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.
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.
Since 2000, the American Lung Association (ALA) has published an annual "State of the Air" report, which assigns grades and ranks United States (U.S.) cities and counties based on data from regulatory monitors reported to the Environmental Protection Agency (EPA). We evaluate satellite-derived gridded data sets for fine particulate matter (PM2.5), as a possible method to support the ALA assessment in counties without regulatory monitors. Our analysis compares two publicly available, annual average, satellite-derived gridded PM2.5 data sets, allocated to U.S. counties using three different methods. Assigning the 90th percentile gridded value of PM2.5 within a county as the county-level indicator yields moderately strong agreement between ground-based monitor PM2.5 results and satellite-derived PM2.5 results, with a spatial correlation coefficient of 0.76 for 8 years of Washington University global (WashU GL) data. This agreement is evaluated with respect to concentration values, rankings, and the definition of counties as "passing" or "failing" the ALA annual PM2.5 benchmark of 9.0 μg/m3 (the 2024 EPA National Ambient Air Quality Standard for annual PM2.5). While most unmonitored counties are "passing" the ALA benchmark, we identify 63 counties that would be classified as "failing" if satellite-based data were used to assign ALA grades. Improving methods to analyze satellite-derived data for comparison with monitor-based metrics can support broader utilization of space-based data in the U.S. and globally. Air pollution is a major public health concern, with fine particulate matter (PM2.5) linked to heart disease, lung disease, and premature death. The American Lung Association provides public‐facing data on air quality; however, data are currently limited to the ∼20% of counties with regulatory monitors for PM2.5. We compared methods of calculating county‐level PM2.5 from satellite‐derived data to best match county‐scale metrics from ground monitors. We found the best agreement by applying the 90th percentile to satellite values within each county. Applying this approach, we identified 63 unmonitored counties could be classified as having unhealthy and “failing” air if satellite data were used in grading. Our findings suggest that new methods can better align satellite data sets with decision‐relevant air quality metrics.
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.
Population-based incidence data for dementia with Lewy bodies (DLB) remain limited, particularly using contemporary diagnostic criteria and across the full adult age range. We aimed to estimate age-specific and sex-specific incidence of DLB in a defined population and to characterize clinical features, sex-related phenotypic differences, and Alzheimer disease (AD) copathology. We conducted a prospective, population-based incidence study in the Salento region of Southern Italy (767,356 residents) from March 1, 2023, to February 28, 2025. Incident DLB cases were identified through a multisource surveillance network and centrally adjudicated. DLB was diagnosed using the 2017 DLB Consortium criteria, with standardized assessment of all core features; AD biomarkers (CSF or amyloid PET) were incorporated when available. Incidence rates per 100,000 person-years were calculated using population denominators with Poisson 95% CIs and were directly standardized to the 2013 European Standard Population. Sixty-two incident DLB cases were identified over 1,534,712 person-years (mean age at diagnosis 77.4 years; 42% female). The crude incidence was 4.04 per 100,000 person-years (95% CI 3.10-5.18), and the age-standardized incidence was 3.77 (95% CI 2.89-4.83). Incidence was higher in men than women (crude 4.87 vs 3.27; standardized 4.55 vs 3.04), increased steeply with age, and peaked at 80-84 years (30.83; 95% CI 18.56-48.14). Men had greater motor severity (Movement Disorder Society-Unified Parkinson's Disease Rating Scale, part III 23.7 vs 14.4; p = 0.002), whereas women had higher neuropsychiatric burden (Neuropsychiatric Inventory total 24.0 vs 13.0; p = 0.041). Young-onset DLB (<65 years) accounted for 6.5% of cases. AD biomarkers were available in 45.2% of patients, of whom 75% were positive. In this prospective population-based study, DLB incidence increased sharply with age, peaked in the early 80s and was higher in men, with a substantial proportion of cases showing AD copathology. We also observed sex-related differences in motor and neuropsychiatric profiles. Limitations include the single-region design, incomplete biomarker testing, and lack of neuropathologic confirmation. These contemporary incidence data help quantify the burden of DLB and can inform service provision, resource allocation, and care planning in aging populations.
