence-based advanced preparedness strategies. This scientific review aims to explore how climate change is facilitating the spread of vector-borne (tick-borne encephalitis, dengue, West Nile virus, leishmaniasis), airborne (by weather conditions like storms), and waterborne infectious diseases (due to floods and droughts) and is triggering new outbreaks among humans.
Background: Most respiratory viruses show pronounced seasonality, but for SARS-CoV-2, this still needs to be documented. Methods: We examined the disease progression of COVID-19 in 6,914 patients admitted to hospitals in Europe and China. In addition, we evaluated progress of disease symptoms in 37,187 individuals reporting symptoms into the COVID Symptom Study application. Findings: Meta-analysis of the mortality risk in seven European hospitals estimated odds ratios per 1-day increase in the admission date to be 0.981 (0.973-0.988, p < 0.001) and per increase in ambient temperature of 1°C to be 0.854 (0.773-0.944, p = 0.007). Statistically significant decreases of comparable magnitude in median hospital stay, probability of transfer to the intensive care unit, and need for mechanical ventilation were also observed in most, but not all hospitals. The analysis of individually reported symptoms of 37,187 individuals in the UK also showed the decrease in symptom duration and disease severity with time. Interpretation: Severity of COVID-19 in Europe decreased significantly between March and May and the seasonality of COVID-19 is the most likely explanation.
BACKGROUND: The comprehensive epidemiology and impact of climate on viral meningitis (VM) in Kazakhstan are unknown. We aimed to study the incidence, in-hospital mortality and influence of climatic indicators on VM from 2014 to 2019. METHODS: Nationwide electronic healthcare records were used to explore this study. ICD-10 codes of VM, demographics, and hospital outcomes were evaluated using descriptive statistics and survival analysis. RESULTS: During the 2014-2019 period, 10,251 patients with VM were admitted to the hospital. 51.35% of them were children, 57.85% were males, and 85.9% were from the urban population. Enteroviral meningitis was the main cause of VM in children. The incidence rate was 13 and 18 cases per 100,000 population in 2014 and 2019, respectively. Case fatality rate was higher in 2015 (2.3%) and 2017 (2.0%). The regression model showed 1°C increment in the daily average temperature might be associated with a 1.05-fold (95% CI 1.047-1.051) increase in the daily rate of VM cases, 1hPa increment in the average air pressure and 1% increment in the daily average humidity might contribute to a decrease in the daily rate of VM cases with IRRs of 0.997 (95% CI 0.995-0.998) and 0.982 (95% CI 0.981-0.983), respectively. In-hospital mortality was 35% higher in males compared to females. Patients residing in rural locations had a 2-fold higher risk of in-hospital death, compared to city residents. Elderly patients had a 14-fold higher risk of in-hospital mortality, compared to younger patients. CONCLUSION: This is the first study in Kazakhstan investigating the epidemiology and impact of climate on VM using nationwide healthcare data. There was a tendency to decrease the incidence with outbreaks every 5 years, and mortality rates were higher for Russians and other ethnicities compared to Kazakhs, for males compared to females, for elder patients compared to younger patients, and for patients living in rural areas compared to city residents. The climatic parameters and the days of delay indicated a moderate interaction with the VM cases.
The global crises of climate change and of the COVID-19 pandemic are straining young peoples’ mental health and their mitigation behaviours. We surveyed German-speaking university students aged 18 to 30 years on their negative emotions regarding both crises repeatedly before and during the COVID-19 crisis. Different emotional patterns emerged for climate change and for COVID-19 with negative emotions regarding COVID-19 increasing during the pandemic. We were further able to differentiate between emotional responses associated with impaired wellbeing and those associated with mitigation efforts. Our findings emphasise the need to focus on a mixture of highly inactivating and activating emotions regarding COVID-19 as they are associated with both reduced wellbeing and mitigation behaviours. The findings broaden the understanding of how young adults react to the burden of two global crises and what role negative emotions play.
COVID-19 pandemic continues to obstruct social lives and the world economy other than questioning the healthcare capacity of many countries. Weather components recently came to notice as the northern hemisphere was hit by escalated incidence in winter. This study investigated the association between COVID-19 cases and two components, average temperature and relative humidity, in the 16 states of Germany. Three main approaches were carried out in this study, namely temporal correlation, spatial auto-correlation, and clustering-integrated panel regression. It is claimed that the daily COVID-19 cases correlate negatively with the average temperature and positively with the average relative humidity. To extract the spatial auto-correlation, both global Moran’s [Formula: see text] and global Geary’s [Formula: see text] were used whereby no significant difference in the results was observed. It is evident that randomness overwhelms the spatial pattern in all the states for most of the observations, except in recent observations where either local clusters or dispersion occurred. This is further supported by Moran’s scatter plot, where states’ dynamics to and fro cold and hot spots are identified, rendering a traveling-related early warning system. A random-effects model was used in the sense of case-weather regression including incidence clustering. Our task is to perceive which ranges of the incidence that are well predicted by the existing weather components rather than seeing which ranges of the weather components predicting the incidence. The proposed clustering-integrated model associated with optimal barriers articulates the data well whereby weather components outperform lag incidence cases in the prediction. Practical implications based on marginal effects follow posterior to model diagnostics.
During the ongoing global COVID-19 pandemic disease, like several countries, Romania experienced a multiwaves pattern over more than two years. The spreading pattern of SARS-CoV-2 pathogens in the Bucharest, capital of Romania is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation. Through descriptive statistics and cross-correlation analysis applied to daily time series of observational and geospatial data, this study aims to evaluate the synergy of COVID-19 incidence and lethality with air pollution and radon under different climate conditions, which may exacerbate the coronavirus’ effect on human health. During the entire analyzed period 1 January 2020-21 December 2021, for each of the four COVID-19 waves were recorded different anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere, and favorable stability conditions during fall-early winter seasons for COVID-19 disease fast-spreading, mostly during the second, and the fourth waves. As the temporal pattern of airborne SARS-CoV-2 and its mutagen variants is affected by seasonal variability of the main air pollutants and climate parameters, this paper found: 1) the daily outdoor exposures to air pollutants (particulate matter PM2.5 and PM10, nitrogen dioxide-NO(2), sulfur dioxide-SO(2), carbon monoxide-CO) and radon – (222)Rn, are directly correlated with the daily COVID-19 incidence and mortality, and may contribute to the spread and the severity of the pandemic; 2) the daily ground ozone-O(3) levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance are anticorrelated with the daily new COVID-19 incidence and deaths, averageingful for spring-summer periods. Outdoor exposure to ambient air pollution associated with radon is a non-negligible driver of COVID-19 transmission in large metropolitan areas, and climate variables are risk factors in spreading the viral infection. The findings of this study provide useful information for public health authorities and decision-makers to develop future pandemic diseases strategies in high polluted metropolitan environments.
Issues continuously compete for attention in the news media and on social media. Climate change is one of the most urgent problems for society and (re)gained wide public attention in 2019 through the global climate strike protest movement. However, we hypothesize that the outbreak of the COVID-19 pandemic in early 2020 challenged the role of climate change as a routine issue. We use extensive news media and Twitter data to explore if and how the pandemic as a so-called killer issue has shifted public attention away from the issue of climate change in Switzerland. Results show that the climate debate fell victim to the impact of the COVID-19 pandemic in the news media and the Twitter-sphere. Given the vast dominance of the pandemic, there is a strong indication this finding applies similarly to various other issues. Additional hashtag co-occurrence analysis shows that some climate activists react to this development and try to connect the issue of climate change to the pandemic. We argue that suppression of climate change by the pandemic is a problem for its long-term resolution, as it seems to have turned climate change back into a struggling issue.
Excess mortality not directly related to the virus has been shown to have increased during the COVID-19 pandemic. However, changes in heat-related mortality during the pandemic have not been addressed in detail. Here, we performed an observational study crossing daily mortality data collected in Portugal (SICO/DGS) with high-resolution temperature series (ERA5/ECMWF), characterizing their relation in the pre-pandemic, and how it aggravated during 2020. The combined result of COVID-19 and extreme temperatures caused the largest annual mortality burden in recent decades (~ 12 000 excess deaths [~ 11% above baseline]). COVID-19 caused the largest fraction of excess mortality during March to May (62%) and from October onwards (85%). During summer, its direct impact was residual, and deaths not reported as COVID-19 dominated excess mortality (553 versus 3 968). A prolonged hot spell led mortality to the upper tertile, reaching its peak in mid-July (+ 45% deaths/day). The lethality ratio (+ 14 deaths per cumulated ºC) was higher than that observed in recent heatwaves. We used a statistical model to estimate expected deaths due to cold/heat, indicating an amplification of at least 50% in heat-related deaths during 2020 compared to pre-pandemic years. Our findings suggest mortality during 2020 has been indirectly amplified by the COVID-19 pandemic, due to the disruption of healthcare systems and fear of population in attending healthcare facilities (expressed in emergency room admissions decreases). While lockdown measures and healthcare systems reorganization prevented deaths directly related to the virus, a significant burden due to other causes represents a strong secondary impact. This was particularly relevant during summer hot spells, when the lethality ratio reached magnitudes not experienced since the 2003 heatwaves. This severe amplification of heat-related mortality during 2020 stresses the need to resume normal healthcare services and public health awareness.
High ambient temperatures pose a significant risk to health. This study investigates the heatwave mortality in the summer of 2020 during the SARS-CoV-2 coronavirus (COVID-19) pandemic and related countermeasures. The heatwaves in 2020 caused more deaths than have been reported since the Heatwave Plan for England was introduced in 2004. The total and cause-specific mortality in 2020 was compared to previous heatwave events in England. The findings will help inform summer preparedness and planning in future years as society learns to live with COVID-19. Heatwave excess mortality in 2020 was similar to deaths occurring at home, in hospitals, and in care homes in the 65+ years group, and was comparable to the increases in previous years (2016-2018). The third heatwave in 2020 caused significant mortality in the younger age group (0-64) which has not been observed in previous years. Significant excess mortality was observed for cardiovascular disease, respiratory disease, and Alzheimer’s and Dementia across all three heatwaves in persons aged 65+ years. There was no evidence that the heatwaves affected the proportional increase of people dying at home and not seeking heat-related health care. The most significant spike in daily mortality in August 2020 was associated with a period of high night-time temperatures. The results provide additional evidence that contextual factors are important for managing heatwave risks, particularly the importance of overheating in dwellings. The findings also suggest more action is also needed to address the vulnerability in the community and in health care settings during the acute response phase of a heatwave.
Hydrometeorological hazards comprise a wide range of events, mainly floods, storms, droughts, and temperature extremes. Floods account for the majority of the related disasters in both developed and developing countries. Flooding alters the natural balance of the environment and frequently establish a favorable habitat for pathogens and vectors to thrive. Diseases caused by pathogens that require vehicle transmission from host to host (waterborne) or a host/vector as part of their life cycle (vector-borne) are those most likely to be affected by flooding. Considering the most notable recent destructive floods events of July 2021 that affected several Central Europe countries, we conducted a systematic literature review in order to identify documented sporadic cases and outbreaks of infectious diseases in humans in Europe, where hydrometeorological hazards, mainly floods, were thought to have been involved. The occurrence of water-, rodent-, and vector-borne diseases in several European countries is highlighted, as flooding and the harsh post-flood conditions favor their emergence and transmission. In this context, strategies for prevention and management of infectious disease outbreaks in flood-prone and flood-affected areas are also proposed and comprise pre- and post-flood prevention measures, pre- and post-outbreak prevention measures, as well as mitigation actions when an infectious disease outbreak finally occurs. Emphasis is also placed on the collision of floods, flood-related infectious disease outbreaks, and the evolving COVID-19 pandemic, which may result in unprecedented multi-hazard conditions and requires a multi-hazard approach for the effective disaster management and risk reduction.
Compared to soil or aquatic ecosystems, the atmosphere is still an underexplored environment for microbial diversity. In this study, we surveyed the composition, variability and sources of microbes (bacteria and fungi) in the near surface atmosphere of a highly populated area, spanning ~ 4,000 Km(2) around the city center of Madrid (Spain), in different seasonal periods along two years. We found a core of abundant bacterial genera robust across space and time, most of soil origin, while fungi were more sensitive to environmental conditions. Microbial communities showed clear seasonal patterns driven by variability of environmental factors, mainly temperature and accumulated rain, while local sources played a minor role. We also identified taxa in both groups characteristic of seasonal periods, but not of specific sampling sites or plant coverage. The present study suggests that the near surface atmosphere of urban environments contains an ecosystem stable across relatively large spatial and temporal scales, with a rather homogenous composition, modulated by climatic variations. As such, it contributes to our understanding of the long-term changes associated to the human exposome in the air of highly populated areas.
In the present study, we analysed how geographical distribution of the fungal pathogen Cryptococcus gattii VGI in Europe and Mediterranean area has evolved in the last four decades based on the climatic changes, and we tried to predict the scenario for the next decade. Niche modelling by Maxent analysis showed that recent climate changes have significantly affected the distribution of the fungus revealing a gradual expansion of the fundamental niche from 1980 to 2009 followed by an impressive increase in the last decade (2010-2019) during which the environmental surface suitable for the fungal survival was more than doubled. In the next decade, our model predicted an increase in the area of distribution of C. gattii VGI from the coasts of the Mediterranean basin towards the more internal sub-continental areas. On the basis of these predictions, an increase of cases of cryptococcosis due to C. gattii VGI is expected in the next decade and a constant monitoring of the epidemiology of this fungal pathogen represents a crucial strategy to detect the onset of future outbreaks.
User behaviour for natural ventilation is known to be strongly corelated to outdoor temperatures. In areas of moderate climate, this leads to an increased fresh air supply in summer, which reduces the exposure level towards airborne pathogens. Modelling of numerous random exposure situations in household, school and various settings, based on the long-term climate data from Berlin, showed that this effect is likely to contribute significantly to the overall seasonality of airborne diseases.
Respiratory syncytial virus (RSV) bronchiolitis is a leading cause of global child morbidity and mortality. Every year, seasonal RSV outbreaks put high pressure on paediatric intensive care units (PICUs) worldwide, including in the Netherlands, and this burden appears to be increasing. Weather conditions have a strong influence on RSV activity, and climate change has been proposed as a potential important determinant of future RSV-related health care utilisation. In this national study spanning a total of 13 years with 2161 PICU admissions for RSV bronchiolitis, we aimed (1) to identify meteorological variables that were associated with the number of PICU admissions for RSV bronchiolitis in the Netherlands and (2) to determine if longitudinal changes in these variables occurred over time as a possible explanation for the observed increase in PICU burden. Poisson regression modelling was used to identify weather variables (aggregated in months and weeks) that predicted PICU admissions, and linear regression analysis was used to assess changes in the weather over time. Maximum temperature and global radiation best predicted PICU admissions, with global radiation showing the most stable strength of effect in both month and week data. However, we did not observe a significant change in these weather variables over the 13-year time period. Based on our study, we could not identify changing weather conditions as a potential contributing factor to the increased RSV-related PICU burden in the Netherlands.
The cold season is usually accompanied by an increased incidence of respiratory infections and increased air pollution from combustion sources. As we are facing growing numbers of COVID-19 cases caused by the novel SARS-CoV-2 coronavirus, an understanding of the impact of air pollutants and meteorological variables on the incidence of respiratory infections is crucial. The incidence of influenza-like illness (ILI) can be used as a close proxy for the circulation of influenza viruses. Recently, SARS-CoV-2 has also been detected in patients with ILI. Using distributed lag nonlinear models, we analyzed the association between ILI, meteorological variables and particulate matter concentration in Bialystok, Poland, from 2013-2019. We found an exponential relationship between cumulative PM(2.5) pollution and the incidence of ILI, which remained significant after adjusting for air temperatures and a long-term trend. Pollution had the greatest effect during the same week, but the risk of ILI was increased for the four following weeks. The risk of ILI was also increased by low air temperatures, low absolute humidity, and high wind speed. Altogether, our results show that all measures implemented to decrease PM(2.5) concentrations would be beneficial to reduce the transmission of SARS-CoV-2 and other respiratory infections.
Background Previous studies investigating the relationship of influenza with acute myocardial infarction (AMI) have not distinguished between AMI types 1 and 2. Influenza and cold temperature can explain the increased incidence of AMI during winter but, because they are closely related in temperate regions, their relative contribution is unknown. Methods and Results The temporal relationship between incidence rates of AMI with demonstrated culprit plaque (type 1 AMI) from the regional primary angioplasty network and influenza, adjusted for ambient temperature, was studied in Madrid region (Spain) during 5 influenza seasons (from June 2013 to June 2018). A time-series analysis with quasi-Poisson regression models and distributed lag-nonlinear models was used. The incidence rate of type 1 AMI according to influenza vaccination status was also explored. A total of 8240 cases of confirmed type 1 AMI were recorded. The overall risk ratio (RR) of type 1 AMI during epidemic periods, adjusted for year, month, and temperature, was 1.23 (95% CI, 1.03-1.47). An increase of weekly influenza rate of 50 cases per 100 000 inhabitants resulted in an RR for type 1 AMI of 1.16 (95% CI, 1.09-1.23) during the same week, disappearing 1 week after. When adjusted for influenza, a decrease of 1ºC in the minimum temperature resulted in an increase of 2.5% type 1 AMI. Influenza vaccination was associated with a decreased risk of type 1 AMI in subjects aged 60 to 64 years (RR, 0.58; 95% CI, 0.47-0.71) and ≥65 years (RR, 0.53; 95% CI, 0.49-0.57). Conclusions Influenza and cold temperature were both independently associated with an increased risk of type 1 AMI, whereas vaccination was associated with a reduced risk among older patients.
Climate extremes affected cities and their populations during the last decades. Future climate projections indicate climate extremes will increasingly impact urban areas during the 21st century. Humidity related fluctuations and extremes directly underpin convective processes, as well as can influence human health conditions. Regional climate models are a powerful tool to understand regional-to-local climate change processes for cities and their surroundings. Convection-permitting regional climate models, operating on very high resolutions, indicate improved simulation of convective extremes, particularly on sub-daily timescales and in regions with complex terrain such as cities. This research aims to understand how crossing spatial resolutions from similar to 12.5 km to similar to 3 km grid size affect humidity extremes and related variables under future climate change for urban areas and its surroundings. Taking Berlin and its surroundings as the case study area, the research identifies two categories of unprecedented future extreme atmospheric humidity conditions happening under 1.5 degrees C and 2.0 degrees C mean warming based on statistical distributions, respectively near surface specific humidity >0.02 kg/kg and near surface relative humidity <30%. Two example cases for each future extreme condition are dynamically downscaled for a two months period from the 0.44 degrees horizontal resolution following a double-nesting approach: first to the 0.11 degrees (similar to 12.5 km) horizontal resolution with the regional climate model REMO and thereafter to the 0.0275 degrees (similar to 3 km) horizontal resolution with the non-hydrostatic version of REMO. The findings show that crossing spatial resolutions from similar to 12.5 km to similar to 3 km grid size affects humidity extremes and related variables under climate change. Generally, a stronger decrease in moisture (up to 0.0007-0.005 kg/kg SH and 10-20% RH) and an increase in temperature (1-2 degrees C) is found on the 0.0275 degrees compared to the 0.11 degrees horizontal resolution, which is more profound in Berlin than in the surroundings. The convection-permitting scale mitigates the specific humidity moist extreme and intensifies the relative humidity dry extreme in Berlin, posing challenges with respect to health for urban dwellers.
Several countries have been affected by natural hazards during the COVID-19 pandemic. The combination of the pandemic and natural hazards has led to serious challenges that include financial losses and psychosocial stress. Additionally, this compound disaster affected evacuation decision making, where to evacuate, volunteer participation in mitigation and recovery, volunteer support acceptance, and interest in other hazard risks. This study investigated the impact of COVID-19 on disaster response and recovery from various types of hazards, with regard to preparedness, evacuation, volunteering, early recovery, awareness and knowledge of different types of hazards, and preparedness capacity development. This study targets hazards such as Cyclone Amphan in India, the Kumamoto flood in Japan, Typhoon Rolly in the Philippines, and the California wildfires in the U.S. This study made several recommendations, such as the fact that mental health support must be taken into consideration during COVID-19 recovery. It is necessary to improve the genral condition of evacuation centers in order to encourage people to act immediately. A pandemic situation necessitates a strong communication strategy and campaign with particular regard to the safety of evacuation centers, the necessity of a lockdown, and the duration required for it to reduce the psychological impact. Both national and local governments are expected to strengthen their disaster risk reduction (DRR) capacity, which calls for the multi-hazard management of disaster risk at all levels and across all sectors.
BACKGROUND: The impacts of non-pharmaceutical interventions (NPIs) and vaccine boosters on the transmission of the largest outbreak of COVID-19 (the fifth wave) in Hong Kong have not been reported. The outbreak, dominated by the Omicron BA.2 subvariant, began to spread substantially after the Spring Festival in February, 2022, when the temperature varied greatly (e.g. a cold surge event). Tightening social distancing measures did not succeed in containing the outbreak until later with the use of rapid antigen tests (RAT) and increased vaccination rates. Temperature has been previously found to have significant impact on the transmissibility. Understanding how the public health interventions influence the number of infections in this outbreak provide important insights on prevention and control of COVID-19 during different seasons. METHODS: We developed a transmission model incorporating stratified immunity with vaccine-induced antibody responses and the daily changes in population mobility, vaccination and weather factors (i.e. temperature and relative humidity). We fitted the model to the daily reported cases detected by either PCR or RAT between 1 February and 31 March using Bayesian statistics, and quantified the effects of individual NPIs, vaccination and weather factors on transmission dynamics. RESULTS: Model predicted that, with the vaccine uptake, social distancing reduced the cumulative incidence (CI) from 58.2% to 44.5% on average. The use of RAT further reduced the CI to 39.0%. Without vaccine boosters in these two months, the CI increased to 49.1%. While public health interventions are important in reducing the total infections, the outbreak was temporarily driven by the cold surge. If the coldest two days (8.5 °C and 8.8 °C) in February were replaced by the average temperature in that month (15.2 °C), the CI would reduce from 39.0% to 28.2%. CONCLUSION: Preventing and preparing for the transmission of COVID-19 considering the change in temperature appears to be a cost-effective preventive strategy to lead people to return to normal life.
