Spatial and Spatio-temporal Epidemiology

IntroductionObesity and related health conditions are unevenly distributed across neighborhoods, often co-occuring with multiple health challenges and socioeconomic disadvantages. Using an ecosyndemic framework, which integrates ecological and social dimensions that contribute to the clustering of health problems, this study examines how adverse obesity-related health outcomes spatially cluster in relation to obesogenic environments and socioeconomic position (SEP) across Dutch neighborhoods. MethodsNationwide neighborhood-level data on health outcomes, obesogenic environmental exposures (food environment, walkability, drivability, bikeability, sports facilities), and SEP were combined for all inhabited Dutch administrative neighborhoods in 2016 (N=12,420). Cluster analysis was used to identify distinct neighborhood profiles and descriptive statistics to characterize each cluster, with spatial patterns visualized using an interactive heatmap and principal component plots. ResultsFive neighborhood clusters were identified. The Ecosyndemic cluster (N=1,070 neighborhoods) exhibited the highest burden of obesity (17% [IQR 16;19), chronic diseases (36% [IQR 33;38%) and risk of anxiety/depression (55% [IQR 51;58]), unhealthy food environments and low SEP. In contrast, the Privileged cluster (N=6,425) had more favorable health outcomes and living conditions, including lower obesity prevalence (12% [IQR 11;14]). The Psychosocial Vulnerability cluster (N=991) was notable for elevated risk of anxiety/depression (47% [IQR 43;51]) combined with relatively low obesity (11% [IQR 8;12]). The Syndemic cluster (N=1,836; obesity 15% [IQR 14;17]) and Towards Privileged cluster (N=2,098; obesity 12% [IQR 10;13]) represented intermediate profiles. ConclusionObesity and related health issues frequently cluster with unfavorable environment and SEP at the neighborhood level. The ecosyndemic framework offers a novel approach for identifying high-risk areas and supports targeted, social and place-based interventions.

Introduction Louisiana ranks last in the United States of America in terms of maternal health outcomes. Previous works have highlighted the impact of some social determinants of health on the incidence of adverse birth outcomes. These works have subjectively selected specific social determinants of health from larger datasets. Here, we attempt to replicate their results with objective variable selection techniques. Methods By deriving principal components from the Agency of Healthcare Research and Quality's parish-level social determinants of health dataset, we were able to objectively find social determinants of health associations instead of the conventional subjective variable selection approach. Then, we applied Bayesian linear mixed-effects models to calculate more conservative parameter estimates about the effects of social determinants of health on adverse birth outcome incidence. Then, we used local Moran's I to identify clusters of spatially autocorrelated parishes. Finally, we combined the results of these two methods and inspected the relationship between important predictors and clusters of spatial autocorrelation. Results We identified several significant effects on the incidence of adverse birth outcomes, including populational composition and economic attainment, and several clusters of high and low incidences of adverse birth outcomes in Louisiana. There was also a concordant relationship between important predictors from our predictive models and the cluster assignments of Local Moran's I. Conclusion Our results validate previous works in the subject area and hold implications for precision development of maternal health interventions in Louisiana.

Suicide is one of the worlds leading public health problems, with more than 720,000 deaths annually. Suicide has traditionally been studied from an individual perspective. However, research has increasingly highlighted the influence of community-level factors on suicide risk. This study aimed to (1) analyse the spatial distribution of suicide mortality at the provincial level in Spain (2018-2022); (2) perform stratified analyses by sex and age group; and (3) compare suicide risk across different phases of the COVID-19 pandemic. We used data from the Spanish National Institute of Statistics on 19,381 suicide deaths in 47 peninsular provinces between 2018 and 2022. Covariates included sociodemographic (e.g. aging rate, population density), economic (e.g. unemployment, GDP), and environmental (e.g. temperature) indicators. Bayesian hierarchical spatial Poisson regression models were fitted to estimate suicide risk and identify significant contextual variables. The general spatial model revealed a higher risk of suicide in provinces with lower population density, higher aging rates, and lower health expenditure. Other covariates such as gross domestic product, unemployment, or temperature were associated with specific sex or age groups. Suicide risk was highest in the northwestern provinces and lowest in the central regions. Stratified analyses showed similar patterns across gender and age groups, and between time periods, with some variations in spatial distribution. This study reveals significant spatial heterogeneity in suicide risk across Spanish regions, influenced by socio-demographic, economic, and environmental factors. These findings underline the importance of regionally tailored suicide prevention policies, especially in aging and low-density areas with low health investment. Key MessagesWe examined spatial patterns and socioeconomic and environmental determinants of suicide mortality in 50 Spanish provinces between 2018 and 2022. We found persistent geographical inequalities in suicide rates, with higher mortality in low-density provinces and those with older populations, and protective effects associated with health expenditure. These findings highlight the importance of place-based suicide prevention strategies that consider regional disparities and socioeconomic vulnerabilities.

