This study analyzes the weather-related health damage of present and future extreme temperatures in Argentina. Focusing on mortality, short-term impacts of temperature are obtained by regressing monthly mortality rates on inter-annual monthly weather variability. For this purpose, a countrywide panel dataset at the municipal level was constructed from the universe of deaths between 2010 and 2019, and daily meteorological records from the ERA5 weather dataset. Then, NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) are used to project future mortality by 2085 under two climate scenarios. Finally, present and future mortality-related economic damages are assessed using the Value of a Statistical Life. The results show that one additional day of extreme temperatures increase all-cause mortality rates relative to mild weather and that the impact of hotter-than-average temperatures is greater in magnitude than that of colder ones. Substantial heterogeneity exists between causes of death and age groups, with older people facing greater risks, while the results for gender are inconclusive. All days of extreme cold in a year generate damage equivalent to 0.64% of GDP, while heat damage is 0.11% of GDP. The total damage by extreme temperatures adds up to 0.75% of the 2019 GDP. When future temperatures are valued, the total damage increases by an additional 1.45% under scenario RCP8.5 because the lower mortality occurring on cold days only partially offsets the increase in the number of hot days. On the contrary, if temperature changes were to be mild (i.e., under scenario RCP4.5), overall mortality would be lower at the national level and the corresponding damages would decrease by 0.02%.
Leptospirosis is a zoonotic disease with a high burden in Latin America, including northeastern Argentina, where flooding events linked to El Niño are associated with leptospirosis outbreaks. The aim of this study was to evaluate the value of using hydrometeorological indicators to predict leptospirosis outbreaks in this region. We quantified the effects of El Niño, precipitation, and river height on leptospirosis risk in Santa Fe and Entre Ríos provinces between 2009 and 2020, using a Bayesian modelling framework. Based on several goodness of fit statistics, we selected candidate models using a long-lead El Niño 3.4 index and shorter lead local climate variables. We then tested predictive performance to detect leptospirosis outbreaks using a two-stage early warning approach. Three-month lagged Niño 3.4 index and one-month lagged precipitation and river height were positively associated with an increase in leptospirosis cases in both provinces. El Niño models correctly detected 89% of outbreaks, while short-lead local models gave similar detection rates with a lower number of false positives. Our results show that climatic events are strong drivers of leptospirosis incidence in northeastern Argentina. Therefore, a leptospirosis outbreak prediction tool driven by hydrometeorological indicators could form part of an early warning and response system in the region.
Climate change is an important driver of the increased spread of dengue from tropical and subtropical regions to temperate areas around the world. Climate variables such as temperature and precipitation influence the dengue vector’s biology, physiology, abundance, and life cycle. Thus, an analysis is needed of changes in climate change and their possible relationships with dengue incidence and the growing occurrence of epidemics recorded in recent decades. OBJECTIVES: This study aimed to assess the increasing incidence of dengue driven by climate change at the southern limits of dengue virus transmission in South America. METHODS: We analyzed the evolution of climatological, epidemiological, and biological variables by comparing a period of time without the presence of dengue cases (1976-1997) to a more recent period of time in which dengue cases and important outbreaks occurred (1998-2020). In our analysis, we consider climate variables associated with temperature and precipitation, epidemiological variables such as the number of reported dengue cases and incidence of dengue, and biological variables such as the optimal temperature ranges for transmission of dengue vector. RESULTS: The presence of dengue cases and epidemic outbreaks are observed to be consistent with positive trends in temperature and anomalies from long-term means. Dengue cases do not seem to be associated with precipitation trends and anomalies. The number of days with optimal temperatures for dengue transmission increased from the period without dengue cases to the period with occurrences of dengue cases. The number of months with optimal transmission temperatures also increased between periods but to a lesser extent. CONCLUSIONS: The higher incidence of dengue virus and its expansion to different regions of Argentina seem to be associated with temperature increases in the country during the past two decades. The active surveillance of both the vector and associated arboviruses, together with continued meteorological data collection, will facilitate the assessment and prediction of future epidemics that use trends in the accelerated changes in climate. Such surveillance should go hand in hand with efforts to improve the understanding of the mechanisms driving the geographic expansion of dengue and other arboviruses beyond the current limits. https://doi.org/10.1289/EHP11616.