Evidence regarding the effects of environmental factors on COVID-19 transmission is mixed. We aimed to explore the associations of air pollutants and meteorological factors with COVID-19 confirmed cases during the outbreak period throughout China. The number of COVID-19 confirmed cases, air pollutant concentrations, and meteorological factors in China from January 25 to February 29, 2020, (36 days) were extracted from authoritative electronic databases. The associations were estimated for a single-day lag as well as moving averages lag using generalized additive mixed models. Region-specific analyses and meta-analysis were conducted in 5 selected regions from the north to south of China with diverse air pollution levels and weather conditions and sufficient sample size. Nonlinear concentration-response analyses were performed. An increase of each interquartile range in PM(2.5), PM(10), SO(2), NO(2), O(3), and CO at lag4 corresponded to 1.40 (1.37-1.43), 1.35 (1.32-1.37), 1.01 (1.00-1.02), 1.08 (1.07-1.10), 1.28 (1.27-1.29), and 1.26 (1.24-1.28) ORs of daily new cases, respectively. For 1°C, 1%, and 1 m/s increase in temperature, relative humidity, and wind velocity, the ORs were 0.97 (0.97-0.98), 0.96 (0.96-0.97), and 0.94 (0.92-0.95), respectively. The estimates of PM(2.5), PM(10), NO(2), and all meteorological factors remained significantly after meta-analysis for the five selected regions. The concentration-response relationships showed that higher concentrations of air pollutants and lower meteorological factors were associated with daily new cases increasing. Higher air pollutant concentrations and lower temperature, relative humidity and wind velocity may favor COVID-19 transmission. Controlling ambient air pollution, especially for PM(2.5), PM(10), NO(2), may be an important component of reducing risk of COVID-19 infection. In addition, as winter months are arriving in China, the meteorological factors may play a negative role in prevention. Therefore, it is significant to implement the public health control measures persistently in case another possible pandemic.
Allergic diseases are increasing globally. Air pollution, climate change, and reduced biodiversity are major threats to human health with detrimental effects on chronic noncommunicable diseases. Outdoor and indoor air pollution and climate change are increasing. Asia has experienced rapid economic growth, a deteriorating environment, and an increase in allergic diseases to epidemic proportions. Air pollutant levels in Asian countries are substantially higher than in developed countries. Moreover, industrial, traffic-related, and household biomass combustion and indoor pollutants from chemicals and tobacco are major sources of air pollutants. We highlight the major components of pollutants and their impacts on respiratory allergies.
Objective To examine countries’engagement with the health impacts of climate change in their formal statements to intergovernmental organizations, and the factors driving engagement. Methods We obtained the texts of countries’annual statements in United Nations (UN) general debates from 2000 to 2019 and their nationally determined contributions at the Paris Agreement in 2016. To measure countries’ engagement, we used a keyword-in-context text search with relevant search terms to count the total number of references to the relationship of health to climate change. We used a machine learning model (random forest predictions) to identify the most important country-level predictors of engagement. The predictors included political and economic factors, health outcomes, climate change-related variables and membership of political negotiating groups in the UN. Findings For both UN general debate statements and nationally determined contributions, low-and middle-income countries discussed the health impacts of climate change much more than did high-income countries. The most important predictors of engagement were health outcomes (infant mortality, maternal deaths, life expectancy), countries’ income levels (gross domestic product per capita), and fossil fuel consumption. Membership of political negotiating groups (such as the Group of 77 and Small Island Developing States) was a less important predictor. Conclusion Our analysis indicated a higher engagement in countries that carry the heaviest climate-related health burdens, but lack necessary resources to address the impacts of climate change. These countries are shouldering responsibility for reminding the global community of the implications of climate change for people’s health. Climate change is taking an increasing toll on people’s health. The increase in heatwaves, drought, floods and other climate hazards is increasing the risk of climate-related illness and death as well as reversing gains made in reducing food insecurity and global hunger.1,2 Air pollution, primarily driven by fossil fuel emissions, is the major environmental risk factor for premature death and has impacts on child health and survival.3-5 Highlighting these human impacts is seen as a way of accelerating climate action
Worldwide, over half of the global population is living in urban areas. The metropolitan areas are highly populated and environmentally non-green regions on the planet. In green space regions, plants, grass, and green vegetation prevent soil erosion, absorb air pollutants, provide fresh and clean air, and minimize the burden of diseases. Presently, the entire world is facing a turmoil situation due to the COVID-19 pandemic. This study investigates the effect of the green space environment on air pollutants particulate matter PM2.5, PM10, carbon monoxide (CO), ozone (O(3)), incidence and mortality of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) in environmentally highly green and less-green countries. We randomly selected 17 countries based on the Environmental Performance Index (EPI) data. The 60% of the EPI score is based on seven categories: biodiversity and habitat, ecosystem, fisheries, climate change, pollution emissions, agriculture, and water resources. However, 40% of the score is based on four categories: air quality, sanitation and drinking water, heavy metals, and waste management. The air pollutants and SARS-CoV-2 cases and deaths were recorded from 25 January 2020, to 11 July 2021. The air pollutants PM2.5, PM10, CO, and O(3) were recorded from the metrological websites, Air Quality Index-AQI, 2021. The COVID-19 daily cases and deaths were obtained from the World Health Organization. The result reveals that air pollutants mean values for PM2.5 110.73 ± 1.09 vs. 31.35 ± 0.29; PM10 80.43 ± 1.11 vs. 17.78 ± 0.15; CO 7.92 ± 0.14 vs. 2.35 ± 0.03 were significantly decreased (p < 0.0001) in environmentally highly green space countries compared to less-green countries. Moreover, SARS-CoV-2 cases 15,713.61 ± 702.42 vs. 3445.59 ± 108.09; and deaths 297.56 ± 11.27 vs. 72.54 ± 2.61 were also significantly decreased in highly green countries compared to less-green countries. The green environment positively impacts human wellbeing. The policymakers must implement policies to keep the living areas, surroundings, towns, and cities clean and green to minimize air pollution and combat the present pandemic of COVID-19.
The COVID-19 pandemic continues to expand, while the relationship between weather conditions and the spread of the virus remains largely debatable. In this paper, we attempt to examine this question by employing a flexible econometric model coupled with fine-scaled hourly temperature variations and a rich set of covariates for 291 cities in the Chinese mainland. More importantly, we combine the baseline estimates with climate-change projections from 21 global climate models to understand the pandemic in different scenarios. We found a significant negative relationship between temperatures and caseload. A one-hour increase in temperatures from 25 degrees C to 28 degrees C tends to reduce daily cases by 15.1%, relative to such an increase from -2 degrees C to 1 degrees C. Our results also suggest an inverted U-shaped nonlinear relationship between relative humidity and confirmed cases. Despite the negative effects of heat, we found that rising temperatures induced by climate change are unlikely to contain a hypothesized pandemic in the future. In contrast, cases would tend to increase by 10.9% from 2040 to 2059 with a representative concentration pathway (RCP) of 4.5 and by 7.5% at an RCP of 8.5, relative to 2020, though reductions of 1.8% and 18.9% were projected for 2080-2099 for the same RCPs, respectively. These findings raise concerns that the pandemic could worsen under the climate-change framework.
Australia’s summer bushfires of 2020-2021 were catastrophic, negatively impacting people, and the natural environment. This climate change-related event exacerbated the influence of the COVID-19 pandemic on public health. Young people are a priority population whose health and livelihoods are significantly impacted by these events. At the same time, young people are active agents for climate action. This exploratory mixed-method study draws on descriptive analyses of survey data (n = 46) and thematic analyses of interview data (n = 6) which demonstrated that some young people, whilst concerned about existential and real impacts of climate change, use contact with nature to cope and as motivation for taking climate actions.
BACKGROUND: From November 2019 to January 2020, eastern Australia experienced the worst bushfires in recorded history. Two months later, Sydney and surrounds were placed into lockdown for six weeks due to the COVID-19 pandemic, followed by ongoing restrictions. Many pregnant women at this time were exposed to both the bushfires and COVID-19 restrictions. OBJECTIVE: To assess the impact of exposure to bushfires and pandemic restrictions on perinatal outcomes. METHODS: The study included 60 054 pregnant women who gave birth between November 2017 and December 2020 in South Sydney. Exposure cohorts were based on conception and birthing dates: 1) bushfire late pregnancy, born before lockdown; 2) bushfires in early-mid pregnancy, born during lockdown or soon after; 3) conceived during bushfires, lockdown in second trimester; 4) conceived after bushfires, pregnancy during restrictions. Exposure cohorts were compared with pregnancies in the matching periods in the two years prior. Associations between exposure cohorts and gestational diabetes, preeclampsia, hypertension, stillbirth, mode of birth, birthweight, preterm birth and small for gestational age were assessed using generalised estimating equations, adjusting for covariates. RESULTS: A decrease in low birth weight was observed for cohort 1 (aOR 0.81, 95%CI 0.69, 0.95). Conversely, cohort 2 showed an increase in low birth weight, and increases in prelabour rupture of membranes, and caesarean sections (aOR 1.18, 95%CI 1.03, 1.37; aOR 1.21, 95%CI 1.07, 1.37; aOR 1.10 (1.02, 1.18) respectively). Cohort 3 showed an increase in unplanned caesarean sections and high birth weight babies (aOR 1.15, 95%CI 1.04, 1.27 and aOR 1.16, 95%CI 1.02, 1.31 respectively), and a decrease in gestational diabetes mellitus was observed for both cohorts 3 and 4. CONCLUSION: Pregnancies exposed to both severe climate events and pandemic disruptions appear to have increased risk of adverse perinatal outcomes beyond only experiencing one event, but further research is needed.
Henan Province in Central China was hit by unprecedented, rain-triggered floods in July 2021 and experienced a recurrence of the COVID-19 pandemic. The current study aims to identify the latent profiles of psychological status and acceptance of change among Henan residents who have been cumulatively exposed to these floods and the COVID-19 pandemic. A total of 977 participants were recruited. Latent profile analysis (LPA) was used to explore underlying patterns of psychological status (i.e., perceived risk of the COVID-19 pandemic, post-traumatic stress symptoms, anxiety and rumination) and acceptance of change. The predictors were evaluated with multinomial logistic regression. LPA identified four patterns of psychological status and acceptance of change: high distress/high acceptance (5.1%), moderate distress/moderate acceptance (20.1%), mild distress/mild acceptance (45.5%), and resilience (29.3%). The additive impact of the floods and COVID-19 pandemic and negative emotion during the floods were the risk factors, while flood coping efficacy, trust, and a closer psychological distance change were the protective factors. The present study therefore provides novel evidence on psychological status after both a natural disaster and a major public health event. The cumulative effects of the floods and the COVID-19 pandemic may have heightened the risk of post-disaster maladaptation. A complex relationship between psychological outcomes and acceptance of change was also found. The findings of this study thus provide a foundation for both disaster management and psychological assistance for particular groups.
Since the outbreak of COVID-19, wearing masks, vaccinations, and maintaining a safe distance has become social behaviors advocated by the government and widely adopted by the public. At the same time, unpredictable natural disaster risks brought by extreme climate change compound difficulties during epidemics and cause systemic risks that influence the existing pattern of epidemic prevention. Therefore, it is necessary to explore the effect of natural disaster risk caused by climate change on the response to outbreaks in the context of the COVID-19 epidemic. This study will focus on individual-level epidemic prevention behaviors, taking as an example the significant risk of severe destructive flooding caused by heavy rains in Henan, China, on July 20, 2021, which claimed 398 lives, to explore the effect of floods on the preventive behaviors of residents in the hardest hit areas against COVID-19. Through the multi-stage stratified random sampling of the affected residents in Zhengzhou, Xinxiang, Hebi, Luoyang, Anyang, and other cities in Henan Province, 2,744 affected people were surveyed via questionnaires. Through the linear regression model and moderating effect analysis, the study found that after floods, the individual’s flood risk perception and response behaviors significantly correlated with the individual’s prevention behaviors against COVID-19. Specifically, both flood risk perception and response behaviors strengthened the individual’s prevention behaviors. Furthermore, the study also found that community risk preparation behavior and social capital can moderate the above relationship to a certain extent. The research can guide risk communication under the compound risk scenario and prevent risky public behavior under the consistent presence of COVID-19 in the community.
OBJECTIVE: This research seeks to identify climate-sensitive infectious diseases of concern with a present and future likelihood of increased occurrence in the geographically vulnerable Torres Strait Islands, Australia. The objective is to contribute evidence to the need for adequate climate change responses. METHODS: Case data of infectious diseases with proven, potential and speculative climate sensitivity were compiled. RESULTS: Five climate-sensitive diseases in the Torres Strait and Cape York region were identified as of concern: tuberculosis, dengue, Ross River virus, melioidosis and nontuberculous mycobacterial infection. The region constitutes 0.52% of Queensland’s population but has a disproportionately high proportion of the state’s cases: 20.4% of melioidosis, 2.4% of tuberculosis and 2.1% of dengue. CONCLUSIONS: The Indigenous Torres Strait Islander peoples intend to remain living on their traditional country long-term, yet climate change brings risks of both direct and indirect human health impacts. Implications for public health: Climate-sensitive infections pose a disproportionate burden and ongoing risk to Torres Strait Islander peoples. Addressing the causes of climate change is the responsibility of various agencies in parallel with direct action to minimise or prevent infections. All efforts should privilege Torres Strait Islander peoples’ voices to self-determine response actions.
BACKGROUND: The effects of extreme temperature on infectious diseases are complex and far-reaching. There are few studies to access the relationship of pulmonary tuberculosis (PTB) with extreme temperature. The study aimed to identify whether there was association between extreme temperature and the reported morbidity of PTB in Shandong Province, China, from 2005 to 2016. METHODS: A generalized additive model (GAM) was firstly conducted to evaluate the relationship between daily reported incidence rate of PTB and extreme temperature events in the prefecture-level cities. Then, the effect estimates were pooled using meta-analysis at the provincial level. The fixed-effect model or random-effect model was selected based on the result of heterogeneity test. RESULTS: Among the 446,016 PTB reported cases, the majority of reported cases occurred in spring. The higher reported incidence rate areas were located in Liaocheng, Taian, Linyi and Heze. Extreme low temperature had an impact on the reported incidence of PTB in only one prefecture-level city, i.e., Binzhou (RR = 0.903, 95% CI: 0.817-0.999). While, extreme high temperature was found to have a positive effect on reported morbidity of PTB in Binzhou (RR = 0.924, 95% CI: 0.856-0.997) and Weihai (RR = 0.910, 95% CI: 0.843-0.982). Meta-analysis showed that extreme high temperature was associated with a decreased risk of PTB (RR = 0.982, 95% CI: 0.966-0.998). However, extreme low temperature was no relationship with the reported incidence of PTB. CONCLUSION: Our findings are suggested that extreme high temperature has significantly decreased the risk of PTB at the provincial levels. The findings have implications for developing strategies to response to climate change.
The aim of this study was to explore the effect of temperature on tuberculosis (TB) incidence using the distributed lag non-linear model (DLNM) from 2017 to 2021 in Kashgar city, the region with higher TB incidence than national levels, and assist public health prevention and control measures. From January 2017 to December 2021, a total of 8730 cases of TB were reported, with the higher incidence of male than that of female. When temperature was below 1 °C, it was significantly correlated with TB incidence compared to the median observed temperature (15 °C) at lag 7, 14, and 21, and lower temperatures showed larger RR (relative risk) values. High temperature produced a protective effect on TB transmission, and higher temperature from 16 to 31 °C has lower RR. In discussion stratified by gender, the maximum RRs were achieved for both male group and female group at - 15 °C with lag 21, reporting 4.28 and 2.02, respectively. At high temperature (higher than 20 °C), the RR value of developing TB for female group was significantly larger than 1. In discussion stratified by age, the maximum RRs were achieved for all age groups (≤ 35, 36-64, ≥ 65) at - 15 °C with lag 21, reporting 3.20, 2.07, and 3.45, respectively. When the temperature was higher than 20 °C, the RR of the 36-64-year-old group and the ≥ 65-year-old group was significantly larger than 1 at lag 21, while significantly smaller than 1 for cumulative RR at lag 21, reporting 0.11, 95% confidence interval (CI) (0.01, 0.83) and 0.06, 95% CI (0.01, 0.44), respectively. In conclusion, low temperature, especially in extreme level, acts as a high-risk factor inducing TB transmission in Kashgar city. Males exhibit a significantly higher RR of developing TB at low temperature than female, as well as the elderly group in contrast to the young or middle-aged groups. High temperature has a protective effect on TB transmission in the total population, but female and middle-aged and elderly groups are also required to be alert to the delayed RR induced by it.
BACKGROUND: The association between the incidence of hand, foot, and mouth disease (HFMD) and ambient temperature has been well documented. Although the severity of symptoms is an important indicator of disease burden and varies significantly across cases, it usually was ignored in previous studies, potentially leading to biased estimates of the health impact of temperature. METHODS: We estimated the disability-adjusted life year (DALY) by considering the severity of symptoms for each HFMD case reported during 2010-2012 in Guangdong and used distributed lag-nonlinear models to estimate the association between the daily average temperature and daily DALY of HFMD cases at the city-level. We investigated the potential effect modifiers on the pathway between temperature and DALY and pooled city-specific estimates to a provincial association using a meta-regression. The overall impact of temperature was further evaluated by estimates of DALYs that could be attributed to HFMD. RESULTS: The overall cumulative effect of daily mean temperature on the DALY of HFMD showed an inverse-U shape, with the maximum effect estimated to be β = 0.0331 (95%CI: 0.0199-0.0463) DALY at 23.8°C. Overall, a total of 6.432 (95%CI: 3.942-8.885) DALYs (attributable fraction = 2.721%, 95%CI: 1.660-3.759%) could be attributed to temperature exposure. All the demographic subgroups had a similar trend as the main analysis, while the magnitude of the peak of the temperature impact tended to be higher among the males, those aged ≥3yrs or from the Pear-River Delta region. Additionally, the impact of temperature on DALY elevated significantly with the increasing population density, per capita GDP, and per capita green space in parks. CONCLUSIONS: Temperature exposure was associated with increased burden of HFMD nonlinearly, with certain groups such as boys and those from areas with greater population density being more vulnerable.
The burden of disease caused by ambient high temperature has become a public health concern, but the associations between high temperature and hand, foot, and mouth disease (HFMD) remain indistinct. We used distributed lag non-linear model (DLNM) to estimate the burden of disease attribute to high temperature, adjusting for long-term trend and weather confounders. Total 18,167,455 cases were reported in 31 Chinese provinces, the incidence of HFMD showed a gradually increasing trend from 2008 to 2017 in China. Minimum morbidity temperature (MMT) was mainly concentrated at 17 to 23 °C in ≤ 5 years old group, 18 to 25 °C in 6 ~ 10 years old group and 19 to 27 °C in > 10 years old group. The greatest relative risk (RR) in age group ≤ 5 years old was 2.06 (95% CI: 1.85 ~ 2.30) in Heilongjiang, and the lowest RR was 1.02 (95% CI: 1.00 ~ 1.05) in Guangdong; the greatest RR in age group 6 ~ 10 years old was 2.24 (95% CI: 1.72 ~ 2.91) in Guizhou, and the lowest RR was 1.01 (95% CI: 0.97 ~ 1.12) in Tianjin; the greatest RR in the age group > 10 years old was 2.53 (95% CI: 1.66 ~ 3.87) in Heilongjiang, and the lowest RR was 1.02 (95% CI: 0.71 ~ 1.46) in Henan. We found the positive association between high temperature and HFMD in China.
BACKGROUND: Hand, foot and mouth disease (HFMD) is one of the common intestinal infectious diseases worldwide and has caused huge economic and disease burdens in many countries. The average annual incidence rate of HFMD was 11.66% in Shaanxi during the time span from 2009 to 2018. There are distinct differences within Shaanxi, as it is a special region that crosses three temperature zones. Hence, in this study, a spatiotemporal analysis of Shaanxi was performed to reveal the characteristics of the distribution of HFMD and to explore the meteorological determinants of HFMD. METHODS: The county-level and municipal data from Shaanxi Province from 2009 to 2018 were applied to research the spatiotemporal characteristics of HFMD and its meteorological determinants. Time series and spatial autocorrelation analyses were applied to assess the spatiotemporal characteristics of HFMD. This study used spatial econometric panel models to explore the relationship between HFMD and meteorological factors based on the data of 107 counties and 10 municipalities. RESULTS: The incidence rate of HFMD displayed no variable trend throughout the whole research period. A high incidence rate of HFMD was observed from June to September, corresponding to a time when the climate is characterized by heavy rain, high temperature, and high humidity. The high-incidence areas were mainly located in the central region in Shaanxi, whereas the low-incidence spots were mainly found in Northern Shaanxi. Regarding the meteorological factors analysed in this study, in general, the incidence rate of HFMD in specific regions was positively associated with the rainfall, temperature and humidity. CONCLUSION: These results could be applied by the government and the general public to take effective measures to prevent disease. Region-targeted policies could be enacted and implemented in the future according to specific situations in different areas and the relevant meteorological determinants. Additionally, meteorological conditions normally extend to a wide-ranging region; thus, cooperation among surrounding regions is necessary.
Tuberculosis (TB) cases have increased drastically over the last two decades and it remains as one of the deadliest infectious diseases in Malaysia. This cross-sectional study aimed to establish the spatial distribution of TB cases and its association with the sociodemographic and environmental factors in the Gombak district. The sociodemographic data of 3325 TB cases such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency, and smoking status from 1st January 2013 to 31st December 2017 in Gombak district were collected from the MyTB web and Tuberculosis Information System (TBIS) database at the Gombak District Health Office and Rawang Health Clinic. Environmental data consisting of air pollution such as air quality index (AQI), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter 10 (PM10,) were obtained from the Department of Environment Malaysia from 1st July 2012 to 31st December 2017; whereas weather data such as rainfall were obtained from the Department of Irrigation and Drainage Malaysia and relative humidity, temperature, wind speed, and atmospheric pressure were obtained from the Malaysia Meteorological Department in the same period. Global Moran’s I, kernel density estimation, Getis-Ord Gi* statistics, and heat maps were applied to identify the spatial pattern of TB cases. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to determine the spatial association of sociodemographic and environmental factors with the TB cases. Spatial autocorrelation analysis indicated that the cases was clustered (p<0.05) over the five-year period and year 2016 and 2017 while random pattern (p>0.05) was observed from year 2013 to 2015. Kernel density estimation identified the high-density regions while Getis-Ord Gi* statistics observed hotspot locations, whereby consistently located in the southwestern part of the study area. This could be attributed to the overcrowding of inmates in the Sungai Buloh prison located there. Sociodemographic factors such as gender, nationality, employment status, health care worker status, income status, residency, and smoking status as well as; environmental factors such as AQI (lag 1), CO (lag 2), NO2 (lag 2), SO2 (lag 1), PM10 (lag 5), rainfall (lag 2), relative humidity (lag 4), temperature (lag 2), wind speed (lag 4), and atmospheric pressure (lag 6) were associated with TB cases (p<0.05). The GWR model based on the environmental factors i.e. GWR2 was the best model to determine the spatial distribution of TB cases based on the highest R2 value i.e. 0.98. The maps of estimated local coefficients in GWR models confirmed that the effects of sociodemographic and environmental factors on TB cases spatially varied. This study highlighted the importance of spatial analysis to identify areas with a high TB burden based on its associated factors, which further helps in improving targeted surveillance.
