Health & Place

Background: Multiple factors can drive heat susceptibility, but are typically analyzed individually. This study investigates high-dimensional heterogeneity across multiple domains in the effect of hot days on emergency hospitalizations across German districts. Methods: We obtained emergency hospitalization rates per 1,000 in 3-day windows for the summers of 2005-2023 from the German Diagnosis-Related Groups-Statistic, linked with spatio-temporal temperature information. A hot day was defined as at least one day above the district-level 98th percentile heat index. Multiple characteristics of the demographic, health/social, and environmental domains were selected as potential effect modifiers. We implemented a generalized random forest, where outcome and propensity scores were modeled with fixed effects estimators to control for seasonality and district-level confounding. We divided the conditional average treatment effects (CATE) into quartiles to map their spatial distribution and identify key effect modifiers via the variable importance and observed indicator profiles across quartiles. Results: We found substantial variation across districts regarding heat-related emergency hospitalizations. The top three indicators for predicting this effect heterogeneity were the percentage of people seeking protection, population below age 6, and air pollution levels. The observed indicator profile for the most susceptible districts was characterized by a larger percentage of vulnerable populations, higher car density, and fewer hospital beds. Conclusion: There is substantial variation in heat susceptibility across and within districts in Germany. We propose an approach that can help understanding such complex interplay of indicators from different domains driving heterogeneity.

Background: Built environment characteristics have been associated with depressive symptoms, whilst active travel behaviour is linked to improved mental health. However, it remains unclear whether active travel buffers associations between increased exposure to the built environment and worse mental health. Methods: In this pre-registered study, participants were 10,310 mothers (mean age = 28.11, SD = 4.83) participating in the Avon Longitudinal Study of Parents and Children (ALSPAC). A latent built environment factor was measured at study enrolment, active travel behaviour at a 2-year follow-up, and depressive symptoms at a 4-year follow-up. Moderation was examined using adjusted linear regression models including an interaction term between the built environment factor and active travel behaviour. Results: The built environment factor was positively associated with depressive symptoms ({beta} = 0.023, p = .012). Active travel behaviour was not associated with depressive symptoms ({beta} = -0.002, p = .854) and did not moderate the association between the built environment factor and depressive symptoms ({beta} = -0.002, p = .865), after adjusting for covariates, including age, familial depressive history, socioeconomic risk factors, physical activity, baseline depressive symptoms and smoking. Conclusion: Contrary to expectations, active travel behaviour does not modify associations between the built environment and depressive symptoms. These findings provide no evidence that active travel mitigates mental health risks associated with the built environment in this population.

Background: Previous studies indicated that regional fluctuations in COVID-19 impact were associated with socioeconomic, demographic and environmental factors early in the pandemic. It is unclear whether factors associated with regional COVID-19 mortality and SARS-CoV-2 infections changed over the course of the pandemic. Methods: We evaluated the association of ecological factors with municipality-specific COVID-19 mortality in the national population of Austria between January 2020 and June 2023. Analyses were conducted separately per calendar year and pooled across years via random-effects meta-analysis. Outcomes were analysed using univariable and multivariable zero-inflated negative binomial (ZINB) regression, including annually averaged environmental exposome exposure (including air pollution and UV radiation), socioeconomic, demographic and infection dissemination predictors with significance threshold set to p<0.005. Secondary analysis modelled SARS-CoV-2 infections using of negative-binomial model (NB). Two additional approaches for smaller predictor set selection were applied as sensitivity analyses. Results: Within the entire observation period 18,430 COVID-19 deaths were reported. In univariable analyses (pooled RR (99.5% CI)), COVID-19 mortality was significantly associated with the proportion of commuters (0.79 (0.73-0.86)), residents aged 75-84 (1.18 (1.01-1.37)) and [&ge;]85 years (1.29 (1.04-1.60)), residents with at most compulsory education (1.15 (1.02-1.29)), employed residents aged 15-64 (0.89 (0.85-0.93)), and homeowners (0.91 (0.83-0.99)), as well as with precipitation (0.94 (0.89-0.99)) and the average time with a mean temperature of 5-15 C (0.95 (0.90-0.99)). In multivariable analyses the share of population aged 85 or older (1.30 (1.09-1.54)) and home ownership (0.92 (0.87-0.98)) were independently significant. Mortality predictor estimates were relatively stable across pandemic years. Across 5,963,882 documented SARS-CoV-2 infections share of employed in 15- to 64-year-olds (1.01 (1.01-1.02)), part-time employment rate (1.01 (1.00-1.02)) and district level nursing care allowance recipiency (0.97 (0.95-0.98)) were associated with detected infections. Overnight stays, population 85 or older, income, compulsory education or less and air pollution all had multiple significant estimates with opposite directionality, leading to non-significant overall associations. Sensitivity analyses outcomes mirrored main analyses outcomes. Conclusions: Throughout the pandemic in Austria, demographic and socioeconomic factors were associated with COVID-19 mortality and infections, but many associations, especially for infections, were unstable over time. These findings underscore the importance of structural vulnerabilities in epidemic impact.

