Environmental research, health : ERH最新文献

Heatwave exposures have been linked to a variety of mental and neurological disorders. Little is known, however, about the potentially differential associations of daytime versus nighttime heatwave intensity with subtypes of mental and neurological disorders. In this time-stratified case-crossover study, we estimated and compared the associations of typically dry daytime and typically humid nighttime heatwave intensities, characterized by heatwave indices (HWIs), with acute care utilizations for various subtypes of mental and neurological disorders in 1412 ZIP Code Tabulation Areas in California from 2006 to 2019. A total of 4309 294 acute care utilizations for mental disorders and 2097 563 for neurological disorders were included in this study. Higher associations with nighttime HWI were found for most disease subtypes, including anxiety disorder, depressive disorder, schizophrenia, bipolar disorder, post-traumatic stress disorder, Alzheimer's disease and related dementias, and Parkinson's disease; while daytime HWI showed a higher impact on conduct disorders (P < .001). On average, during the warm season in California, nighttime heatwaves accounted for about 70.6% and 34.0% of acute care utilizations for mental and neurological disorders that were attributable to heatwaves, respectively. Our findings highlight the detrimental impacts of humid nighttime heatwaves on mental and neurological health and call for innovative heat preparedness actions and increased awareness among public health practitioners as more nighttime heatwaves are anticipated under climate change.

In Sub-Saharan African (SSA) cities, elementary school environments may significantly contribute to children's exposure to environmental pollution, potentially affecting their health, development, and learning. Despite children spending much of their day at school, limited data exists regarding levels, inequalities, and determinants of air and noise pollution in school settings, particularly in rapidly urbanizing regions. As part of the Accra School Health and Environment Study (ASHES), we assessed air and noise pollution in primary schools across the Greater Accra Metropolitan Area, one of SSA's fastest-growing metropolises, and explored determinants of pollution levels around these schools. We conducted weeklong measurements of fine particulate matter (PM_2.5), black carbon (BC), and sound pressure levels in 90 schoolyards (74% public, 26% private). We assessed schoolyard characteristics (surface type, greenness, road proximity) and examined their associations with pollutants using generalized additive models. Additionally, we evaluated 1037 child responses to noise annoyance surveys. Annual equivalent PM_2.5 concentrations exceeded WHO guidelines by 2-13 times (11-65 µg m^-3). Median noise levels (57 dBA) surpassed Ghana EPA standards at >60% of schools, coinciding with 60% of students reporting high noise annoyance. BC and noise were higher in public and more urban schools. In the most urbanized district, all pollutants were inversely associated with neighborhood socioeconomic status. Lower greenness correlated with higher BC levels; associations with other spatial factors were weak or not statistically significant. These findings underscore the need to reduce air and noise pollution at urban SSA schools and promote healthier, quieter environments that support learning and development.

We examine the cost-benefit of selected physical and behavioural interventions to reduce indoor temperatures and associated health risks in UK care homes during periods of hot weather typical of the projected future climate. Cases of heat-related mortality under selected temperature scenarios were modelled for three care home settings. Published temperature-mortality functions for care home residents of England and Wales were applied to life tables for care home populations. Building physics modelling was used to assess the effect of interventions on summer indoor temperatures and associated mortality risks. The monetised value of quality-adjusted life years gained was assessed in relation to the capital, energy and maintenance costs of each intervention over its lifespan under a range of assumptions. Sensitivity analyses were used to characterise model uncertainty. Under the assumption that those who die of heat have the life expectancy of an average care home resident, we found evidence for cost-effective interventions. The cost-effectiveness of interventions varied across care homes, depending on each building's specific characteristics. In a large, highly insulated care home with low thermal mass, increased thermal mass combined with window and door opening rules was the most cost-effective intervention; in a small care home with limited insulation and high thermal mass, active cooling in lounges using portable air-conditioners was most cost-effective; and in a medium sized care home with moderate insulation and thermal mass, shading combined with window and door opening rules was most cost-effective. Our results show that several interventions have the potential to be cost-effective in reducing heat-related risks to residents across a range of care home types. They also demonstrate that behavioural interventions, such as window and door opening, may be equally effective as physical interventions at reducing exposure to heat-related risks, provided they are fully adhered to.