ObjectiveThe objectives of this study were to: (1) quantify toddlers total physical activity (TPA) and guideline adherence using a machine learning method; and (2) explore socio-ecological predictors (e.g., sex, childcare) of TPA. MethodsToddlers (n=103, 21.4 {+/-} 6.9 months, 52% female) from the Hamilton, Canada region completed a gross motor assessment (Peabody Developmental Motor Scales 2nd ed; PDMS-2) and wore an ActiGraph wGT3X-BT accelerometer on the right hip for 4-8 days. Parents completed demographics and physical activity surveys. TPA was estimated using a validated machine learning model and reported using descriptive statistics. Multiple linear regression explored potential predictors of TPA: age, sex, household income, older sibling, BMI-for-age z-score, gross motor z-score, childcare arrangement, parent physical activity, and temperature, controlling for accelerometer wear time. ResultsToddlers had an average of 200.3 {+/-} 44.0 minutes of daily TPA. Most (72%) met the PA guideline of 180 min/day when averaged across days, while only 27% met the guideline on all days. The regression model was significant and explained 57% of the variation in TPA (F13,79 = 8.09, p < 0.0001). Controlling for wear time, the only significant positive predictors were age and PDMS-2 z-score. ConclusionAlmost three quarters of toddlers met the TPA guidelines. Older toddlers and toddlers with more advanced gross motor skills for their age participated in more daily TPA. Future research should continue to apply machine learning methods in more diverse samples and could build on modifiable predictors (e.g., motor skill) to design interventions to improve toddlers PA levels.

IntroductionAtmospheric conditions under climate change increase pressure on healthcare systems. Especially, the intensive care units (ICU) are vulnerable due to low buffer capacity and high utilization rates. MethodsDaily ICU cases from 2009 to 2023 were derived from the German statutory health insurance data of eleven regional AOK insurances. Cases were stratified by age and sex. Generalized additive models were used to investigate the associations between daily ICU cases and lagged atmospheric variables. Thirteen intensive care relevant diseases were analyzed using disease-specific predictor sets. Analyses were conducted for regions derived from a human-biometeorological characterization of Germany. Model performance was assessed using (weighted) explained deviance. ResultsOver the 15-year study period, 9,970,548 ICU patients were recorded (44% women), 74.3% aged [&ge;]60 years. Trauma was the most common ICU-related disease, followed by non-ST elevation myocardial infarction (NSTEMI), pneumonia and ischemic stroke. ICU demand was most sensitive (p [&le;] 0.05) to pressure-related factors, thermo-physiological parameters and ozone concentration. In terms of sex-age differences, atmospheric factors affected men more frequently, while women were more impacted by cold weather and particulate matter (PM10). Heat was more relevant for patients aged [&ge;]60 years. The NSTEMI model in Central Eastern Germany performed best (weighted explained deviance of 49.3%). In males [&ge;]60 years, heatwaves were associated with a reduced risk of ICU cases (Relative Risk = 0.94, 95%-Confidence Interval 0.89 to 0.99). ConclusionThe study identified key atmospheric factors for ICU, enabling the German healthcare system to prepare better for short-term impacts of meteorological and air quality factors. KEY MESSAGESWhat is already known on this topic: O_LIThe atmospheric changes have a direct impact on public health and the inpatient care, particularly in intensive care units. C_LIO_LIConsequently, there is a necessity to investigate the influence of atmospheric factors on intensive care in order to prepare the healthcare system for the new circumstances. C_LI What this study adds: O_LIThe study provides evidence that atmospheric factors influence the intensive care in Germany and describes age and sex-specific aspects. C_LIO_LIThe results offer valuable insights into how different atmospheric factors affect the demand for intensive care in hospitals. C_LI How this study might affect research, practice or policy: O_LIThe study enables the German healthcare system to better prepare for short-term effects of atmospheric factors, and structural or resource-related adjustments could be made in hospitals to anticipate for short-term fluctuations in intensive care demand. C_LI

