Frequent natural disasters like floods pose a major threat to India, with significant implications for public health. Low birth weight (LBW) is a critical global health concern, contributing to neonatal mortality. However, the association between floods and LBW remains underexplored. This study aims to address this gap by investigating the association between flood hazards and LBW in India using a geospatial approach. By analyzing data from the National Family Health Survey (NFHS-5) and flood zonation maps, the study aims to uncover the spatial dynamics of this association, offering insights into the implications of floods on birth weight across diverse geographical regions. The study used the fifth round of NFHS data, 2019-21, which involved 202,194 children selected through a multi-stage stratified sampling technique. The Vulnerability Atlas of India 2019 maps were also utilized to classify areas as flood or non-flood zones. Birth weight data from the NFHS-5 were categorized into three groups: very low, low, and normal birth weight (VLBW, LBW and NBW). Control variables including flood exposure, socio-demographic attributes, and geographic region were considered. Bivariate analysis and multinomial logistic regression were employed for statistical analysis. The spatial analysis involved Moran’s I statistics and Geographically Weighted Regression to explore spatial dynamics of the association between floods and birth weight in India. RESULTS: Floods predominantly affect India’s lower Himalayan belts and western coastal regions. Flood-affected areas show higher proportions of VLBW and LBW infants. Groundwater usage and unimproved sanitation are associated with higher risk of VLBW and LBW. Sex, wealth, maternal education, residence type, and geographic region significantly influence birth weights. Multinomial logistic regression reveals 8 % and 27 % higher risks for LBW and VLBW in flood-affected regions. LISA cluster maps identify high-risk areas for both LBW and floods. Geographically Weighted Regression highlights 52 % of the variability in LBW occurrences can be attributed to the influence of flood hazards. Families hailing from the poorest wealth background and exposed to flood hazards bear a 5 % heightened likelihood of delivering LBW infants, in stark contrast to their counterparts from the same economic background yet unaffected by floods. The significant association between floods and LBW underscores the importance of robust disaster preparedness and public health strategies. By unraveling the spatial intricacies of flood-induced LBW disparities, this research provides valuable insights for promoting healthier birth outcomes and reducing child mortality rates, particularly in flood-prone regions. These findings emphasize the importance of holistic policies that address both environmental challenges and socioeconomic inequalities to safeguard maternal and infant health across the nation.
Visceral leishmaniasis or Kala-azar (KA) is a Vector-Borne Disease (VBD) that remains the second-largest parasitic killer across the globe (mortality rate: 75-95%). More than 60% of KA cases originate in South Asia, wherein India accounts for 2/3rd of the cases, and Bihar, a state in India, alone accounts for more than 50% of the Indian cases. Past studies suspected climate change vulnerabilities as a driving cause of KA outbreaks. The VBDs-based epidemic prediction systems have been developed to mitigate recurrent outbreaks; however, Machine Learning (ML) based approaches still need to be explored for modeling changing climate impacts on KA cases. This study, for the first time, develops a Radial Basis Function (RBF) kernel-based Support Vector Regression (SVR), hereinafter RBF-kernel-based-SVR model for the most-affected endemic districts of Bihar (northern-India), using the data from 2016 and 2021. Forward selection, backward elimination, and stepwise regression procedures were adopted while selecting influential climatic variables, followed by the k-fold cross-validation technique and, then, the RBF-kernel-based-SVR algorithm for classification. Results suggested that temperature, wind speed, rainfall, and population density significantly contributed to the KA outbreaks. This study also developed Multiple Linear Regression (MLR) and Multilayer Perceptron (MLP) models to compare SVR with other classification models. Findings indicated that the proposed RBF-kernel-based-SVR model [Correlation Coefficient (CC) = 0.82, Root-Mean-Square Error (RMSE) = 12.20, and Nash-Sutcliffe Efficiency (NSE) = 0.66] outperformed MLR (0.81, 14.20, 0.48) and MLP (0.81, 12.95, 0.61). Study recommends using the RBF-kernel-based-SVR model as a quick and efficient model capable of detecting KA cases with high predictability even under limited data availability. Such models can assist public health authorities, given monitoring KA spread, learning the climate impacts of outbreaks, and ensuring timelier health services.
India is at a high risk of heat stress-induced health impacts and economic losses owing to its tropical climate, high population density, and inadequate adaptive planning. The health impacts of heat stress across climate zones in India have not been adequately explored. Here, we examine and report the vulnerability to heat stress in India using 42 years (1979-2020) of meteorological data from ERA-5 and developed climate-zone-specific percentile-based human comfort class thresholds. We found that the heat stress is usually 1-4 °C higher on heatwave (HW) days than on nonheatwave (NHW) days. However, the stress on NHW days remains considerable and cannot be neglected. We then showed the association of a newly formulated India heat index (IHI) with daily all-cause mortality in three cities – Delhi (semiarid), Varanasi (humid subtropical), and Chennai (tropical wet and dry), using a semiparametric quasi-Poisson regression model, adjusted for nonlinear confounding effects of time and PM(2.5). The all-cause mortality risk was enhanced by 8.1% (95% confidence interval, CI: 6.0-10.3), 5.9% (4.6-7.2), and 8.0% (1.7-14.2) during “sweltering” days in Varanasi, Delhi, and Chennai, respectively, relative to “comfortable” days. Across four age groups, the impact was more severe in Varanasi (ranging from a 3.2 to 7.5% increase in mortality risk for a unit rise in IHI) than in Delhi (2.6-4.2% higher risk) and Chennai (0.9-5.7% higher risk). We observed a 3-6 days lag effect of heat stress on mortality in these cities. Our results reveal heterogeneity in heat stress impact across diverse climate zones in India and call for developing an early warning system keeping in mind these regional variations.
Exposure to heat is associated with a substantial burden of disease and is an emerging issue in the context of climate change. Heat is of particular concern in India, which is one of the world’s hottest countries and also most populous, where relatively little is known about personal heat exposure, particularly in rural areas. Here, we leverage data collected as part of a randomized controlled trial to describe personal temperature exposures of adult women (40-79 years of age) in rural Tamil Nadu. We also characterize measurement error in heat exposure assessment by comparing personal exposure measurements to the nearest ambient monitoring stations and to commonly used modeled temperature data products. We find that temperatures differ across individuals in the same area on the same day, sometimes by more than 5 °C within the same hour, and that some individuals experience sharp increases in heat exposure in the early morning or evening, potentially a result of cooking with solid fuels. We find somewhat stronger correlations between the personal exposure measurements and the modeled products than with ambient monitors. We did not find evidence of systematic biases, which indicates that adjusting for discrepancies between different exposure measurement methods is not straightforward.
Dengue is a rapidly spreading viral disease transmitted to humans by Aedes mosquitoes. Due to global urbanization and climate change, the number of dengue cases are gradually increasing in recent decades. Hence, an early prediction of dengue continues to be a major concern for public health in countries with high prevalence of dengue. Creating a robust forecast model for the accurate prediction of dengue is a complex task and can be done through various data modelling approaches. In the present study, we have applied vector auto regression, generalized boosted models, support vector regression, and long short-term memory (LSTM) to predict the dengue prevalence in Kerala state of the Indian subcontinent. We consider the number of dengue cases as the target variable and weather variables viz., relative humidity, soil moisture, mean temperature, precipitation, and NINO3.4 as independent variables. Various analytical models have been applied on both datasets and predicted the dengue cases. Among all the models, the LSTM model was outperformed with superior prediction capability (RMSE: 0.345 and R(2):0.86) than the other models. However, other models are able to capture the trend of dengue cases but failed in predicting the outbreak periods when compared to LSTM. The findings of this study will be helpful for public health agencies and policymakers to draw appropriate control measures before the onset of dengue. The proposed LSTM model for dengue prediction can be followed by other states of India as well.
Warmer global climate and urban heat islands (UHIs) interact, by exacerbating heatwaves and increasing the extreme heat days in cities. The implications of added heat stress in urban environments due to intensifying surface UHIs (SUHIs) is of utmost concern. Seasonal, annual and decadal nighttime SUHI intensities (SUHIIs), from 2001 to 2020, for nine major populated cities of India are analyzed. This includes five megacities- Delhi, Mumbai, Kolkata, Bangalore, and Chennai, and four incipient megacities- Hyderabad, Ahmedabad, Surat, and Pune. The key role of increasing urbanization (pre- and post-2010) in expansion and intensification of nighttime SUHIs in India is highlighted. For all cities either pre-monsoon (MAM) or winter (December-February; DJF) seasons show the strongest SUHII development. During the 2001-2010, and the 2011-2020 decade, a nighttime SUHII maxima of respectively (i) 2.1 degrees C and 2.5 degrees C for Delhi, (ii) 1.3 degrees C and 1.5 degrees C for Mumbai, (iii) 1.3 degrees C and 1.5 degrees C for Kolkata, (iv) 0.6 degrees C and 1.0 degrees C Bangalore, (v) 1.7 degrees C and 1.9 degrees C for Chennai, (vi) 1.8 degrees C and 2.3 degrees C for Hyderabad, (vii) 2.8 degrees C and 3.1 degrees C for Ahmedabad, (viii) 1.9 degrees C and 2.4 degrees C for Surat, and (ix) 0.8 degrees C and 1.3 degrees C for Pune is noted. Further, all incipient megacities showed a mean annual growth rate of nighttime SUHII of over 0.007 degrees C/year, substantially greater than in the megacities. High SUHII magnitudes, greater growth rates of SUHII, and huge populations, severely compounds the vulnerability of Indian cities to excessive heat exposure risk, especially during MAM heatwaves. Lastly, the implications of nighttime SUHII findings from the present study, on the increase in heat stress, the loss of labor productivity and the rise in heat-related mortality rate is emphasized. The study recommends implementation of city-specific action plans to mitigate the heat stressed urban environment. Targeted use of cooling strategies in localized hotspots within the urban areas where high intensity SUHIs are likely to form is also suggested.
The northeast region of India is highlighted as the most vulnerable region for malaria. This study attempts to explore the epidemiological profile and quantify the climate-induced influence on malaria cases in the context of tropical states, taking Meghalaya and Tripura as study areas. Monthly malaria cases and meteorological data from 2011 to 2018 and 2013 to 2019 were collected from the states of Meghalaya and Tripura, respectively. The nonlinear associations between individual and synergistic effect of meteorological factors and malaria cases were assessed, and climate-based malaria prediction models were developed using the generalized additive model (GAM) with Gaussian distribution. During the study period, a total of 216,943 and 125,926 cases were recorded in Meghalaya and Tripura, respectively, and majority of the cases occurred due to the infection of Plasmodium falciparum in both the states. The temperature and relative humidity in Meghalaya and temperature, rainfall, relative humidity, and soil moisture in Tripura showed a significant nonlinear effect on malaria; moreover, the synergistic effects of temperature and relative humidity (SI=2.37, RERI=0.58, AP=0.29) and temperature and rainfall (SI=6.09, RERI=2.25, AP=0.61) were found to be the key determinants of malaria transmission in Meghalaya and Tripura, respectively. The developed climate-based malaria prediction models are able to predict the malaria cases accurately in both Meghalaya (RMSE: 0.0889; R(2): 0.944) and Tripura (RMSE: 0.0451; R(2): 0.884). The study found that not only the individual climatic factors can significantly increase the risk of malaria transmission but also the synergistic effects of climatic factors can drive the malaria transmission multifold. This reminds the policymakers to pay attention to the control of malaria in situations with high temperature and relative humidity and high temperature and rainfall in Meghalaya and Tripura, respectively.
The current study on spatiotemporal variability of temperature presents a holistic approach for quantifying the joint space-time variability of extreme temperature indices over the physio-climatically heterogeneous Tapi River basin (TRB) using two unsupervised machine learning algorithms, i.e., principal component analysis (PCA) and cluster analysis. The long-term variability in extreme temperature indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), was evaluated for 1951-2016. The magnitude and statistical significance of the temporal trend in extreme temperature indices were estimated using non-parametric Sen’s slope estimator and modified Mann Kendall (MMK) tests, respectively. The multivariate assessment of temporal trends using PCA resulted in four principal components (PCs) encapsulating more than 90% variability. The cluster analysis of corresponding PCs resulted in two spatial clusters exhibiting homogeneous spatiotemporal variability. Cluster 1 is characterized by significantly increasing hottest, very hot, and extremely hot days with rising average maximum temperature and intraday temperature variability. On the other hand, cluster 2 showed significantly rising coldest nights, mean minimum, mean temperature, and Tx37 with significantly decreasing intraday and interannual temperature variability, very cold, and extremely cold nights with reducing cold spell durations. The summertime heat stress computation revealed that the Purna sub-catchment of the Tapi basin is more vulnerable to various health issues and decreased work performance (> 10%) for more than 45 days per year. The current study dealing with the associated effects of rising temperature variability on crop yield, human health, and work performance would help policymakers formulate better planning and management strategies to safeguard society and the environment.
Floods have claimed the lives of countless people and caused significant property damage in many countries, putting their livelihoods in the jeopardy. The Vembanad lake system (VLS) in Kerala, India, has faced adverse mishappening during 2018, 2019, and 2021 floods in the state due to torrential rainfall. The goal of this research is to construct effective decision tree-based machine learning models such as adaptive boosting (AdaBoost), random forest (RF), gradient boosting machines (GBMs), and extreme gradient boosting (XGBoost) for integrating data, processing, and generating flood susceptibility maps. There are 18 conditioning parameters considered, which include seven categories and 11 numerical data. These seven categorical data were converted to numerical data, bringing the total amount of input data to 61. The recursive feature elimination (RFE) was utilized as the feature selection technique, and a total of 22 layers were chosen to feed into the machine learning models to generate the flood susceptibility maps. The efficiencies of the models were evaluated using receiver operating characteristic (ROC)-area under the ROC curve (AUC), F1 score, accuracy, and kappa. According to the results, the performance of all four models demonstrated their practical application; however, XGBoost fared well in terms of the model’s metrics. For the testing data set, the ROC-AUC values of XGBoost, GBM, and AdaBoost are 0.90, whereas it was 0.89 for RF. The accuracy varied significantly among the four models, with XGBoost scoring 0.92, followed by GBM (0.88), RF (0.87), and AdaBoost (0.87). As a result, this map may be utilized for early mitigation actions during future floods, as well as for land-use planners and emergency managers, assisting in the reduction of flood risk in regions prone to this hazard.
The majority of malaria cases in Southeast Asia occur in India. It is a major public health problem in India, which accounts for substantial morbidity, mortality, and economic loss. The spatial distribution of malaria widely varies due to geo-ecological diversity, multi-ethnicity, and wide distribution of the different anopheline vectors. The predominant malaria parasites in India for malaria are P. Falciparum (Pf) and P. Vivax (Pv). This study analyzes the spatial patterns of malaria cases, specifically the two dominant malaria vectors, at the regional level and its relation to seasonal precipitation. The results of our study revealed an overall decline in malaria cases in the later years. The spatial spread of malaria cases was more widespread during the normal monsoon years vs drought years, which can be attributed to more conducive environment for mosquitos to breed. The correlation analysis revealed a stronger correlation between malaria case burden and monsoon precipitation. Spatially, the strongest correlation between seasonal and annual precipitation, and malaria case burden were located across the northern plains and northeast India. The results of this research further our understanding of the relationship between seasonal precipitation and malaria case burden at the regional level across India.
Abundant literature is available on an extremely high temperature associated with mortality for cities of the developed world, but there is a dearth in the literature for coastal, desert and dry cities of the developing world, especially for India. We examined all-cause mortality and extreme high temperature in three Indian cities representing coastal, desert and dry areas for summer months (March to June) from 2006 to 2015. We obtained the data on temperature and all-cause mortality for ten years for the summer months. The city-specific effect of ambient heat on all-cause mortality was assessed through time series ordinary least square linear regression model. A total of 75,571, 122,117 and 53,042 deaths for 1,203, 1,220 and 1,180 summer days from 2006 to 2015 were analysed with ambient temperature for Jaipur, Hyderabad and Surat, respectively. There were 994 (27.6%) out of 3,603 summer days having temperature & GE;40 & DEG;C and 2,495 (69.3%) out of 3,602 summer days having feel temperature/heat index (HI) of & GE;41 & DEG;C. According to the Indian Meteorological Department (IMD) criteria for the heatwave, Surat has the maximum number of 75 days with a maximum temperature of & GE;40 & DEG;C, whereas Hyderabad has only 4 days and Jaipur faced 35 days with a maximum temperature of & GE;45 & DEG;C during the study period. The per-day mean all-cause mortality increased to 39% and 11% for Jaipur and Hyderabad, respectively, at & GE;45 & DEG;C and 20% for the coastal city of Surat at & GE;40 & DEG;C as per IMD heatwave criteria. A time-series linear regression model shows that adjusted R-squared is 0.593, 0.629 and 0.348, which explained the variation of 59.3%, 62.9% and 34.8% for all-cause mortality (dependent variable) by independent variables (maximum temperature, humidity and HI) for Jaipur, Hyderabad and Surat, respectively. The maximum temperature threshold (cut-off) for all-cause mortality for Jaipur, Hyderabad and Surat is 42 & DEG;C, 41 & DEG;C and 40 & DEG;C, respectively. The impact of ambient heat in the rise of all-cause mortality for all study sites was evident. Hence, findings support the efforts for reducing the public health burden of high ambient temperature through developing and implementing city-specific heat action plans.
This paper attempts to assess the vulnerability to river bank erosion of human communities in different mouzas of selected blocks of Diara sub division of Malda District of India. A primary household survey has been done to collect data on socio demographic profile, livelihood strategy, health, food, water, social network, natural disaster and river bank erosion indicators which were selected for Livelihood Vulnerability Index (LVI) and Livelihood Vulnerability Index-Intergovernmental Panel on Climate Change (LVI-IPCC) analyses to predict and compare the vulnerability of mouzas currently suffering from frequent flooding, river bank erosion and embankment breaching on an annual basis. Secondary data are collected from the Human Development Report of Malda district; Regional Agriculture office and analyzed through relevant charts, diagrams and calculating index values. A GPS survey has been conducted to identify locations of affected mouzas due to river bank erosion. The results indicate that the study area has experienced rise in water level, higher amount of water discharge, riverbank line change, constant land loss, embankment breaching and changing land use, which have had impact on vulnerability, particularly of poorer riverine people. From the result of both LVI and LVI- IPCC, high to moderate vulnerability condition has existed within the selected mouzas. The high vulnerable mouzas are Dharampur, Manikchak, Kesarpur, Mirpur, Mathurapur, Jot Bhabani and relatively less vulnerable mouzas are Suksena, Duani Tafir and Paschim Narayanpur in respect to both indices. The poor conditions of LVI components of the selected mouzas in the study area make them more expose and sensitive and decrease their adaptive capacity. These findings enable policymakers to formulate and implement effective strategies and programs to reduce vulnerability and enhance resilience by improving the livelihoods of the vulnerable riverine community of all other parts in India as well as world.
Crimean Congo Hemorrhagic Fever (CCHF), is an emerging zoonosis globally and in India. The present study focused on identifying the risk factors for occurrence of CCHF in the Indian state of Gujarat and development of risk map for India. The past CCHF outbreaks in India were collated for the analyses. Influence of land use change and climatic factors in determining the occurrence of CCHF in Gujarat was assessed using Bayesian spatial models. Change in maximum temperature in affected districts was analysed to identify the significant change points over 110 years. Risk map was developed for Gujarat using Bayesian Additive Regression Trees (BART) model with remotely sensed environmental variables and host (livestock and human) factors. We found the change in land use patterns and maximum temperature in affected districts to be contributing to the occurrence of CCHF in Gujarat. Spatial risk map developed using CCHF occurrence data for Gujarat identified density of buffalo, minimum land surface temperature and elevation as risk determinants. Further, spatial risk map for the occurrence of CCHF in India was developed using selected variables. Overall, we found that combination of factors such as change in land-use patterns, maximum temperature, buffalo density, day time minimum land surface temperature and elevation led to the emergence and further spread of the disease in India. Mitigation measures for CCHF in India could be designed considering disease epidemiology and initiation of surveillance strategies based on the risk map developed in this study.
BACKGROUND: Cities are becoming increasingly important habitats for mosquito vectors of disease. The pronounced heterogeneity of urban landscapes challenges our understanding of the effects of climate and socioeconomic factors on mosquito-borne disease dynamics at different spatiotemporal scales. Here, we quantify the impact of climatic and socioeconomic factors on urban malaria risk, using an extensive dataset in both space and time for reported Plasmodium falciparum cases in the city of Surat, northwest India. METHODS: We analysed 10 years of monthly P falciparum cases resolved at three nested spatial resolutions (seven zones, 32 units, and 478 worker units) with a Bayesian hierarchical mixed model that incorporates the effects of population density, poverty, relative humidity, and temperature, in addition to random effects (structured and unstructured). To reduce dimensionality and avoid correlation of covariates, socioeconomic variables from survey data were summarised into main axes of variation using principal component analysis. With model selection, we identified the main drivers of spatiotemporal variation in malaria incidence rates at each of the three spatial resolutions. We also compared observations to model-fitted cases by quantifying the percentage of predictions within five discrete levels of malaria risk. FINDINGS: The spatial variation of urban malaria cases was stationary over time, whereby locations with high and low yearly cases remained largely consistent across years. Local socioeconomic variation could be summarised with three principal components accounting for approximately 80% of the variance. The model that incorporated local temperature and relative humidity together with two of these principal components, largely representing population density and poverty, best explained monthly malaria patterns in models formulated at the three different spatial scales. As model resolution increased, the effect size of humidity decreased, whereas those of temperature and the principal component associated with population density increased. Model predictions accurately captured aggregated total monthly cases for the city; in space-time, they more closely matched observations at the intermediate scale, with around 57% of units estimated to fall in the observed category on average across years. The mean absolute error was lower at the intermediate level, showing that this is the best aggregation level to predict the space-time dynamics of malaria incidence rates across the city with the selected model. INTERPRETATION: This statistical modelling framework provides a basis for development of a climate-driven early warning system for urban malaria for the units of Surat, including spatially explicit prediction of malaria risk several weeks to months in advance. Results indicate environmental and socioeconomic covariates for which further measurement at high resolution should lead to model improvement. Advanced warning combined with local surveillance and knowledge of disease hotspots within the city could inform targeted intervention as part of urban malaria elimination efforts. FUNDING: US National Institutes of Health.
Due to climate change, rapid warming and its further intensification over different parts of the globe have been recently reported. This has a direct impact on human health, agriculture, water availability, power generation, various ecosystems, and socioeconomic conditions of the exposed population. The current study thus investigates the frequency and duration of heatwaves, human discomfort, and exposure of the human population to these extremes using the high-resolution regional climate model experiments under two Representative Concentration Pathways (RCP2.6, RCP8.5) over India. We find that more than 90% of India will be exposed to uncomfortable warm nights by the end of the 21st century with the highest rise over western India, Madhya Pradesh (MP), Uttar Pradesh (UP), Punjab, and the Haryana region. States like Odisha, Chhattisgarh, eastern parts of MP and UP, and some parts of J&K will be the worst hit by the intense and frequent heatwaves and human discomfort followed by the densely populated Indo-Gangetic plains under RCP8.5. Strict enforcement of the stringent policies on stabilization of population growth, improvement of local adaptive capacities, and economic status of the vulnerable population along with enforcing effective measures to curb greenhouse gas emissions are important to reduce human exposure to future heat stress. We demonstrate that a proper mitigation-based development (RCP2.6) instead of a business-as-usual scenario (RCP8.5) may help to reduce 50-200 heatwave days, 3-10 heatwave spells, and 10-35% warm nights over the Indian region. Consequently, this can avoid the exposure of 135-143 million population to severe discomfort due to extreme heat conditions by the end of the 21st century.
As a widespread natural hazard, droughts impact several aspects of human society adversely. Thus, the present study aims to answer the following research questions; (i) What are the expected variabilities in different drought conditions over India in the future? (ii) How the population exposure to drought varies under different climate change and population scenarios? (iii) How is the total exposure attributed to the individual exposure (climate, population, and interaction) in future climate change scenarios? In this sense, the study is performed under four Shared Socioeconomic Pathways scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) using thirteen Global Climate Models from Coupled Model Intercomparison Project Phase 6 and Standardized Precipitation Evapotranspiration Index as a drought indicator. The future period is divided into two parts i.e., 2023-2061 (T1) and 2062-2100 (T2), and compared with the historical period during 1967-2005. The results show that the severe (56 % to 72 % of the area) and extreme (99 % of the area) droughts are likely to increase under all the scenarios for 3-month scale conditions, respectively. The drought intensity is projected to increase under 3-and 12-month scale drought conditions. The population exposure to the extreme drought severity is anticipated to increase for both the drought conditions and the highest exposure is noticed under the SSP3-7.0 scenario. The significant contribution from climate or interaction effects is observed in the case of 3- and 9-month scale extreme drought conditions. The present study necessitates a call for effective measures to alleviate the risk, especially in the high-risk areas of India.
There is scant information on early manifestation of trauma due to catastrophic natural events and its relation with stress-related disorders. The specific objective of this study was to estimate and compare the prevalence of post-traumatic stress and depression on day 3 (D3) and week 6 (W6) following the 2018 flood in Kerala, India. In a cross-sectional study, symptoms of post-traumatic stress and depression were studied at D3 using primary care Post-Traumatic Stress Disorder screen for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) (PC-PTSD-5), and then at W6 using PC-PTSD-5, Screening Questionnaire for Disaster Mental Health, PTSD Checklist for DSM-5 (PCL-5), and Becks Depression Inventory. Majority (70 percent) were screen positive at D3 (n = 20) compared with 30 percent at W6 (n = 50), with a decreased frequency of all symptoms. Being PC-PTSD-5 screen positive at W6 was significantly associated with the presence of threat to life, physical injury, and death of relatives or neighbors. According to PCL-5, at W6, 46 percent had possible PTSD. Except damage to property, other disaster related or sociodemographic variables were not associated with the risk of having PTSD. Positive predictive value of PC-PTSD-5 (D3) for PTSD (PCL-5) at W6 was 64.3 percent. Depression and possibility of PTSD were significantly associated. A considerable proportion of victims continued to have post-traumatic stress and depression although the -frequency decreased over time. A simple screening measure may help to identify victims with possible PTSD.
This study is an attempt to review the climate change phenomenon through the theoretical lens of “Political Ecology of Health.” It identifies the strategic implications of climate change policies on health and well-being in the Sundarbans region of India and other related factors which come into play in determining the health status there. It encapsulates the existing vulnerabilities observed in this eco-sensitive region and tries to reflect on the community’s perception of the climate change crises inflicted upon them. The paper presents a comprehensive review of current conditions in the region. In addition to utilizing electronic databases, the author also reached out to experts from pertinent organizations with related expertise to gather supplementary references. Adapting to climate change is crucial to cope with the changing surroundings and elevate competency. Since Sundarbans’ adaptation strategies are intricately dependent on historical positionality, community behavior, gender roles, social capital, and ecoregion sensitivity, the well-being and recovery in the community are very much context-specific. Thus, it must give space to discourses of newer politics of adaptation, emerging from a rigorous ecological standpoint. Looking into the matter through the lens of “situated knowledge, political economy and socio-ecological relationships” brought out important issues like land-ownership conflict, fading away of the traditional knowledge system, conspicuous utilization of funds and a poor public health system.
BACKGROUND: Natural disasters cause much hardship and suffering, loss of property, and increased morbidity and mortality amongst those affected. Timely and effective response for relief and rescue services go a long way in mitigating these consequences. MATERIAL AND METHODS: This population-based cross-sectional, descriptive study conducted in the immediate aftermath of the catastrophic flood that occurred in Kerala, South India, in 2018, documents the experiences of the victims, the community’s preparedness, and response to the disaster. RESULTS: Flood waters reached levels of over four feet within the premises of 55% of the houses and nearly 97% had water flooding inside their homes. More than 93% of the households were evacuated to safer locations and relief camps. The elderly and those with chronic illnesses were the worst sufferers, unable to access medical aid. Many families (62%) received help from neighbors. CONCLUSION: However, the loss of lives was minimal, and could be attributed to the immediate response of the local community in rescue and relief work. This experience underscores the vital importance of the local community as first responders, and their preparedness for disasters.
In the scenario of global warming and climate change, human thermal discomfort is about to rise. A rise in human thermal discomfort will undermine human health and well-being. It will also undermine labour productivity (as workers have to reduce work intensity and take longer breaks from work to prevent heat stress-related illness and injuries) and boost energy demand (as people will have to use more cooling instruments such as ACs, coolers, fan, etc., to get relief from thermal discomfort). Hence an assessment of spatio-temporal variability of thermal discomfort is necessary to develop a national strategy for the sustainable development of the country under changing climate scenarios. In this study, we have tried to analyze spatio-temporal variations of summertime thermal discomfort in India with the help of the Discomfort Index (DI). To calculate the DI, we have used high resolution (0.25 degrees x0.25 degrees) ERA-5 hourly 2-m air temperature and 2-m dewpoint temperature data. It is seen that March is the month of minimum discomfort and June is the month of maximum discomfort. In June, maximum discomfort occurs in the western region. The east coastal region and western region of India, particularly Rajasthan, experience maximum discomfort in terms of severity and prolonged discomfort hours. We have also calculated trends in DI, RH and temperature over the Indian region for March to June and observed a generally increasing trend with some spatial variations across India. It is also observed that the DI trend is more prominent in the western region in March and April, the southern region in May and the eastern region in June. We have also calculated the diurnal variations of thermal discomfort and the number of days with DI greater than 27 degrees C and 29 degrees C for different regions. It is observed that in most of the regions, DI reaches its peak around 09-10Z. Except for the north region, most of the regions show increasing trends in the number of discomfort days in April, May and June.
Cardiovascular diseases (CVDs), the leading cause of death worldwide, are sensitive to temperature. In light of the reported climate change trends, it is important to understand the burden of CVDs attributable to temperature, both hot and cold. The association between CVDs and temperature is region-specific, with relatively few studies focusing on low-and middle-income countries. This study investigates this association in Puducherry, a district in southern India lying on the Bay of Bengal, for the first time. METHODS: Using in-hospital CVD mortality data and climate data from the Indian Meteorological Department, we analyzed the association between apparent temperature (T(app)) and in-hospital CVD mortalities in Puducherry between 2011 and 2020. We used a case-crossover model with a binomial likelihood distribution combined with a distributed lag non-linear model to capture the delayed and non-linear trends over a 21-day lag period to identify the optimal temperature range for Puducherry. The results are expressed as the fraction of CVD mortalities attributable to heat and cold, defined relative to the optimal temperature. We also performed stratified analyses to explore the associations between T(app) and age-and-sex, grouped and considered together, and different types of CVDs. Sensitivity analyses were performed, including using a quasi-Poisson time-series approach. RESULTS: We found that the optimal temperature range for Puducherry is between 30°C and 36°C with respect to CVDs. Both cold and hot non-optimal T(app) were associated with an increased risk of overall in-hospital CVD mortalities, resulting in a U-shaped association curve. Cumulatively, up to 17% of the CVD deaths could be attributable to non-optimal temperatures, with a slightly higher burden attributable to heat (9.1%) than cold (8.3%). We also found that males were more vulnerable to colder temperature; females above 60 years were more vulnerable to heat while females below 60 years were affected by both heat and cold. Mortality with cerebrovascular accidents was associated more with heat compared to cold, while ischemic heart diseases did not seem to be affected by temperature. CONCLUSION: Both heat and cold contribute to the burden of CVDs attributable to non-optimal temperatures in the tropical Puducherry. Our study also identified the age-and-sex and CVD type differences in temperature attributable CVD mortalities. Further studies from India could identify regional associations, inform our understanding of the health implications of climate change in India and enhance the development of regional and contextual climate-health action-plans.
Compound dry hot events (CDHEs), where hot events and droughts coexist, have received a lot of attention lately due to their catastrophic effects on the economy, environment and human health. In this study, we use two CDHE indices, the Standardized Compound Event Indicator (SCEI) and the Standardized Dry and Hot Index (SDHI), to assess changes in CDHE characteristics (severity, frequency, spatial extent) over the historical past and future CMIP6 simulations across the Indian subcontinent. To understand the role of the drought index selected on CDHE characterization two drought indices namely the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are employed in the calculation of the CDHE indices. Further, the role of climatic oscillations such as El Nino Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Equatorial Indian Ocean Monsoon Oscillation and Indian Ocean Dipole in modulating CDHE characteristics have also been explored. Results show that SPI and SPEI based CDHE indices exhibit contrasting trends in northern India, while similar trends are observed in southern India in the historical past. Also, more frequent, extended, and severe CDHEs are reported by CDHE indices calculated using SPEI than by SPI. Temperature is found to be the dominant factor contributing to increases in CDHEs in the recent past and ENSO phases significantly modulate the severity and frequency of CDHE events in India. CMIP6 simulations generally report an increase in CDHE events for a 3 degrees C global warming scenario. Overall, our findings show that the choice of the drought index has a greater impact on CDHE characterization than the choice of the CDHE index itself. Results from this study provide useful information towards understanding the risk of CDHEs in India under global warming and urge for the development and implementation of adaptation and mitigation measures.
Advancements in early warning, efficient evacuation, and previous experience with cyclones have largely restricted disaster casualties in the Sundarban delta in India and Bangladesh. Despite the limited loss of life, post-cyclone soil, inland water salinity, and loss of agriculture productivity remain among the impeding factors affecting people’s lives. Two severe cyclones, cyclone Am-phan, which hit on May 20, 2020, and cyclone Yaas, which hit the delta on May 28, 2021, caused significant damage to the life and livelihood of the local people. In this paper, we inquired into the mental health and socio-psychological aspects of cyclone Amphan and Yaas-induced water in-security in the Indian Sundarban delta using both qualitative and quantitative research methods. The empirically derived data from Focus Group Discussions (N = 17; Number of participants = 157) and household surveys (N = 121) were subjected to various qualitative and quantitative analyses to understand the key determinants of post-cyclone water-induced mental health and well-being. Results suggest varying levels of stress and anxiety among the respondents, which include indecisiveness, general anxiety, fear of waterlessness, irritation, loss of self-esteem, and multiple other mental health issues. The FGD participants pointed out the severe impacts of the cyclones including increased seawater inundation, salinization of agricultural lands, flooding of the freshwater sources and increased water prices. Among all these impacts, seawater inunda-tion and salinization of water and soil have the most long-lasting impact on the life and livelihood of local people. In the quantitative analysis, we observed that the perceived lack of fresh water, including both quality and quantity, alongside, the experience of water-borne diseases, loss of so-cial relations, and loss of livelihood are the key determinants of the self-reported emotional stress of the respondents. In the lieu of existing knowledge gap on disaster-induced water insecurity and its impact on the mental health of affected people, the present research findings emphasize the need for resilient water structures on one side and socio-psychological counselling at the community level on the other side to ensure a sound recovery of the damaged resources as well as make the community a part of the recovery process that can improve their mental health.
