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).

Background: Environmental noise and air pollution are both shaped by road traffic and the built environment, and exposure assessment increasingly folds them into composite indices or proxies both by traffic exposure. Whether the two share a social distribution has rarely been tested against direct measurement of several exposures in the same communities, and community noise is almost always characterized by A-weighted levels alone, which discount low-frequency energy. Methods: At 176 sites across Rhode Island, spanning the contiguous urban area of Providence, Central Falls, and Pawtucket together with four rural municipalities, we measured the acoustic environment under A- and C-weighting (LAeq, LCeq), fine particulate matter (PM2.5), night-time illuminance, and relative humidity across four session types over roughly one year (704 site-sessions). Exposures were linked to census-tract composition (American Community Survey), and mixed-effects models were fitted for each of eight area-level markers of disadvantage, adjusting for campaign and session. Relative humidity was carried through the identical model as a negative control. Results: A-weighted noise was consistently higher in more disadvantaged tracts, rising with non-White, poverty, renter, and no-vehicle shares and falling with income and older-resident share (six of eight markers significant; 1.3 to 1.8 dBA per standard deviation; 6.6 dBA between the least and most racially diverse neighborhoods). C-weighted levels followed the same gradient on every marker and exceeded their A-weighted counterparts at block-group scale for renter occupancy and vehicle absence. Night-time illuminance was also socially patterned, whereas short-term PM2.5 was roughly an order of magnitude weaker and relative humidity showed no gradient. The acoustic gradient persisted within the urban core alone. Conclusions: Measured burden was carried by the acoustic environment, including its low-frequency component, and by night-time light, not by short-term particulates. The exposure metric and the averaging time determine which disparities are visible at all.

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.

Maternal depressive symptoms are a major public health concern in low- and middle-income countries (LMICs), yet evidence on their relationship with early childhood development (ECD) remains mixed. This study examined the association between maternal depressive symptoms and ECD outcomes during infancy and toddlerhood, and whether household composition moderated these associations. Additionally, we examined whether child stimulation activities were associated with ECD outcomes across both timepoints. We analysed data from 769 mother-child dyads drawn from a cluster-randomised controlled trial conducted in the Cajamarca region of Peru. Maternal depressive symptoms were assessed during infancy (approximately 5 months) using the DASS-21 depression subscale. Child development was assessed during infancy (mean age 5.2 months) using the CREDI and during toddlerhood (mean age 28.7 months) using the GSED Long Form. Associations were examined using linear regression models, adjusting for demographic, household, social, and caregiving covariates. Effect modification by number of siblings under 6 years and number of adults in the household was assessed. Approximately one in six mothers (17.4%) reported any depressive symptoms. Maternal depressive symptoms were neither associated with child development outcomes in infancy (adjusted {beta} = 0.01, 95% CI [-0.01, 0.01]) nor in toddlerhood (adjusted {beta} = -0.01, 95% CI [-0.07, 0.05]). No significant moderation was observed for either household composition variable. Among the covariates examined in the adjusted models, child stimulation activities showed the strongest independent association with child development at both timepoints, regardless of maternal depressive symptom status. We found no evidence that maternal depressive symptoms were associated with child development during infancy or toddlerhood, and household composition did not moderate these associations. These findings suggest that the relationship between maternal depressive symptoms and early child development may be more complex than previously assumed in this rural Andean context. However, the absence of an association in this study should not be interpreted as evidence that maternal depressive symptoms are unimportant, as previous research has documented important effects on maternal wellbeing, family functioning, and child outcomes.

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.

