Health & Place

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.

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.

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.

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.

Background We examined whether two key housing quality indicators, energy efficiency and household overcrowding, were associated with lower respiratory tract infection (LRTI) hospital admissions in infants. Methods We used a cohort of all singleton births in Scotland 2010-2012, created through linked vital statistics and health data. LRTI admissions were characterised in hospital records. Overcrowding (defined using the national room standard) and median postcode-level energy efficiency were defined using maternal Census and postcode-level Energy Performance Certificate data linked to the cohort, respectively. We used logistic regression to model the odds of at least one infant LRTI admission. Results The cohort included 136,123 infants of whom 4.0% had at least one LRTI admission. Overcrowding was more common among infants of younger mothers and those in rented housing. Energy efficiency was lower among infants of older mothers, living in owner occupied homes, in less deprived areas. Compared with infants living in homes with excess rooms (under-occupied housing), those whose homes were below, or met, the minimum room standard had higher odds of LRTI admission (adjusted odds ratio 1.07, 95% CI 0.98-1.17; 1.10, 95% CI 1.03-1.17, respectively). Postcode-level energy efficiency was not associated with LRTI admission odds. Conclusion Overcrowding was more common in socioeconomically disadvantaged households and associated with increased risk of LRTI admission in infancy. Lower energy efficiency was associated with factors commonly linked to socioeconomic advantage and was not associated with LRTI admissions. Improving access to housing with adequate living space may reduce the burden of LRTIs in early life.

Purpose: This study assesses the association between colorectal cancer (CRC) screening and a validated, housing-based measure of individual-level socioeconomic status (SES, called HOUSES hereafter) within rural communities and determines whether HOUSES-integrated geospatial analysis can be used to tailor interventions. Methods: We used CRC screening data from a subset of Mayo Clinic Midwest patients living in cities without ready access to routine care in the Mayo Clinic Health System in 2019 to represent rural communities. At the individual level, we assessed the association between CRC screening rates and the HOUSES index, adjusting for age, sex, race/ethnicity, comorbidity, distance from home address to clinic, and area deprivation index, using a multilevel mixed-effects logistic regression model. Additionally, we conducted geospatial analysis to examine the correlation between hotspots of 1) lower CRC screening rates and 2) lower SES of the subject population (HOUSES quartile 1). Findings: Among 34,489 individuals (median age 64.0 years, 52.4% female), those with the lowest SES (HOUSES Q1) had 37% lower odds of being CRC screening adherent than those with the highest SES (HOUSES Q4) (adj. OR [95% CI]: 0.63 [0.58-0.69]). In the 14 identified HOUSES Q1 hotspots, there was a significant correlation in counts of HOUSES Q1 and low CRC screening (correlation coefficient=0.81). Conclusion: Lower SES was significantly associated with lower CRC screening among rural populations. HOUSES-enabled geospatial analysis identified geographic hotspots with lower CRC screening rates for targeted interventions to address disparities in CRC screening in rural communities. HOUSES may be a useful digital tool for cancer preventive care and research.

Background: Neighborhood disadvantage indices used in public health research and policy include multiple economic, social, and housing items. However, research has failed to question whether it is necessary to include a multitude of economic, social, and housing variables in a single index. The purpose of this work was to examine three different neighborhood indices: a multidimensional disadvantage index, a unidimensional disadvantage index, and a unidimensional affluence index, and examine their performance with respect to distinguishing between healthy and unhealthy census tract neighborhoods in the United States. Methods: The 2022 disadvantage and affluence indices came from the National Neighborhood Data Archive, which are derived from census tract data from the American Community Survey 5-year estimates (2018-2022). The multidimensional disadvantage index included seven economic, social (e.g., single parent households), and housing items; the unidimensional disadvantage index included three poverty and income items; the unidimensional affluence index included 3 items capturing greater social and economic resources. Data on neighborhood health status (census tract prevalence of obesity, diabetes, and coronary heart disease) was obtained from the Population Level Analysis and Community EStimates database for 2022 and linked to the disadvantage and affluence indices for 83,522 census tracts. Contingency tables examined the degree of correspondence in quintiles across the three different indices and the corresponding disease prevalence in each cell. Generalized linear mixed models regressed the disease prevalence variables on index quintiles to determine the predicted prevalence of disease across the disadvantage gradient for each index. Results: Compared to the unidimensional disadvantage and affluence indices, the multidimensional disadvantage index underestimated disease burden in the most disadvantaged census tracts, and overestimated disease burden in the least disadvantaged tracts. Conclusions: Using a disadvantage or affluence index with a more parsimonious set of items would have greater precision in identifying communities at risk for poor health.