Urban floods are more concerned in recent days due to their substantial effect in loss of human lives and properties. Due to climate change, urban floods are frequently observed in many parts of the world. Flood events in Chennai city are a frequent scenario due to rapid increase in the density of population. Adyar river watershed and surrounding urban cover are focused in the present study. The present study aims at mapping flooded region using Sentinel 1A datasets over Adyar watershed. Series of Sentinel 1A image is collected before, during and after floods for mapping the extent of flood and mapping risk zones in Adyar watershed. Methodologies such as ISODATA Technique, Multi-Temporal Analysis, Thresholding Method, PCA and ICA Analysis and Grey Level Co-Occurrence Matrix are adopted for the extraction of flooded extent from the SAR datasets. Analysis performed over the Adyar watershed provided promising results in the extraction of flooded extent with Thresholding Method and Grey Level Co-Occurrence Matrix being the dominant of all the methods. Though higher accuracy is obtained in the extraction of flooded extent, limitation of layover, foreshortening and shadow is experienced in the built up region for the extraction of flooded pixels.
India’s land use pattern has witnessed significant changes over time. Various studies have pointed out that land use changes in India indicate a trend towards increasing urbanisation and deforestation (particularly for native forests). A priori, such changes in land use patterns may lead to a higher incidence of natural disasters. The study examines whether these land use changes have led to higher fatalities (and damages) due to floods and other natural disasters , controlling for various socio-economic factors. Our results indicate that land use changes, specifically deforestation, and urbanisation, are detrimental to environmental health, causing greater flood damages and natural disaster fatalities. Specifically, ‘forest cover’ is found to have a negative impact on ‘flood damages’, whereas ‘urbanisation’ has a positive impact, as per a priori expectations. Similarly, forest cover is found to be inversely related to total ‘natural disaster fatalities’ (which includes deaths due to floods, cyclones, landslides, heat waves, cold waves, and lightning), whereas ‘urbanisation’ is found to have a positive impact on disaster fatalities, according to a priori expectations. Our results confirm that land use changes in the direction of deforestation and urbanisation have increased fatalities and damages due to natural disasters. Another important finding of our study is that financial development has a mitigating impact on flood fatalities as well as overall natural disaster fatalities.
Changes in global temperature have adverse influences on the environment, crop production, and public health. Temperature extreme investigation is necessary for those areas whose cropland and work cultures rely on effective climatic conditions. Therefore, identifying trends in climatic scenarios is important in determining the pattern of extreme temperatures. The previously mentioned trend analysis was based on a complete period that did not investigate the recent variability in temperature. We scrutinized a different decadal zone using the innovative trend analysis method (ITAM). We selected the annual and seasonal extreme temperature variations for 70 years (1951-2020) at 11 grid points in the Kangsabati-Silabati-Keliaghai-Dwarkeswar basins of West Bengal. The outcomes from two different time zones, 1951-2020 and 2001-2020, show some surprising results. For 1951-2020, Tmax shows negative sloping patterns in winter and summer, whereas for 2001-2020, it shows the opposite pattern: year-round, winter and summer with excessive magnitudes. Similarly, for Tmin, all the seasons from 1951 to 2020 show positive trends, but in recent decades (2001-2020), except the monsoon season, other seasons show negative trends. The normalized difference in vegetation and water indices also supports the results of trends. From the results obtained through the use of ITAM, we recognize that the results of the recent trends are more sensitive and of a higher magnitude of slope in nature than in the historical decade. This study may serve as scientific support for detecting and strategically minimizing the effects of climate change on water resources to reduce the risk of adverse weather soon.
Climate change-mediated rise in sea level and storm surges, along with indiscriminate exploitation of groundwater along populous coastal regions have led to seawater intrusion. Studies on groundwater salinization and heavy metal contamination trends are limited. Present study investigated the heavy metal contamination, associated risks and provided initial information on the impacts of groundwater salinization on heavy metals along the coastal plains of Odisha, India. Total 50 groundwater samples (25 each in post- and pre-monsoon) were collected and analysed. Concentrations of Fe (44%), Mn (44%), As (4%) and Al (4%) in post-monsoon and Fe (32%), Mn (32%), As (4%), B (8%) and Ni (16%) in pre-monsoon exceeded Bureau of Indian Standards (BIS) drinking water limits. High concentrations of heavy metals (Fe, Sr, Mn, B, Ba, Li, Ni and Co) and high EC (>3000 μS/cm) indicated that the groundwater-seawater mixing process has enhanced the leaching and ion exchange of metallic ions in central part of the study area. Multivariate statistical analysis suggested leaching process, seawater intrusion and agricultural practices as the main heavy metal sources in the groundwater. 4% of samples in post- and 16% in pre-monsoon represented high heavy metal pollution index (HPI). Pollution indices indicated the central and south-central regions are highly polluted due to saline water intrusion and high agricultural activities. Ecological risks in the groundwater systems found low (ERI <110) in both seasons. Children population found more susceptible to health risks than adults. Hazard index (HI > 1) has shown significant non-carcinogenic risks where Fe, Mn, As, B, Li and Co are the potential contributors. Incremental lifetime cancer risk (ILCR >1.0E-03) has suggested high carcinogenic risks, where As and Ni are the major contributors. The study concluded that groundwater salinization could increase the heavy metal content and associated risks. This would help policymakers to take appropriate measures for sustainable coastal groundwater management.
The study investigates the impact of flooding on mental health and resilience among adolescent students in the South Indian State of Kerala. It explores the interrelationships between the impact generated by floods, depression, anxiety, stress, and resilience among adolescent students. Two groups of fifty adolescent students each, from within the age range of twelve to eighteen years, were selected for the study. The first group had experienced floods and their devastation firsthand, while the second group were yet to experience floods or their devastation. The study compares the variables pertaining to both sets of respondents and identifies potential areas of psychological impact. A convergent mixed-method research design was employed to achieve these objectives, and self-report questionnaires and in-depth interviews were used for data collection. The key findings reveal that flood-affected adolescents score higher in metrics like impact of events, depression, anxiety, and stress, whereas their counterparts who are not affected by floods display higher resilience. Thematic analysis identified significant themes, including livelihood challenges, problems related to schooling, an apprehension surrounding potential flooding, and a lack of knowledge about and preparedness for disasters, while a surprisingly positive theme could be discerned when it came to the outlook of the respondents regarding coping with the outcomes. The results underscore the need for new policies to mitigate the psychological impact of disasters on adolescents, emphasizing the importance of providing psychological support services and preparedness training.
The survival of life on earth depends on equilibrium between the organisms and the environment. The monsoon is a seasonal variation prevailing in the Indian sub-continent. Monsoon has two seasons which are separated by a transition. The infectious diseases epidemiology is affected by both climatic and societal influences. An interaction of climatic and societal influence favours the infectious disease exposure in a population. The infectious diseases affecting the population can be broadly classified as vector borne diseases, food borne diseases, water borne diseases, and respiratory diseases. The rainfall associated change in temperature and floods favours the survival of infectious diseases and their transmitting vectors. The changing global climatic trends including the EL nino Southern oscillation bring undue rainfall during other seasons. The drastic events associated with these climatic changes affect the heath and sanitation infrastructure. India being a developing country has more vulnerability to such infections. A better strengthening of the infrastructure and health policies is the need of the hour to curb the infections.
Human health in Odisha is directly vulnerable to climate change in the form of mortality as a result of climate-induced natural disasters (CINDs) and heatwaves. More frequent and intensified CIND has become an inevitable part of the state and its impact on human health has been detrimental. The magnitude of the impact of climate change on human health depends on the vulnerability and adaptation approaches of the state. The objectives of the paper are to study the changing pattern of climatic variability over 20 years in the state and to analyze the direct impact of climate change on human health in Odisha. Linear trend analysis is performed for annual average, pre-monsoon, monsoon, and post-monsoon rainfall as well as annual maximum and minimum temperature and for the heatwave period to show the changing pattern of climate in the state over 20 years. Regression analysis is performed between the indexed value of vulnerability and adaptation coefficients considered in the study as independent variables and mortality due to CIND as the dependent variable to analyze the impact of climate change on human health in the state. Also, correlation analysis is conducted to show the association between heatwave mortality and the maximum temperature of the heatwave period. The rainfall trend of the state for 20 years from 2000 to 2020 is found to increase in pre-monsoon and post-monsoon periods, while the annual average rainfall of the state for 20 years is slightly increasing and the monsoon period rainfall has remained consistent throughout the years. The annual maximum and minimum temperature and the heatwave period are found to be increasing. The regression analysis has shown a significant positive relationship between vulnerability coefficients and mortality as a direct impact of CIND on human health, whereas adaptation coefficients exhibit negative relation with it. Also, there is a moderate but significant association between the maximum temperature of the heatwave period with heatwave mortality. Odisha has been vulnerable to climate change during 2000-2020 as indicated by the high vulnerability score compared to the adaptation score for each year. However, years with better adaptive approaches, having high adaptive index scores, experienced less human mortality even with high vulnerability scores.
An effort is made to understand the role of El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) events on the malaria transmission intensity over India during the period 1951-2020 (70 years) with the help of a realistically simulated dynamical malaria model. The results suggest that the La Nina years pose a greater threat of malaria disease, especially in the densely populated Indian states. During El Nino years, the malaria transmission intensity and distribution over India greatly reduce, except in the regions such as Orissa, Chhattisgarh, Jharkhand, Western Ghats, parts of Madhya Pradesh, and Andhra Pradesh. It is found that in the positive IOD years, the malaria transmission intensity increases (decreases) over the entire central Indian region and along coastal regions of Tamil Nadu and Kerala (southern peninsular states of India and northeast India). An almost opposite behavior is seen during the negative IOD years. The malaria transmission variability over India is becoming increasingly heterogeneous in recent decades during the El Nino and La Nina years as a result of global warming. The period of 1986-2020 witnessed a substantial decrease (increase) in the malaria transmission intensity during the positive (negative) IOD years, except for a few regions of India. The implications of the results presented in the paper linking the ENSO and IOD signals with the intensity and distribution of malaria over India in a warming world are enormous, especially for the densely populated Indian states.
Air pollution is a growing concern in India, and its adverse health effects are well documented. Climate change is likely to exacerbate this problem by altering weather patterns and increasing the frequency and severity of extreme events. This paper examines the potential impact of climate change on ambient air pollution in India and its implications for policy design. Our analysis reveals that pollution in India is highly sensitive to variation in weather, particularly in the densely populated Indus-Gangetic Plain. Using our estimated relationship between weather and pollution, we predict that changing weather patterns will increase average PM2.5 concentrations by 3.1 mu g/m3, leading to a loss of 364 million years of life expectancy. To address this challenge, we propose an emissions fee calibrated to be highest in regions most vulnerable to persistently high levels of pollution and most sensitive to future deterioration in air quality due to climate change.
Indian cities have frequently observed intense and severe heat waves for the last few years. It will be primarily due to a significant increase in the variation in heat wave characteristics like duration, frequency, and intensity across the urban regions of India. This study will determine the impact of future climate scenarios like SSP 245 and 585 over the heat wave characteristics. It will present the comparison between heat waves characteristics in the historical time (1981 to 2020) with future projections, i.e., D(1) (2021-2046), D(2) (2047-2072), and D(3) (2073-2098) for different climate scenarios across Indian smart cities. It is observed that the Coastal, Interior Peninsular, and North-Central regions will observe intense and frequent heat waves in the future under SSP 245 and 585 scenarios. A nearly two-fold increase in heat wave’ mean duration will be observed in the smart cities of the Interior Peninsular, Coastal, and North Central zones. Thiruvananthapuram city on the west coast has the maximum hazard associated with heat waves among all the smart cities of India under both SSPs. This study assists smart city policymakers in improving the planning and implementation of heat wave adaptation and mitigation plans based on the proposed framework for heat action plans and heat wave characteristics for improving urban health well-being under hot weather extremes in different homogeneous temperature zones.
Changes in extreme human bioclimate conditions are accepted evidence for and serve as a broad measure of anthropogenic climate change. The essential objective of the current study was to investigate past and future thermal bioclimate conditions across West Bengal (WB), India. The daily physiologically equivalent temperature (PET) was calculated by considering definite climate variables as inputs. These meteorological variables were captured from the Coordinated Regional Downscaling Experiment (CORDEX)-South Asia. The initial results from this research work present the mean monthly distribution of each PET class over the considered stations of WB during the period (1986-2005) and three future time periods: (i) near future (2016-2035), (ii) mid-future (2046-2065), and (iii) far future (2080-2099). It was observed that the months from April to June comprise heat stress months in terms of human thermal perception, whereas thermally acceptable conditions begin in November and continue until March for most stations. Results from future PET changes over WB in the context of the reference period (1986-2005) reveal a prominent increase in warm and hot PETs for all future time periods in two different greenhouse gas emission scenarios. During the far-future time period, stations within a kilometer of the Bay of Bengal such as Digha, Diamond Harbour, Canning, and Baruipur account for the highest percentage in the warm PET class (35.7-43.8 degrees C) in high-end emission scenarios. Simultaneously, during the period from 2080 to 2099, Kolkata, Dum Dum, Kharagpur, and Siliguri will experience a PET greater than 43.8 degrees C for close to 10% of the days in the year and more than 10% in Sriniketan, Malda, Asansol, and Birbhum. During the far-future period, a negative change in the very cool PET class (<3.3 degrees C) indicating a decrease in cold days was the largest for Darjeeling.
Fetal life and infancy are extremely sensitive to adverse environmental conditions. This study aimed to assess the effect of exposure to a natural disaster (cyclone Aila) in utero or during infancy on fine and gross motor functions in preadolescent Indian children. The study was conducted in West Bengal, India, and included approximately 700 children (7-10 years old) who were prenatally or postnatally exposed to cyclone Aila and a nonaffected group. Anthropometric measures included height, weight, and birthweight. Socioeconomic status was based on parental education, family size, and income. Motor functions were assessed using the short form of Bruininks-Oseretsky Test of Motor Proficiency (BOT-2). Statistical analyses included, for example, generalized linear models. There were no differences in motor functions relative to the timing of the exposure (trimester) during pregnancy. Compared to the controls, prenatal Aila exposure resulted in poorer performance in all BOT-2 subtests, except for fine motor precision, strength, and balance (the last in boys), while postnatal Aila exposure, compared to the controls, resulted in poorer performance in manual dexterity, bilateral coordination, balance (girls only), and speed and agility. Early life exposure to a natural disaster has long-term adverse effect on motor proficiency in children. By inference, the welfare of pregnant women and infants should be of particular concern for emergency and health services during an environmental cataclysm.
The links between climate change, food security and women’s wellbeing remain an under-investigated area. This paper contributes to this area through a thorough examination of how women experience food insecurity in farming households in rural India. The households are located in four agro-climatic regions in India. These regions experience varied climatic pressures, and this diversity allows us to explore a wider variety of women’s experiences in their attempts to maintain household food security as the climate changes. The study finds that women, even in comparatively more food-secure households, suffer from food insecurity. One of the reasons for this is that women’s food habits and mealtimes have altered in recent years due to the increase in their work pressures. The worst effects are to be found in drought-prone areas, and there are greater vulnerabilities among women-headed households, indicating that the impacts of climate change are exacerbated by cultural norms that further hinder the role of women in farm activities.
Nitrate and fluoride are two of the most prevalent pollutants in drinking water and exposure to their high concentrations could cause methemoglobinemia and fluorosis. This study attempted to evaluate the groundwater quality (pH: 4.4-9) from a relatively understudied part of the southwestern coast in India (i.e., Alappuzha, Kerala state) and assessed the associated health risks from exposures to nitrate (0.2-5.8 mg/l) and fluoride (0.2-1.9 mg/l) present in the groundwater. Pollution index (PIG: 0.35-5.43) grouped about 21% samples in high pollution and very high pollution categories because of fluoride content above the WHO guidelines. The total hazard index (THI) for adult male (0.17-1.70; average: 0.75), adult female (0.19-1.85; average: 0.81) and children (0.35-3.40; average: 1.50) suggested more non-carcinogenic risks for children from 41.6% samples compared to adult male and female from 33.3% samples in the absence of any mitigation measure. These results provide additional data from the country with highest population and the largest groundwater use in the context of sustainability in availability and supply of groundwater under the increasing risks of population growth, climate change and industrial development.
BACKGROUND: Malaria remains a public health challenge across several African and South-East Asia Region countries, including India, despite making gains in malaria-related morbidity and mortality. Poor climatic and socioeconomic factors are known to increase population vulnerability to malaria. However, there is scant literature from India exploring this link using large population-based data. OBJECTIVES: This study aims to study the role of climatic and socioeconomic factors in determining population vulnerability to malaria in India. MATERIALS AND METHODS: We used logistic regression models on a nationally representative sample of 91,207 households, obtained from the National Sample Survey Organization (69(th) round), to study the determinants of household vulnerability. RESULTS: Households that resided in high (odds ratio [OR]: 1.876, P < 0.01) and moderately high (OR: 3.427, P < 0.01), compared to low climatically vulnerable states were at greater odds of suffering from malaria. Among households that faced the problem of mosquitoes/flies compared to the reference group, the urban households were at higher risk of suffering from malaria (OR: 8.318, P < 0.01) compared to rural households (OR: 2.951, P < 0.01). Households from the lower income quintiles, caste, poor physical condition of their houses, poor garbage management, and water stagnation around the source of drinking water, strongly predicted malaria vulnerability. CONCLUSION: Household's vulnerability to malaria differed according to state climatic vulnerability level and socioeconomic factors. More efforts by integrating local endemicity, epidemiological, and entomological information about malaria transmission must be considered while designing malaria mitigation strategies for better prevention and treatment outcomes.
Among the diverse Vibrio spp. autochthonous to coastal ecosystems, V. cholerae, V. fluvialis, V. vulnificus and V. parahaemolyticus are pathogenic to humans. Increasing sea-surface temperature, sea-level rise and water-related disasters associated with climate change have been shown to influence the proliferation of these bacteria and change their geographic distribution. We investigated the spatio-temporal distribution of Vibrio spp. in a tropical lake for 1 year at a 20-day interval. The abundance of Vibrio spp. was much higher during the south-west monsoon in 2018, when the lake experienced a once-in-a-century flood. The distribution of Vibrio spp. was influenced by salinity (r = 0.3, p < 0.001), phosphate (r = 0.18, p < 0.01) and nitrite (r = 0.16, p < 0.02) in the water. We isolated 470 colonies of Vibrio-like organisms and 341 could be revived further and identified using 16S rRNA gene sequencing. Functional annotations showed that all the 16 Vibrio spp. found in the lake could grow in association with animals. More than 60% of the isolates had multiple antibiotic resistance (MAR) index greater than 0.5. All isolates were resistant to erythromycin and cefepime. The proliferation of multiple antibiotic-resistant Vibrio spp. is a threat to human health. Our observations suggest that the presence of a diverse range of Vibrio spp. is favoured by the low-saline conditions brought about by heavy precipitation. Furthermore, infections caused by contact with Vibrio-contaminated waters may be difficult to cure due to their multiple antibiotic resistances. Therefore, continuous monitoring of bacterial pollution in the lakes is essential, as is the generation of risk maps of vibrio-infested waters to avoid public contact with contaminated waters and associated disease outbreaks.
Indian Sundarban is highly susceptible to tropical cyclones and resultant impacts such as storm surge-induced floods, embankment breaching, and saline water intrusion. It affects life and livelihood in severe ways. Mitigation and policy measures are therefore very important, based on information gathered at the grassroots level. Hence, this study is designed to assess inter-village variation in cyclone vulnerability, considering physical vulnerability, social vulnerability, and mitigation capacity. This study also highlights livelihood challenges faced by coastal dwellers. Geospatial and quantitative methods were used to assess the composite vulnerability index (CVI). Remote sensing data and climatic data were integrated to assess physical vulnerability and various socioeconomic data were incorporated to determine the social vulnerability. Moreover, an intensive field survey (2020-2021) was also conducted to understand the livelihood challenges of local people and accordingly suggest mitigation measures to cope with natural hazards. According to this analysis, nearly 18% of the total population living in the southern and eastern parts of the Matla-Bidya inter-estuarine area (MBI) are extremely vulnerable (CVI > 0.544) due to their geographical location and high exposure to coastal hazards. Almost 51% of the total populations inhabited in 46% of the total MBI villages are experiencing high to moderate vulnerability. Conversely, MBI villages in the northern part, where 32% of the total population lives, show low vulnerability (CVI < 0.387) due to less exposure and high resilience. Coastal low-lying villages are often hardest hit by tropical cyclones. Therefore, effective mitigation strategies and coping mechanisms are essentially needed to reduce the adverse impacts of cyclones.
Livelihood vulnerability index (LVI) was employed to identify the differential vulnerability of two villages to climate change effects, which are located at different altitudes (Chushul at 4000-5000 m and Shey at 3000-4000 m above sea level). Primary data were collected from 165 households on indicators of health, food, water, demographic profile, livelihood strategies, social networks, and their perception of climate variability and natural disasters. A composite index was employed to aggregate the data. The vulnerability of different components or indicators of the village were compared to estimate the differential vulnerability. The vulnerability of indicators ranges from (0) as the least vulnerable and (1) as the most vulnerable. The results suggest that both high-altitude village (HAV) and low-altitude village (LAV) were highly vulnerable in terms of climate variability and natural disasters. The vulnerability of livelihood strategies and food for HAV was higher than that of LAV; however, both villages were vulnerable to water shortages. Comparatively, HAV was more vulnerable to water shortages than LAV. It was found that both villages are the least vulnerable in terms of social networks and health facilities. Overall, the livelihood vulnerability index for HAV was 0.668, which was higher than that of LAV, with a vulnerability index of 0.443. The pragmatic LVI approach can be used to estimate the vulnerability of any region by altering the indicators and selecting those indicators suitable for the study region. Furthermore, the livelihood vulnerability index results may have an inference for government institutions and stakeholders to carry out developmental works and adaptation strategies in both the villages.
This study was performed in order to understand the effect of climatological variables on the malaria situation in the north-east region of India, which is prolonged by the disease. Time-series analysis of major climate parameters like rainfall, maximum temperature, minimum temperature, mean temperature, relative humidity, and soil moisture distributions is carried out, and their correlation with the malaria incidence is quantified state-wise, which is the unique part of the study. The correlation analysis reveals that malaria is significantly related with the maximum temperature and soil moisture in three out of eight states in NE India. To assess the climate variability, the inter-dependency between the meteorological parameters is obtained and the state wise correlation matrix for all states are reported. The analysis shows that maximum and mean temperature has highest positive correlation whereas minimum temperature and relative humidity has negative correlation. The climate-malaria relation is being carried out in the study region using the regression analysis and the results revealed that the regional climate has the most impact for the malaria incidence in the state of Arunachal Pradesh, Meghalaya, Tripura and Nagaland and in other states the impact is moderate. Analysis of variance modelling in the regions also indicates the degree of the fitment of both the data sets with the regression model and it is observed that the relation is also significant in the same 4 states. As a case study the impact of large scale oscillations like El Niño-Southern Oscillation on the malaria load is also assessed which can be a good indicator in the prediction of the climate and in turn the malaria incidences over the region.
Developing a better scientific understanding of anthropogenic climate change and climate variability, especially the prediction/projection of climate futures with useful temporal and geographical resolution and quantified uncertainties, and using that knowledge to inform adaptation planning and action will become crucially important in the coming years. Generating such policy-relevant knowledge may be particularly important for developing countries such as India. It is with this backdrop that, in this paper, we analyze future heat waves in India by using observations and a large number of model simulations of historical, + 1.5 degrees C, and + 2.0 degrees C warmer worlds. In both the future scenarios, there is an increased probability of heat waves during June and July when the Indian monsoon is in full swing and humidity is high, which makes the heat events even more of a health risk. While the highest temperatures in heat waves may not increase much in future climates, the duration and areal extent of the heat waves will most likely increase, leading to the emergence of new heat wave-prone zones in India. The results indicate that the joint frequencies of the longest duration and large area events could be nearly threefold greater in the + 1.5 degrees C and fivefold greater in the + 2.0 degrees C future scenarios compared to historical simulations. Thus, overall, the study indicates a substantial increase in the risk of heat events that typically elicit warnings from forecasters. The likely widespread and persistent nature of heat wave events in the future, as revealed by this study, will require planning and adaptation measures beyond the short-term disaster planning frameworks currently in place. Exploring what these measures might look like is beyond the scope of this study, but it underlines the importance of developing climate knowledge with high temporal and geographical resolution capable of informing adaptation policy and planning.
Although the ecological and economic services rendered by the wetland ecosystems are innumerable, the exposure of inhabitants to hazardous climatic events is on the rise. For instance, the Kuttanad wetland ecosystem in Kerala, India, faces uneven rainfall patterns, leading to recurrent flooding. The present study examines people’s vulnerability to elevated flooding risk in the region, factors responsible for migration in the wake of climate change and their adaptive capacity to such events. The primary survey-based study follows the theoretical framework of vulnerability and adaptive capacity. Physical asset loss, sinking houses, elevated health risks and loss of livelihood are factors identified for increased vulnerability to flood risks. The exacerbating vulnerability translates into the mass migration of local inhabitants. The Probit regression underscores the role of households’ socio-economic background in migrating from the region, seeking safe havens. Marginalised social groups and people reliant on the local environment are most vulnerable. As per the study, the absence of pre and post-flood measures affects the adaptive capacity of the inhabitants. Given the gravity of flooding risk, the study suggests channelised policy measures that are quintessential to improve their resilience and adaptive capacity.
Based on a desk review and three rounds of the Delphi method, this study examines the impacts of climate change-induced water-related threats on food security in the Indian Sundarbans, and develops management strategies to address the issues. Results show climate change, through its impacts on water, has lowered agricultural output, endangered traditional livelihoods, reduced access to food, and affected food utilization by impacting freshwater availability and creating health hazards. In addition, intensified weather extremes are likely to threaten food security further. A combination of local-level adaptation measures and global-level mitigation initiatives is necessary to ensure food security in this region.
Climate change has far-reaching impacts on human health, with low- and middle-income countries, including India, being particularly vulnerable. While there have been several advances in the policy space with the development of adaptation plans, little remains known about how stakeholders who are central to the strengthening and implementation of these plans perceive this topic. We conducted a qualitative study employing key interviews with 16 medical doctors, researchers, environmentalists and government officials working on the climate change agenda from Puducherry, India. The findings were analysed using the framework method, with data-driven thematic analysis. We elucidated that despite elaborating the direct and indirect impacts of climate change on health, there remains a perceived gap in education and knowledge about the topic among participants. Knowledge of the public health burden and vulnerabilities influenced the perceived health risks from climate change, with some level of scepticism on the impacts on non-communicable diseases, such as cardiovascular diseases. There was also a felt need for multi-level awareness and intervention programmes targeting all societal levels along with stakeholder recommendations to fill these gaps. The findings of this study should be taken into consideration for strengthening the region’s climate change and health adaptation policy. In light of limited research on this topic, our study provides an improved understanding of how key stakeholders perceive the impacts of climate change on health in India.
Evidence of the health impact of climate change has been extensively documented in recent scholarly literature. In order to mitigate the adverse health effects induced by climate change, the need for conducting vulnerability assessments (VAs) has been emphasised. A higher vulnerability to climate change is often linked with substantial risks to human lives and built environment. Despite the potential of VAs in alleviating risks posed by climate change, only a limited amount of scholarly work in this domain has been conducted in the Indian context. The present research addresses this lacuna and contributes to the limited scholarship on climate change and health VAs in India. Drawing on the VA framework introduced by the fourth assessment report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), this paper estimates district-level health vulnerabilities caused by climate change using multi-dimensional indices. The indices are multi-dimensional since they integrate 50 district-level indicators from 8 data sources for all 640 Census 2011 districts. The statistical technique of Principal Component Analysis (PCA) has been used for integrating the indicators. The findings of this paper indicate that nearly 56% of India’s population in 344 districts is highly vulnerable to the health impact of climate change. The results show that high vulnerability in certain districts is mediated by high adaptive capacity (AC). Since climate exposure varies across districts, the paper highlights the need for local-level responses and Complex Adaptive System (CAS) thinking to understand the implications of climate change and human health.
Extreme climate events are related to women’s exposure to different forms of violence. We examined the relationship between droughts and physical, sexual, and emotional intimate partner violence (IPV) in India by using two different definitions of drought: precipitation-based drought and socio-economic drought. We analyzed data from two rounds of a nationally representative survey, the National Family Health Survey, where married women were asked about their experiences of IPV in the past year (2015-16 and 2019-21; N=122,696). Precipitation-based drought was estimated using remote sensing data and GIS mapping, while socio-economic drought status was collected from government records. Logistic regression models showed precipitation-based drought to increase the risk of experiencing physical IPV and emotional IPV. Similar findings were observed for socio-economic drought; women residing in areas classified as drought-impacted by the government were more likely to report physical IPV, sexual IPV, and emotional IPV. These findings support the growing body of evidence regarding the relationship between climate change and women’s vulnerability, and highlight the need for gender responsive strategies for disaster management and preparedness.
The frequency and intensity of extreme thermal stress conditions during summer are expected to increase due to climate change. This study examines sixteen models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that have been bias-adjusted using the quantile delta mapping method. These models provide Universal Thermal Climate Index (UTCI) for summer seasons between 1979 and 2010, which are regridded to a similar spatial grid as ERA5-HEAT (available at 0.25° × 0.25° spatial resolution) using bilinear interpolation. The evaluation compares the summertime climatology and trends of the CMIP6 multi-model ensemble (MME) mean UTCI with ERA5 data, focusing on a regional hotspot in northwest India (NWI). The Pattern Correlation Coefficient (between CMIP6 models and ERA5) values exceeding 0.9 were employed to derive the MME mean of UTCI, which was subsequently used to analyze the climatology and trends of UTCI in the CMIP6 models.The spatial climatological mean of CMIP6 MME UTCI demonstrates significant thermal stress over the NWI region, similar to ERA5. Both ERA5 and CMIP6 MME UTCI show a rising trend in thermal stress conditions over NWI. The temporal variation analysis reveals that NWI experiences higher thermal stress during the summer compared to the rest of India. The number of thermal stress days is also increasing in NWI and major Indian cities according to ERA5 and CMIP6 MME. Future climate projections under different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) indicate an increasing trend in thermal discomfort conditions throughout the twenty-first century. The projected rates of increase are approximately 0.09 °C per decade, 0.26 °C per decade, and 0.56 °C per decade, respectively. Assessing the near (2022-2059) and far (2060-2100) future, all three scenarios suggest a rise in intense heat stress days (UTCI > 38 °C) in NWI. Notably, the CMIP6 models predict that NWI could reach deadly levels of heat stress under the high-emission (SSP5-8.5) scenario. The findings underscore the urgency of addressing climate change and its potential impacts on human well-being and socio-economic sectors.
OBJECTIVES: Almost a quarter of the global burden of disease and mortalities is attributable to environmental causes, the magnitude of which is projected to increase in the near future. However, in many low- and middle-income settings, there remains a large gap in the synthesis of evidence on climate-sensitive health outcomes. In India, now the world’s most populous country, little remains known about the impacts of climate change on various health outcomes. The objective of this study is to better understand the challenges faced in conducting climate change and health research in Puducherry, India. DESIGN AND SETTING: In this study, we employed key informant interviews to deepen the understanding of the perceived research barriers in Puducherry. The findings were analysed using data-driven qualitative thematic analysis to elaborate the major perceived barriers to conducting environmental health research. PARTICIPANTS: This study was conducted among 16 public health professionals, including medical researchers, and professionals involved in environmental policies and planning in Puducherry. RESULTS: We identify three key barriers faced by public health professionals as key stakeholders, namely: (1) political and institutional barriers; (2) education and awareness barriers; and (3) technical research barriers. We show there is a need, from the professionals’ perspective, to improve community and political awareness on climate change and health; strengthen technical research capacity and collaboration among researchers; and strengthen health surveillance, resource allocation and access to health data for research. CONCLUSION: Evidence informed policies and interventions are a key element in the adaptation response for countries. In the context of the paucity of data on environmental health from India, despite recognised climate change related health vulnerabilities, these findings could contribute to the development and improvement of relevant interventions conducive to a strong research environment.
BACKGROUND: Postnatal care is crucial to prevent the child mortality. Despite the improvement in the PNC coverage for the neonates, it is still far away from the universal health coverage. Along with, some specific regions mostly are natural hazard prone areas of India show very under coverage of PNC for the neonates. Considering the substantial spatial variation of PNC coverage and natural hazard prevalence, present study aimed to examine spatial variation of PNC coverage and its association with natural hazard at the district level. METHODS: The cross-sectional exploratory study utilized National Family Health Survey, 2019-21, which included 1,76,843 children using multistage stratified sampling method to examine postnatal care within 42 days for neonates born within five years prior to the survey. Additionally, the study utilized Vulnerability Atlas of India,2019 maps to categorize regions into hazardous (flood, earthquake, and landslide) and non-hazardous areas. Spatial univariate and bivariate analyses, logistic and geographically weighted regressions were conducted using ArcGIS Pro, GeoDa, and Stata 16.0 software to identify associations between PNC coverage, hazard exposure, and spatial variation. RESULTS: The univariate spatial analysis showed some specific regions such as north, east, and north-east region of India had a high concentration of natural hazard and low access of PNC coverage. Bivariate analysis also showed that PNC coverage was low in flood (75.9%), earthquake (68.3%), and landslide (80.6%) effected areas. Compared to the national PNC coverage (81.1%), all these natural hazards effected areas showed low coverage. Further, logic regression showed that these hazard prone areas were less (OR:0.85 for flood, 0.77 for earthquake, and 0.77 for landslide) likely to get PNC coverage than their counterparts. LISA cluster maps significantly showed low PNC and high disaster concentration in these disaster-prone areas. Geographic weighted regression results also showed similar result. CONCLUSIONS: The present study elucidates notable heterogeneity in the coverage of postnatal care (PNC) services, with lower concentrations observed in disaster-prone areas. In order to enhance the accessibility and quality of PNC services in these areas, targeted interventions such as the deployment of mobile health services and fortification of health systems are recommended.