Visceral leishmaniasis (VL), a deadly neglected tropical disease, remains a persistent public health challenge in Brazil, where transmission is shaped by interacting climatic, environmental, and sociodemographic factors. Despite evidence that weather conditions influence VL dynamics, they remain underutilized for outbreak prediction. This study evaluates whether climate-informed machine learning can support early warnings for VL in Brazil. We developed machine learning models to forecast monthly VL case counts and classify outbreak risk using data from 2007 to 2024 across 113 Brazilian municipalities. A cutting-edge sliding window approach enabled models to capture both short- and long-term trends using lagged meteorological data combined with land-use and sociodemographic variables. Risk classification models were developed for a subset of 22 municipalities following the Brazilian Ministry of Health's prioritization framework to enable direct policy alignment. Predictive performance and variable importance were evaluated across locations. Weather patterns and indicators of human land-use pressure consistently ranked among the strongest predictors of VL risk. However, the relative importance of predictors varied across municipalities, reflecting local differences in transmission dynamics. Overall, forecasting models successfully captured long-term trends in observed case counts, and risk classification models, offering particularly timely and actionable signals for targeted intervention, achieved area under the curve scores above 0.80 in 86% of municipalities. Weather-informed machine learning models can provide timely, locally tailored predictions of VL risk in Brazil. As weather variability intensifies, integrating environmental data into existing surveillance systems may improve preparedness and reduce disease burden in vulnerable communities.

Wildfires continue to increase in size, intensity, and duration. There is growing evidence that wildfire smoke adversely impacts clinical outcomes; however, few studies have assessed the impact of wildfires on household air quality and subclinical cardiovascular health indicators. We measured continuous indoor and outdoor fine particulate matter (PM_2.5) concentrations from July-October 2022 at 20 residences in the rural, mountainous state of Montana in the United States. We used a combination of satellite-derived smoke plume data from the National Oceanic and Atmospheric Administration's Hazard Mapping System and household-level daily mean PM_2.5 concentrations to classify wildfire-impacted days. One participant from each household self-reported in-home blood pressure (BP) on weekly electronic surveys. We used linear mixed-effects regression models to assess associations between air pollution exposures (PM_2.5 concentrations; number of wildfire-impacted days) and systolic BP (SBP) and diastolic BP (DBP). Models were adjusted for potential time-variant confounders including temperature, humidity, and self-reported exercise. Compared to survey periods with 0 wildfire days, SBP was 3.83 mmHg higher (95% Confidence Interval [95% CI]: 0.22, 7.44) and DBP was 2.36 mmHg higher (95% CI: -0.06, 4.78) during periods with 4+ wildfire days. Across the entire study period, a 10 µg m^-3 increase in indoor PM_2.5 was associated with 1.34 mmHg higher SBP (95%CI: 0.39, 2.29) and 0.71 mmHg higher DBP (95% CI: 0.07, 1.35). We observed that wildfire-impacted days and increasing household-level PM_2.5 concentrations are associated with higher in-home BP. Our results support growing literature which indicates that wildfires adversely impact subclinical cardiovascular health. Clinical and public health messaging should emphasize the cardiovascular health impacts of wildfire smoke and educate on exposure-reduction strategies such as indoor air filtration.

An estimated 18 million Americans reside within 1.6 km (1 mile) of an oil and gas development (OGD) facility. OGD often creates cycles of economic boom-and-busts, resulting in precarious employment, social disruptions, and environmental stressors, which may have mental health consequences. Among counties with OGD, we used Medicaid claims data to calculate annual county-level counts of inpatient hospitalizations with psychiatric diagnoses (n = 3.6 million hospitalizations, 2001-2011). Each county-year combination was classified by the trajectory of OGD resource production: boom (economic growth), bust (economic decline), and status quo (comparison group). Using a quasi-experimental panel data study design, we observed a small increase in annual county-level inpatient psychiatric hospitalization rates for the bust period (incidence rate ratio [IRR]: 1.05, 95% CI: 1.00, 1.11) but not the boom period (IRR: 1.02, 95% CI: 0.96, 1.07). Associations in the bust period were stronger among beneficiaries who identified as White race, resided in rural areas, and lived in a county with the lowest tertile of median household income. In cause-specific models, the size of the effect estimate was larger among the categories for attention disorders, anxiety disorders, and mood disorders. Stratified models by sociodemographic characteristics and cause-specific hospitalizations were broadly null in the boom period. Our results suggest that cycles of economic boom-and-busts, as measured by oil and gas production, may have deleterious impacts in the bust period on the mental health of the Medicaid population. However, future research is needed to elucidate the complex impacts of boom-and-bust cycles for resource-dependent communities.

Extreme heat events (EHEs) are the deadliest weather hazards in the United States (U.S.). Local health jurisdictions (LHJs) in the U.S. are frontline responders during EHEs and other public health emergencies. This study aims to clarify the factors influencing EHE preparedness and response implementation. From January to March 2023, we conducted and thematically analyzed four focus group discussions with 17 representatives from U.S. LHJs. The Consolidated Framework for Implementation Research was used to guide the discussion. Participants described barriers, facilitators, and needs surrounding extreme heat preparedness and response implementation. The focus group discussions identified four factors that influence EHE preparedness and response implementation: local conditions (environmental, political, planning); engaging communities and tailoring strategies; partnerships and relational connections; and available resources. Focus group discussions emphasized the need for EHE preparedness and response activities to be targeted and scaled to the unique climate, population, and needs of the implementing jurisdiction. Local conditions, community engagement, partnerships, and available resources shape LHJ priorities. The study emphasizes the need for scalable resources and comprehensive plans, and identifies research gaps to be addressed in the future.