PurposeMost spatio-temporal models identify COVID-19 sociodemographic and socioeconomic risk factors using methods that assume a single spatial dependency pattern across the city, which may not reflect reality. The purpose of this study is to apply a spatially and temporally localized Bayesian model to identify COVID-19 risk factors that account for localized context. MethodsFor this study, a spatio-temporal localized Bayesian Hierarchical Model (ST-LCAR) was used to assess the relationships between population factors (age, sex, income, visible minority status, and education) and COVID-19 relative risk. The ST-LCAR model accounts for spatial and temporal autocorrelation through spatio-temporal random effects along with piecewise intercepts to capture step changes in relative risk patterns that might be reflective of underlying local contexts. This study focuses on the first four complete waves of the COVID-19 pandemic across Forward Sortation Areas (FSAs) in the City of Toronto. ResultsA 10-percentage-point increase in the proportion of residents who identify as visible minorities was associated with a 3% increase in COVID-19 relative risk; however, this association varied across different social contexts. On the other hand, a 10-percentage-point increase in the proportion of residents with post-secondary education was associated with a 22% decrease in relative risk. Beyond quantitative relationships, our model identified 3 times higher COVID-19 relative risk in the northwestern portion of the city, with patterns varying over time. ConclusionThe different COVID-19 patterns in the city of Toronto may have been shaped by the complex and diverse social contexts, products of ingrained systems of structural inequities that influence the living, working, and economic conditions of city residents. Public health interventions and pandemic preparedness should integrate an equity-focused lens that considers the diverse social contexts across the city and how it shapes health outcomes.

Background. Young sexual and gender minorities of color face compound health risks shaped by interlocking systems of racism, cisgenderism, and class inequality. Spatial health research documents that place shapes health, but existing methods cannot specify the mechanisms through which spatial configurations produce different health outcomes for differently positioned people. This gap prevents targeted intervention. ObjectiveTo develop and pilot test the Spatial Intersectionality Health Framework (SIHF), which specifies three mechanisms through which space produces intersectional health inequities: Layered (multiple oppressive systems activating simultaneously), Positional (the same space producing different health pathways by intersectional position), and Conditional (nominally protective spaces carrying hidden costs for specific positions). We also introduce and validate Intersectional Geographically-Explicit Ecological Momentary Assessment (IGEMA) as the methodology operationalizing SIHF across three data levels. MethodsThe GeoSense study enrolled 32 young sexual and gender minorities of color (ages 18-29) in New York City. IGEMA was implemented across three integrated levels: (1) GPS mobility tracking via participants personal smartphones, linked to census tract structural exposure indices across n=19 participants; (2) ecological momentary assessment of intersectional discrimination with multilevel modeling of mood, stress, and sleep outcomes; and (3) map-guided qualitative interviews with SIHF mechanism coding and intercoder reliability assessment across 92 coded records from 18 participants. This study was conducted as the pilot for NIH R01HL169503. ResultsAll three SIHF mechanisms were empirically detectable. A compound structural gendered racism index outperformed every single-axis alternative in predicting daily mood (b=-0.048, p=.001) and stress (b=0.121, p<.001). The Positional mechanism accounted for 71% of coded harm experiences. Intercoder reliability for mechanism assignment reached kappa=0.824 at Stage 2 reconciliation. Daily intersectional discrimination predicted greater sleep disturbance (b=1.308, p=.004). ConclusionsSIHF and IGEMA together provide an empirically testable framework for specifying how space produces intersectional health inequities. Mechanism specification, not spatial location alone, is the condition for designing research and intervention that reaches the source of harm for multiply marginalized populations.

