Geohealth最新文献

The adjoint of the U.S. EPA's Community Multiscale Air Quality (CMAQ) model is extended for hemispheric scale applications and is used to estimate location-specific health impacts from primary PM_2.5, and PM_2.5 precursor emissions (NH₃, NO_X and SO₂). We estimate the monetized health burden due to mortality caused by chronic PM_2.5 exposure among adults living in the northern hemisphere, using a generalized concentration-response function. The health impact sensitivities show large spatial variability over the northern hemisphere and exhibit a great deal of seasonal variability, especially for inorganic precursor emissions. The largest marginal impacts are seen for NH₃ and primary PM_2.5. The estimated health impacts for a 10% reduction in emissions reveal a hemispheric burden of 513,700 avoided mortality and monetized health benefits at above 1.2 trillion USD₂₀₁₆. The largest regional contribution to hemispheric mortality is found to be in East and South Asia, particularly China and India (183,760 and 123,440 for a 10% reduction in emissions, respectively). Monetized health burdens are estimated to be highest in China and Europe (∼365 and ∼252 million USD for a 10% reduction in emissions) while it is relatively similar in India (∼175 million USD) as in Canada and the United States (∼177 million USD). Sectoral source contribution analysis demonstrates that the agriculture (19%) and residential (15%) sectors are the largest contributors to the northern hemispheric scale health burden, however, regional differences exist in the results. Examining location- and sector-specific health impacts can inform more effective regulatory measures.

Malaria imposes a major health burden in the Peruvian Amazon, and its early warning is essential for effective disease prevention. The tropical sea surface temperature (SST) variability, fundamentally shaping the global weather patterns, may also alter malaria transmission and potentially improve its long-lead predictability. In this study, we propose a machine learning-based methodology that leverages comprehensive tropical SST variability for malaria prediction in the Peruvian Amazon. First, we demonstrate that significant correlations broadly exist between tropical SST anomalies and Peruvian malaria occurrence across different seasons and time lags, confirming the potential predictability from the tropical ocean. Then, we apply the self-organizing map to synthesize the spatiotemporally varying SST-malaria relationship and identify a unique dynamic SST index for Peruvian malaria. The dynamic SST index provides better performance (higher correlation coefficients and lower root mean square errors) in the generalized linear model, compared to the traditional El Niño–Southern Oscillation (ENSO) index, with lead times exceeding 3 months. Furthermore, the dynamic SST index captures the evolution of the ENSO life cycle from its precursor climate mode (Pacific Meridional Mode) and appears to influence Peruvian malaria by altering the local near-surface air temperature and specific humidity. Such underlying mechanisms provide the physically plausible basis for the long-lead predictability of Peruvian malaria using a machine learning-based remote predictor. Last but not least, we provide open-source code for broad applications in linking tropical SST variability and vector-borne disease transmission, or other climate-sensitive socioeconomic issues.

Groundwater is a major source of drinking water in the United States (US). Groundwater chemistry can contribute to lead leaching from water supply pipes due to factors such as pH and mineral content that influence corrosion. Lead exposure disproportionately affects children from low-income neighborhoods. We evaluated the association of county-level groundwater chemicals with the percentage of children with blood lead levels >5 μg/dL (BLL5%) in 1,104 US counties served by public water utilities using groundwater. Out of the 4,844 BLL5% observations, 3,525 had values of “NA” for BLL5%. We used weighted least squares regression to evaluate the associations, adjusting for covariates such as county-level median household income, educational attainment, and poverty rates. Bayesian Kernel Machine Regression (BKMR) was used to assess the joint effects of all chemicals on BLL5%. Sensitivity analyses tested the robustness of our results by imputing missing BLL5% values. A one mg/L increase in arsenic, copper, dissolved oxygen, and selenium was associated with increases in BLL5% of 0.0512% (95% CI: 0.0002%, 0.1023%), 0.0358% (95% CI: 0.0208%, 0.0508%), 0.0956% (95% CI: 0.0225%, 0.1687%), and 0.3038% (95% CI: 0.1747%, 0.4420%), respectively. Alkalinity, pH, calcium, bicarbonate, and dissolved solids were not found to be statistically significant. BKMR identified calcium, lithium, and alkalinity (posterior inclusion probabilities = 1,000) as important, though with minimal effects. Sensitivity analyses showed variability in results depending on assumptions about missing data. Our findings highlight the importance of monitoring groundwater quality and implementing interventions to reduce childhood lead exposure risks in vulnerable populations, particularly minority, and low-income children.