BACKGROUND: Hantavirus Pulmonary Syndrome (HPS) is a rodent-borne zoonosis in the Americas, with up to 50% mortality rates. In Argentina, the Northwestern endemic area presents half of the annually notified HPS cases in the country, transmitted by at least three rodent species recognized as reservoirs of Orthohantavirus. The potential distribution of reservoir species based on ecological niche models (ENM) can be a useful tool to establish risk areas for zoonotic diseases. Our main aim was to generate an Orthohantavirus risk transmission map based on ENM of the reservoir species in northwest Argentina (NWA), to compare this map with the distribution of HPS cases; and to explore the possible effect of climatic and environmental variables on the spatial variation of the infection risk. METHODS: Using the reservoir geographic occurrence data, climatic/environmental variables, and the maximum entropy method, we created models of potential geographic distribution for each reservoir in NWA. We explored the overlap of the HPS cases with the reservoir-based risk map and a deforestation map. Then, we calculated the human population at risk using a census radius layer and a comparison of the environmental variables’ latitudinal variation with the distribution of HPS risk. RESULTS: We obtained a single best model for each reservoir. The temperature, rainfall, and vegetation cover contributed the most to the models. In total, 945 HPS cases were recorded, of which 97,85% were in the highest risk areas. We estimated that 18% of the NWA population was at risk and 78% of the cases occurred less than 10 km from deforestation. The highest niche overlap was between Calomys fecundus and Oligoryzomys chacoensis. CONCLUSIONS: This study identifies potential risk areas for HPS transmission based on climatic and environmental factors that determine the distribution of the reservoirs and Orthohantavirus transmission in NWA. This can be used by public health authorities as a tool to generate preventive and control measures for HPS in NWA.
Extreme heat events result in higher indoor temperatures in buildings, increased energy consumption, and more frequent health problems, mainly between the children, the elderly over 65, and vulnerable low-income people. The indoor environment plays a key role in reducing the effects of extreme heat events. While the benefits of passive cooling measures on thermal and environmental aspects are well known, their effects on resilience are less well explored. This paper aims at studying the indoor environment in low-income housing from the energy and heat resilience points of view, during extreme hot periods, together with possible passive cooling measures to be applied in the houses in order to improve both, heat resilience and energy efficiency. A low-income neighbourhood in La Pampa, central Argentina, was selected as a case study. Transient thermal simulation, electricity consumption bills obtained from the Energy Company, and health statistics from the data-base of the nearby hospital were used. We conclude that the houses are not capable to manage hot/heat wave periods in a resilient way because of their energy inefficient design. Moreover, the cooling equipment is sub-used due to economic reasons. Indoor temperatures exceeded 33 degrees C and Heat Index reached “Extreme caution” health risk level. Sudden changes in the meteorological conditions seems to increase the number of consultations of health disorders previous or after the hot periods. The best set of passive strategies is to favour night ventilation together with shading of the envelope (i.e., by trees, climbing plants, green walls, or by installing ventilated opaque facades) and an improved roof (light colour coating and addition of thermal insulation). These strategies could both, improve the heat resilience and the thermal behavior of the indoor environment while reducing the electricity consumption in the hottest months of summer. (C) 2020 Elsevier B.V. All rights reserved.
Increased frequency of heat waves (HWs) is one of the prominent consequences of climate change. Its impact on human health has been mostly reported in the northern hemisphere but has been poorly studied in the southern hemisphere. The aim of this study was to analyze the effects of the HWs waves occurred in the warm season 2013-14 on mortality in the center-north region of Argentina, where 22 million people live. It was carried out an observational study of ecological-type contrasting the mortality occurred during the HWs of the summer 2013-14 with the mortality in the summers 2010-11 to 2012-13, free from HWs. The mortality was analyzed according to the following variables: place of residence, age, sex and cause of death. During the HWs of the summer 2013-14, 1877 (RR=1.23, 95%CI 1.20-1.28) deaths in excess were registered. Moreover, the death risk significantly increased in 13 of the 18 provinces analyzed. The mortality rates by sex revealed heterogeneous behaviour regarding both the time and spatial scale. The death risk increased with age; it was particularly significant in four provinces for the 60-79 years group and in six provinces in people of 80 years and over. The death causes that showed significantly increments were respiratory, cardiovascular, renal diseases and diabetes.