Compliance with the U.S. National Ambient Air Quality Standards (NAAQS) traditionally relies on a sparse network of ground-based monitors. We compare two methods for assessing whether U.S. counties are above or below the annual fine particulate matter (PM2.5) NAAQS of 9.0 μg/m3: the monitor-based methodology used to calculate county design values (CDVs; an EPA metric representing the maximum, in-county PM2.5 monitor value over a 3-year annual average), and a satellite-based methodology that computes county design value equivalents (CDVEs) from Washington University's global satellite-derived PM2.5 data product. The satellite-based approach uses the 90th percentile PM2.5 grid value in a county and shows strong overall agreement with monitor-based CDVs across 536 monitored U.S. counties (r = 0.76; r s = 0.74). Counties were classified as aligned positives (APs), aligned negatives (ANs), non-aligned positives (NPs), or non-aligned negatives (NNs) based on whether CDVEs align or don't align with CDVs on NAAQS status. Risk factors likely contributing to larger differences between CDVEs and CDVs within non-aligned counties include ≤2 monitors, low (<7 μg/m3) or high (>10 μg/m3) CDVs, low (<0.05%) or high (>0.1%) monitor coverage, large county size, wildfires, mountains, deserts, and low urbanization. In non-aligned counties, differences grow with the number of risk factors, with seven-risk-factor counties showing 5x higher median differences than those with one. High error-risk counties cluster in the Western U.S.; low error-risk counties are in the Midwest and East. These findings highlight trade-offs between sparse and contiguous data for regulatory assessments and support integration of satellite-derived data into policy frameworks. The majority of U.S. counties lack ground‐based monitors to track health‐damaging fine particulate matter (PM2.5). Satellite data may be used to calculate PM2.5, but satellite‐derived values can differ from ground monitors. We compared county‐level satellite‐derived PM2.5 with county‐level EPA monitor data across the U.S. to understand where and why these two data sets agree or disagree. We found that large counties with wildfires, mountains, deserts, few monitors, and either low or high pollution levels were more likely to show mismatches. These differences were most common in the western U.S. and grew larger as more risk factors were present. These findings highlight where caution is needed when using both satellite and monitor data for air quality management and policy.
Schizophrenia (SCZ) is a serious psychiatric condition. While PM2.5 exposure has been linked to SCZ, the specific effects of its components remain poorly understood. This study aimed to explore the relationships between PM2.5 constituents (including BC: black carbon, OM: organic matter, SO4 2-: sulfate, NH4 +: ammonium, and NO3 -: nitrate) and SCZ. It incorporated the hospitalization records of 16,082 SCZ patients from Nanning Fifth People's Hospital, spanning from 1 January 2014, to 31 December 2023. The daily concentration data of PM2.5 and its five chemical components were sourced from Tracking Air Pollution in China (TAP). A distributed lag nonlinear model (DLNM) was employed to measure the dynamic correlation between PM2.5 components and the risk of hospitalization for SCZ. Further analysis was conducted by stratifying based on gender, age, and cold/warm seasons to identify susceptible populations. Our study revealed that OM and BC demonstrated lagged effects on SCZ hospitalization, with significant associations observed at lag 3 day(lag3)and lag4. The strongest effect was identified at lag4, with OM showing an relative risks (RR) of 1.010 (95%CI: 1.001, 1.019) and BC exhibiting a higher RR of 1.010 (1.001, 1.019). And the lag effect of the OM relative percentage was identified at lag 3 (RR = 1.013, 95% CI: 1.005-1.022). PM2.5, SO4 2-, and NH4 + showed lagged response trends but no statistically significant effects (p > 0.05). Subgroup analysis indicated that males, 45 years and younger, and those exposed during the warm season had higher risks associated with SO4 2-, OM, BC, and PM2.5. Short-term exposure to OM is significantly related to SCZ hospitalization. PM2.5 is a recognized risk factor for schizophrenia, yet its underlying mechanisms remain unclear. The toxicological properties of PM2.5 are governed by its components, but systematic studies on their roles in schizophrenia are scarce. This study investigates the impact of PM2.5 and its five major chemical components on schizophrenia hospitalization, aiming to identify specific components contributing to the disease and provide evidence for source‐specific PM2.5 pollution control.