OBJECTIVES: Evidence of the relationship between climate variability, air pollution and human parainfluenza virus (HPIV) infections has been inconsistent. We assessed this in a paediatric population from a highly urbanized tropical city-state. METHODS: We analysed all reports of HPIV infections in children <5 years old obtained from a major specialist women and children's hospital in Singapore. Assuming a negative binomial distribution and using multivariable fractional polynomial modelling, we examined the relations between climate variability, air quality and the risk of HPIV infections, adjusting for time-varying confounders. RESULTS: We identified 6393 laboratory-confirmed HPIV infections from 2009 to 2019. Every 1 °C decline in temperature was associated with a 5.8% increase (RR: 0.943, 95% Confidence Interval [95% CI]: 0.903-0.984) in HPIV infection risk 6 days later. Every 10% decrease in relative humidity was associated with a 15.8% cumulative increase in HPIV risk over the next 6 days (cumulative RR: 0.842, 95% CI: 0.771-0.919). Rainfall was positively associated with HPIV risk 2 days later (RR: 1.021, 95% CI: 1.000-1.043). A within-year seasonal rise of HPIV was driven by HPIV-3 and HPIV-1 and preceded by a seasonal decline in temperature. Gender was an effect modifier of the climate-HPIV relationship. Air quality was not associated with HPIV risk. CONCLUSIONS: This study demonstrates a close association between HPIV infection risk and tropical climate variability. The climate dependence and seasonal predictability of HPIV can inform the timing of community campaigns aimed at reducing infection risk and the development of hospital resources and climate adaption plans.
BACKGROUND: Acute respiratory infections (ARIs) are among the most common human illnesses globally. Previous studies that examined the associations between climate variability and ARIs or ARI pathogens have reported inconsistent findings. Few studies have been conducted in Southeast Asia to date, and the impact of climatic factors are not well-understood. This study aimed to investigate the short-term associations between climate variability and ARIs in Singapore. METHODS: We obtained reports of ARIs from all government primary healthcare services from 2005 to 2019 and analysed their dependence on mean ambient temperature, minimum temperature and maximum temperature using the distributed lag non-linear framework. Separate negative binomial regression models were used to estimate the association between each temperature (mean, minimum, maximum temperature) and ARIs, adjusted for seasonality and long-term trend, rainfall, relative humidity, public holidays and autocorrelations. For temperature variables and relative humidity we reported cumulative relative risks (RRs) at 10th and 90th percentiles compared to the reference value (centered at their medians) with corresponding 95% confidence intervals (CIs). For rainfall we reported RRs at 50th and 90th percentiles compared to 0 mm with corresponding 95% CIs. RESULTS: Statistically significant inverse S-curve shaped associations were observed between all three temperature variables (mean, minimum, maximum) and ARIs. A decrease of 1.1 °C from the median value of 27.8 °C to 26.7 °C (10th percentile) in the mean temperature was associated with a 6% increase (RR: 1.06, 95% CI: 1.03 to 1.09) in ARIs. ARIs also increased at 23.9 °C (10th percentile) compared to 24.9 °C of minimum temperature (RR: 1.11, 95% CI: 1.07 to 1.16). The effect of maximum temperature for the same comparison (30.5 °C vs 31.7 °C) was non-significant (RR: 1.02, 95% CI: 0.99 to 1.05). An increase in ambient temperature to 28.9 °C (90th percentile) was associated with an 18% decrease (RR: 0.82, 95% CI: 0.80 to 0.83) in ARIs. Similarly, ARIs decreased with the same increase to 90th percentile in minimum (RR: 0.84, 95% CI: 0.80 to 0.87) and maximum (RR: 0.89, 95% CI: 0.86 to 0.93) temperatures. Rainfall was inversely associated with ARIs and displayed similar shape in all three temperature models. Relative humidity, on the other hand, exhibited a U-shaped relationship with ARIs. CONCLUSION: Our findings suggest that lower temperatures increase the risk of ARIs. Anticipated extreme weather events that reduce ambient temperature can be used to inform increased healthcare resource allocation for ARIs.
The unfavorable effects of global climate change, which are mostly the result of human activities, have had a particularly negative effect on human health and the planet’s ecosystems. This study attempted to determine the seasonality and association of air pollution, in addition to climate conditions, with two respiratory infections, influenza and pneumonia, in Chiang Mai, Thailand, which has been considered the most polluted city on Earth during the hot season. We used a seasonal-trend decomposition procedure based on loess regression (STL) and a seasonal cycle subseries (SCS) plot to determine the seasonality of the two diseases. In addition, multivariable negative binomial regression (NBR) models were used to assess the association between the diseases and environmental variables (temperature, precipitation, relative humidity, PM(2.5), and PM(10)). The data revealed that influenza had a clear seasonal pattern during the cold months of January and February, whereas the incidence of pneumonia showed a weak seasonal pattern. In terms of forecasting, the preceding month’s PM(2.5) and temperature (lag1) had a significant association with influenza incidence, while the previous month’s temperature and relative humidity influenced pneumonia. Using air pollutants as an indication of respiratory disease, our models indicated that PM(2.5) lag1 was correlated with the incidence of influenza, but not pneumonia. However, there was a linear association between PM(10) and both diseases. This research will help in allocating clinical and public health resources in response to potential environmental changes and forecasting the future dynamics of influenza and pneumonia in the region due to air pollution.
The relationship between meteorological factors and the risk of hand, foot, and mouth disease (HFMD) has been well documented. However, researchers have failed to consider the complex interactive relationships among meteorological factors. The weekly number of HFMD cases along with meteorological factors were collected between 2009 to 2017 in four cities in Guangdong Province. We used Bayesian kernel machine regression to investigate the nonlinear and interactive relationship between meteorological factors, such as temperature and humidity, on the risk of HFMD. Multivariate meta-analysis was used to pool the city-specific effect estimates and identify factors underlying the inter-city heterogeneity. The risk ratios (RRs) for each percentile increase in temperature from the 50th percentile value, while humidity was at its 10th, 50th, and 90th percentile values, were 1.621(95%CI: 1.226, 2.141), 2.638(2.169, 3.208), and 3.734(2.908, 4.792), respectively (Q= 19.132, P (interaction)< 0.001). In contrast, the RRs for each percentile increase in humidity from its 50th percentile, while holding temperature at its 10th, 50th, and 90th percentile values, were 0.901(95%CI: 0.592, 1.369), 2.026(1.679, 2.448), and 0.884(0.632, 1.238), respectively (Q= 24.876, P (interaction) < 0.001). Increased wind speed and sunshine duration were also observed to strengthen the impact of other meteorological factors. Furthermore, we found increased gross domestic product per capita and per capital area of parks and green land in city tended to significantly strengthen the interactive effects of humidity on other meteorological factors including sunshine duration (P = 0.013 and 0.042), rainfall (P = 0.017 and 0.035), temperature (P = 0.021 and 0.031), win speed (P = 0.011 and 0.045), and pressure (P = 0.013 and 0.042). Our study contributed further understanding of complex interactions between meteorological factors on the risk of HFMD. Our findings provide epidemiological evidence for meteorological interactions on HFMD, which may provide knowledge for future research on the health effects of meteorological factors.
BACKGROUND: Hand, foot, and mouth disease (HFMD) is a febrile rash infection caused by enteroviruses, spreading mainly via the respiratory tract and close contact. In the past two decades, HFMD has been prevalent mainly in Asia, including China and South Korea, causing a huge disease burden and putting the lives and health of children at risk. Therefore, a further study of the factors influencing HFMD incidences has far-reaching implications. In existing studies, the environmental factors affecting such incidences are mainly divided into two categories: meteorological and air. Among these studies, the former are the majority of studies on HFMD. Some scholars have studied both factors at the same, but the number is not large and the findings are quite different. METHODS: We collect monthly cases of HFMD in children, meteorological factors and atmospheric pollution in Urumqi from 2014 to 2020. Trend plots are used to understand the approximate trends between meteorological factors, atmospheric pollution and the number of HFMD cases. The association between meteorological factors, atmospheric pollution and the incidence of HFMD in the Urumqi region of northwest China is then investigated using multiple regression models. RESULTS: A total of 16,168 cases in children are included in this study. According to trend plots, the incidence of HFMD shows a clear seasonal pattern, with O(3) (ug/m(3)) and temperature (°C) showing approximately the same trend as the number of HFMD cases, while AQI, PM(2.5) (ug/m(3)), PM(10) (ug/m(3)) and NO(2) (ug/m(3)) all show approximately opposite trends to the number of HFMD cases. Based on multiple regression results, O(3) (P = 0.001) and average station pressure (P = 0.037) are significantly and negatively associated with HFMD incidences, while SO(2) (P = 0.102), average dew point temperature (P = 0.072), hail (P = 0.077), and thunder (P = 0.14) have weak significant relationships with them.
Hand, foot, and mouth disease (HFMD) is a global infectious disease severely threatening children’s health. It has been recognized that climate factors play an important role in the transmission of HFMD. In this paper, the bootstrap test in the geographically weighted regression (GWR) literature is extended to geographically and temporally weighted regression (GTWR) models for identifying homogeneous explanatory variables and spatiotemporally heterogeneous ones. The resulting mixed GTWR model is then used to investigate spatiotemporal effect of climate factors on the HFMD incidence in Inner Mongolia, China, a provincial autonomous region with extensive area and different climatic conditions. The results demonstrate that the effect of relative humidity is global over space and time, while that of air temperature, air pressure and wind speed varies spatiotemporally. The extended bootstrap test provides a solid statistical basis for model selection. The findings from the study may provide not only a deep understanding of spatiotemporal variation characteristics of the climatic effect on the HFMD incidence, but also some useful evidences for taking measures of the disease prevention and control at the county level in different seasons.
Seasonal cycles of AHF are causally attributed to the seasonal pattern of respiratory tract infections. However, this assumption has never been formally validated. We aimed to determine whether the increase in winter admissions for acute heart failure (AHF) can be explained by seasonal infectious diseases. We studied 12,147 patients admitted for AHF over a period of 11 years (2005-2015). Detailed virology and bacteriology data were collected on each patient. Meteorological information including daily temperature and relative humidity was obtained for the same period. The peak-to-low ratio, indicating the intensity of seasonality, was calculated using negative binomial regression-derived incidence rate ratios (IRR). AHF admissions occurred with a striking annual periodicity, peaking in winter (December-February) and were lowest in summer (June-August), with a seasonal amplitude (January vs. August) of 2.00 ([95% CI 1.79-2.24]. Occurrence of confirmed influenza infections was low (1.59%). Clinical diagnoses of respiratory infections, confirmed influenza infections, and influenza-like infections also followed a strong seasonal pattern (P < 0.0001; Peak/low ratio 2.42 [95% CI 1.394-3.03]). However, after exclusion of all respiratory infections, the seasonal variation in AHF remained robust (Peak/low ratio January vs. August, 1.81 [95% CI 1.60-2.05]; P < 0.0001). There was a strong inverse association between AHF admissions and average monthly temperature (IRR 0.95 per 1℃ increase; 95% CI 0.94 to 0.96). In conclusion, these is a dominant seasonal modulation of AHF admissions which is only partly explained by the incidence of winter respiratory infections. Environmental factors modify the susceptibility of heart failure patients to decompensation.
Despite high incidence of acute kidney injury (AKI) among patients hospitalised for influenza, no previous work has attempted to analyse and quantify the association between the two. Herein, we made use of Hong Kong’s surveillance data to evaluate the time-varying relationship between seasonal influenza and risk of AKI with adjustment for potential environmental covariates. Generalized additive model was used in conjunction with distributed-lag non-linear model to estimate the association of interest with daily AKI admissions as outcome and daily influenza admissions as predictor, while controlling for environmental variables (i.e. temperature, relative humidity, total rainfall, nitrogen dioxide, and ozone). Results suggested a positive association between risk of AKI admission and number of influenza hospitalisation cases, with relative risk reaching 1.12 (95% confidence interval, 1.10-1.15) at the 95th percentile. Using median as reference, an almost U-shaped association between risk of AKI admission and temperature was observed; the risk increased significantly when the temperature was low. While ozone was not shown to be a risk factor for AKI, moderate-to-high levels of nitrogen dioxide (50-95th percentile) were significantly associated with increased risk of AKI admission. This study mentioned the possibility that AKI hospitalisations are subject to environmental influences and offered support for a positive association between seasonal influenza and AKI occurrence in Hong Kong. Authorities are urged to extend the influenza vaccination program to individuals with pre-existing renal conditions to safeguard the health of the vulnerable. Given that adverse health effects are evident at current ambient levels of nitrogen dioxide, the government is recommended to adopt clean-air policies at the earliest opportunity to protect the health of the community.
Many studies have shown that the relationship between ambient temperature, relative humidity and mumps has been highlighted. However, these studies showed inconsistent results. Therefore, the goal of our study is to conduct a meta-analysis to clarify this relationship and to quantify the size of these effects as well as the potential factors. Systematic literature researches on PubMed, Embase.com, Web of Science Core Collection, Cochrane library, Chinese BioMedical Literature Database (CBM) and China National Knowledge Infrastructure (CNKI) were performed up to February 7, 2022 for articles analyzing the relationships between ambient temperature, relative humidity and incidence of mumps. Eligibility assessment and data extraction were conducted independently by two researchers, and meta-analysis was performed to synthesize these data. We also assessed sources of heterogeneity by study region, regional climate, study population. Finally, a total of 14 studies were screened out from 1154 records and identified to estimate the relationship between ambient temperature, relative humidity and incidence of mumps. It was found that per 1 °C increase and decrease in the ambient temperature were significantly associated with increased incidence of mumps with RR of 1.0191 (95% CI: 1.0129-1.0252, I(2) = 92.0%, Egger’s test P = 0.001, N = 13) for per 1 °C increase and 1.0244 (95% CI: 1.0130-1.0359, I(2) = 86.6%, Egger’s test P = 0.077, N = 9) for per 1 °C decrease. As to relative humidity, only high effect of relative humidity was slightly significant (for per 1 unit increase with RR of 1.0088 (95% CI: 1.0027-1.0150), I(2) = 72.6%, Egger’s test P = 0.159, N = 9). Subgroup analysis showed that regional climate with temperate areas may have a higher risk of incidence of mumps than areas with subtropical climate in cold effect of ambient temperature and low effect of relative humidity. In addition, meta-regression analysis showed that regional climate may affect the association between incidence of mumps and cold effect of ambient temperature. Our results suggest ambient temperature could affect the incidence of mumps significantly, of which both hot and cold effect of ambient temperature may increase the incidence of mumps. Further studies are still needed to clarify the relationship between the incidence of mumps and ambient temperature outside of east Asia, and many other meteorological factors. These results of ambient temperature are important for establishing preventive measures on mumps, especially in temperate areas. The policy-makers should pay more attention to ambient temperature changes and take protective measures in advance.
Mumps is an acute and common childhood disease caused by paramyxovirus. It has been reported that the occurrence of mumps is influenced by seasonality. However, the role of meteorological variables in the incidence of mumps remains unclear. The purpose of this study was to explore the relationship between meteorological factors and the incidence of mumps infection. Poisson regression analysis was used to study the relationship between weather variability and the incidence of mumps in Taiwan. Between 2012 and 2018, 5459 cases of mumps cases were reported to the Centers for Disease Control, Taiwan (Taiwan CDC). The occurrence of mumps virus infections revealed significant seasonality in the spring and summer seasons in Taiwan. The incidence of mumps virus infections began to increase at temperatures of 15°C and started to decline if the temperature was higher than 29°C (r2 = 0.387, P = .008). Similarly, the number of mumps cases began to increase at a relative humidity of 65% to 69% (r2 = 0.838, P < .029). The number of mumps cases was positively associated with temperature and relative humidity during the period preceding the infection. This study showed that the occurrence of mumps is significantly associated with increasing temperature and relative humidity in Taiwan. Therefore, these factors could be regarded as early warning signals and indicate the need to strengthen the intervention and prevention of mumps.
OBJECTIVE: In this study, we aimed to investigate the clinical epidemiology of lower respiratory tract infections with different respiratory syncytial virus (RSV) subtypes in hospitalized children in Suzhou and their correlation with climatic and environmental factors. METHOD: In this retrospective cross-sectional study, we collected nasopharyngeal secretion samples from children hospitalized with acute lower respiratory tract infection. We collected the clinical data of children with RSV infection, and compared and analyzed their epidemiological characteristics. RESULTS: RSV-B was the dominant strain in 2016. In 2018, RSV-A was the dominant strain. The positive detection rate of RSV-A was negatively correlated with monthly mean temperature, monthly mean wind speed, total monthly rainfall, and O(3) concentration and positively correlated with PM2.5, PM10, and NO(2), SO(2), and CO concentrations. The positive detection rate of RSV-B was negatively correlated with monthly average temperature, monthly total rainfall, monthly sunshine duration, and O(3) concentration and positively correlated with CO concentration. CONCLUSIONS: RSV-A was the main subtype detected in this study. The positive detection rate of RSV-A was related to temperature, wind speed, rainfall, PM2.5. PM10, and NO(2), SO(2), CO, and O(3) concentrations. The positive detection rate of RSV-B was related to temperature, rainfall, sunshine time, and O(3) concentration.
BACKGROUND: While influenza infections and environmental factors have been documented as potential drivers of tuberculosis, no investigations have simultaneously examined their impact on tuberculosis at a population level. This study thereby made use of Hong Kong’s surveillance data over 22 years to elucidate the temporal association between environmental influences, influenza infections, and tuberculosis activity. METHODS: Weekly total numbers of hospital admissions due to tuberculosis, meteorological data, and outdoor air pollutant concentrations in Hong Kong during 1998-2019 were obtained. All-type influenza-like illness positive (ILI+) rate and type-specific ILI+ rates were used as proxies for influenza activity. Quasi-Poisson generalized additive models together with distributed lag non-linear models were used to assess the association of interest. RESULTS: A total of 164,116 hospital admissions due to tuberculosis were notified in public settings over a period of 22 years. The cumulative adjusted relative risk (ARR) of hospital admission due to tuberculosis was 1.07 (95% CI, 1.00-1.14) when the mean ambient temperature increased from 15.1 °C (the 5th percentile) to 24.5 °C (median). Short-term exposure to air pollutants was not found to be statistically significantly related to tuberculosis hospitalization. Accounting for the environmental covariates in the analysis, the cumulative ARR of tuberculosis admission was elevated to 1.05 (95% CI, 1.01-1.08) when the rate of ILI+ total increased from zero to 19.9 per 1000 consultations, the 95th percentile. CONCLUSION: Our findings demonstrated that increased influenza activity and higher temperature were related to a higher risk of tuberculosis admissions. Stepping up the promotion of influenza vaccination, especially before the summer season, may lower the risk of tuberculosis infection/reactivation for vulnerable groups (e.g. elderly born before the launch of Bacillus Calmette-Guérin vaccination programme).
Background: Existing evidence suggests that mumps epidemics, a global public health issue, are associated with meteorological factors and air pollutants at the population scale. However, the interaction effect of meteorological factors and air pollutants on mumps remains underexplored.Methods: Daily cases of mumps, meteorological factors, and air pollutants were collected in Ningxia, China, from 2015 to 2019. First, a distributed lag nonlinear model (DLNM) was employed to assess the confounding-adjusted relationship between meteorological factors, ambient air pollutants, and mumps incidences. According to the results of DLNM, stratification in both air pollutants and meteorological factors was adopted to further explore the interaction effect of particulate matter less than or equal to 2.5 mu m in aerodynamic diameter (PM2.5) and ground-level ozone (O-3) with temperature and relative humidity (RH).Results: We reported significant individual associations between mumps incidences and environmental factors, including temperature, relative humidity, PM2.5, and O-3. Evident multiplicate and additive interactions between meteorological factors and PM2.5 were found with interaction relative risk (IRR) of 1.14 (95%CI: 1.01, 1.29) and relative excess risk due to interaction (RERI) of 0.17 (95%CI: 0.02, 0.32) for a moderate level of temperature at 12 degrees C, and IRR of 1.37 (95%CI: 1.14, 1.66), RERI of 0.36 (95%CI: 0.11, 0.60) for a high level of temperature at 20 degrees C, respectively. These results indicated that PM2.5 and temperature have a significant synergistic effect on the cases of mumps, while no interaction between relative humidity and PM2.5 is observed. Regarding O-3 and meteorological factors (temperature = 12 degrees C, 20 degrees C), IRR and RERI were 1.33 (95%CI: 1.17, 1.52) and 0.30 (95%CI: 0.16, 0.45), 1.91 (95%CI: 1.46, 2.49) and 0.69 (95%CI: 0.32, 1.07), respectively. And IRR of 1.17 (95%CI: 1.06, 1.29), RERI of 0.13 (95%CI: 0.04, 0.21) for a middle level of relative humidity at 48%.Conclusion: Our findings indicated that meteorological factors and air pollutants imposed a significantly lagged and nonlinear effect on the incidence of mumps. The interaction between low temperature and O-3 showed antagonistic effects, while temperature (medium and high) with PM2.5 and O-3 presented synergistic effects. For relative humidity, the interaction with O-3 is synergistic. These results provide scientific evidence to relevant health authorities for the precise disease control and prevention of mumps in arid and semi-arid areas.
Big data can be used to correlate diseases and climatic factors. The prevalence of influenza (flu) virus, accounting for a large proportion of respiratory infections, suggests that the effect of climate variables according to seasonal dynamics of influenza virus infections should be investigated. Here, trends in flu virus detection were analyzed using data from 9,010 tests performed between January 2012 and December 2018 at Dankook University Hospital, Cheonan, Korea. We compared the detection of the flu virus in Cheonan area and its association with climate change. The flu virus detection rate was 9.9% (894/9,010), and the detection rate was higher for flu virus A (FLUAV; 6.9%) than for flu virus B (FLUBV; 3.0%). Both FLUAV and FLUBV infections are considered an epidemic each year. We identified 43.1% (n = 385) and 35.0% (n = 313) infections in children aged < 10 years and adults aged > 60 years, respectively. The combination of these age groups encompassed 78.1% (n = 698/894) of the total data. Flu virus infections correlated with air temperature, relative humidity, vapor pressure, atmospheric pressure, particulate matter, and wind chill temperature (P < 0.001). However, the daily temperature range did not significantly correlate with the flu detection results. This is the first study to identify the relationship between long-term flu virus infection with temperature in the temperate region of Cheonan.
