This paper evaluated user feedback surrounding the inclusivity of a national GeoHealth informatics tool prototype that visualized community-level indicators across domains related to digital health accessibility. There is a critical gap in the literature regarding the end-user needs of GeoHealth tools, including usability testing. Formal usability testing was conducted with participants representing the patient population on a GeoHealth tool prototype. Participants used the "think-aloud" technique to help observers capture qualitative insights. Results revealed barriers linked to expert-oriented design, specialized terminology (e.g., "domains," "indices"), and insufficient contextual guidance. Users often perceived the tool as intended for professionals, which led to hesitancy and limited exploration. This research contributed to a deeper understanding of the considerations needed when designing GeoHealth tools. Patient inclusivity requires plain-language interfaces, intuitive cues, and other embedded supports. These strategies can bridge the expert-lay gap and enhance equitable participation across the digital health landscape. This study highlights the importance of ensuring patient inclusiveness in design and usability for GeoHealth platforms' roles in the health informatics landscape.
In El Bagre, Nechí, Zaragoza, and three other municipalities that comprise the Bajo Cauca region of Antioquia, an estimated total of 28.000 ha have been affected by illegal gold mining, where environmental liabilities such as mine tailings are distributed throughout the area. Urban areas, especially the main urban centers, may be exposed to environmental contaminants and their associated adverse effects. Studies that contribute to the characterization and prioritization of environmental liabilities are necessary to improve environmental management and public policies. The aim of this study was to assess the presence of As, Cd, Cr, and Pb associated with mine tailings in El Bagre, Nechí, and Zaragoza and to implement a methodology for their characterization and prioritization through the calculation of risk index (RI) values. The RI considered both the concentrations of potentially toxic elements (PTEs) and the proximity of mine tailings to sensitive receptors, including urban areas and environmental features such as surface water bodies. Spatial distribution maps of PTE concentrations and RI values were subsequently developed to identify priority environmental liabilities for management. One of the main results of the study is the identification of five mining tailings as prioritized environmental liabilities for environmental management, which was unknown prior to the completion of this study. No environmental liabilities classified as high risk were identified in any of the three municipalities. In El Bagre, 9/11 environmental liabilities were classified as low risk, while 2/11 were classified as medium risk. In Zaragoza, 4/7 environmental liabilities were classified as low risk and 3/7 as medium risk. In Nechí, 6/6 environmental liabilities were classified as low risk. To the best of our knowledge, this study is the first to apply a methodology for the classification and prioritization of mining tailings in the study area. Another important result was the spatial mapping of the distribution of PTEs across the three municipalities, as this outcome allows the spatial identification of PTE concentrations and serves as an input for environmental planning of the territories. In conclusion, the results provide scientific evidence that will contribute to the development of a methodology for the categorization and prioritization of environmental liabilities associated with gold mining, serving as a resource for environmental authorities and guiding decision-making based on scientific and technical evidence.
In a rapidly warming climate, heatwaves pose an increasing threat to human health. However, there is limited knowledge of heatwave impacts on health outcomes, or the role of humidity in the tropical and arid climates of Australia's Northern Territory (NT). Using a space-time-stratified case-crossover design and conditional Poisson regression models, we analysed the association between heatwaves and emergency department (ED) presentations from 2001 to 2023, across all six NT public hospitals. Heatwaves were identified using the Excess Heat Factor (EHF) method, with both temperature-only (heatwaves_T) and temperature-plus-humidity, heat-index (heatwaves_TH) metrics. We undertook sub-group analyses by sociodemographic characteristics and principal diagnosis. All-cause ED presentations increased by 4.4% (RR = 1.044, 95%CI 1.018-1.071) for severe/extreme and 1.6% (RR = 1.016, 95%CI 1.002-1.030) for low-intensity heatwaves_T. For heatwaves_TH, presentations increased by 6.1% (RR = 1.061, 95%CI 1.025-1.098, severe/extreme) and 0.9% (RR = 1.009, 95%CI 0.995-1.024, low-intensity). Subpopulation increases for severe/extreme heatwaves_T occurred for ages 19-49 years (RR = 1.052; 95%CI 1.018-1.087), visitors (RR = 1.162, 95%CI 1.038-1.301) and skin conditions (RR = 1.116, 95%CI 1.048-1.189). Specific to severe/extreme heatwaves_TH, presentations increased for Aboriginal peoples (RR = 1.059, 95%CI 1.006-1.114), ages 50-64 years (RR = 1.141, 95%CI 1.059-1.230) and cardiovascular conditions (RR = 1.111, 95%CI 1.015-1.216). Comparing heatwave indexes, 57.1% of heatwave_T days were not captured by heatwave_TH, and conversely 49.6% of heatwaves_TH days were not captured by heatwave_T. These findings call for dual heatwave warning systems in the NT, incorporating both EHF temperature and heat-index, and further humidity-inclusive studies in varied climates. Preventative interventions should target high-risk populations, prioritizing resources for severe and extreme heatwaves. Our research investigates how dry‐heat and humid heatwaves impact emergency department presentations in the Northern Territory, Australia. We find that both dry‐heat and humid heatwaves cause emergency department presentations to rise, particularly for visitors during dry‐heat heatwaves and for Aboriginal people during humid heatwaves. This means that heatwave warning systems must also include measures for humidity, and target high‐risk groups.
