Geohealth最新文献

Previous evidence on heatwaves’ impact on mental health outpatient visits is limited, especially uncertainty on how different heatwave definitions affect this relationship. In this time-series study, we assessed the association between heatwaves and outpatient visits for mental disorders in Guangzhou, China. Daily outpatient visits for mental disorders and its specific categories (schizophrenia, mood, and neurotic disorders) were sourced from the Urban Resident-based Basic Medical Insurance (URBMI) and the Urban Employee-based Basic Medical Insurance (UEBMI) claims databases in Guangzhou from 2010 to 2014. The study employed nine heatwave definitions, based on combinations of three daily mean temperature thresholds (90th, 92.5th, and 95th percentiles) and durations (2, 3, and 4 days). Using quasi-Poisson generalized linear models (GLMs), we estimated the risks (at lag 0 day) and cumulative effects (lag 0–10 days) of heatwaves on mental disorder outpatient visits. Age, gender, types of medical insurance were considered as potential effect modifiers. We observed a positive association between heatwaves and increased total outpatient visits for mental disorders, both at lag 0 day and during lag 0–10 days. The impact of heatwave was significant at lag 0 day for schizophrenia, mood and neurotic disorders visits, it remained significant for neurotic and mood disorders visits during lag 0–10 days. Heatwave durations lasting more than 4 days were associated with higher relative risks of mental disorders at lag 0 day. Older adults had relatively higher effect estimations than younger individuals. This research highlights the effects of extreme heat on mental health.

Wet-bulb globe temperature (WBGT)—a standard measure for workplace heat stress regulation—incorporates the complex, nonlinear interaction among temperature, humidity, wind and radiation. This complexity requires WBGT to be calculated iteratively following the recommended approach developed by Liljegren and colleagues. The need for iteration has limited the wide application of Liljegren's approach, and stimulated various simplified WBGT approximations that do not require iteration but are potentially seriously biased. By carefully examining the self-nonlinearities in Liljegren's model, we develop a zero-iteration analytic approximation of WBGT while maintaining sufficient accuracy and the physical basis of the original model. The new approximation slightly deviates from Liljegren's full model—by less than 1°C in 99% cases over 93% of global land area. The annual mean and 75%–99% percentiles of WBGT are also well represented with biases within $���\pm �0.5��$°C globally. This approximation is clearly more accurate than other commonly used WBGT approximations. Physical intuition can be developed on the processes controlling WBGT variations from an energy balance perspective. This may provide a basis for applying WBGT to understanding the physical control of heat stress.

Floods can have adverse health effects and impose a burden on healthcare systems. However, the potential consequences of floods on specific medical causes in densely populated metropolitan cities has not been characterized yet. Therefore, we evaluate the changes in healthcare utilization patterns after the 2022 Seoul flood using nationwide health insurance data. Based on the flood inundation map, districts within the flooded municipalities of Seoul were classified as severe-(n = 12), mild-(n = 22), or non-(n = 38) flood-affected districts. Capitalizing on the timing of the flood as a natural experiment, a generalized synthetic control method was applied to estimate changes in the number of disease-specific hospital visits in flood-affected districts during 2 weeks after the flood. We found excess hospital visits for external injuries (20.2 visits, 95% CI: −6.0, 45.2) and fewer visits for pregnancy and puerperium (−3.0 visits, 95% CI: −5.1, −0.5) in residents of flooded districts. When comparing severe- and non-flood districts, the increase in hospital visits for external injuries (56.2 visits, 95% CI: 17.2, 93.2) and a decrease in hospital visits related to pregnancy and puerperium (−5.3 visits, 95% CI: −8.4, −1.6) were prominent in residents living in severe-flood affected districts. Disease specific analysis showed an increase in hospital visits for injuries to the elbow and forearm, ankle and foot injuries, and chronic lower respiratory diseases in severe-flood-affected districts. However, these impacts were not observed when comparing the mild- and non-flood-affected districts. Our study suggests an immediate and substantial change in medical demand following flood exposure, highlighting the importance of public health responses after flood events.

Dengue is a rapidly spreading mosquito-borne infectious disease that is sensitive to climate factors and poses a major public health concern worldwide. We analyzed dengue incidence trends and the relationship between annual mean minimum temperatures (AMMTs) and dengue incidence rates from 1990 to 2019 in 122 countries using the Global Burden of Disease and TerraClimate data sets. We also projected global dengue incidence rates under different carbon emission scenarios using temperature data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) data set. Our results reveal a significant increase in global dengue cases from 1990 to 2019 and a positive correlation between temperature and dengue incidence. The association between AMMT and dengue incidence strengthened at temperatures exceeding 21°C. Central and eastern sub-Saharan Africa, as well as Oceania, were identified as the regions most sensitive to dengue; males and individuals aged 15–19 or 70–84 years were the most susceptible to dengue under rising temperatures. Our projections suggest that global dengue incidence will substantially increase by 2050 and 2100. By 2100, regions including Africa, the Arabian Peninsula, the southern United States, southern China, and island countries in the Pacific and Indian Oceans are projected to become year-round dengue-endemic under a high-emission climate scenario. Our findings underscore the importance of implementing effective measures to mitigate the impact of climate change on dengue transmission. Identifying high-risk areas and susceptible populations, along with understanding the projected expansion of dengue-endemic regions, will provide valuable guidance for targeted interventions to address this growing global health challenge in the face of changing climatic conditions.