Flood is always a source of social lamentation, huge infrastructural losses and disruption to economic activities in Bhagirathi Sub-basin in India. Climate variability and increasing flood incidents have created a dilemma for social, economic and environmental conditions of the affected communities. These implications necessitate assessing overall flood vulnerability to minimize their short and long-term impacts. This study presents a comprehensive analysis of composite vulnerability among the flood affected communities in Bhagirathi Sub-basin. Data for analyzing composite flood vulnerability were derived from an in-depth survey of 432 households selected through stratified random sampling method in the Sub-basin. Domains of vulnerability such as quality of life, social & economic status, health impacts, ecological implications, losses and adaptation were examined. A total of 95 indicators of these domains were considered to prepare composite vulnerability index of the selected villages. Relationship between vulnerability and households’ characteristics was ascertained using cross tabulation and multinomial logistic regression. Analysis of composite vulnerability index (CVI) revealed very high vulnerability in Nutanhat, Bakkhali, Jhara, Gopalpur, Jayarampur, Titiha, Uchildaha and Mohanpur villages. High vulnerability was observed in Banagram, Mayapur, Amravati, Gobindapur, Raichak Boltala, Talim Nagar Minakhan and Majhirmana villages while Kalna Municipality was found under moderate vulnerability. High losses, ecological & health implications and low socioeconomic conditions of the households aggravated very high to moderate vulnerability in these villages. Gender, income and land possession were found strongly associated with high vulnerability while flood insurance, farming purposes and changes in rainfall pattern were identified inducing moderate vulnerability. CVI analysis assisted in identifying the priority villages for effective policy implications. The study calls for policy implications for lessening the impact of flood in the Sub-basin.
Drought is a natural hazard that is characterized by a low amount of precipitation in a region. In order to evaluate the drought-related issues that cause chaos for human well-being, drought indices have become increasingly important. In this study, the monthly precipitation data from 1964 to 2013 (about 50 years) of the Jodhpur district in the drought-prone Rajasthan state of India was used to derive the effective drought index (EDI). The machine learning models hybridized with evolutionary optimizers such as the genetic algorithm adaptive neurofuzzy inference system (GA-ANFIS) and particle swarm optimization ANFIS (PSO-ANFIS) were used in addition to the generalized regression neural network (GRNN) to predict the EDI index. Using the partial autocorrelation function (PACF), models for forecasting the monthly EDI were constructed with 2-, 3- and 5-input combinations to evaluate their outcomes based on various performance indices. The results of the different combination models were compared. With reference to 2-input and 3-input combination models, both GA-ANFIS and PSOANFIS show better performance results with R-2 = 0.75, while among the models with 5-input combination, GA-ANFIS depicts better performance results compared to other models with R-2 = 0.78. The results are presented suitably with the aid of scatter plots, Taylor’s diagram and violin plots. Overall, the GA-ANFIS and PSO-ANFIS models outperformed the GRNN model.
Simple Summary Dirofilariasis is caused by Dirofilaria spp. worm infections, transmitted by mosquitoes, and affects humans and animals worldwide. Often, infected animals show symptoms relating to the cardiopulmonary system (heart and lung) and subcutaneous tissue (eye and skin). This study assessed the current published data on the distribution and prevalence of dirofilariasis across Sri Lanka and India. This analysis found that almost all cases of human dirofilariasis reported in Sri Lanka and India are presented as subcutaneous infections, with the eye being the most commonly affected organ. Both heartworm and subcutaneous infections are found in the dog populations in India. However, only subcutaneous infections have so far been reported in Sri Lanka, and the rationale behind this geographical distribution of infection patterns of dirofilariasis remains unknown and warrants further research. There was a low infection rate in the pet and working dog populations in India and Sri Lanka, but this may change due to climate change and emerging anti-parasitic drug resistance. It was identified in this study that some regions within India and Sri Lanka have not yet been surveyed for dirofilariasis, and future studies need to target these unsurveyed areas to better understand the geographical and species distribution of dirofilariasis in these two countries. Dirofilariasis is an emerging vector-borne tropical disease of public health importance that mainly affects humans and dogs. Dirofilaria immitis and D. repens are the two well-documented dirofilariasis-causing filarioid helminths of both medical and veterinary concerns in India and Sri Lanka. This systematic review and meta-analysis aimed to describe and summarize the current evidence of dirofilariasis prevalence and distribution in India and Sri Lanka. Interestingly, D. repens is reported to circulate in both dogs (prevalence of 35.8% (95% CI: 11.23-60.69)) and humans (97% of published case reports) in India and Sri Lanka, but D. immitis is reported to be present in the dog populations in India (prevalence of 9.7% (95% CI: 8.5-11.0%)), and so far, it has not been reported in Sri Lanka. This peculiar distribution of D. immitis and D. repens in the two neighbouring countries could be due to the interaction between the two parasite species, which could affect the pattern of infection of the two worm species in dogs and thus influence the geographical distribution of these two filarial worms. In medical and veterinary practice, histopathology was the most commonly used diagnostic technique (31.3%; 95% CI 2.5-60.2%). The low specificity of histopathology to speciate the various Dirofilaria spp. may lead to misdiagnosis. It was identified in this study that several regions of India and Sri Lanka have not yet been surveyed for dirofilariasis. This limits our understanding of the geographical distribution and interspecies interactions of the two parasites within these countries. Parasite distribution, disease prevalence, and interspecies interactions between the vectors and the host should be targeted for future research.
Climate change-driven temperature increases worsen air quality in places where coal combustion powers electricity for air conditioning. Climate solutions that substitute clean and renewable energy in place of polluting coal and promote adaptation to warming through reflective cool roofs can reduce cooling energy demand in buildings, lower power sector carbon emissions, and improve air quality and health. We investigate the air quality and health co-benefits of climate solutions in Ahmedabad, India-a city where air pollution levels exceed national health-based standards-through an interdisciplinary modeling approach. Using a 2018 baseline, we quantify changes in fine particulate matter (PM(2.5)) air pollution and all-cause mortality in 2030 from increasing renewable energy use (mitigation) and expanding Ahmedabad’s cool roofs heat resilience program (adaptation). We apply local demographic and health data and compare a 2030 mitigation and adaptation (M&A) scenario to a 2030 business-as-usual (BAU) scenario (without climate change response actions), each relative to 2018 pollution levels. We estimate that the 2030 BAU scenario results in an increase of PM(2.5) air pollution of 4.13 µg m(-3) from 2018 compared to a 0.11 µg m(-3) decline from 2018 under the 2030 M&A scenario. Reduced PM(2.5) air pollution under 2030 M&A results in 1216-1414 fewer premature all-cause deaths annually compared to 2030 BAU. Achievement of National Clean Air Programme, National Ambient Air Quality Standards, or World Health Organization annual PM(2.5) Air Quality Guideline targets in 2030 results in up to 6510, 9047, or 17 369 fewer annual deaths, respectively, relative to 2030 BAU. This comprehensive modeling method is adaptable to estimate local air quality and health co-benefits in other settings by integrating climate, energy, cooling, land cover, air pollution, and health data. Our findings demonstrate that city-level climate change response policies can achieve substantial air quality and health co-benefits. Such work can inform public discourse on the near-term health benefits of mitigation and adaptation.
Compound warm-dry spells over land, which is expected to occur more frequently and expected to cover a much larger spatial extent in a warming climate, result from the simultaneous or successive occurrence of extreme heatwaves, low precipitation, and synoptic conditions, e.g., low surface wind speeds. While changing patterns of weather and climate extremes cannot be ameliorated, effective mitigation requires an understanding of the multivariate nature of interacting drivers that influence the occurrence frequency and predictability of these extremes. However, risk assessments are often focused on univariate statistics, incorporating either extreme temperature or low precipitation; or at the most bivariate statistics considering concurrence of temperature versus precipitation, without accounting for synoptic conditions influencing their joint dependency. Based on station-based daily meteorological records from 23 urban and peri-urban locations of India, covering the 1970-2018 period, this study identifies four distinct regions that show temporal clustering of the timing of heatwaves. Further, combining joint probability distributions of interacting drivers, this analysis explored compound warm-dry potentials that result from the co-occurrence of warmer temperature, scarcer precipitation, and synoptic wind patterns. The results reveal 50-year severe heat stress solely based on the temperature at each location tends to be more frequent and is expected to become 5 to 17-year compound warm-dry events considering interdependence between attributes. Notably, considering dependence among drivers, a median 6-fold amplification (ranging from 3 to 10-fold) in compound warm-dry spell frequency is apparent relative to the expected annual number of a local (univariate) 50-year severe heatwave episode, indicating warming-induced desiccation is already underway over most of the urbanized areas of the country. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00382-022-06324-y.
BACKGROUND: COVID-19 and the climate crisis have caused unprecedented disruptions across the world. Climate change has affected the mental health and wellbeing of children and adolescent. Young people with a mental illness and without social support are at an increased risk of climate change induced mental ill-health. COVID-19 resulted in a marked increase of psychological distress. Increase in depression, anxiety and insomnia have increased due to the upheavals that people were experiencing including loss of livelihood and breaking of social bonds. METHODS: This exploratory study adopted a cross sectional survey design using quantitative methods to understand the perceptions, thoughts, and feelings of young people on both the climate and COVID-19 crises, their concerns, and desires for the future and to understand their sense of agency to contribute to the changes that they want to see. FINDINGS: The findings show that most of the respondents from the sample studied reported nearly similar interference of climate change and COVID-19 on their mental wellbeing. Their climate concern and COVID-19 concern scores were comparable. Tangible experiences of extreme weather events, that were personally experienced or that impacted their family members had a negative impact on their lives, while action around improving the environment had a positive impact.Loss of income, loss of mobility and loss of social contact due to COVID-19 had negative impacts on the respondents, while indulging in leisure activities and bonding with the family had positive impacts. Although the majority of the participants reported having both climate and COVID agency, it did not translate into action to improve the environment. INTERPRETATION: Young people’s activism on climate change and COVID-19 has a positive impact on their mental wellbeing hence more opportunities and platforms must be provided to enable young people to take action on both these crises. FUNDING: None.
Purpose The purpose of this paper is to develop a replicable model that ensures Household Water Treatment and Safe Storage as well as water treatment facilities at the community level by providing total service coverage at community scale. An intervention was implemented in one of the low-lying areas of Basirhat Municipality (West Bengal, India) that included a number of action programs in order to address household- and community-level water-induced challenges. Design/methodology/approach A research study was undertaken to identify the root causes of the problems that are generally spawned from geomorphological, hydro-fluvial, climatic factors and processes and the situation becomes complicated when many other cumulative problem-contexts layovers the existing ones. A number of social and technological innovations were tested in the field and this paper critically examined the intervention processes and outcomes. It was implemented through participatory process by involving related stakeholders working at that scale so that necessary public acceptance is received for scaling up, at least, in the similar physical, social, economic and institutional contexts. Findings The problem conceptualization process, spatial assessment for contextualizing the problem, design of interventions for different scales, development of project deployment strategies from field-based learnings contributed in developing a total solution based on fusing of household-level technical solutions, social innovations and actions for community engagements towards sustainability. Mobilized community members in addressing local inundation and waterlogging crisis. Satellite image-based maps shown to make them understand the upper-lower connection of drainage. People also developed their own action plans and engaged themselves in resuscitation of an old canal, removed the garbage that resulted in improved drainage conditions in the area. Research limitations/implications Pandemic due to COVID 19 and its related prolonged lock down, West Bengal State Assembly Election, closure of municipal governance system due to the forthcoming municipal election, closure of educational institutions, closure of Anganwadi Centre in the field area were the limitations. Due to the lock down, it was difficult for the team to maintain the time frame as well as the budget. As per the Election’s Code of Conduct gets released no public meeting was allowed without permission, people in the vicinity became suspicious, hence movement of the team members got restricted. Practical implications Due to the COVID protocols, the team could not organise mass training programs. It was difficult for the team members to commute in public/private transport, hence filed work got impacted. As the team could not access data from the health department, they developed a strategy of generation data on body mass index, mid-upper arm circumferences and waist-to-hip ratios to understand the status of health and nutrition of the community. It was difficult to access the Public Health Engineering Department’s laboratory situated in the municipality for water sample test. Cost escalated due to extension of the project time. Social implications During the second phase (wave) when people lost access to health facilities they requested the team to stop field visit. Women’s empowerment through acquiring knowledge and skill on treatment and safe storage of drinking water at home. Men appreciated and recognized this, which improved the status of women in the society. Children after expressing their willingness to learn the new technology of water purification were given handholding training by their mothers and knowledge transfer has taken place in the next generation. Mobilized community members in addressing local inundation and waterlogging crisis. Satellite image-based maps to understand the upper-lower connection of drainage helped them develop their own action plans and engaged themselves in resuscitation of an old canal, removed the garbage that resulted in improved drainage conditions in the area. Originality/value Household-level solutions include supply of low cost, easy operable, sustainable water purifiers, community-level solution focused on securing water-related challenges at social/public gathering places and wider catchment area level solutions include the engagement of local communities to drain out stagnant waters by clearing drains, creating/digging small canals through collective actions. Geo-spatial techniques (topographical mapping, spatial survey, water quality tests) along with social methods such as participatory appraisals for gathering information on human health, public awareness campaigns and partnership development with local government agencies were the major activities performed as part of the implementation of interventions. It is imperative to mention that water-related challenges in the low-lying settlement areas of Basirhat Municipality have effectively been addressed by relying on necessary theoretical underpinnings (Disaster risk reduction/humanitarian principles) transmitted through application of scientific techniques and mediated through local people and their agencies.
Malaria is an endemic disease in India and targeted to eliminate by the year 2030. The present study is aimed at understanding the epidemiological patterns of malaria transmission dynamics in Assam and Arunachal Pradesh followed by the development of a malaria prediction model using monthly climate factors. A total of 144,055 cases in Assam during 2011-2018 and 42,970 cases in Arunachal Pradesh were reported during the 2011-2019 period observed, and Plasmodium falciparum (74.5%) was the most predominant parasite in Assam, whereas Plasmodium vivax (66%) in Arunachal Pradesh. Malaria transmission showed a strong seasonal variation where most of the cases were reported during the monsoon period (Assam, 51.9%, and Arunachal Pradesh, 53.6%). Similarly, the malaria incidence was highest in the male population in both states (Asam, 55.75%, and Arunachal Pradesh, 51.43%), and the disease risk is also higher among the > 15 years age group (Assam, 61.7%, and Arunachal Pradesh, 67.9%). To predict the malaria incidence, Bayesian structural time series (BSTS) and Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMAX) models were implemented. A statistically significant association between malaria cases and climate variables was observed. The most influencing climate factors are found to be maximum and mean temperature with a 6-month lag, and it showed a negative association with malaria incidence. The BSTS model has shown superior performance on the optimal auto-correlated dataset (OAD) which contains auto-correlated malaria cases, cross-correlated climate variables besides malaria cases in both Assam (RMSE, 0.106; MAE, 0.089; and SMAPE, 19.2%) and Arunachal Pradesh (RMSE, 0.128; MAE, 0.122; and SMAPE, 22.6%) than the SARIMAX model. The findings suggest that the predictive performance of the BSTS model is outperformed, and it may be helpful for ongoing intervention strategies by governmental and nongovernmental agencies in the northeast region to combat the disease effectively.
Air pollution is a major threat to human health in India. More than three-quarters of the people in India are exposed to pollution levels higher than the limits recommended by the National Ambient Air Quality Standards in India and significantly higher than those recommended by the World Health Organization. Despite the poor air quality, the monitoring of air pollution levels is limited even in large urban areas in India and virtually absent in small towns and rural areas. The lack of data results in a minimal understanding of spatial and temporal patterns of air pollutants at local and regional levels. This paper is the second in a planned series of papers presenting particulate air pollution trends monitored in small cities and towns in India. The findings presented here are important for framing state and regional level policies for addressing air pollution problems in urban areas, and achieve the sustainable development goals (SDGs) linked to public health, reduction in the adverse environmental impact of cities, and adaptation to climate change, as indicated by SDGs 3.9, 11.6 and 11.b.
Human beings are adversely affected by climate extremes, pertinent to an increase in frequency and intensity of warm temperatures, eventually inducing warming on a global and regional scale. In a tropical nation like India, high summer temperature and increased moisture with the arrival of the southwest monsoon (hereafter referred to as monsoon) aggravate the sultriness of the ambient environment. Irrespective of global climate change, cities alter their climate due to urban materials’ impervious surfaces and thermal properties, which upsurge moisture and temperature in urban settings. Thus, urban dwellers are peculiarly vulnerable to heat stress health hazards. Heat stress indices allow quantitative assessment of thermal stress to determine the safe limits of thermal exposure. In the present study, statistical trends in Heat Index were evaluated to analyze heat stress over 41 urban stations of southern peninsular India over the summer and monsoon season from 1969 to 2015. Results indicated that almost all stations registered a significant increase at 95% confidence level in heat stress except for an insignificant decrease at a few stations. Changepoint detection depicted an increase in heat stress initiated in the late 1990s and early years of the decade 2000 at most urban stations. Hierarchical cluster analysis partitioned data into seven spatial units. Accordingly, the highest magnitude of increase was observed over cities located in the northeastern part of the study area and the southern tip of peninsular India. The study demands attention to perilous health risks related to India’s increasing heat stress casualties and the need for an indigenous thermal stress alerts system.
Heatwaves in the summer and extreme rainfall in the following summer monsoon season over the same regions in India pose severe challenges for adaptation in agriculture, infrastructure, and public health. However, the risks and drivers of the sequential extremes in India remain unrecognized. Here, we show that the mega-heatwaves in summer and extreme rainfall in the summer monsoons of 1995 and 1998 affected 20% and 8% of India, respectively. The area affected by the sequential extremes is significantly (p < 0.05) higher during the positive phase (El Nino) than the negative phase. The fraction of the total population and urban area exposed to sequential extremes will increase rapidly if the global mean temperature rises above 1.5 degrees C from the pre-industrial level. A considerable improvement in socioeconomic livelihood and infrastructure will be needed to reduce vulnerability and maintain the same level of risk at 1.5 degrees C at higher global warming levels.
The wellbeing of mountain communities is determined by the availability and accessibility of ecosystem goods and services. We assessed the relationship between forest quality and wellbeing of local communities of Nanda Devi Biosphere Reserve (NDBR) in the Upper Ganga River Basin, Western Himalayas, India. We used 14 relevant Sustainable Development Goals of the United Nations as indicators to assess wellbeing. Data on these indicators were collected in 22 villages that were selected based on secondary demographic information, remoteness, and the state of the forest resources, which we classified into degraded and less-degraded. Semi-structured questionnaire-based interviews were conducted in randomly selected households (n = 764). The households located close to forests scored higher on wellbeing indicators than the households located further away from forests as they have better accessibility to forest resources and freshwater, which provides alternatives to market and agricultural products. Households with access to less-degraded resources also had access to wild fruits, vegetables, and medicinal plants adding to their food and health security. Our study found that the combination of climate change, declining forest resources, and the expansion of the market-based economy is leading to shifts in traditional cropping patterns and hence the nutritional status and forest use patterns of local people, making them vulnerable to diseases and hunger. Accessibility to an intact forest patch near a village contributes to the wellbeing of people and increases their resilience in the face of climate change and the changes dictated by the market forces.
The rapid pace of urbanization makes it imperative that we better understand the influence of climate forcing on urban malaria transmission. Despite extensive study of temperature effects in vector-borne infections in general, consideration of relative humidity remains limited. With process-based dynamical models informed by almost two decades of monthly surveillance data, we address the role of relative humidity in the interannual variability of epidemic malaria in two semi-arid cities of India. We show a strong and significant effect of humidity during the pre-transmission season on malaria burden in coastal Surat and more arid inland Ahmedabad. Simulations of the climate-driven transmission model with the MLE (Maximum Likelihood Estimates) of the parameters retrospectively capture the observed variability of disease incidence, and also prospectively predict that of ‘out-of-fit’ cases in more recent years, with high accuracy. Our findings indicate that relative humidity is a critical factor in the spread of urban malaria and potentially other vector-borne epidemics, and that climate change and lack of hydrological planning in cities might jeopardize malaria elimination efforts.
Aim: Climate and weather conditions play a crucial role in the dynamics and distribution of ticks and tick-borne diseases. In this study, we explored the influence of a heavy rainfall (flood) occurrence on the seasonal activity and density of host-seeking Haemaphysalis tick vectors in Wayanad district, Kerala, India. Methodology: Wayanad district in Kerala state was selected as the study area. Ticks were collected from December 2017 to May 2019, monthly for five consecutive days by dragging method. Tick density was analyzed with climate data obtained from the meteorological station. Results: The total number of ticks collected post-flood decreased to 59% in Kurichiyad (site 1) and 63% in Muthanga (site 2), and the seasonal nymphal peak density was shifted. A seasonal peak of tick activity was normally observed from December to February. This peak occurrence was missing after flood in the study areas created with waterlogging and vegetation overgrowth. Interpretation: The present study revealed the effect of flood events in the study sites with significant differences in the abundance of five Haemaphysalis tick species during pre and post-flood periods and forest and wildlife habitats. This difference in the changing climatic conditions and increasing annual flood seasons in the Western Ghats may shift this region’s ticks questing activity and tick-borne disease ecology.
The study of human biometeorological conditions is becoming increasingly important in climate perception for the improvement of public health system. The present study investigates the long-term thermal bioclimate conditions in four stations of West Bengal, India. Kolkata, the capital city of West Bengal, and three suburban stations, namely, Dum Dum, Canning and Diamond Harbour, located in the adjacent districts of Kolkata, have been selected. The biometeorological conditions have been estimated by physiological effective temperature (PET) and modified physiologically equivalent temperature (mPET) at 1130 h and 1730 h (IST) based on 42 years of meteorological data. The initial purpose of this study is to present the monthly distribution of PET and mPET categories and further highlight the structure of each thermal index in four tropical climate locations. The results from this analysis reveal higher human thermal stress in Kolkata compared to other neighbouring stations during the period from 1979 to 2018. Reverse behaviour was observed from 2018 to 2020 indicating that Diamond Harbour and Canning are warmer in terms of human thermal stress compared to Kolkata and Dum Dum. The results captured has also been validated by mean monthly, mean seasonal PET and mPET index difference between Kolkata (urban station) and other three stations (suburban areas). During the past period (1979-2018), highest differences in PET and mPET were recorded in Canning and Diamond Harbour for the months September to November (SON), varying between 4 and 5 degrees C both at prenoon and evening. The second highest differences of indices ranging from 2.5 to 3.5 degrees C were observed during December to February (DJF). For the last two years (2018-2020), the seasonal differences of PET and mPET are negative, implying that Dum Dum, Canning and Diamond Harbour at 1130 h are warmer by a maximum of 2 degrees C in comparison to Kolkata. Finally, the mean annual thermal indices of each year show a growing trend in all the four stations with a variation of 0.4 degrees C to 0.7 degrees C and 1.1 degrees C to 1.3 degrees C in early noon and evening measurements respectively for 40 years.
It is important to study the recent malaria incidence trends in urban areas resulting from rapid urbanization that can lead to changes in environmental conditions for malaria. This retrospective study assessed trends in malaria patients, their distribution according to parasite species, patient demographics, and weather data for the past 8 years at a malaria clinic in the National Institute of Malaria Research, New Delhi, India. We overlaid the effects of environmental factors such as rainfall, relative humidity, and temperature on malaria incidence. The malaria data were digitized for a period spanning 2012 to 2019, during which 36,892 patients with fever attended the clinic. Of these, 865 (2.3%) were diagnosed with malaria microscopically. Plasmodium vivax was predominant (96.2%), and very few patients were of Plasmodium falciparum (3.5%) or mixed infections (0.3%). The patients with malaria were within a 10-km radius of the clinic. Males (70.9%) were more commonly affected than females (29.1%). Of the total malaria patients, a majority (∼78%) belonged to the > 15-year age group. A total of 593 malaria patients (68.6%) received primaquine. These patients were most commonly diagnosed in April through October. Furthermore, there was a lag of 1 month between the rainfall peak and the malaria case peak. The peak in malaria cases corresponded to a mean temperature of 25 to 30°C and a relative humidity of 60% to 80%. This analysis will be useful for policymakers in evaluating current interventions and in accelerating malaria control further in urban areas of India.
Floods are the most commonly occurring natural disasters in India due to India’s unique geographical location and socioeconomic conditions. Frequent flooding causes enormous loss of human lives and damage crops and public utilities. Furthermore, floods adversely affect economic development and increase the government’s financial burden by increasing spending on various disaster mitigation measures. Recent empirical literature based on cross-national comparisons shows that disaster fatalities and damages are monotonically decreasing in per capita income. We challenge this view on the monotonic negative relationship between income and flood damages. We examine the non-monotonic (inverted U-shaped) relationship between per capita income and flood impact in terms of deaths, people affected, and damages due to floods in 19 major Indian states from 1980 to 2011, using Poisson and Tobit estimation methods. In particular, deaths and the population affected by floods increase with a turning point of income up to 882 US$ and 578 US$, respectively, and diminishes thereafter. Our results confirm an inverted U-shaped relationship between income and fatalities and the population affected by floods. In addition to income, we argue that government responsiveness plays an essential role in mitigating the risk of floods. We employ the fixed-effect Poisson estimation method to examine the government’s role in protecting people against disaster risk, focusing on regional differences in India. Deaths from floods remain non-linear and follow the inverted U-pattern with respect to government responsiveness. However, the effect of government responsiveness on flood fatalities and flood damages is statistically insignificant. Our results further suggest that high-income states experience a lower death toll from floods. The high-income (rich) states are capable of incurring a higher threshold level of income and higher natural calamity expenditure to reduce flood fatalities and protect the population affected by floods than the low-income (poor) states. The poor states have minimal resources and face severe financial constraints to reduce the death toll from floods. From the perspective of public policy, the poor states, in particular, require an increase in income, better governance, and effective disaster management policies to mitigate flood impact.
Fetal life and infancy are critical periods when adverse environmental conditions, such as natural disasters, may alter a developing organism, leading to life-lasting unfavorable health outcomes, such as central body fat distribution. Therefore, the aim of this study was to assess the effect of the exposure to cyclone Aila in utero or during infancy on the relative subcutaneous adiposity distribution in preadolescent Indian children. The study included children prenatally (N = 336) or postnatally (during infancy, N = 212) exposed to Aila and a non-affected group (N = 284). Anthropometric indices involved, i.e., subscapular, suprailiac, triceps, and biceps skinfolds. The relative adiposity distribution (PC1) and socioeconomic status (SES) were assessed using principal component analysis. An analysis of covariance and Tukey’s post hoc test for unequal samples were performed to assess the effect of exposure to a natural disaster on the PC1, controlling for age, sex, Z-BMI, and SES. Prenatally and postnatally Aila-exposed children revealed a significantly more central-oriented pattern of relative subcutaneous fat distribution compared to the controls (p < 0.05). Early-life exposure to a natural disaster was related to an adverse pattern of relative adipose tissue distribution in preadolescent children.
Since the adoption of Sustainable Development Goals (SDGs) in 2015, various countries across the world have started programmes to achieve the relevant targets under SDGs. The advancements in research and development play a crucial role in achieving these targets. Motivated by this a few studies have tried to map the research publications with their relevance to specific SDGs. However, there are no existing detailed studies with reference to India. Therefore, this article attempts to measure the research activities on SDGs in India. It utilises standard bibliometrics approach and textual analysis of data collected from Dimensions database for a five-year period (2016-2020). The results show a positive response from the Indian research community towards the SDGs. About 12 percent of the total research output from India is found directly related to SDGs. The three SDGs namely SDG 3 (Good Health and Well-being), SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Change) have received most attention from the Indian research community. Technical subjects such as, Engineering, Medical and Health Sciences, and Chemical Sciences are the main contributors. The major contributing institutions, authors and journals are identified.
Many children in India face the double burden of high exposure to ambient (AAP) and household air pollution, both of which can affect their linear growth. Although climate change mitigation is expected to decrease AAP, climate policies could increase the cost of clean cooking fuels. Here, we develop a static microsimulation model to project the air pollution-related burden of child stunting in India up to 2050 under four scenarios combining climate change mitigation (2 degrees C target) with national policies for AAP control and subsidised access to clean cooking. We link data from a nationally representative household survey, satellite-based estimates of fine particulate matter (PM2.5), a multi-dimensional demographic projection and PM2.5 and clean cooking access projections from an integrated assessment model. We find that the positive effects on child linear growth from reductions in AAP under the 2 degrees C Paris Agreement target could be fully offset by the negative effects of climate change mitigation through reduced clean cooking access. Targeted AAP control or subsidised access to clean cooking could shift this trade-off to result in net benefits of 2.8 (95% uncertainty interval [UI]: 1.4, 4.2) or 6.5 (UI: 6.3, 6.9) million cumulative prevented cases of child stunting between 2020-50 compared to business-as-usual. Implementation of integrated climate, air quality, and energy access interventions has a synergistic impact, reducing cumulative number of stunted children by 12.1 (UI: 10.7, 13.7) million compared to business-as-usual, with the largest health benefits experienced by the most disadvantaged children and geographic regions. Findings underscore the importance of complementing climate change mitigation efforts with targeted air quality and energy access policies to concurrently deliver on carbon mitigation, health and air pollution and energy poverty reduction goals in India.
The aim of this study was to assess the relation between early exposure to stressful events and symptoms of the Attention-Deficit/Hyperactivity Disorder (ADHD) in children, based on the outcomes from a natural experiment. It was hypothesized that children pre- and postnatally exposed to cyclone Aila have increased ADHD symptoms compared to the control group, and the effect depends on the timing of the exposure. Indian children (8-11 years) prenatally (N = 336) and early postnatally (N = 216) exposed to cyclone Aila were compared to a non-exposed control peer group (N = 285). ADHD symptoms were assessed using the Conner’s Teacher Rating Scale Revised. The main effect of exposure to the cyclone on the total ADHD symptoms’ score, ADHD index, Hyperactivity and Oppositional symptoms was significant and independent to covariates: age and sex of children, gestational age and birth weight, maternal stress during the year before the study and the socioeconomic status of a family. The timing of exposure and sex of the children were found to be a significant moderator of the relation between early exposure to the natural disaster and ADHD symptoms. The prenatal, but also early postnatal exposure to stressful experiences such as a natural disaster, may disturb the development of cognitive functions and behavioural control, thus increasing the risk of ADHD in children.
Due to the increase in extreme rainfall events in India, there is an urgent need for prior communication of the expected impacts and appropriate responses in order to mitigate the losses of lives and damage to property. Extreme rainfall events cause numerous casualties, damage to property and infrastructure and vast displacement of people. Hence, the development of an approach where the rainfall forecasts are well analyzed, associated risks are identified, and the probable impacts are clearly communicated to relevant stakeholders is required. In this study, we aim to develop a framework for generating the impact-based forecasts (IBF) and associated warning matrices over the selected districts of eastern Uttar Pradesh, India, by integrating the rainfall forecasts and the socio-economic characteristics such as population, economy and agriculture. The selected districts lack proper infrastructure, have poor socio-economic conditions and have been historically prone to frequent extreme rainfall. The basic idea is to estimate the impacts that could occur over various sectors of population, economy and agriculture and suggest appropriate actions in order to mitigate the severity of the impacts. To this end, we identify the vulnerable districts based on the frequency of the number of extreme rainfall forecasts (ERFs) in the past four years (2017-2020) and the nature of socio-economic conditions. We selected three vulnerable districts based on the expected impacts, i.e., Shravasti (high category), Gorakhpur (medium category) and Jaunpur (low category) and subsequently, the corresponding IBFs are generated. Furthermore, a warning matrix is created for each district which provides updated information regarding the potential risk for a district a few days in advance. This study is significant since it identifies the different levels of potential impact over multiple sectors of society, presents a framework to generate impact-based forecasts and warnings, informs about the expected impacts, and suggests mitigation actions to reduce potential damage and losses.
This paper presents the groundwater quality assessment of the upper Brahmaputra floodplains of Assam on a seasonal basis. A total of 88 samples were analyzed for the presence of potentially toxic elements in two seasons. In addition, an attempt is made to identify any possible associated health risks to the residents via the drinking water pathway. The study reveals the presence of various potentially toxic elements, in particular, manganese, iron, nickel, and fluoride concentration exceeding the drinking water specifications set by BIS and WHO drinking water standards. The degree of groundwater contamination was assessed using the Water Quality Index, Heavy metal Pollution Index, Heavy metal Evaluation Index, and Degree of Contamination. The spatial distribution maps of groundwater quality were prepared using geographical information system. The non-carcinogenic health risk was evaluated using hazard quotients and hazard index as per the United States Environmental Protection Agency methodology. The hazard quotient of fluoride and manganese have values > 1, which exceeds USEPA recommended benchmark. The health risk assessment identified that the risk was highest during the pre-monsoon season, and the child population is more vulnerable to non-carcinogenic risk than the adults. Findings of cancer risk identified that pre-monsoon groundwater samples from the Golaghat District pose the highest health risks in the upper Brahmaputra floodplains. The risk is highest in the southwest of the study area, followed by the south and then by the north.
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.