Background Italy experiences a substantial heat-related mortality burden, but evidence on cause-specific mortality across its heterogeneous climatic settings remains limited. We aimed to quantify cause-specific mortality attributable to summer heat in Italy at national and ecological region levels and to map its distribution across municipalities. Methods We analysed daily deaths classified by underlying cause in eight ICD-10 groups among individuals aged 18-64 years and 65 years or older across 7,864 municipalities during June-August, 2004-2023. Mortality data were linked to area-weighted ERA5-Land daily maximum temperature. Nonlinear temperature-mortality associations were estimated with Bayesian hierarchical Poisson models, fitted separately for each cause, age group, and ecological region, with municipality-level attributable fractions. Findings High temperatures were associated with mortality from a broad range of causes, particularly among individuals aged 65 years or older. Among the study population, heat-attributable fractions were highest for nervous system diseases (14.93%, 95% credible interval 14.22-15.69) and mental and behavioural disorders (14.47%, 13.55-15.39). Circulatory diseases accounted for the largest absolute burden, with 85,524 heat-attributable deaths (82,762-88,271) across the summers of the 20 years considered. Interpretation The mortality burden associated with summer heat in Italy extended well beyond circulatory and respiratory diseases, highlighting the relevance of a broader range of health outcomes. These findings support heat-health strategies that account for cause-specific and geographical differences in heat-related risk, with greater consideration of neurological and mental health outcomes alongside established cardiorespiratory risks. Funding NextGenerationEU (CUP: B53C23002660006); Imperial College Research Fellowship; NIHR Imperial Biomedical Research Center (BRC: NIHR203323).

Abstract Background: Rising global temperatures are intensifying exposure to extreme heat, yet access to air conditioning (AC) remains unequal which leaves vulnerable populations without protection against the heat. Household survey data on AC ownership is scarce in low- and middle-income countries limiting the ability to identify areas of cooling gaps. Methods: In this study, we develop a geospatial machine learning framework that integrates household data from the Demographic and Health Surveys (DHS) program with high resolution climatic, socioeconomic and demographic covariates to generate a 5km resolution prediction of AC ownership across low- and middle-income countries in Africa and Asia for the year 2023. Results: The resulting map reveals strong spatial inequalities with high predicted prevalence in wealthier and urban regions, and near zero prevalence across much of Central and West Africa and rural areas. These patterns reflect the adaptation cooling gap where those most exposed to extreme heat are least able to adapt through access to cooling. Conclusion: By producing the first continental-scale, high resolution map of AC ownership, this study lays the foundation for designing and targeting equitable cooling strategies, support heat-health planning particularly in communities most affected and informing sustainable energy policy.