Objective: To synthesize the methodologies of studies that evaluate the impacts of heat exposure on morbidity and mortality. Methods: Embase, MEDLINE, Web of Science, and Scopus were searched from date of inception until 1 March 2023 for English language literature on heat exposure and health outcomes. Records were collated, deduplicated and screened, and full texts were reviewed for inclusion and data abstraction. Eligibility for inclusion was determined as any article with climate-related heat exposure and an associated morbidity/mortality outcome. Results: Of 13,136 records initially identified, 237 articles were selected for analysis. The scope of research represented 43 countries, with most studies conducted in China (62), the USA (44), and Australia (16). Across all studies, there were 141 unique climate data sources, no standard threshold for extreme heat, and 200 unique health outcome data sources. The distributed lag non-linear model (DLNM) was the most common analytic method (48.1% of studies) and had high usage rates in China (68.9%) and the USA (31.8%); Australia frequently used conditional logistic regression (50%). Conditional logistic regression was most prevalent in case-control studies (5 of 8 studies, 62.5%) and in case-crossover studies (29 of 70, 41.4%). DLNMs were most common in time series studies (64 of 111, 57.7%) and ecological studies (13 of 20, 65.0%). Conclusions: This review underscores the heterogeneity of methods in heat impact studies across diverse settings and provides a resource for future researchers. Underrepresentation of certain countries, health outcomes, and limited data access were identified as potential barriers.

Heat is associated with increased risk of morbidity and mortality. People who are incarcerated are especially vulnerable to heat exposure due to demographic characteristics and their conditions of confinement. Evaluating heat exposure in prisons, and the characteristics of exposed populations and prisons, can elucidate prison-level risk to heat exposure. We leveraged a high-resolution air temperature data set to evaluate short and long-term patterns of heat metrics for 1,614 prisons in the United States from 1990 to 2023. We found that the most heat-exposed facilities and states were mostly in the Southwestern United States, while the prisons with the highest temperature anomalies from the historical record were in the Pacific Northwest, the Northeast, Texas, and parts of the Midwest. Prisons in the Pacific Northwest, the Northeast, and upper Midwest had the highest occurrences of days associated with an increased risk of heat-related mortality. We also estimated differences in heat exposure at prisons by facility and individual-level characteristics. We found higher proportions of non-white and Hispanic populations in the prisons with higher heat exposure. Lastly, we found that heat exposure was higher in prisons with any of nine facility-level characteristics that may modify risk to heat. This study brings together distinct measures of exposure, vulnerability, and risk, which would each inform unique strategies for heat-interventions. Community leaders and policymakers should carefully consider which measures they want to apply, and include the voices of directly impacted people, as the differing metrics and perspectives will have implications for who is included in fights for environmental justice.

The paucity of fine particulate matter (PM_2.5) measurements limits estimates of air pollution mortality in Sub-Saharan Africa. Well calibrated low-cost sensors can provide reliable data especially where reference monitors are unavailable. We evaluate the performance of Clarity Node-S PM monitors against a Tapered element oscillating microbalance (TEOM) 1400a and develop a calibration model in Mombasa, Kenya's second largest city. As-reported Clarity Node-S data from January 2023 through April 2023 was moderately correlated with the TEOM-1400a measurements (R² = 0.61) and exhibited a mean absolute error (MAE) of 7.03 μg m⁻³. Employing three calibration models, namely, multiple linear regression (MLR), Gaussian mixture regression and random forest (RF) decreased the MAE to 4.28, 3.93, and 4.40 μg m⁻³ respectively. The R² value improved to 0.63 for the MLR model but all other models registered a decrease (R² = 0.44 and 0.60 respectively). Applying the correction factor to a five-sensor network in Mombasa that was operated between July 2021 and July 2022 gave insights to the air quality in the city. The average daily concentrations of PM_2.5 within the city ranged from 12 to 18 μg m⁻³. The concentrations exceeded the WHO daily PM_2.5 limits more than 50% of the time, in particular at the sites nearby frequent industrial activity. Higher averages were observed during the dry and cold seasons and during early morning and evening periods of high activity. These results represent some of the first air quality monitoring measurements in Mombasa and highlight the need for more study.