A study was conducted to understand if the disaster death in Odisha, India across five categories, viz. tropical cyclone, lightning, heat wave, cold wave and extreme precipitation events underwent any significant change during 2001–14. It was based on timeseries data available at the National Data Portal of India. Results of the study suggest that the number of fatalities from sporadic meso-scale meteorological hazards like cyclones and heavy precipitation have drastically reduced due to better forecasting and effective evacuation strategies adopted by the Government. However, fatalities due to more frequent recurring extreme events, such as lightning and heat stress are on the rise. Male adults and middle-aged people (30–44 and 45–59 years respectively) constituted the most vulnerable groups affected by lightning and heat stress which account for maximum number of deaths in the state. Older population (especially older women) were more vulnerable towards cold wave due to reduced thermoregulatory mechanism. The finding is significant, because often deaths due to lightning injury, heat stress and cold wave either go unnoticed or are under-reported. We expect that the present study which focuses on gender and age disaggregated death would help in adopting more targeted mitigation or adaptation strategies in Odisha. The study also points out the need of a single and detailed spatio-temporal data infrastructure for all kinds of disaster deaths for more in-depth and insightful analysis.
The aim of this study was to describe the correlation between the meteorological and air pollution parameters with the temporal pattern of presentation of recent onset allergic eye disease (AED). This cross-sectional hospital-based study included new patients (≤21 years of age) presenting between January 2016 and August 2018 from the district of Hyderabad with a clinical diagnosis of AED and an acute exacerbation of recent onset of symptoms of less than 3 months duration. Correlation analysis was performed with the local environmental rainfall, temperature, humidity, windspeed, and air pollution. Of the 25,354 new patients hailing from the district of Hyderabad, 2494 (9.84%) patients were diagnosed with AED, of which 1062 (4.19%) patients had recent onset of symptoms. The mean monthly prevalence in this cohort was 4.13%, and the month of May (6.09%) showed the highest levels. The environmental parameters of humidity (r(2) = 0.83/p = < 0.0001) and rainfall (r(2) = 0.41/p = 0.0232) showed significant negative correlation, while temperature (r(2) = 0.43/p = 0.0206) and ground-level ozone (r(2) = 0.41/p = 0.0005) showed significant positive correlation with the temporal pattern of AED in the population. An increase in rainfall and humidity was associated with a lower prevalence, and an increase of temperature and ground-level ozone was associated with a higher prevalence of AED cases during the year among children and adolescents.
Gender mediates climate vulnerability and adaptation action. Consequently, climate change adaptation policy has seen a push towards ‘mainstreaming’ gender and prioritizing ‘gender-responsive’ climate action. However, it is unclear to what extent this mainstreaming advances or obscures gender considerations and whether current climate policies reflect developments in the gender and climate change literature. This paper explores how gender is operationalized in adaptation policy in India through a policy review of 28 State Action Plans on Climate Change. We juxtapose normative goals around reducing differential vulnerability with policy approaches to mainstreaming gender and propose entry points that link advances in gender and feminist studies with climate change adaptation policy. Our analysis indicates that most subnational climate policies in India explicitly mention gender as a mediator of vulnerability and adaptive capacity but operationalize it inadequately and unevenly. We also reflect on how the heuristics of mainstreaming get operationalized in policies (gender-blind, gender-sensitive, to gender-transformative approaches) and what that means for addressing gendered vulnerability.
The study of various air pollutants and meteorological parameters are very important for all the researchers. Baleswar was known to be a seaside districts of odisha which is the economic and cultural heart of Northern Odisha.The aim of this study is to measure the air pollutants, meteorological parameters and to enumerate the air pollution index at three specific sites (Sahadevkhunta, Mallikashpur, Rasalpur) according to CPCB procedures. The air pollutants analysed by supplying through specific absorbing reagents and the pollutants were analysed up to 3 year (2017, 2018 and 2019) with a regularity of thrice per week. Analyses of our data sets showing that SO2 and NO2 concentration during summer, rainy and winter season are within the prescribe standard of NAAOS by CPCB but PM10 and PM2.5 are above the prescribed standard except PM2.5 concentration of rainy season in year 2019. Air pollution index is remaining in the condition between clean air (CA) to moderate air pollution (MAP) and it shows that the pollution index in all the sites are reducing from the year 2017 to 2019 may be due to enhancing technologies to reduce the pollutant concentration in air.
OBJECTIVES: Despite periodic outbreaks, the causes and risk factors of acute encephalitis syndrome (AES) in children of Muzaffarpur, Bihar, India, remain unknown. We explored the correlation between AES caseload and the climate parameters. METHODS: Data for 1318 hospitalized children with AES during 2012-20 were used. The correlation between AES cases and daily climate parameters (temperature, sunshine, rainfall, humidity and wind speed) for the previous 24, 48 and 72 h were examined using Pearson’s and Spearman’s rank-order correlation and Poisson regression or negative binomial regression analyses. RESULTS: Most (91.8%) of the AES cases occurred during the summer season (May-July months), especially June month. Pearson’s and Spearman’s rank-order correlation analyses revealed that AES caseload had positive correlations with maximum (r = 0.275, ρ = 0.293) and minimum (r = 0.306, ρ = 0.306) temperatures during past 24 h and heat index (r = 0.325, ρ = 0.325) and negative correlation with humidity (r = -0.222, ρ = -0.222) and rainfall (r = -0.183, ρ = -0.183) (all p < 0.05). The correlation was consistent for the climate parameters for the past 24, 48 and 72 h. Regression analysis also documented a significant association of AES cases with daily maximum (β: 0.32-0.36) and minimum (β: 0.53-0.62) temperatures and heat index (β: 0.92-1.03) over past 24, 48 and 72 h (all p < 0.01). The number of AES cases exponentially increased when the daily maximum and minimum temperatures crossed 40°C and 31°C, respectively. CONCLUSIONS: The climate parameters, especially temperature appears to be a risk factor for AES in children. The definite aetiological role of heat for AES in children needs further exploration.
PURPOSE: Big data is the new gold, especially in health care. Advances in collecting and processing electronic medical records (EMR) coupled with increasing computer capabilities have resulted in an increased interest in the use of big data in health care. Ophthalmology has been an area of focus where results have shown to be promising. The objective of this study was to determine whether the EMR at a multi-tier ophthalmology network in India can contribute to the management of patient care, through studying how climatic and socio-demographic factors relate to eye disorders and visual impairment in the State of Telangana. METHODS: The study was designed by merging a dataset obtained from the Telangana State Development Society to an existing EMR of approximately 1 million patients, who presented themselves with different eye symptoms and diagnosed with several diseases from the years (2011-2019). The dataset obtained included weather and climatic variables to be tested alongside eye disorders. AI creative featuring techniques have been used to narrow down the variables most affected by climatic and demographic factors, with the application of the Cynefin Framework as a guide to simplify and structure the dataset for analysis. RESULTS: Our findings revealed a high presence of cataract in the state of Telangana, mostly in rural areas and throughout the different weather seasons in India. Males tend to be the most affected as per the number of visits to the clinic, while home makers make the most visit to the hospital, in addition to employees, students, and laborers. While cataract is most dominant in the older age population, diseases such as astigmatism, conjunctivitis, and emmetropia, are more present in the younger age population. CONCLUSION: The study appeared useful for taking preventive measures in the future to manage the treatment of patients who present themselves with eye disorders in Telangana. The use of clinical big datasets helps to identify the burden of ocular disorders in the population. The overlaying of meteorological data on the clinical presentation of patients from a geographic region lends insight into the complex interaction of environmental factors on the prevalence of ocular disorders in them.
Globally, billions of people are experiencing food insecurity and malnutrition. The United Nations has set a global target to end hunger by 2030, but we are far from reaching it. Over the decade, climate change, population growth and economic slowdown have impacted food security. Many countries are facing the challenge of both undernutrition and over nutrition. Thus, there is a need to transform the food system to achieve food and nutrition security. One of the ways to reach closer to our goal is to provide an affordable healthy and nutritious diet to all. Millets, the nutri-cereals, have the potential to play a crucial role in the fight against food insecurity and malnutrition. Nutri-cereals are an abundant source of essential macro- and micronutrients, carbohydrates, protein, dietary fiber, lipids, and phytochemicals. The nutrient content and digestibility of millets are significantly influenced by the processing techniques. This review article highlights the nutritional characteristics and processing of Indian millets, viz. foxtail, kodo, proso, little, and pearl millets. It also envisages the effect of traditional and modern processing techniques on millet’s nutritional properties. An extensive literature review was conducted using the research and review articles related to processing techniques of millets such as fermentation, germination, dehulling, extrusion, cooking, puffing, popping, malting, milling, etc. Germination and fermentation showed a positive improvement in the overall nutritional characteristics of millets, whereas excessive dehulling, polishing, and milling resulted in reduction of the dietary fiber and micronutrients. Understanding the changes happening in the nutrient value of millets due to processing can help the food industry, researchers, and consumers select a suitable processing technique to optimize the nutrient value, increase the bioavailability of nutrients, and help combat food and nutrition security.
In low-and-middle-income countries, the provisioning of safe drinking water is a challenge that will likely worsen with climate change. Securing water will require more work and time, burdening women and children the most. Currently, the consequences of this time burden to children’s development remain understudied. To address this gap, we examine the tradeoff between children’s household water collection responsibilities and learning achievement. Using nationally representative data from India, we measure the effect of daily fetching time on primary school children’s learning achievement in a two-stage regression model, with rainfall as the instrument. Our analyses indicate that higher fetching times predict lower mathematics (-1.23 standard deviations, 95CI[-2.32, -0.14]), reading (-1.13 standard deviations, 95CI[-2.07, -0.19]), and writing (-1.21 standard deviations, 95CI[-1.89, -0.51]) test scores. These effects are heterogeneous across sex and infrastructure type. For example, we find girls’ mathematical and reading skills profit more from reductions in fetching time than boys’ (score less affected for boys by β amount: mathematics: β=0.26 points, 95CI[0.095, 0.42]; reading: β=0.27 points, 95CI[0.054, 0.49]). Children using hand pumps, open wells, or tube wells are hurt more academically in mathematics and writing by increases in fetching time than children with mostly off-premises piped access (e.g., writing scores more affected by β amount: hand pump: β=-0.18, 95CI[-0.29, -0.081]; open well: β=-0.18, 95CI[-0.33, -0.040]; tube well: β=-0.14, 95CI[-0.29, -0.00072]). Given these results, we recommend off-premises piped infrastructure in the absence of piped-to-premises water in water-insecure contexts and offer guidance for targeting infrastructure investments in India.
PURPOSE: The aim of this study was to describe the correlation between the temporal pattern of presentation of acute microsporidial keratoconjunctivitis (MKC) with meteorological parameters such as environmental temperature, rainfall, humidity, windspeed, and air pollution. METHODS: This cross-sectional hospital-based study included 182,789 patients presenting between January 2016 and December 2019 hailing from the district of Hyderabad. Patients with a clinical diagnosis of MKC in at least one eye with an acute onset (≤1 week) of presentation were included as cases. Correlation analysis was performed with the local environmental temperature, rainfall, humidity, and windspeed (Telangana State Development and Planning Society) and air pollution (Central Pollution Control Board, Government of India). RESULTS: Overall, 84 (0.05%) patients were diagnosed with acute onset MKC from the district of Hyderabad. The mean monthly prevalence in this cohort was 0.05% with peak prevalence in the months of July (0.08%), August (0.09%), September (0.12%), and October (0.08%). The environmental parameters of rainfall (r(2) = 0.87/P = < 0.0001), humidity (r(2) = 0.78/P = 0.0001), windspeed (r(2) = 0.38/P = 0.0321) were significantly positively correlated and the air pollution parameters such as ground level ozone (r(2) = 0.89/P = < 0.0001), particulate matter PM(10) (r(2) = 0.65/P = 0.0013), PM(2.5) (r(2) = 0.50/P = 0.0095), nitrogen dioxide (r(2) = 0.53/P = 0.0071), and carbon monoxide (r(2) = 0.69/P = 0.0008) were significantly negatively correlated with the temporal pattern of MKC in the population. CONCLUSION: Parasitic infections like MKC show a distinct temporal trend peaking during the monsoon season in the population. An increase in humidity, wind speed, and especially rainfall contributes to a higher prevalence of MKC cases during the year. An increase in ground-level ozone seems to be protective against infection.
Skills are an important predictor of labour, education, and wellbeing outcomes. Understanding the origins of skills formation is important for reducing future inequalities. This paper analyses the effect of shocks in-utero on human capital outcomes in childhood and adolescence in India. Combining historical rainfall data and longitudinal data from Young Lives, we estimate the effect of rainfall shocks in-utero on cognitive and non-cognitive skills development over the first 15 years of life. We find negative effects of rainfall shocks on receptive vocabulary at age 5, and on mathematics and non-cognitive skills at age 15. The negative effects on cognitive skills are driven by boys, while the effect for both cognitive and non-cognitive skills are driven by children of parents with lower education, suggesting that prenatal shocks might exacerbate pre-existing inequalities. Our findings support the implementation of policies aiming at reducing inequalities at very early stages in life.
INTRODUCTION: The impact of climate change on agriculture and food security has been examined quite thoroughly by researchers globally as well as in India. While existing studies provide evidence on how climate variability affects the food security and nutrition, research examining the extent of effect vulnerability of agriculture to climate change can have on nutrition in India are scarce. This study examined a) the association between the degree of vulnerability in agriculture to climate change and child nutrition at the micro-level b) spatial effect of climate vulnerability on child nutrition, and c) the geographical hotspots of both vulnerability in agriculture to climate change and child malnutrition. METHODS: The study used an index on vulnerability of agriculture to climate change and linked it to child malnutrition indicators (stunting, wasting, underweight and anaemia) from the National Family Health Survey 4 (2015-16). Mixed-effect and spatial autoregressive models were fitted to assess the direction and strength of the relationship between vulnerability and child malnutrition at macro and micro level. Spatial analyses examined the within-district and across-district spill-over effects of climate change vulnerability on child malnutrition. RESULTS: Both mixed-effect and spatial autoregressive models found that the degree of vulnerability was positively associated with malnutrition among children. Children residing in districts with a very high degree of vulnerability were more like to have malnutrition than those residing in districts with very low vulnerability. The analyses found that the odds of a child suffering from stunting increased by 32%, wasting by 42%, underweight by 45%, and anaemia by 63% if the child belonged to a district categorised as very highly vulnerable when compared to those categorised as very low. The spatial analysis also suggested a high level of clustering in the spatial distribution of vulnerability and malnutrition. Hotspots of child malnutrition and degree of vulnerability were mostly found to be clustered around western-central part of India. CONCLUSION: Study highlights the consequences that vulnerability of agriculture to climate change can have on child nutrition. Strategies should be developed to mitigate the effect of climate change on areas where there is a clustering of vulnerability and child malnutrition.
Mountains are considered as the early indicators of climate change. The study aims to understand how the Himalayan communities perceive climate change, and how this change has impacted the livelihood and sus-tenance of local people particularly in the remote and rural areas of the region. In view of this, 994 house-holds of 25 villages were interviewed from five basins (five villages per basin) of the Indian Himalayan Region. Their perceptions mainly of climate change were validated/compared with the available climatic indices. People perceived rainfall pattern to be less predictable, greater change in land-use pattern, adverse impacts on forests and human health and overall reduction in their harvests. Seasonal increase in temperature was also reported. Capacity-building programmes for the inhabitants, including the most vulnerable communities in the wake of climate change would be significantly fruitful by way of mitigation and adaptation strategies.
Does deprivation of assets imply an increased likelihood of vulnerability to climate change? Our study attempts to answer this question by analyzing the multidimensional poverty in rural Bihar, followed by deciphering the link between the developed poverty index and vulnerability of agriculture to climate change. Vulnerability index used in the present study was developed under the National Initiative on Climate Resilient Agriculture (NICRA) by the Government of India. As most people in rural areas and those dependent on rural based livelihood activities (such as agriculture, forestry and livestock) are more vulnerable to vagaries of weather, related attributes (i.e. land ownership, livestock ownership as well as access to agriculture equipments) are included in our analysis. We found that the extent of multidimensional poverty in rural Bihar was 0.278 indicating that rural poor were deprived in 27.8 percent of the total potential deprivations that they could experience overall. It varied from 0.19 to 0.39 across districts. The findings highlight that a majority of the population were deprived in living standard dimension, followed by health and education dimensions. Further, the districts where the multidimensional poverty was high were also more vulnerable to climate change. The study advocates for formulation of district specific programs that can target major contributing factors resulting in reducing the extent of multidimensional poverty and vulnerability.
Toxic gaseous organic air pollutants such as benzene, toluene, ethylbenzene, and xylene isomers (m, p, and o-x) (BTEX) are considered hazardous due to its adverse impacts on human health and on climate change. This review identifies the major research questions addressed so far and the research gap in research articles, published between 2001 and 2022, focusing on the ambient BTEX concentrations in different locations in India along with its sources, ozone formation potential (OFP), and associated health risks. The ambient levels of BTEX were also compared with those of other Asian countries. A comparison of ambient BTEX levels with different microenvironments in India is also presented. BTEX concentrations were found in the range of 30.95 to 317.18 mu g m(-3) and multi-fold higher in urban environments than those measured in the rural air. In most reported studies, the order of occurrence of BTEX compounds was toluene > benzene > xylene isomers > ethylbenzene and winter had higher concentrations than in other seasons, including summer. As far as BTEX levels in classified areas of urban environments are concerned, traffic locations have shown the highest BTEX concentrations, followed by residential, commercial, and industrial locations. OFP indicated that xylene isomers and toluene contributed to ozone formation. The major gaps in reported studies on BTEX measurement are (1) source apportionment; (2) impact on lower tropospheric chemistry, human health, and climate change; and (3) removal techniques from air.
The effects of climate on infectious diseases could influence the health impacts, particularly in children in countries with the unfair socioeconomic conditions. In a prospective cohort of 461 children under 16-years-of-age in Varanasi city, India, the association of maximum-temperature (Tmax), relative humidity (RH), absolute humidity (AH), rainfall (RF), wind-speed (WS), and solar radiation (SLR) with prevalent infectious diseases (Diarrhea, Common cold and flu, Pneumonia, Skin-disease and Malaria, and Dengue) was examined using binomial-regression, adjusting for confounders and effect modifiers (socioeconomic-status; SES and child anthropometry), from January 2017 to January 2020. Attributable-fraction (AFx) was calculated due to each climate variable for each infectious disease. The result showed that each unit (1 °C) rise in Tmax was associated with an increase in diarrhea and skin-disease cases by 3.97% (95% CI: 2.92, 5.02) and 3.94% (95% CI: 1.67, 6.22), respectively, whereas, a unit decline in Tmax was associated with an increase in cold and flu cases by 3.87% (95% CI: 2.97, 4.76). Rise in humidity (RH) was associated with increase in cases of cold and flu by 0.73% (95% CI: 0.38, 1.08) and malaria (AH) by 7.19% (95% CI: 1.51, 12.87) while each unit (1 g/m(3)) decrease in humidity (AH) observed increase in pneumonia cases by 3.02% (95% CI: 0.75, 5.3). WS was positively associated with diarrhea (14.16%; 95% CI: 6.52, 21.80) and negatively with dengue (17.40%; 12.32, 22.48) cases for each unit change (kmph). RF showed marginal association while SLR showed no association at all. The combined AFx due to climatic factors ranged from 9 to 18%. SES and anthropometric parameters modified the climate-morbidity association in children with a high proportion of children found suffering from stunting, wasting, and underweight conditions. Findings from this study draw the attention of government and policymakers to prioritize effective measures for child health as the present association may increase disease burden in the future under climate-change scenarios in already malnourished paediatric population through multiple pathways.
Climate change and air pollution have been a matter of serious concern all over the world in the last few decades. The present review has been carried out in this concern over the Indian cities with significant impacts of both the climate change and air pollution on human health. The expanding urban areas with extreme climate events (high rainfall, extreme temperature, floods, and droughts) are posing human health risks. The intensified heat waves as a result of climate change have led to the elevation in temperature levels causing thermal discomfort and several health issues to urban residents. The study also covers the increasing air pollution levels above the prescribed standards for most of the Indian megacities. The aerosols and PM concentrations have been explored and hazardous health impacts of particles that are inhaled by humans and enter the respiratory system have also been discussed. The air quality during COVID-2019 lockdown in Indian cities with its health impacts has also been reviewed. Finally, the correlation between climate change, air pollution, and urbanizations has been presented as air pollutants (such as aerosols) affect the climate of Earth both directly (by absorption and scattering) and indirectly (by altering the cloud properties and radiation transfer processes). So, the present review will serve as a baseline data for policy makers in analyzing vulnerable regions and implementing mitigation plans for tackling air pollution. The adaptation and mitigation measures can be taken based on the review in Indian cities to reciprocate human health impacts by regular air pollution monitoring and addressing climate change as well.
Global temperature rises in response to accumulating greenhouse gases is a well-debated issue in the present time. Historical records show that greenhouse gases positively influence temperature. Lockdown incident has brought an opportunity to justify the relation between greenhouse gas centric air pollutants and climatic variables considering a concise period. The present work has intended to explore the trend of air quality parameters, and air quality induced risk state since pre to during the lockdown period in reference to India and justifies the influence of pollutant parameters on climatic variables. Results showed that after implementation of lockdown, about 70% area experienced air quality improvement during the lockdown. The hazardous area was reduced from 7.52% to 5.17%. The spatial association between air quality components and climatic variables were not found very strong in all the cases. Still, statistically, a significant relation was observed in the case of surface pressure and moisture. From this, it can be stated that pollutant components can control the climatic components. This study recommends that pollution source management could be a partially good step for bringing climatic resilience of a region.
This study was carried out to evaluate the heavy metals (Lead (Pb), Nickel (Ni), Chromium (Cr), Copper (Cu), Cadmium (Cd) and Zinc (Zn)) pollution in the Noyyal River of South India by collecting 130 river water samples (65 each in pre- and post-monsoon). The heavy metals were measured using Atomic Absorption Spectrophotometer (AAS). The data were used to calculate the associated health hazards for the inhabitants consume river water. Correlation analyses and average concentration of heavy metals denoted that post-monsoon metal concentrations were lesser compared to the pre-monsoon due to dilution effect. Modified Contamination Degree (MCD) indicated that 45% of pre-monsoon and 25% of post-monsoon samples were classified under extremely polluted category. Heavy metal pollution index (HPI) showed that all the regions fall under highly polluted category except ‘Region I’ where 20% of samples were under safe category during the pre-monsoon, whereas 9%,28%, 17% and 26% of samples in Regions I, II, III and IV were highly polluted during the post-monsoon season, respectively. Ecological Risk Index (ERI) revealed that high risks attained in Regions II (78%) and III (82%) during pre-monsoon, and reduced risks found in Regions II (28%) and III (45%) during post-monsoon season due to dilution by monsoon rainfall. Non-carcinogenic risks as inferred by the Hazard Index (HI) indicated that 78% and 52% of samples for infants, 75% and 49% of samples for teens and 71% and 45% of samples for adults exceeded the threshold limits of USEPA (HI > 1) and possessed risks during pre- and post-monsoon, respectively. The cancer risk assessment based on ingestion of heavy metals indicated that the order of risk is Ni > Cr > Cu. The HI for infants and teens was notably high to that of adults in both the seasons. This study will be useful to develop effective strategies for improving river water quality and to reduce human health hazards.
Rickettsial infections and Q fever are a common cause of acute febrile illness globally. Data on the role of climate and altitude on the prevalence of these infections in lacking from Southern India. In this study, we determined the sero-prevalence of scrub typhus (ST), spotted fever (SF), murine typhus (MT) and Q Fever (QF) in 8 eight geographical regions of North Tamil Nadu by detecting IgG antibodies using ELISA. Totally we tested 2565 people from 86 localities. Among the 27.3% positives, approximately 5% were IgG positive for two or more infections. Sero-prevalence to rickettsioses and Q fever was highest for individuals from rural areas and increased with age (> 30 years). Those in the Nilgiris highlands (wetter and cooler) and Erode, which has the most land under irrigation, demonstrated the least exposure to rickettsioses and Q fever. Lowland plains (AOR: 8.4-22.9; 95% CI 3.1-55.3) and highland areas up to 1000 m (AOR: 6.1-10.3; 95% CI 2.4-23.9) showed the highest risk of exposure to scrub typhus. For spotted fever, the risk of exposure was highest in Jawadhi (AOR:10.8; 95% CI 2.6-44.3) and Kalrayan (AOR:16.6; 95% CI 4.1-66.2). Q fever positivity was most likely to be encountered in Salem (AOR: 5.60; 95% CI 1.01-31.08) and Kalrayan hills (AOR:12.3; 95% CI 2.9-51.6). Murine typhus risk was significant only in Tiruvannamalai (AOR:24.2; 95% CI 3.3-178.6). Our study suggests that prevalence of rickettsial infections and Q fever is low in areas which receive rainfall of ≥ 150 cm/year, with average minimum and maximum temperatures between 15 and 25 °C and elevation in excess of 2000 m. It is also less in well irrigated lowlands with dry climate. These preliminary findings need confirmation by active surveillance in these areas.
OBJECTIVE: To understand how climate change vulnerability is associated with women and children’s health (WCH) at the district level in India. METHODS: The district-specific climate change vulnerability index was mapped to the district level NFHS-5 data (N = 674). Fractional regression and spatial analyses were performed to examine the strength of association and the presence of geographic clustering. RESULTS: Bivariate analysis revealed that the levels of WCH indicators were lower in districts with a high vulnerability index than in those with a low vulnerability index. Multivariable analyses suggested that with a 1% increase in the vulnerability index, the proportion of modern contraceptive use was reduced by 0.22, four or more prenatal care visits by 0.14, postnatal care by 0.11, and full immunization by 0.12; whereas wasting and underweight proportions increased by 0.07 and 0.10, respectively. The spatial analysis found that in about 70-118 districts, mostly in eastern India, where climate vulnerability was high the WCH outcomes were also poor. CONCLUSION: There is a macro-level association between climate change vulnerability and WCH, as districts that had high levels of climate change vulnerability also performed poorly in WCH. There is a need for an integrated approach that considers geography-specific climate change threats to develop health programs.
Indian Himalayan Region (IHR) is prone to climate shock and is highly sensitive to minor climate variance. Yet, there is a dearth of studies evaluating the adaptive capacity and vulnerability of the socio-ecological system. We assessed the household (n = 1346) and village (n = 77) level adaptive capacity and vulnerability to climate stress in Beas, Bhagirathi, and Teesta basins of IHR following a bottom-up approach. The estimation of adaptive capacity scores for surveyed households and villages were done based on preselected indicator scores of the natural, human, financial, and physical capital assets. The exposure to climate shock was obtained from Coupled Model Intercomparison Project (CMIP5) data of different scenarios. The present and future vulnerability scores were assessed based on the adaptive capacity, exposure and sensitivity to climatic shock. The villages and households were grouped in resilient and vulnerable clusters, and major indicators determining the vulnerability were identified. Our result revealed the household and village level adaptive capacity were low in the Bhagirathi and Teesta basin compared to the Beas basin, so as the present and future climate shock. We found that access to different resources as well as natural and financial capital assets were the major governing factor for the adaptive capacity of the villages. We suggest future policy interventions to be on climate sensitive sectors, mostly the natural capital of the region as part of the adaptation and mitigation strategy to climate change.
Climate change induced frequent disasters pose severe threats to agro-based rural livelihoods. Perceptions of risks play a critical role in planning and averting disasters. Lack of analytical documentation concerning how vulnerable communities perceive climate risks is a barrier to addressing and averting disasters and maladaptation. Applying a mixed approach, this study examines the perception of households concerning climate change and analyses the impacts of climate change on livelihood in Arunachal Pradesh, the largest northeastern state of India, with severe climate related challenges. Conceptual livelihood vulnerability index (LVI) framework of Intergovernmental Panel on Climate Change is adopted to analyse the climate change induced vulnerability on livelihood. A total 450 households from 18 villages located in the districts of Arunachal Pradesh were surveyed during October, 2021 for retrieving the ground complexities in the region. Decrease in yields, frequent landslides and floods, livestock losses and unpredictable weather condition were perceived by the sampled households. The LVI analysis indicated that households are vulnerable in Arzoo, Perum, Pekong and Amliang villages requiring priority for lessening livelihood vulnerability and increasing coping capacity of the communities. Correlation analysis indicated that climate variability, natural disaster, health, food and social components attributed to livelihood vulnerability in the study area. Alternate livelihood, enhancing preparedness to disasters, inclusion of women in workforce, sustainable livelihood practices and government assistance are some of the suggestions made to enhance the adaptation of local communities in a sustainable way.
This study attempts to investigate the simulation of heavy precipitation events (HPEs) over the West Coast of India associated with atmospheric rivers (ARs) using the Advanced Research Weather Research and Forecasting (ARW-WRF) model. The study evaluates the sensitivity of five microphysical (Lin, WSM6, Goddard, Thompson, and Morrison) and cumulus (KF, BMJ, Grell3D, Tidtke, and GD) parameterization schemes to explore the capability to reproduce the AR associated HPEs. The model simulations were reasonably successful at reproducing key structural and synoptic characteristics of atmospheric rivers, including well-defined corridors of strong water vapor transport, meteorological variables and circulation features. Deviations in Rainfall and Wind profiles were observed in simulations among the different parameterization schemes. The model better simulated the AR related precipitation using the Lin, Thompson MP and KF, Grell3D CU schemes when compared to observations, and attributed to the moisture laden tendency of the schemes. Nonetheless, differences in precipitation distribution and overestimation of winds among the model runs using different microphysical and cumulus physics schemes were noted. The study highlights that simulation of AR associated HPEs using high-resolution mesoscale mode with suitable representations of physical parameterization schemes are useful for disaster management and to minimize the loss of fatalities and property.
OBJECTIVES: The study aimed to document the association between intussusception in Indian children and meteorological parameters and examine regional variations. DESIGN: A bidirectional (retrospective and prospective) surveillance between July 2010 and September 2017. SETTING: At 20 hospitals in India, retrospective case record review during July 2010 and March 2016 and prospective surveillance during April 2016 and September 2017 were performed. PARTICIPANTS: 2161 children aged 2-24 months with first intussusception episode were included. INTERVENTIONS: The monthly mean meteorological parameters (temperature, sunshine, rainfall, humidity and wind speed) for the study sites were collected. METHODS: The association between monthly intussusception cases and meteorological parameters was examined at pooled, regional and site levels using Pearson (r) and Spearman’s rank-order (ρ) correlation, factorial analysis of variance, and Poisson regression or negative binomial regression analyses. RESULTS: The intussusception cases were highest in summer and lowest in autumn seasons. Pearson correlation analysis showed that temperature (r=0.056; p<0.05), wind speed (r=0.134; p<0.01) and humidity (r=0.075; p<0.01) were associated with monthly intussusception cases. Spearman's rank-order correlation analysis found that temperature (ρ=0.049; p<0.05), wind speed (ρ=0.096; p<0.01) and sunshine (ρ=0.051; p<0.05) were associated with monthly intussusception cases. Poisson regression analysis resulted that monthly intussusception case was associated with rising temperature (North region, p<0.01 and East region, p<0.05), sunshine (North region, p<0.01), humidity (East region, p<0.01) and wind speed (East region, p<0.01). Factorial analysis of variance revealed a significant seasonal difference in intussusception cases for pooled level (p<0.05), 2-6 months age group (p<0.05) and North region (p<0.01). Significant differences in intussusception cases between summer and autumn seasons were observed for pooled (p<0.01), children aged 2-6 months (p<0.05) and 7-12 months (p<0.05). CONCLUSIONS: Significant correlations between intussusception cases and temperature, humidity, and wind speed were observed at pooled and regional level in India. A peak in summer months was noted, which may be used for prediction, early detection and referral for appropriate management of intussusception.
Strategic location of coastal areas across the world causes them to be prone to disaster risks. In the global south, the Indian coast is one of the most susceptible to oceanic extreme events, such as cyclones, storm surge and high tides. This study provides an understanding of the risk experienced (currently as well as back in 2001) by the districts along the Indian coastline by developing a quantitative risk index. In the process, it attempts to make a novel contribution to the risk literature by following the definition of risk as a function of hazard, exposure and vulnerability as stated in the most recent (Fifth) assessment report of the Intergovernmental Panel on Climate Change (IPCC). Indicators of bio-physical hazards (such as cyclones, storm surge, tides and precipitation), and socio-economic contributors of vulnerability (such as infrastructure, technology, finance and social nets) and exposure (space), are combined to develop an overall risk index at a fine administrative scale of district-level over the entire coastline. Further, the study employs a multi-attribute decision-making (MADM) method, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to combine the contributing indicators and generate indices on hazard, exposure and vulnerability. The product of these three components is thereafter defined as risk. The results suggest that most districts of the eastern coast have higher risk indices compared to those in the west, and the risk has increased since 2001. The higher risk can be attributed to the higher hazard indices in the eastern districts which are aggravated by their higher vulnerability index values. This study is the first effort made to map risk for the entire coastline of India – which in turn has resulted in a new cartographic product at a district-scale. Such assessments and maps have implications for environmental and risk-managers as they can help identify the regions needing adaptive interventions.
OBJECTIVE: In recent times, increased rainfall from tropical cyclones due to climate change affects the agricultural sector, mainly the paddy fields. High windspeed with excessive rain causes lodging of paddy crops, which is difficult to harvest. Mechanized harvesting systems are ineffective in this situation due to waterlogging in the fields. Manual harvesting with a traditional sickle is the only way to harvest lodged crops to save food security crises and economic losses. Collecting the lodged paddy stems lying on the ground for harvesting manually is time-consuming and harvesters need to maintain an awkward posture for a prolonged period compared to harvesting un-lodged crops. METHODS: Seventy-five female harvesters aged 35-75 years were selected for the study from both lodged and un-lodged small-scale farming lands of Kerala, a southwestern coastal state of India. A comparative ergonomic assessment was conducted to measure body pain, perceived exertion, postural risks, and rate of production under both harvesting conditions. RESULTS: The harvesters reported significant higher rates of body pain, perceived exertion, high postural risks, and low productivity in lodged conditions compared to un-lodged condition. CONCLUSION: Harvesting lodged crops involves high risks with low productivity and needs immediate ergonomic design intervention for the well-being of the harvesters.