To investigate whether the reported additional effects of heatwaves on mortality are truly independent or instead reflect incomplete control for lagged temperature effects, and to assess whether this relationship varies by heatwave definition (e.g., minimum duration and intensity thresholds), as a potential explanation for heterogeneity in findings across the literature. We collected daily all-cause mortality records spanning 19 years (2000-2018) in Amman, Jordan. Heatwaves were identified using 20 definitions with varying temperature thresholds (percentile-based) and durations (2-5 consecutive days). Generalized Additive Models with penalized splines and Distributed Lag Non-linear Models were used to assess temperature-mortality associations and investigate how lag adjustment impacts the observed heatwave added effect. Without accounting for the lagged effects of temperature, heatwaves showed a significant additional effect on mortality in 7 out of 20 definitions. However, after properly controlling for these lagged effects, none of the heatwave definitions based on duration and threshold demonstrated a significant additional effect on mortality beyond what can be attributed to the cumulative (same day plus lagged) effect of daily temperature. The apparent added effect of heatwaves on mortality is largely explained by the cumulative impact of daily temperatures, suggesting that research and efforts might be more effective if focused on the cumulative effect of temperatures instead. Heatwaves are often thought to cause additional deaths beyond those expected from hot weather. However, many studies have used different ways to define heatwaves and have not always accounted for the delayed effects of temperature over several days. We studied daily temperatures and deaths in Amman, Jordan, over 19 years to see whether heatwaves have an effect on mortality that is separate from the overall impact of high temperatures. We tested 20 different ways of defining heatwaves and compared models that either did or did not include delayed temperature effects. We found that when delayed temperature effects were not considered, heatwaves appeared to increase mortality. But when these delayed effects were properly included, the added effect of heatwaves disappeared. This suggests that the real driver of increased deaths is the cumulative effect of high temperatures, not heatwaves by themselves. Focusing public health efforts on protecting people from overall temperature extremes may therefore be more effective than concentrating only on periods defined as heatwaves.
The present study proposes a high-resolution, multi-component framework to estimate human exposure to air pollution in tropical urban environments, addressing complex topography and limited historical mobility and exposure data. This framework is applied to the city of Medellín in the Aburrá Valley in Colombia. To assess population exposure to particulate matter, numerical simulations, mobility and toxicity data were integrated. The LOTOS-EUROS Chemical Transport Model, driven by the Weather Research and Forecasting (WRF) model, was used to simulate air quality at a spatial resolution of 1 km  ×  1 km for the year 2019. Mobility dynamics were derived from the Origin-Destination Survey (ODS), comprising over 180,000 trips across 240 traffic analysis zones. The utilisation of this data set facilitated the estimation of time-weighted exposure levels for diverse demographic groups during weekdays. The morpho-chemical properties of the particulate matter samples were analyzed to determine the presence of metals and carbonaceous fractions due to significant health implications. The exposure model is a weighted model that integrates exposure time, PM 2.5 and PM 10 concentration, and cytogenotoxic indicators. The application of cluster analysis to the available data, resulted in the identification of areas of elevated health risk, indicating that the central and southern zones, approximately 60% of the metropolitan population and main highway corridors, exhibited mean particulate matter exposure levels that exceeded 40 μg/m3 during peak hours, thus surpassing the WHO air quality guidelines. These zones demonstrated the highest levels of exposure, as indicated by cluster significance levels, suggesting more epidemiological studies and public health interventions. This study sets out a detailed methodology for the estimation of human exposure to particulate matter in Medellín and the Aburrá Valley (Colombia) integrating air quality simulations using LOTOS‐EUROS (driven by WRF meteorology), mobility data from 180,000 trips, and toxicity analysis of particulate matter. The model incorporates PM 2.5 and PM 10 concentrations, exposure duration, and biological effects. Central and southern regions, characterized by high population density and traffic intensity, exhibited concentrations exceeding 40 μg/m3 during peak hours, thereby demonstrating the highest levels of cytogenotoxic risk. The framework facilitates precise identification of high‐risk zones and supports focused public health responses.