Over the past two decades, the surge in warehouse construction near seaports and in economically lower-cost land areas has intensified product transportation and e-commerce activities, particularly affecting air quality and health in nearby socially disadvantaged communities. This study, spanning from 2000 to 2019 in Southern California, investigated the relationship between ambient concentrations of PM_2.5 and elemental carbon (EC) and the proliferation of warehouses. Utilizing satellite-driven estimates of annual mean ambient pollution levels at the ZIP code level and linear mixed effect models, positive associations were found between warehouse characteristics such as rentable building area (RBA), number of loading docks (LD), and parking spaces (PS), and increases in PM_2.5 and EC concentrations. After adjusting for demographic covariates, an Interquartile Range increase of the RBA, LD, and PS were associated with a 0.16 μg/m³ (95% CI = [0.13, 0.19], p < 0.001), 0.10 μg/m³ (95% CI = [0.08, 0.12], p < 0.001), and 0.21 μg/m³ (95% CI = [0.18, 0.24], p < 0.001) increase in PM_2.5, respectively. For EC concentrations, an IQR increase of RBA, LD, and PS were each associated with a 0.021 μg/m³ (95% CI = [0.019, 0.024], p < 0.001), 0.014 μg/m³ (95% CI = [0.012, 0.015], p < 0.001), and 0.021 μg/m³ (95% CI = [0.019, 0.024], p < 0.001) increase. The study also highlighted that disadvantaged populations, including racial/ethnic minorities, individuals with lower education levels, and lower-income earners, were disproportionately affected by higher pollution levels.

Childhood stunting is a serious public health concern in Rwanda. Although stunting causes have been documented, we still lack a more in-depth understanding of their local factors at a more detailed geographic level. We cross-sectionally examined 615 height-for-age prevalence observations in the Northern Province of Rwanda, linked with their related covariates, to explore the spatial heterogeneity in the low height-for-age prevalence by fitting linear and non-linear spatial regression models and explainable machine learning. Specifically, complemented with generalized additive models, we fitted the ordinary least squares (OLS), a standard geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR) models to characterize the imbalanced distribution of stunting risk factors and uncover the nonlinear effect of significant predictors, explaining the height-for-age variations. The results reveal that 27% of the children measured were stunted, and that likelihood was found to be higher in the districts of Musanze, Gakenke, and Gicumbi. The local MGWR model outperformed the ordinary GWR and OLS, with coefficients of determination of 0.89, 0.84, and 0.25, respectively. At specific ranges, the study shows that height-for-age decreases with an increase in the number of days a child was left alone, elevation, and rainfall. In contrast, land surface temperature is positively associated with height-for-age. However, variables like the normalized difference vegetation index, slope, soil fertility, and urbanicity exhibited bell-shaped and U-shaped non-linear associations with the height-for-age prevalence. Identifying areas with the highest rates of stunting will help determine the most effective measures for reducing the burden of undernutrition.

Unpredictable emergency department (ED) admissions challenge healthcare systems, causing resource allocation inefficiencies. This study analyses associations between air pollutants, meteorological factors, and 2,655,861 cause-specific ED admissions from 2014 to 2018 across 12 categories. Generalized additive models were used to assess non-linear associations for each exposure, yielding Incidence Rate Ratios (IRR), while the population attributable fraction (PAF) calculated each exposure's contribution to cause-specific ED admissions. IRRs revealed increased risks of ED admissions for respiratory infections (IRR: 1.06, 95% CI: 1.01–1.11) and infectious and parasitic diseases (IRR: 1.09, 95% CI: 1.03–1.15) during increased rainfall (13.21–16.97 mm). Wind speeds >12.73 km/hr corresponded to increased risks of ED admissions for respiratory infections (IRR: 1.12, 95% CI: 1.03–1.21) and oral diseases (IRR: 1.58, 95% CI: 1.31–1.91). Higher concentrations of air pollutants were associated with elevated risks of cardiovascular disease (IRR: 1.16, 95% CI: 1.05–1.27 for PM₁₀) and respiratory infection-related ED admissions (IRR: 2.78, 95% CI: 1.69–4.56 for CO). Wind speeds >12.5 km/hr were predicted to contribute toward 10% of respiratory infection ED admissions, while mean temperatures >28°C corresponded to increases in the PAF up to 5% for genitourinary disorders and digestive diseases. PM₁₀ concentrations >60 μg/m³ were highly attributable toward cardiovascular disease (PAF: 10%), digestive disease (PAF: 15%) and musculoskeletal disease (PAF: 10%) ED admissions. CO concentrations >0.6 ppm were highly attributable to respiratory infections (PAF: 20%) and diabetes mellitus (PAF: 20%) ED admissions. This study underscores protective effects of meteorological variables and deleterious impacts of air pollutant exposures across the ED admission categories considered.