The transmission of leptospirosis is conditioned by climatic variables. In northeastern Argentina leptospirosis outbreaks occur mainly in coincidence with periods of abundant precipitation and high hydrometric level. A Susceptible-Infectious-Recovered Epidemiological Model (SIR) is proposed, which incorporates hydroclimatic variables for the three most populated cities in the area (Santa Fe, Paraná and Rosario), during the 2009-2018 period. Results obtained by solving the proposed SIR model for the 2010 outbreak are in good agreement with the actual data, capturing the dynamics of the leptospirosis outbreak wave. However, the model does not perform very well in the last months of the year when isolated cases appear outside the outbreak periods, probably due to non- climatic factors not explicitly considered in the present version of the model. Nevertheless, the dynamic modeling of infectious diseases considering hydroclimatic variables constitutes a climatic service for the public health system, not yet available in Argentina.
In the last two decades dengue cases increased significantly throughout the world, giving place to more frequent outbreaks in Latin America. In the non-endemic city of San Ramón de la Nueva Orán, located in Northwest Argentina, large dengue outbreaks alternate with several years of smaller ones. This pattern, as well as the understanding of the underlying mechanisms, could be essential to design proper strategies to reduce epidemic size. We develop a stochastic model that includes climate variables, social structure, and mobility between a non-endemic city and an endemic area. Climatic variables were input of a mosquito population ecological model, which in turn was coupled to a meta-population, spatially explicit, epidemiological model. Human mobility was included into the model given the high border crossing to the northern country of Bolivia, where dengue transmission is sustained during the whole year. We tested different hypotheses regarding people mobility as well as climate variability by fitting numerical simulations to weekly clinical data reported from 2009 to 2016. After assessing the number of imported cases that triggered the observed outbreaks, our model allows to explain the observed epidemic pattern. We found that the number of vectors per host and the effective reproductive number are proxies for large epidemics. Both proxies are related with climate variability such as rainfall and temperature, opening the possibility to test these meteorological variables for forecast purposes.
We evaluated species richness, abundance, alpha diversity, and true diversity of Phlebotominae sand flies temporal changes in domiciles within the northern Argentina city of Corrientes. A total of 16 sampling nights were conducted seasonally throughout the years 2012-2014 through light traps supplemented with CO2. Meteorological and remote sensing environmental factors were used to assessed for vectors implications in disease transmission through Generalized Mixt Models. Lutzomyia longipalpis was the most abundant and common species, followed by Nyssomyia neivai and Migonemyia migonei. Lutzomyia longipalpis was more abundant in urban areas, Ny. neivai was associated with vegetation in periurban areas, both were found all sampling years with higher abundance during the rainy season. Positive association of Lu. longipalpis with precipitation and relative humidity and negative association with temperature were observed. Models showed humidity and vegetation as making effects on Lu. longipalpis abundance. Precipitation was significant for Mg. migonei models, with higher abundance in periurban and periurban-rural environments. For Ny. neivai models, relative humidity was the most important variable, followed by precipitation frequency. Our findings led to identify high risk areas and develop predictive models. These are useful for public health stakeholders giving tolls to optimized resources aim to prevent leshmaniasis transmission on the area.