Background Short-term weather conditions can trigger paediatric hospital admissions, but evidence remains limited on the health relevance of intraday temperature variability, humidity, and precipitation, and generally on which subgroups are most susceptible. Methods We conducted a nationwide case time-series study of Swedish children and adolescents aged 6-18 years using linked register data. Associations between daily mean temperature, intraday temperature variability, relative humidity, and precipitation and inpatient hospitalisations were estimated with Poisson regression (individual fixed effects) combined with distributed lag non-linear models over lags 0-21 days. Outcomes included all-cause hospitalisation and cause-specific admissions. Effect modification was examined by sex, age group, socioeconomic indicators, region, and calendar periods. Findings Among 4,638,247 children and adolescents in the cohort, 1,070,702 (23.1%) experienced at least one inpatient hospitalisation, contributing 1,736,169 hospitalisation events. All-cause hospitalisation increased with higher mean temperature, with risk highest at hot extremes. Intraday temperature variability showed a U-shaped association, with higher rates at both lowest and highest exposure levels. Relative humidity showed no association with hospitalisation risk. Higher precipitation was associated with increased hospitalisation risk, but estimates at high rainfall levels were imprecise. Stratified analyses indicated modest differences by sex and socioeconomic indicators, but clearer heterogeneity by age and region, with the highest risks among younger children and in East Sweden; associations varied across calendar periods and were imprecise for most cause-specific outcomes. Sensitivity analyses mostly showed that the main curve shapes were robust. Interpretation Short-term exposure to hot temperatures, both low and high intraday temperature variability, and higher levels of precipitation were associated with an increased risk of all-cause hospitalisation among children in Sweden, with clearer variation by age group and region than by sex or socioeconomic position. These findings support targeting paediatric climate-health preparedness and early-warning systems toward younger children and higher-risk regions such as eastern Sweden and extending such systems beyond mean temperature to include intraday temperature variability, a distinct, currently overlooked driver of risk that heat-focused systems are not designed to capture. Fundings This research was funded by Formas the Swedish Research Council for Sustainable Development (grant no. 2023-01774 and 2022-01845) and the Swedish Research Council (2025-02804 and 2022-06599).

BackgroundSocial isolation (SI) is an established risk factor for chronic disease and mortality. How people travel, including the modes of transportation they use, may shape opportunities for social interaction and influence the impacts of SI. This study assessed heterogeneity of the association between SI and mortality across primary modes of non-commute transportation. MethodsWe analyzed cohort data from the UK Biobank, following participants aged 40-69 years from 2006 to 2022. SI at baseline was derived from three items and dichotomized. Cox models were used to estimate the association between SI and mortality, adjusting for socioeconomic factors, health behaviours, and comorbidities. Effect modification by participants primary mode of non-commute transportation was assessed using interaction terms with SI. ResultsThere were 490,114 participants with complete baseline exposure data. SI was associated with increased hazard of all-cause (Hazard Ratio [HR] = 1.34, 95% Confidence Interval [CI]: 1.31-1.38) and cardiovascular mortality (HR = 1.49, CI: 1.41-1.57). The association with cardiovascular mortality was stronger among participants who only used public transport compared with those who only used a car (interaction HR = 1.17, CI: 1.00-1.37). In contrast, using a car alongside active or public transport, compared to using a car alone, attenuated the association between SI and all-cause mortality (interaction HR = 0.93, CI: 0.87-0.99). ConclusionMixed transport, including a car, showed the greatest reduction in the association between SI and all-cause mortality. Our findings motivate further exploration of the role of transportation modes in enhancing social connection, mobility, and health.

Background: Non-optimal air temperature increases the risk of preterm birth, but how temperature-related risks may translate into future population burden remains uncertain. We aimed to estimate the association between temperature and preterm birth in Sweden and assess how preterm birth burden may change under plausible future temperature conditions. Methods: We conducted a nationwide population-based cohort study of singleton births in Sweden from 1973 to 2018. Weekly mean temperature was assigned at the parish level according to maternal residence. Cox proportional hazards models with a distributed lag nonlinear model were used to estimate nonlinear and delayed associations between temperature and preterm birth. The estimated exposure response relationship and baseline hazards were then incorporated into an individual-based microsimulation of gestational progression under projected temperatures for 2025 to 2055. Future projections were examined across multiple climate models under SSP1-2.6 and SSP3-7.0 scenarios. Outcomes included the annual number and cumulative incidence of preterm births and the temperature-attributable fraction. Results: Among 4,338,825 singleton births, 211,397 (4.9%) were preterm. Temperature preterm birth associations were modest overall. The clearest increase in hazard was observed following exposure to very low temperatures, particularly -10C at short lags. Higher temperatures showed no clear increase in risk. From 2025 to 2055, the annual cumulative incidence of preterm birth and the temperature-attributable fraction was expected to remain broadly stable, with limited differences between emissions scenarios. In contrast, the absolute number of preterm births was expected to over the projection period, primarily reflecting increases in the projected number of pregnancies rather than substantial changes in temperature-associated risk. Conclusions: Very low temperatures were associated with a modest short-term increase in preterm birth risk in Sweden, hot temperatures showed little evidence of increased risk. Applying these associations to future climate conditions suggested limited changes in preterm birth incidence or the proportion attributable to temperature through 2055. However, the absolute burden of preterm birth may increase as the population at risk grows. This highlights the importance of considering demographic change alongside climate projections when assessing future maternal and neonatal health burden.