BACKGROUND: Sentinel physician surveillance in communities has played an important role in detecting early signs of epidemics. The traditional approach is to let the primary care physician voluntarily and actively report diseases to the health department on a weekly basis. However, this is labor-intensive work, and the spatio-temporal resolution of the surveillance data is not precise at all. In this study, we built up a clinic-based enhanced sentinel surveillance system named “Sentinel plus” which was designed for sentinel clinics and community hospitals to monitor 23 kinds of syndromic groups in Taipei City, Taiwan. The definitions of those syndromic groups were based on ICD-10 diagnoses from physicians. METHODS: Daily ICD-10 counts of two syndromic groups including ILI and EV-like syndromes in Taipei City were extracted from Sentinel plus. A negative binomial regression model was used to couple with lag structure functions to examine the short-term association between ICD counts and meteorological variables. After fitting the negative binomial regression model, residuals were further rescaled to Pearson residuals. We then monitored these daily standardized Pearson residuals for any aberrations from July 2018 to October 2019. RESULTS: The results showed that daily average temperature was significantly negatively associated with numbers of ILI syndromes. The ozone and PM2.5 concentrations were significantly positively associated with ILI syndromes. In addition, daily minimum temperature, and the ozone and PM2.5 concentrations were significantly negatively associated with the EV-like syndromes. The aberrational signals detected from clinics for ILI and EV-like syndromes were earlier than the epidemic period based on outpatient surveillance defined by the Taiwan CDC. CONCLUSIONS: This system not only provides warning signals to the local health department for managing the risks but also reminds medical practitioners to be vigilant toward susceptible patients. The near real-time surveillance can help decision makers evaluate their policy on a timely basis.
The bacteria (including pathogenic bacteria) attached to road deposited sediments (RDS) may interrelate with the microbe in the atmosphere, soil and water through resuspension and wash-off, and is of great significance to human and ecological health. However, the characteristics of bacterial communities with different time scale on RDS were unknown to dates. Climate change prolonged the dry days between rain events in many areas, making the varied trend of bacterial communities might be more significant in short term. This study revealed the characteristics of bacterial communities on RDS in urban and suburban areas through seasonal and daily scale. The correlations between other factors (land use, particle size, and chemical components) and the bacterial communities were also analyzed. It was found that the season showed a higher association with the bacterial community diversity than land use and particle size in urban areas. The bacterial community diversity increased substantially throughout the short-term study period (41 days) and the variation of dominant bacteria could be fitted by quadratic function in suburbs. In addition, urbanization notably increased the bacterial community diversity, while the potential pathogenic bacteria were more abundant in the suburban areas, coarse RDS (>75 μm), and in spring. The chemical components on RDS showed special correlations with the relative abundance of dominant bacteria. The research findings would fill the knowledge gap on RDS bacterial communities and be helpful for the future research on the assembly process of bacterial communities.
This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (-24.88%; t = -5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (-16.69%; t = -4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.
BACKGROUND: Although exposure to air pollution has been linked to many health issues, few studies have quantified the modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China. METHODS: The data of daily incidence of influenza and the relevant meteorological data and air pollution data in Ningbo from 2014 to 2017 were retrieved. Low, medium and high temperature layers were stratified by the daily mean temperature with 25th and 75th percentiles. The potential modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo was investigated through analyzing the effects of air pollutants stratified by temperature stratum using distributed lag non-linear model (DLNM). Stratified analysis by sex and age were also conducted. RESULTS: Overall, a 10 μg/m(3) increment of O(3), PM(2.5), PM(10) and NO(2) could increase the incidence risk of influenza with the cumulative relative risk of 1.028 (95% CI 1.007, 1.050), 1.061 (95% CI 1.004, 1.122), 1.043 (95% CI 1.003, 1.085), and 1.118 (95% CI 1.028, 1.216), respectively. Male and aged 7-17 years were more sensitive to air pollutants. Through the temperature stratification analysis, we found that temperature could modify the impacts of air pollution on daily incidence of influenza with high temperature exacerbating the impact of air pollutants. At high temperature layer, male and the groups aged 0-6 years and 18-64 years were more sensitive to air pollution. CONCLUSION: Temperature modified the relationship between air pollution and daily incidence of influenza and high temperature would exacerbate the effects of air pollutants in Ningbo.
Airborne bacteria have an important role in atmospheric processes and human health. However, there is still little information on the transmission and distribution of bacteria via the airborne route. To characterize the impact of foggy, haze, haze-fog (HF) and dust days on the concentration and viability of bacteria in atmospheric aerosols, size-segregated bioaerosol samples were collected in the Qingdao coastal region from March 2018 to February 2019. The total airborne microbes and viable/non-viable bacteria in the bioaerosol samples were measured using an epifluorescence microscope after staining with DAPI (4′, 6-diamidino-2-phenylindole) and a LIVE/DEAD® BacLight Bacterial Viability Kit. The average concentrations of total airborne microbes on haze and dust days were 6.75 × 10(5) and 1.03 × 10(6) cells/m(3), respectively, which increased by a factor of 1.3 and 2.5 (on average), respectively, relative to those on sunny days. The concentrations of non-viable bacteria on haze and dust days increased by a factor of 1.2 and 3.6 (on average), respectively, relative to those on sunny days. In contrast, the concentrations of viable bacteria on foggy and HF days were 7.13 × 10(3) and 5.74 × 10(3) cells/m(3), decreases of 38% and 50%, respectively, compared with those on sunny days. Foggy, haze, dust and HF days had a significant effect on the trend of the seasonal variation in the total airborne microbes and non-viable bacteria. Bacterial viability was 20.8% on sunny days and significantly higher than the 14.1% on foggy days, 11.2% on haze days, 8.6% during the HF phenomenon and 6.1% on dust days, indicating that special weather is harmful to some bacterial species. Correlation analysis showed that the factors that influenced the bacterial concentration and viability depended on different weather conditions. The main influential factors were temperature, NO(2) and SO(2) concentrations on haze days, and temperature, particulate matter (PM(2.5)) and NO(2) concentrations on foggy days. The median size of particles containing viable bacteria was 1.94 μm on sunny days and decreased to 1.88 μm and 1.74 μm on foggy and haze days, respectively, but increased to 2.18 μm and 2.37 μm on dust and HF days, respectively.
BACKGROUND: As the global climate changes, the number of cases of hand-foot-and-mouth disease (HFMD) is increasing year by year. This study comprehensively considers the association of time and space by analyzing the temporal and spatial distribution changes of HFMD in the Ili River Valley in terms of what climate factors could affect HFMD and in what way. METHODS: HFMD cases were obtained from the National Public Health Science Data Center from 2013 to 2018. Monthly climate data, including average temperature (MAT), average relative humidity (MARH), average wind speed (MAWS), cumulative precipitation (MCP), and average air pressure (MAAP), were obtained from the National Meteorological Information Center. The temporal and spatial distribution characteristics of HFMD from 2013 to 2018 were obtained using kernel density estimation (KDE) and spatiotemporal scan statistics. A regression model of the incidence of HFMD and climate factors was established based on a geographically and temporally weighted regression (GTWR) model and a generalized additive model (GAM). RESULTS: The KDE results show that the highest density was from north to south of the central region, gradually spreading to the whole region throughout the study period. Spatiotemporal cluster analysis revealed that clusters were distributed along the Ili and Gongnaisi river basins. The fitted curves of MAT and MARH were an inverted V-shape from February to August, and the fitted curves of MAAP and MAWS showed a U-shaped change and negative correlation from February to May. Among the individual climate factors, MCP coefficient values varied the most while MAWS values varied less from place to place. There was a partial similarity in the spatial distribution of coefficients for MARH and MAT, as evidenced by a significant degree of fit performance in the whole region. MCP showed a significant positive correlation in the range of 15-35 mm, and MAAP showed a positive correlation in the range of 925-945 hPa. HFMD incidence increased with MAT in the range of 15-23 °C, and the effective value of MAWS was in the range of 1.3-1.7 m/s, which was positively correlated with incidences of HFMD. CONCLUSIONS: HFMD incidence and climate factors were found to be spatiotemporally associated, and climate factors are mostly non-linearly associated with HFMD incidence.
Hand, foot, and mouth disease (HFMD) remains a serious health threat to young children. Urumqi is one of the most severely affected cities in northwestern China. This study aims to identify the spatiotemporal distribution characteristics of HFMD, and explore the relationships between driving factors and HFMD in Urumqi, Xinjiang. METHODS: HFMD surveillance data from 2014 to 2018 were obtained from the China Center for Disease Control and Prevention. The center of gravity and geographical detector model were used to analyze the spatiotemporal distribution characteristics of HFMD and identify the association between these characteristics and socioeconomic and meteorological factors. RESULTS: A total of 10,725 HFMD cases were reported in Urumqi during the study period. Spatially, the morbidity number of HFMD differed regionally and the density was higher in urban districts than in rural districts. Overall, the development of HFMD in Urumqi expanded toward the southeast. Temporally, we observed that the risk of HFMD peaked from June to July. Furthermore, socioeconomic and meteorological factors, including population density, road density, GDP, temperature and precipitation were significantly associated with the occurrence of HFMD. CONCLUSIONS: HFMD cases occurred in spatiotemporal clusters. Our findings showed strong associations between HFMD and socioeconomic and meteorological factors. We comprehensively considered the spatiotemporal distribution characteristics and influencing factors of HFMD, and proposed some intervention strategies that may assist in predicting the morbidity number of HFMD.
The study aims to depict the temporal and spatial distributions of hand-foot-and-mouth disease (HFMD) in Xinjiang, China and reveal the relationships between the incidence of HFMD and meteorological factors in Xinjiang. With the national surveillance data of HFMD in Xinjiang and meteorological parameters in the study area from 2008 to 2016, in GeoDetector Model, we examined the effects of meteorological factors on the incidence of HFMD in Xinjiang, China, tested the spatial-temporal heterogeneity of HFMD risk, and explored the temporal-spatial patterns of HFMD through the spatial autocorrelation analysis. From 2008 to 2016, the HFMD distribution showed a distinct seasonal pattern and HFMD cases typically occurred from May to July and peaked in June in Xinjiang. Relative humidity, precipitation, barometric pressure and temperature had the more significant influences on the incidence of HFMD than other meteorological factors with the explanatory power of 0.30, 0.29, 0.29 and 0.21 (P<0.000). The interaction between any two meteorological factors had a nonlinear enhancement effect on the risk of HFMD. The relative risk in Northern Xinjiang was higher than that in Southern Xinjiang. Global spatial autocorrelation analysis results indicated a fluctuating trend over these years: the positive spatial dependency on the incidence of HFMD in 2008, 2010, 2012, 2014 and 2015, the negative spatial autocorrelation in 2009 and a random distribution pattern in 2011, 2013 and 2016. Our findings revealed the correlation between meteorological factors and the incidence of HFMD in Xinjiang. The correlation showed obvious spatiotemporal heterogeneity. The study provides the basis for the government to control HFMD based on meteorological information. The risk of HFMD can be predicted with appropriate meteorological factors for HFMD prevention and control.
Hand, foot and mouth (HFM) disease is a common childhood illness. The paper aims to capture the spatiotemporal characters, and investigate the influence factors of the HFM epidemic in 15 regions of Xinjiang province from 2008 to 2017, China. Descriptive statistical analysis shows that the children aged 0-5 years have a higher HFM incidence, mostly boys. The male-female ratio is 1.5:1. Through the scanning method, we obtain the first cluster high-risk areas. The cluster time is usually from May to August every year. A spatiotemporal model is proposed to analyze the impact of meteorological factors on HFM disease. Comparing with the spatial model, the model is more effective in terms of R2, AIC, deviation, and mean-square error. Among meteorological factors, the number of HFM cases generally increases with the intensity of rainfall. As the temperature increases, there are more HFM patients. Some regions are mostly influenced by wind speed. Further, another spatiotemporal model is introduced to investigate the relationship between HFM disease and socioeconomic factors. The results show that socioeconomic factors have significant influence on the disease. In most areas, the risk of HFM disease tends to rise with the increase of the gross domestic product, the ratios of urban population and tertiary industry. The incidence is closely related to the number of beds and population density in some regions. The higher the ratio of primary school, the lower the number of HFM cases. Based on the above analysis, it is the key measure to prevent and control the spread of the HFM epidemic in high-risk areas, and influence factors should not be ignored.
Previous studies have reported that temperature is the main meteorological factor associated with influenza activity. This study used generalized additive models (GAMs) to explore the relationship between temperature and influenza activity in China. From the national perspective, the average temperature (AT) had an approximately negative linear correlation with the incidence of influenza, as well as a positive rate of influenza H1N1 virus (A/H1N1). Every degree that the monthly AT rose, the influenza cases decreased by 2.49% (95%CI: 1.24%-3.72%). The risk of influenza cases reached a peak at -5.35 °C with RRs of 2.14 (95%CI: 1.38-3.33) and the monthly AT in the range of -5.35 °C to 18.31 °C had significant effects on the incidence of influenza. Every degree that the weekly AT rose, the positive rate of A/H1N1 decreased by 5.28% (95%CI: 0.35%-9.96%). The risk of A/H1N1 reached a peak at -3.14 °C with RRs of 4.88 (95%CI: 1.01-23.75) and the weekly AT in the range of -3.14 °C to 17.25 °C had significant effects on the incidence of influenza. Our study found that AT is negatively associated with influenza activity, especially for A/H1N1. These findings indicate that temperature could be integrated into the current influenza surveillance system to develop early warning systems to better predict and prepare for the risks of influenza.
BACKGROUND: The effects of multiple meteorological factors on influenza activity remain unclear in Chongqing, the largest municipality in China. We aimed to fix this gap in this study. METHODS: Weekly meteorological data and influenza surveillance data in Chongqing were collected from 2012 to 2019. Distributed lag nonlinear models (DLNMs) were conducted to estimate the effects of multiple meteorological factors on influenza activity. RESULTS: Inverted J-shaped nonlinear associations between mean temperature, absolute humidity, wind speed, sunshine and influenza activity were found. The relative risks (RRs) of influenza activity increased as weekly average mean temperature fell below 18.18°C, average absolute humidity fell below 12.66 g/m3, average wind speed fell below 1.55 m/s and average sunshine fell below 2.36 hours. Taking the median values as the references, lower temperature, lower absolute humidity and windless could significantly increase the risks of influenza activity and last for 4 weeks. A J-shaped nonlinear association was observed between relative humidity and influenza activity; the risk of influenza activity increased with rising relative humidity with 78.26% as the break point. Taking the median value as the reference, high relative humidity could increase the risk of influenza activity and last for 3 weeks. In addition, we found the relationship between aggregate rainfall and influenza activity could be described with a U-shaped curve. Rainfall effect has significantly higher RR than rainless effect. CONCLUSIONS: Our study shows that multiple meteorological factors have strong associations with influenza activity in Chongqing, providing evidence for developing a meteorology-based early warning system for influenza to facilitate timely response to upsurge of influenza activity.
BACKGROUND: Influenza is a seasonal infectious disease, and meteorological parameters critically influence the incidence of influenza. However, the meteorological parameters linked to influenza occurrence in semi-arid areas are not studied in detail. This study aimed to clarify the impact of meteorological parameters on influenza incidence during 2010-2019 in Lanzhou. The results are expected to facilitate the optimization of influenza-related public health policies by the local healthcare departments. METHODS: Descriptive data related to influenza incidence and meteorology during 2010-2019 in Lanzhou were analyzed. The exposure-response relationship between the risk of influenza occurrence and meteorological parameters was explored according to the distributed lag no-linear model (DLNM) with Poisson distribution. The response surface model and stratified model were used to estimate the interactive effect between relative humidity (RH) and other meteorological parameters on influenza incidence. RESULTS: A total of 6701 cases of influenza were reported during 2010-2019. DLNM results showed that the risk of influenza would gradually increase as the weekly mean average ambient temperature (AT), RH, and absolute humidity (AH) decrease at lag 3 weeks when they were lower than 12.16°C, 51.38%, and 5.24 g/m(3), respectively. The low Tem (at 5th percentile, P(5)) had the greatest effect on influenza incidence; the greatest estimated relative risk (RR) was 4.54 (95%CI: 3.19-6.46) at cumulative lag 2 weeks. The largest estimates of RRs for low RH (P(5)) and AH (P(5)) were 4.81 (95%CI: 3.82-6.05) and 4.17 (95%CI: 3.30-5.28) at cumulative lag 3 weeks, respectively. An increase in AT by 1°C led to an estimates of percent change (95%CI) of 3.12% (-4.75% to -1.46%) decrease in the weekly influenza case counts in a low RH environment. In addition, RH showed significant interaction with AT and AP on influenza incidence but not with wind speed. CONCLUSION: This study indicated that low AT, low humidity (RH and AH), and high air pressure (AP) increased the risk of influenza. Moreover, the interactive effect of low RH with low AT and high AP can aggravate the incidence of influenza.
Under the variant climate conditions in the transitional regions between tropics and subtropics, the impacts of climate factors on influenza subtypes have rarely been evaluated. With the available influenza A (Flu-A) and influenza B (Flu-B) outbreak data in Shenzhen, China, which is an excellent example of a transitional marine climate, the associations of multiple climate variables with these outbreaks were explored in this study. Daily laboratory-confirmed influenza virus and climate data were collected from 2009 to 2015. Potential impacts of daily mean/maximum/minimum temperatures (T/T(max)/T(min)), relative humidity (RH), wind velocity (V), and diurnal temperature range (DTR) were analyzed using the distributed lag nonlinear model (DLNM) and generalized additive model (GAM). Under its local climate partitions, Flu-A mainly prevailed in summer months (May to June), and a second peak appeared in early winter (December to January). Flu-B outbreaks usually occurred in transitional seasons, especially in autumn. Although low temperature caused an instant increase in both Flu-A and Flu-B risks, its effect could persist for up to 10 days for Flu-B and peak at 17 C (relative risk (RR) = 14.16, 95% CI: 7.46-26.88). For both subtypes, moderate-high temperature (28 C) had a significant but delayed effect on influenza, especially for Flu-A (RR = 26.20, 95% CI: 13.22-51.20). The Flu-A virus was sensitive to RH higher than 76%, while higher Flu-B risks were observed at both low (< 65%) and high (> 83%) humidity. Flu-A was active for a short term after exposure to large DTR (e.g., DTR = 10 C, RR = 12.45, 95% CI: 6.50-23.87), whereas Flu-B mainly circulated under stable temperatures. Although the overall wind speed in Shenzhen was low, moderate wind (2-3 m/s) was found to favor the outbreaks of both subtypes. This study revealed the thresholds of various climatic variables promoting influenza outbreaks, as well as the distinctions between the flu subtypes. These data can be helpful in predicting seasonal influenza outbreaks and minimizing the impacts, based on integrated forecast systems coupled with short-term climate models.
BACKGROUND: Influenza epidemics occur during winter in temperate zones, but have less regular seasonality in the subtropics and tropics. Here we quantified the role of environmental drivers of influenza seasonality in temperate and subtropical China. METHODS: We used weekly surveillance data on influenza virus activity in mainland China and Hong Kong from 2005 through 2016. We estimated the transmissibility via the instantaneous reproduction number (R(t)), a real-time measure of transmissibility, and examined its relationship with different climactic drivers and allowed for the timing of school holidays and the decline in susceptibility in the population as an epidemic progressed. We developed a multivariable regression model for R(t) to quantify the contribution of various potential environmental drivers of transmission. FINDINGS: We found that absolute humidity is a potential driver of influenza seasonality and had a U-shaped association with transmissibility and hence can predict the pattern of influenza virus transmission across different climate zones. Absolute humidity was able to explain up to 15% of the variance in R(t), and was a stronger predictor of R(t) across the latitudes. Other climatic drivers including mean daily temperature explained up to 13% of variance in R(t) and limited to the locations where the indoor measures of these factors have better indicators of outdoor measures. The non-climatic driver, holiday-related school closures could explain up to 7% of variance in R(t). INTERPRETATION: A U-shaped association of absolute humidity with influenza transmissibility was able to predict seasonal patterns of influenza virus epidemics in temperate and subtropical locations.
OBJECTIVES: From 2013 to 2017, the avian influenza A (H7N9) virus frequently infected people in China, which seriously affected the public health of society. This study aimed to analyze the spatial characteristics of human infection with the H7N9 virus in China and assess the risk areas of the epidemic. METHODS: Using kernel density estimation, standard deviation ellipse analysis, spatial and temporal scanning cluster analysis, and Pearson correlation analysis, the spatial characteristics and possible risk factors of the epidemic were studied. Meteorological factors, time (month), and environmental factors were combined to establish an epidemic risk assessment proxy model to assess the risk range of an epidemic. RESULTS: The epidemic situation was significantly correlated with atmospheric pressure, temperature, and daily precipitation (P < 0.05), and there were six temporal and spatial clusters. The fitting accuracy of the epidemic risk assessment agent-based model for lower-risk, low-risk, medium-risk, and high-risk was 0.795, 0.672, 0.853, 0.825, respectively. CONCLUSIONS: This H7N9 epidemic was found to have more outbreaks in winter and spring. It gradually spread to the inland areas of China. This model reflects the risk areas of human infection with the H7N9 virus.
(1) Background: To explore whether meteorological factors have an impact on the prevalence of mumps, and to make a short−term prediction of the case number of mumps in Chongqing. (2) Methods: K−means clustering algorithm was used to divide the monthly mumps cases of each year into the high and low case number clusters, and Student t−test was applied for difference analysis. The cross−correlation function (CCF) was used to evaluate the correlation between the meteorological factors and mumps, and an ARIMAX model was constructed by additionally incorporating meteorological factors as exogenous variables in the ARIMA model, and a short−term prediction was conducted for mumps in Chongqing, evaluated by MAE, RMSE. (3) Results: All the meteorological factors were significantly different (p < 0.05), except for the relative humidity between the high and low case number clusters. The CCF and ARIMAX model showed that monthly precipitation, temperature, relative humidity and wind velocity were associated with mumps, and there were significant lag effects. The ARIMAX model could accurately predict mumps in the short term, and the prediction errors (MAE, RMSE) were lower than those of the ARIMA model. (4) Conclusions: Meteorological factors can affect the occurrence of mumps, and the ARIMAX model can effectively predict the incidence trend of mumps in Chongqing, which can provide an early warning for relevant departments.
BACKGROUND: Small-scale studies have identified temperature and other meteorological factors as risk factors for human health. However, only a few have quantified the specific impact of meteorological factors on mumps. A quantitative examination of the exposure-response relationship between meteorological factors and mumps is needed to provide new insights for multi-city analysis. METHODS: The daily recorded number of mumps cases and meteorological data in 17 cities of Shandong Province from 2009 to 2017 were collected. A two-stage model was built to explore the relationship between meteorological factors and mumps. RESULTS: A total of 104,685 cases of mumps were recorded from 2009 to 2017. After controlling for seasonality and long-term trends, the effect of low temperature on mumps was significant at the provincial level, with a cumulative RR of 1.035 (95%CI: 1.002-1.069) with a 1-day lagged effect. The proportion of primary and middle school students was determined as an effect modifier, which had a significant impact on mumps (Stat = 8.374, p = 0.039). There was heterogeneity in the combined effect of temperature on mumps (Q = 95.447, p = 0.000), and its size was I(2) = 49.7%. CONCLUSIONS: We have identified a non-linear relationship between mumps and temperature in Shandong Province. In particular, low temperatures could bring more cases of mumps, with certain lagged effects. More public health measures should be taken to reduce the risks when temperatures are low, especially for cities with a high proportion of primary and secondary school students.