Due to legacy leaded products, soil Pb is generally higher in older urban centers triggering substantial implications for environmental equity. By integrating a gridded soil Pb analysis of about 150 samples each in two historically industrial cities (Hartford, CT and Springfield, MA) with block-level census data, we tested the hypotheses that (a) high soil Pb areas correlate spatially with older housing, (b) historical processes of discrimination have caused long-lasting environmental injustices with respect to Pb exposure, and (c) multiple social, demographic and geographic factors intersect in determining areas in need of targeted remediation efforts. Our data and geospatial analysis showed higher Pb concentrations in Hartford than Springfield and confirmed the prevalence of elevated (>200 ppm) Pb in soils closer to older homes in both cities. Using a decision tree statistical partition, we show that exposure to elevated soil Pb was most prevalent in communities with children population higher than the city median, who live in multi-family housing. In Springfield, ethnicity was a significant factor in exposure as census blocks populated by non-Hispanic Whites were least likely to contain lead in soils above 200 ppm. Our analysis highlights that historical discriminatory practices, including redlining, have anchored environmental injustices in the studied communities, creating an invisible legacy challenge recorded in the land and carried across decades. The use of decision trees in the context of soil lead contamination provides a new method to help identify vulnerabilities of marginalized populations, providing quantitative tools to advocate for targeted mitigation and remediation. Our analysis of soils in Hartford, CT and Springfield, MA reveals elevated soil lead levels that are associated with older houses. Using recent census data we show that Hispanic households and communities of color, particularly those living in multi‐family housing, are most at risk of soil lead exposure in these two cities. We argue that these vulnerabilities are remnants of past discriminatory practices in urban contexts, including redlining policies.
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.
This paper aims to comprehensively investigate the content of potentially toxic elements (PTEs) in 11 tremolite samples to better understanding of their potential effects on human health. Accurate characterization of trace element concentrations in asbestos mineral fibers is crucial to elucidate their potential synergistic contribution to the mechanisms of asbestos-induced carcinogenesis and related pathologies, particularly in light of the documented involvement of elements such as Ni and Cr in the etiology of lung cancer. Samples were collected from diverse geological settings: San Severino Lucano and Iacolinei (Basilicata region, South Italy), Val Malenco (Lombardy region, North Italy), Praborna and Verrayes (Aosta Valley, North Italy), Monastero di Lanzo, Bracchiello, Caprie (Piedmont region, North Italy), Reventino (Calabria region, South Italy), Campolungo (Ticino Alps, Swiss), and Fowler (St. Lawrence Co., New York, USA). PTEs concentrations were determined using Inductively Coupled Plasma Optical Emission Spectrometry. The distribution of PTEs among different tremolite types was compared and discussed to provide a comprehensive overview of the data set.Tremolite asbestos samples showed variable concentrations of trace elements, with Mn (691.5 ppm) and Ni (474.2 ppm) being the most abundant. Samples from Monastero di Lanzo exhibited the highest total PTEs content (4,709 ppm). Statistical analyses revealed a consistent geochemical contrast: asbestos tremolite is systematically enriched in Mn and Ni, leading to higher overall PTEs levels, while prismatic tremolite is defined by very low Mn-Ni contents. The observed elemental variability reflects distinct geological settings that influence PTEs incorporation and potentially affect toxicity. Tremolite asbestos is a known cause of severe diseases such as lung cancer. In this study, we investigated whether the concentration of potentially toxic elements (PTEs) differs between asbestos and non‐asbestos (prismatic) tremolite, which could contribute to their differing pathogenic effects. We analyzed 11 tremolite samples collected from different geological settings in Italy, Switzerland, and the United States. Using laboratory‐based analytical techniques, we quantified PTEs (e.g., Ni, Cr, Mn) due to their established role in carcinogenesis. Our results revealed a clear and consistent geochemical distinction: asbestos tremolite contains significantly higher concentrations of these PTEs, whereas non‐asbestos (prismatic) tremolite shows very low levels. These findings suggest that the toxicity of asbestos tremolite may be enhanced not only by its fibrous morphology but also by its distinct chemical composition. Understanding this difference is important for better assessing health risks related to natural asbestos exposure and for improving prevention and regulatory strategies.