Disability is an important population-health concern, but adult-specific evidence on its distribution and socioeconomic patterning remains limited in Somaliland. This study estimated the prevalence and distribution of reported disability among adults in Somaliland and examined socioeconomic inequality, reported mental/psychological illness, and their co-occurrence. We conducted a cross-sectional secondary analysis of the 2020 Somaliland Health and Demographic Survey. The analysis included 14,579 adults aged [&ge;]18 years who were usual residents, had the disability module administered, and had a defined reported mental/psychological illness measure. Reported disability was defined as at least one positive response across seven disability domains: sight, hearing, speech, learning, mobility, self-care, and mental. Survey-weighted prevalence estimates and design-based comparisons were calculated. Survey-weighted logistic regression assessed associations with household wealth and other covariates. Socioeconomic inequality was assessed using a concentration index and the normalized Erreygers concentration index. Co-occurrence was evaluated by comparing observed joint prevalence with that expected under statistical independence and by survey-weighted logistic regression. The survey-weighted prevalence of any reported disability was 7.30% (95% CI 6.65-8.02). Sight was the most frequently reported domain (3.83%, 95% CI 3.19-4.59), followed by hearing (1.54%, 95% CI 1.20-1.97) and mobility (1.39%, 95% CI 1.14-1.70). Disability prevalence increased markedly with age, from 4.15% among adults aged 18-29 years to 23.96% among those aged [&ge;]60 years. The crude wealth pattern was non-monotonic and was not statistically detectable after adjustment for residence and region. The concentration index for reported disability was 0.0584 (SE 0.0284, p=0.0415). Reported mental/psychological illness had a weighted prevalence of 0.71% (95% CI 0.50-1.00). Both reported disability and reported mental/psychological illness occurred in 0.5032% of adults, compared with 0.05185% expected under statistical independence; the observed-to-expected ratio was 9.70 (95% CI 7.60-11.81). After adjustment for age group and sex, reported disability was strongly associated with reported mental/psychological illness (OR 42.99, 95% CI 18.45-100.18). Reported disability affected approximately one in fourteen adults in Somaliland and showed a marked age gradient and substantial geographic variation. The crude socioeconomic pattern was attenuated after adjustment for geographic context, while concentration analysis indicated modest marginal concentration toward higher wealth ranks. Reported mental/psychological illness was uncommon but co-occurred with reported disability more often than expected under independence. These findings provide an adult population baseline for Somaliland while highlighting the need to distinguish survey-reported disability from the separate mental/psychological illness construct and to interpret the findings within the limitations of cross-sectional, survey-reported measures.

Features of urbanicity have been linked to poor psychiatric health in adulthood, but little is known about their relationship with biological age and cognition earlier in life. Using the Avon Longitudinal Study of Parents and Children (ALSPAC), we tested associations between the built environment and epigenetic ages (EpiAgeAltum, EpiAgeHorvath, EpiAgePACE) (n=2,452), brain age (n=751), and cognition (n=12,199). Analyses were replicated in Generation R and triangulated in the Millennium Cohort Study. The built environment was not associated with epigenetic ages ({beta}Altum range = 0.013-0.015, p range = .466-.689; {beta}Horvath range = -0.011-0.023, p range = .182-.728; {beta}PACE range = -0.045-0.013, p range = .131-.524) or brain age ({beta} range = 0.022-0.028, p range = .337-.429). A negative association with cognition was observed in Generation R. We provide no consistent evidence that childhood built environment exposure relates to biological age and cognition in adolescence, suggesting these relationships may emerge in later life.