Varicella (chickenpox) is a serious public health problem in China, with the most reported cases among childhood vaccine-preventable infectious diseases, and its reported incidence has increased over 20-fold since 2005. Few previous studies have explored the association of multiple meteorological factors with varicella and considered the potential confounding effects of air pollutants. It is the first study to investigate and analyze the effects of multiple meteorological factors on varicella incidence, controlling for the confounding effects of various air pollutants. Daily meteorological and air pollution data and varicella cases were collected from January 1, 2015, to December 31, 2020, in Lu’an, Eastern China. A combination of the quasi-Poisson generalized additive model (GAM) and distributed lag nonlinear model (DLNM) was used to evaluate the meteorological factor-lag-varicella relationship, and the risk of varicella in extreme meteorological conditions. The maximum single-day lag effects of varicella were 1.288 (95%CI, 1.201-1.381, lag 16 day), 1.475 (95%CI, 1.152-1.889, lag 0 day), 1.307 (95%CI, 1.196-1.427, lag 16 day), 1.271 (95%CI, 0.981-1.647, lag 4 day), and 1.266 (95%CI, 1.162-1.378, lag 21 day), when mean temperature, diurnal temperature range (DTR), mean air pressure, wind speed, and sunshine hours were -5.8°C, 13.5°C, 1035.5 hPa, 6 m/s, and 0 h, respectively. At the maximum lag period, the overall effects of mean temperature and pressure on varicella showed W-shaped curves, peaked at 17.5°C (RR=2.085, 95%CI: 1.480-2.937) and 1035.5 hPa (RR=5.481, 95%CI: 1.813-16.577), while DTR showed an M-shaped curve and peaked at 4.4°C (RR=6.131, 95%CI: 1.120-33.570). Sunshine hours were positively correlated with varicella cases at the lag of 0-8 days and 0-9 days when sunshine duration exceeded 10 h. Furthermore, the lag effects of extreme meteorological factors on varicella cases were statistically significant, except for the extremely high wind speed. We found that mean temperature, mean air pressure, DTR, and sunshine hours had significant nonlinear effects on varicella incidence, which may be important predictors of varicella early warning.
OBJECTIVE: To analyze the spatiotemporal dynamic distribution and detect the related meteorological factors of scarlet fever from an ecological perspective, which could provide scientific information for effective prevention and control of this disease. METHODS: The data on scarlet fever cases in mainland China were downloaded from the Data Center of the China Public Health Science, while monthly meteorological data were extracted from the official website of the National Bureau of Statistics. Global Moran’s I, local Getis-Ord G(i)(⁎) hotspot statistics, and Kulldorff’s retrospective space-time scan statistical analysis were used to detect the spatial and spatiotemporal clusters of scarlet fever across all settings. A spatial panel data model was conducted to estimate the impact of meteorological factors on scarlet fever incidence. RESULTS: Scarlet fever in China had obvious spatial, temporal, and spatiotemporal clustering, high-incidence spatial clusters were located mainly in the north and northeast of China. Nine spatiotemporal clusters were identified. A spatial lag fixed effects panel data model was the best fit for regression analysis. After adjusting for spatial individual effects and spatial autocorrelation (ρ = 0.5623), scarlet fever incidence was positively associated with a one-month lag of average temperature, precipitation, and total sunshine hours (all P-values < 0.05). Each 10 °C, 2 cm, and 10 h increase in temperature, precipitation, and sunshine hours, respectively, was associated with a 6.41% increment and 1.04% and 1.41% decrement in scarlet fever incidence, respectively. CONCLUSION: The incidence of scarlet fever in China showed an upward trend in recent years. It had obvious spatiotemporal clustering, with the high-risk areas mainly concentrated in the north and northeast of China. Areas with high temperature and with low precipitation and sunshine hours tended to have a higher scarlet fever incidence, and we should pay more attention to prevention and control in these places.
The relationship between air temperature and the hospital admission of adult patients with community-acquired pneumonia (CAP) was analyzed. The hospitalization data pertaining to adult CAP patients (age ≥ 18 years) in two tertiary comprehensive hospitals in Baotou, Inner Mongolia Autonomous Region, China from 2014 to 2018 and meteorological data there in the corresponding period were collected. The exposure-response relationship between the daily average temperature and the hospital admission of adult CAP patients was quantified by using a distributed lag non-linear model. A total of 4466 cases of adult patients with CAP were admitted. After eliminating some confounding factors such as relative humidity, wind speed, air pressure, long-term trend, and seasonal trend, a lower temperature was found to be associated with a higher risk of adult CAP. Compared to 21 °C, lower temperature range of 4 to -12 °C was associated with a greater number of CAP hospitalizations among those aged ≥ 65 years, and the highest relative risk (RR) was 2.80 (95% CI 1.15-6.80) at a temperature of - 10 °C. For those < 65 years, lower temperature was not related to CAP hospitalizations. Cumulative lag RRs of low temperature with CAP hospitalizations indicate that the risk associated with colder temperatures appeared at a lag of 0-7 days. For those ≥ 65 years, the cumulative RR of CAP hospitalizations over lagging days 0-5 was 1.89 (95% CI 1.01-3. 56). In brief, the lower temperature had age-specific effects on CAP hospitalizations in Baotou, China, especially among those aged ≥ 65 years.
BACKGROUND: Most existing studies have only investigated the delayed effect of meteorological factors on pulmonary tuberculosis (PTB). However, the effect of extreme climate and the interaction between meteorological factors on PTB has been rarely investigated. METHODS: Newly diagonsed PTB cases and meteorological factors in Urumqi in each week between 2013 and 2019 were collected. The lag-exposure-response relationship between meteorological factors and PTB was analyzed using the distributed lag non-linear model (DLNM). The generalized additive model (GAM) was used to visualize the interaction between meteorological factors. Stratified analysis was used to explore the impact of meteorological factors on PTB in different stratification and RERI, AP and SI were used to quantitatively evaluate the interaction between meteorological factors. RESULTS: A total of 16,793 newly diagnosed PTB cases were documented in Urumqi, China from 2013 to 2019. The median (interquartile range) temperature, relative humidity, wind speed, and PTB cases were measured as 11.3°C (-5.0-20.5), 57.7% (50.7-64.2), 4.1m/s (3.4-4.7), and 47 (37-56), respectively. The effects of temperature, relative humidity and wind speed on PTB were non-linear, which were found with the “N”-shaped, “L”-shaped, “N”-shaped distribution, respectively. With the median meteorological factor as a reference, extreme low temperature was found to have a protective effect on PTB. However, extreme high temperature, extreme high relative humidity, and extreme high wind speed were found to increase the risk of PTB and peaked at 31.8°C, 83.2%, and 7.6 m/s respectively. According to the existing monitoring data, no obvious interaction between meteorological factors was found, but low temperature and low humidity (RR = 1.149, 95%CI: 1.003-1.315), low temperature and low wind speed (RR = 1.273, 95%CI: 1.146-1.415) were more likely to cause the high incidence of PTB. CONCLUSION: Temperature, relative humidity and wind speed were found to play vital roles in PTB incidence with delayed and non-linear effects. Extreme high temperature, extreme high relative humidity, and extreme high wind speed could increase the risk of PTB. Moreover, low temperature and low humidity, low temperature and low wind speed may increase the incidence of PTB.
In the present paper, an association between the growth rate of PTB and the environmental impacting elements in the pearl river delta region and the closed industry related cities in China is studied. We summarized the characteristics of different industry characteristics in this region by three echelons of urban agglomerations conducted by K-means clustering model on the time series of their monthly AQI data. To determine the impact of environmental factors on the increase of PTB, the SMLR in GLM has been applied. We then measured the seasonal effect and suggest the spring to be the leading season which keep the highest possibility of the incidence of PTB. Besides giving the analysis by fixed meteorological factors, we presented a sensitive analysis with a variation of precipitation. The Genetic algorithms (GAs) is used to determine the “tolerant” interval and as the results, the width of “tolerant” almost keep a declining trend as the precipitation increasing except when the precipitation comes the interval [68,74]. In addition, with the precipitation increasing higher than 64 mm, the “tolerant” for the AQI values from the first and the second echelon both trend to decline, and a lenient environmental policy currently may easily cause a rapid development of PTB growth rate.
BACKGROUND: Most studies on associations between meteorological factors and tuberculosis (TB) were conducted in a single city, used different lag times, or merely explored the qualitative associations between meteorological factors and TB. Thus, we performed a multicenter study to quantitatively evaluate the effects of meteorological factors on the risk of pulmonary tuberculosis (PTB). METHODS: We collected data on newly diagnosed PTB cases in 13 study sites in Jiangsu Province between January 1, 2014, and December 31, 2019. Data on meteorological factors, air pollutants, and socioeconomic factors at these sites during the same period were also collected. We applied the generalized additive mixed model to estimate the associations between meteorological factors and PTB. RESULTS: There were 20,472 newly diagnosed PTB cases reported in the 13 study sites between 2014 and 2019. The median (interquartile range) weekly average temperature, weekly average wind speed, and weekly average relative humidity of these sites were 17.3 °C (8.0-24.1), 2.2 m/s (1.8-2.7), and 75.1% (67.1-82.0), respectively. In the single-meteorological-factor models, for a unit increase in weekly average temperature, weekly average wind speed, and weekly average relative humidity, the risk of PTB decreased by 0.9% [lag 0-13 weeks, 95% confidence interval (CI): -1.5, -0.4], increased by 56.2% (lag 0-16 weeks, 95% CI: 32.6, 84.0) when average wind speed was <3 m/s, and decreased by 28.1% (lag 0-14 weeks, 95% CI: -39.2, -14.9) when average relative humidity was ≥72%, respectively. Moreover, the associations remained significant in the multi-meteorological-factor models. CONCLUSIONS: Average temperature and average relative humidity (≥72%) are negatively associated with the risk of PTB. In contrast, average wind speed (<3 m/s) is positively related to the risk of PTB, suggesting that an environment with low temperature, relatively high wind speed, and low relative humidity is conducive to the transmission of PTB.
The autoregressive integrated moving average with exogenous regressors (ARIMAX) modeling studies of pulmonary tuberculosis (PTB) are still rare. This study aims to explore whether incorporating air pollution and meteorological factors can improve the performance of a time series model in predicting PTB. We collected the monthly incidence of PTB, records of six air pollutants and six meteorological factors in Ningbo of China from January 2015 to December 2019. Then, we constructed the ARIMA, univariate ARIMAX, and multivariate ARIMAX models. The ARIMAX model incorporated ambient factors, while the ARIMA model did not. After prewhitening, the cross-correlation analysis showed that PTB incidence was related to air pollution and meteorological factors with a lag effect. Air pollution and meteorological factors also had a correlation. We found that the multivariate ARIMAX model incorporating both the ozone with 0-month lag and the atmospheric pressure with 11-month lag had the best performance for predicting the incidence of PTB in 2019, with the lowest fitted mean absolute percentage error (MAPE) of 2.9097% and test MAPE of 9.2643%. However, ARIMAX has limited improvement in prediction accuracy compared with the ARIMA model. Our study also suggests the role of protecting the environment and reducing pollutants in controlling PTB and other infectious diseases.
Few studies have examined the effects of inbound overseas travelers and meteorological conditions on the shift in human respiratory syncytial virus (HRSV) season in Japan. This study aims to test whether the number of inbound overseas travelers and meteorological conditions are associated with the onset week of HRSV epidemic season. The estimation of onset week for 46 prefectures (except for Okinawa prefecture) in Japan for 4-year period (2014-2017) was obtained from previous papers based on the national surveillance data. We obtained data on the yearly number of inbound overseas travelers and meteorological (yearly mean temperature and relative humidity) conditions from Japan National Tourism Organization (JNTO) and Japan Meteorological Agency (JMA), respectively. Multi-level mixed-effects linear regression analysis showed that every 1 person (per 100,000 population) increase in number of overall inbound overseas travelers led to an earlier onset week of HRSV epidemic season in the year by 0.02 week (coefficient -0.02; P<0.01). Higher mean temperature and higher relative humidity were also found to contribute to an earlier onset week by 0.30 week (coefficient -0.30; P<0.05) and 0.18 week (coefficient -0.18; P<0.01), respectively. Additionally, models that included the number of travelers from individual countries (Taiwan, South Korea, and China) except Australia showed that both the number of travelers from each country and meteorological conditions contributed to an earlier onset week. Our analysis showed the earlier onset week of HRSV epidemic season in Japan is associated with increased number of inbound overseas travelers, higher mean temperature, and relative humidity. The impact of international travelers on seasonality of HRSV can be further extended to investigations on the changes of various respiratory infectious diseases especially after the coronavirus disease 2019 (COVID-19) pandemic.
We investigated the association between climate variables and pulmonary tuberculosis (PTB) incidence in Brunei-Muara district, Brunei Darussalam. Weekly PTB case counts and climate variables from January 2001 to December 2018 were analysed using distributed lag non-linear model framework. After adjusting for long-term trend and seasonality, we observed positive but delayed relationship between PTB incidence and minimum temperature, with significant adjusted relative risk (adj.RR) at 25.1 °C (95th percentile) when compared to the median, from lag 30 onwards (adj.RR = 1.17 [95% Confidence Interval (95% CI): 1.01, 1.36]), suggesting effect of minimum temperature on PTB incidence after 30 weeks. Similar results were observed from a sub-analysis on smear-positive PTB case counts from lag 29 onwards (adj.RR = 1.21 [95% CI: 1.01, 1.45]), along with positive and delayed association with total rainfall at 160.7 mm (95th percentile) when compared to the median, from lag 42 onwards (adj.RR = 1.23 [95% CI: 1.01, 1.49]). Our findings reveal evidence of delayed effects of climate on PTB incidence in Brunei, but with varying degrees of magnitude, direction and timing. Though explainable by environmental and social factors, further studies on the relative contribution of recent (through primary human-to-human transmission) and remote (through reactivation of latent TB) TB infection in equatorial settings is warranted.
OBJECTIVES: The aim of this study was to explore whether indoor or outdoor relative humidity (RH) modulates the influenza epidemic transmission in temperate and subtropical climates. METHODS: In this study, the daily temperature and RH in 1558 households from March 2017 to January 2019 in five cities across both temperate and subtropical regions in China were collected. City-level outdoor temperature and RH from 2013 to 2019 were collected from the weather stations. We first estimated the effective reproduction number (R(t)) of influenza and then used time-series analyses to explore the relationship between indoor/outdoor RH/absolute humidity and the R(t) of influenza. Furthermore, we expanded the measured 1-year indoor temperature and the RH data into 5 years and used the same method to examine the relationship between indoor/outdoor RH and the R(t) of influenza. RESULTS: Indoor RH displayed a seasonal pattern, with highs during the summer months and lows during the winter months, whereas outdoor RH fluctuated with no consistent pattern in subtropical regions. The R(t) of influenza followed a U-shaped relationship with indoor RH in both temperate and subtropical regions, whereas a U-shaped relationship was not observed between outdoor RH and R(t). In addition, indoor RH may be a better indicator for R(t) of influenza than indoor absolute humidity. CONCLUSION: The findings indicated that indoor RH may be the driver of influenza seasonality in both temperate and subtropical locations in China.
The early identification and prediction of hand-foot-and-mouth disease (HFMD) play an important role in the disease prevention and control. However, suitable models are different in regions due to the differences in geography, social economy factors. We collected data associated with daily reported HFMD cases and weather factors of Zibo city in 2010 similar to 2019 and used the generalised additive model (GAM) to evaluate the effects of weather factors on HFMD cases. Then, GAM, support vectors regression (SVR) and random forest regression (RFR) models are used to compare predictive results. The annual average incidence was 129.72/100 000 from 2010 to 2019. Its distribution showed a unimodal trend, with incidence increasing from March, peaking from May to September. Our study revealed the nonlinear relationship between temperature, rainfall and relative humidity and HFMD cases and based on the predictive result, the performances of three models constructed ranked in descending order are: SVR > GAM > RFR, and SVR has the smallest prediction errors. These findings provide quantitative evidence for the prediction of HFMD for special high-risk regions and can help public health agencies implement prevention and control measures in advance.
OBJECTIVES: Hand, foot, and mouth disease (HFMD) is a viral infectious disease that poses a substantial threat in the Asia-Pacific region. It is widely reported that meteorological factors are associated with HFMD. However, the relationships between air pollutants and HFMD are still controversial. In addition, the interactive effects between meteorological factors and air pollutants on HFMD remain unknown. To fill this research gap, we conducted a time-series study. DESIGN: A time-series study. SETTING AND PARTICIPANTS: Daily cases of HFMD as well as meteorological and air pollution data were collected in Chengdu from 2011 to 2017. A total of 184 610 HFMD cases under the age of 15 were included in our study. OUTCOME MEASURES: Distributed lag nonlinear models were used to investigate the relationships between HFMD and environmental factors, including mean temperature, relative humidity, SO(2), NO(2), and PM(10). Then, the relative excess risk due to interaction (RERI) and the proportion attributable to interaction were calculated to quantitatively evaluate the interactions between meteorological factors and air pollutants on HFMD. Bivariate response surface models were used to visually display the interactive effects. RESULTS: The cumulative exposure-response curves of SO(2) and NO(2) were inverted ‘V’-shaped and ‘M’-shaped, respectively, and the risk of HFMD gradually decreased with increasing PM(10) concentrations. We found that there were synergistic interactions between mean temperature and SO(2), relative humidity and SO(2), as well as relative humidity and PM(10) on HFMD, with individual RERIs of 0.334 (95% CI 0.119 to 0.548), 0.428 (95% CI 0.214 to 0.642) and 0.501 (95% CI 0.262 to 0.741), respectively, indicating that the effects of SO(2) and PM(10) on HFMD were stronger under high temperature (>17.3°C) or high humidity (>80.0%) conditions. CONCLUSIONS: There were interactive effects between meteorological factors and air pollutants on HFMD. Our findings could provide guidance for targeted and timely preventive and control measures for HFMD.
Despite a conspicuous exacerbation of asthma among patients hospitalized due to influenza infection, no study has attempted previously to elucidate the relationship between environmental factors, influenza activity, and asthma simultaneously in adults. In this study, we examined this relationship using population-based hospitalization records over 22 years. Daily numbers of hospitalizations due to asthma in adults of 41 public hospitals in Hong Kong during 1998-2019 were obtained. The data were matched with meteorological records and air pollutant concentrations. We used type-specific and all-type influenza-like illness plus (ILI+) rates as proxies for seasonal influenza activity. Quasi-Poisson generalized additive models together with distributed-lag non-linear models were used to examine the association. A total of 212,075 hospitalization episodes due to asthma were reported over 22 years. The cumulative adjusted relative risk (ARR) of asthma hospitalizations reached 1.15 (95 % confidence interval [CI], 1.12-1.18) when the ILI+ total rate increased from zero to 20.01 per 1000 consultations. Compared with the median temperature, a significantly increased risk of asthma hospitalization (cumulative ARR = 1.10, 95 % CI, 1.05-1.15) was observed at the 5(th) percentile of temperature (i.e., 14.6 °C). Of the air pollutants, oxidant gas was significantly associated with asthma, but only at its extreme level of concentrations. In conclusion, cold conditions and influenza activities are risk factors to asthma exacerbation in adult population. Influenza-related asthma exacerbation that appeared to be more common in the warm and hot season, is likely to be attributable to influenza A/H3N2. The heavy influence of both determinants on asthma activity implies that climate change may complicate the asthma burden.
Varicella is a rising public health issue. Several studies have tried to quantify the relationships between meteorological factors and varicella incidence but with inconsistent results. We aim to investigate the impact of temperature and relative humidity on varicella, and to further explore the effect modification of these relationships. In this study, the data of varicella and meteorological factors from 2011 to 2019 in 21 cities of Guangdong Province, China were collected. Distributed lag nonlinear models (DLNM) were constructed to explore the relationship between meteorological factors (temperature and relative humidity) and varicella in each city, controlling in school terms, holidays, seasonality, long-term trends, and day of week. Multivariate meta-analysis was applied to pool the city-specific estimations. And the meta-regression was used to explore the effect modification for the spatial heterogeneity of city-specific meteorological factors and social factors (such as disposable income per capita, vaccination coverage, and so on) on varicella. The results indicated that the relationship between temperature and varicella in 21 cities appeared nonlinear with an inverted S-shaped. The relative risk peaked at 20.8 ℃ (RR = 1.42, 95% CI: 1.22, 1.65). The relative humidity-varicella relationship was approximately L-shaped, with a peaking risk at 69.5% relative humidity (RR = 1.25, 95% CI: 1.04, 1.50). The spatial heterogeneity of temperature-varicella relationships may be caused by income or varicella vaccination coverage. And varicella vaccination coverage may contribute to the spatial heterogeneity of the relative humidity-varicella relationship. The findings can help us deepen the understanding of the meteorological factors-varicella association and provide evidence for developing prevention strategy for varicella epidemic.
Hand, foot, and mouth disease (HFMD) poses a great disease burden in China. However, there are few studies on the relationship between temperature variability (TV) and HFMD. Moreover, whether air pollutions have modified effects on this relationship is still unknown. Therefore, this study aims to explore the modified effects of air pollutants on TV-HFMD association in Zibo City, China. Daily data of HFMD cases, meteorological factors, and air pollutants from 2015 to 2019 were collected for Zibo City. TV was estimated by calculating standard deviation of minimum and maximum temperatures over the exposure days. We used generalized additive model to estimate the association between TV and HFMD. The modified effects of air pollutants were assessed by comparing the estimated TV-HFMD associations between different air stratums. We found that TV increased the risk of HFMD. The effect was strongest at TV03 (4 days of exposure), when the incidence of HFMD increased by 3.6% [95% CI: 1.3-5.9%] for every 1℃ increases in TV. Males, children aged 0-4 years, were more sensitive to TV. We found that sulfur dioxide (SO(2)) enhanced TV’s effects on all considered exposure days, while ozone (O(3)) reduced TV’s effects on some exposure days in whole concerned population. However, we did not detect significant effect modification by particulate matter less than 10 microns in aerodynamic diameter (PM(10)). These findings are of significance in developing policies and public health practices to reduce the risks of HFMD by integrating changes in temperatures and air pollutants.