Aim: Neighbourhood food outlet availability influences dietary behaviours and nutrition related health outcomes. However, food environment research across Europe remains limited by inconsistent spatial data, heterogenous food outlet classifications, and the lack of publicly available high-resolution retail food data. We developed and evaluated a scalable framework to acquire, classify, validate, and analyse food-related points of interest (POIs) from Google Maps across 39 countries in Europe and Turkey. We also developed a method for characterizing the joint co-occurrence of multiple outlet types as neighbourhood-level food environment exposures. Methods: We harmonized ~3.26 million food-related POIs. Outlets were classified into five main categories (cafes and bakeries, restaurants, fast-food and snack outlets, groceries and food retail, and bars and pubs) and 25 subcategories. We assessed data quality against official registers in five regions (the Netherlands, United Kingdom, Denmark, Madrid (Spain), and Brno (Czech Republic)), testing agreement in positional accuracy, completeness, spatial clustering (Nearest Neighbour Index), and spatial density (kernel density estimation (KDE)). To enable cross-country comparisons, POIs were aggregated to national and city scales, with food outlet density calculated as outlets per 1,000 residents and standardised using z-scores. To characterize neighbourhood-level co-exposures, we applied Principal Component Analysis (PCA) followed by Latent Class Analysis (LCA) to z-standardized outlet densities across 500 m hexagonal grid cells in eight major European cities. Results: Google Maps data showed high positional accuracy, with 91% of outlets located within 30m of registry records. Completeness varied by region, while KDE comparisons showed moderate-to-strong spatial agreement with city-specific variation. PCA identified two components explaining 68.4% of the variance (PC1: 51.3%, PC2: 17.1%), with PC2 differing between cafe/bar and fast-food/supermarket densities. LCA identified four neighbourhood classes: low-access, moderate-mixed, dining-out, and high-density-mixed. Conclusion: Our harmonized high-resolution framework supports cross-national monitoring of retail food environments and spatial epidemiological research across European settings.

Socioeconomic inequality in exposure to air pollution possess a significant public health challenge, yet little is known about how the disparities vary across the various economic status areas in Nairobi. Globally, studies have shown that exposure to air pollution is unequal across communities hence disparities in harm to human health. This study examined the association between socioeconomic characteristics and perceived air quality among residents of low- and high-socioeconomic status areas in Nairobi, Kenya. Two regions within Nairobi County were selected for this study: Mukuru kwa Njenga (representing the Low Socioeconomic Status) and Langata (representing the High Socioeconomic Status) with a sample size of 384 in HSES areas and 368 in LSES areas. A cross-sectional study was conducted among 752 respondents residing in selected LSES and HSES areas of Nairobi. Data was collected using a structured questionnaire assessing sociodemographic characteristics, income, education, employment, perceived air quality, and self-reported health outcomes associated with air pollution exposure. Descriptive statistics were used to summarize participant characteristics and perceived air quality. Chi-square tests were used to examine associations between residential area and categorical health outcomes, while ordinal logistic regression was used to assess the association between socioeconomic characteristics and perceived air-quality ratings. Perceived air quality differed significantly between residential socioeconomic groups. Respondents in LSES areas were more likely to rate air quality as poor or very poor, with 45.4% rating it as very poor, compared with only 1.6% of respondents in HSES areas. In contrast, 12.2% of HSES respondents rated air quality as good compared with 0.3% in LSES areas. The association between area of residence and perceived air-quality rating was statistically significant, {chi}2(3) = 282.672, p < 0.001. In the ordinal logistic regression model, HSES residence was associated with significantly lower odds of reporting poorer perceived air quality compared with LSES residence (OR = 0.135, 95% CI: 0.095-0.190, p < 0.001). Income was also significantly associated with perceived air quality, while respondents with no formal education had higher odds of reporting poorer perceived air quality compared with those with secondary education (OR = 3.254, 95% CI: 1.388-7.638, p = 0.007). Significant differences were also observed for several self-reported health outcomes. Respiratory problems were more prevalent among respondents in LSES areas than HSES areas (72.7% versus 50.4%; {chi}2(1) = 29.081, p < 0.001; Cramer's V = 0.224). However, allergies, eye irritation, and headaches were reported more frequently in HSES areas than in LSES areas, with significant associations observed for allergies ({chi}2(1) = 106.479, p < 0.001; Cramer's V = 0.429), eye irritation ({chi}2(1) = 136.577, p < 0.001; Cramer's V = 0.486), and headaches ({chi}2(1) = 149.180, p < 0.001; Cramer's V = 0.508). No statistically significant association was observed for cardiovascular problems, likely reflecting the very low number of reported cases. Substantial socioeconomic disparities in perceived air quality and self-reported respiratory health outcomes were observed. Residents of low-socioeconomic status areas consistently perceived poorer air quality and reported a higher burden of respiratory problems, highlighting the need for targeted interventions to reduce environmental health inequalities.