Background Staying physically active is essential for healthy ageing. Exposure to high levels of air pollutants such as particulate matter (PM) is associated with less active, more sedentary lifestyles in older adults. Whether results extend to typical fluctuations in daily PM exposure is unclear. We examined associations of higher-than-usual personal exposure to PM (PM2.5 and PM10) with next-day movement behaviour in older adults, assessed using concurrently worn personal air quality monitors and accelerometers. Methods The present study used 304 days of data from 67 adults aged 50-85 years living in the UK. For five days, participants wore accelerometers 24-hours per day and personal air quality monitors during waking hours. Average daily time spent in movement behaviours (moderate-to-vigorous physical activity [MVPA], light physical activity [LPA], sedentary behaviour [SB]) and step count was extracted from accelerometers, and average daily exposure to PM2.5 from personal air quality monitors. PM10 was examined in secondary exploratory analyses. We used linear mixed models to examine within-person associations of deviation from usual PM exposure with next-day movement outcomes, with estimates presented for 'usual' (individual mean) and 'higher-than-usual' (+1 interquartile range, equivalent to 2.6 g/m3 beyond individual mean PM2.5 exposure and 2.8 g/m3 beyond individual mean PM10 exposure) exposure. Analyses were repeated using area-based PM estimates. Results Compared with usual exposure, higher-than-usual PM2.5 was associated with 8 minutes less LPA (95% CI=-14 to -2) and 483 fewer steps (-855 to -82) on the following day. Exposure to higher than usual PM10 was associated with 8 minutes less LPA (-13 to -3), 8 minutes more SB (0 to 15), and 412 fewer steps (-739 to -85) on the following day. There was no association with MVPA, and no associations when area-level PM estimates were used as opposed to estimates from personal air quality monitors. Conclusions Typical daily variability in PM exposure in a setting with relatively low overall PM levels was associated with modest next-day differences in movement behaviour in older adults. Higher-than-usual PM exposure was associated with less next-day activity and more sedentary time. Personal monitoring may be important for capturing short-term exposure-behaviour associations. Trial registration N/A

Health inequalities are often examined through single axes such as class or ethnicity, obscuring how these dimensions interlock. This study applies a longitudinal intersectional approach to examine physical and mental health trajectories across five generations in the UK. We analysed 14 waves of the UK Household Longitudinal Study (2009-2024) using longitudinal Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA). We modelled SF-12 physical and mental health trajectories for 398 intersectional strata defined by gender, ethnicity, occupational class, and generation. Physical health inequalities widened with age, consistent with cumulative disadvantage, while mental health inequalities were more evident among later-born cohorts. The 'Boomer' generation exhibited a relatively flat mental health trajectory across its observed age range. In contrast, 'Generation Z' had lower predicted mental health, particularly among White British women with no work history - a group with poorer predicted trajectories than their South Asian peers. Our study shows how health inequalities are structurally differentiated by cohort and intersectional position. Policy responses should support proportionate universalism, combining broad population-level support with intensified action for groups showing poorer health.