Objective: To examine if food insecurity moderates the relationship between rurality and mental health outcomes (suicide mortality, poor mental health days, frequent mental distress) and to assess if these effects vary across U.S. Census divisions. Methods: This county-level (n=2,397) cross-sectional study used OLS and spatial error regression to analyze public data from sources including the County Health Rankings and USDA. We modeled suicide mortality, poor mental health days, and frequent mental distress as functions of the Index of Relative Rurality (IRR) and food insecurity, controlling for median income and provider rates. The suicide model was also tested across nine U.S. Census divisions. Results: Baseline models revealed a paradox: rurality was a direct risk factor for suicide (B=0.400) but protective for poor mental health days (B=-0.224). The national multivariable model revealed a significant, positive rurality-food insecurity interaction for suicide mortality (B=0.861), indicating a synergistic risk. This interaction was not significant for general mental distress, which was more strongly predicted by income and food insecurity. Regional analysis confirmed the suicide interaction was potent in five divisions, including the Pacific (B=3.048) and Mountain (B=1.712) , but absent in others (e.g., South Atlantic). Conclusions: The drivers of suicide are distinct from those of general mental distress and are geographically heterogeneous. The interaction of rurality and food insecurity creates a compounded risk for suicide. Suicide prevention must be regionally-tailored and address structural inequalities, such as food insecurity, alongside clinical care.

Abstract Background Breast and cervical cancer screening in Ghana remains low, and several analyses of the Ghana Demographic and Health Survey (GDHS) have already shown that wealth, education, and place of residence pattern who gets screened [1-3]. Whether this patterning clusters geographically below the level of administrative region has not been tested for this population, and whether cluster-aware machine learning adds anything to the standard regression approach used so far remains open. Methods We analyzed the 2022 Ghana Demographic and Health Survey women's file (N = 15,014; primary sample of women aged 25-49 years, n = 9,510) linked to cluster geographic coordinates for 618 enumeration areas. Clinical breast examination and cervical cancer testing were the two outcomes. We estimated survey-weighted prevalence across demographic and socioeconomic strata, tested global spatial autocorrelation with Moran's I, mapped local clustering with Getis-Ord Gi* statistics, and separately fitted gradient-boosted classifiers on individual-level socioeconomic covariates, validated under cluster-held-out five-fold cross-validation to prevent within-cluster information leakage. Feature contributions to the breast-screening model were interpreted with an additive, feature-level explanation technique, and socioeconomic inequality was quantified with both the ordinary and Erreygers-corrected concentration index. The predictive models did not include geographic coordinates or survey weights; both are noted as limitations. Results Weighted prevalence among women aged 25-49 years was 22.5% (standard error 0.77) for breast examination and 6.9% (standard error 0.46) for cervical testing. Both rose with education and wealth and were roughly double in urban areas relative to rural ones. Moran's I was positive and significant for both outcomes (breast: 0.217, z = 11.69, p < 0.001; cervical: 0.125, z = 6.77, p < 0.001), and local cluster statistics located discrete hotspots around Greater Accra and parts of Ashanti and Bono, with coldspots concentrated across the north, though these local tests were not adjusted for multiple comparisons. Cross-validated discrimination reached an area under the curve of 0.716 for breast examination and 0.730 for cervical testing, without confidence intervals or calibration assessment; education, wealth, and age were the dominant predictors for both. The Erreygers index put breast examination as the more wealth-concentrated outcome (0.231 versus 0.095 for cervical testing), reversing the ranking implied by the uncorrected index. Conclusions Screening uptake in Ghana is spatially clustered at a resolution that regional reporting cannot show, and this clustering is compositionally associated with, though not formally shown to be mediated by, the socioeconomic makeup of individual clusters. Cluster-level spatial analysis and a model-based risk ranking may offer a useful complement to regional targeting, but calibration, external geographic validation, and comparison against a regional-allocation baseline are needed before any operational use.