Malaria eradication requires reducing transmission in endemic areas and maintaining these reductions in areas where elimination has been achieved. In Altamira, a municipality in the Xingu Basin of the Brazilian Amazon, a malaria control program was implemented during the construction of the Belo Monte Dam which eliminated local malaria cases, but resurged in rural areas after these efforts ceased. To elucidate different drivers of malaria transmission in Altamira, before (2006-2012) and after (2017-2020) the construction of Belo Monte Dam and the localized malaria control program, we analyzed annual notified malaria cases alongside geospatial variables including forest edge, population size and travel time to urban centers. Due to the low incidence, malaria case data was clustered based on the geographic proximity of health centers. A total of 17,578 malaria cases were reported in Altamira between 2006 and 2020. Malaria cases exceeded 1,200 per year prior to dam construction, then dropped below 100 during construction, resurging to over 700 cases by 2020 predominantly in rural clusters. Using generalized additive models we found that malaria cases increased significantly with increases in forest edge habitats and local population size, both before and after dam construction, while neither distance to deforestation nor climatic variables explained incidence variation. This study emphasizes that interruptions to control efforts, even when localized, can rapidly reverse gains, and that understanding landscape epidemiology is critical for achieving and maintaining malaria elimination in Amazonian settings. Malaria is a serious mosquito‐borne disease that remains a challenge in the Brazilian Amazon. In the municipality of Altamira, malaria cases dropped sharply during the construction of the Belo Monte Dam when local health authorities increased control efforts. However, after construction ended, malaria cases rose again, especially in rural areas. This study examined malaria case data from 2006 to 2020 alongside information about the environment and population to understand what caused these changes. We found that malaria increased in areas with more forest edge habitat and higher numbers of people before and after the dam construction. These results show that both the environment and human populations influence malaria risk over time. The findings also reveal that stopping control programs can lead to a quick return of malaria, highlighting the need for continuous efforts, especially in rural and environmentally changing areas. Sustained malaria control and careful monitoring in these settings are important to prevent resurgence and help Brazil achieve its goal of malaria elimination.
Understanding how climate modulates infectious disease dynamics is critical for anticipating epidemic patterns. After a hot debate on potential facilitation of population spread by climate factors during the first 2 years of the COVID-19 pandemics, not much attention has been devoted to assess whether the ensuing SARS-CoV-2 waves also displayed similar climatic facilitation, or conversely, whether immunity dismissed any such forcing and potential signatures. This study examines the association between climatological variables-specifically temperature and absolute humidity (AH)-and the global incidence of the SARS-CoV-2 Omicron variant (B.1.1.529) during its massive epidemic wave (2021-2022). Using global-scale epidemiological and climate data, we applied the scale-dependent correlation analysis to identify transient, scale-specific non-linear associations between factors and the disease across regions. We found consistent significant local negative correlations between incidence and both temperature ( r s = - 0.4 to - 0.6 ) and AH ( r s = - 0.3 to - 0.5 ), particularly in mid-latitudes during colder months. Threshold analyses revealed temperature and humidity ranges of 12°C-21°C and 8-12 g m - 3 , respectively, where transmission was most favored, fully in line with previous results obtained for the first COVID-19 variants. A stochastic population-based model incorporating a temperature-modulated infection rate reproduced epidemic dynamics more accurately than constant or seasonal formulations, reducing normalized residuals by approximately 40%-50%. These results largely demonstrate a robust climatic influence also on Omicron transmission yet underscoring the value of integrating environmental drivers into epidemic modeling and early-warning frameworks. Climate can influence the spread of diseases, but the role of climate in COVID‐19 outbreaks is not yet fully understood. In this study, we examined the impact of temperature and humidity on the global spread of the Omicron variant in 2021–2022. By combining global COVID‐19 case data with climate records, we found that Omicron spread more when temperatures and humidity were lower, especially in regions with colder winters. We also tested mathematical models of disease spread and found that models including climate effects matched real case patterns better than models without them. These results suggest that climate conditions yet played an important role in shaping Omicron outbreaks and that climate information could help improve future epidemic predictions and public health planning.
The increasing frequency of climate extreme events and persistent air pollution challenges pose compound environmental risks, yet the spatiotemporal patterns and trends of compound extreme temperature-air pollution events remain poorly understood globally. In this paper, we quantify the temporal trends and spatial distributions of compound extreme temperature-PM2.5 pollution events across 10,067 monitor locations in 43 countries spanning 2003-2023. We find that compound extreme heat and PM2.5 pollution (heat + PM2.5) events decreased in most countries in our sample (35/43), while compound extreme cold and PM2.5 pollution (cold + PM2.5) events showed diverse trends across different nations. Despite the increase in global temperature, cold + PM2.5 events still represent a substantial component of the compound extreme temperature and PM2.5 pollution events. Sensitivity analyses using alternative thresholds and MERRA-2 PM2.5 data sets yield generally consistent results. The spatial heterogeneity in the compound events is linked to the diverse temperature-PM2.5 relationships, for example, we find higher PM2.5 concentrations during cold conditions in China, India, and Europe, while the USA and Australia show higher PM2.5 concentrations during hot conditions. Concentrated populations in high-frequency regions further amplify the exposure burden, with India, Pakistan, and China exhibiting disproportionately higher exposure levels relative to the compound events. Given the likely health impacts of these compound events, our findings suggest that future policies should target temperature-dependent emission activities that will amplify pollution under extreme temperature conditions. Extreme temperatures and air pollution are major environmental and public health threats, yet their co‐occurrence can be more harmful than either hazard alone. Analyzing data from more than 10,000 air quality monitoring stations in 43 countries over the past two decades, we quantified when and where extreme temperatures and high fine particulate matter (PM2.5) pollution happen together. We find that both “heat + PM2.5” and “cold + PM2.5” events occur frequently around the world, but their patterns differ from place to place. In many countries, heat + PM2.5 events have become less common despite the increasing heat extremes. However, cold + PM2.5 events show a wider variety of trends across regions. These differences are linked to how PM2.5 levels respond to temperature as well as the changing trends in PM2.5 under different temperature conditions. For example, PM2.5 levels declined across all temperature conditions in the USA over the years, while PM2.5 levels reduce the most under cold conditions in India. Our results highlight that policies aimed at protecting public health must consider these compound events, and how temperature extremes influence pollution levels.