Rural communities are dependent on their native environment for supporting their customs, traditions, and other rural activities. This study attempts to understand the effects of the changing climate on rural individuals by investigating their feelings and experiences of perceived changes in the home environment and village life. Thirty-four in-depth interviews were conducted during the months of May-June 2019 in two districts-Gaya and Jehanabad of South Bihar, India. The findings reveal that the rural population have experienced changes in climate such as a rise in the incidence of heatwaves, erratic rainfall patterns, delay in monsoon onset, early drying of water resources, and loss of particular tree and bird species. Worries and uncertainties of the rural population have emerged from the experiences of involuntary separation from traditional farm activities, forced adaptation strategies, loss of cultural and religious practices, and reduced self-worth in coping with the deteriorating environment. The changing climate instigates feelings of emotional distress, resulting in adverse mental health and psychological well-being outcomes. It is concluded that the changing climate is responsible for the loss of traditional village customs and nature-related cultural practices, subsequently inducing solastalgia among the rural population.
BACKGROUND & OBJECTIVES: Issues such as emerging and re-emerging infectious diseases, antimicrobial resistance, food security, biosafety and biosecurity are associated with changes in land use, population growth, urbanization, global travel and trade and climate change. As a result, a trans-disciplinary approach among human, animal and environmental health disciplines gained support. The Indian Council of Medical Research (ICMR) and Indian Council of Agricultural Research (ICAR) decided to establish a National Institute of One Health at Nagpur, Maharashtra, India. In this context, two collaborative research projects, funded by the ICAR and ICMR were initiated to conduct the epidemiological surveillance of selected zoonotic diseases in Central India. METHODS: Disease surveillance and molecular detection employing standard techniques like enzyme linked immunosorbent assay (ELISA), immuno-fluroscent assay (IFA), standard tube agglutination test (STAT) , Rose Bengal plate test (RBPT) and polymerase chain reaction (PCR) were undertaken based on the disease to be screened. RESULTS: In animals, the seropositivities for listeriosis (7.66%) and brucellosis (11.69%) were recorded. The occurrence of tuberculosis (3.8%) and leptospirosis (6.33%) was detected by PCR. Through cross-sectional studies from suspected human population with associated risk factors for zoonotic diseases, the seropositivity of brucellosis (1.83-11%), listeriosis (1.01-10.18 %), leptospirosis (8.14-12.67%) and scrub typhus (1.78-20.34%) was recorded. The investigations on scrub typhus indicated bimodal pattern during the months of pre-monsoon and post-monsoon season with a peak in post-monsoon in human cases. Ornithonyssus bacoti mites were identified from the rodents as a vector harbouring Orientia tsutsugamushi. The bovine tuberculosis was detected in 1.43 per cent human cases employing molecular assay. INTERPRETATION & CONCLUSIONS: The data indicated the occurrence of important zoonotic diseases adversely affecting the livestock health and human wellbeing. The scientific collaboration between veterinary and medical faculties has set an example for effective implementation of One Health (OH) programme for the establishment of National Institute of OH.
Integrating noncommunicable disease (NCD) in health care delivery during emergency response posed a major challenge post-floods in Kerala. Kerala experienced an abnormally high rainfall during mid-2018 where more than 400 people lost their lives. State health officials and the Disaster Response Team were sensitized about the importance of including NCDs in the response action. More than 80% of patients with hypertension and diabetes were not under control in Kerala. Under the state NCD cell, an NCD expert group was consulted for drafting the treatment and referral strategies. Steps to tackle NCDs during the disaster response were formulated. The state NCD cell decided to integrate NCDs in the response measures. The technical guidance document by the World Health Organization South-East Asia Region was consulted to formulate actions. The activities were implemented in 6 steps: prioritizing of major NCDS, patient estimation and drug stock preparation, standard treatment protocol, mapping of referral facilities, public engagement, and daily reporting of NCD consultations. Prioritizing the continuum of care of NCDs during floods among the program managers and care providers was crucial. The health education and communication campaign was done to sensitize the known NCD patients to seek early care. Daily reporting of consultations was established.
Communities are ever-evolving, cities are constantly expanding, and the threat of natural hazards has escalated like never before. Cities can develop and prosper only if their society is resilient to external shocks. Measuring community resilience over time is crucial with the influence of technology and change in community lifestyles. With the frequent onset of floods in Kerala in recent years, the community must be well-prepared for future calamities. Thus, this paper develops a community resilience index for Kerala’s urban flood-prone areas (CRIF) through a rigorous bottom-up approach. The criteria for the index were developed using multi-criteria decision analysis that covered a fuzzy Delphi study, an empirical study using multi-variate probit regression, and an AHP analysis. The fuzzy Delphi study selected seven criteria: ‘social’, ‘economical’, ‘governance/political’, ‘health’, ‘communication/coordination, ‘education’, and ‘infrastructure’ from 65 experts. The empirical study helped apprehend the public’s viewpoints under each criterion. Finally, the AHP analysis helped assign appropriate weights to the criteria which 28 experts designated. The index is also designed according to the Sendai Framework for Disaster Risk Reduction (2015-2030). Further, the CRIF Index is put into action through a case study of the Kochi Municipal Corporation area, and the results are also validated using the Spearman’s rank correlation coefficient method. Results from validation returned a value of 0.7209 for the perceived CRIF method and 0.5798 for the external validation method, which corresponds to a ‘high’ and ‘moderate’ correlation, respectively. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11069-022-05299-7.
Recurring floods and erosion result in displacement, which adversely impacts women who are ‘left behind’ when men migrate. Policy and programme measures for disaster response and climate adaptation often perceive women as homogenous, vulnerable groups, instead of addressing underlying structural and conceptual barriers and strengthening their adaptive capacities to disasters and displacement. This article draws upon a political ecology lens to understand gendered recovery processes following disasters across four districts in Assam, northeastern India using empirical research from 2012 to 2018. The findings add nuances to the displacements of women in Assam as being ‘climate-induced’ by showing the different mechanisms of displacement and how it impacts particular groups of women, as well as their differential ways of coping with these changes. This article draws on sustained long-term qualitative research among rural villagers, particularly women, in Assam where migration is connected to riverbank erosion, exacerbated by the construction of a new embankment, and disrupted due to waterlogging caused by embankments and government relocation schemes in order to construct further dams/embankments.
Objective Flood is an annual phenomenon in Assam. This study aims to understand the after flood effects on children in the worst flood-hit districts of Assam during the last decades most devastating flood where more than 1,00,000 people were affected. Study design A cross sectional population-based study was done covering 7512 children of 0-12 years of age in 35 worse flood-affected villages in three districts of lower Assam for nutritional risk profiling, low birth weight and morbidity pattern. Method Anthropometric measurements of the children were compared with standard WHO definitions for nutritional risk profiling. ANOVA was conducted to see any relationship of nutritional status between the age groups and BMI for the districts. Two-factor ANOVA to analyse the effect of ARI on the morbidity status of different districts was done. District-wise and village-wise heat maps were generated to understand different nutritional parameters for analysing which areas within each district are more affected and why. Results The moderate (< 2SD) and severely underweight (< 3SD) children under 5 years of age were highest in the Barpeta district (45.5% and 24.2% respectively). The moderate and severe stunting was highest in Morigaon (47.6%) and Barpeta (24.3%) respectively. The moderate and severe wasting was highest in the Morigaon district (46.1% and 20.6% respectively). All the nutritional indicators were less than the WHO standard. The age-wise and district-wise distribution of nutritional status shows variations in all three districts. Severe underweight was observed highest among 24-35 months' children (50.9%) in Barpeta. The moderate and severe undernutrition status was found to be higher among the age group of 10-12 years of age compared to 5 to 10 years. Conclusion This survey has provided a comprehensive picture of the nutritional status of the targeted children in the worst flood-affected areas. However, attention to its impact on health particularly among those exposed at very early stages in life is still lacking. This kind of rapid assessment helps to understand the health and nutritional status of the vulnerable groups in a deeper way and to plan robust region-specific interventions.
India witness floods during the summer monsoon (June-September) that disproportionately affect the socioeconomic well-being of millions of people. Nonstructural measures such as flood early warning systems play a crucial role in mitigating the impacts; however, these require a proper understanding of flood drivers. The drivers of floods in the Indian river basins have not been examined for the observed and projected future climate. Here using a novel framework, we examine antecedent moisture conditions and precipitation characteristics before high flow events. We estimate the probability of occurrence of flood drivers and their association with peak flood magnitude under the observed and projected future climate in Indian river basins. Multiday precipitation, a proxy to heavy precipitation on wet soil conditions, was found as the predominant flood driver in the observed and projected future climate. We show that multiday precipitation is more prominent driver than extreme soil moisture conditions in larger rivers basins while extreme precipitation drives floods in smaller river basins. The frequency of major drivers of floods is projected to rise in the future, which may pose a greater risk to agriculture and infrastructure under the warming climate.
Landslides being a widespread disaster are associated with susceptibility, vulnerability and risk. The physical factors inducing landslides are relatively well-known. However, how landslide susceptibility will be exacerbated by climate change, impede the attainment of the sustainable development goals and increase health vulnerability is relatively less explored. We present an integrated assessment of landslide susceptibility, health vulnerability and overall risk to understand these interconnected dimensions using Arunachal Pradesh, India, as a case study, which is susceptible to landslides due to its topography and climate conditions. Landslide susceptibility was examined using twenty landslide conditioning parameters through the fuzzy analytical hierarchy process (FAHP). The susceptibility map was validated using the area under the ROC curve (AUC). National Family Health Survey (NFHS 4) data were used to analyze the health vulnerability, while the overall risk was computed through the integration of susceptibility and vulnerability. Landslide susceptibility analysis indicated that nearly 22% area of the state is characterized by moderate susceptibility followed by high (17%) and very high susceptibility (13%). High elevation, slope, rainfall, SPI, drainage density and complex geology were identified as the causative factors of landslides. In the case of health vulnerability, East Kameng and Lohit districts were found to be very highly vulnerable, while Papum Pare, Changlang and Tirap districts experience high health vulnerability due to high degree of exposure and sensitivity. Overall risk analysis revealed over 16.8% area of the state is under moderate risk followed by high (9.8%) and very high (4.2%) risk. Linking this analysis with the climate change projections and SDG goals attainment revealed that Papum Pare, Upper Subansiri, Tirap and West Kameng require priority for lessening susceptibility, vulnerability and risk for achieving sustainable development. A strong correlation (99%) between HVI and risk further demonstrates the need for lessening health vulnerability and risk in the study area. Furthermore, our study contributes additional insights into landslide susceptibility by considering heal vulnerability and risk which may help in planning sustainable development strategies in a changing climate.
BACKGROUND: Early Identification of disaster victims with mental health problems may be useful, but information within a short period after a disaster is scarce in developing countries. This study examined anxiety, depression, and post-traumatic stress symptoms at 1 month following 2019 Cyclone Fani in Odisha, India. METHOD: Post-traumatic stress symptoms (PTSS) were assessed by the Primary care PTSD screen for DSM 5 (PC-PTSD-5), anxiety symptoms by the Generalised Anxiety Disorder (GAD-7), and depression by the Patient Health Questionnaire (PHQ-9). The survey included participants’ disaster experience e.g., evacuation, fear of death, injury, death in family, damage to house, difficulty for food, displacement, and effect on livelihood. RESULTS: Proportion of sample (n = 80) with probable PTSD was 42.9%, with severe anxiety was 36.7%, moderately severe depression was 16.5%, and severe depression was 3.8%. Suicidal cognitions were reported to increase by 14%. Comorbidity was common; with significant (P < 0.01) correlation between PTSS and anxiety (r = 0.69), depression (r = 0.596), and between anxiety and depression (r = 0.63). Damage of house and displacement were associated significantly with PTSD; evacuation and displacement with moderate and severe depression; and displacement with severe anxiety. No specific demographic factors were significantly linked to the psychiatric morbidities. CONCLUSION: A considerable proportion of victims had psychiatric morbidities at 1 month. Associated risk factors included housing damages, evacuation, and displacement, suggesting the need to improve the disaster-management process.
OBJECTIVE: This study was undertaken to assess the health status of newborns discharged from Sick Newborn Care Units (SNCU) of the Cyclone Fani affected districts of Odisha, which is amongst the highest neonatal mortality rate states in the country. METHODS: Cyclone Fani hit the coast of Odisha on May 3, 2019. This cross-sectional study was conducted in 5 districts and targeted the babies discharged from SNCU’s from January to May 2019. A telephonic interview of the caregivers was conducted to assess the health status of the newborns. Data was collected in a web-based portal and analyzed by statistical package for social sciences SPSS (IBM Corp., Armonk, New York, USA). RESULTS: We inquired about 1840 babies during the study period but only 875 babies could be followed up, with the highest proportion of the babies from the most affected district. Out of 875 babies, 111 (12.7%) had 1 or more illnesses during follow up. Distance from the health facility and time constraints were the major reasons for not seeking health care. Of the babies, 35.7% were reported as being underweight. Poor breastfeeding (14.1%) and kangaroo mother care (31.7%) practices were reported. Only 32% of the babies were completely immunized. CONCLUSION: The health status of the babies discharged from the SNCUs was found to be poor. Newborn care can be strengthened by improving home-based and facility-based newborn care.
Urban parks play an essential role in urban settings; significantly contribute to the health of every age group person. Parks provide opportunities for families to connect with nature and breathe in the fresh air. Due to global climate change and increased urbanisation in the past few decades, extreme heat can be experienced in urban areas. Mental and physical health issues arise primarily due to a sedentary lifestyle in cities. Staying at parks for a longer duration could promote stress reduction and perceived physical health. The present study aims to assess the thermal comfort conditions at an urban park in the hot semi-arid climate(BSh) of Haryana, India. The present study investigated the outdoor thermal comfort range and thermal sensations of visitors at a park during the summer season using the onsite monitoring of the microclimate parameters and questionnaire survey in the hot-semi arid region of India. Thermal comfort indices, Physiological equivalent temperature (PET) and Universal Thermal Climate Index (UTCI) and Wet bulb globe temperature(WBGT) have been applied to investigate the outdoor thermal comfort conditions. The seven-point sensation scale has been used to record the visitors’ thermal sensations. The results indicated that:1) WBGT was found to be the most suitable index to investigate the OTC conditions. The neutral UTCI, PET, and WBGT ranged within 28.03 degrees C to 35.6 degrees C, 24.04 degrees C to 37.5 degrees C, and 23.5 degrees C to 26.1 degrees C, respectively. 2) The neutral PET ,UTCI, and WBGT were found to be 30.8 degrees C, 31.8 degrees C, and 24.8 degrees C, respectively.3) Dry bulb temperature is the most significant thermal comfort parameter affecting visitors’ thermal sensations, followed by mean radiant temperature.4) Thermal comfort indices were found to be most significantly affected by globe temperature. The study’s outcome could provide theoretical design reference to urban designers to develop new parks and existing parks, ultimately promoting public health. Copyright (c) 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the F-EIR Conference 2021 on Environment Concerns and its Remediation: Materials Science
BACKGROUND: Globally, post traumatic stress disorder (PTSD) is one of the most common psychiatric illnesses following a disaster. We aimed to evaluate the relationship between the socio-economic and flood exposure factors with PTSD, depression and anxiety among the flood-affected populations in Kerala, India. METHODS: A cross-sectional household survey was conducted from November 2019 to January 2020 in Kozhikode district of Kerala, India. Adults (≥ 18 years), who were permanent residents and had been directly exposed to the flood, were invited to take part in the study. Individuals with a history of mental health issues and those who had other stressful situations in the past were excluded. The survey questionnaire was based on three screening tools: (1) PTSD Checklist for DSM-5 (PCL-5); (2) patient health questionnaire (PHQ-9); and (3) generalized anxiety disorder (GAD-7). Data included sociodemographic factors and flood exposure variables. The primary outcome variable was psychiatric morbidity (PTSD, anxiety and depression). RESULTS: A total of 276 respondents (150 males/126 females) participated in the study. A significant correlation was observed between total score on PCL-5 and GAD-7 (r=0.339, p=0.001) and PHQ-9 (r=0.262, p=0.001). Females had significantly higher total PTSD symptom severity scores (8.24±5.88 vs. 6.07±5.22; p=0.001), severity of symptoms of intrusion (4.66±3.60 vs. 3.69±3.20; p=0.04), increased level of anxiety (2.54±1.94 vs. 1.79±1.53; p=0.001) and depression (3.02±2.26 vs. 2.04±1.67; p=0.001) compared to males. However, the gender difference for PTSD symptoms disappeared when controlling for age. CONCLUSION: The findings of this survey revealed that the vast majority of respondents (92 percent females and 87 percent males) still had subclinical psychiatric symptoms one year after the flood. Therefore, tailored psychological interventions are warranted to counter the long-lasting impact of flooding on the mental health of individuals.
INTRODUCTION: Disasters can have deep physical and psychological impact among survivors. An extraordinary southwest monsoon has unleashed floods and landslides in Kerala state in 2018. Adolescents are more vulnerable to psychological impairment after a disaster and trauma during initial stages of life can etch an indelible signature in the individual’s development and may lead to future disorders. OBJECTIVES: 1. To screen for PTSD and associated factors among adolescents 8 months post floods in selected schools in flood-affected areas of Alleppey district of Kerala 2. To compare the proportion of adolescents screened positive for PTSD in public and private schools. METHODOLOGY: A 3-month, Cross-sectional study was done among 670 adolescents in private and public schools using stratified sampling in Alleppey district. The study tool included a structured questionnaire that collected information on sociodemographics, flood-related variables, Trauma screening questionnaire and academic performance. RESULTS: The mean age of the participants was 16.03 ± 0.73 years with almost equal gender distribution. One-third of students reported flood-related damage to house/property, and a few lost their pets. Nearly 50% of the students reported that they still re-experience and get upsetting memories about flood events. The prevalence of probable PTSD noted to be 34.9%. We observed that 31% of students in public school screened positive for PTSD compared to 38.8% of private school students. (odds ratio = 1.409, CI 1.024-1.938). Male gender (Odds ratio = 1.503, CI 1.093-2.069), higher age (Odds ratio = 1.701, CI 1.120-2.585), damage during floods (Odds ratio = 2.566, CI 1.814-3.630), presence of morbidity (Odds ratio = 3.568, CI 1.888-6.743), camp stay (Odds ratio = 3.788, CI 2.364-6.067) and loss of pets (Odds ratio = 3.932, CI 2.019-7.657) were the factors significantly associated with PTSD. We noted a deterioration in academic performance in 45.9% of students who screened positive for PTSD. CONCLUSION AND RECOMMENDATIONS: High prevalence of stress disorder highlights the need for early identification and intervention for PTSD and including trained counsellors as a part of the disaster management team in future.
The Tista floodplain is one of the major food baskets of North Bengal and is sensitive to a multitude of issues regarding vulnerability. The riparian areas and the river island or charland of the lower Tista River basin in India, specifically from Sevoke to the Indo-Bangladesh border, generally suffer due to flood-prone, river course shifting, limited livelihood activities, low adaptive capacity, and poor accessibility. The present work is conducted to assess the livelihood vulnerability based on the livelihood vulnerability index (LVI) framework of the agriculture-dependent riparian villages and the charlands of the River Tista in the Jalpaiguri district. Total 337 households of five villages from the Mal and Maynaguri block at the left bank of the Tista River were selected to conduct the field survey. The livelihood vulnerability was assessed based on eight major components (viz., socio-demographic profile, health condition, livelihood strategies, food support, water support, climatic variability, flood hazards, and social safety) and 42 sub-components. The three contributing factors, i.e., adaptive capacity, sensitivity, and exposure, have been combined to calculate the livelihood vulnerability employing the LVI and LVI-IPCC methodologies. The outcome of the study exhibit that LVI scored highest in Premganj Majhiali (0.436), followed by Basusuba (0.403), Uttar Marichbari (0.349), Domohani (0.335), and Chat Rarpur village (0.328). According to the LVI-IPCC results, Basusuba has the most vulnerability (0.015), whereas Domohani has the least (0.007). In terms of flood hazard, variations were noticed based on increasing distance from the river. Lack of adaptive capacity prevailed in the villages with significant flooding events. Building awareness of the inhabitants will be an effective way to improve the adaptive capacity of the rural villagers. Therefore, giving priority to the policies depending on the natural environment of the active flood-prone region would make long-term sustainability.
INTRODUCTION: Extreme heat is a significant cause of morbidity and mortality, and the incidence of acute heat illness (AHI) will likely increase secondary to anthropogenic climate change. Prompt diagnosis and treatment of AHI are critical; however, relevant diagnostic and surveillance tools have received little attention. In this exploratory cross-sectional and diagnostic accuracy study, we evaluated three tools for use in the prehospital setting: 1) case definitions; 2) portable loggers to measure on-scene heat exposure; and 3) prevalence data for potential AHI risk factors. METHODS: We enrolled 480 patients who presented to emergency medical services with chief complaints consistent with AHI in Ahmedabad, India, from April-June 2016 in a cross-sectional study. We evaluated AHI case definition test characteristics in reference to trained prehospital provider impressions, compared on-scene heat index measured by portable loggers to weather station measurements, and identified AHI behavioral and environmental risk factors using logistic regression. RESULTS: The case definition for heat exhaustion was 23.8% (12.1-39.5%) sensitive and 93.6% (90.9-95.7%) specific. The positive and negative predictive values were 33.5% (20.8-49.0%) and 90.1% (88.5-91.5%), respectively. Mean scene heat index was 6.7°C higher than the mean station heat index (P < 0.001), and station data systematically underestimated heat exposure, particularly for AHI cases. Heat exhaustion cases were associated with on-scene heat index ≥ 49°C (odds ratio [OR] 2.66 [1.13-6.25], P = 0.025) and a history of recent exertion (OR 3.66 [1.30-10.29], P = 0.014), while on-scene air conditioning was protective (OR 0.29 [0.10-0.85], P = 0.024). CONCLUSION: Systematic collection of prehospital data including recent activity history and presence of air conditioning can facilitate early AHI detection, timely intervention, and surveillance. Scene temperature data can be reliably collected and improve heat exposure and AHI risk assessment. Such data may be important elements of surveillance, clinical practice, and climate change adaptation.
Extreme weather conditions, especially heatwave, are a threat to society, affecting livability, wellbeing, and social interactions. The present study aims to assess the monthly heat stress in the outdoor environment from 2010 to 2019 in Sonepat’s municipality, representing a hot semi-arid climate. The authors applied three heat stress indices, namely, Wet bulb globe temperature (WBGT), Physiological equivalent temperature (PET), and Universal thermal climate index (UTCI), to estimate the grade of heat stress. While calculations, the highest average WBGT was found in July (33.4 +/- 0.77 degrees C), demonstrating July in the “Extreme heat stress” category. The highest mean PET was found in June (42.47 +/- 2.34 degrees C), indicating June in the “Extreme heat stress” category. The highest mean UTCI was found in June (38.58 +/- 1.82 degrees C), demonstrating “Very strong heat stress.” The dry bulb temperature was found to be the most dominant parameter among meteorological parameters promoting extreme heat stress. It was concluded that extreme heat stress was observed in the Pre-monsoon hot weather season and summer monsoon season (especially in June), making the population vulnerable to mortality and morbidity. The findings could provide valuable information to people from various disciplines like Climate scientists, landscape designers, architects, and all relevant stakeholders to develop a heatwave action plan against adverse heat stress.(c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the F-EIR Conference 2021 on Environment Concerns and its Remediation: Materials Science.
OBJECTIVES: Global warming and more intense heat wave periods impact health. Heat illness during heat waves has not been studied in the prehospital setting of a low- and middle-income country (LMIC). Early intervention in the community and in the prehospital setting can improve outcomes. Hence, this paper aims to describe the characteristics of heat illness patients utilizing the ambulance service in Telangana state, India with the aim of optimizing public prevention and first aid strategies and prehospital response to this growing problem. METHODS: This retrospective observational study reviewed patients presenting to Telangana’s prehospital emergency care system with heat illness symptoms during the heat wave period from March through June in 2018 and 2019. Descriptive analysis was done on the prehospital, dispatch, and environmental data looking at the patients’ characteristics and prehospital intervention. RESULTS: There were 295 cases in 2018 and 230 cases in 2019 from March-June. The overall incidence of calls with heat illness symptoms was 1.5 cases per 100,000 people. The Scheduled Tribes (ST) had the highest incidence of 4.5 per 100,000 people. Over 96% were from the white income group (below poverty line) while two percent were from the pink income group (above poverty line). From geospatial mapping of the cases, the highest incidence of calls came from the rural, tribal areas. However, the time to response in rural areas was longer than that in an urban area. Males with an average age of 47 were more likely to be affected. The three most common symptoms recorded by the first responders were vomiting (44.4%), general weakness (28.7%), and diarrhea (15.9%). The three most common medical interventions on scene were oxygen therapy (35.1%), oral rehydration salt (ORS) solution administration (26.9%), and intravenous fluid administration (27.0%), with cold sponging infrequently mentioned. CONCLUSION: This descriptive study provides a snapshot of the regions and groups of people most affected by heat illness during heat waves and the heterogeneous symptom presentation and challenges with management in the prehospital setting. These data may aid planning of prehospital resources and preparation of community first responders during heat wave periods.
This study investigated children’s perceptions and adaptive behaviors related to indoor thermal conditions of classrooms in primary schools with no air-conditioning systems during both summer and winter in Dehradun City, Uttarakhand, India. Responses were collected from 5297 school children aged 6-13 years. During the measurement periods, 100% and 94% of the samples were obtained under conditions outside an 80% thermally acceptable comfort range in winter and summer, respectively. The analysis using receiver operating characteristics suggested that the students had the least sensitivity to the temperature variation for all scales of the thermal sensation vote (TSV). Approximately 95.1% of students were “very satisfied”, “satisfied”, or “slightly satisfied” with the thermal conditions under the condition of “extreme caution” or “danger” of heat risk. In contrast, adaptive thermal behaviors, such as adjusting clothing insulation ensembles, opening or closing classroom windows and doors, and utilizing ceiling fans, were found to be the most affordable options for optimizing indoor thermal comfort. Children’s reports of thermal sensations and thermal satisfaction did not correspond to the actual physical environment. This draws attention to the adequacy of applying widely used methods of TSV-based identification of the thermal comfort range in classrooms for children, especially in hot environments. The findings of this study are expected to serve as an evidence-based reference for local governments and authorities to take appropriate measures to mitigate heat risks for schoolchildren in the future.
The climate of a place has a decisive role in human adaptations. Man’s health, adaptability, behavioural patterns, food, shelter, and clothing are mainly influenced by the temperatures of the area. Hence, a study is undertaken to analyse the spatial distribution, frequency, and trend in the heat waves over the country. The statistical characteristics of heat waves over India are addressed in this study. Gridded daily temperature data sets for the period 1951-2019 were used to compute the arithmetic mean (AM), standard deviation (SD), coefficient of variation (CV), and trends of monthly maximum temperature. The number of heat wave days were identified using the criteria given by India Meteorological Department (IMD) i.e., a heat wave is recognized when the daily normal maximum temperature of a station is less than or equal to (greater than) 40 degrees C than it will be considered as a heat wave if the daily maximum temperature exceeds the daily normal maximum temperature by 5 degrees C (4 degrees C). The analysis was confined to the two summer months of April and May only. The spatial distribution of the AM shows higher values during May, and the core hot region with temperatures exceeding 40 degrees C lies over central India extending towards the northwest. The SD distribution shows higher values over the northeast of central India decreasing towards the southwest. The CV distribution shows higher values over the north decreasing toward the south. Higher numbers of heat waves are observed during May and the number is higher over Andhra Pradesh and south Telangana regions of southeast India. This study concludes that a moderate hot region experiences a higher number of heat wave days over India.
In the present research work, the authors investigated the seasonal thermal environment and thermal perception of university subjects in a naturally ventilated workshop building under the composite climate of India. Total 1460 subjective responses were collected during the field study in the year, 2019. Standard Effective Temperature (SET*) has been used as a rationally derived thermal comfort index to study the combined effects of air temperature, relative humidity and airspeed on perceived thermal sensation and occupant’s preference under high metabolic rates. Probit analysis showed more than 80% of subjects were voting comfortable (+/- 1 Thermal sensation votes) when SET* ranged between 25 degrees C-33 degrees C. Seasonal mean comfort temperature varied more than 4.8 degrees C, while, preferred temperature was noted about 3 degrees C lower than their mean Griffiths comfort temperature. The adaptive relation developed from the collected database under high metabolic activities was compared with existing national and international comfort standards. The slope coefficient for adaptive relation was observed close to the adaptive model of ASHRAE Standard 55-2017 but lower than the National Building Code of India, 2016. Further, adaptive use of fans and windows were analyzed using logistic regression models and predicted about 80% of fans and windows were in operation at 30 degrees C. To confirm the adaptive mechanism, the interrelation of other contextual factors like gender, clothing insulation, airspeed, metabolic activities, etc. to thermal comfort expectations of subjects were also studied.
BACKGROUND: Studies have documented a significant association between temperature and all-cause mortality for various cities but such data are unavailable for Hyderabad City. OBJECTIVE: The objective of this work was to assess the association between the extreme heat and all-cause mortality for summer months (March to June) from 2006 to 2015 for Hyderabad city population. METHODS: We obtained the data on temperature and all-cause mortality for at least ten years for summer months. Descriptive and Bivariate analysis were conducted. Pearson correlation coefficient was used to study the relationship between heat and all-cause mortality for lag time effect. RESULTS: A total of 122,117 deaths for 1,220 summer days (2006 to 2015) were analyzed with mean daily all-cause mortality was 100.1±21.5. There is an increase of 16% and 17% per day mean all-cause mortality at the maximum temperature of less than or equal to 40 degrees C and for extreme danger days (Heat Index greater than 54 degrees C) respectively. The mean daily all-cause mortality shows a significant association with maximum temperature (P < 0.001) and Heat Index from caution to extreme danger risk days (P<0.0183). The lag effect of extreme heat on all-cause mortality for the study period (2006 to 2015) was at peak on same day of the maximum temperature (r = 0.273 at p<0.01). CONCLUSION: The study concludes that the impact of ambient heat in the rise of all-cause mortality is clearly evident (16% mean deaths/day). There was no lag effect from the effect of extreme heat on all-cause mortality as the peak period was the same as the maximum temperature. Hence heat action plans are needed. However, extreme heat-related mortality merits further analysis.
The present paper is an attempt to study the heat waves associated fatalities over space and time in India. For this, ‘Disastrous Weather Events’ reports statistics have been used for the period 1978-2014. The analysis has shown that a total of 660 heat wave events have caused 12,273 fatalities (about 332 fatalities every year). Only five states namely, Andhra Pradesh (42%), Rajasthan (17%), Odisha (10%), Uttar Pradesh (7%) and Bihar (7%) have accounted more than 80% of the heat wave fatalities, although nine states namely, Arunachal Pradesh, Nagaland, Manipur, Meghalaya, Tripura, Sikkim, Mizoram, Uttarakhand and Goa have never reported heat wave events and fatalities during 1978-2014. Interestingly, each event has resulted about 104 fatalities in Andhra Pradesh state. Further, fatality and density rates have been witnessed to the tune of 0.35 and 3.81 respectively. Temporally, heat wave events have displayed large differences with a significant increasing trend (P < 0.01), whereas no trend could be noticed in fatalities. Majority of events have been witnessed in May and June months. It has been observed that men have been more harshly affected compared to women and children. Finally, it is believed that this study may provide new insight towards making better disaster management guidelines for minimizing the shocks of harsh temperature.
Heat waves are often termed as the silent killer and have become even more important as recent studies suggest that the heat wave have become second most devastating extreme weather events in terms of human deaths and losses. It is also been largely realised by scientific community that it is not just the high temperatures which are responsible for the gruesome effect of heat waves but several other meteorological parameters play a vital role in aggravating the impact and causing much more damages. In view of the above the attention of scientific community, weather forecasters as well as disaster managers has shifted to also take into account the different meteorological parameters like maximum and minimum temperatures, relative humidity, wind speed, duration/spell of heat waves and its intensity which are aggravating the impact of heat stress. In this background, this study is undertaken as an attempt to quantify the effect of different meteorological parameters on heat wave on different regions of India for different summer months (March, April, May and June). In this study the impact of individual meteorological parameter as well their cumulative effect is studied based on data of 30 years (1981-2010) for 300 stations. The effect of different meteorological parameters is identified for different months for different regions of the country. Also the cumulative scores are calculated for different regions considering different meteorological parameters, as a first initiative to perform heat hazard analysis and zonation over the entire country. This could serve as initial step for planning mitigation and adaptation strategies throughout the country. These scores as thresholds for different regions may be also useful for operational forecaster’s for early impact based warning services as well as for the disaster managers, for taking effective and timely actions.
The heatwave is a disastrous hazard having significant impacts on health and society. This study analyses the heatwave hazards and risk for India’s current and future scenarios using socioeconomic vulnerability and temperature datasets during the summer (April-June) season. The Census of India (CoI) 2011 datasets were considered to assess current vulnerability and projected from the SocioEconomic Data And Application Center (SEDAC) population at Shared Socioeconomic Pathway (SSP) 4 for future vulnerability. Whereas IMD temperature data used for hazard assessment for the present scenario (1958-2005) while projected temperature data from regional earth system model REMO-OASIS-MPIOM (ROM) were used for the future (2006-2099) scenario. The study exhibited the most hazardous, vulnerable, and risk-prone regions identified as the south-eastern coast and Indo-Gangetic plains and some populous districts with metropolitan regions (Mumbai, Delhi, and Kolkata) under the current scenario. The coupled model ROM has efficiently captured the critical districts with higher and lower risk, showing its future projection capability. The study highlighted that the heatwave hazard-risk would significantly worsen in future scenarios in all districts under enhanced global warming and largely affecting the districts in the eastern and middle Indo-Gangetic plains and Malabar region. The present study will provide sufficient insights into designing mitigation strategies and future adaptive planning for the heatwave risk, which is one of the targets under Sustainable Development Goal 13 (Goal 13: Climate Action).