Respiratory infectious diseases (RID) are the major form of infectious diseases in China, and are highly susceptible to climatic conditions. Current research mainly focuses on the impact of weather on RID, but there is a lack of research on the effect of El Niño-Southern Oscillation (ENSO) on RID. Therefore, this paper uses the system generalized method of moments (SYS-GMM) and the data of 31 provinces in China from 2007 to 2018 to construct a dynamic panel model to empirically test the causality between ENSO and RID morbidity. Moreover, this paper considers the moderating effects of per capita disposable income and average years of education on this causality. The results show that ENSO can positively and significantly impact RID morbidity, which is 5.842% higher during El Niño years than normal years. In addition, per capita disposable income and average years of education can effectively weaken the relationship between ENSO and RID morbidity. Thus, this paper is of great significance for improving the RID early climate warning system in China and effectively controlling the spread of RID.
BACKGROUND: Acute lower respiratory infections (ALRIs) caused by respiratory viruses are common and persistent infectious diseases worldwide and in China, which have pronounced seasonal patterns. Meteorological factors have important roles in the seasonality of some major viruses, especially respiratory syncytial virus (RSV) and influenza virus. Our aim was to identify the dominant meteorological factors and to model their effects on common respiratory viruses in different regions of China. METHODS: We analysed monthly virus data on patients hospitalised with ALRI from 81 sentinel hospitals in 22 provinces in mainland China from Jan 1, 2009, to Sept 30, 2013. We considered seven common respiratory viruses: RSV, influenza virus, human parainfluenza virus, adenovirus, human metapneumovirus, human bocavirus, and human coronavirus. Meteorological data of the same period were used to analyse relationships between virus seasonality and seven meteorological factors according to region (southern vs northern China). The geographical detector method was used to quantify the explanatory power of each meteorological factor, individually and interacting in pairs, on the respiratory viruses. FINDINGS: 28 369 hospitalised patients with ALRI were tested, 10 387 (36·6%) of whom were positive for at least one virus, including RSV (4091 [32·0%] patients), influenza virus (2665 [20·8%]), human parainfluenza virus (2185 [17·1%]), adenovirus (1478 [11·6%]), human bocavirus (1120 [8·8%]), human coronavirus (637 [5·0%]), and human metapneumovirus (615 [4·8%]). RSV and influenza virus had annual peaks in the north and biannual peaks in the south. Human parainfluenza virus and human bocavirus had higher positive rates in the spring-summer months. Human metapneumovirus had an annual peak in winter-spring, especially in the north. Adenovirus and human coronavirus exhibited no clear annual seasonality. Temperature, atmospheric pressure, vapour pressure, and rainfall had most explanatory power on most respiratory viruses in each region. Relative humidity was only dominant in the north, but had no significant explanatory power for most viruses in the south. Hours of sunlight had significant explanatory power for RSV and influenza virus in the north, and for most viruses in the south. Wind speed was the only factor with significant explanatory power for human coronavirus in the south. For all viruses, interactions between any two of the paired factors resulted in enhanced explanatory power, either bivariately or non-linearly. INTERPRETATION: Spatiotemporal heterogeneity was detected for most viruses in this study, and interactions between pairs of meteorological factors were found to enhance their influence on virus variation. These findings might be helpful to guide government planning, such as public health interventions, infection control practice, and timing of passive immunoprophylaxis, and might facilitate the development of future vaccine strategies. FUNDING: National Natural Science Foundation of China, the Ministry of Science and Technology of China, and the Technology Major Project of China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.
BACKGROUND: This study reports a systematic review of association between meteorological parameters and hand, foot and mouth disease (HFMD) in mainland China. METHODS: Using predefined study eligibility criteria, three electronic databases (PubMed, Web of Science, and Embase) were searched for relevant articles. Using a combination of search terms, including “Hand foot and mouth disease,” “HFMD,” “Meteorological,” “Climate,” and “China,” After removal of duplicates, our initial search generated 2435 studies published from 1990 to December 31, 2019. From this cohort 51 full-text articles were reviewed for eligibility assessment. The meta-analysis was devised in accordance with the published guidelines of the Cochrane Collaboration and Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA). Effect sizes, heterogeneity estimates and publication bias were computed using R software and Review Manager Software. RESULTS: The meta-analysis of 18 eligible studies showed that the meteorological parameters played an important role in the prevalence of HFMD. Lower air pressure may be the main risk factor for the incidence of HFMD in Chinese mainland, and three meteorological parameters (mean temperature, rainfall and relative humidity) have a significant association with the incidence of HFMD in subtropical regions. CONCLUSION: Lower air pressure might be the main risk factor for the incidence of HFMD in Chinese mainland. The influence of meteorological parameters on the prevalence of HFMD is mainly through changing virus viability in aerosols, which may be different in different climate regions. In an environment with low air pressure, wearing a mask that filters the aerosol outdoors may help prevent HFMD infection.
(1) Background: Assignment of pathogens to the correct genus, species, and type is vital for controlling infectious epidemics. However, the role of different enteroviruses during hand, foot, and mouth disease (HFMD) epidemics and the major contributing factors remain unknown. (2) Methods: HFMD cases from 2016 to 2018 in Guangzhou, China were collected. The relationship between HFMD cases and genotype frequency, as well as the association between genotype frequency and climate factors, were studied using general linear models. We transformed the genotype frequency to the isometric log-ratio (ILR) components included in the model. Additionally, vaccination rates were adjusted in the climate-driven models. (3) Results: We observed seasonal trends in HFMD cases, genotype frequency, and climate factors. The model regressing case numbers on genotype frequency revealed negative associations with both the ILRs of CAV16 (RR = 0.725, p < 0.001) and EV71 (RR = 0.421, p < 0.001). The model regressing genotype frequency on driven factors showed that the trends for EV71 proportions were inversely related to vaccination rate (%, β = -0.152, p = 0.098) and temperature (°C, β = -0.065, p = 0.004). Additionally, the trends for CVA16 proportions were inversely related to vaccination rate (%, β = -0.461, p = 0.004) and temperature (°C, β = -0.068, p = 0.031). The overall trends for genotype frequency showed that EV71 decreased significantly, while the trends for CVA16 increased annually. (4) Conclusions: Our findings suggest a potential pathway for climate factors, genotype frequency, and HFMD cases. Our study is practical and useful for targeted prevention and control, and provides environmental-based evidence.
Background: As an emerging infectious disease, the prevention and control of hand, foot, and mouth disease (HFMD) poses a significant challenge to the development of public health in China. In this study, we aimed to explore the mechanism of the seasonal transmission characteristics of HFMD and to reveal the correlation and potential path between key meteorological factors and the transmissibility of HFMD. Methods: Combined with daily meteorological data such as average temperature, average relative humidity, average wind velocity, amount of precipitation, average air pressure, evaporation capacity, and sunshine duration, a database of HFMD incidence and meteorological factors was established. Spearman rank correlation was used to calculate the correlation between the various meteorological factors and the incidence of HFMD. The effective reproduction number (R (eff) ) of HFMD was used as an intermediate variable to further quantify the dynamic relationship between the average temperature and R (eff) . Results: A total of 43,659 cases of HFMD were reported in Xiamen from 2014 to 2018. There was a significantly positive correlation between the average temperature and the incidence of HFMD (r = 0.596, p < 0.001), and a significantly negative correlation between the average air pressure and the incidence of HFMD (r = -0.511, p < 0.001). There was no correlation between the average wind velocity (r = 0.045, p > 0.05) or amount of precipitation (r = 0.043, p > 0.05) and incidence. There was a temperature threshold for HFMD’s transmissibility. Owing to the seasonal transmission characteristics of HFMD in Xiamen, the temperature threshold of HFMD’s transmissibility was 13.4-18.4°C and 14.5-29.3°C in spring and summer and in autumn and winter, respectively. Conclusions: HFMD’s transmissibility may be affected by the average temperature; the temperature threshold range of transmissibility in autumn and winter is slightly wider than that in spring and summer. Based on our findings, we suggest that the relevant epidemic prevention departments should pay close attention to temperature changes in Xiamen to formulate timely prevention strategies before the arrival of the high-risk period.
The coronavirus disease 2019 (COVID-19) pandemic caused a crisis worldwide, due to both its public health impact and socio-economic consequences. Mental health was consistently affected by the pandemic, with the emergence of newly diagnosed psychiatric disorders and the exacerbation of pre-existing ones. Urban areas were particularly affected by the virus spread. In this review, we analyze how the urban environment may influence mental health during the COVID-19 pandemic, considering two factors that profoundly characterize urbanization: air pollution and migration. Air pollution serves as a possibly risk factor for higher viral spread and infection severity in the context of urban areas and it has also been demonstrated to play a role in the development of serious mental illnesses and their relapses. The urban environment also represents a complex social context where minorities such as migrants may live in poor hygienic conditions and lack access to adequate mental health care. A global rethinking of the urban environment is thus required to reduce the impact of these factors on mental health. This should include actions aimed at reducing air pollution and combating climate change, promoting at the same time a more inclusive society in a sustainable development perspective.
Global health threats including epidemics and climate change, know no political borders and require regional collaboration if they are to be dealt with effectively. This paper starts with a review of the COVID-19 outbreak in Israel, Palestine and Jordan, in the context of the regional health systems, demography and politics. We suggest that Israel and Palestine function as one epidemiological unit, due to extensive border crossing of inhabitants and tourists, resulting in cross-border infections and potential for outbreaks’ transmission. Indeed, there is a correlation between the numbers of confirmed cases with a 2-3 weeks lag. In contrast, Jordan has the ability to seal its borders and better contain the spread of the virus. We then discuss comparative public health aspects in relation to the management of COVID-19 and long term adaptation to climate change. We suggest that lessons from the current crisis can inform regional adaptation to climate change. There is an urgent need for better health surveillance, data sharing across borders, and more resilient health systems that are prepared and equipped for emergencies. Another essential and currently missing prerequisite is close cooperation within and across countries amidst political conflict, in order to protect the public health of all inhabitants of the region.
Purpose This article examines US official and public responses to the COVID-19 pandemic for insights into future policy and pubic responses to global climate change. Design/methodology/approach This article compares two contemporary global threats to human health and well-being: the COVID-19 pandemic and climate change. We identify several similarities and differences between the two environmental phenomena and explore their implications for public and policy responses to future climate-related disasters and disruptions. Findings Our review of research on environmental and public health crises reveals that though these two crises appear quite distinct, some useful comparisons can be made. We analyze several features of the pandemic for their implications for possible future responses to global climate change: elasticity of public responses to crises; recognition of environmental, health, racial, and social injustice; demand for effective governance; and resilience of the natural world. Originality/value This paper examines public and policy responses to the coronavirus pandemic for their implications for mitigating and adapting to future climate crises.
The COVID-19 pandemic has brought profound social, political, economic, and environmental challenges to the world. The virus may have emerged from wildlife reservoirs linked to environmental disruption, was transmitted to humans via the wildlife trade, and its spread was facilitated by economic globalization. The pandemic arrived at a time when wildfires, high temperatures, floods, and storms amplified human suffering. These challenges call for a powerful response to COVID-19 that addresses social and economic development, climate change, and biodiversity together, offering an opportunity to bring transformational change to the structure and functioning of the global economy. This biodefense can include a “One Health” approach in all relevant sectors; a greener approach to agriculture that minimizes greenhouse gas emissions and leads to healthier diets; sustainable forms of energy; more effective international environmental agreements; post-COVID development that is equitable and sustainable; and nature-compatible international trade. Restoring and enhancing protected areas as part of devoting 50% of the planet’s land to environmentally sound management that conserves biodiversity would also support adaptation to climate change and limit human contact with zoonotic pathogens. The essential links between human health and well-being, biodiversity, and climate change could inspire a new generation of innovators to provide green solutions to enable humans to live in a healthy balance with nature leading to a long-term resilient future.
Gender is a critical factor in how people respond to, and recover from major disruptions such as natural disasters or disease outbreaks. Climate-related disasters are known to pose-gender specific problems that disproportionately affect more women than men. Similarly, the COVID-19 pandemic’s impacts along gender lines are enormous, with women being the worst-affected. Existing studies have drawn connections between COVID-19 and climate change, with most arguing that responses to the pandemic provide an opportunity to tackle climate change through emission reduction strategies as part of recovery efforts. We introduce a new dimension to this connection by demonstrating that though different phenomena, COVID-19 and climate change are not so dissimilar in terms of their gendered socioeconomic impacts. Through a systematic review of the available literature, we establish a nexus between these impacts, and examine how the gender responses to COVID-19 can be leveraged to address gender-related climate impacts. We find that social protection, labor market, economic, and violence against women measures adopted in response to the pandemic provide a good opportunity to address the gender impacts of climate change as well. However, current COVID-19 gender responses do not incorporate the interconnections between the gender impacts of the pandemic and climate change. Adopting a nexus approach could help to leverage COVID-19 responses to address the gendered socioeconomic impacts of both crises.
Outbreaks of the novel coronavirus disease (severe acute respiratory syndrome coronavirus 2: SARS-CoV-2) (coronavirus disease 2019; COVID-19) remind us once again of the mechanisms of zoonotic outbreaks. Climate change and the expansion of agricultural lands and infrastructures due to population growth will ultimately reduce or eliminate wildlife and avian habitats and increase opportunities for wildlife and birds to come into contact with livestock and humans. Consequently, infectious pathogens are transmitted from wildlife and birds to livestock and humans, promoting zoonotic diseases. In addition, the spread of diseases has been associated with air pollution and social inequities, such as racial discrimination, gender inequality, and racial, economic, and educational disparities. The COVID-19 pandemic is a fresh reminder of the significance of excessive greenhouse gas excretion and air pollution, highlighting social inequities and distortions. This provides us with an opportunity to reflect on the appropriateness of our trajectory. Therefore, this review glances through the COVID-19 pandemic and discusses our future.
Numerous studies have linked outdoor levels of PM2.5, PM10, NO2, O-3, SO2, and other air pollutants to significantly higher rates of Covid 19 morbidity and mortality, although the rate in which specific concentrations of pollutants increase Covid 19 morbidity and mortality varies widely by specific country and study. As little as a 1-mu g/m(3) increase in outdoor PM2.5 is estimated to increase rates of Covid 19 by as much as 0.22 to 8%. Two California studies have strongly linked heavy wildfire burning periods with significantly higher outdoor levels of PM2.5 and CO as well as significantly higher rates of Covid 19 cases and deaths. Active smoking has also been strongly linked significantly increased risk of Covid 19 severity and death. Other exposures possibly related to greater risk of Covid 19 morbidity and mortality include incense, pesticides, heavy metals, dust/sand, toxic waste sites, and volcanic emissions. The exact mechanisms in which air pollutants increase Covid 19 infections are not fully understood, but are probably related to pollutant-related oxidation and inflammation of the lungs and other tissues and to the pollutant-driven alternation of the angiotensin-converting enzyme 2 in respiratory and other cells.
The coincidence of floods and coronavirus disease 2019 (COVID-19) is a genuine multihazard problem. Since the beginning of 2020, many regions around the World have been experiencing this double hazard of serious flooding and the pandemic. There have been 70 countries with flood events occurring after detection of the country’s first COVID-19 case and hundreds of thousands of people have been evacuated. The main objective of this article is to assess challenges that arise from complex intersections between the threat multipliers and to provide guidance on how to address them effectively. We consider the limitations of our knowledge including “unknown unknowns.” During emergency evacuation, practicing social distancing can be very difficult. However, people are going to take action to respond to rising waters, even if it means breaking quarantine. This is an emergency manager’s nightmare scenario: two potentially serious emergencies happening at once. During this unprecedented year (2020), we are experiencing one of the most challenging flood seasons we have seen in a while. Practical examples of issues and guides for managing floods and COVID-19 are presented. We feel that a new approach is needed in dealing with multiple hazards. Our main messages are: a resilience approach is needed whether in response to floods or a pandemic; preparation is vital, in addition to defense; the responsible actors must be prepared with actions plans and command structure, while the general population must be involved in the discussions so that they are aware of the risk and the reasons for the actions they must take. This article is categorized under:Engineering Water > Methods.
Converging data would indicate the existence of possible relationships between climate change, environmental pollution and epidemics/pandemics, such as the current one due to SARS-CoV-2 virus. Each of these phenomena has been supposed to provoke detrimental effects on mental health. Therefore, the purpose of this paper was to review the available scientific literature on these variables in order to suggest and comment on their eventual synergistic effects on mental health. The available literature report that climate change, air pollution and COVID-19 pandemic might influence mental health, with disturbances ranging from mild negative emotional responses to full-blown psychiatric conditions, specifically, anxiety and depression, stress/trauma-related disorders, and substance abuse. The most vulnerable groups include elderly, children, women, people with pre-existing health problems especially mental illnesses, subjects taking some types of medication including psychotropic drugs, individuals with low socio-economic status, and immigrants. It is evident that COVID-19 pandemic uncovers all the fragility and weakness of our ecosystem, and inability to protect ourselves from pollutants. Again, it underlines our faults and neglect towards disasters deriving from climate change or pollution, or the consequences of human activities irrespective of natural habitats and constantly increasing the probability of spillover of viruses from animals to humans. In conclusion, the psychological/psychiatric consequences of COVID-19 pandemic, that currently seem unavoidable, represent a sharp cue of our misconception and indifference towards the links between our behaviour and their influence on the “health” of our planet and of ourselves. It is time to move towards a deeper understanding of these relationships, not only for our survival, but for the maintenance of that balance among man, animals and environment at the basis of life in earth, otherwise there will be no future.
PURPOSE OF REVIEW: This paper reviews the empirical literature on exposures to disaster or terrorism and their impacts on the health and well-being of children with disabilities and their families since the last published update in 2017. We also review the literature on studies examining the mental health and functioning of children with disabilities during the COVID-19 pandemic. RECENT FINDINGS: Few studies have examined the effects of disaster or terrorism on children with disabilities. Research shows that children with disabilities and their families have higher levels of disaster exposure, lower levels of disaster preparedness, and less recovery support due to longstanding discriminatory practices. Similarly, many reports of the COVID-19 pandemic have documented its negative and disproportionate impacts on children with disabilities and their families. In the setting of climate change, environmental disasters are expected to increase in frequency and severity. Future studies identifying mitigating factors to disasters, including COVID-19; increasing preparedness on an individual, community, and global level; and evaluating post-disaster trauma-informed treatment practices are imperative to support the health and well-being of children with disabilities and their families.
The new severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) pandemic was first recognized at the end of 2019 and has caused one of the most serious global public health crises in the last years. In this paper, we review current literature on the effect of weather (temperature, humidity, precipitation, wind, etc.) and climate (temperature as an essential climate variable, solar radiation in the ultraviolet, sunshine duration) variables on SARS-CoV-2 and discuss their impact to the COVID-19 pandemic; the review also refers to respective effect of urban parameters and air pollution. Most studies suggest that a negative correlation exists between ambient temperature and humidity on the one hand and the number of COVID-19 cases on the other, while there have been studies which support the absence of any correlation or even a positive one. The urban environment and specifically the air ventilation rate, as well as air pollution, can probably affect, also, the transmission dynamics and the case fatality rate of COVID-19. Due to the inherent limitations in previously published studies, it remains unclear if the magnitude of the effect of temperature or humidity on COVID-19 is confounded by the public health measures implemented widely during the first pandemic wave. The effect of weather and climate variables, as suggested previously for other viruses, cannot be excluded, however, under the conditions of the first pandemic wave, it might be difficult to be uncovered. The increase in the number of cases observed during summertime in the Northern hemisphere, and especially in countries with high average ambient temperatures, demonstrates that weather and climate variables, in the absence of public health interventions, cannot mitigate the resurgence of COVID-19 outbreaks.
Corona virus is highly uncertain and complex in space and time. Atmospheric parameters such as type of pollutants and local weather play an important role in COVID-19 cases and mortality. Many studies were carried out to understand the impact of weather on spread and severity of COVID-19 and vice-versa. A review study is conducted to understand the impact of weather and atmospheric pollution on morbidity and mortality. Studies show that aerosols containing corona virus generated by sneezes and coughs are major route for spread of virus. Viability and virulence of SARS-CoV-2 stuck on the surface of particulate matter is not yet confirmed. Studies found that an increase in particulate matter concentration causes more COVID-19 cases and mortality. Gaseous pollutant and COVID-19 cases are positively correlated. Local meteorology plays crucial role in the spread of corona virus and thus mortality. Decline in number of cases with rising temperature observed. Few studies also find that lowest and highest temperatures were related to lesser number of cases. Similarly humidity shows negative or no relationship with COVID-19 cases. Rainfall was not related whilst wind-speed plays positive role in spread of COVID-19. Solar radiation threats survival of virus, areas with lower solar radiation showed high exposure rate. Air quality tremendously improved during lockdown. A significant reduction in PM10, PM2.5, BC, NOx, SO(2), CO and VOCs concentration were observed. Lockdown had a healing effect on ozone; significant increase in its concentration was observed. Aerosols Optical Depths were found to decrease up to 50%.
SARS-CoV-2 virus infections in humans were first reported in December 2019, the boreal winter. The resulting COVID-19 pandemic was declared by the WHO in March 2020. By July 2020, COVID-19 was present in 213 countries and territories, with over 12 million confirmed cases and over half a million attributed deaths. Knowledge of other viral respiratory diseases suggests that the transmission of SARS-CoV-2 could be modulated by seasonally varying environmental factors such as temperature and humidity. Many studies on the environmental sensitivity of COVID-19 are appearing online, and some have been published in peer-reviewed journals. Initially, these studies raised the hypothesis that climatic conditions would subdue the viral transmission rate in places entering the boreal summer, and that southern hemisphere countries would experience enhanced disease spread. For the latter, the COVID-19 peak would coincide with the peak of the influenza season, increasing misdiagnosis and placing an additional burden on health systems. In this review, we assess the evidence that environmental drivers are a significant factor in the trajectory of the COVID-19 pandemic, globally and regionally. We critically assessed 42 peer-reviewed and 80 preprint publications that met qualifying criteria. Since the disease has been prevalent for only half a year in the northern, and one-quarter of a year in the southern hemisphere, datasets capturing a full seasonal cycle in one locality are not yet available. Analyses based on space-for-time substitutions, i.e., using data from climatically distinct locations as a surrogate for seasonal progression, have been inconclusive. The reported studies present a strong northern bias. Socio-economic conditions peculiar to the ‘Global South’ have been omitted as confounding variables, thereby weakening evidence of environmental signals. We explore why research to date has failed to show convincing evidence for environmental modulation of COVID-19, and discuss directions for future research. We conclude that the evidence thus far suggests a weak modulation effect, currently overwhelmed by the scale and rate of the spread of COVID-19. Seasonally modulated transmission, if it exists, will be more evident in 2021 and subsequent years.
Respiratory viruses, including coronaviruses, are known to have a high incidence of infection during winter, especially in temperate regions. Dry and cold conditions during winter are the major drivers for increased respiratory tract infections as they increase virus stability and transmission and weaken the host immune system. The novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) emerged in China in December 2020 and swiftly spread across the globe causing substantial health and economic burdens. Several countries are battling with the second wave of the virus after a devastating first wave of spread, while some are still in the midst of their first wave. It remains unclear whether SARS-CoV-2 will eventually become seasonal or will continue to circulate year-round. In an attempt to address this question, we review the current knowledge regarding the seasonality of respiratory viruses including coronaviruses and the viral and host factors that govern their seasonal pattern. Moreover, we discuss the properties of SARS-CoV-2 and the potential impact of meteorological factors on its spread.