Hurricane-related flooding can mobilize microbial and chemical contaminants, while limited well testing and uneven disaster response capacity leave many households relying on private wells at elevated risk, particularly during the early recovery phase when contamination risk is acute. To address this challenge, we propose a data-driven framework for quantifying post-hurricane private well contamination risk. The framework produces a numerical risk score by integrating 78 geospatial variables across three modules representing hazard, physical vulnerability, and social capacity. The score is constructed using a hybrid approach that combines supervised and unsupervised learning to generate interpretable indices grounded in theory and calibrated to real-world data. We applied the framework to western North Carolina following Hurricane Helene and evaluated its performance using post-hurricane well testing data from the North Carolina Department of Health and Human Services together with community-informed assessments in two counties. Higher risk scores were significantly associated with increased total coliform contamination (p = 0.006, Wilcoxon rank-sum test), demonstrating the value of the framework for identifying areas with elevated contamination risk. These findings suggest that the framework can help identify areas with elevated contamination risk following extreme weather events, although predictive performance remains moderate and further systematic evaluation is needed. The framework is designed to be transferable and can be adapted to other storms and regions where geospatial and well testing data are available. Overall, this work provides a practical, data-informed tool to support disaster preparedness, prioritize well testing, and protect private well users after extreme weather events. Millions of people in the United States rely on private wells for drinking water, especially in rural areas. Unlike public water systems, private wells are not regulated, so contamination often goes unnoticed unless homeowners test their water. After hurricanes and flooding, the risk of contamination increases because floodwaters can carry bacteria and chemicals into wells. At the same time, testing resources are limited, making it hard for health agencies to know where help is most urgently needed. In this study, we developed a data‐driven approach to estimate the risk of private well contamination after hurricanes. The method creates a single risk score by combining information on flooding, well and land characteristics, and community resources that may reduce risk. We applied this approach in western North Carolina after Hurricane Helene and evaluated it using well water test results and field observations in local communities. We found that wells with higher risk scores were more likely to have bacterial contamination. This suggests the framework can help identify areas where testing and assistance should be prioritized. Although demonstrated for one hurricane, the approach can be used in other regions and storms to support disaster preparedness and protect private well users after extreme weather.
Dengue, commonly known as breakbone fever, has been prevalent in Bangladesh since 2000. Monsoon conditions and warmer temperatures create suitable breeding environment for the vectors Aedes aegypti and Aedes albopictus. Although dengue outbreaks peak during the post-monsoon months (September and October), 2023 showed an unusual shift, with cases peaking as early as June. The case fatality rate (0.54%) in 2023 was the highest in the past two decades. This study offers an exhaustive examination of the spatiotemporal dynamics of dengue incidence clusters and composite risk across Bangladesh using daily dengue case data from 2019 to 2024. Spatial autocorrelation analysis (Local Moran's I) revealed that the central and south coastal districts, especially Dhaka, Manikganj, and Barisal were the major dengue hotspots during the peak outbreak years (2019, 2023, and 2024). Jaccard Similarity Index (JSI) analysis further indicated strong seasonal and interannual reorganization of dengue hotspots, especially during the post-monsoon season, with increasing hotspot concentration in coastal districts after 2020. To assess hazard, vulnerability and risk, we applied a GIS-based multi-criteria decision-making model (MCDM) framework and compared both weighted (AHP-based) and unweighted approaches. The weighted AHP model was more accurate (AUC = 0.75) than the unweighted model (AUC = 0.55) in predicting dengue risk zones. A negative binomial mixed-effects regression showed that lagged temperature, precipitation and population density increased dengue incidence, while higher normalized difference built-up index was associated with lower incidence. These findings emphasize the need for integrated dengue control strategies targeting high-risk areas and equal healthcare access. Dengue fever, transmitted by Aedes mosquitoes, is a major public health concern in Bangladesh. This study investigated dengue outbreaks from 2019 to 2024 to identify seasonal changes and risk factors driving the spread of the disease. Findings showed a significant rise in dengue cases in recent years, with 2023 recording the highest number of infections and deaths, along with an unusually early peak beginning in June. Central Dhaka and south coastal districts, particularly Barisal, consistently reported high dengue incidence. Dengue activity increased during the monsoon and post‐monsoon periods, likely due to higher rainfall and warmer temperatures that favor mosquito breeding. Statistical analysis showed that higher temperature, rainfall in the previous month and population density were associated with increased dengue incidence, while higher built‐up land intensity was linked to lower incidence. Using geospatial analysis and hotspot analysis techniques, this study identified both persistent and shifting dengue hotspots across Bangladesh. Coastal regions emerged as hotpots after 2020 placing additional pressure on public health systems. The findings also revealed that climatic, social and environmental factors contribute differently to dengue risk, underscoring the importance of targeted prevention.