Background: Rising rates of young people Not in Education, Employment, or Training (NEET) in the UK have recently coincided with declining youth physical and mental health but no study has asked whether this reflects a growing proportion of young people with health problems (prevalence) or those with health problems becoming more likely to be NEET (penalty). Methods: Using 15 years of Understanding Society data (2009-23), we analysed 15,242 respondents aged 16-24 (66,160 observations). We employed three complementary approaches: descriptive trends, Blinder-Oaxaca-Kitagawa (BO) probit decomposition comparing 2009-2013 and 2019-2023 against a 2014-2018 reference period, and fixed-effects (FE) Poisson models with lagged health status. Exposures included self-reported health conditions or disability (SRHD), psychological distress , diagnosed conditions and socio-demographic factors. Findings: NEET rates were lowest in 2014-18 (10.5-11.5%) and higher in 2009-13 (12-15%) and 2019-23 (15-16%). Higher prevalence of SRHD, psychological distress, diagnosed depression and multimorbidity explained changes in NEET prevalence across both the 2009-13 to 2014-2018 and 2014-18 to 2019-23 periods. No change in penalty was observed for any health variable across periods, except for an increase in the penalty for SRHD between the 2009-13 to 2014-18 periods. Interpretation: Rising NEET rates among UK youth are driven largely by more young people having physical and psychological ill health. Whilst labour market and education accommodations remain important, reducing NEET rates will require reversing the decline in youth health, not just accommodating it.

Communities exposed to flooding can experience markedly different mental health outcomes, yet conventional resilience indicators capture only part of the social and contextual conditions that may explain this variation. This study develops a multilevel and predictive framework for examining community resilience and depressive symptoms following flood exposure in Indonesia. Data were drawn from 20,303 respondents aged 15 years and older nested within 312 communities in the Indonesia Family Life Survey (IFLS-5). Depressive symptoms were assessed using the 10-item Centre for Epidemiologic Studies Depression Scale (CES-D-10), with Rasch Partial Credit Model calibration used to examine measurement properties. Bayesian multilevel models quantified between-community heterogeneity and assessed how far observable structural resources accounted for this variation. Community resilience was represented through two complementary constructs: structural resilience, based on observable socioeconomic and social-capital resources, and Latent Community Protective Capacity (LCPC), a model-derived proxy for residual contextual variation in depressive-symptom risk. Approximately 6 percent of variation was attributable to between-community differences, while observable structural resources explained only part of this heterogeneity. Structural resilience and LCPC were weakly correlated (r = 0.155). Moderation analyses provided no clear evidence that structural resilience altered the flood-depression association, while LCPC showed a directionally consistent but uncertain buffering pattern. Predictive models incorporating community-level information improved discrimination, with the best-performing model reaching an ROC-AUC of approximately 0.71. The findings suggest that observable resource-based indices provide an incomplete account of community-level mental health vulnerability and that residual contextual measures may provide complementary information, while requiring cautious interpretation and independent validation.

Objectives: To describe age-group differences in inability to afford dental treatment and cost related dental delay, among the US community-dwelling population. Study design: Descriptive analysis of nationally representative survey data. Methods: Using nationally representative Medical Expenditure Panel Survey data (2018-2021), we examined trends in inability to afford dental treatment and cost-related dental treatment delays across four age groups (2-17, 18-39, 40-64, [&ge;]65 years). Weighted analyses accounted for the complex survey design; statistical significance was set at p<0.001. Results: Cost-related delays declined modestly from 2018 to 2021 but remained most prevalent among adults aged 40-64 (4.8% for ages 40-64, 3.4% for ages 18- 64, 2.2% for ages>65 in 2021; p<0.001). Conclusion: Middle-aged adults seem to experience delays due to cost, underscoring the need for dental coverage to expand dental coverage for this group and to reduce their out-of-pocket costs.