Introduction: Raised blood pressure is an important cardiovascular risk factor in Cambodia; however, national averages may obscure substantial local geographic heterogeneity. We assessed the spatial distribution of raised blood pressure and its demographic, metabolic, behavioural, built-environment, and environmental correlates using the 2023 Cambodia STEPwise approach to noncommunicable disease (NCD) risk factor surveillance (STEPS) survey. Methods: We analysed 289 georeferenced primary sampling units (PSUs) from the 2023 Cambodia STEPS Survey. PSU-level case counts and numbers of adults with valid blood pressure data were modelled using a frequentist binomial generalised additive model with a two-dimensional Gaussian-process smooth. An explanatory model included selected PSU-level and contextual covariates; a separate mapping model used spatially continuous predictors to generate predictions on a 5-km grid. Design-based national and residence-specific prevalence estimates were calculated separately using the survey design and Step-2 weights. Results: Among 4,225 adults with valid blood pressure data, unweighted prevalence of raised blood pressure was 28.6%, compared with 18.8% after Step-2 weighting; weighted prevalence was 17.5% in rural areas and 21.7% in urban/suburban areas. Mean observed unweighted PSU-level prevalence was 29.0%. In the adjusted spatial explanatory model, the proportion aged 60-69 years ({beta}=1.969, SE=0.327; p<0.001) and overweight/obesity prevalence ({beta}=0.996, SE=0.265; p<0.001) were positive ecological correlates. The spatial model had lower Akaike information criterion and residual deviance than its non-spatial counterpart, although Bayesian information criterion was higher. Prediction uncertainty was greater in some sparsely sampled areas. Conclusions: Raised blood pressure showed significant geographic heterogeneity across Cambodia. Older age composition and overweight/obesity prevalence were positive PSU-level correlates, not individual-level causal effects. Predicted prevalence should be interpreted alongside uncertainty and should complement, rather than replace, design-based STEPS prevalence estimates for geographically informed NCD planning.

Background: Unpaid caregiving is a recognised social determinant of health, yet most research treats it as a fixed exposure measured at a single time point, and little is known about how patterns of caregiving transitions relate to health behaviours. Methods: Secondary analysis of the UK Household Longitudinal Study (waves 2 to 13, approximately 2010 to 2023; n=25,049). Latent class analysis was applied to binary caregiving status across 12 waves to identify caregiving trajectories. Associations between trajectories and four health behaviours (physical inactivity, fruit and vegetable consumption, problematic drinking, current smoking) at waves 7 to 13 were estimated using logistic and linear regression, adjusting for sociodemographic, health and baseline behavioural confounders. Results: An eight-class solution identified a non-caregiving class comprising 62.9% (n=15,760) of the sample. Among caregivers (n=9,289), trajectories were Temporary (20.0%), Former-short (20.2%), Former-long (10.9%), Emerging-short (15.4%), Emerging-long (11.6%), Long-term (15.7%) and Recurrent (6.3%). In fully adjusted models, Recurrent caregivers had higher odds of smoking (OR=1.67, 95%CI: 1.17 to 2.40) compared with non-caregivers but lower odds of physical inactivity (OR=0.65, 95%CI: 0.53 to 0.81) and problematic drinking (OR=0.75, 95%CI: 0.59 to 0.94). Associations for other trajectory classes were largely attenuated after adjustment. Conclusion: The pattern and timing of caregiving transitions are an under-explored dimension of the caregiving experience. Recurrent caregivers had a distinctive profile, with elevated smoking but lower physical inactivity and problematic drinking. Recurrent caregiving should be recognised alongside intensity and duration, and support systems better equipped for people who move into and out of care repeatedly.

Background. Stunting and wasting drive child mortality and are central to Sustainable Development Goal target 2.2. In 2025, 24.0% of Bangladeshi children under five were stunted and 12.9% wasted, meeting the national stunting target but missing the 8% wasting target. We compared where the two occur and tested whether environmental factors explain the patterns. Methods. We linked the 2025 Bangladesh Multiple Indicator Cluster Survey to cluster GPS locations and environmental data. Moran's I tested clustering and Getis-Ord Gi* found hotspots across 64 districts and 3,144 clusters. We tested whether six environmental and access factors explained the hotspots, estimated what share of affected children each hotspot list reached, and used a joint model to compare factor associations between outcomes. Results. Stunting clustered by region (district Moran's I 0.21, P=0.002), with a north-eastern hotspot centred on Sylhet and Sunamganj. Six environmental factors together removed this clustering (I -0.01, P=0.45) and about 60% of between-district variation (95% CI 28 to 84), more than rainfall alone, with the gain over generic regional terms uncertain. Wasting showed no clear regional clustering (I 0.05, P=0.17), and district stunting and wasting rates were unrelated (r -0.01). Stunting hotspot districts held 5.9% of wasted children, the same as their 5.9% population share. Children with both conditions (902 cases) did not cluster. Altitude and rainfall related differently to stunting than to wasting (all P<0.004), but altitude associations were sensitive to how altitude was modelled. Conclusion. Stunting in Bangladesh is concentrated in the north-east, and six environmental and access factors together account for much of this, though no single factor, including rainfall, does. Wasting shows no clear regional pattern. A stunting hotspot list reached no more wasted children than population share, so surveillance may need both measures, with finer-scale data for wasting.