Road injuries are a leading cause of mortality and morbidity worldwide. Years of international efforts have aimed to strengthen policy engagement, including the 2020 UN General Assembly's proclamation of the Second Decade of Action for Road Safety (2021-30), targeting a 50% reduction in road traffic deaths and serious injuries by 2030. The aim of this study is to provide estimates to monitor progress and identify intervention gaps. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2023, we estimated incidence, mortality, and morbidity of road injuries for 204 countries and territories from 1990 to 2023. Four road injury types and 47 nature-of-injury categories were examined. Morbidity and mortality data from clinical records, vital registration, and police reports were harmonised using meta-analytic techniques to ensure consistency and correct for systematic bias. Incidence was modelled with the meta-regression tool Disease Modelling-Meta-Regression version 2.1 and cause-specific mortality with the Cause of Death Ensemble model, both incorporating location-specific covariates to support interpolation. Years of life lived with disability (YLDs) were estimated from the prevalence and severity of the nature of road injury, and years of life lost (YLLs) from the number of cause-specific deaths multiplied by the standard life expectancy at the age of death. Disability-adjusted life-years (DALYs) were the sum of YLLs and YLDs. All metrics were calculated with 95% uncertainty intervals (UIs). In 2023, there were 50·9 million (95% UI 46·1-56·1) road injury incident cases, 1·34 million (1·04-1·58) deaths, and 75·3 million (59·8-89·2) DALYs globally. Road injuries were the leading global cause of death among males aged 10-39 years. Between 1990 and 2023, age-standardised incidence decreased by 38·3% (95% UI 36·9-39·7) and mortality decreased by 32·3% (6·1-49·0), but progress varied widely by World Bank income group. Mortality in low-income countries (43·8 [95% UI 31·7-56·0] deaths per 100 000 population) was approximately six times higher than in high-income countries (7·5 [7·1-7·9] deaths per 100 000), despite the high-income countries showing the highest age-standardised incidence rates (858·1 [95% UI 781·9-947·1] cases per 100 000). In the past decade, many countries achieved notable reductions in road injuries, but others, including Ghana and the USA, saw increases. More severe injuries tended to occur in low-income and middle-income countries. Although global incidence, mortality, and DALY rates from road injuries have declined, progress remains uneven, with pronounced disparities across income groups reflecting systemic inadequacies in infrastructure, vehicle standards, enforcement, and post-crash care. Strengthening emergency response, improving road design, enforcing safety measures, and adapting policies to the evolving demographics remain essential. Gates Foundation.
Drought is one of the most widespread and disruptive natural hazards globally, with environmental and societal effects that may increase psychological distress. Yet, its association with suicide in the U.S. remains understudied. We examined the relationship between drought and suicide mortality across the contiguous U.S. Drought severity was measured using the Evaporative Demand Drought Index, and suicide data from the National Center for Health Statistics. We used Generalized Additive Models (GAMs) to estimate incidence rate ratios (IRRs) and absolute risk differences (ARDs), with 95% confidence intervals. Analyses were stratified by age, sex, and urbanicity. From 2000 to 2018, the U.S. recorded 350,434 firearm-related and 323,225 non-firearm suicide deaths. Drought affected 37.4% of county-months, and both suicide types were positively associated with drought-especially under severe conditions. For firearm suicides, worsening drought was linked to an IRR of 1.109 (95% CI: 1.091-1.128) and ARD of 0.704 (95% CI: 0.595-0.811); improving drought had an IRR of 1.094 (95% CI: 1.076-1.112) and ARD of 0.608 (95% CI: 0.501-0.715). For non-firearm suicides, worsening drought was associated with an IRR of 1.057 (95% CI: 1.037-1.077) and ARD of 0.347 (95% CI: 0.232-0.461), while improving drought had an IRR of 1.073 (95% CI: 1.054-1.093) and ARD of 0.456 (95% CI: 0.344-0.568). Severe drought was associated with higher suicide mortality, including firearm-related deaths, across several subgroups such as older adults, women, and individuals living in non-metro areas; these subgroup-specific findings were not statistically compared. This study explores how drought, a prolonged period of unusually dry weather, affects suicide rates in the United States. Using data from all counties between 2000 and 2018, we analyzed over 670,000 deaths by suicide to determine if dry conditions were linked to higher risk. We found that both firearm and nonfirearm suicides increased during all stages of drought, with the highest risk seen during the most severe drought periods. Certain groups—including older adults, women, and people living in rural areas—were more affected. The risk was especially high for suicides involving firearms. These findings suggest that dry weather may create or worsen stress, especially for people who are already vulnerable. By better understanding when and where these risks increase, we can improve public health strategies. Adding mental health support to drought preparation and response plans could help save lives, particularly in rural communities and during times of severe environmental stress.