Early life adversity is a recognised risk factor for poor health in later life, but its relationship with allostatic load (AL; a measure of cumulative physiological stress) remains underexplored across populations around the world. This study builds evidence to test a life course approach to AL using harmonised data from six longitudinal ageing cohorts: Health and Retirement Study (US, N=2,427), English Longitudinal Study of Ageing (England, N=2,038), The Irish Longitudinal Study on Ageing (Ireland, N=8,184), Survey of Health Ageing and Retirement in Europe (Europe, N=4,079), China Health and Retirement Longitudinal Study (China, N=5,220), and Indonesia Family Life Survey (Indonesia, N=1,243). Childhood information at aged ten to sixteen was collected retrospectively from adults aged 65 on average. An AL score is constructed using biomarker data and a latent construct of early life adversity constructed to address recall bias. Results show that early life poverty is significantly associated with raised AL in Ireland, China, and Indonesia, supporting the impact of early life experiences on older adulthood. However, studies from other countries do not confirm statistical significance. These findings document that experiencing poverty during development phase in some settings is associated with higher AL in later life, however further analyses are required to explore possible variations across different populations and their social and economic context. These results add further evidence that social determinants of health, including child poverty, in some settings, contribute to cumulative physiological stress across the life course, and that policies and actions to reduce child poverty can have beneficial effects not only as children, but also as older adults.

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.

Many urban health outcomes are shaped by environmental stressors that occur together rather than in isolation, yet methods for measuring such co-occurrence at the neighbourhood scale remain underdeveloped. We developed a multi-metric framework for joint co-exposure assessment and applied it to characterise the joint spatial distribution of summer surface heat and fine particulate matter (PM2.5) across 42,304 census tracts in 48 large US metropolitan areas during summers 2015 to 2020, covering approximately 174.6 million residents. The framework combines a composite co-exposure index, a joint exceedance indicator, a conditional exceedance ratio that compares observed joint occurrence to within-group statistical independence, and an upper tail dependence parameter estimated using both the non-parametric Caperaa-Fougeres-Genest estimator and a Gumbel copula, with bias-corrected and accelerated (BCa) confidence intervals obtained from a 5,000-replicate metropolitan-area block bootstrap. Among residents of predominantly Black tracts, 13.21% lived in neighbourhoods that simultaneously exceeded the within-metropolitan-area 80th percentile for both heat and PM2.5, compared with 3.33% of residents of predominantly White tracts; the corresponding heat-only and PM2.5-only ratios were 2.88 and 2.48. Residents of Home Owners Loan Corporation grade D tracts had 3.97 times the odds (95% confidence interval 2.79 to 5.66) of joint hotspot residence compared with grade A residents after adjustment for contemporary tract racial composition, poverty, renter-occupancy, and pre-1960 housing. The within-group conditional exceedance ratio at the 80th percentile was 2.29 in predominantly White tracts (95% BCa CI 1.81 to 2.78), 1.27 in predominantly Black tracts (0.71 to 1.56), and 1.13 in predominantly Hispanic tracts (0.70 to 1.41); the White interval excluded one while the Black and Hispanic intervals included one, which we interpret as power-limited given fewer contributing CBSAs. Magnitudes attenuated under near-surface air temperature surfaces but the direction and statistical significance of the primary findings were preserved. The framework is portable to other compound-exposure questions and supports cumulative-impact assessment.