As climate change intensifies, hurricanes and weather-related disasters have been increasingly frequent and severe, impacting regions like the U.S. Gulf Coast with repeated hurricanes. While acute and short-term health impacts are well-described, impacts on longer-term and chronic conditions such as hypertension remain underexplored. This study examines the association between repeated hurricane exposure and hypertension risk in survivors. We used data from the Resilience in Survivors of Katrina project, a longitudinal (2003-2018) cohort of predominantly Black, low-income mothers affected by Hurricane Katrina. A sample of 505 women who were not hypertensive pre-Katrina was analyzed. Cumulative exposure was defined as the number of hurricanes experienced post-Katrina, assessed at several survey waves over 12 years. Logistic regression estimated associations between hurricane exposure and hypertension in 2016-18, with mediation analyses exploring the indirect effect via psychological distress (PD). In adjusted models, exposure to two hurricanes was associated with a 61% increase in hypertension odds (OR = 1.61, 95% CI: 1.00, 2.63) and exposure to three or more with 87% increased odds (OR = 1.87, 95% CI: 1.01, 3.47), relative to exposure to only one hurricane. The indirect effect from hurricane exposure to hypertension via PD was statically significant (95% CI: 1.01, 1.09). Findings highlight a novel link between cumulative disaster exposure and hypertension, with PD as a potential mediator. This suggests that repeated exposure to hurricanes not only impacts mental health but may also contribute to adverse physical health outcomes. Addressing both mental and physical health in disaster response, especially for vulnerable populations, is crucial.

Climate change will increase the frequency of extreme weather events. This means climate-driven events like wildfires and power outages will likely co-occur more often, potentially magnifying their health risks. We characterized three types of climate-driven events-anomalously warm temperatures, wildfire burn zone disasters, and long power outages-in 58 California counties during 2018-2019. We defined county-day anomalously warm temperatures when daily average temperatures exceeded 24 °C and the 85th percentile of the long-term county average. We defined county-day wildfire burn zone disasters when an active wildfire burn zone intersected a county, burned 1+ structures, killed a civilian, or received a Federal Emergency Management Agency Fire Management Declaration, and overlapped with a community. For a subset of the 38 counties (66%), long power outage county days were identified using PowerOutage.us data when an outage affected >0.5% of county customers for 8+ h. Co-occurring events were when 2+ of these events occurred on the same county day. Using the CDC/ATSDR Social Vulnerability Index (SVI), we determined whether co-occurring events disproportionately affected vulnerable populations. Nearly every county (97%) experienced at least one day of anomalously warm temperatures, 57% had at least one wildfire burn zone disaster day, and 63% (24/38 counties with available data) had at least one long power outage day. The most common co-occurring events (anomalously warm temperatures and wildfire burn zone disasters) impacted 24 (41%) counties for 144 total county-days. We did not find a clear connection between co-occurring events and social vulnerability. We observed an inverse correlation between co-occurring wildfire burn zone disasters and long power outage days with SVI, and a positive correlation between co-occurring anomalously warm and long power outage days with SVI. This analysis can inform regional resource allocation and other state-wide planning and policy objectives to reduce the adverse effects of climate-driven events.

Quantifying how hurricanes disrupt educational attainment is essential to evaluating the burden of climate-related disasters. Here, we examine the association between hurricane-force tropical cyclones and educational attainment among elementary and middle school students in all affected areas in the United States during the 2008/2009-2017/2018 school years. Educational performance was based on county-level average standardized test scores in math and reading/language arts (RLAs). Hurricane-force tropical cyclone-exposed counties were those that experienced a sustained maximal wind speed ⩾64 knots. We estimated the association between hurricane-force tropical cyclone exposure and long-term test scores using a Bayesian hierarchical linear model, accounting for time-varying covariates at the county and grade cohort level. For hurricane-exposed counties, compared with the rest of the state, there were better test scores in Florida in math (β = 0.14; 95% CrI: 0.02, 0.26; PP[β > 0] = 99.0%) and RLA (β = 0.11; 95% CrI: 0.02, 0.22; PP[β > 0] = 99.2%), and worse math scores in North Carolina (β = -0.16; 95% CrI: -0.29, -0.03; PP[β < 0] = 99.4%). Grade cohorts with more racialized and minoritized (e.g. Black, Hispanic, Indigenous) and socioeconomically disadvantaged students tended to have lower test scores, while grade cohorts with greater shares of students racialized as Asian and counties with more college-educated adults tended to have higher scores regardless of hurricane exposure. Disaster preparedness must maximize resilience to climate-related stressors' impacts on academic achievement, especially for vulnerable populations.