A considerable association between temperature and all-cause mortality has been documented in various studies. Further insights can be obtained from studying the impact of temperature and heat index (HI) for Jaipur city’s all-cause mortality. The objective of this work was to assess the association between the extreme heat (daily maximum temperature, daily minimum temperature, daily mean temperature, relative humidity and HI) and all-cause mortality for summer months (March to June) from 2006 to 2015 for urban population of Jaipur. For summer months, we collected the data on various temperature and all-cause mortality parameters for at least 10 years. The student’s t-test and ANOVA were used to analyse variations in mean temperature, maximum temperature and HI. The Pearson correlation coefficient was used to study the relationship between ambient heat and lag time effect all-cause mortality. A total of 75,571 deaths (all-cause mortality) for 1,203 summer days (2006-2015) were analysed in relation to temperature and relative humidity. The mean daily all-cause mortality has been estimated at 62.8 +/- 15.2 for the study period. There is a significant increase of 39% per day all-cause mortality at the maximum temperature of 45 degrees C and above. However only 10% rise per day all-cause mortality for extreme danger days (HI > 54 degrees C). The mean daily all-cause mortality shows a significant association with daily maximum temperature (F = 34.6, P < .0001) and HI (discomfort index) from caution to extreme danger risk days (F = 5.0, P < .0019). The lag effect of extreme heat on all-cause mortality for the study period (2006 to 2015) was at a peak period on the same day of the maximum temperature (r = 0.245 at P < .01) but continues up to four days. The study concludes that the effect of ambient heat on all-cause mortality increase is clearly evident (rise of 39% deaths/day). Accordingly, focus should be put on developing adaptation measures against ambient heat. This analysis may satisfy policy makers' needs. Extreme heat-related mortality needs further study to reduce adverse effects on health among Jaipur's urban population.
Heat waves are quite frequent over the Indian subcontinent during the summer season (April-July) owing to an increase in anthropogenic activities and global temperatures. These extreme heat conditions induce a high level of outdoor discomfort, adverse health effects and mortality, depending on the degree of thermal stress. The present study investigates the climatology of thermal stress and its trends over northwest (NW) India during the summer. The Universal Thermal Climate Index (UTCI) derived from Human thErmAl comforT (ERAS-HEAT) dataset was used for the period of 1981-2019. The monthly and seasonal climatological mean of UTCI exhibits moderate to strong thermal stress over NW India (ranges from 27 to 34.5 degrees C) than in the rest of the country (below 25.5 degrees C), with a peak during the months of June (34.5 degrees C) and July (33.5 degrees C) months. The seasonal mean UTCI shows significant rising trends (0.9 degrees C per 39 years) over NW India and entire India (0.6 degrees C per 39 years), indicating that the thermal discomfort amplifies at a faster pace compared to the rest of India. Similar rising trends are also noticed in the major cities of the study region. Surface temperature and relative humidity also exhibit a substantial increasing trend, which resulted in the intensification of thermal discomfort over NW India. Furthermore, the number of thermal discomfort days over NW India exhibits an increasing trend during 1981-2019. The composite analysis of UTCI greater than 32 degrees C (referred to as strong heat stress) depicts the highest thermal discomfort conditions in NW India. During summer, strong soil temperatures and high sensible heat fluxes over the study region may enhance the warming at the surface during UTCI (> 32 degrees C) days as it depends on surface radiative fluxes through the mean radiant temperature. In addition to high temperatures, a substantial amount of moisture transported by strong westerly wind from the Arabian Sea towards the NW India during strong thermal stress days seems to have contributed to high thermal stress conditions in the region.
This study is an assessment of the effects of outdoor air pollution and extreme weather events on the health of outdoor workers in Delhi, including auto rickshaw drivers, street vendors, and sweepers. To carry it out, a cross-sectional and perception-based epidemiological research design was used, and the primary tool used for data collection was a questionnaire. Two hundred twenty-eight people participated in the survey, and a pulmonary function test (PFT) was performed on 63 participants. Most of the respondents from different occupational groups complained about headaches/giddiness, nausea, and muscular cramps during extreme heat events due to the physically demanding nature of their jobs in the outdoor environment. Furthermore, autorickshaw drivers reported the highest prevalence of ophthalmic symptoms, such as eye redness (44%) and eye irritation (36%). In comparison, vendors reported a higher prevalence of headaches (43%) and eye redness (40%) due to increased exposure to vehicular emissions. Among sweepers, musculoskeletal problems like joint pain (40%), backache (38%), and shoulder pain (35%) were most prevalent due to occupation-related ergonomic factors. In addition, the majority of autorickshaw drivers (47%), vendors (47%), and sweepers (48%) considered that air quality had a severe impact on their health. PFT results showed that most respondents had restricted lung function. Binary logistic regression analysis showed that lung function impairment had a significant association with smoking (p = 0.023) and age (0.019). The odds ratio for smoking, which was around 4, indicated that respondents who smoked had a nearly four times greater risk of developing lung impairment. The study also highlighted the need for using personal protective equipment and developing guidelines to reduce their exposure level.
BACKGROUND: Exposure to high and low ambient temperatures is associated with morbidity and mortality across the globe. Most of these studies assessing the effects of non-optimum temperatures on health and have been conducted in the developed world, whereas in India, the limited evidence on ambient temperature and health risks and has focused mostly on the effects of heat waves. Here we quantify short term association between all temperatures and mortality in urban Pune, India. METHODS: We applied a time series regression model to derive temperature-mortality associations based on daily mean temperature and all-cause mortality records of Pune city from year January 2004 to December 2012. We estimated high and low temperature-mortality relationships by using standard time series quasi-Poisson regression in conjunction with a distributed lag non-linear model (DLNM). We calculated temperature attributable mortality fractions for total heat and total cold. FINDINGS: The analysis provides estimates of the total mortality burden attributable to ambient temperature. Overall, 6∙5% [95%CI 1.76-11∙43] of deaths registered in the observational period were attributed to non-optimal temperatures, cold effect was greater 5.72% [95%CI 0∙70-10∙06] than heat 0∙84% [0∙35-1∙34]. The gender stratified analysis revealed that the highest burden among men both for heat and cold. CONCLUSION: Non-optimal temperatures are associated with a substantial mortality burden. Our findings could benefit national, and local communities in developing preparedness and prevention strategies to reduce weather-related impacts immediately due to climate change.
In the Indian subcontinent, the annual average extreme weather events (EWEs) are reported to be increasing during the last few decades. The impact of increased EWEs on mortality has become a key issue in terms of minimizing it, even with the increasing population. In the present study, based on 50 years’ data (1970-2019) of India Meteorological Department, mortality rates of different EWEs viz., floods, tropical cyclones, heat waves, cold waves, lightning, etc. were analysed, both at the national and state level. The analysis was done based on different periods, i.e. annual, decadal and twenty-year slice periods. Various statistical analyses were carried out. Out of these EWEs, floods accounted for maximum mortality of 46.1%, followed by tropical cyclones with 28.6% mortality. Over the decades, despite a significant rise in EWEs (except for tropical cyclones), there has been a decrease in the mortality rate (mortalities per year per million population). The number of mortalities per event had a significant negative trend for heatwaves and floods, during the last 50 years. The total EWEs had a mortality rate of 3.86 during 1980-1999 and it reduced to 2.14 during 2000-2019. The mortality rate of tropical cyclones reduced by 94% in the past 20 years, whereas for heatwaves and lightning it increased by 62.2% and 52.8%, respectively. However, the change in mortality rate was not found to be statistically significant due to high year to year variability in mortality associated with floods, lightning, and tropical cyclones in the last two decades as compared to earlier decades. In India, among the major states, Odisha, Andhra Pradesh, Assam, Bihar, Kerala, and Maharashtra were found to be having maximum mortality rates due to EWEs in the last two decades and thus there is a need to consider these states with priority for developing disaster management action plans.
The Intergovernmental Panel on Climate Change (IPCC) report highlights the projected increase in heat wave (HW) frequency, intensity, and duration. Globally, HW events have caused massive deaths in the past. India has also experienced severe HWs and thousands have reportedly died during the past decade. The study uses the Local Climate Zone (LCZ) classification developed by Stewart and Oke (2012) for evaluating heat stress at the city level during the summer period. Stationery surveys were conducted to collect micro-meteorological data in different LCZs. The study analyses the unique behaviour of mapped LCZs in Nagpur, a tropical landlocked Indian city using widely adopted heat indices (heat index and humidex). It investigates two kinds of probabilities, the distribution of heat stress levels in a particular LCZ and how vulnerable are various LCZs to a given heat stress level. It adopts a statistical approach fitting a predictive logit model to estimate the probability of heat stress in various LCZs. The results show that temperature regimes differ significantly across the LCZs. Secondly, heat stress varies greatly depending upon the LCZs. The mapping scheme and the corresponding heat stress provides indispensable information for targeted heat response planning and heat stress mitigation strategies in heat-prone areas.
The unplanned and uncontrolled urbanization of Indian cities has put them under different ecological and environmental threats. Urban heat island (UHI) is one such critical ecological hazard, whereby an urban area is experiencing higher land surface temperature (LST) as compared to the surrounding rural area. In the present study, the relationship of LST and surface urban heat island (SUHI) with the degree of impervious surface (IS) and green spaces (GS) in four rapidly growing Indian cities is presented. This study utilizes different geospatial techniques, including urban-rural gradient analysis, surface urban heat island estimation using Landsat OLI/TIRS data. The results signify a strong negative correlation of LST with the IS for Ahmedabad, Jodhpur, and Nagpur, while a positive correlation is seen over Guwahati. The negative correlation is the manifestation of the urban cool island, pertaining to higher LST over rural areas. On the other hand, Guwahati is surrounded by green vegetation, which provides natural cooling and thus lowers the LST, resulting in positive SUHI. The density of GS is found to be a significant contributor of SUHI in Guwahati city, whereas in the other three cities, its impact is insignificant due to its presence in very less amount in rural surroundings.
In a rapidly warming world, sustainable cooling is directly related to the protection of fresh and nutritious food, medicines, and the population from extreme heat for work conditions, the economic productivity of the working population, and income generation. This study aimed to understand how rural communities are meeting their nutrition, livelihood, health, living space, and mobility requirements regarding the role of cooling. We selected three villages as case studies in Maharashtra, India and conducted household surveys, in-depth interviews of key informants, focus group discussions (FGDs), and social mapping building typology study. The objective was to assess the rural community cooling to propose a community cooling hub (CCH) framework that could be economically, environmentally, and socially sustainable for the three villages. Our study showed that agriculture, dairy, buildings (domestic and commercial), and healthcare require cooling intervention in the studied communities. Based on the needs assessment for cooling, we proposed a CCH framework to provide cooling solutions in an integrated system for rural contexts.
Extreme heat and heat waves have been established as disasters which can lead to a great loss of life. Several studies over the years, both within and outside of India, have shown how extreme heat events lead to an overall increase in mortality. However, the impact of extreme heat, similar to other disasters, depends upon the vulnerability of the population. This study aims to assess the extreme heat vulnerability of the population of four cities with different characteristics across India. This cross-sectional study included 500 households from each city across the urban localities (both slum and non-slum) of Ongole in Andhra Pradesh, Karimnagar in Telangana, Kolkata in West Bengal and Angul in Odisha. Twenty-one indicators were used to construct a household vulnerability index to understand the vulnerability of the cities. The results have shown that the majority of the households fell under moderate to high vulnerability level across all the cities. Angul and Kolkata were found to be more highly vulnerable as compared to Ongole and Karimnagar. Further analysis also revealed that household vulnerability is more significantly related to adaptive capacity than sensitivity and exposure. Heat Vulnerability Index can help in identifying the vulnerable population and scaling up adaptive practices.
Climate change and rapid urbanization currently pose major challenges for equitable development in megacities of the Global South, such as Delhi, India. This study considers how urban social inequities are distributed in terms of burdens and benefits by quantifying exposure through an urban heat risk index (UHRI), and proximity to greenspace through the normalized difference vegetation index (NDVI), at the ward level in Delhi. Landsat derived remote sensing imagery for May and September 2011 is used in a sensitivity analysis of varying seasonal exposure. Multivariable models based on generalized estimating equations (GEEs) reveal significant statistical associations (p < 0.05) between UHRI/NDVI and several indicators of social vulnerability. For example, the proportions of children (β = 0.922, p = 0.024) and agricultural workers (β = 0.394, p = 0.016) are positively associated with the May UHRI, while the proportions of households with assets (β = -1.978, p = 0.017) and households with electricity (β = -0.605, p = 0.010) are negatively associated with the May UHRI. In contrast, the proportions of children (β = 0.001, p = 0.633) and agricultural workers (β = 0.002, p = 0.356) are not significantly associated with the May NDVI, while the proportions of households with assets (β = 0.013, p = 0.010) and those with electricity (β = 0.008, p = 0.006) are positively associated with the May NDVI. Our findings emphasize the need for future research and policies to consider how socially vulnerable groups are inequitably exposed to the impact of climate change-related urban heat without the mitigating effects of greenspace.
Heat waves are expected to intensify around the globe in the future, with a potential increase in heat stress and heat-induced mortality in the absence of adaptation measures. India has high current exposure to heat waves, and with limited adaptive capacity, impacts of increased heat waves might be quite severe. This paper presents a comparative analysis of urban heat stress/heatwaves by combining temperature and vapour pressure through two heat stress indices, i.e., Wet Bulb Globe Temperature (WBGT) and humidex index. For the years 1970-2000 (historical) and 2041-2060 (future), these two indicators were estimated in Jaipur. Another goal of this research is to better understand Jaipur land use changes and urban growth. For the land use study, Landsat 5 TM and Landsat 8 OLI satellite data from the years 1993, 2010, and 2015 were examined. During the research period, urban settlement increased and the majority of open land is converted to urban settlements. In the coming term, all months except three, namely July to September, have seen an increase in the WBGT index values; however, these months are classified as dangerous. Humidex’s historical value has been 21.4, but in RCP4.5 and RCP8.5 scenarios, it will rise to 25.5 and 27.3, respectively, and slip into the danger and extreme danger categories. The NDVI and SAVI indices are also used to assess the city’s condition during various periods of heat stress. The findings suggest that people’s discomfort levels will rise in the future, making it difficult for them to work outside and engage in their usual activities.
Urban climate changes and the warming of the cities are serious issues that cannot be overlooked. One of the most common inferences for these changes is unprecedented and unplanned urbanization, which further causes a rise in local, regional, and even global temperatures. Although the rate of urbanisation defines and greatly influences the city’s socioeconomic worth and GDP per capita, if the urban expansion is hap-hazardous, it can cause serious environmental harm.There has been a steep rise in global urban population over the past three decades, and the highest growth rates have been observed in Asian and African cities. These two continents have been predicted to contribute to almost 90% of the total urban growth from the present to 2050. India is one of the few highly susceptible countries to the harsh effects of climate change in terms of rise in temperatures. After 1990s’, India has observed substantial changes in the landscape due to urbanization, which has led to a significant rise in the surface and ambient air temperatures, further affecting the planet’s health. Elevated temperature drastically affects the health of urban dwellers leading to a rise in stress and discomfort levels. Estimation of Land Surface Temperature (LST) can play a vital role in understanding the region-specific alterations in temperatures as it uses satellite data that captures the entire region and provides the information in the form of pixels. Traditionally, the temperature was measured at meteorological stations and extrapolated for the entire region,whichinduces inaccuracies. This ambiguity can be amended by developing a relationship between LST and ambient air temperature. This communication focuses on LST estimation using Radiative Transfer Equation algorithm corresponding to various Landuse categories. The study also attempts to create a relationship between the LST and the ambient air temperature observed at two meteorological stations. An overall assessment of the number of days under stress for the residents was also performed over several years. Kolkata Metropolitan Area was considered the study area to represent the results and understand the complete analysis. A rise of 6.77 degrees C was observed in LST over the study period (2000-2019) due to an increment of 200% in the urban area. Analysis of the number of days under stress showed an increasing trend for the study area due to alterations in urban temperatures. These results and the suggestions from the scientific community, urban planners, and climate experts will help develop or modify the current policy frameworks for creating a balance between development and the environment, thus creating sustainable urban development.
We investigated the time evolution of heat waves and warm nights over the 7 agroclimatic zones of Tamil Nadu, India, during the period 1951-2016, including the spatiotemporal patterns of concurrent hot day and hot night (CHDHN) episodes and the concurrent warm spells in daytime temperature and drought (CWD) episodes. The research relied upon gridded temperature and rainfall observations from the India Meteorological Department. We used the Heat-Wave Magnitude Index daily to study the warm spells in daytime and nighttime temperature, while the analysis of droughts was based on the Standardized Precipitation Evapotranspiration Index. We observed a considerable increase in the count, intensity and duration of heat waves and warm night episodes across Tamil Nadu between the periods 1951-1983 and 1984-2016. Particularly, the number of heat wave events almost doubled in the second half of the study period. We observed a west-east gradient in the severity of heat waves. The intensity and duration of warm night events increased up to 3-fold in the second half of the study period, especially over central Tamil Nadu. The study recorded a multi-fold increase in the number and frequency of CHDHN episodes and the number of CWD episodes during 1984-2016 compared to the base period 1951-1983. More importantly, the incidence of compound events that coexisted with anomalous phases of sea surface temperatures registered a statistically significant spike in many locations. These changes in temperature-induced extremes pose an exceptional public health threat that can increase morbidity and mortality, disproportionately affecting vulnerable sections of Tamil Nadu’s populace engaged in outdoor work.
Due to global warming, increase in air temperature is a growing concern at present. This rise in temperature may cause mild to severe thermal discomfort and heat related hazards mostly for the people who are engaged in outside activities throughout the day. The present study shows the inter-spatial monthly distribution of thermal patches over major stations of Madhya Pradesh, viz., Bhopal, Gwalior, Indore, Jabalpur, Hoshangabad, Rewa, Ratlam, Ujjain, Dhar etc. In this study, various Heat Indices applicable for tropical climate including Wet Bulb Globe Temperature (WBGT) are used to estimate the thermal stress by analyzing the meteorological data of Summer-2018 in Madhya Pradesh. Study was carried out for computing indoor, shady and outdoor heat stress separately and heat transfer rates to identify the places vulnerable to severe heat stroke in the month of March, April and May in 2018.It is observed that declaration of heat wave alone at any station is not sufficient for the administration and health organizations to take precautionary actions; also, discomfort indices should be referred for impact based monitoring and making work schedules. It is found that March and April fall in the partial discomfort category for at least half of the districts in Madhya Pradesh. It is interesting to note that several districts fall in discomfort category in outdoor conditions but not in indoor or shady conditions in May month. Severe stresses are observed mainly in the West and Central Madhya Pradesh during April and May months. Comparison of various Heat Indices is too performed along with computing Tropical Summer Index (TSI) and Apparent Temperature (AT) to indicate real feel-like temperatures in Madhya Pradesh during extreme temperature events.
BACKGROUND: Studies on high temperatures and mortality have not focused on underdeveloped tropical regions and have reported the associations of different temperature metrics without conducting model selection. METHODS: We collected daily mortality and meteorological data including ambient temperatures and humidity in Ahmedabad during summer, 1987-2017. We proposed two cross-validation (CV) approaches to compare semiparametric quasi-Poisson models with different temperature metrics and heat wave definitions. Using the fittest model, we estimated heat-mortality associations among general population and subpopulations. We also conducted separate analyses for 1987-2002 and 2003-2017 to evaluate temporal heterogeneity. FINDINGS: The model with maximum and minimum temperatures and without heat wave indicator gave the best performance. With this model, we found a substantial and significant increase in mortality rate starting from maximum temperature at 42 °C and from minimum temperature at 28 °C: 1 °C increase in maximum and minimum temperatures at lag 0 were associated with 9.56% (95% confidence interval [CI]: 6.64%, 12.56%) and 9.82% (95% CI: 6.33%, 13.42%) increase in mortality risk, respectively. People aged ≥65 years and lived in South residential zone where most slums were located, were more vulnerable. We observed flatter increases in mortality risk associated with high temperatures comparing the period of 2003-2017 to 1987-2002. INTERPRETATION: The analyses provided better understanding of the relationship of high temperatures with mortality in underdeveloped tropical regions and important implications in developing heat warning system for local government. The proposed CV approaches will benefit future scientific work.
The elderly are one of the most vulnerable groups to heat-related illnesses and mortality. In tropical countries like India, where heat waves have increased in frequency and severity, few studies have focused on the level of stress experienced by the elderly. The study presented here included 130 elderly residents of Kolkata slums and 180 elderly residents of rural villages about 75 km south of Kolkata. It used miniature monitoring devices to continuously measure temperature, humidity, and heat index experienced during everyday activities over 24-h study periods, during hot summer months. In the Kolkata slum, construction materials and the urban heat island effect combined to create hotter indoor than outdoor conditions throughout the day, and particularly at night. As a result, elderly slum residents were 4.3 times more likely to experience dangerous heat index levels (≥ 45°C) compared to rural village elderly. In both locations, the median 24-h heat indexes of active elderly were up to 2°C higher than inactive/sedentary elderly (F = 25.479, p < 0.001). Among Kolkata slums residents, there were no significant gender differences in heat exposure during the day or night, but in the rural village, elderly women were 4 times more likely to experience dangerous heat index levels during the hottest times of the day compared to elderly men. Given the decline in thermoregulatory capacity associated with aging and the increasing severity of extreme summer heat in India, these results forecast a growing public health challenge that will require both scientific and government attention.
The impact of heat stress among the elderly in India-particularly the elderly poor-has received little or no attention. Consequently, their susceptibility to heat-related illnesses is virtually unknown, as are the strategies they use to avoid, or deal with, the heat. This study examined perceptions of comfort, heat-related symptoms, and coping behaviors of 130 elderly residents of Kolkata slums and 180 elderly residents of rural villages south of Kolkata during a 90-day period when the average 24-h heat indexes were between 38.6 °C and 41.8 °C. Elderly participants in this study reported being comfortable under relatively warm conditions-probably explained by acclimatization to the high level of experienced heat stress. The prevalence of most heat-related symptoms was significantly greater among elderly women, who also were more likely to report multiple symptoms and more severe symptoms. Elderly women in the rural villages were exposed to significantly hotter conditions during the day than elderly men, making it likely that gender differences in symptom frequency, number and severity were related to gender differences in heat stress. Elderly men and elderly village residents made use of a greater array of heat-coping behaviors and exhibited fewer heat-related symptoms than elderly women and elderly slum residents. Overall, heat measurements and heat-related symptoms were less likely to be significant predictors of most coping strategies than personal characteristics, building structures and location. This suggests that heat-coping behaviors during hot weather were the result of complex, culturally influenced decisions based on many different considerations besides just heat stress.
Cities are becoming hotter day-by-day because heat is trapped near the earth’s surface due to a decrease in green cover, rapid urbanization, energy-intensity activities, and concrete structures. The four major metropolitan cities of India, i.e. Kolkata, Chennai, Delhi and Mumbai, have experienced heat waves and heat stress frequently during the summer season. This study analyses heat wave and heat stress patterns in these cities using 30 years of data from 1990 to 2019 during the summer season. We used daily maximum temperature, relative humidity, wind speed and solar radiation datasets for the above mentioned period in this study. To understand the episode of a heat wave, we have used the 95th percentile method. Furthermore, we have also used Humidity Index (HD) to evaluate the degree of discomfort and the Universal Thermal Climate Index (UTCI) to categorize the level of heat stress. The analysis indicates that the number of heat wave events in the Delhi region is 26.31%, 31.58% and 63.16% higher than Kolkata, Chennai, and Mumbai regions respectively. It is also seen that the risks of extreme heat stress and dangerous-heat stroke events in the Chennai region during heat wave periods are higher than that experienced in other metropolitan cities because of high temperature with higher values of relative humidity. The risk of extreme heat stress is less in Delhi because of lower relative humidity compared to other metropolitan cities although temperature is higher in this region. However, the risk of extreme heat stress is lower in Mumbai region because of relatively lower temperature than Chennai during summer season. The likelihood of experiencing great discomfort during heat wave periods in Kolkata city is higher than that experienced in other metropolitan cities in India, however, during non-heat wave periods the probability of extreme discomfort is higher in Chennai.
Extreme heat events (EHEs) have been linked to increased mortality rates, rendering them a valuable research topic in both climate and public health. Early warning systems are highly impactful in prevention and management of heat-related illnesses. We aimed to determine the preliminary maximum temperature thresholds for Nagpur and Rajkot city of India by analyzing the meteorological and mortality data to enable the heat-health response system based on the heat wave disaster risk of a particular state and city. We conducted a trend analysis with daily maximum temperature and all-cause mortality data of Nagpur and Rajkot (2003-2017) cities, also city-specific thresholds evaluated for both cities. There was a significant association between all-cause mortality and extreme heat events and it was more profound when temperatures were above 40.1 degrees C, but V-shaped relationship of mortality-temperature was noted only for Nagpur city. The dose-response relationship between maximum temperatures and deaths alert thresholds to activate heat health response for red alert set at 46 degrees C and 44 degrees C for Nagpur and Rajkot city respectively. This study suggests that determining local thresholds is important for developing and implementing scientific early warning systems to prevent heat-related illnesses.
BACKGROUND: Diarrhea and typhoid, ancient water-borne diseases which are highly connected to rainfall are serious public health challenges in the blocks of Kalahandi district of Odisha, India. OBJECTIVES: Corroboration of rainfall and waterborne diseases are available in abundance; therefore, the objective of this article is to calculate the climate and disease vulnerability index (CDVI) value for each block of Kalahandi. METHODS: We have applied the livelihood vulnerability index with some modifications and classify the three major categories, i.e., exposure, sensitivity, and adaptive capacity into six subcategories. These six subcategories are further divided into 26 vulnerability indicators based on a detailed literature review. RESULTS: The result indicated that the Thuamul Rampur block, the southernmost part of the district is highly exposed to the annual and seasonal mean rainfall, and the Madanpur Rampur block lies in the northernmost part of the district is highly exposed to diarrhea and typhoid. Based on the calculation of the final CDVI value, nearly 50% of blocks of the Kalahandi district fall in the category of very high to high vulnerable zones. Furthermore, it has been observed that factors such as rainfall and disease distribution, vulnerable population and infrastructure, and education and health-care capacities had a notable influence on vulnerability. CONCLUSION: It is rare to find a health vulnerability-related study in India at this microlevel based on the suitable indicators selected for a tribal and backward region.
Melioidosis is a seasonal infectious disease in tropical and subtropical areas caused by the soil bacterium Burkholderia pseudomallei. In many parts of the world, including South West India, most cases of human infections are reported during times of heavy rainfall, but the underlying causes of this phenomenon are not fully understood. India is among the countries with the highest predicted melioidosis burden globally, but there is very little information on the environmental distribution of B. pseudomallei and its determining factors. The present study aimed (i) to investigate the prevalence of B. pseudomallei in soil in South West India, (ii) determine geochemical factors associated with B. pseudomallei presence and (iii) look for potential seasonal patterns of B. pseudomallei soil abundance. Environmental samplings were performed in two regions during the monsoon and post-monsoon season and summer from July 2016 to November 2018. We applied direct quantitative real time PCR (qPCR) together with culture protocols to overcome the insufficient sensitivity of solely culture-based B. pseudomallei detection from soil. A total of 1,704 soil samples from 20 different agricultural sites were screened for the presence of B. pseudomallei. Direct qPCR detected B. pseudomallei in all 20 sites and in 30.2% (517/1,704) of all soil samples, whereas only two samples from two sites were culture-positive. B. pseudomallei DNA-positive samples were negatively associated with the concentration of iron, manganese and nitrogen in a binomial logistic regression model. The highest number of B. pseudomallei-positive samples (42.6%, p < 0.0001) and the highest B. pseudomallei loads in positive samples [median 4.45 × 10(3) genome equivalents (GE)/g, p < 0.0001] were observed during the monsoon season and eventually declined to 18.9% and a median of 1.47 × 10(3) GE/g in summer. In conclusion, our study from South West India shows a wide environmental distribution of B. pseudomallei, but also considerable differences in the abundance between sites and within single sites. Our results support the hypothesis that nutrient-depleted habitats promote the presence of B. pseudomallei. Most importantly, the highest B. pseudomallei abundance in soil is seen during the rainy season, when melioidosis cases occur.
Cholera is a water-borne infectious disease that affects 1.3 to 4 million people, with 21,000 to 143,000 reported fatalities each year worldwide. Outbreaks are devastating to affected communities and their prospects for development. The key to support preparedness and public health response is the ability to forecast cholera outbreaks with sufficient lead time. How Vibrio cholerae survives in the environment outside a human host is an important route of disease transmission. Thus, identifying the environmental and climate drivers of these pathogens is highly desirable. Here, we elucidate for the first time a mechanistic link between climate variability and cholera (Satellite Water Marker; SWM) index in the Bengal Delta, which allows us to predict cholera outbreaks up to two seasons earlier. High values of the SWM index in fall were associated with above-normal summer monsoon rainfalls over northern India. In turn, these correlated with the La Niña climate pattern that was traced back to the summer monsoon and previous spring seasons. We present a new multi-linear regression model that can explain 50% of the SWM variability over the Bengal Delta based on the relationship with climatic indices of the El Niño Southern Oscillation, Indian Ocean Dipole, and summer monsoon rainfall during the decades 1997-2016. Interestingly, we further found that these relationships were non-stationary over the multi-decadal period 1948-2018. These results bear novel implications for developing outbreak-risk forecasts, demonstrating a crucial need to account for multi-decadal variations in climate interactions and underscoring to better understand how the south Asian summer monsoon responds to climate variability.
BACKGROUND: Chengannur, a town in the south Indian state of Kerala, was 1 of the worst affected towns during the floods of 2018. Post-flood, Kerala state was under the threat of many infectious diseases including leptospirosis, but did not report any leptospirosis infections. OBJECTIVES: This study was conducted with the following objectives: (1) Assess the knowledge, attitude and practices regarding the prevention of leptospirosis among the flood affected population and Accredited Social Health Activists (ASHAs) of Chengannur; and (2) Analyze the factors responsible for and contributing to leptospirosis control in the area post flood. METHODOLOGY: A cross-sectional questionnaire based observational study was conducted among 2 groups: the flood affected population, and ASHA. The questionnaire was divided into 3 parts. Part A contained the socio-demographic information. Part B contained questions on assessment of knowledge, attitude, and practices regarding the prevention, and control of leptospirosis. Part C was only for the ASHA involved. RESULTS: The final sample size was 331 (244 from the general population and 87 ASHAs). With respect to knowledge, attitude, and practice, the responses were dichotomized into correct and wrong responses. The mean knowledge score was 9.01 ± 1.08 (maximum score of 10), mean attitude score was of 3.61 ± 0.55 (maximum score of 4) and the mean practice score was 4.12 ± 1.05 (maximum score of 5). CONCLUSION: Knowledge and attitude scores did not significantly differ between the general population and ASHA, but the practice score showed a higher score among the ASHA, all of which could have probably contributed to the prevention of a leptospirosis outbreak in the region.
Climate change has increased the frequency of drought occurrence in various parts of the world. Drought as a complex phenomenon causes severe impacts on ecological and socio-economic status. Short-term and long-term occurrences of drought have made many regions vulnerable globally. This paper makes an attempt to assess drought vulnerability in Godavari Middle Sub-basin of India. Twenty-four site specific socio-economic and environmental factors were identified based on the extensive literature review. Drought frequency was assessed using standardized precipitation index (SPI). These datasets were divided into training (70%) and testing (30%) data. Frequency ratio (FR) model was utilized to establish relationship among drought conditioning factors and drought frequency. Weights obtained from the FR model were used as input to the adaptive neuro-fuzzy inference systems (ANFIS) model. Drought vulnerability results were validated using the testing data and receiver operating characteristic (ROC). The accuracy of ANFIS models for 1-month (0.957), 3-months (0.882), 6-months (0.964) and 12-months (0.938) showed high suitability of ANFIS model for the assessment of drought vulnerability. The findings revealed that very low normalized difference vegetation index (NDVI) and increasing trend of highest maximum and mean maximum temperature were major environmental factors which influenced high drought vulnerability in the sub-basin. High proportion of area under fallow land, high infant mortality rate (IMR) and moderate literacy rate were identified as major socio-economic factors making watersheds vulnerable during short and long-term droughts. Largest area of the sub-basin was found under high vulnerability for 3-months, followed by 6-months and 12-months droughts. Thus, the study calls for policy intervention towards lessening the impact of drought in highly vulnerable watersheds.
The Indian summer monsoon rainfall (ISMR) is vital for the livelihood of millions of people in the Indian region; droughts caused by monsoon failures often resulted in famines. Large volcanic eruptions have been linked with reductions in ISMR, but the responsible mechanisms remain unclear. Here, using 145-year (1871-2016) records of volcanic eruptions and ISMR, we show that ISMR deficits prevail for two years after moderate and large (VEI > 3) tropical volcanic eruptions; this is not the case for extra-tropical eruptions. Moreover, tropical volcanic eruptions strengthen El Niño and weaken La Niña conditions, further enhancing Indian droughts. Using climate-model simulations of the 2011 Nabro volcanic eruption, we show that eruption induced an El Niño like warming in the central Pacific for two consecutive years due to Kelvin wave dissipation triggered by the eruption. This El Niño like warming in the central Pacific led to a precipitation reduction in the Indian region. In addition, solar dimming caused by the volcanic plume in 2011 reduced Indian rainfall.
Background: Personnel deployed at an altitude ranging from 9000 ft to 23,000 ft are exposed to sub-zero temperatures up to -40 degrees C. These conditions lead to the development of various cold injuries which presents in varying grades and severity. Aim: The aim of this study is to study the epidemiological trends and assess risk factors/conditions those are contributing to the development of cold weather injuries (CWI) at extreme cold climate in high altitude areas. Methodology: This is a retrospective, observational study on cold injury cases evacuated from the northern glaciers of India. The data were collected and tabulated in MS-Excel sheets, and analysis was done using percentage, mean, median, linear regression, and P value calculation. SPSS statistical analysis software version 23 was employed for generating the results. P < 0.05 was considered for statistically significant. Results: The annual incidence of cold injuries calculated for troops deployed at high altitude (>9000 feet) with extreme cold climate is 6.4/1000/year. The average duration of exposure for the development of CWI was found to be 4.85 h with a standard deviation = 2.88 h. Statistically significant association was found between the median temperatures and number of cold injury cases evacuated monthly with a strong negative coefficient of correlation (Pearson’s) value r = -0.8214, and P = 0.001063. No correlation was found between the severity of frostbites and duration of exposure as the coefficient of correlation r (Pearson’s) was weakly positive with a value of 0.19 and statistically not significant with P = 0.127. Conclusion: This study highlights the magnitude of problem, high risk zones, and predisposing activities. Statistical association has been drawn between altitude, temperature and duration of exposure with burden of cold injury. This study provides an insight with respect to associations and risk factors for the development of CWI, in Indian perspective and may be beneficial for better planning and preventive measures to reduce burden of CWI.