Rhodnius prolixus is the most common and abundant kissing bug found in Royal and other native palms from western Venezuela. R. prolixus is a dominant vector of Trypanosoma cruzi, the parasite causing Chagas disease. Here we use species distribution models (SDMs) to estimate habitat suitability for R. prolixus. Based on habitat suitability we estimate the population at risk of Chagas disease transmission. We fitted an ensemble SDM with 250 m spatial resolution using remote sensing covariates, processed for the same time when kissing bugs were sampled, for modeling R. prolixus habitat suitability, based on 67 samples (from 41 locations) collected between 2004 and 2012. The ensemble SDM included prediction using six different machine learning algorithms, which include: generalized linear model, multiple adaptive regression splines, regression trees, random forests, generalized boosted regression trees, and extreme gradient boosting. The final SDM included 9 out of 13 variables selected using variable importance. The final ensemble SDM had an average receiver operating curve (±SD) of 0.833 ± 0.114 for the best model. The model suggested a high habitat suitability for R. prolixus along the eastern slope of the Venezuelan Andean Cordillera spread along the states of Merida, Barinas, Tachira, Trujillo, and Portuguesa. Validation with an independent data set, collected between 2012 and 2018, showed that higher suitability predicted occurrence of R. prolixus (p < 0.025). The results show how ensemble SDMs can provide high spatial resolution distribution information for R. prolixus, which can be used to accurately estimate Chagas disease transmission risk. Rhodnius prolixus is a kissing bug that transmits parasites causing Chagas disease. Using tools to map its distribution can help to estimate how many people live near kissing bugs and might be at risk of contracting Chagas disease. Here, we use a model to illustrate the preferred environmental conditions this kissing bug prospers in. We found that this kissing bug lives at low elevations, in a warm environment with a temperature that does not change much, while preferred vegetation conditions, can vary from very sparse to very dense. When we mapped the model, we found this kissing bug was very common along the eastern slope of the Venezuelan Andean Cordillera. Based on the area where this kissing bug is likely present we estimated the population at risk of contracting Chagas disease. Current estimates range from 82,296 to 1,943,934 people at risk of Chagas disease for the studied area in 2020.
Hantaviruses are not emerging pathogens in the strict sense, but their public health relevance is being reshaped by climate change, environmental disruption, land-use change, and increasing human mobility. In Southeast Europe, where Dobrava-Belgrade virus and Puumala virus remain endemic, these infections should no longer be viewed only as sporadic rodent-borne diseases of rural or forested environments. Changing ecological conditions can alter reservoir abundance, virus circulation, and human exposure, while fragmented surveillance and variable diagnostic capacity may obscure the true burden of disease. Although human infection is still driven primarily by environmental exposure to infected rodent excreta, rare person-to-person transmission of Andes virus and recent travel-associated clusters illustrate how traditionally localized zoonoses can acquire wider international relevance. This Opinion argues that hantaviruses should be approached as a climate-sensitive One Health challenge, requiring closer integration of human, veterinary, ecological, and meteorological surveillance. Strengthening regional preparedness is essential before environmental change further expands the conditions for transmission.
Extreme heat exposure leads to excess cardiovascular morbidity and mortality among older adults, but misalignment in the geographic scales of health data and intraurban heat exposure make it challenging to inform local interventions. We introduce an adaptable framework for developing health burden estimates at finer geographic scales by connecting small area analysis (SAA) techniques with an epidemiologic model of heat related health risks in three steps: (a) estimating an exposure response function using individual level cardiovascular disease (CVD) hospitalizations for adults aged 65 and over; (b) downscaling daily CVD hospitalization incidence rates at the ZCTA-level to census block groups (CBG) using SAA, adjusting for individual- and community-level demographic factors, and (c) linking exposure-response with daily incidence rates to estimate heat-attributable burden at the CBG scale. Using the data for a metropolitan area in the southeastern United States for the summer of 2018, we estimated 1.8 to 22.0 excess hospitalizations per 10,000 people across CBG. We demonstrate the utility of this 3-step approach to help inform localized intervention strategies by classifying neighborhoods as one of four risk groups: Low, Health-driven, Heat-driven, and Dual-channel (driven by both heat and health). In comparison, using coarse-resolution temperature resulted in a significantly smaller heat-attributable health burden with different geographic distribution across the CBGs, highlighting the importance of using appropriately scaled exposure data. The findings from this study can support more effective heat interventions to address heat-health related outcomes, rather than exposure alone. Exposure to hot temperatures can result in health issues, especially for older adults. These health issues can be avoided by modifying the physical environment to make temperatures cooler and implementing policies that help people avoid dangerous temperatures. However, data on health outcomes is often not available at the same geographic scale as temperature, making it difficult for policymakers to identify places where interventions are most needed. In this study, we introduce an approach to align the geographic scale of temperature and health data and estimate neighborhood‐level heat‐related health outcomes. We demonstrate how this approach works using data from a metropolitan area in the Southeastern United States. During the summer of 2018, we estimated that there were 163 excess cardiovascular disease (CVD) hospitalizations due to high temperatures among older adults, although the rate of heat‐related hospitalizations varies by neighborhood. Nearly one‐third of older adults live in areas with high rates of CVD and high temperatures, which result in above‐average heat‐attributable health burden in these neighborhoods. We find that using less detailed temperature data changes the results. The findings from this study can help local decision makers identify the most effective approaches for preventing heat‐related health issues.