Background In sub-Saharan Africa, women are navigating overlapping burdens of undernutrition and rising overweight/obesity, often within fragile health system and rapidly changing food environments. In the DRC, theses tensions may be intensified by rapid urbanization, socioeconomic disparities, insecurity and shifting lifestyles. Despite those changes, national level evidence on who is the most affected by excess weight and why remains scarce. This study assessed the determinant of overweight and obesity among Congolese women of reproductive age, aiming to highlight the social and geographic inequalities. Methods We analysed nationally representative data from the 2023 DHS. The analysis included 10,740 non-pregnant women aged 15-49 years with valid anthropometric measurements. Overweight/obesity was defined as BMI [&ge;] 25 Kg/m2. We examined a broad range of potential associated factors, including province, residence, socioeconomic status, household structure, education level, marital status, occupation, dietary diversity score, healthy diet related indicators, media exposure, internet use and health service utilisation. Weighted analyses accounted for the DHS sampling design. Variables associated at p value < 0.20 were retained for multivariable modelling. Multicollinearity was assed via adjusted GVIFs. Four hierarchical weighted logistic regression were built; the fully adjusted model guided final interpretation. Results Nearly on five women of reproductive age (19.5%) lived overweight or obesity. However, this burden was not evenly distributed. Women from Kongo Central and Tshuapa exhibited significantly lower odds, while those in Bas-Uele, Nord-Kivu, Sud-Kivu and Maniema were substantially more affected, highlighting spatial inequities. Women living in rural areas had lower odds of overweight/obesity compared with their urban counterparts (aOR=0.6; 95% CI: 0.48-0.79; p<0.001). A pronounced socioecomic gradient was observed. Compared with the poorest households, the likelihood of excess weight increases progressively among women in middle income household (aOR=1.65;95% CI:1.13-2.41), rich households (aOR=2.41; 95%CI:1.62-3.60), and was highest among the richest (aOR=4.19; 95%CI: 2.45-7.16). Larger households appeared protective, with lower odds observed in household of 4-5 members (aOR=0.68; 95%CI:0.5-0.92), 6-7 (aOR=0.72;95% CI: 0.54-0.97) and [&ge;]8 members (aOR=0.69; 95%CI:0.50-0.95) compared with smaller household. Age was the strongest predictor, with risk sharply accelerating after 30 years. Being married or in union was associated with higher odds. Notably, frequent internet use independently predicted overweight/obesity. In contrast, dietary diversity and unhealthy food indicators were not significantly significant in the fully adjusted models. Conclusion Overweight and obesity are rising among Congolese women, but unevenly and unjustly. Urban residence, socioeconomic status, age and digital exposure strongly sharply shape who is the most affected, revealing deep social and geographic inequities. Addressing this growing epidemic requires equity-oriented, province specific actions, alongside stronger primary prevention. Key-word: Overweight-obesity-associated factors, DRCongo, DHS

Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) describes health inequalities through three components: (i) specific contextual effects (SCE), (ii) general contextual effects (GCE), and (iii) discriminatory accuracy of the context. We present Simple-Means MAIHDA (S-MAIHDA), which estimates each stratum directly from its observed individuals, with no distributional assumption. The observed proportions are unbiased whatever the stratum size, and their confidence intervals report the uncertainty honestly. S-MAIHDA operationalises the three components on the probability scale. The SCE are the raw and standardised stratum prevalences and the modification of the sociodemographic average differences by the area. The GCE are the variance partition coefficient (VPC) and the contextual structuring of the between-stratum inequality, expressed as the contextual clustering of inequalities, the additive sociodemographic differences, and the contextual modification of inequalities (CMI). The contextual discriminatory accuracy is expressed by the area under the ROC curve (AUC), and the sensitivity and specificity at the population prevalence as the threshold for a possible intervention. Because its estimates are the observed data themselves, S-MAIHDA is the canonical description, and the compare diagnostic quantifies how Random-Effects MAIHDA (RE-MAIHDA), the usual implementation, departs from it: RE shrinkage pulls small strata towards the overall mean and can hide the very inequalities the analysis seeks. The approach is implemented in the smaihda Stata command and reproduced in free Python code. We illustrate S-MAIHDA on register data from Malmo, Sweden (43,291 individuals; 300 area-sociodemographic strata), showing how the three components separate two contrasting outcomes: psychotropic medication use, almost purely sociodemographic, stable across areas, with weak contextual structuring (VPC {approx} 4%, CMI {approx} 0%); and choice of a private general practitioner, strongly geographical (VPC {approx} 11%, CMI {approx} 17%), with the sociodemographic differences reshaped and amplified in wealthy areas. RE-MAIHDA attenuated inequalities. For describing inequalities, S-MAIHDA preserves what the data show.