This study assessed the cost-effectiveness of Low-Intensity mental health Support via a Telehealth Enabled Network (LISTEN) for adults experiencing diabetes distress, facilitated by diabetes health professionals. A within-trial cost-utility analysis included 428 participants randomized to either LISTEN (diabetes-specific problem-solving therapy) or usual care (web-based diabetes distress and generic mental health resources). Quality-adjusted life-years (QALYs) were derived using the Assessment of Quality of Life-4D. Costs were measured from health sector and societal perspectives using a self-reported resource use questionnaire. Incremental cost-effectiveness ratios were calculated and nonparametric bootstrapping estimated 95% confidence intervals. Costs were presented in Australian dollars (A$) for the 2021-2022 financial year. The mean direct cost of delivering LISTEN was A$262 per person. Over 6 months, the LISTEN group had higher total QALYs (0.024; 95% confidence intervals [CI] 0.007 to 0.041). Compared with usual care, mean health sector cost (-A$3271; 95% CI -A$5134 to A$1008) and societal cost (-A$3588; 95% CI -A$5913 to A$860) were lower, although these differences were not statistically significant. At a A$50 000 per QALY willingness-to-pay threshold, LISTEN was dominant with a 94% probability of being cost-effective from both perspectives. Findings should be interpreted cautiously given the substantial missing data. LISTEN significantly improves health-related quality of life of adults with diabetes distress and is likely to be cost-effective. Point estimates show lower mean costs alongside QALY gains, but cost differences were not statistically significant.
Exposure to ambient air pollution increases the risk of respiratory tract infections (RTIs). This systematic review and meta-analysis quantified the association between ambient air pollution and RTIs in low- and middle-income countries (LMICs). We searched Ovid Medline, Embase, and related databases for studies published between January 2000 and December 2024 reporting on ambient air pollution and RTIs in LMICs. Two reviewers independently screened studies, assessed risk of bias using the RoBANS tool, and conducted a random-effects meta-analysis. Studies reporting odds ratios (ORs) were included in a random-effects meta-analysis, and separately for each pollutant-outcome combination for studies reporting multiple pollutants. Of 2,201 records identified, 111 full texts were assessed, 17 studies were included in the systematic review and 7 in the meta-analysis. Exposure to ambient air pollution was associated with increased odds of respiratory tract infections (pooled OR: 1.25, 95% CI: 1.04-1.49), with substantial heterogeneity (I² = 65.2%). Subgroup analyses suggested stronger associations in studies conducted in Africa (OR: 1.94, 95% CI: 1.46-2.58) compared to Asia (OR: 1.16, 95% CI: 0.92-1.45), and for pneumonia (OR: 1.74, 95% CI: 1.38-2.18) compared to other respiratory tract infections; though based on few studies. Among pollutants, PM2.5 showed the most consistent association with respiratory tract infections (OR: 1.05, 95% CI: 1.03-1.08), while associations for NO₂ and NOx were not statistically significant. Evidence of publication bias was suggested by funnel plot asymmetry. Meta-regression indicated a declining trend in effect estimates over time from 2018 to 2024. Ambient air pollution, particularly PM2.5, is significantly associated with increased RTI risk, with stronger effects in African settings and for pneumonia. The association appears to weaken in more recent studies, potentially reflecting changing exposure patterns or research methodological advancements. PROSPERO Registration: CRD42024586784.
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.
Autism spectrum disorder (ASD) in children is a major public health challenge. The potential epidemiological links between heavy metal exposure and ASD remains a controversial issue. This critical review examines the epidemiological discrepancies, including variations in exposure assessment, study design, and population differences that contribute to the ongoing debate. Key research gaps, such as the need for longitudinal studies and mechanistic insights, are addressed. Finally, we outline future priorities to advance understanding of heavy metals' role in ASD. Lead (Pb), cadmium (Cd), mercury (Hg), and arsenic (As) were selected heavy metals. The PubMed and Scopus databases, as well as the Google Scholar search engine, were searched to retrieve original research articles on human epidemiological studies that utilized the selected "exposure keywords" in conjunction with the "outcome keywords." Finally, 36 full-length articles, irrespective of age, sex, regions, and race/ethnicity, were included for the present review. We revealed inconsistent associations between prenatal and childhood urinary, blood, and hair, As and Cd exposure, and ASD outcomes. In contrast, elevated prenatal and early childhood Pb and Hg concentrations in blood and hair samples showed a significant consistent association with both increased ASD risk and symptom severity, even after adjustment for key demographic and environmental confounders. The findings are inconsistent across metals and studies, and should be interpreted with caution due to potential residual confounding and heterogeneity in exposure assessment methods. Large prospective cohort studies are needed to clarify causal relationships. The path analysis of relevant biomarkers is also warranted to establish biological mechanism. This review examined whether exposure to lead, cadmium, mercury, and arsenic is linked to autism spectrum disorder (ASD) by analyzing 36 human studies. The findings were metal‐specific. For arsenic and cadmium, most studies found no clear association. Mercury results were highly variable: about half reported positive links, while others found none or inverse relationships. Lead showed the most consistent signal, with 60% of studies linking higher levels to increased ASD risk or severity, although many well‐conducted studies found no association. Overall, the evidence does not support a blanket conclusion that heavy metals cause ASD. Rather, lead and mercury may contribute to ASD risk in certain populations or under specific exposure conditions, but these findings require cautious interpretation due to methodological differences and potential biases. Long‐term prospective mother‐child cohort studies with significant participants are needed to clarify whether these metals truly play a causal role.