Background: The retail food environment is a widely used exposure in behavioural-nutrition and obesity research, on the premise that nearby food retailers shape diet and obesity risk. Over the past quarter-century, grocery stores have declined across rural and small-town America while limited-assortment discount ("dollar") stores have proliferated. Standard food-environment indices classify retailers as healthy or less-healthy but typically exclude dollar stores, now the fastest-growing food-retail format. As a result, a single classification decision may alter how the food environment is measured and the conclusions drawn from it. We develop a dollar-aware index, quantify how counting dollar stores changes the measured exposure, and derive a longitudinal trajectory typology. Methods: Using establishment-level data from Data Axle for all 878 Mississippi census tracts (1997-2024), we classified food retailers into five mutually exclusive categories using a previously validated approach and calculated the modified Retail Food Environment Index (mRFEI) in both its standard and dollar-aware forms, with the latter counting dollar stores as less-healthy outlets. We fitted Nagin-style group-based trajectory models to the tract-level dollar-aware index, related class membership to the Social Vulnerability Index (SVI) and urbanicity with multinomial regression, and characterised spatial clustering (Getis-Ord Gi*, join-counts) and grocery access. Results: Grocery stores fell from 1,616 to 716 while dollar stores rose from 315 to 1,005, intersecting in 2018. Counting dollar stores lowered the index by a margin that widened over time, and a growing number of tracts had only dollar-store retail, undefined under the standard index. Six trajectory classes emerged: stable adequate (5.6% of tracts), steady decline (13.1%), early collapse (11.1%), late collapse (6.7%), persistently constrained (34.1%) and chronic desert (29.3%); only the stable-adequate class (5.2% of children) stayed adequate throughout. Constrained and steady-decline membership rose steeply with vulnerability (RRR 11.7 and 9.9); chronic desert was urban (RRR 5.2, a food-swamp pattern); collapse classes had no cross-sectional social signature. Conclusions: In the US state with the highest adult obesity prevalence, a single retailer-classification decision substantially changes the measured food environment. The dollar-aware index and trajectory typology offer a transferable, time-varying exposure for behavioural-nutrition and obesity research and establish a foundation for future childhood-obesity studies.

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

Residential environmental exposures might be associated with biological ageing, but evidence has largely focused on a limited set of single exposures. We investigated associations between the multidomain residential external exposome and epigenetic ageing in 3307 adults from the nationally representative UK Understanding Society study. We linked 30 exposures across six domains spanning social, built, physico-chemical, retail, health and weather environments, to residential postcodes and examined associations with first-, second- and third-generation epigenetic clocks. Across four complementary approaches capturing individual, joint, domain-level and clustered exposures, adverse social, built and physico-chemical environments showed the most consistent associations with biological ageing. Area deprivation, neighbourhood crime, lower social cohesion and greenness, air pollution and higher temperatures were associated with faster pace of ageing measured by third-generation clocks (particularly DunedinPACE) and, to a lesser extent, with PhenoAge. Greater distance to leisure services was associated with older biological age on Horvath s clock and PhenoAge. Clustering identified urban and rural exposure profiles, with faster ageing in the urban profile. Residential environmental exposures are associated with epigenetic ageing, with later-generation clocks appearing more sensitive to the biological imprint of the external residential exposome.

Urban air pollution remains a significant public health concern, contributing to premature deaths and adverse health outcomes. However, there is little causal research evaluating the effectiveness of policies designed to improve air quality. This study assesses the impact of all three stages of London's Ultra Low Emission Zone (ULEZ) on air pollution, via PM2.5 levels, and respiratory health, via prescription records for bronchodilator and respiratory corticosteroid medications. Analyses are at general practice level, using a generalised synthetic control method to estimate causal impacts. Stage 1 was associated with a statistically significant but negligible 0.77% reduction in PM2.5 levels, with no corresponding change in prescribing. Stage 2 produced a paradoxical 2.69% increase in PM2.5, alongside a 4.44% decrease in inhaled corticosteroid quantity but a 12.51% increase in average daily quantity (ADQ) usage, suggesting a worsening of disease severity among existing patients. Stage 3 yielded a 2.69% PM2.5 reduction and a modest 2.18% decrease in bronchodilator ADQ usage. Spillover effects beyond the ULEZ boundary were statistically significant, but negligible. We find overall that the ULEZ had minimal effects on both air quality and respiratory prescribing across all three stages. These findings provide new insights into the effectiveness of ULEZ policies in reducing air pollution and its associated health impacts, suggesting the zone's effects are considerably smaller than previously reported, and that integration with broader policy measures may be necessary to achieve meaningful public health gains.