Introduction: Pesticide exposure has been associated with poorer mental health, yet little is known about whether seasonal fluctuations in agricultural pesticide use are associated with cyclical changes in mood among people living in agricultural communities. We evaluated associations between 10 peak and off-peak pesticide spray seasons and ecological momentary assessment (EMA)-derived mood in a floricultural region in Ecuador. Methods: 496 participants completed repeated EMA surveys across 10 assessment administrations over 2.5 years (2022-2024), including one single-assessment administration and nine 10-day bursts (Nobservations=14,332). Mood outcomes included negative affect (sadness, anxiety, stress, anger, tiredness, difficulty concentrating; overall mean=2.79, Standard Deviation [SD]=1.32), positive affect (happiness, motivation; overall mean=4.21, SD=1.60), and behavioral dysregulation (aggression and impulsivity, overall mean=1.86, SD=1.29). Generalized linear mixed models estimated pooled, between-person and within-person associations between peak pesticide spray seasons and mood, adjusting for demographic and longitudinal factors Effect modification by sex, floricultural occupation, and cohabitation with a floricultural worker was assessed. Results: Peak spray seasons were associated with higher negative affect ({beta}=0.11 [0.06, 0.16]) and behavioral dysregulation ({beta}=0.10 [0.05, 0.15]) but not positive affect ({beta}=0.01 [-0.05, 0.07]). Associations were driven by within-person changes. Sex modified associations with negative affect, which were stronger in males than females. Floricultural occupation modified associations, with poorer mood, greater behavioral dysregulation and lower positive affect among floricultural workers than non-workers. Discussion: Peak pesticide spray seasons were associated with worsening of negative affect and behavioral dysregulation. EMA may provide a sensitive approach for detecting short-term neurobehavioral changes associated with seasonal pesticide exposure.

Background: In coastal Bangladesh, saline drinking water has been linked to raised blood pressure. The 2025 Multiple Indicator Cluster Survey (MICS) measured electrical conductivity (EC), a salinity indicator, in source water, but its report gave no population counts or district estimates. Objectives: To estimate how many people live in households whose tested source has high EC, and to describe patterns by place, household characteristics, source type and arsenic. Methods. EC was measured in source water of 6,040 households in 64 districts, February to June. In 14.5% of households the tested source was not the main drinking-water source. Primary outcomes were EC above 1,500 and 3,000 microsiemen/cm, survey risk classes that are not health-based standards. Estimates were survey weighted; comparisons were observational. Results: EC exceeded 1,500 microsiemen/cm in 5.3% of households (95% CI 4.7 to 5.9), about 9.0 million people, including 1.8 million above 3,000 microsiemen/cm. Coastal-zone districts held 26% of households but 70% of those above 1,500 microsiemen/cm. Shares varied little by wealth or in households with young children or pregnant women. Nationally, deep tube wells more often had high EC because most are coastal. In the coastal zone, deep and shallow wells had similar shares (16.1% and 17.9%). After district adjustment, EC was about 18% lower in deep wells (geometric mean ratio 0.82, 0.73 to 0.92). Within upazilas (restricted sample) it was 0.93 (0.80 to 1.07). Arsenic above 10 ppb was more common where EC was high, but this link weakened after district adjustment. Conclusions: Millions of people live in households whose tested source has high EC, mostly in coastal and south-central districts. Deep wells had modestly lower EC after geographic adjustment, but the difference shrank within upazilas and depended on the handling of implausibly low readings. The findings support measuring EC alongside arsenic, with documented meter calibration.