Background: General illness-related sick leave among teachers represents a relevant public health and workforce management issue. However, long-term population-based evidence describing its distribution and associated factors in Latin American urban educational systems remains limited. This study aimed to characterize the occurrence, distribution, duration, and sociodemographic, occupational, temporal, and territorial factors associated with general illness-related sick leave among public school teachers in Bogota between 2010 and 2025. Methods: A retrospective cohort study was conducted using integrated administrative databases from the Bogota District Department of Education. The primary outcome was the occurrence of at least one general illness-related sick leave episode in a teacher-month observation. Descriptive analyses were performed to characterize sociodemographic and occupational patterns. A multivariable logistic regression model was used to estimate associations. Month and year were included as temporal fixed effects to account for seasonal patterns, academic-calendar effects, pandemic-related disruption, and secular changes. Results: The cohort included 59,697 unique teachers, contributing 537,025 teacher-year observations from teachers with an active employment record between January 1, 2010, and July 31, 2025. Overall, 41.59% of teacher-year observations included at least one general illness-related sick leave episode, and 83.26% of teachers had at least one episode at any time during follow-up. Respiratory diseases accounted for the largest share of episodes, followed by musculoskeletal and infectious diseases. Mean duration varied substantially by diagnostic category, ranging from short respiratory and infectious episodes to longer absences related to neoplasms, circulatory diseases, injuries, and mental health conditions. In the multivariable teacher-month model, sick leave occurrence was associated with age, sex, occupational role, teaching area, contract type, locality, calendar month, and calendar year. Lower odds were observed among male teachers, principals, and teachers with provisional contracts, while temporal and territorial variation was observed across months, years, and localities. Conclusions: General illness-related sick leave among public school teachers in Bogota showed consistent sociodemographic, occupational, temporal, and territorial patterns. Respiratory and musculoskeletal conditions accounted for the largest share of episodes, while chronic, neoplastic, injury-related, circulatory, and mental health conditions were associated with longer durations. These findings provide population-level evidence to inform occupational health surveillance, seasonal preparedness, and workforce planning strategies within urban educational systems.

Unhealthy retail outlets, including liquor stores, bars, convenience stores, and fast food, are concentrated in lower-income neighbourhoods. This is a well-documented cross-sectional fact; the process that sustains it is not. A neighbourhood can hold more because more open there or because those already there survive longer, and these point to different responses. We assembled an establishment-level panel of every business in Rhode Island from 2016 to 2025 (480,923 geocoded establishment-years across nine annual cross-sections), following the same outlets year to year, and classified and counted unhealthy outlets by census tract. We estimated the tract income gradient three ways (negative binomial regression, a concentration index, and a Bayesian spatial model), tested its stability, and decomposed it into openings and closures. The gradient was strong, stable, and robust: about 30 percent fewer unhealthy outlets per resident per standard deviation of higher income, with racial composition and poverty no longer associated once income was included. It was reproduced through entry, not survival: closures were even-handed across income, while new unhealthy outlets opened about 2.2 times as often per resident in the lowest-income tracts as in the highest. This entry was not unhealthy-specific: new healthy food retail tilted toward lower-income tracts at least as strongly, and the unhealthy share of openings did not rise as income fell. The standing burden was nonetheless dominated by convenience stores and off-premise alcohol. Efforts to reshape the retail environment will have more leverage on new openings than on the existing stock, through instruments defined by outlet type.

The analysis uses a global 2010-2023 panel comprising 3,038 economy-years across 217 economies. It explicitly separates between-economy and within-economy estimands and tests the longitudinal interpretation using an identical-sample temporal analysis with cluster-aware coefficient contrasts, a formal isometric log-ratio sensitivity analysis, independent fixed-effects replication, and wild-cluster-bootstrap inference. The central finding is deliberately calibrated: cross-economy agreement cannot validate national sugar availability for longitudinal obesity surveillance. The study identifies temporal and construct instability without claiming that sugar is protective or that the mechanisms producing the instability have been identified. The manuscript aligns well with PLOS ONEs emphasis on technically sound, transparent and reproducible research of broad relevance. All data required to reproduce the findings, complete metadata, executable code, full-precision results, diagnostic outputs and a completed STROBE checklist are provided as S1-S5. Figures are provided separately as compliant 350-dpi TIFF files. The study used only publicly available, aggregated economy-year statistics and involved no individual participants, identifiable information or biological specimens; institutional ethics review and consent were therefore not required. This is original work; it is not under consideration elsewhere, and the sole author has approved the submission and accepts responsibility for its content. Funding and competing-interest declarations will be entered accurately in the submission portal. An Academic Editor with expertise in nutritional epidemiology, global health metrics, longitudinal panel methods, or food-system surveillance would be well placed to assess the work.