The world is currently shadowed by the pandemic of COVID-19. Confirmed cases and the death toll has reached more than 12 million and more than 550,000 respectively as of 10 July 2020. In the unsettling pandemic of COVID-19, the whole Earth has been on an unprecedented lockdown. Social distancing among people, interrupted international and domestic air traffic and suspended industrial productions and economic activities have various far-reaching and undetermined implications on air quality and the climate system. Improvement in air quality has been reported in many cities during lockdown, while the death rate of COVID-19 has been found to be higher in more polluted cities. The relationship between the spread of the SARS-CoV-2 virus and air quality is under investigation. In addition, the battle against COVID-19 could bring short-lived and long-lasting and positive and negative impacts to the warming climate. The impacts on the climate system and the role of the climate in modulating the COVID-19 pandemic are the foci of scientific inquiry. The intertwined relationship among environment, climate change and public health is exemplified in the pandemic of COVID-19. Further investigation of the relationship is imperative in the Anthropocene, in particular, in enhancing disaster preparedness. This short article intends to give an up-to-date glimpse of the pandemic from air quality and climate perspectives and calls for a follow-up discussion.
The co-occurrence of the 2020 Atlantic hurricane season and the ongoing coronavirus disease 2019 (COVID-19) pandemic creates complex dilemmas for protecting populations from these intersecting threats. Climate change is likely contributing to stronger, wetter, slower-moving, and more dangerous hurricanes. Climate-driven hazards underscore the imperative for timely warning, evacuation, and sheltering of storm-threatened populations – proven life-saving protective measures that gather evacuees together inside durable, enclosed spaces when a hurricane approaches. Meanwhile, the rapid acquisition of scientific knowledge regarding how COVID-19 spreads has guided mass anti-contagion strategies, including lockdowns, sheltering at home, physical distancing, donning personal protective equipment, conscientious handwashing, and hygiene practices. These life-saving strategies, credited with preventing millions of COVID-19 cases, separate and move people apart. Enforcement coupled with fear of contracting COVID-19 have motivated high levels of adherence to these stringent regulations. How will populations react when warned to shelter from an oncoming Atlantic hurricane while COVID-19 is actively circulating in the community? Emergency managers, health care providers, and public health preparedness professionals must create viable solutions to confront these potential scenarios: elevated rates of hurricane-related injury and mortality among persons who refuse to evacuate due to fear of COVID-19, and the resurgence of COVID-19 cases among hurricane evacuees who shelter together.
This article compares two concurrent global crises: the decades-long climate change crisis and the months-long COVID-19 pandemic. These have many similarities. We draw attention to seven parallels and implications. Three of these feature change: business as usual is not acceptable; timeliness in relation to tipping points is critical; and communities can adapt to change with support. Two other points highlight the importance of data: decisions about policy, planning and management need to be based on evidence; and preparation needs to be based on expert advice, warnings, and long-term strategies. Two additional comments involve institutions and relationships: integrated multi-level governance is most effective to deal with global crises; and a sense of a shared burden on humanity globally is essential. We learn that adaptation can take place without having all the facts but accepting the trends, timing is critical, and political will is vital.
The humanity is currently facing the COVID-19 pandemic challenge, the largest global health emergency after the Second World War. During summer months, many countries in the northern hemisphere will also have to counteract an imminent seasonal phenomenon, the management of extreme heat events. The novelty this year concerns that the world population will have to deal with a new situation that foresees the application of specific measures, including adjunctive personal protective equipment (i.e. facemasks and gloves), in order to reduce the potential transmission of the SARS-CoV-2 virus. These measures should help to decrease the risk of the infection transmission but will also represent an aggravating factor to counteract the heat effects on the population health both at occupational and environmental level. The use of a specific heat health warning system with personalized information based on individual, behavioural and environmental characteristics represents a necessary strategy to help a fast adaptation of the population at a time where the priority is to live avoiding SARS-CoV-2 infection.
The geopolitics of pandemics and climate change intersect. Both are complex and urgent problems that demand collective action in the light of their global and trans-boundary scope. In this article we use a geopolitical framework to examine some of the tensions and contradictions in global governance and cooperation that are revealed by the pandemic of coronavirus disease 2019 (COVID-19). We argue that the pandemic provides an early warning of the dangers inherent in weakened international cooperation. The world’s states, with their distinct national territories, are reacting individually rather than collectively to the COVID-19 pandemic. Many countries have introduced extraordinary measures that have closed, rather than opened up, international partnership and cooperation. Border closures, restrictions on social mixing, domestic purchase of public health supplies and subsidies for local industry and commerce may offer solutions at the national level but they do not address the global strategic issues. For the poorest countries of the world, pandemics join a list of other challenges that are exacerbated by pressures of scarce resources, population density and climate disruption. COVID-19’s disproportionate impact on those living with environmental stresses, such as poor air quality, should guide more holistic approaches to the geopolitical intersection of public health and climate change. By discussing unhealthy geopolitics, we highlight the urgent need for a coordinated global response to addressing challenges that cannot be approached unilaterally.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors, and the daily reported infected cases caused by novel coronavirus in India. The daily positive infected cases, concentrations of air pollutants, and meteorological factors in 288 districts were collected from January 30, 2020, to April 23, 2020, in India. Spearman’s correlation and generalized additive model (GAM) were applied to investigate the correlations of four air pollutants (PM(2.5), PM(10), NO(2), and SO(2)) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS, and WD) with COVID-19-infected cases. The study indicated that a 10 ?g/m(3) increase during (Lag0-14) in PM(2.5), PM(10), and NO(2) resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01), and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of Coronavirus Disease 2019 (COVID 19)-infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19-infected cases respectively. In addition, SO(2) and relative humidity were negatively associated with COVID-19-infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO(2) and for relative humidity respectively. The study recommended that there are significant correlations between air pollutants and meteorological factors with COVID-19-infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.
AIM: As the COVID-19 pandemic has been spreading rapidly all over the world, there are plenty of ongoing works to shed on light to unknown factors related to disease. One of the factors questioned is also to be the factors affecting the disease course. In this study, our aim is to determine the factors that affect the course of the disease in the hospitalised patients because of COVID-19 infection and to reveal whether the seasonal change has an effect on the disease course. METHODS: Our study was conducted on 1950 PCR test positive patients who were hospitalised for COVID-19 disease between March 16 and July 15. RESULTS: As the seasonal temperature increases, decrease in WBC, PLT and albumin levels and increase in LDH and AST levels were observed. Risk of need for ICU has been found statistically significant (P < .05) with the increase in the age, LDH levels and CRP levels and with the decrease in the Ca and Albumin levels. CONCLUSIONS: It is predicted with these results that, seasonal change might have affects on the clinical course of the disease, although it has no affect on the spread of the disease. And it might beneficial to check biochemical parameters such as LDH, CRP, Ca and Albumin to predict the course of the disease.
Scholars argue that personal experience with climate change related impacts can increase public engagement, with mixed empirical evidence. Previous studies have almost exclusively focussed on individuals’ experience with extreme weather events, even as scientific research on health impacts of climate change is burgeoning. This article extends previous research in the domain of public perceptions about climate-related public health impacts. Results from a nationally representative sample survey in New Zealand indicates that subjective attribution of infectious disease outbreaks to climate change and to human impact on the environment is positively associated with mitigation behavioural intentions and climate-focussed COVID-19 economic recovery policies. In contrast, knowledge about COVID-19 and self-reported economic impact due to COVID-19 is not associated with policy support. Moreover, significant interaction between political affiliation and subjective attribution to climate change on policy support indicate that learning about the links between health and climate change will particularly help increase mitigation engagement among right-leaning individuals. Subjective attribution may be the key to help translate personal experience to personal engagement.
The health, economic, and social impact of COVID-19 has been significant across the world. Our objective was to evaluate the association between air pollution (through NO(2) and PM(2.5) levels) and COVID-19 mortality in Spanish provinces from February 3, 2020, to July 14, 2020, adjusting for climatic parameters. An observational and ecological study was conducted with information extracted from Datadista repository (Datadista, 2020). Air pollutants (NO(2) and PM(2.5) levels) were analyzed as potential determinants of COVID-19 mortality. Multilevel Poisson regression models were used to analyze the risk of mortality after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Models were adjusted by four climatic variables (hours of solar radiation, precipitation, daily temperature and wind speed) and population size. The mean levels of PM(2.5) and NO(2) across all provinces and time in Spain were 8.7 ?g/m(3) (SD 9.7) and 8.7 ?g/m(3) (SD 6.2), respectively. High levels of PM(2.5) (IRR?=?1.016, 95% CI: 1.007-1.026), NO(2) (IRR?=?1.066, 95% CI: 1.058-1.075) and precipitation (IRR(NO2)?=?0.989, 95% CI: 0.981-0.997) were positively associated with COVID-19 mortality, whereas temperature (IRR(PM2.5)?=?0.988, 95% CI: 0.976-1.000; and IRR(NO2)?=?0.771, 95% CI: 0.761-0.782, respectively) and wind speed (IRR(NO2)?=?1.095, 95% CI: 1.061-1.131) were negatively associated with COVID-19 mortality. Air pollution can be a key factor to understand the mortality rate for COVID-19 in Spain. Furthermore, climatic variables could be influencing COVID-19 progression. Thus, air pollution and climatology ought to be taken into consideration in order to control the pandemic. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11869-021-01062-2.
Global warming and air pollution affect the transmission pathway and the survival of viruses, altering the human immune system as well. The first wave of the COVID-19 pandemic dramatically highlights the key roles of climate and air chemistry in viral epidemics. The elongated form of the Italian peninsula and the two major islands (the largest in Europe) is a perfect case study to assess some of these key roles, as the fate of the virus is mirroring the industrialization in the continental part of our country. Fine particulate matter (PM(2.5)), geography, and climate explain what is happening in Italy and support cleaner air actions to address efficiently other outbreaks. Besides the environmental factors, future works should also address the genetic difference among individuals to explain the spatial variability of the human response to viral infections.
The 2020 summer Olympic and Paralympic Games in Tokyo were postponed to July-September 2021 due to the coronavirus disease 2019 (COVID-19) pandemic. While COVID-19 has emerged as a monumental health threat for mass gathering events, heat illness must be acknowledged as a potentially large health threat for maintaining health services. We examined the number of COVID-19 admissions and the Tokyo rule for emergency medical care, in Tokyo, from March to September 2020, and investigated the weekly number of emergency transportations due to heat illness and weekly averages of the daily maximum Wet Bulb Globe Temperature (WBGT) in Tokyo in the summer (2016-2020). The peak of emergency transportations due to heat illness overlapped the resurgence of COVID-19 in 2020, and an increase of heat illness patients and WBGT has been observed. Respect for robust science is critical for the decision-making process of mass gathering events during the pandemic, and science-based countermeasures and implementations for COVID-19 will be warranted. Without urgent reconsiderations and sufficient countermeasures, the double burden of COVID-19 and heat-related illnesses in Tokyo will overwhelm the healthcare provision system, and maintaining essential health services will be challenging during the 2021 summer Olympic and Paralympic Games.
People with chronic obstructive pulmonary disease, cardiovascular disease, or hypertension have a high risk of developing severe coronavirus disease 2019 (COVID-19) and of COVID-19 mortality. However, the association between long-term exposure to air pollutants, which increases cardiopulmonary damage, and vulnerability to COVID-19 has not yet been fully established. We collected data of confirmed COVID-19 cases during the first wave of the epidemic in mainland China. We fitted a generalized linear model using city-level COVID-19 cases and severe cases as the outcome, and long-term average air pollutant levels as the exposure. Our analysis was adjusted using several variables, including a mobile phone dataset, covering human movement from Wuhan before the travel ban and movements within each city during the period of the emergency response. Other variables included smoking prevalence, climate data, socioeconomic data, education level, and number of hospital beds for 324 cities in China. After adjusting for human mobility and socioeconomic factors, we found an increase of 37.8% (95% confidence interval [CI]: 23.8%-52.0%), 32.3% (95% CI: 22.5%-42.4%), and 14.2% (7.9%-20.5%) in the number of COVID-19 cases for every 10-?g/m(3) increase in long-term exposure to NO(2), PM(2.5), and PM(10), respectively. However, when stratifying the data according to population size, the association became non-significant. The present results are derived from a large, newly compiled and geocoded repository of population and epidemiological data relevant to COVID-19. The findings suggested that air pollution may be related to population vulnerability to COVID-19 infection, although the extent to which this relationship is confounded by city population density needs further exploration.
OBJECTIVE: The wildfire allied environmental pollution is highly toxic and can cause significant wide-ranging damage to the regional environment, weather conditions, and it can facilitate the transmission of microorganisms and diseases. The present study aims to investigate the effect of wildfire allied pollutants, particulate matter (PM-2.5 ?m), and carbon monoxide (CO) on the dynamics of daily cases and deaths due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in San Francisco, USA. MATERIALS AND METHODS: For this study, we selected San Francisco, one of the regions affected by the wildfires allied pollution in California, USA. The data on the COVID-19 pandemic in San Francisco, including daily new cases and new deaths were recorded from Worldometer Web. The daily environmental pollutants particulate matter (PM-2.5 ?m) and carbon monoxide (CO) were recorded from the metrological web “BAAQMD”. The daily cases, deaths, particulate matter (PM-2.5 ?m) and carbon monoxide were documented from the date of the occurrence of the first case of (SARS-CoV-2) in San Francisco, CA, USA, from March 20, 2020 to Sept 16, 2020. RESULTS: The results revealed a significant positive correlation between the environmental pollutants particulate matter (PM2.5 ?m) and the number of daily cases (r=0.203, p=0.007), cumulative cases (r=0.567, p<0.001) and cumulative deaths (r=0.562, p<0.001); whereas the PM2.5 ?m and daily deaths had no relationship (r=-0.015, p=0.842). In addition, CO was also positively correlated with cumulative cases (r=0.423, p<0.001) and cumulative deaths (r=0.315, p<0.001), however, CO had no correlation with the number of daily cases (r=0.134, p=0.075) and daily deaths (r=0.030, p=0.693). In San Francisco, one micrometer (?g/m3) increase in PM2.5 caused an increase in the daily cases, cumulative cases and cumulative deaths of SARS-COV-2 by 0.5%, 0.9% and 0.6%, respectively. Moreover, with a 1 part per million (ppm) increase in carbon monoxide level, the daily number of cases, cumulative cases and cumulative deaths increased by 5%, 9.3% and 5.3%, respectively. On the other hand, CO and daily deaths had no significant relationship. CONCLUSIONS: The wildfire allied pollutants, particulate matter PM-2.5?m and CO have a positive association with an increased number of SARS-COV-2 daily cases, cumulative cases and cumulative deaths in San Francisco. The metrological, disaster management and health officials must implement the necessary policies and assist in planning to minimize the wildfire incidences, environmental pollution and COVID-19 pandemic both at regional and international levels.
The global risks report of 2020 stated, climate-related issues dominate all of the top-five long-term critical global risks burning the planet and according to the report, “as existing health risks resurge and new ones emerge, humanity’s past successes in overcoming health challenges are no guarantee of future results.” Over the last few decades, the world has experienced several pandemic outbreaks of various pathogens and the frequency of the emergence of novel strains of infectious organisms has increased in recent decades. As per expert opinion, rapidly mutating viruses, emergence and re-emergence of epidemics with increasing frequencies, climate-sensitive vector-borne diseases are likely to be increasing over the years and the trends will continue and intensify. Susceptible disease hosts, anthropogenic activities and environmental changes contribute and trigger the ‘adaptive evolution’ of infectious agents to thrive and spread into different ecological niches and to adapt to new hosts. The overarching objective of this paper is to provide insight into the human actions which should be strictly regulated to help to sustain life on earth. To identify and categorize the triggering factors that contribute to disease ecology, especially repeated emergence of disease pandemics, a theory building approach, ‘Total Interpretive Structural Modeling’ (TISM) was used; also the tool, ‘Impact Matrix Cross-Reference Multiplication Applied to a Classification’ analysis (MICMAC) was applied to rank the risk factors based on their impacts on other factors and on the interdependence among them. This mathematical modeling tool clearly explains the strength, position and interconnectedness of each anthropogenic factor that contributes to the evolution of pathogens and to the frequent emergence of pandemics which needs to be addressed with immediate priority. As we are least prepared for another pandemic outbreak, significant policy attention must be focused on the causative factors to limit emerging outbreaks like COVID 19 in the future.
Since the first report in December 2019, the novel coronavirus (COVID-19) has spread to most parts of the world, with over 21.5 million people infected and nearly 768,000 deaths to date. Evidence suggests that transmission of the virus is primarily through respiratory droplets and contact routes, and airborne carriers such as atmospheric particulates and aerosols have also been proposed as important vectors for the environmental transmission of COVID-19. Sewage and human excreta have long been recognized as potential routes for transmitting human pathogens. The causative agent of the COVID-19 pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been detected in human feces and urine, where it could remain viable for days and show infectivity. Urban flooding, a common threat in summer caused by heavy rainfalls, is frequently reported in urban communities along with sewage overflows. With summer already underway and economy re-opening in many parts of the world, urban flooding and the often-accompanied sewage overflows could jeopardize previous mitigation efforts by posing renewed risks of virus spread in affected areas and communities. In this article, we present the up-to-date evidence and discussions on sewage-associated transmission of COVID-19, and highlighted the roles of sewage overflow and sewage-contaminated aerosols in two publicized events of community outbreaks. Further, we collected evidence in real-life environments to demonstrate the shortcuts of exposure to overflowed sewage and non-dispersed human excreta during a local urban flooding event. Given that communities serviced by combined sewer systems are particularly prone to such risks, local municipalities could prioritize wastewater infrastructure upgrades and consider combined sewer separations to minimize the risks of pathogen transmission via sewage overflows during epidemics.
INTRODUCTION: Bacterial meningitis still constitutes an important threat in Africa. In the meningitis belt, a clear seasonal pattern in the incidence of meningococcal disease during the dry season has been previously correlated with several environmental parameters like dust and sand particles as well as the Harmattan winds. In parallel, the evidence of seasonality in meningitis dynamics and its environmental variables remain poorly studied outside the meningitis belt. This study explores several environmental factors associated with meningitis cases in the Democratic Republic of Congo (DRC), central Africa, outside the meningitis belt area. METHODS: Non-parametric Kruskal-Wallis’ tests were used to establish the difference between the different health zones, climate and vegetation types in relation to both the number of cases and attack rates for the period 2000-2018. The relationships between the number of meningitis cases for the different health zones and environmental and socio-economical parameters collected were modeled using different generalized linear (GLMs) and generalized linear mixed models (GLMMs), and different error structure in the different models, i.e., Poisson, binomial negative, zero-inflated binomial negative and more elaborated multi-hierarchical zero-inflated binomial negative models, with randomization of certain parameters or factors (health zones, vegetation and climate types). Comparing the different statistical models, the model with the smallest Akaike’s information criterion (AIC) were selected as the best ones. 515 different health zones from 26 distinct provinces were considered for the construction of the different GLM and GLMM models. RESULTS: Non-parametric bivariate statistics showed that there were more meningitis cases in urban health zones than in rural conditions (?2 = 6.910, p-value = 0.009), in areas dominated by savannah landscape than in areas with dense forest or forest in mountainous areas (?2 = 15.185, p-value = 0.001), and with no significant difference between climate types (?2 = 1.211, p-value = 0,449). Additionally, no significant difference was observed for attack rate between the two types of heath zones (?2 = 0.982, p-value = 0.322). Conversely, strong differences in attack rate values were obtained for vegetation types (?2 = 13.627, p-value = 0,001) and climate types (?2 = 13.627, p-value = 0,001). This work demonstrates that, all other parameters kept constant, an urban health zone located at high latitude and longitude eastwards, located at low-altitude like in valley ecosystems predominantly covered by savannah biome, with a humid tropical climate are at higher risk for the development of meningitis. In addition, the regions with mean range temperature and a population with a low index of economic well-being (IEW) constitute the perfect conditions for the development of meningitis in DRC. CONCLUSION: In a context of global environmental change, particularly climate change, our findings tend to show that an interplay of different environmental and socio-economic drivers are important to consider in the epidemiology of bacterial meningitis epidemics in DRC. This information is important to help improving meningitis control strategies in a large country located outside of the so-called meningitis belt.
Flooding displaces large populations each season, which potentially increases the exposure of the vulnerable societies. Having failed to curve down the number of people infected with COVID-19 in the first wave of the pandemic, many states in the United States (U.S.) are now at high risk of the concurrence of the two disasters. Assessing this compound risk before the country enters the flood season is of vital importance. Therefore, we provide a prompt tool to assess the compound risk of COVID-19 at the county level over the U.S. We find that (1) the number of flood insurance house claims can proxy the displaced population accurately with more spatiotemporal detail, and (2) the high-risk areas of both flooding and COVID-19 are concentrated along the southern and eastern coasts and some parts of the Mississippi River. Our findings may trigger the interest of further exploring the topics related to the concurrence of COVID-19 and flooding.
Thermal comfort standards are essential to ensure comfortable and enjoyable indoor conditions, and they also help in optimizing energy use. Thermal comfort studies, either climate chamber-based or field investigation, are conducted across the globe in order to ascertain the comfort limits as per the climatic and other adaptive features. However, very few studies are conducted when the occupants are subjected to a stressed condition, like the COVID-19 lockdown, which may not only have the health impacts but also have psychological impacts on the adaptation. In this paper, we present the results of the online study conducted regarding the status of thermal comfort during the COVID-19 lockdown in India. A total of 406 complete responses were collected from subjects located across 3 different climatic regions of India, that is, cold climate, composite climate, and hot and humid climate. Variations in clothing insulation, thermal sensation, and preference were noted across the different climatic regions. We also present the variation in opening of windows and running of fans with the variation in outdoor mean air temperature. The self-judged productivity, comfort, desire to go outdoors, and effectiveness of working from home were seen to vary with the increase in the days of lockdown.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), universally recognized as COVID-19, is currently is a global issue. Our study uses multivariate regression for determining the relationship between the ambient environment and COVID-19 cases in Lima. We also forecast the pattern trajectory of COVID-19 cases with variables using an Auto-Regressive Integrated Moving Average Model (ARIMA). There is a significant association between ambient temperature and PM10 and COVID-19 cases, while no significant correlation has been seen for PM2.5. All variables in the multivariate regression model have R-2 = 0.788, which describes a significant exposure to COVID-19 cases in Lima. ARIMA (1,1,1), during observation time of PM2.5, PM10, and average temperature, is found to be suitable for forecasting COVID-19 cases in Lima. This result indicates that the expected high particle concentration and low ambient temperature in the coming season will further facilitate the transmission of the coronavirus if there is no other policy intervention. A suggested sustainable policy related to ambient environment and the lessons learned from different countries to prevent future outbreaks are also discussed in this study.
