Geohealth最新文献

Ambient air pollutants are reported to have adverse health impacts, which could be better assessed by examining human exposure to multiple criteria pollutants or through a multi-pollutant Air Quality Index (AQI). As compared to AQIs developed using in situ measurements of pollutants across sparse monitoring stations, satellite-based products such as ambient PM_2.5 or NO₂ provide better spatiotemporal patterns. Here, we examine the long-term (2005-2019) air pollution exposure and health impacts over India with the novel Satellite-Based Multi-Pollutant Index (SMPI). The high spatial resolution (1 km × 1 km) SMPI over India reveals pollution exposure hotspots in most states and districts over the Indo-Gangetic Plain (IGP) and eastern states. The spatiotemporal patterns in SMPI exposure and its urban-rural heterogeneity were different from either PM_2.5 or NO₂, highlighting the importance of multi-pollutant indices to comprehensively assess air quality. Furthermore, we found a strong association (1.05, 95% UI: 1.025-1.075) between SMPI and odds of Chronic Obstructive Pulmonary Disease (COPD), which increased with increasing co-pollutant exposures [1.03 (1.018-1.042) to 1.184 (1.156-2.212)]. The SMPI-COPD association was significantly higher among males, middle class, tribes, and older sub-populations, highlighting the need for future environmental policies prioritized for vulnerable population subgroups for larger health benefits.

Exposure to extreme temperatures during pregnancy can have adverse effects on birth weight, however, there is little evidence from Latin America. We used birth records in 2011-2020. Mean, minimum, and maximum daily temperatures were obtained from meteorological stations in 26 municipalities representing different climatic zones of Chile. We explored different windows of exposure (entire pregnancy, trimester, and gestational week) and calculated temperature percentiles based on climatic zone for each window. The 50th percentile served as the reference for comparison. General additive models and distributed lag nonlinear models (DLNM) adjusted for month and year of last menstrual cycle, maternal and paternal: age, education, and employment. Exposure to cold mean temperatures (≤10th percentile) in the total pregnancy period and each trimester was associated with a lower mean birth weight (-28.7 g for the total period, -45.9, -36.1, and -83.4 g for trimester 1, 2, and 3, respectively), whereas exposure to warm mean temperatures was associated with higher birth weight (21.3 g for >90th percentile). For extreme temperatures, exposure to both cold (≤10th percentile for minimum) and hot (>90th percentile for maximum) in the total pregnancy period related to lower birth weight: -48.7 g (95% CI -49.7; -47.6) and -17.48 g (95% CI -18.5; -16.4), respectively, with similar effects by trimester. In DLNM, consistent effects were observed later in pregnancy. Lower birthweight was observed with exposure to extreme cold and heat, while warmer mean temperatures were associated with higher birthweight. Findings from Chile underscore regional impacts of climate change on child health.

As the United States (US) has increased its domestic production of natural gas, transmission pipeline infrastructure continues to expand. Previous research highlights the environmental justice implications of this situation in the US, including fugitive methane emissions and the disproportionate concentration of pipelines in counties with high social vulnerability. However, gaps in publicly-available data make it difficult to understand the intersection of social factors, pipeline prevalence, and race, particularly in rural areas and at community-level spatial scales. This study begins to address this gap by examining the relationship between natural gas pipeline prevalence and demographics at the census block group level. This study uses North Carolina as a case study due to the state's dramatic increase in natural gas consumption driving an increase in pipeline infrastructure in recent years. This work highlights two critical findings: First, African American and American Indian people make up a disproportionately large share of the population living in block groups characterized by high social vulnerability and high densities of natural gas pipelines. Second, our main finding is insensitive to the threshold used to determine disproportionality, suggesting these results are robust. These data demonstrate a need for more equitable methods for energy infrastructure planning and maintenance. These results underscore the need for geospatial analysts to critically evaluate their methods for identifying disparities.