Farmworkers are particularly vulnerable to heat stress, which can escalate into heat-related illnesses such as heat exhaustion or heat stroke, and can also impact productivity loss. Heat exposure varies considerably depending on season, work-shift timing, acclimatization, and workload. We examine the frequency of heat stress exceedance in the Imperial and Coachella Valleys of southern California using wet bulb globe temperature (WBGT) calculated using the outputs from Weather Research and Forecasting (WRF) model for a reference year of 2020. The critical WBGT threshold of 80℉ is exceeded for more than 500 hr in August, with considerable exceedance in all key harvesting months of April, May, and June. The threshold is exceeded most frequently in August, with daytime (nighttime) exceedance of 96.3% (38.0%) in the Imperial and 72.5% (11.9%) in the Coachella Valleys. Occupational WBGT limits are also exceeded in most crop environments for unacclimatized workers, and even for acclimatized workers during date and sugarcane harvests. Adjusting the work schedule to an early morning or late evening shift can reduce workers' productivity loss by up to 40%. Based on our findings, we propose several strategies to mitigate heat risks among farmworkers: (a) adjusting work hours to include cooler morning or evening hours (b) using WBGT instead of air temperature to monitor heat exposure (c) adopting variable rest-break schedules (d) reducing nighttime heat exposure during summer to allow recovery from daytime heat (e) taking extra precaution in high-risk crop zones (f) avoiding nighttime irrigation during summer, and (g) revising thresholds and definitions used to identify heatwaves. Farmworkers are at high risk of heat stress because they work outdoors for prolonged hours under strenuous conditions. This can lead to serious illnesses such as heat stroke and can reduce productivity. We examined how often heat exposure exceeds safety limits in California's agricultural heartlands, the Imperial and Coachella Valleys, using the Wet‐Bulb Globe Temperature (WBGT), a scientific heat metric. We found that WBGT exceeded the safety limit for hundreds of hours during key harvest months such as April, May, and June. Based on our results, we recommend several heat‐exposure mitigation strategies to protect farmworkers: (a) adjusting work hours to include cooler morning or evening periods; (b) using WBGT rather than air temperature to monitor heat exposure; (c) adopting a variable rest‐break schedule; (d) reducing nighttime heat exposure during summer to support recovery from daytime heat; (e) taking extra precautions in high‐risk crop zones; (f) avoiding nighttime irrigation during summer; and (g) revising definitions used to identify heatwaves.
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.
Communicating the risks posed by extreme weather events remains a challenge for researchers and policy makers. This study evaluates the gap between public perceptions on climate risk and scientific estimates of mortality burden from climate-related extreme weather events. Results show each mortality risk perception gap follows a similar spatial pattern to the observed attributable mortality rate. The perception that "global warming is happening" was significantly associated with smaller risk perception gaps for all extreme weather events. Improving population-level understanding of climate change is critical to reducing health risks associated with extreme weather events and to motivate climate-oriented actions. The health risks posed by extreme weather events are poorly understood across most United States counties. This study identifies where and by how much the public's perceptions of risk do not align with the scientific estimates of mortality burden to reduce health risks and motivate climate‐oriented actions.