Background: High rates of young people who are not in education, employment or training (NEET) are a major societal concern in the UK. Whilst other studies have highlighted that adolescent health can predict NEET status in young adulthood, robust and recent longitudinal evidence remains limited. Methods: This study used data from the Millennium Cohort Study, a longitudinal study of people born in the UK in the early 2000s, to estimate the extent to which mental health conditions, physical health conditions and health behaviours during adolescence predict NEET status in early adulthood (median age: 23). Co-occurrence of exposures was also considered and population attributable fractions were calculated to account for differences in exposure prevalence. Results: Among 8,374 young people, 12.5% were NEET at age 23; approximately two thirds were seeking work and one third were economically inactive. Estimates adjusted for demographic factors indicated that multiple health exposures increased risk of being NEET at age 23, with mental health conditions predicting greater risk than physical health conditions and health behaviours. For instance, a longstanding mental health condition more than doubled the risk of being NEET (adjusted relative risk [aRR] = 2.39, 95% CIs = 1.85, 3.09), while autism (aRR = 3.60, 95% CIs = 2.69, 4.83) and ADHD (aRR = 3.25, 95% CIs = 2.38, 4.44) more than tripled the risk. A greater number of reported adolescent mental health conditions was associated with greater risk of being NEET in young adulthood. Obesity predicted being NEET at age 23 (aRR = 1.54, 95% CIs = 1.18, 2.01) and obesity accompanied by a mental health condition further increased risk (aRR = 2.01, 95% CIs = 1.38, 2.93). Follow-up analyses indicated that associations between adolescent mental health and young adult NEET status were more pronounced for females than males and for the economically inactive than those seeking work. Conclusions: Findings indicate that adolescent health, especially mental health, strongly predicts being NEET in early adulthood. Early, integrated health and education interventions may help reduce later educational and labour market disengagement.

Importance: While increased physical activity (PA) and decreased sedentary behavior (SB) are associated with favorable health outcomes, evidence regarding their causal effects on healthcare costs remains limited. Objective: To assess the causal effects of PA and SB on healthcare costs. Design: A two-sample Mendelian randomization (MR) study. Setting: Separate, non-overlapping cohorts with genetic instruments for self-reported and device-based PA and SB, and healthcare costs. Participants: The instruments used to assess self-reported PA were derived from a genome-wide meta-analysis of 606,820 individuals across 51 cohorts. Two large genome-wide association studies (GWASs) were used for self-reported SB (leisure screen time N=526,725; television watching N=408,815), while accelerometer-based GWASs (N=89,683-91,105) were used for device-based PA and SB. The instruments used to assess the outcome data were obtained from the FinnGen cohort (N=373,160). Exposures: Genetically predicted PA and SB. Main Outcomes and Measures: Validated genetic instruments for log-transformed annual healthcare costs derived from registers, including primary care, secondary care, and medication costs. Inverse variance weighting was used as the primary MR measure, while the sensitivity analyses included MR-Egger, weighted median, simple mode, weighted mode, F-score, Cochran's Q, and leave-one-out analysis. Results: Higher genetically predicted self-reported PA was associated with lower healthcare costs (causal estimate, {beta} = -0.166; 95% CI, -0.270 to -0.062). In contrast, higher genetically predicted SB (leisure screen time or television watching) was associated with higher healthcare costs across self-reported datasets ({beta} = 0.097; 95% CI, 0.064 to 0.130; {beta} = 0.114; 95% CI, 0.063 to 0.165, respectively). No associations were observed for device-based PA ({beta} = -0.014; 95% CI, -0.040 to 0.014) or SB ({beta} = -0.009; 95% CI, -0.197 to 0.179). Conclusions and Relevance: Findings based on genetically predicted PA and SB support a causal association between these behaviors and healthcare costs, suggesting that increasing population's leisure-time PA and reducing SB may decrease healthcare expenditure. This highlights the importance of promoting PA for both population health and long-term sustainability of healthcare systems. However, causal evidence remains partly limited, particularly for device-based measures of these behaviors.