Importance: Long-term exposure to higher average air pollutant concentrations is associated with poorer cognitive health, but annual averages can obscure substantial differences in day-to-day exposure. Whether recurrent high pollution days are associated with long-term cognitive aging trajectories among people with similar overall air pollution exposure is unclear. Objective: To determine whether the annual number of days exceeding World Health Organization daily guideline values for nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10) is associated with subsequent cognitive decline after accounting for annual mean air pollutant exposure. Design: Prospective cohort study using data from waves 1-10 (2002-03 to 2021-23) of the English Longitudinal Study of Ageing. Setting: Population-based cohort of community-dwelling adults in England. Participants: 12,235 adults aged 50+ years at analytic baseline. Exposures: Number of days exceeding World Health Organization daily guideline values ('exceedance days') for NO2, PM2.5, and PM10, derived from residence-linked environmental estimates during the calendar year preceding the analytic baseline. Main Outcomes and Measures: Baseline cognitive performance and model-estimated 14-year cognitive decline (corresponding to the 75th percentile of follow-up duration) in episodic memory and verbal fluency. Results: Exceedance days were not associated with baseline memory or verbal fluency after adjustment for annual mean air pollutant exposure. More NO2 exceedance days were associated with faster cognitive decline: comparing the 75th percentile with the median of NO2 exceedance days, the difference in 14-year decline was -0.09 SD (95% CI, -0.17 to -0.01; p = 0.02) for memory and -0.10 SD (95% CI, -0.19 to 0.00; p = 0.04) for fluency. PM2.5 and PM10 exceedance days were not associated with cognitive decline. Conclusions and Relevance: In this cohort study of older adults, a higher number of NO2 exceedance days was associated with faster subsequent cognitive decline after adjustment for annual mean air pollutant exposure. This suggests that daily exposure to high NO2 concentrations may be related to accelerated cognitive aging even when offset by lower exposure on other days, potentially providing a new rationale for identifying and prioritizing areas with recurrent high NO2 exposure and the local emission sources contributing to it.

Community-led urban greening may contribute to neighborhood safety, but its relationship with crime remains uncertain. We examined whether Keep Indianapolis Beautiful Project GreenSpaces in Indianapolis were associated with changes in violence and total crime. We used a matched-pair difference-in-differences design comparing funded sites with matched unfunded applicant sites. The primary analysis included 29 matched pairs with two years of pre- and post-grant follow-up. Monthly Part I violent offenses and total crime within 600-m buffers were modeled using Poisson pseudo-maximum likelihood. In the first post-grant year, the violent-crime estimate was lower but imprecise (IRR, 0.903; 95% CI, 0.805-1.014). In the second post-grant year, the relative rate was lower (IRR, 0.830; 95% CI, 0.718-0.961). Total crime estimates showed no clear post-grant change. Additional analyses indicated some sensitivity to spatial scale, but no sustained divergence in pre-grant crime patterns. Analyses of short-term crime dependence showed clear month-to-month persistence in violent crime within sites but limited evidence of stable spatial dependence across nearby sites. Therefore, within the two-year post-grant period examined, Project GreenSpaces was associated most consistently with lower relative violent-crime rates in the second year, while no corresponding decrease was observed for total recorded crime. Longer follow-up is needed to determine whether these patterns persist or change as projects mature.

BackgroundIn rural Alaska, where most communities lack road access, air ambulances serve as a critical link between remote communities and hospital services. ObjectiveTo examine how air ambulance utilization decisions are made within the rural Alaskan health organization ecosystem. MethodsWe conducted semi-structured interviews with five key stakeholders. Transcripts were analyzed thematically using the Practical, Robust Implementation and Sustainability Model (PRISM). ResultsFour themes emerged that collectively define the structure of the air ambulance decision-making system, each reflecting interactions across multiple PRISM domains: limited formal guidance, logistical constraints affecting transport options, variability driven by communication and clinician risk tolerance, and reliance on unwritten rules. Together, these describe decision-making in a limited information state with incomplete feedback, leading to experiential and locally evolved practices. ConclusionAir ambulance use reflects clinical-logistical constraints rather than critical illness alone. Linking transport decisions to outcomes and developing structured decision support may improve consistency and optimize care delivery.