Cardiovascular diseases (CVDs) remain a leading cause of mortality globally, with environmental risk factors playing a significant role in their prevalence. This review aims to critically evaluate the current methodologies employed in spatiotemporal analyses of CVDs and provides recommendations to enhance the accuracy and practical application of these models. A systematic search of the literature was conducted using Scopus, PubMed, and Embase databases. Studies were selected based on their use of spatiotemporal models to assess the relationship between environmental factors and CVDs. We evaluated the methodological quality of included studies using the Spatial Methodology Appraisal of Research Tool (SMART). Significant challenges were noted, including the need for higher spatial resolution data sets and improved methods for addressing the modifiable areal and temporal unit problems and ecological bias. Additionally, the visualization of spatiotemporal data remains underutilized and underdeveloped, limiting the practical utility of the findings. We also discuss combining parameters to form an indicator that better represents environmental conditions, as well as cases where ground, satellite, or modeled data products are suitable. These recommendations could extend to other acquired chronic diseases and their relationship with environmental risk factors to improve the utility of spatiotemporal models. While spatiotemporal modeling holds considerable promise in understanding and mitigating CVD risks associated with environmental factors, appropriate data selection, addressing methodological pitfalls and reporting spatial and temporal model outcomes are necessary to enhance their reliability and impact.

The spatial resolution of environmental exposure and sociodemographic population data is often mismatched given limited publicly available population data that complies with privacy requirements for individuals. To address this limitation, we developed a novel matching algorithm to construct a synthetic population at the address-level. To demonstrate how our approach can improve environmental justice (EJ) analyses and health impact assessments (HIAs), we examined sociodemographic patterns of residential proximity to major roadways in Greater Boston (Massachusetts) and HIA results, comparing our method with a random address allocation method. The synthetic population was developed at a census tract-level using US Census microdata and combinatorial optimization methods and then downscaled to address-level parcels by matching building attributes to synthetic households. We designated households within 50 m of a major road “high exposure” and households below state median household income “low income”.We found misclassification for individual households (21% of the high exposure/low-income households in the matched data set were identified as such in the random allocation data set). We found modest aggregate differences in matched allocation (3.3% of low-income households had high exposure) compared to random allocation (3.4%). In a HIA, the difference between random and matched allocation would be stronger when there is a strong interactive effect between a sociodemographic effect modifier and exposure on the outcome. Address-level exposure assignment based on synthetic populations can provide more significant and nuanced health impact and EJ analyses. Our novel method can be applied to other regions of the US and expanded to other dimensions of population vulnerability.

Long-term exposure to particulate matter (PM) pollution may directly increase the risk of developing tuberculosis (TB). Despite the known link, the multi–scale spatiotemporal variations in the burden of TB attributable to long-term PM exposure remain largely unclear in China. In this study, we conducted a nationwide, multi-scale risk assessment of the burden of TB attributable to long-term PM_2.5, PM_2.5–10, and PM₁₀ exposure from 2013 to 2019, employing the multivariate distributed lag nonlinear model (MVDLNM), Lorenz curve and Gini index. Our health impact assessments indicate that PM exposure has resulted in significant increases in TB burden. Specifically, approximately $1,202 million (95% CI: 801–1,573 million), $486 million (95% CI: 398–572 million), and $944 million (95% CI: 767–1,115 million) of health economic costs could be attributed to long-term exposure to PM_2.5, PM_2.5–10, and PM₁₀, respectively. Although the overall the burden of TB attributable to PM exposure was significantly reduced from 2013 to 2019, regional inequalities have become more pronounced. The Gini index reveals a clear disparity in the burden of TB related to PM exposure across provincial, city, and county levels. These disparities are most pronounced at the county level (0.4914–0.6801), followed by the city level (0.4135–0.6382), and are least evident at the province level (0.3672–0.6078). Overall, the regional inequalities in the burden of TB are more pronounced at finer spatial scales. Our study highlights the health impacts of long-term exposure to PM on the incidence of TB across different spatiotemporal scales, and the findings provide strong scientific evidence for pollution mitigation and efforts to reduce regional inequality.

Downscaled climate projections provide valuable information needed to better understand the impacts of climate change on health outcomes and to inform adaptation and mitigation strategies at local to regional scales. Because downscaled climate products vary in their representations of fine-scale spatiotemporal patterns, due to of multiple interacting factors, epidemiologic analyses need to consider how differences across downscaling approaches impact projections of health impacts into the future. We evaluate the projected seasonality of coccidioidomycosis in response to projected temperature and precipitation estimated using global climate models from CMIP6 included in California's Fifth Climate Change Assessment, downscaled using two approaches: (a) dynamical downscaling using the Weather Research and Forecasting model; and (b) hybrid statistical downscaling using the Localized Constructed Analogs approach. Our results indicate that by end of century, coccidioidomycosis transmission is projected to start earlier, end later, and last longer across the California endemic region; however, the magnitude of these changes varies by downscaling method. Specifically, LOCA2-hybrid projected a season onset that is 4.2 weeks earlier and an end that is 4.1 weeks later than historical conditions, while the dynamical approach projected a 4 week earlier onset and a 3.8 week later end compared to the historical period. Overall, the LOCA2-hybrid product estimates that the transmission season will last about 0.3 weeks longer than what is projected using dynamical downscaling by end of century. This analysis highlights the sensitivity of coccidioidomycosis seasonality projections to choice of downscaling product, underscoring the need to account for these differences in mitigation and adaptation planning.