Soils are reservoirs of pathogenic bacteria that cause human illness, particularly after mobilizing events such as extreme rain. Land-use patterns (e.g., proximity to agriculture) and soil properties (e.g., moisture) are associated with abundance of individual pathogenic bacteria. However, there are major uncertainties in (a) the importance of local/regional land-use decisions relative to overall natural variability of pathogenicity and (b) the correlations among pathogen abundance, climate-linked physical processes increasing pathogen mobility, and the vulnerability of human receptors. This impairs identification of priority areas for outbreak surveillance, which has traditionally focused on food and water distribution networks, and the development of process-based risk screening models. Here, we analyze a novel data set of 622 soil samples covering 42 of the 48 contiguous United States. We describe (a) the relationship between putative pathogenicity and natural and land-use drivers and (b) how hotspots of putative pathogen abundance intersect with climate-linked hazard of mobilization via fire, floods, wind, and fluvial transport, and the social vulnerability of local human populations. Variability in putative pathogenicity can be partially explained by known drivers, with natural variables having greater explanatory power than land-use variables. Relative abundance of putative pathogens is generally higher in forested ecoregions, notably in the eastern and southeastern United States and in proximity to surface waters. Higher relative abundance of putative pathogens, climate risks promoting pathogen mobility, and a relatively vulnerable rural population intersect in the southeastern United States. Integrated sampling and modeling are needed to monitor and forecast health risks from soilborne pathogens.

This study reviews the use of the distributed lag non-linear model (DLNM) in public health research, focusing on environmental-exposure, health–outcome relationships, and providing recommendations for future studies. Embase, PubMed, Web of Science, and Scopus databases were searched for literature published from January 2020 to November 2024 using the DLNM to analyze the environmental exposures and health outcomes. After screening, removing duplicates, and reviewing full-text articles, eligible studies were assessed using the DLNM to examine the health effects related to environmental exposure, particularly temperature and other environmental factors. From 2,847 studies, 274 studies from 36 countries were selected for analysis, primarily from China (164), Europe (28), and North America (23). There were 174 exclusive climate data sources, no standardized heat thresholds, and 131 unique sources of air pollutant data. Among the 53 adverse health outcomes identified using the DLNM, morbidity was the most prevalent ($��n�=�102�$), followed by hospitalization ($��n�=�39�$), hospital admission ($��n�=�40�$), and emergency room visits ($��n�=�22�$). This review highlights the utility of the DLNM in capturing complex temporal relationships between environmental exposure and health, clarifying lagged effects. Despite the challenges of standardization and computational efficiency, ongoing developments are enhancing the utility of the DLNM. Future research should focus on advanced statistical techniques, such as machine learning and neural networks, and extend applications to other environmental health scenarios.

Untreated wastewater enters the ocean at an outfall in Mexico and spreads to the San Diego-Tijuana (USA-Mexico) border region, posing significant risks to human health. Here, we developed a risk assessment tool for coastal communities, leveraging hindcast oceanographic simulations (2017–2019), to link changes in temperature and salinity at the coastline to high wastewater concentrations. We first calculated the modeled timescales (i.e., duration and return time) of wastewater exposure for popular beaches in the region. Most high wastewater exposure events occurred about once a month and lasted less than a week at the southern locations (e.g., Imperial Beach), and occurred less frequently and for shorter periods of time further north (e.g., Coronado). Using the same hindcast simulations, we then identified relationships between anomalous environmental conditions and wastewater concentration along the coastline. High wastewater concentrations were typically associated with lower salinity and temperature, reflecting the low salinity of wastewater and the colder temperatures of water originating south of the USA-Mexico border. Statistical models with only parameters of salinity and temperature anomalies captured a large proportion of the variation in wastewater-associated risk of illness (R² = 0.63–0.78). We tested the risk assessment approach with several months of recent observations (January–December 2024) to show how this tool may be practically applied. This study provides an efficient method for developing risk models that utilize commonly measured environmental data, with applications to other pollution-impacted coastal locations.

Approximately 39% of U.S. homes are now located in the Wildland-Urban Interface (WUI) and are at elevated risk of burning during wildfires. WUI fires emit a cocktail of chemicals from the combustion of anthropogenic materials, including compounds that may differ from the burning of biogenic-only materials. There is currently limited knowledge on the mixture composition of combustible materials in WUI homes, representing a data gap and need to further characterize exposure chemistries and toxicological impacts of WUI-relevant smoke exposures. To address this issue, this study integrated combustible materials in an average American WUI home to derive what we are referring to as the “Burning ExposHome.” Items such as structural materials, plumbing, furnishings, and appliances were included in the Burning ExposHome. Calculations were based on an average American household, a 2,016 sq. ft. single family home of four bedrooms, using materials typical to California due to the high incidence of WUI fires in that geographic region. All materials were sorted and summed by type of base material such as wood materials, plastics, textiles, and metals. This list is notably modular and detailed per item, allowing for the addition/subtraction of components to address future study designs. In summary, the total combustible mass of an average American home was around 46,500 kg, including 81% wood materials, 6% plastics, and 2% metals. This list of materials serves as a foundational mixture of home materials to integrate into exposure characterization, mechanistic toxicology, and ecological/human health research addressing wildfires occurring at the growing WUI.