Environmental variables related to vegetation and weather are some of the most influential factors that impacting Aedes (Stegomya) aegypti, a mosquito vector of dengue, chikungunya and Zika viruses. In this paper, we aim to develop temporal predictive models for Ae. aegypti oviposition activity utilizing vegetation and meteorological variables as predictors in Córdoba city (Argentina). Eggs were collected using ovitraps placed throughout the city from 2009 to 2012 that were replaced weekly. Temporal generalized linear mixed models were developed with negative binomial distributions of errors that model average number of eggs collected weekly as a function of vegetation and meteorological variables with time lags. The best model included a vegetation index, vapor pressure of water, precipitation and photoperiod. With each unit of increment in vegetation index per week the average number of eggs increased by 1.71 in the third week. Furthermore, each millimeter increase of accumulated rain during 4 weeks was associated with a decrease of 0.668 in the average number of eggs found in the following week. This negative effect of precipitation could occur during abundant rainfalls that fill containers completely, thereby depriving females of oviposition sites and leading them to search for other suitable breeding sites. Furthermore, the average number of eggs increased with the photoperiod at low values of mean vapor pressure; however the average number of eggs decreased at high values of mean vapor pressure, and the positive relationship between the response variable and mean vapor pressure was stronger at low values of photoperiod. Additionally, minimum temperature was associated positively with oviposition activity and that low minimum temperatures could be a limiting factor in Ae. aegypti oviposition activity. Our results emphasize the important role that climatic variables such as temperature, precipitation, and vapor pressure play in Ae. aegypti oviposition activity and how these variables along with vegetation indices can be used to inform predictive temporal models of Ae. aegypti population dynamics that can be used for informing mosquito population control and arbovirus mitigation strategies.
Aedes albopictus (Diptera: Culicidae) distribution is bounded to a subtropical area in Argentina, while Aedes aegypti (Diptera: Culicidae) covers both temperate and subtropical regions. We assessed thermal and photoperiod conditions on dormancy status, development time and mortality for these species from subtropical Argentina. Short days (8 light : 16 dark) significantly increased larval development time for both species, an effect previously linked to diapause incidence. Aedes albopictus showed higher mortality than Ae. aegypti at 16?°C under long day treatments (16 light : 8 dark), which could indicate a lower tolerance to a sudden temperature decrease during the summer season. Aedes albopictus showed a slightly higher percentage of dormant eggs from females exposed to a short day, relative to previous research in Brazilian populations. Since we employed more hours of darkness, this could suggest a relationship between day-length and dormancy intensity. Interestingly, local Ae. aegypti presented dormancy similar to Ae. albopictus, in accordance with temperate populations. The minimum dormancy in Ae. albopictus would not be sufficient to extend its bounded distribution. We believe that these findings represent a novel contribution to current knowledge about the ecophysiology of Ae. albopictus and Ae. aegypti, two species with great epidemiological relevance in this subtropical region.
BACKGROUND: Rodent-borne hantaviruses (genus Orthohantavirus) are the etiologic agents causing two human diseases: hemorrhagic fever with renal syndrome (HFRS) in Euroasia; and hantavirus pulmonary syndrome (HPS) in North and South America. In South America fatality rates of HPS can reach up to 35%-50%. The transmission of pathogenic hantaviruses to humans occurs mainly via inhalation of aerosolized excreta from infected rodents. Thus, the epidemiology of HPS is necessarily linked to the ecology of their rodent hosts and the contact with a human, which in turn may be influenced by climatic variability. Here we examined the relationship between climatic variables and hantavirus transmission aim to develop an early warning system of potential hantavirus outbreaks based on ecologically relevant climatic factors. METHODOLOGY AND MAIN FINDINGS: We compiled reported HPS cases in northwestern Argentina during the 1997-2017 period and divided our data into biannual, quarterly, and bimestrial time periods to allow annual and shorter time delays to be observed. To evaluate the relationship of hantavirus transmission with mean temperature and precipitation we used dynamic regression analysis. We found a significant association between HPS incidence and lagged rainfall and temperature with a delay of 2 to 6 months. For the biannual and quarterly models, hantavirus transmission was positively associated with lagged rainfall and temperature; whereas the bimestrial models indicate a direct relationship with the rainfall but inverse for temperature in the second lagged period. CONCLUSIONS/SIGNIFICANCE: This work demonstrates that climate variability plays a significant role in the transmission of hantavirus in northwestern Argentina. The model developed in this study provides a basis for the forecast of potential HPS outbreaks based on climatic parameters. Our findings are valuable for the development of public health policies and prevention strategies to mitigate possible outbreaks. Nonetheless, a surveillance program on rodent population dynamics would lead to a more accurate forecast of HPS outbreaks.