Climatic factors may influence stroke patterns, but data from sub-Saharan Africa are scarce. This study assessed the relationship between weather variables and stroke incidence, severity, mortality, and hospital stay in Douala, Cameroon. A retrospective review was conducted using medical records from three referral hospitals in Douala from January 2011 to December 2020. Adults (≥18 years) with neuroimaging-confirmed ischemic or hemorrhagic strokes were included. Weather data: temperature, humidity, wind speed, precipitation, atmospheric pressure, and sunshine duration, were obtained from the national meteorological agency (ASECNA). Associations between weather parameters and stroke-related outcomes were analyzed using univariate and multivariate logistic regression. Among 1,349 stroke cases (mean age 61 ± 13 years; 53% male), 65% were ischemic strokes. Stroke incidence peaked during the long rainy season (p = 0.053). Severe strokes were associated with the long dry season (OR = 1.88), low precipitation (OR = 1.74), and low sunshine (OR = 0.62), while the long rainy season was inversely associated with severity (OR = 0.60). Mortality was higher during the short rainy season, linked to high temperatures (p = 0.046) and moderate rainfall (p = 0.04). Longer hospital stays were associated with the long rainy season (mean difference of 2.3 days, p = 0.01), and were influenced by high wind (p = 0.023), heavy rain (p = 0.013), and low sunshine (p = 0.002). Weather conditions significantly affect stroke incidence and outcomes. Climate-informed public health strategies could improve stroke prevention and care in tropical regions.

Floods can increase the risk of adverse health outcomes through multiple pathways, including contamination of food and water. Remotely sensed (RS) inundation extents can help identify regions with expected heightened flood-related health risks, but variations across inundation data sets and their integration into health risk assessments may affect intervention targeting. We examined if the association between census tract (CT) flooding and intestinal infectious disease related emergency department (IID-ED) visits differed by RS-based exposure estimation methods. Two Hurricane Harvey Inundation data sets with different spatiotemporal resolutions were used to estimate CT-level exposure as percent land flooded and percent population flooded, yielding four exposure variables. These were linked to ED visits by residential CT, and the effect estimates for association between IID-ED visits and flooding were derived. A 10% increase in land flooded was associated with a 6% (1%–10%) higher risk of IID-ED visits, while percent population flooded was not significantly associated with IID-ED visits. No statistically significant differences were found in the effect estimates between the inundation data sets or the exposure representation methods. Combining data sets to identify flooded CTs improved model fitness compared to using either alone, indicating a 1.30 (1.16–1.45) times greater risk of IID-ED visits in flooded CTs compared to non-flooded CTs. CTs where the data sets disagreed also showed a 25% (8%–10%) higher risk of IID-ED visits compared to the mutually agreed non-flooded CTs. Combining remotely sensed inundation data sets of different specifications can address limitations of individual products and improve identifying intervention areas to mitigate flood-related health risks.

Hurricanes pose a wide range of health and safety threats, from wind and flooding to less recognized hazards such as heat stress. Although heat exposure has been documented after hurricanes, little research has examined how it affects disaster relief workers during recovery operations. This study evaluated the heat stress conditions faced by emergency response personnel deployed to southeastern Texas following Hurricane Beryl in July 2024, a period marked by prolonged power outages and extreme heat. Heat hazard scenarios were assessed for the Houston area using occupational exposure limits—the Recommended Alert Limit (RAL) and Recommended Exposure Limit—in combination with wet bulb globe temperature (WBGT) data and factors like worker acclimatization status, work intensity, work/rest schedules, and use of personal protective equipment (PPE). Depending on the scenario, WBGT values exceeded critical safety thresholds throughout this period. For unacclimatized workers engaged in medium to very heavy labor with minimal rest, conditions exceeded the RAL between 74% and 100% of the time. Even heat acclimatized workers deployed outdoors would have faced considerable heat stress, especially during heavy work levels. The presence of restrictive PPE significantly increased heat stress, with all scenarios surpassing safety thresholds. These findings underscore the heightened vulnerability of disaster response personnel to heat-related health risks in the aftermath of hurricanes. Acclimatization, workload, rest breaks, and PPE use are key factors influencing heat health risks. Tailored heat mitigation strategies are needed to safeguard workers operating in high-pressure, resource-limited environments where standard workplace safety practices may be difficult to implement.