Black carbon (BC) is an essential short-lived climate pollutant and a critical component of health-damaging fine particulate matter (PM2.5), posing significant risks to public health and the climate system. Despite its dual impact, comprehensive assessments of population-level BC exposure, its associated mortality burden, and its disparities across population subgroups remain limited in India. We used an advanced machine learning algorithm to estimate annual BC mass concentration at a 1-km × 1-km spatial scale from 2016 to 2021. Following the global burden of disease (GBD) framework, we integrated the meta-analysis-derived risk estimates with demographic and epidemiologic attributes and estimated BC-attributable all-cause, cardiovascular disease, and respiratory mortality (per 100,000 population). Further, utilizing the socio-demographic information from the National Family Health Survey, we assessed the disparity in BC exposure and attributable burden across sub-populations. Annual population-weighted BC exposure varied from 0.4 to 13.7 μg/m3 in India between 2016 and 2021. While BC exposure remained stagnant over the years, its disparity across most demographic subgroups has diminished in recent years. We estimated annual BC-attributable all-cause, cardiovascular disease, and respiratory mortality (per 100,000 population) to be 133 (95% uncertainty intervals: 113-155), 7 (5-9), and 9 (5-14), respectively. BC-attributable mortality was found to be higher among females, other backward classes, and economically deprived subgroups than their demographic counterparts. Our results demonstrate that prioritizing BC emission reduction would improve ambient air quality, result in a larger health benefit (for every unit reduction in concentration), and slow down regional warming, thereby creating a win-win situation for India. Black carbon (BC) is a harmful air pollutant that affects both human health and the climate. In India, where air pollution is extremely high, we still know little about how BC exposure impacts different social subgroups and contributes to early deaths. In this study, we used machine learning to estimate BC pollution across India from 2016 to 2021 at a fine spatial scale. We combined these estimates with health data to calculate how many deaths were linked to BC exposure, especially from heart and lung diseases. We also looked at how BC exposure varies across income, gender, and social subgroups. Our findings show that while BC levels remained mostly stable over time, vulnerable sub‐populations such as women, poorer communities, and socially disadvantaged subgroups continue to face greater health risks. On average, BC exposure contributed to 133 deaths (per 100,000 population) per year in India. Reducing BC pollution would not only save lives but also help reduce the pace of climate change. These findings highlight the importance of targeting BC emissions as a public health and environmental priority.
Acute lymphoblastic leukemia (ALL) is the leading cause of death in Mexican children, yet water-mediated environmental pathways remain largely unexplored as contributors to its spatial distribution. We analyzed ALL mortality records for individuals aged 0-19 years across all Mexican municipalities from 2003 to 2023, using publicly available national death statistics and population estimates. Age-specific mortality rates were calculated at the municipal level and aggregated to the state level to characterize broad geographic gradients. A discrete Poisson spatial scan statistic, implemented in SaTScan, identified 10 statistically significant mortality clusters, five with elevated risk and five with reduced risk, which were then compared against hydrogeological and industrial variables derived from global remote sensing and infrastructure databases. The national mortality rate averaged 1.64 per 100,000 children aged 0-19, with state-level rates ranging more than two-fold from 1.01 in Durango to 2.35 in Tabasco. High-mortality clusters are concentrated along the Gulf of Mexico coast and southeastern states; low-mortality zones lie predominantly in the arid west and northwest. High-mortality areas are systematically characterized by greater annual precipitation, lower aridity, permeable sedimentary geology, and higher forest cover-landscape conditions that collectively maximize infiltration and groundwater contamination risk. All five high-mortality clusters spatially overlap with oil and gas infrastructure, with three coinciding with zones of intensive extraction along the Gulf coast. These findings suggest that groundwater vulnerability and industrial contamination, rather than genetic predisposition, are primary spatial determinants of childhood ALL mortality in Mexico, pointing toward preventable, structurally driven disease burden. Childhood leukemia is the leading cancer among Mexican children, with death rates particularly high in some regions. While better treatments have helped more diagnosed children survive leukemia, we still don't know what in the environment is causing so many children to get sick in the first place. This study mapped where children are dying from leukemia across Mexico to see if patterns emerge. We analyzed data at a detailed geographic scale to avoid missing important local patterns that broader averages might hide. We found that areas with the most deaths tend to get more rain, sit on rock types that let water seep through easily, and have more forest cover, conditions that could help pollution spread through water systems like rivers and aquifers to which vulnerable children may be exposed. Many of these high‐risk areas also have a strong presence of oil and gas operations, which can release cancer‐causing chemicals. Our findings suggest that contaminated water may be an important and underappreciated reason why leukemia kills so many children in certain parts of Mexico. If confirmed, this would mean that a significant share of these deaths could be prevented by focusing prevention efforts where environmental risks are highest, not just treating the disease after it occurs.