This study aimed to evaluate the relationship between weather factors (temperature, humidity, solar radiation, wind speed, and rainfall) and COVID-19 infection in the State of Rio de Janeiro, Brazil. Solar radiation showed a strong (-0.609, p < 0.01) negative correlation with the incidence of novel coronavirus (SARS-CoV-2). Temperature (maximum and average) and wind speed showed negative correlation (p < 0.01). Therefore, in this studied tropical state, high solar radiation can be indicated as the main climatic factor that suppress the spread of COVID-19. High temperatures, and wind speed also are potential factors. Therefore, the findings of this study show the ability to improve the organizational system of strategies to combat the pandemic in the State of Rio de Janeiro, Brazil, and other tropical countries around the word.
Pollen is an important component of bioaerosol and the distribution of pollen and its relationship with meteorological parameters can be analyzed to better prevent hay fever. Pollen assemblages can also provide basic data for analyzing the relationship between bioaerosol and PM. We collected 82 samples of airborne pollen using a TSP large flow pollen collector from June 1, 2015 to June 1, 2016, from central Zhanjiang city in South China. We also conducted a survey of the nearby vegetation at the same time, in order to characterize the major plant types and their flowering times. We then used data on daily temperature, relative humidity, precipitation, vapor pressure and wind speed from a meteorological station in the center of Zhanjiang City to assess the relationship between the distribution of airborne pollen and meteorological parameters. Our main findings and conclusions are as follows: (1) We identified 15 major pollen types, including Pinus, Castanopsis, Myrica, Euphorbiaceae, Compositae, Gramineae, Microlepia and Polypodiaceae. From the vegetation survey, we found that the pollen from these taxa represented more than 75% of local pollen, while the pollen of Podocarpus, Dacrydium and other regional pollen types represented less than 25%. (2) The pollen concentrations varied significantly in different seasons. The pollen concentrations were at a maximum in spring, consisting mainly of tree pollen; the pollen concentrations were at an intermediate level in autumn and winter, consisting mainly of herb pollen and fern spores; and the pollen concentrations in summer were the lowest, consisting mainly of fern spores. (3) Analysis of the relationship between airborne pollen concentrations and meteorological parameters showed that variations in the pollen concentrations were mainly affected by temperature and relative humidity. In addition, there were substantial differences in these relationships in different seasons. In spring, pollen concentrations were mainly affected by temperature; in summer, they were mainly affected by the direction of the maximum wind speed; in autumn, they were mainly affected by relative humidity and temperature; and in winter, they were mainly affected by relative humidity and wind speed. Temperature and relative humidity promote plant growth and flowering. Notably, the variable wind direction in summer and the increased wind speed in winter and spring are conductive to pollen transmission. (4) Of the 15 major pollen types, Moraceae, Artemisia and Gramineae are the main allergenic pollen types, with peaks in concentration during April-May, August-September, and October-December, respectively. (5) Atypical weather conditions have substantial effects on pollen dispersal. In South China, the pollen concentrations in the sunny day were usually significantly higher than that of the rainy day. The pollen concentrations increased in short rainy days, which usually came from the Herb and Fern pollen. The pollen concentrations decreased in continuous rainy days especially for the Tree and Shrub pollen. the pollen concentrations in the sunny days were usually significantly higher than that in the rainy days. The pollen concentrations increased in short and strong rainfall.
Knowing how people perceive multiple risks is essential to the management and promotion of public health and safety. Here we present a dataset based on a survey (N?=?4,154) of public risk perception in Italy and Sweden during the COVID-19 pandemic. Both countries were heavily affected by the first wave of infections in Spring 2020, but their governmental responses were very different. As such, the dataset offers unique opportunities to investigate the role of governmental responses in shaping public risk perception. In addition to epidemics, the survey considered indirect effects of COVID-19 (domestic violence, economic crises), as well as global (climate change) and local (wildfires, floods, droughts, earthquakes, terror attacks) threats. The survey examines perceived likelihoods and impacts, individual and authorities’ preparedness and knowledge, and socio-demographic indicators. Hence, the resulting dataset has the potential to enable a plethora of analyses on social, cultural and institutional factors influencing the way in which people perceive risk.
The coronavirus disease 2019 (COVID-19) pandemic is the most severe global health and socioeconomic crisis of our time, and represents the greatest challenge faced by the world since the end of the Second World War. The academic literature indicates that climatic features, specifically temperature and absolute humidity, are very important factors affecting infectious pulmonary disease epidemics – such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS); however, the influence of climatic parameters on COVID-19 remains extremely controversial. The goal of this study is to individuate relationships between several climate parameters (temperature, relative humidity, accumulated precipitation, solar radiation, evaporation, and wind direction and intensity), local morphological parameters, and new daily positive swabs for COVID-19, which represents the only parameter that can be statistically used to quantify the pandemic. The daily deaths parameter was not considered, because it is not reliable, due to frequent administrative errors. Daily data on meteorological conditions and new cases of COVID-19 were collected for the Lombardy Region (Northern Italy) from 1 March, 2020 to 20 April, 2020. This region exhibited the largest rate of official deaths in the world, with a value of approximately 1700 per million on 30 June 2020. Moreover, the apparent lethality was approximately 17% in this area, mainly due to the considerable housing density and the extensive presence of industrial and craft areas. Both the Mann-Kendall test and multivariate statistical analysis showed that none of the considered climatic variables exhibited statistically significant relationships with the epidemiological evolution of COVID-19, at least during spring months in temperate subcontinental climate areas, with the exception of solar radiation, which was directly related and showed an otherwise low explained variability of approximately 20%. Furthermore, the average temperatures of two highly representative meteorological stations of Molise and Lucania (Southern Italy), the most weakly affected by the pandemic, were approximately 1.5 °C lower than those in Bergamo and Brescia (Lombardy), again confirming that a significant relationship between the increase in temperature and decrease in virulence from COVID-19 is not evident, at least in Italy.
BACKGROUND AND AIMS: As, the COVID-19 has been deemed a pandemic by World Health Organization (WHO), and since it spreads everywhere throughout the world, investigation in relation to this disease is very much essential. Investigation of pattern in the occurrence of COVID-19, to check the influence of different meteorological factors on the incidence of COVID-19 and prediction of incidence of COVID-19 are the objectives of this paper. METHODS: For trend analysis, Sen’s Slope and Man-Kendall test have been used, Generalized Additive Model (GAM) of regression has been used to check the influence of different meteorological factors on the incidence and to predict the frequency of COVID-19, and Verhulst (Logistic) Population Model has been used. RESULTS: Statistically significant linear trend found for the daily-confirmed cases of COVID-19. The regression analysis indicates that there is some influence of the interaction of average temperature (AT) and average relative humidity (ARH) on the incidence of COVID-19. However, this result is not consistent throughout the study area. The projections have been made up to 21st May, 2020. CONCLUSIONS: Trend and regression analysis give an idea of the incidence of COVID-19 in India while projection made by Verhulst (Logistic) Population Model for the confirmed cases of the study area are encouraging as the sample prediction is as same as the actual number of confirmed COVID-19 cases.
A probe of a patient, seeking help in an emergency ward of a French hospital in late December 2019 because of Influenza like symptoms, was retrospectively tested positive to COVID-19. Despite the early appearance of the virus in Europe, the prevalence and virulence appeared to be low for several weeks, before the spread and severity of symptoms increased exponentially, yet with marked spatial and temporal differences. Here, we compare the possible linkages between peaks of fine particulate matter (PM2.5) and the sudden, explosive increase of hospitalizations and mortality rates in the Swiss Canton of Ticino, and the Greater Paris and London regions. We argue that these peaks of fine particulate matter are primarily occurring during thermal inversion of the boundary layer of the atmosphere. We also discuss the influence of Saharan dust intrusions on the COVID-19 outbreak observed in early 2020 on the Canary Islands. We deem it both reasonable and plausible that high PM2.5 concentrations-favored by air temperature inversions or Saharan dust intrusions-are not only modulating but even more so boosting severe outbreaks of COVID-19. Moreover, desert dust events-besides enhancing PM2.5 concentrations-can be a vector for fungal diseases, thereby exacerbating COVID-19 morbidity and mortality. We conclude that the overburdening of the health services and hospitals as well as the high over-mortality observed in various regions of Europe in spring 2020 may be linked to peaks of PM2.5 and likely particular weather situations that have favored the spread and enhanced the virulence of the virus. In the future, we recommended to monitor not only the prevalence of the virus, but also to consider the occurrence of weather situations that can lead to sudden, very explosive COVID-19 outbreaks.
It is often difficult to define the relationship and the influence of climate on the occurrence and distribution of disease. To examine this issue, the effects of climate indices on the distributions of malaria and meningitis in Nigeria were assessed over space and time. The main purpose of the study was to evaluate the relationships between climatic variables and the prevalence of malaria and meningitis, and develop an early warning system for predicting the prevalence of malaria and meningitis as the climate varies. An early warning system was developed to predetermine the months in a year that people are vulnerable to malaria and meningitis. The results revealed a significant positive relationship between rainfall and malaria, especially during the wet season with correlation coefficient R-2 >= 60.0 in almost all the ecological zones. In the Sahel, Sudan and Guinea, there appears to be a strong relationship between temperature and meningitis with R-2 > 60.0. In all, the results further reveal that temperatures and aerosols have a strong relationship with meningitis. The assessment of these initial data seems to support the finding that the occurrence of meningitis is higher in the northern region, especially the Sahel and Sudan. In contrast, malaria occurrence is higher in the southern part of the study area. We suggest that a thorough investigation of climate parameters is critical for the reallocation of clinical resources and infrastructures in economically underprivileged regions.
SARS CoV-2 (COVID-19) Coronavirus cases are confirmed throughout the world and millions of people are being put into quarantine. A better understanding of the effective parameters in infection spreading can bring about a logical measurement toward COVID-19. The effect of climatic factors on spreading of COVID-19 can play an important role in the new Coronavirus outbreak. In this study, the main parameters, including the number of infected people with COVID-19, population density, intra-provincial movement, and infection days to end of the study period, average temperature, average precipitation, humidity, wind speed, and average solar radiation investigated to understand how can these parameters effects on COVID-19 spreading in Iran? The Partial correlation coefficient (PCC) and Sobol’-Jansen methods are used for analyzing the effect and correlation of variables with the COVID-19 spreading rate. The result of sensitivity analysis shows that the population density, intra-provincial movement have a direct relationship with the infection outbreak. Conversely, areas with low values of wind speed, humidity, and solar radiation exposure to a high rate of infection that support the virus’s survival. The provinces such as Tehran, Mazandaran, Alborz, Gilan, and Qom are more susceptible to infection because of high population density, intra-provincial movements and high humidity rate in comparison with Southern provinces.
The transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the severity of the related disease (COVID-19) are influenced by a large number of factors. This study aimed to investigate the correlation of COVID-19 case and death rates with possible causal climatological and sociodemographic factors for the March to May 2020 (first wave) period in a worldwide scale by statistically processing data for over one hundred countries. The weather parameters considered herein were air temperature, relative humidity, cumulative precipitation, and cloud cover, while sociodemographic factors included population density, median age, and government measures in response to the pandemic. The results of this study indicate that there is a statistically significant correlation between average atmospheric temperature and the COVID-19 case and death rates, with chi-square test p-values in the 0.001-0.02 range. Regarding sociodemographic factors, there is an even stronger dependence of the case and death rates on the population median age (p = 0.0006-0.0012). Multivariate linear regression analysis using Lasso and the forward stepwise approach revealed that the median age ranks first in importance among the examined variables, followed by the temperature and the delays in taking first governmental measures or issuing stay-at-home orders.
OBJECTIVE: The weather-related conditions change the ecosystem and pose a threat to social, economic and environmental development. It creates unprecedented or unanticipated human health problems in various places or times of the year. Africa is the world’s second largest and most populous continent and has relatively changeable weather conditions. The present study aims to investigate the impact of weather conditions, heat and humidity on the incidence and mortality of COVID-19 pandemic in various regions of Africa. MATERIALS AND METHODS: In this study, 16 highly populated countries from North, South, East, West, and Central African regions were selected. The data on COVID-19 pandemic including daily new cases and new deaths were recorded from World Health Organization. The daily temperature and humidity figures were obtained from the weather web “Time and Date”. The daily cases, deaths, temperature and humidity were recorded from the date of appearance of first case of “Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)” in the African region, from Feb 14 to August 2, 2020. RESULTS: In African countries, the daily basis mean temperature from Feb 14, 2020 to August 2, 2020 was 26.16±0.12°C, and humidity was 57.41±0.38%. The overall results revealed a significant inverse correlation between humidity and the number of cases (r= -0.192, p<0.001) and deaths (r= -0.213, p<0.001). Similarly, a significant inverse correlation was found between temperature and the number of cases (r= -0.25, p<0.001) and deaths (r=-0.18, p<0.001). Furthermore, the regression results showed that with 1% increase in humidity the number of cases and deaths was significantly reduced by 3.6% and 3.7% respectively. Congruently, with 1°C increase in temperature, the number of cases and deaths was also significantly reduced by 15.1% and 10.5%, respectively. CONCLUSIONS: Increase in relative humidity and temperature was associated with a decrease in the number of daily cases and deaths due to COVID-19 pandemic in various African countries. The study findings on weather events and COVID-19 pandemic have an impact at African regional levels to project the incidence and mortality trends with regional weather events which will enhance public health readiness and assist in planning to fight against this pandemic.
The ongoing pandemic of 2019 novel coronavirus disease (COVID-19) is challenging global public health response system. We aim to identify the risk factors for the transmission of COVID-19 using data on mainland China. We estimated attack rate (AR) at county level. Logistic regression was used to explore the role of transportation in the nationwide spread. Generalized additive model and stratified linear mixed-effects model were developed to identify the effects of multiple meteorological factors on local transmission. The ARs in affected counties ranged from 0.6 to 9750.4 per million persons, with a median of 8.8. The counties being intersected by railways, freeways, national highways or having airports had significantly higher risk for COVID-19 with adjusted odds ratios (ORs) of 1.40 (p = 0.001), 2.07 (p < 0.001), 1.31 (p = 0.04), and 1.70 (p < 0.001), respectively. The higher AR of COVID-19 was significantly associated with lower average temperature, moderate cumulative precipitation and higher wind speed. Significant pairwise interactions were found among above three meteorological factors with higher risk of COVID-19 under low temperature and moderate precipitation. Warm areas can also be in higher risk of the disease with the increasing wind speed. In conclusion, transportation and meteorological factors may play important roles in the transmission of COVID-19 in mainland China, and could be integrated in consideration by public health alarm systems to better prevent the disease.
Coronavirus Disease 2019 (COVID-19) pandemic poses extreme threat to public health and economy, particularly to the nations with higher population density. The disease first reported in Wuhan, China; later, it spreads elsewhere, and currently, India emerged as COVID-19 hotspot. In India, we selected 20 densely populated cities having infection counts higher than 500 (by 15 May) as COVID-19 epicenters. Daily COVID-19 count has strong covariability with local temperature, which accounts approximately 65-85% of the explained variance; i.e., its spread depends strongly on local temperature rise prior to community transmission phase. The COVID-19 cases are clustered at temperature and humidity ranging within 27-32°C and 25-45%, respectively. We introduce a combined temperature and humidity profile, which favors rapid COVID-19 growth at the initial phase. The results are highly significant for predicting future COVID-19 outbreaks and modeling cities based on environmental conditions. On the other hand, CO(2) emission is alarmingly high in South Asia (India) and entails high risk of climate change and extreme hot summer. Zoonotic viruses are sensitive to warming induced climate change; COVID-19 epicenters are collocated on CO(2) emission hotspots. The COVID-19 count distribution peaks at 31.0°C, which is 1.0°C higher than current (2020) and historical (1961-1990) mean, value. Approximately, 72% of the COVID-19 cases are clustered at severe to record-breaking hot extremes of historical temperature distribution spectrum. Therefore, extreme climate change has important role in the spread of COVID-19 pandemic. Hence, a strenuous mitigation measure to abate greenhouse gas (GHG) emission is essential to avoid such pandemics in future.
The purpose of the present study is to explore the associations between novel coronavirus disease 2019 (COVID-19) case counts and meteorological factors in 30 provincial capital cities of China. We compiled a daily dataset including confirmed case counts, ambient temperature (AT), diurnal temperature range (DTR), absolute humidity (AH) and migration scale index (MSI) for each city during the period of January 20th to March 2nd, 2020. First, we explored the associations between COVID-19 confirmed case counts, meteorological factors, and MSI using non-linear regression. Then, we conducted a two-stage analysis for 17 cities with more than 50 confirmed cases. In the first stage, generalized linear models with negative binomial distribution were fitted to estimate city-specific effects of meteorological factors on confirmed case counts. In the second stage, the meta-analysis was conducted to estimate the pooled effects. Our results showed that among 13 cities that have less than 50 confirmed cases, 9 cities locate in the Northern China with average AT below 0 °C, 12 cities had average AH below 4 g/m(3), and one city (Haikou) had the highest AH (14.05 g/m(3)). Those 17 cities with 50 and more cases accounted for 90.6% of all cases in our study. Each 1 °C increase in AT and DTR was related to the decline of daily confirmed case counts, and the corresponding pooled RRs were 0.80 (95% CI: 0.75, 0.85) and 0.90 (95% CI: 0.86, 0.95), respectively. For AH, the association with COVID-19 case counts were statistically significant in lag 07 and lag 014. In addition, we found the all these associations increased with accumulated time duration up to 14 days. In conclusions, meteorological factors play an independent role in the COVID-19 transmission after controlling population migration. Local weather condition with low temperature, mild diurnal temperature range and low humidity likely favor the transmission.
The need for healthcare workers (HCWs) to wear personal protective equipment (PPE) during the coronavirus disease 2019 (COVID-19) pandemic heightens their risk of thermal stress. We assessed the knowledge, attitudes, and practices of HCWs from India and Singapore regarding PPE usage and heat stress when performing treatment and care activities. One hundred sixty-five HCWs from India (n = 110) and Singapore (n = 55) participated in a survey. Thirty-seven HCWs from Singapore provided thermal comfort ratings before and after ice slurry ingestion. Differences in responses between India and Singapore HCWs were compared. A p-value cut-off of 0.05 depicted statistical significance. Median wet-bulb globe temperature was higher in India (30.2 °C (interquartile range [IQR] 29.1-31.8 °C)) than in Singapore (22.0 °C (IQR 18.8-24.8 °C)) (p < 0.001). Respondents from both countries reported thirst (n = 144, 87%), excessive sweating (n = 145, 88%), exhaustion (n = 128, 78%), and desire to go to comfort zones (n = 136, 84%). In Singapore, reports of air-conditioning at worksites (n = 34, 62%), dedicated rest area availability (n = 55, 100%), and PPE removal during breaks (n = 54, 98.2%) were higher than in India (n = 27, 25%; n = 46, 42%; and n = 66, 60%, respectively) (p < 0.001). Median thermal comfort rating improved from 2 (IQR 1-2) to 0 (IQR 0-1) after ice slurry ingestion in Singapore (p < 0.001). HCWs are cognizant of the effects of heat stress but might not adopt best practices due to various constraints. Thermal stress management is better in Singapore than in India. Ice slurry ingestion is shown to be practical and effective in promoting thermal comfort. Adverse effects of heat stress on productivity and judgment of HCWs warrant further investigation.
The 2020 Atlantic hurricane season was extremely active and included, as of early November, six hurricanes that made landfall in the United States during the global coronavirus disease 2019 (COVID-19) pandemic. Such an event would necessitate a large-scale evacuation, with implications for the trajectory of the pandemic. Here we model how a hypothetical hurricane evacuation from four counties in southeast Florida would affect COVID-19 case levels. We find that hurricane evacuation increases the total number of COVID-19 cases in both origin and destination locations; however, if transmission rates in destination counties can be kept from rising during evacuation, excess evacuation-induced case numbers can be minimized by directing evacuees to counties experiencing lower COVID-19 transmission rates. Ultimately, the number of excess COVID-19 cases produced by the evacuation depends on the ability of destination counties to meet evacuee needs while minimizing virus exposure through public health directives. These results are relevant to disease transmission during evacuations stemming from additional climate-related hazards such as wildfires and floods.
We investigated the geographical character of the COVID-19 infection in China and correlated it with satellite- and ground-based measurements of air quality. Controlling for population density, we found more viral infections in those prefectures (U.S. county equivalent) afflicted by high Carbon Monoxide, Formaldehyde, PM 2.5, and Nitrogen Dioxide values. Higher mortality was also correlated with relatively poor air quality. When summarizing the results at a greater administrative level, we found that the 10 provinces (U.S. state equivalent) with the highest rate of mortality by COVID-19, were often the most polluted but not the most densely populated. Air pollution appears to be a risk factor for the incidence of this disease, despite the conventionally apprehended influence of human mobility on disease dynamics from the site of first appearance, Wuhan. The raw correlations reported here should be interpreted in a broader context, accounting for the growing evidence reported by several other studies. These findings warn communities and policymakers on the implications of long-term air pollution exposure as an ecological, multi-scale public health issue.
INTRODUCTION: Environmental factors such as wind, temperature, humidity, and sun exposure are known to affect influenza and viruses such as severe acute respiratory syndrome (SARS) and Middle East Respiratory Syndrome (MERS) transmissions. COVID-19 is a new pandemic with very little information available about its transmission and association with environmental factors. The goal of this paper is to explore the association of environmental factors on daily incidence rate, mortality rate, and recoveries of COVID-19. METHODS: The environmental data for humidity, temperature, wind, and sun exposure were recorded from metrological websites and COVID-19 data such as the daily incidence rate, death rate, and daily recovery were extracted from the government’s official website available to the general public. The analysis for each outcome was adjusted for factors such as lock down status, nationwide events, and the number of daily tests performed. Analysis was completed with negative binominal regression log link using generalised linear modelling. RESULTS: Daily temperature, sun exposure, wind, and humidity were not significantly associated with daily incidence rate. Temperature and nationwide social gatherings, although non-significant, showed trends towards a higher chance of incidence. An increase in the number of daily testing was significantly associated with higher COVID-19 incidences (effect size ranged from 2.17-9.96). No factors were significantly associated with daily death rates. Except for the province of Balochistan, a lower daily temperature was associated with a significantly higher daily recovery rate. DISCUSSION: Environmental factors such as temperature, humidity, wind, and daily sun exposure were not consistently associated with COVID-19 incidence, death rates, or recovery. More policing about precautionary measures and ensuring diagnostic testing and accuracy are needed.