Forecast errors of severe weather events aggravate economic damage and degrade public mental health. Whether forecast errors are underestimated or overestimated can shape public emotional responses differently, which remains unknown. In this study, we investigate the socio-psychological impacts of forecast errors during the landfall of Typhoon Khanun over the Korean Peninsula. We evaluate the predictive performance of multiple lead-hour precipitation forecasts against observational data and conduct a sentimental analysis of over 43,000 online discourses from the NAVER Weather Report Talk platform. Multiple lead-hour precipitation forecasts demonstrate underestimation in the eastern and southeastern regions of the Korean Peninsula and overestimation in the western and southwestern regions. The spatial discrepancies of precipitation forecasts are associated with distinct emotional responses: overestimation (underestimation) makes anxiety and worry (stress and confusion) the dominant emotion types in the discourses from the NAVER Report Talk platform. The findings of this study suggest that expectation-reality mismatch is a key mechanism in risk communication, and the direction of this mismatch differentiates public response. This study provides insights into the potential value of improved forecast accuracy on reducing emotional distress and strengthening public resilience during extreme weather events. This study looks at how mistakes in weather forecasts affect people's emotions during a landfalling tropical cyclone over the Korean Peninsula. This study compares predicted rainfall with what happened. This study also analyzes over 43,000 online comments to see how people reacted emotionally. This study finds that forecasts sometimes underestimated rainfall in the eastern and southeastern regions and overestimated it in the western and southwestern regions. These different directions of errors led to different emotional responses. When the forecast predicted more rain than occurred (overestimation), people felt anxious and worried. When it predicted less rain than observed (underestimation), people felt stressful and confused. Overall, the study shows that when there is a mismatch between what people expect and what really happens, it strongly affects how they feel. It also suggests that improving the accuracy of weather forecasts could help reduce emotional distress and help people cope better during extreme weather events.
Chronic Respiratory Diseases (CRDs) are a major global health burden, causing significant mortality and morbidity. The World Health Organization reports CRDs led to 4.1 million deaths in 2023, affecting 454.6 million people (5.54% globally). While tobacco smoking is the leading risk factor, others include air pollution, occupational hazards, childhood respiratory infections, and genetic predisposition. This study examines hydrology's role in CRD prevalence and spatial distribution, alongside household-level factors. Using remote sensing, the Topographical Wetness Index (TWI) was calculated at 30 m resolution and linked to CRD prevalence data from 1,533 households across 15 income-stratified neighborhoods in Dar es Salaam, Tanzania. Generalized linear mixed models, geovalidation, and ground truthing were employed for analysis. Results showed that CRD prevalence was significantly associated with higher TWI scores (OR = 1.18 [1.04, 1.33] per TWI unit increase, p = 0.009), with flood-prone areas (TWI ≥ 15) exhibiting a ninefold greater risk (OR = 8.63 [2.13, 35]). Other independent risk factors included household size (OR = 1.15 [1.05, 1.25] per person, p = 0.003) and communal skip bin use (OR = 4.04 [1.04, 15.80], p = 0.045). Conversely, air conditioning reduced CRD risk (OR = 0.22 [0.06, 0.77], p = 0.017). Notably, neighborhood income was not directly associated with CRD prevalence after adjusting for other factors, indicating poverty acts as an indirect, rather than proximal, determinant. These findings link moist, mold-prone environments to CRD risk, especially in urbanizing cities. Poor households often settle in flood-prone areas due to affordability, underscoring hydrology's role in CRD disparities. Chronic respiratory diseases (CRDs) cause serious health problems worldwide, leading to millions of deaths every year. While smoking is a major cause, other factors like polluted air and lung infections also play a role. This study explored how wet, flood‐prone land areas affect CRD rates in Dar es Salaam, Tanzania. We combined detailed satellite data on land wetness with health surveys from over 1,500 households in different income areas. The results showed that people living in wetter, flood‐prone places had a much higher risk of CRD up to nine times more. Larger households and shared waste bins also increased risk, while air conditioning lowered it. Income alone did not directly predict disease risk when other factors were considered. This study highlights how living in damp environments contributes to lung disease, especially for poorer communities in cities facing flooding challenges. Understanding these links can help improve health policies and reduce disease risks in vulnerable urban areas.
Stratospheric ozone (O3) losses and consequent increases in surface ultraviolet (UV) radiation remain a health concern due to close association with skin cancers and cataracts. This study estimated how emissions of ozone-depleting substances (ODS) affect, via O3 reductions, surface UV radiation and associated health impacts in Australia compared to the United States. We used climatological atmospheric data and the U.S. Environmental Protection Agency's Atmospheric Health Effects Framework (AHEF) model to estimate these effects for historic and projected ODS emissions. Compared to the United States, the Australian population is potentially exposed to ca. 24% more biologically weighted UV radiation because of the closer proximity of its population to the equator, less stratospheric O3 at comparable latitudes, and seasonal variations in the Earth-Sun distance. Surface UV increments owing to ODS-caused stratospheric O3 depletion were largest in the 1990s, while the estimated health impacts peak several decades later at ca. 0.5% for cataracts, 1% for melanoma incidence, and 5% for keratinocyte cancer incidence (relative to 1980), and are similar in both countries. The study found that differences in population-weighted surface UV can only partly explain Australia's substantially higher incidence and mortality rates for UV-related maladies. Other factors like genetic susceptibility of skin types, exposure behaviors, and diagnosis practices likely play a role. Ozone in the upper atmosphere helps block harmful ultraviolet (UV) radiation from reaching Earth's surface. Chemicals known as ozone‐depleting substances (ODS) can reduce ozone levels, allowing more UV radiation through. Increased UV exposure increases the risk of health problems like skin cancer and cataracts. The amount of UV that reaches the surface varies by location. In this study, we used a model to compare UV exposure and resulting skin cancer and cataract rates between the United States and Australia. Australians receive about 24% more harmful UV radiation under clear skies. Our study explains that this is due mostly to Australia's closer proximity to the equator, and partly due to climatological differences in stratospheric ozone and seasonal variations in the Earth‐Sun distance. We further estimate that ozone depletion has increased incidence rates of UV‐induced illness similarly in both Australia and the USA, and by less than 5 percent. Overall, the study found that differences in UV radiation only partly explain the significantly higher rates of UV‐induced illness in Australia than in the USA, suggesting that other factors like genetic susceptibility of skin types, exposure behaviors, and diagnosis practices likely play a role.