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.
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.
Climate change is a concerning matter nowadays. It has a long-term effect on human health by spreading vector-borne diseases throughout the world, and West Bengal is not an exception. Vector-borne diseases are life-threatening risk for human; approximately 27,437 people have been infected (2016) every year by this giant killer in West Bengal of India. Temperature and rainfall, two important parameters, have directly influenced the vector-borne diseases. An association between vector-borne diseases and climatic conditions has been established by using geographically weighted regression (GWR) technique. GWR resulted overall r square value more than 0.523 in every case of diseases signifies that the climatic parameters (temperature and rainfall) and vector-borne diseases (Dengue, Malaria, Japanese Encephlities) are strongly correlated. The climatic parameters and positive cases of diseases were mapped out by using inverse distance weight (IDW) interpolation technique in this study. Artificial neural network (ANN) was performed to predict and forecast the climatic condition. The predicted findings have been validated by root mean square error (RMSE) (temperature: 0.301; rainfall: 0.380, i.e., acceptable). This study revealed an insight between climate variables and vector-borne cases in different districts of West Bengal to better understand the effects of climate variability on these diseases. A novel approach of this study is to forecast the spreading of vector-borne diseases for incoming day in West Bengal. After a critical analysis, temperature and rainfall were found to be potent factors for the development of vectors (Aedes Aegypti and Aedes albopictus), and based on this, the risk of vector-borne diseases has been predicted for upcoming years. Forecasted climatic parameters showed that almost all the districts of West Bengal would be reached in a climatic condition where there would be a chance of spreading of vector-borne diseases.
BACKGROUND: In the climate change discourse, a body of scholarship focusing on how people perceive climate change and its impact is increasing. However, in the Indian context, such scholarship is limited. OBJECTIVE: This paper aims to describe the perceptions of people on climate change and its health impacts, which were captured as part of a larger study. METHODOLOGY: A cross-sectional study was conducted in randomly selected 983 households in four districts spread across Madhya Pradesh and Jammu and Kashmir. A semi-structured questionnaire was used to collect the data. RESULTS: For 72% of respondents, the perception was not related to climate change per se. Their perceptions were contextual and were based on the anomalies which are observed in the immediate weather conditions. The health impacts of climate change were also not understood at the first place, but with probing 64% of respondents were able to report seasonal diseases. CONCLUSION: Perceptions of the people regarding climate change are more linked to their own experiences with their local weather conditions rather than the overall concept. This also explains their lack of comprehension about the health impact of climate change, but a sound understanding of seasonal diseases.
OBJECTIVES: Dakshina Kannada is one of the districts of Karnataka state of India with high incidences of mosquito-borne diseases, especially malaria and dengue. The larval stages of the mosquitoes are very important in determining the prevalence of adult mosquitoes and associated diseases. Hence, the occurrence of mosquito species was investigated by sampling different water bodies present in the Dakshina Kannada district from June 2014 to May 2017. METHODS: Random sampling was carried out from permanent and temporary, artificial and natural water bodies belonging to 11 types of microhabitats using dippers and suction pumps. RESULTS: A maximum of 37 mosquito species belonging to 12 genera were recorded with the dominant genera being Culex. Most species have been recorded from temporary bodies of water with the highest number of species in receptacles. Monsoon is the most productive season, both in terms of occurrence and abundance followed by post-monsoon and pre-monsoon. The abundance of mosquito larvae was significantly higher in temporary water bodies compared to the permanent. INTERPRETATION & CONCLUSION: Abundant rainfall in the study area which produces many natural and domestic temporary water bodies accounts for mosquito breeding throughout the year.
BACKGROUND & OBJECTIVES: In India, Kyasanur Forest Disease has been reported from the states of Karnataka, Kerala, Goa, and Maharashtra. The relationship between climatic factors and transmission of KFD remains untouched, therefore, the present study was undertaken. METHODS: Based on the occurrence of cases, Shivamogga district (Karnataka) and Wayanad district in Kerala and northern Goa (Goa state) were selected for the study. Data on the incidence of KFD and climate factors were collected from concerned authorities. To determine the relationship between dependent and independent variables, spearman’s correlation was calculated for monthly as well as with lag months. RESULTS: KFD cases and temperature (°C) were found significantly correlated up to 1 months’ lag period (p<0.05) while with precipitation relationship was found negatively significant for 0-3 months' lag. The range of suitable temperature for KFD in Shivamogga, Goa and Wayanad was found as 20-31°C, 25-29°C and 27-31°C respectively. The cumulative precipitation during transmission months (November-May) ranged from <150-500mm, while in non-transmission months (June-October) from >1100-2400mm. INTERPRETATION & CONCLUSION: The analysis of three sites revealed that with the increase in temperature, the intensity of KFD transmission decreases as corroborated by the seasonal fluctuations in Shivamogga, Goa and Wayanad. High precipitation from June to October rovides suitable ecology to tick vector and sets in transmission season from November to May when cumulative precipitation is <500 mm.
Introduction: Dengue is a mosquito borne viral disease. found in tropical and subtropical countries. Dengue virus (DENV) infected mosquitoes of Aedes species are crucial for the transmission of disease. It has emerged as a threat to the public health systems. Dengue is endemic in many parts of India but still the status of dengue cases in Rewa Madhya Pradesh is not reported convincingly. Aim: To investigate the presence of dengue in Rewa district of Madhya Pradesh. Materials and Methods: This cross-sectional study was conducted in the Department of Microbiology at Shyam Shah Medical college Rewa under National Vector Borne Disease Control Programme (NVBDCP), Rewa, Madhya Pradesh, India, including 1113 Outpatient/Inpatient Department samples received during March 2021 to October 2021. Blood samples were collected from patients having febrile illness and after serum separation, serum were subjected to NS1 Enzyme Linked Immunosorbent Assay (ELISA) test. Descriptive statistics and Chi-square tests were applied for data analysis. Results: A total of 1113 sample were received and tested for dengue NS1 out of that 108 sample were found NS1 positive by ELISA. The cases of dengue started from the month of July 2021. But in the month of October dengue positivity was highest in number. Dengue cases reported were 297 (6.73%) in the rainy season (July-August), but the dengue positivity increased (713, 9.3%) in the post rainy season (September-October). Overall prevalence of dengue was higher in the 21-30 years (34.3%) age group followed by 11-20 years (24.1%), 31-40 years (18.5%), 41-50 years (18.5%), 51-60 years (7.4%) and >60 years (3.70%) age groups with respect to total positive cases. The prevalence of dengue was higher in male (12.94%) in comparison to females (5.54%). Conclusion: This study warrants the dengue virus infection as one of the important causes of fever during rainy and post rainy season in this region. Early diagnosis and reporting of cases are important for the better management of disease.
In recent decades, dengue has been expanding rapidly in the tropical cities. Even though environmental factors and landscape features profoundly impact dengue vector abundance and disease epidemiology, significant gaps exist in understanding the role of local environmental heterogeneity on dengue epidemiology in India. In this study, we assessed the role of remotely sensed climatic factors (rainfall, temperature and humidity) and landscape variables (land use pattern, vegetation and built up density) on dengue incidence (2012-2019) in Bhopal city, Central India. Dengue hotspots in the city were assessed through geographical information system based spatial statistics. Dengue incidence increased from 0.59 cases in 2012 to 9.11 cases in 2019 per 10,000 inhabitants, and wards located in Southern Bhopal were found to be dengue hotspots. Distributed lag non-linear model combined with quasi Poisson regression was used to assess the exposure-response association, relative risk (RR), and delayed effects of environmental factors on dengue incidence. The analysis revealed a non-linear relationship between meteorological variables and dengue cases. The model shows that the risk of dengue cases increases with increasing mean temperature, rainfall and absolute humidity. The highest RR of dengue cases (~2.0) was observed for absolute humidity ≥60 g/m3 with a 5-15 week lag. Rapid urbanization assessed by an increase in the built-up area (a 9.1% increase in 2020 compared to 2014) could also be a key factor driving dengue incidence in Bhopal city. The study sheds important insight into the synergistic effects of both the landscape and climatic factors on the transmission dynamics of dengue. Furthermore, the study provides key baseline information on the climatic variables that can be used in the micro-level dengue prediction models in Bhopal and other cities with similar climatic conditions.
INTRODUCTION: The study aimed to develop a reproducible, open-source, and scalable framework for extracting climate data from satellite imagery, understanding dengue’s decadal trend in India, and estimating the relationship between dengue occurrence and climatic factors. MATERIALS AND METHODS: A framework was developed in the Open Source Software, and it was empirically tested using reported annual dengue occurrence data in India during 2010-2019. Census 2011 and population projections were used to calculate incidence rates. Zonal statistics were performed to extract climate parameters. Correlation coefficients were calculated to estimate the relationship of dengue with the annual average of daily mean and minimum temperature and rainy days. RESULTS: Total 818,973 dengue cases were reported from India, with median annual incidence of 6.57 per lakh population; it was high in 2019 and 2017 (11.80 and 11.55 per lakh) and the Southern region (8.18 per lakh). The highest median annual dengue incidence was observed in Punjab (24.49 per lakh). Daily climatic data were extracted from 1164 coordinate locations across the country for the decadal period (4,249,734 observations). The annual average of daily temperature and rainy days positively correlated with dengue in India (r = 0.31 and 0.06, at P < 0.01 and 0.30, respectively). CONCLUSION: The study provides a reproducible algorithm for bulk climatic data extraction from research-level satellite imagery. Infectious disease models can be used to understand disease epidemiology and strengthen disease surveillance in the country.
India has witnessed a five-fold increase in dengue incidence in the past decade. However, the nation-wide distribution of dengue vectors, and the impacts of climate change are not known. In this study, species distribution modeling was used to predict the baseline and future distribution of Aedine vectors in India on the basis of biologically relevant climatic indicators. Known occurrences of Aedes aegypti and Aedes albopictus were obtained from the Global Biodiversity Information Facility database and previous literature. Bio-climatic variables were used as the potential predictors of vector distribution. After eliminating collinear and low contributing predictors, the baseline and future prevalence of Aedes aegypti and Aedes albopictus was determined, under three Representative Concentration Pathway scenarios (RCP 2.6, RCP 4.5 and RCP 8.5), using the MaxEnt species distribution model. Aedes aegypti was found prevalent in most parts of the southern peninsula, the eastern coastline, north eastern states and the northern plains. In contrast, Aedes albopictus has localized distribution along the eastern and western coastlines, north eastern states and in the lower Himalayas. Under future scenarios of climate change, Aedes aegypti is projected to expand into unsuitable regions of the Thar desert, whereas Aedes albopictus is projected to expand to the upper and trans Himalaya regions of the north. Overall, the results provide a reliable assessment of vectors prevalence in most parts of the country that can be used to guide surveillance efforts, despite minor disagreements with dengue incidence in Rajasthan and the north east, possibly due to behavioral practices and sampling efforts. Plain Language Summary Climatic parameters derived from temperature and humidity affect the development and survival of mosquitoes that spread diseases. In the past decade, India has witnessed an alarming rise in dengue, a viral disease that spreads through the bite of the mosquitoes Aedes aegypti and Aedes albopictus. We used machine learning based modeling algorithm to predict the present and future abundance of these mosquitoes in India, based on biologically relevant climatic factors. The results project expansion of Aedes aegypti in the hot arid regions of the Thar Desert and Aedes albopictus in cold upper Himalayas as a result of future climatic changes. The results provide a useful guide for strengthening efforts for entomological and dengue surveillance.
This study investigated the influence of climate factors on malaria incidence in the Sundargarh district, Odisha, India. The WEKA machine learning tool was used with two classifier techniques, Multi-Layer Perceptron (MLP) and J48, with three test options, 10-fold cross-validation, percentile split, and supplied test. A comparative analysis was carried out to ascertain the superior model among malaria prediction accuracy techniques in varying climate contexts. The results suggested that J48 had exhibited better skill than MLP with the 10-fold cross-validation method over the percentile split and supplied test options. J48 demonstrated less error (RMSE = 0.6), better kappa = 0.63, and higher accuracy = 0.71), suggesting it as most suitable model. Seasonal variation of temperature and humidity had a better association with malaria incidents than rainfall, and the performance was better during the monsoon and post-monsoon when the incidents are at the peak.
Meeting global and national malaria elimination targets requires identifying challenges as early as possible so that strategies can be modified to stay on track. This qualitative study of stakeholders who have a major influence on malaria programs across the Southeast Asian region, including those at a state level in India and at a national level in Cambodia, Myanmar, Thailand and Vietnam, shows that most believe Plasmodium falciparum malaria elimination targets are attainable, but are less optimistic for meeting Plasmodium vivax targets. Across these countries, stakeholders reported large variations in access to malaria diagnosis and treatment; the effectiveness of strategies for reaching migrants and hardto-serve populations; and securing sufficient numbers of skilled workers for both diagnosis and compliance with artemisinin-combination treatments and the need to optimise use of insecticides. Additionally, there was optimism about coordinated surveillance and response, but this was counterbalanced with a sense that national and regional collaboration opportunities have been missed. Climate change impacts were seen as a potential threat by all stakeholders in this study and in need of further research.
Increased levels of CO(2) and various greenhouse gases cause global warming and, in combination with pollutants from fossil fuel combustion and vehicular and industrial emissions, have been driving increases in noncommunicable diseases across the globe, resulting a higher mortality and morbidity. Respiratory diseases and associated allergenic manifestations have increased worldwide, with rates higher in developing countries. Pollen allergy serves as a model for studying the relationship between air pollution and respiratory disorders. Climate changes affect the quality and amount of airborne allergenic pollens, and pollutants alter their allergenicity, resulting in greater health impacts, especially in sensitized individuals.
A large concern with estimates of climate and health co-benefits of “clean” cookstoves from controlled emissions testing is whether results represent what actually happens in real homes during normal use. A growing body of evidence indicates that in-field emissions during daily cooking activities differ substantially from values obtained in laboratories, with correspondingly different estimates of co-benefits. We report PM(2.5) emission factors from uncontrolled cooking (n = 7) and minimally controlled cooking tests (n = 51) using traditional chulha and angithi stoves in village kitchens in Haryana, India. Minimally controlled cooking tests (n = 13) in a village kitchen with mixed dung and brushwood fuels were representative of uncontrolled field tests for fine particulate matter (PM(2.5)), organic and elemental carbon (p > 0.5), but were substantially higher than previously published water boiling tests using dung or wood. When the fraction of nonrenewable biomass harvesting, elemental, and organic particulate emissions and modeled estimates of secondary organic aerosol (SOA) are included in 100 year global warming commitments (GWC(100)), the chulha had a net cooling impact using mixed fuels typical of the region. Correlation between PM(2.5) emission factors and GWC (R(2) = 0.99) implies these stoves are climate neutral for primary PM(2.5) emissions of 8.8 ± 0.7 and 9.8 ± 0.9 g PM(2.5)/kg dry fuel for GWC(20) and GWC(100), respectively, which is close to the mean for biomass stoves in global emission inventories.
Despite low per capita emissions, with over a billion population, India is pivotal for climate change mitigation globally, ranking as the third largest emitter of greenhouse gases. We linked a previously published multidimensional population projection with emission projections from an integrated assessment model to quantify the localised (i.e. state-level) health benefits from reduced ambient fine particulate matter in India under global climate change mitigation scenarios in line with the Paris Agreement targets and national scenarios for maximum feasible air quality control. We incorporated assumptions about future demographic, urbanisation and epidemiological trends and accounted for model feedbacks. Our results indicate that compared to a business-as-usual scenario, pursuit of aspirational climate change mitigation targets can avert up to 8.0 million premature deaths and add up to 0.7 years to life expectancy (LE) at birth due to cleaner air by 2050. Combining aggressive climate change mitigation efforts with maximum feasible air quality control can add 1.6 years to LE. Holding demographic change constant, we find that climate change mitigation and air quality control will contribute slightly more to increases in LE in urban areas than in rural areas and in states with lower socio-economic development.
This paper examines the effect of biomass burning on adolescent health in India. The biomass burning problem is quite acute especially in North India, with some states experiencing forest fires and few states actively engaging in crop burning practice. We combine remote sensing data on biomass burning events with a pan-India survey on teenage girls (TAG survey). We exploit regional and temporal variation in our data to establish the link between occurrence of extremely high levels of biomass burning during early life and adolescent height for girls in India. Our results indicate that exposure to extremely high level of biomass burning during prenatal and postnatal period is associated with lower height (by 0.7 percent or 1.07 cm) later in life. Girls from North India are found to be especially vulnerable to the harmful effects of exposure to biomass burning. (c) 2021 Elsevier B.V. All rights reserved.
This study examines point and non-point sources of air pollution and particulate matter and their associated socioeconomic and health impacts in South Asian countries, primarily India, China, and Pakistan. The legislative frameworks, policy gaps, and targeted solutions are also scrutinized. The major cities in these countries have surpassed the permissible limits defined by WHO for sulfur dioxide, carbon monoxide, particulate matter, and nitrogen dioxide. As a result, they are facing widespread health problems, disabilities, and causalities at extreme events. Populations in these countries are comparatively more prone to air pollution effects because they spend more time in the open air, increasing their likelihood of exposure to air pollutants. The elevated level of air pollutants and their long-term exposure increases the susceptibility to several chronic/acute diseases, i.e., obstructive pulmonary diseases, acute respiratory distress, chronic bronchitis, and emphysema. More in-depth spatial-temporal air pollution monitoring studies in China, India, and Pakistan are recommended. The study findings suggest that policymakers at the local, national, and regional levels should devise targeted policies by considering all the relevant parameters, including the country’s economic status, local meteorological conditions, industrial interests, public lifestyle, and national literacy rate. This approach will also help design and implement more efficient policies which are less likely to fail when brought into practice.
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.
In the present scenario, tick-borne diseases (TBDs) are well known for their negative impacts on humans as well as animal health in India. The reason lies in their increased incidences due to global warming, environmental and ecological changes, and availability of suitable habitats. On a global basis, they are now considered a serious threat to human as well as livestock health. The major tick-borne diseases in India include Kyasanur forest disease (KFD), Crimean-congo hemorrhagic fever (CCHF), Lyme disease (LD), Q fever (also known as coxiellosis), and Rickettsial infections. In recent years, other tick-borne diseases such as Babesiosis, Ganjam virus (GANV), and Bhanja virus (BHAV) infections have also been reported in India. The purpose of this paper is to review the history and the current state of knowledge of tick-borne diseases in the country. The conclusion of this review is extending the requirement of greater efforts in research and government management for the diagnosis and treatment and as well as prevention of these diseases so that tick-borne disease burden should be minimizing in India.
Climate change is expected to have severe consequences for the world, some of which are already being felt. According to projections, in some regions, droughts will be more frequent and intense in the 21st century. This calls for purposeful interventions by governments to mitigate the impacts. Drought-affected communities are more vulnerable to famine. The effects of drought are felt in people’s education levels, nutrition, health, sanitation, and women and the safety of children in these communities. The impact of drought can be seen in the livelihoods of people affected by it. Against this backdrop, there is the need to document the effects of drought on women and children’s health in the affected communities. Such a study calls for a systematic approach. This study explores the various dimensions of the effects of droughts. It accessed electronic databases, including Google Scholar, Scopus, Pub-Med, JSTOR to identify a substantial number of studies using key words and expressions. To begin with, the word drought was kept constant in all combinations of keywords and phrases. The search was then refined by using the word drought with keywords, such as livelihood, vulnerability, sustainable development, adaption and mitigation, migration, health impact, and risk management to search the required articles. Only studies conducted in the period 2000 – 2019 were considered for this review. The review’s findings show that due to a lack of water during a drought, the burden of work on women and children increased considerably. Most faced severe health issues like malnutrition and anemia. The livelihoods of women were also affected because of which they were forced to adopt various strategies to overcome the problems posed by droughts. Droughts occur every year in different parts of India. Actions are required to mitigate the effects of drought, including the provision of drinking water, food, aid and relief aid to distressed farmers, employment support, support for changes in livelihoods, water security, and drought-proofing. State policies and actions must give particular attention to women and children because they are the most vulnerable. Employment-generation actions should also include youth by providing appropriate training for developing appropriate skills.
Kerala is one of India’s most vulnerable states in India when it comes to climate-induced disasters. Kerala’s public health department grappled with a flood of unprecedented magnitude in August 2018. Situating the flood in the context of Kerala’s state and society, this paper addresses three questions: What was the level of flood-prevention preparedness? What were the public health effects and how were they managed? Finally, what policy lessons were learned? Drawing from reports of relevant national and state agencies responsible for disaster management as well as first-hand accounts of nongovernmental organizations and media coverage, this paper argues that while Kerala’s flood-prevention preparedness was far from ideal, its postflood response in mounting a rapid and effective rescue and relief operation as well as in preventing a public health crisis was commendable. The paper also shows that impressive achievements in climate-disaster health management can be achieved through a decentralized and participatory public health system in which coordinated public action is managed by a capacious state with the active collaboration of civil society.
To protect public health, heat-related policies are increasingly being adopted by city authorities to address the unequal impact of heatwaves. Ahmedabad’s Heat Action Plan (HAP) is an acclaimed and successful policy response in India and beyond. While the pilot evaluation of the initiative suggests that almost a thousand deaths were avoided annually after its implementation, it is not yet clear whose lives were saved, and to what extent this statistic was due to the HAP, rather than other factors. By reviewing the published and grey literature centering on the HAP target groups, outreach strategies, and impacts on urban services, this paper points out major knowledge gaps concerning the potentials and impacts of the HAP, which may lead to the systematical exclusion of vulnerable and disadvantaged groups from the intended benefits. In this paper, it is argued that the effectiveness and inclusiveness of the HAP predominantly depend on its integration into urban development projects, which is a challenging task given the existing horizontal and vertical fragmentation in the planning of city projects. Moreover, urban plans and policies, including the HAP, are shown to be overly focused on technology, and as a consequence, they do not realize their limited scope in addressing the associated issues, which are fundamentally social, deep, and structural, such as spatial inequality in Indian cities.
As the world deals with COVID-19, there is increasing attention to the threat of emerging and re-emerging infectious diseases. India is especially vulnerable to climate-induced health risks and a hotspot for infectious diseases. In this study we use a scoping review to synthesize evidence on the impact of climate on infectious diseases. We use this to uncover gaps and understand the implications for policymaking and health system preparedness. There is a strong evidence base linking climate change to disease outbreaks, both directly and indirectly. Socio-economic factors are the modifiers that determine disease severity in different populations and locations.
Efforts have been made to quantify the spatio-temporal malaria transmission intensity over India using the dynamical malaria model, namely, Vector-borne Disease Community Model of International Centre for Theoretical Physics Trieste (VECTRI). The likely effect of climate change in the variability of malaria transmission intensity over different parts of India is also investigated. The Historical data and future projection scenarios of the rainfall and temperature derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) model output are used for this purpose. The Entomological Inoculation Rate (EIR) and Vector are taken as quantifiers of malaria transmission intensity. It is shown that the maximum number of malaria cases over India occur during the Sept-Oct months, whereas the minimum during the Feb-Apr months. The malaria transmission intensity as well as length of transmission season over India is likely to increase in the future climate as a result of global warming.
Poverty and discrimination compound vulnerability to disasters. Yet, people who experience these are some of the least involved groups in Disaster Risk Reduction (DRR) dialogue and research. This study aims to fill that gap by narrating the lived experience of underprivileged flood-affected communities. We conducted in-depth interviews (N = 48) with community members (n = 36) and staff members of collaborating non-governmental organisations (n = 12). We also conducted focus group discussions with staff members of the same NGOs. The results describe how systemic issues entrenched with socio-economic and cultural factors impact a community?s ability to prepare for floods. These communities received no warning or timely evacuation messages, and perceived the received support as inadequate and unfair. Communities recovered through their resourcefulness and thoughtfulness. They resented the government for its lack of action throughout the disaster cycle. Priorities for future efforts involve actively engaging these vulnerable groups and tailoring DRR activities for them.
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.
INTRODUCTION: Enteric Fever (EF) affects over 14.5 million people every year globally, with India accounting for the largest share of this burden. The water-borne nature of the disease makes it prone to be influenced as much by unsanitary living conditions as by climatic factors. The detection and quantification of the climatic effect can lead to improved public health measures which would in turn reduce this burden. METHODOLOGY: We obtained a list of monthly Widal positive EF cases from 1995 to 2017 from Ahmedabad and Surat Municipalities. We obtained population data, daily weather data, and Oceanic Niño Index values from appropriate sources. We quantified the association between extreme weather events, phases of El Niño Southern Oscillations (ENSO) and incidence of EF. RESULTS: Both cities showed a seasonal pattern of EF, with cases peaking in early monsoon. Risk of EF was affected equally in both cities by the monsoon season — Ahmedabad (35%) and Surat (34%). Extreme precipitation was associated with 5% increase in EF in Ahmedabad but not in Surat. Similarly, phases of ENSO had opposite effects on EF across the two cities. In Ahmedabad, strong El Niño months were associated with 64% increase in EF risk while strong La Niña months with a 41% reduction in risk. In Surat, strong El Niño was associated with 25% reduction in risk while moderate La Niña with 21% increase in risk. CONCLUSIONS: Our results show that the risk of EF incidence in Gujarat is highly variable, even between the two cities only 260 kms apart. In addition to improvements in water supply and sewage systems, preventive public health measures should incorporate variability in risk across season and phases of ENSO. Further studies are needed to characterize nationwide heterogeneity in climate-mediated risk, and to identify most vulnerable populations that can benefit through early warning systems.
The surface water is a significant feature in the hydrological system and is a vital compound for life growth. Assessment of trace elements in the water bodies is essential since it poses huge threats to aquatic organisms and humans if present in high concentrations. This study was carried out to assess the seasonal changes in the dissolved trace elements concentration in Bhavani river, which is one of the major rivers of Tamil Nadu, southern India and also to assess the human health risk due to its consumption. A total of 46 surface water samples were collected along the river during pre-monsoon and post-monsoon of 2018 and were analyzed for various trace elements such as Zn, Cu, Fe, Ni, and Pb. The variation in trace element concentration is observed spatially, where higher concentration is found in samples from agricultural and urban areas than the samples from the undisturbed natural-mountain terrains. The results highlighted that the concentrations of trace elements differ temporally where the concentration is greater during the monsoon due to increased discharge of sewage and agricultural run off to the river. Multivariate statistical analysis indicates stronger relationship between trace elements and other physio-chemical parameters hinting that natural and anthropogenic sources alters the riverine chemistry. Thus, the rainfall-runoff characteristics along with lithology, topography, and landuse of the basin plays a dominant role in the seasonal variation of dissolved trace elements. The water quality index value shows “good/excellent” during pre-monsoon and “marginal/fair” during monsoon season and the Heavy Metal Pollution Index values were also low during both the seasons. The river water samples which defy these indices were found to be either from urban or agricultural lands. The oral and dermal ingestion health risk to adults was assessed, which indicates that the risks posed to humans by consumption of water were minimal. The trace metal concentration of the river was then compared with the other rivers of world and India, where it shows that Zn, Cu, and Ni concentration was higher in Bhavani than in most of the rivers. Thus, the study highlighted that the urban settlements and agricultural lands have a considerable influence on river quality thereby triggering the increase in trace element concentrations. Therefore, the study necessitates on the continuous monitoring of river along with adoption of stringent discharge protocols.
Urban green spaces (UGS) are known for providing a cooling effect by evapotranspiration, shade, and by altering the albedo. Heat mitigation by UGS reduces the space cooling demand, provides comfort, and enhances productivity. Rapid urbanization in developing countries has resulted in dwindling green spaces and their protective role is often neglected. We have quantified the heat mitigation by UGS using the InVEST model (Integrated Valuation of Ecosystem Services and Trade-offs) for present and future scenarios of Nagpur City which is situated in a heatwave-affected zone. Four future plausible scenarios were generated with a combination of drivers-economic development and commitment to promoting UGS, using the two-axis scenario analysis method. The simulated UGS in each future scenario (by allowing 10% variation in the land use) is utilized for quantification of heat mitigation and energy conserved. In comparison with the present situation, 21-29% less space cooling energy is conserved in scenarios driven by economic development (least commitment to UGS), whereas 17% more energy is conserved when UGS are promoted. In similar lines, the average temperature is increased by 0.5-0.7 degrees C when UGS are neglected, while the temperature dropped by 0.5 degrees C when UGS are promoted in Nagpur City. The methodology presents an integration of scenario analysis with heat mitigation modeling which can enable urban planners and researchers to improve their understanding of the ecological structure of urban centers and can aid in appreciating the potential of UGS in heat mitigation for human wellbeing.
Globally, mountain systems are unevenly exposed to risks of extreme precipitation. Within the Himalayan region, precipitation extremes are a rising concern, but their current understanding is limited. In this study, we use 113?years of precipitation data to rank and characterize precipitation extremes in the Indian Western Himalayas (IWH). Our statistical ranking method integrates precipitation spatial extent and its intensity across different durations for determining the severity of extreme events. The proposed ranking method accounts for multi-day duration ranking method to capture persistent precipitation episodes. Results show that the method accurately detects and ranks the most extreme precipitation events that occurred in the IWH and indicate locations of these events. Our results highlight that critical long duration events in the region (e.g., 10?days) are missed at ranks at shorter duration (e.g., 2–3?days), thereby highlighting the importance to multi-day precipitation extremes ranking. In addition, the proposed ranking method provides information about the event duration that will be associated with the highest impact on society, carrying high significance. Our findings are valuable for flood risk management and disaster risk reduction.
Soaring temperatures cause deaths in large numbers in various parts of India. The number of deaths vary with region and are influenced by the demographics and socioeconomic characteristics of the region. This study tried to estimate the number of deaths associated with exposure to heat in the different states of India. Secondary data was used, which was collected from the website data.gov.in, an Open Government Data (OGD) Platform of the Indian government. Descriptive statistics were applied using Microsoft Excel-10. It was found that there 3014 men died from heat-related causes in 2001-05, which increased to 5157 in the period 2011-15. For women the number of deaths in the corresponding periods were 849 and 1254 respectively. Deaths caused by heatwaves were found to be higher than those resulting from avalanches, exposure to cold, cyclone, tornado, starvation due to natural calamity, earthquake, epidemic, flood, landslide, torrential rain and forest fire. The study revealed that there are regional variations in the number deaths due to heatstroke. From the perspective of disaster preparedness, it is important to note that deaths from heat strokes occur every year. With rising temperatures, the numbers are likely to increase. The findings of the study highlight this concern. Therefore, there is a need for targeted region-specific interventions for reducing the number of deaths due to heatwaves.
As the world’s population is expected to be over 2/3rd urban by 2050, climate action in cities is a growing area of interest in the inter-disciplines of development policy, disaster mitigation and environmental governance. The climate impacts are expected to be quite severe in the developing world, given its urban societies are densely packed, vastly exposed to natural elements while possessing limited capabilities. There is a notable ambiguity and complexity that inhibits a methodical approach in identifying urban resilience measures. The complexity is due to intersection of large number of distinct variables in climate geoscience (precipitation and temperature anomalies at different locations, RCPs, timeline), adaptation alternatives (approach, priority, intervention level) and urban governance (functional mandate, institutional capacity, and plans & policies). This research examines how disparate and complex knowledge and information in these inter-disciplines can be processed for systematic ‘negotiation’ to situate, ground and operationalize resilience in cities. With India as a case, we test this by simulating mid-term and long-run climate scenarios (2050 & 2080) to map regional climate impacts that shows escalation in the intensity of climate events like heat waves, urban flooding, landslides and sea level rise. We draw on suitable adaptation measures for five key urban sectors- water, infrastructure (including energy), building, urban planning, health and conclude a sleuth of climate resilience building measures for policy application through national/ state policies, local urban plans and preparation of city resilience strategy, as well as advance the research on ‘negotiated resilience’ in urban areas.
The present study explored the association between daily ambient air pollution and daily emergency room (ER) visits due to acute respiratory symptoms in children of Delhi. The daily counts of ER visits (ERV) of children (?15 years) having acute respiratory symptoms were obtained from two hospitals of Delhi for 21 months. Simultaneously, data on daily concentrations of particulate matter (PM(10) and PM(2.5)), nitrogen dioxide (NO(2)), sulfur dioxide (SO(2)), carbon monoxide (CO), and ozone (O(3)) and weather variables were provided by the Delhi Pollution Control Committee. K-means clustering with time-series approach and multi-pollutant generalized additive models with Poisson link function was used to estimate the 0-6-day lagged change in daily ER visits with the change in multiple pollutants levels. Out of 1,32,029 children screened, 19,120 eligible children having acute respiratory symptoms for ?2 weeks and residing in Delhi for the past 4 weeks were enrolled. There was a 29% and 21% increase in ERVs among children on high and moderate level pollution cluster days, respectively, compared to low pollution cluster days on the same day and previous 1-6 days of exposure to air pollutants. There was percentage increase (95% CI) 1.50% (0.76, 2.25) in ERVs for acute respiratory symptoms for 10 ?g/m(3) increase of NO(2) on previous day 1, 46.78% (21.01, 78.05) for 10 ?g/m(3) of CO on previous day 3, and 13.15% (9.95, 16.45) for 10 ?g/m(3) of SO(2) on same day of exposure. An increase in the daily ER visits of children for acute respiratory symptoms was observed after increase in daily ambient air pollution levels in Delhi.