Coccidioidomycosis (Valley fever, VF) is a climate-sensitive infectious disease caused by inhaling soil-dwelling fungus Coccidioides, mostly reported in southwestern USA. Although soil moisture (SM) and soil temperature (ST) are known to shape the fungal lifecycle, their effects on coccidioidomycosis remain understudied. Most prior studies have relied on their proxies—precipitation and air temperature—that might not accurately capture soil hydrothermal dynamics. We conducted multivariable negative binomial regressions to estimate seasonal associations between incidence and climate drivers—including SM, ST, and wind speed from the North American Land Data Assimilation Phase 2 (NLDAS-2), and PM₁₀-based dusty-day counts—in Arizona's hyperendemic counties (Maricopa, Pima, and Pinal) from 2000 to 2022. We found higher incidence in areas with hotter, drier soils and more seasonal dusty days. Multi-year soil hydrothermal cycles—alternating wet–dry and cool–hot periods along with concurrent dry, dusty conditions—significantly influenced incidence. Notably, no antecedent dry–cool seasons were linked to increased incidence, indicating moisture and/or heat are prerequisites for fungal growth and dispersal. SM showed more consistent and widespread effects than ST across seasons and lags, with winter and spring soils most influential. Higher incidence followed wetter winters and monsoons, and dry, hot springs and falls. Our models using NLDAS-2 SM and ST data showed robust performance and generalizability across exposure seasons. Our results support adding multi-year soil indicators—with up to 3-year lead times—into early-warning systems to enhance VF forecasting and better prepare endemic regions for the challenges of a warming, drying, and increasingly variable climate.

With the many health implications of droughts and floods known, and the many adverse secondary and tertiary effects of the Covid-19 pandemic still lingering, it is important to study the complex interactions of epidemics, droughts, and floods. To gain insights into this issue, we have constructed epidemic, drought, and flood indices for the Ming and Qing dynasties of China in a period of more than 400 years. Using adaptive fractal analysis, we find that the time series of epidemic, drought, and flood indices possess long-range correlations of different degrees in different regions of China. More importantly, the scaling behavior for the cross correlations between the epidemic and the drought indices in Northern China is characterized by a non-stationary emergent behavior rather than by a long-range correlation, while this scaling behavior is close to the boundary of stationarity and non-stationarity in the Central China. This scaling is up to about 16 years, highlighting that on average, outbreak of large-scale epidemics may occur not shorter than once every 32 years. Interestingly, the emergent behavior can be characterized as a Zipf's law for the ranked size of the epidemics, mostly in the Northern China, and sometimes also involving some regions in the Central China.

We investigate the effect of extreme heat on home births in India, proposing that such extreme weather events may impede access to health facilities for childbirth. Utilizing geocoded data from the 2019–2021 Demographic and Health Survey for India, we identified the place of delivery of 208,368 births as home versus health facility. We incorporated maximum values for gridded wet-bulb globe temperatures (WBGT_max) and dry-bulb temperatures (DBT_max) corresponding to delivery dates and maternal residences. We defined context-specific extreme heat events using several percentile-based thresholds (between 80th and 95th) over varying durations (1–5 days). We used Generalized Estimating Equations (GEE) with inverse probability of treatment weighting, incorporating socioeconomic factors and state-level fixed effects, and adjusted for seasonality. We tested for effect-measure-modification by socio-economic factors (e.g., caste, wealth), healthcare access factors (e.g., rural/urban place of residence, difficultly in accessing healthcare), and contextual factors (e.g., long-term mean temperature, prevalence of institutional delivery). Both WBGT_max and DBT_max-based heatwave exposures were associated with increased likelihood of home births, with WBGT exposures demonstrating an earlier onset of significant associations at lower percentile thresholds while DBT showed stronger associations at higher thresholds and longer durations. Effect modification analyses revealed heightened impacts in warmer regions, states not designated as high-focus under the Janani Suraksha Yojana program, and non-Hindu populations. We find that extreme heat may compromise delivery at health facilities in India. Findings call for improved health system preparedness via early warning systems and advanced resource allocation to mitigate some of these effects.