High suicide rates remain a critical public health challenge in the United States. Using census block group-level data, we examine spatial clustering of suicidal behavior in Salt Lake City (SLC), Utah, and identify key environmental and socioeconomic determinants. We find that green space and walkability are associated with lower suicidal behavior, likely by promoting stress relief, physical activity, and social interaction. In contrast, greater access to hotels is linked to higher risk, as these spaces may offer privacy that limits timely intervention. Neighborhoods with higher incomes and larger families showed greater resilience, consistent with reduced financial strain and stronger emotional support. Areas with higher labor force participation face increased risk, possibly reflecting pressures from job competition and wage stagnation. Contextual factors further moderate these effects. The protective effect of green space is weaker in neighborhoods with larger shares of non-Hispanic African American residents and in higher-income or larger-family areas, but stronger where the jobs-housing mix is higher. Spatial heterogeneity analyses reveal that mixed land use near downtown is associated with higher risk, likely because of traffic noise, waste, and crime. Meanwhile, higher proportions of non-Hispanic African American residents are associated with lower risk in eastern SLC, where these populations tend to live in higher-income neighborhoods with better living conditions. Overall, this study highlights the interplay of environmental, socioeconomic, and contextual factors underlying suicidal behavior and offers a comprehensive understanding of how physical settings and neighborhood characteristics shape vulnerability. These insights inform the development of more targeted and effective suicide prevention strategies. This study examines the spatial clustering and determinants of suicidal behavior in Salt Lake City (SLC), Utah. The results indicate that green space and walkability are associated with lower levels of suicidal behavior, whereas greater access to hotels corresponds to higher levels. Moreover, neighborhoods with higher incomes and larger families exhibit greater resilience to suicide risk, while those with larger proportions of labor force participants experience greater vulnerability. Also, the relationships between green space, hotel access, and suicidal behavior are further shaped by local contexts. The protective effect of green space is weaker in neighborhoods with higher proportions of non‐Hispanic African American residents or with higher income and larger family size, but stronger in neighborhoods with a high degree of jobs‐housing mix. Spatial heterogeneity analyses additionally reveal that jobs‐housing mix near downtown is associated with increased suicide risk, while higher proportions of non‐Hispanic African American residents correspond to lower levels of suicidal behavior in eastern SLC.
Wildfire activity in the United States is increasing due to climate change, land management practices, and human ignitions, reversing decades of air quality progress. Wildfire is an essential process in fire-adapted ecosystems, but fine particulate matter (PM2.5) from wildfire smoke poses significant health risks both near the fire source and in communities far from fire-prone areas. Public health and forest management are often viewed as having conflicting goals-reducing smoke exposure versus restoring fire to ecosystems-but opportunities for collaboration exist. We analyzed an interdisciplinary panel discussion from the 2024 Rocky Mountain Wildfire Smoke Symposium (RMWSS) using thematic analysis and the RADaR technique to identify such opportunities. Four major themes emerged: (a) coordinated communication between stakeholders, (b) barriers and facilitators to bridge building across disciplines, (c) impacts of climate change and (d) priorities and perspectives across disciplines. Additionally, we synthesized the panel discussion and audience polling data into a figure that categorizes solutions by perceived investment, impact, and stakeholder responsibility. High impact objectives included advancing climate resilient community infrastructure, expanding resource sharing, and securing full-time equivalent (FTE) funding for smoke specialists and communication liaisons. Collaboration across disciplines, combined with long-term policy that reduces barriers for safe fire management while investing in clean air will be critical to addressing the wildfire crisis. Wildfires are becoming more frequent and intense and smoke from these fires can harm people's health—even in communities far from the flames. At the same time, fire is important in maintaining healthy forests. Balancing forest management and public health requires stronger coordination across disciplines. We examined a discussion among experts in public health, forest management, and fire science to identify ways to better prepare for a future with more fire and smoke. The conversation highlighted common challenges, including gaps in communication, limited coordination across agencies, and a lack of resources. It also highlighted ways to build bridges across disciplines including consistent public messaging, better access to clean indoor air spaces, and policy investments that support both healthy forests and clean air.