India has reported a high prevalence of Intimate Partner Violence (IPV) against women over the years. Previous Western research has found an increased IPV risk among women in the aftermath of natural disasters, underscoring the need for such studies in India. We could not locate any study focusing on the impact of slow-onset versus rapid-onset disasters, which might have differing impacts on the vulnerable, especially on the incidence of IPV in India. Using data on ever-married women from the National Family Health Survey-4 (2015-16), we investigated the association of residing in districts exposed to a drought (N = 31,045), and separately, to two cyclones (N = 8469), with three forms of self-reported IPV against women (emotional, physical, and sexual). Survey-adjusted logistic regression models showed that exposure to cyclone was positively associated with emotional IPV (AOR: 1.59, 95% CI: 1.20, 2.10) after adjusting for sociodemographic covariates. Although not statistically significant, exposure to cyclone was also positively associated with physical and sexual IPV, and drought with physical IPV. However, we did not find an association of drought with emotional and sexual violence. We corroborated previous findings that women from wealthier households, with greater education, and whose husbands had no history of alcohol consumption, were less likely to experience any form of IPV independent of the influence of other factors. These results highlight the potential increased risk of IPV following natural disasters. In patriarchal societies such as India vulnerable to climate-change, these sobering results highlight the need to prepare for the social disasters that might accompany natural disasters.
Soil-transmitted helminthiasis is a major disease burden in developing countries, with a considerable share borne by India. Currently, the principal strategy of the World Health Organization for the control of soil-transmitted helminths (STHs) is mass deworming in the high-risk population based on the prevalence and intensity of infection in a region. However, the disease load of STH remains unknown in many regions. A cross-sectional study was conducted in 2017 among children in the age group of 5-13 years in Barpeta, Assam, to ascertain the prevalence of STH infection in school-aged children and its probable risk factors. Socio-demographic and epidemiologic data were gathered using a piloted questionnaire. Geohelminths were identified by the Kato-Katz method. Association with probable risk-factors was analyzed by binomial logistic regression. Overall, 16.3% [95% confidence interval (CI) = 12.9-19.8] of children were found to be infected with one or more of the three STHs. Ascaris, hookworm, and Trichuris infections were observed in 9.4%, 7.4%, and 5.3%, respectively. The strongest predictors for the presence of any STH with multivariable analysis were open defecation (habitual or occasional), lack of proper handwashing, living in homes affected by flood, and age group of 8-10 years. The availability of proper handwashing stations in schools was found to be protective against Trichuris. Awareness among the people regarding sanitation and personal hygiene, particularly in the post-flood scenario, is imperative for sustainable control of STH infections. Preventive deworming should be continued; however, the time and frequency must be adjusted according to the prevailing climatic conditions in the region under study.
To curb the staggering health burden attributed to air pollution, the sustainable solution for India would be to reduce emissions in future. Here we project ambient fine particulate matter (PM2.5) exposure in India for the year 2030 under two contrasting air pollution emission pathways for two different climate scenarios based on Representative Concentration Pathways (RCP4.5 and RCP8.5). All-India average PM2.5 is expected to increase from 41.4 +/- 26.5 mu g m(-3) in 2010 to 61.1 +/- 40.8 and 58.2 +/- 37.5 mu g m(-3) in 2030 under RCP8.5 and RCP4.5 scenarios, respectively if India follows the current legislation (baseline) emission pathway. In contrast, ambient PM2.5 in 2030 would be 40.2 +/- 27.5 (for RCP8.5) and 39.2 +/- 25.4 (for RCP4.5) mu g m(-3) following the short-lived climate pollutant (SLCP) mitigation emission pathway. We find that the lower PM2.5 in the mitigation pathway (34.2% and 32.6%, respectively for RCP8.5 and RCP4.5 relative to the baseline emission pathway) would come at a cost of 0.3-0.5 degrees C additional warming due to the direct impact of aerosols. The premature mortality burden attributable to ambient PM2.5 exposure is expected to rise from 2010 to 2030, but 381,790 (5-95% confidence interval, CI 275,620-514,600) deaths can be averted following the mitigation emission pathway relative to the baseline emission pathway. Therefore, we conclude that given the expected large health benefit, the mitigation emission pathway is a reasonable tradeoff for India despite the meteorological response. However, India needs to act more aggressively as the World Health Organization (WHO) annual air quality guideline (10 mu g m(-3)) would remain far off.
Major heat waves are occurring over India during the hottest months of May and June. Since the temperature extremes have major impact on human health and agriculture, better understanding the dynamics behind its evolution and propagation will help us to develop effective mitigation strategies. Understanding the spatio-temporal distribution, evolution and dynamics associated with heat waves is lacking over this region, due to the lack of high-resolution weather information. Here, we developed a high-resolution (4 x 4 km) dynamically downscaled hourly climate data for April to June during period of 2001-2016. The downscaled daily surface temperature is in good agreement with station observations, which is also in agreement with the observed features of temperature distribution during this period. Based on the Indian meteorological department definition, intensity of the heat waves is identified and re-classified into minor and severe category. The spatio-temporal distribution of each heat wave shows variation in its spatial coverage and also in its intensity. The distributions of heat waves are mainly over central India, North-West India and states such as Odisha, Andhra Pradesh and Telangana during pre-monsoon season. Results show that the increase in meridional heat transport is higher than the zonal advection component, and intensification of heat waves is linked with heat accumulation over a particular region associated with weakening of heat transport. The further amplification associated with depletion of soil moisture will result in the reduction in evaporative cooling, and it will further amplify the surface air temperature.
The coal-dominated electricity system poses major challenges for India to tackle air pollution and climate change. Although the government has issued a series of clean air policies and low-carbon energy targets, a key barrier remains enforcement. Here, we quantify the importance of policy implementation in India’s electricity sector using an integrated assessment method based on emissions scenarios, air quality simulations, and health impact assessments. We find that limited enforcement of air pollution control policies leads to worse future air quality and health damages (e.g., 14?200 to 59?000 more PM(2.5)-related deaths in 2040) than when energy policies are not fully enforced (5900 to 8700 more PM(2.5)-related deaths in 2040), since coal power plants with end-of-pipe controls already emit little air pollution. However, substantially more carbon dioxide will be emitted if low-carbon and clean coal policies are not successfully implemented (e.g., 400 to 800 million tons more CO(2) in 2040). Thus, our results underscore the important role of effectively implementing existing air pollution and energy policy to simultaneously achieve air pollution, health, and carbon mitigation goals in India.
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.
OBJECTIVES: Indian agriculture is mostly dependent on monsoon. Poor and irregular rainfall may result in crop failure and food shortage among the vulnerable population. This study examined the variations in drought condition and its association with under age 5 child malnutrition across the districts of India. METHODS: Using remote sensing and National Family Health Survey (NFHS-4) data, univariate Moran’s I and bivariate local indicator of spatial autocorrelation (LISA) maps were generated to assess the spatial autocorrelation and clustering. To empirically check the association, we applied multivariate ordinary least square and spatial autoregressive models. RESULTS: The study identified highly significant spatial dependence of drought followed by underweight, stunting, and wasting. Bivariate LISA maps showed negative spatial autocorrelation between drought and child malnutrition. Regression results suggest agricultural drought is substantially associated with stunting. An increasing value of drought showed statistical association with the decreasing (??=?-?8.251; p value?
Vector control is one of the main aspects to reach the target of eliminating visceral leishmaniasis from Indian sub-continent as set by the World Health Organisation. Data on different aspects of vector like ecology, behaviour, population dynamics and their association with environmental factors are very important for formulating an effective vector control strategy. The present work was designed to study the species abundance and impact of environmental factors on population dynamics of vector P. argentipes in a visceral leishmaniasis endemic area of Malda district, West Bengal. Adult sand flies were collected using light traps and mouth aspirators from twelve kala-azar affected villages of Habibpur block of Malda district, on a monthly basis from January to December, 2018. Morphological and molecular methods were used for species identification. Population dynamics were assessed by man hour density and per night per trap collection. Data were analysed using SPSS software to determine the impact of environmental factors on vector population P. argentipes was found to the predominant species and prevalent throughout the year. A significantly higher number of sand flies were collected from cattle sheds than human dwellings and peri-domestic vegetation. A portion of the P. argentipes population was exophilic and exophagic as evidenced by their collection from peri-domestic vegetation. The highest population density was recorded during April to September. Population dynamics were mostly influenced by average temperature along humidity and rain fall. Resting behaviour of sand flies was not restricted to the lower portion of the wall but equally distributed throughout the wall and ceiling. Programme officials should consider management of outdoor populations of the sand flies and timings of indoor residual spray for chemical control purpose.
Summertime heat stress future projections from multi-model mean of 18 CMIP5 models show unprecedented increasing levels in the RCP 4.5 and RCP 8.5 emission scenarios over India. The estimated heat stress is found to have more impact on the coastal areas of India having exposure to more frequent days of extreme caution to danger category along with the increased probability of occurrence. The explicit amount of change in temperature, increase in the duration and intensity of warm days along with the modulation in large scale circulation in future are seemingly connected to the increasing levels of heat stress over India. A decline of 30 to 40% in the work performance is projected over India by the end of the century due to the elevated heat stress levels which pose great challenges to the country policy makers to design the safety mechanisms and to protect people working under continuous extreme hot weather conditions.
BACKGROUND: Often quoted as “heaven on earth,” Kashmir forms one of the two divisions of the Union territory of Jammu and Kashmir. A high-altitude region with abundant precipitation and snowfall, the people of Kashmir experience peculiar dermatoses not commonly seen in the majorly tropical subcontinent of India. In this study, we focussed on cold dermatoses as a comprehensive cluster and attempted to study them as a group. AIMS: To determine the prevalence of cold dermatoses in Kashmir valley and study their epidemiological characteristics. METHODS: This observational, cross-sectional community-based study was conducted on native Kashmiri population in three districts of the valley, exclusively during the winter season of the year 2016-17 and 2017-18. The data were tabulated and analyzed with Chi-square test for discrete variables and t-test for continuous variables, using OpenEpi. A P value of less than 0.05 was taken as significant. RESULTS: The study included a total of 1200 cases with 602 males and 598 females. Perniosis was most commonly encountered dermatoses in our study with a prevalence of 12.2%. Frostbite had a prevalence of 0.83%. Raynaud’s phenomenon and asteatotic eczema were seen in 1.5% and 1.67% of the population, respectively. Cold panniculitis, cold urticaria, and livedo reticularis were each seen in 0.08% of the population. CONCLUSIONS: Cold dermatoses form an important source of morbidity among the native population of Kashmir. These can be easily prevented by ensuring adequate protection against cold. Creating awareness regarding these disorders and probable association with connective tissue disorders is also imperative.
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.
We analyze the combined effect of political violence and adverse climatic and health shocks on child nutrition using longitudinal data from Andhra Pradesh, India. The paper shows three key results using two-stage least square (2SLS) models: (i) the presence of political violence reduces the mean height-for-age z-scores of children by between 0.4 and 0.9 standard deviations and reduces the mean weight-for-age z-scores of children by between 0.3 and 0.6 standard deviations; (ii) political violence generates such a large negative effect on the long-term nutrition of children (measured by height-for-age z-scores) through a reduction of the ability of households to cope with drought and illness; and (iii) drought and illness have an adverse effect on child nutrition in Andhra Pradesh only in violence-affected communities. The 2SLS results are robust to a wide range of robustness tests. Potential mechanisms explaining the strong joint welfare effect of conflict and adverse shocks are the failure of economic coping strategies in areas of violence and restricted access to public goods and services.
INTRODUCTION: In August 2018, India’s southern state of Kerala experienced its worst flooding in over a century. This report describes the relief efforts in Kozhikode, a coastal region of Kerala, where Operation Navajeevan was initiated. SOURCES: Data were collected from a centralized database at the command center in the District Medical Office as well as first-hand accounts from providers who participated in the relief effort. OBSERVATIONS: From August 15 through September 8, 2018, 36,846 flood victims were seen at 280 relief camps. The most common cause for presentation was exacerbation of an on-going chronic medical condition (18,490; 50.2%). Other common presentations included acute respiratory infection (7,451; 20.2%), traumatic injuries (3,736; 10.4%), and psychiatric illness (5,327; 14.5%). ANALYSIS: The prevalence of chronic disease exacerbation as the primary presentation during Operation Navajeevan represents an epidemiologic shift in disaster relief in India. It is foreseeable that as access to health care improves in low- and middle-income countries (LMICs), and climate change increases the prevalence of extreme weather events around the world, that this trend will continue.
Outdoor Thermal Comfort (OTC) is largely influenced by urban morphology and geometry of the urban landscape. In this study, the Local Climatic Zones (LCZs) approach was adopted to assess the OTC in different settings of Sriniketan-Santiniketan Planning Area (SSPA) during the summer season. The basic objective of this study is to assess OTC from both subjective and objective perspectives over eight LCZs. This study assessed OTC over LCZs using both field measurements and questionnaire survey. Non-parametric tests such as ANOVA and Kruskal-Wallis tests were also performed to find out the significant difference of perception across LCZs. The result of ANOVA and Krushkal-Walls test showed that subjective perception of OTC across LCZs varied due to diversified physical landscape settings. The result also showed that the maximum (above 40 degrees C) and minimum (28 degrees C) temperature was recorded in built types (particularly compact low rise) and natural land cover types (dense forest and water) respectively. Highest PET was also recorded over the built-up LCZs (about 50 degrees C) that led to this planning region thermally very hot or extreme heat stress. The respondents living in LCZ3 and LCZ6 were more sensitive to the thermal sensation as compared to those living in other LCZs.This study was probably the first attempt dealing with the assessment of OTC over the tropical planning region using LCZ approach from subjective and objective perspectives. Therefore, this research study has an immense potentiality to formulate strategies to deal with the outdoor thermal conditions as well to implement climate sensitive planning for urban sustainability in tropical cities.
Mountains are characterized by their specificities such as fragility, marginality and remoteness. They are prone to various hazards such as drought, flood, forest fire, landslide and therefore physical, ecological and social systems of the mountains are at risk. Climate change adds to intensifying the magnitude of multi-hazard risk in mountains. The present study attempts to evaluate risk induced by multi-hazard and climate change in the Indian Himalayan Region (IHR) using the Intergovernmental Panel on Climate Change (IPCC) framework. The proposed multi-hazard risk index was based on indicators from a broader domain and applied on 109 administrative districts of IHR. Exposure, sensitivity, adaptive capacity, and coping capacity were defined using comprehensive and sub-regional indicators identified through inductive and deductive approaches. The result showed that the differential risks among the districts of IHR were governed by the multiplicity of the factor such as demography, amenities, natural capital, partnership, technology and spatial specificities of the districts. The result highlighted the need of inclusion of spatial specificities for the risk mitigation in the IHR and therefore a Mountain Specific Risk Management Framework (MSMRMF) was proposed for sustaining the mountainous communities. The proposed MSMRMF contained two broad components as risk assessment and risk addressal. The framework detailed the risk mitigation and coping strategies (based on adjustment of internal and external strengths) for addressing risks. Risk mitigation was proposed to achieved through habitation resilience, natural capital enhancement, external partnerships, climate change adaptation, and technological interventions. The framework would provide an insight of risk and risk management strategies for the multi-hazard prone mountain regions for the sustainable development under the global change.
Rising temperature and heat stress risks in the changing climate scenario might potentially affect workers globally, especially the ones with strenuous workload in tropical settings. We used a cross-sectional study design to profile the heat exposures of similar to 1900 workers from eight industrial sectors using a QuesTemp Wet Bulb Globe Temperature (WBGT) monitor, quantified select heat-strain indicators viz., rise in Core Body Temperature, Sweat Rate, and Urine Specific Gravity and evaluated the perceived health impacts of heat stress using a structured questionnaire. Heat exposures (average WBGT: 30.1 +/- 2.6 degrees C) exceeded the Threshold Limit Value for 67% workers and was positively associated with the rise in Core Body Temperature >1 degrees C in 13% and elevated Urine Specific Gravity >1.020 in 9% workers. Heat-related health concerns were reported by 86% workers, and the heat-exposed workers had 2.3 times higher odds of adverse health outcomes compared to unexposed workers (p < 0.0001). Exposure to higher WBGT and adverse renal health among salt-pan workers were significantly associated (p = 0.004), and steel workers had 9% prevalence of kidney stones. Evidence presented clearly points to heat stress as a health and productivity risk factor that could have long-term and irreversible health impacts. In-depth assessments are urgently needed to develop scientifically sound preventative interventions and protective labor policies to avert the adverse occupational health and productivity consequences for millions of workers globally, thereby aiding poverty reduction.
Intensive irrigation in India has been demonstrated to decrease surface temperature, but the influence of irrigation on humidity and extreme moist heat stress is not well understood. Here we analysed a combination of in situ and satellite-based datasets and conducted meteorological model simulations to show that irrigation modulates extreme moist heat. We found that intensive irrigation in the region cools the land surface by 1 degrees C and the air by 0.5 degrees C. However, the decreased sensible heat flux due to irrigation reduces the planetary boundary layer height, which increases low-level moist enthalpy. Thus, irrigation increases the specific and relative humidity, which raises the moist heat stress metrics. Intense irrigation over the region results in increased moist heat stress in India, Pakistan, and parts of Afghanistan-affecting about 37-46 million people in South Asia-despite a cooler land surface. We suggest that heat stress projections in India and other regions dominated by semi-arid and monsoon climates that do not include the role of irrigation overestimate the benefits of irrigation on dry heat stress and underestimate the risks. Intensive irrigation in India cools the land surface, but increases the moist heat stress in South Asia, according to an analysis of observational datasets and meteorological models.
BACKGROUND: India is expected to experience an increase in the frequency and intensity of extreme weather events in the coming decades, which poses serious risks to human health and wellbeing in the country. OBJECTIVE: This paper aims to shed light on the possible detrimental effects of monsoon weather shocks on childhood undernutrition in India using the Demographic and Health Survey 2015-16, in combination with geo-referenced climate data. METHODS: Undernutrition is captured through measures of height-for-age, weight-for-height, stunting and wasting among children aged 0-59 months. The standardised precipitation and evapotranspiration index (SPEI) is used to measure climatic conditions during critical periods of child development. RESULTS: The results of a multivariate logistic regression model show that climate anomalies experienced in utero and during infancy are associated with an increased risk of child undernutrition; exposure to excessive monsoon precipitation during these early periods of life elevates the risk of stunting, particularly for children in the tropical wet and humid sub-tropical regions. In contrast, the risk of stunting is reduced for children residing in the mountainous areas who have experienced excessive monsoon precipitation during infancy. The evidence on the short-term effects of climate shocks on wasting is inconclusive. We additionally show that excessive precipitation, particularly during the monsoon season, is associated with an increased risk of contracting diarrhoea among children under five. Diseases transmitted through water, such as diarrhoea, could be one important channel through which excessive rainfall increases the risk of stunting. CONCLUSIONS: We find a positive association between childhood undernutrition and exposure to excessive monsoon precipitation in India. Pronounced differences across climate zones are found. The findings of the present analysis warn of the urgent need to provide health assistance to children in flood-prone areas.
Background: It is widely acknowledged that climate change will lead to more frequent natural disasters and extreme weather events. This is a matter of concern, especially for countries like India which is amongst the most vulnerable drought-prone countries in the world. In 2015 the Government of Maharashtra had declared a drought in state. The severe drought situation forced millions of people to migrate from the Marathwada region to the bigger cities. Objective: The objective of the study was to examine the sanitation, hygiene and living conditions of migrants who were forced to leave their homes because of the drought. The focus of the study was on the health problems of pregnant migrant women and children in their destinations. Methods: The study adopted a qualitative approach to explore this phenomena. Fifteen in-depth interviews were conducted which included ten pregnant women and five women who had children less than two years of age. Data analysis were carried out with thematic analysis using NVivo software. Results: The study shows that pregnant mothers and women with children are at greater risk of diseases at place of destination. In particular, the pregnant migrant mothers could not access the necessary antenatal care. Moreover, they could not sleep or rest during the day due to non-availability of place.
Malaria, a vector-borne disease, is a significant public health problem in Keonjhar district of Odisha (the malaria capital of India). Prediction of malaria, in advance, is an urgent need for reporting rolling cases of disease throughout the year. The climate condition do play an essential role in the transmission of malaria. Hence, the current study aims to develop and assess a simple and straightforward statistical model of an association between malaria cases and climate variates. It may help in accurate predictions of malaria cases given future climate conditions. For this purpose, a Bayesian Gaussian time series regression model is adopted to fit a relationship of the square root of malaria cases with climate variables with practical lag effects. The model fitting is assessed using a Bayesian version of R(2) (RsqB). Whereas, the predictive ability of the model is measured using a cross-validation technique. As a result, it is found that the square root of malaria cases with lag 1, maximum temperature, and relative humidity with lag 3 and 0 (respectively), are significantly positively associated with the square root of the cases. However, the minimum and average temperatures with lag 2, respectively, are observed as negatively (significantly) related. The considered model accounts for moderate amount of variation in the square root of malaria cases as received through the results for RsqB. We also present Absolute Percentage Errors (APE) for each of the 12 months (January-December) for a better understanding of the seasonal pattern of the predicted (square root of) malaria cases. Most of the APEs obtained corresponding to test data points is reasonably low. Further, the analysis shows that the considered model closely predicted the actual (square root of) malaria cases, except for some peak cases during the particular months. The output of the current research might help the district to develop and strengthen early warning prediction of malaria cases for proper mitigation, eradication, and prevention in similar settings.
Dengue is one of the most serious vector-borne infectious diseases in India, particularly in Kolkata and its neighbouring districts. Dengue viruses have infected several citizens of Kolkata since 2012 and it is amplifying every year. It has been derived from earlier studies that certain meteorological variables and climate change play a significant role in the spread and amplification of dengue infections in different parts of the globe. In this study, our primary objective is to identify the relative contribution of the putative drivers responsible for dengue occurrences in Kolkata and project dengue incidences with respect to the future climate change. The regression model was developed using maximum temperature, minimum temperature, relative humidity and rainfall as key meteorological factors on the basis of statistically significant cross-correlation coefficient values to predict dengue cases. Finally, climate variables from the Coordinated Regional Climate Downscaling Experiment (CORDEX) for South Asia region were input into the statistical model to project the occurrences of dengue infections under different climate scenarios such as Representative Concentration Pathways (RCP4.5 and RCP8.5). It has been estimated that from 2020 to 2100, dengue cases will be higher from September to November with more cases in RCP8.5 (872 cases per year) than RCP4.5 (531 cases per year). The present research further concludes that from December to February, RCP8.5 leads to suitable warmer weather conditions essential for the survival and multiplication of dengue pathogens resulting more than two times dengue cases in RCP8.5 than in RCP4.5. Furthermore, the results obtained will be useful in developing early warning systems and provide important evidence for dengue control policy-making and public health intervention.
India and other Southeast Asian countries are severely affected by Japanese encephalitis (JE), one of the deadliest vector-borne disease threat to human health. Several epidemiological observations suggest climate variables play a role in providing a favorable environment for mosquito development and virus transmission. In this study, generalized additive models were used to determine the association of JE admissions and mortality with climate variables in Gorakhpur district, India, from 2001-2016. The model predicted that every 1 unit increase in mean (Tmean;°C), and minimum (Tmin;°C) temperature, rainfall (RF; mm) and relative humidity (RH; %) would on average increase the JE admissions by 22.23 %, 17.83 %, 0.66 %, and 5.22 % respectively and JE mortality by 13.27 %, 11.77 %, 0.94 %, and 3.27 % respectively Conversely, every unit decrease in solar radiation (Srad; MJ/m(2)/day) and wind speed (WS; Kmph) caused an increase in JE admission by 17% and 11.42% and in JE mortality by 9.37% and 4.88% respectively suggesting a protective effect at higher levels. The seasonal analysis shows that temperature was significantly associated with JE in pre-monsoon and post-monsoon while RF, RH, Srad, and WS are associated with the monsoon. Effect modification due to age and gender showed an equal risk for both genders and increased risk for adults above 15 years of age, however, males and age groups under 15 years outnumbered females and adults. Sensitivity analysis results to explore lag effects in climate variables showed that climate variables show the strongest association at lag 1 to 1.5 months with significant lag effect up tp lag 0-60 days. The exposure-response curve for climate variables showed a more or less linear relationship, with an increase in JE admissions and mortality after a certain threshold and decrease were reported at extreme levels of exposure. The study concludes that climate variables could influence the JE vector development and multiplication and parasite maturation and transmission in the Gorakhpur region whose indirect impact was noted for JE admission and mortality. In response to the changing climate, public health interventions, public awareness, and early warning systems would play an unprecedented role to compensate for future risk.
In the freshwater environment of north India, cholera appears seasonally in form of clusters as well as sporadically, accounting for a significant piece of the puzzle of cholera epidemiology. We describe a number of cholera outbreaks with an average attack rate of 96.5/1000 but an overall low case fatality (0.17). Clinical cholera cases coincided with high rainfall and elevated temperatures, whereas isolation of V. cholerae non-O1 non-O139 from water was dependent on temperature (p??0.05). However, isolation from plankton samples correlated with increased temperature and pH (p?
Background: Around 2-3% of hospitalizations have been reported due to dermatological adverse drug reactions. Recent studies suggest that climatic variations affect the skin barrier function and extreme conditions aggravate skin disorders.
Objective: The present study was designed to compare the impact of climatic variations on drug-induced skin reactions in the Northern and Eastern regions of India. Methods: We performed a one-year retrospective study to evaluate the impact of climatic variations (temperature and humidity) in Eastern (Kalyani, West Bengal) and Northern (Karnal, Haryana) regions on drug-induced skin reactions. Drug-induced skin reactions were reported month-wise in both the Eastern and Northern regions. Temperature and humidity level were also noted month-wise in both the regions. The direct correlation between climatic variations and number of drug reactions were assessed using Pearson’s correlation and quadratic regression analysis.
Results and Discussion: Overall, 99 and 81 dermatological adverse drug reactions were reported in tertiary care hospitals in the Northern and Eastern regions, respectively. During the summer season, the humidity level was found to be low in the Northern region as compared to the Eastern region. During this period, drug-induced skin reactions were reported significantly (p<0.05) more in the Northern region as compared to the Eastern region. Furthermore, quadratic regression analysis revealed that climatic variations contributed to drug reaction variability in the Northern region (68.5%) and Eastern region (23.5%).
Conclusion: Therefore, the difference in the prevalence of drug-induced skin reactions may be related to the different climatic conditions among these two regions. Further studies in controlled climatic conditions should be performed for definitive correlations and to look into possible solutions.
.The number of serious and extreme drought events is increasing, causing a serious threat to ecosystems, food security, livelihood security, social stability, and sustainable development. The Marathwada region of India is highly vulnerable to the impacts of drought and has been severely affected because of consecutive drought events from 2012 to 2016. This article aims to understand the rural farming household’s perceptions of the impacts of drought, their adaptation and mitigation measures, and also attempts to assess the level of satisfaction of rural households with government mitigation measures. This study is based on primary and secondary sources of data collected from 192 farming households following a structured questionnaire survey. The survey reveals that crop failure, livelihood insecurity, declines in livestock production, livestock loss, water conflicts, and problems in meeting agricultural expenses, increased school dropout rates of children, and both psychological and health problems, were the most immediate socio-economic impacts of drought. The various environmental impacts of drought perceived by farmers included depleted groundwater levels, poor groundwater quality, land degradation, a decrease in seasonal river flows, degradation of pastures and declines in soil fertility. It was found that small and medium sized farmers were highly affected by drought compared with marginal and large scale farmers because of their high dependency on agriculture and poor adaptation strategies.
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.
We examine the impact of extreme heat during pregnancy on infant mortality and check if public interventions can serve as effective adaptation strategies. We show that 2 children die as infants out of 1000 births in India for high temperature during pregnancy, tentatively due to reduced agricultural yields, wages, and greater disease prevalence like diarrhea. The heat-infant mortality relationship holds in rural India only. Using phased introduction of an employment guarantee program and partial introduction of a community health care worker program for identification, we find that only the health program is effective in modifying the temperature-infant mortality relationship in rural India.
BACKGROUND: Heat-related illness is a common medical emergency. There is failure of thermoregulatory mechanisms of the body resulting in multiple organ dysfunction syndrome which if not identified and treated urgently can result in high mortality rate and permanent neurological damage. This study provides description of clinical profile patients presenting with heat-related illness and identifies clinical and laboratory variables resulting in poor outcome. METHODS: This retrospective study was done identifying adult patients admitted with a diagnosis of heat-related illness from April to August 2019 in tertiary care center. Their clinical profile, laboratory investigations and outcome were extracted from medical records and variables associated with poor outcome were analyzed for statistical significance. RESULTS: Mean age of the patients in the study was 61 years with mean heat index of the localities being 39.6-degree C. 66% of patients had multiple organ dysfunction with central nervous system dysfunction (77%) followed by respiratory distress syndrome (61%) as the most common organ derangement. Evaporative cooling measures were incorporated in management of all patients, followed by cold saline infusion in 60%. Higher J-ERATO score at admission was found to be a predictor for underlying multiple organ dysfunction syndrome (P value < 0.029). The mortality rate associated with heat-related illness in this study was 11.1%. CONCLUSIONS: Multiple organ dysfunction is seen in majority of the patients and calculation of simple admission J-ERATO score helps in predicting the same. Declining mortality rate observed in our study as compared to the earlier studies could be attributed to increased awareness, prompt diagnosis and initiation of rapid cooling measures.
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.
BACKGROUND: Although many studies have provided evidence for all-cause mortality attributed to extreme temperature across India, few studies have provided a systematic analysis of the association between all-cause mortality and temperature. OBJECTIVE: To estimate the risk associated with heat waves during two major heat waves of Nagpur occurred in 2010 and 2014. METHODS: The association between temperature and mortality was measured using a distributed lag non-linear model (DLNM) and the attributable deaths associated with the heat waves with forward perspective in the DLNM framework. RESULTS: From the ecological analysis, we found 580 and 306 additional deaths in 2010 and 2014, respectively. Moving average results also gave similar findings. DLNM results showed that the relative risk was 1.5 for the temperature above 45 °C; forward perspective analysis revealed that the attributable deaths during 2010 and 2014 were 505 and 376, respectively. Results from different methods showed that heat waves in different years had variable impacts for various reasons. However, all the results were consistent during 2010 and 2014; there were 30% and 14% extra-mortalities due to heat comparing to non-heat wave years. CONCLUSION: We strongly recommend the city Government to implement the action plans based on this research outcome to reduce the risk from the heat wave in future.
The present study explores the spatial and temporal pattern of cold wave related mortalities over India. The data for this study has been obtained from the annual reports pertaining to ‘Disastrous Weather Events’ published by India Meteorological Department, Pune for 37-years (1978-2014). The analysis reveals that a total of 8520 mortalities have been caused by 606 cold wave events, with an average of 230 mortalities per year. Only two states i.e. Bihar (44%) and Uttar Pradesh (31%) account for approximately 75% of total cold waves mortalities, while eleven states namely, Arunachal Pradesh, Assam, Goa, Karnataka, Kerala, Manipur, Mizoram, Nagaland, Sikkim, Tamil Nadu and Tripura have never experienced cold wave events and mortalities. Interestingly, each cold wave event has caused approximately 43 mortalities alone in Bihar state. Furthermore, mortality (standardized by population) and density rates (standardized by area) in India have been observed to be 0.24 and 2.65, respectively. In temporal terms, cold wave events and mortalities have shown large interannual variations without any significant increasing or decreasing trend. Most of the cold wave events and mortalities have been observed in January and December months. Males have been found to be more severely affected by the cold waves than females and children. Overall, the results of this research may provide an understanding to develop effective disaster management guidelines for temperature extremes safety and preparedness.
Background: Rotavirus diarrhea is often referred as “winter diarrheal disease” as it causes nearly 50% of the pediatric hospitalizations during winter season. This study was done with the objective of bringing out the epidemiological nexus of rotavirus cases with different seasonal parameters like maximum, minimum temperature, humidity, and average rainfall. Methods: This prospective observational study was conducted in a tertiary care teaching hospital of Eastern India from February 2016 to December 2018. Data on daily maximum and minimum temperature, relative humidity, and rainfall were collected. Result: Of 964 children admitted, 768 stool samples were collected for rotavirus assay. A total of 222 children (29%) were positive. The maximum, minimum temperature, average rainfall, and average humidity of 83.4 mm, 79.2%, 28.1, and 21.9, respectively, were significantly associated with positive rotaviral cases. Conclusions: The incidence of rotavirus positivity cases was found to be inversely associated with average temperature, humidity, and rainfall. The knowledge about the seasonal pattern in a particular geographical area would help in the reallocation of hospital services (staff and bed) to tackle the epidemic or emergency situations resulting from clustering of cases.
In India, a reduction in wheat crop yield would lead to a widespread impact on food security. In particular, the most vulnerable people are severely exposed to food insecurity. This study estimates the climate change vulnerability of wheat crops with respect to heterogeneities in time, space, and weighting methods. The study uses the Intergovernmental Panel on Climate Change (IPCC) framework of vulnerability while using composite indices of 27 indicators to explain exposure, sensitivity, and adaptive capacity. We used climate projections under current (1975-2005) conditions and two future (2021-2050) Representation Concentration Pathways (RCPs), 4.5 and 8.5, to estimate exposure to climatic risks. Consistency across three weighting methods (Analytical Hierarchy Process (AHP), Principal Component Analysis (PCA), and Equal Weights (EWs)) was evaluated. Results of the vulnerability profile suggest high vulnerability of the wheat crop in northern and central India. In particular, the districts Unnao, Sirsa, Hardoi, and Bathinda show high vulnerability and high consistency across current and future climate scenarios. In total, 84% of the districts show more than 75% consistency in the current climate, and 83% and 68% of the districts show more than 75% consistency for RCP 4.5 and RCP 8.5 climate scenario for the three weighting methods, respectively. By using different weighting methods, it was possible to quantify “method uncertainty” in vulnerability assessment and enhance robustness in identifying most vulnerable regions. Finally, we emphasize the importance of communicating uncertainties, both in data and methods in vulnerability research, to effectively guide adaptation planning. The results of this study would serve as the basis for designing climate impacts adjusted adaptation measures for policy interventions.