Sylvatic Yellow Fever (YF) is an infectious mosquito-borne disease with significant epidemiological relevance due to its widespread distribution and high lethality for human and non-human primates, particularly in tropical regions of the planet such as in Brazil. Identifying regions and periods of high environmental suitability for the occurrence of YF is essential for preventing or mitigating its burden, as it enables the efficient allocation of surveillance efforts, prevention, and implementation of control measures. Environmental modeling of YF occurrence has proven to be an effective approach toward this goal; however, its effectiveness strongly depends on the modeling framework's capabilities as well as the spatial and temporal precision of all associated data. We propose a fine-scale geospatial modeling of YF environmental suitability that is based on a generative machine-learning ensemble method built on a large set of high-resolution environmental covariates. First, we take the spatiotemporal statistical description of the environment of each of the 545 YF cases from 2019--2024 up to 30 m/monthly resolution at three buffer scales: 100 m, 500 m, and 1000 m ratios. Then, we perform a feature selection and train hundreds of One-Class Support Vector Machine submodels to form a robust ensemble model, whose predictions are projected to a 1x1 km resolution grid of Brazil under several metrics, exceeding seven million ensemble evaluations. The predictions ranked the Southern Brazil region with the highest mean suitability for YF, with a level of 0.64; Southeast comes next with 0.46, followed closely by Central-West region (0.44), North (0.39), and finally Northeast (0.28). The model exhibited high uncertainty for the North region, indicating that data collection efforts are much needed in this region. As for the environmental covariates, a feature analysis pointed out that Land use and cover accounts for the largest influence in the model output.

PurposeTo create a publicly available, interactive data platform to visualize various data measures reflecting Colorado and its residents to support research and outreach efforts, specifically focusing on cancer burden and disparities throughout the state. This platform, named ECCO (Exploring Cancer in Colorado), aims to integrate diverse public data sources into a unified, user-friendly interface, accessible to researchers, community members, and outreach programs alike. MethodsA multi-disciplinary team developed ECCO, leveraging public data sources like Cancer InFocus, State Cancer Profiles, and the Colorado Department of Public Health and Environment. The platforms architecture employs a three-tiered web application model, utilizing a PostgreSQL database, a backend API built with FastAPI, and a Vue 3 frontend with an Open Layers map. Data is organized geographically at the county and/or census tract levels, categorized into measure categories (e.g., socio-demographics, cancer risk factors), and further filterable by demographic characteristics. An automated Extract-Transform-Load (ETL) data pipeline ensures regular updates of the data. ResultsThe platform visualizes data such as socio-demographics, cancer risk factors, screening adherence, and cancer incidence and mortality rates. Additionally, ECCO incorporates location-specific data for cancer care facilities, health services, environmental exposures, and political boundaries. To date, ECCO has had 1.1K unique visitors and over 19K pageviews according to Google Analytics. ConclusionThe ECCO platform provides a valuable tool for understanding and addressing cancer disparities in Colorado. By integrating diverse data sources and offering interactive visualization, ECCO enhances the ability of researchers, community members, and outreach programs to identify populations at risk, inform interventions, and support research priorities. AvailabilityThe application and code are available at https://coe-ecco.org/ and https://github.com/colorado-cancer-center/ecco. CONTENT SUMMARYO_ST_ABSKey ObjectiveC_ST_ABSThis work sought to develop ECCO (Exploring Cancer in Colorado), an interactive, easily-accessible data platform designed to visualize and understand diverse cancer-related data measures reflective of Colorado and its residents. Knowledge generatedECCO integrates public data from sources like Cancer InFocus, State Cancer Profiles, and the Colorado Department of Public Health and Environment, visualizing measures such as socio-demographics, cancer risk factors, screening adherence, and cancer incidence and mortality rates at both county and census tract levels. The platform also incorporates location-specific data on cancer care facilities, health services, environmental exposures, and political boundaries.

Recognizing the challenges faced by primary caregivers regarding the health of children with congenital craniofacial anomalies (CCAs) contributes to strengthening healthcare programs according to patient[s] and families differential needs. This qualitative study presents the experiences of 25 caregivers of children with CCAs from Bogota and Cali, Colombia, identified from care registries and consultation statistics provideed from public high-complexity healthcare institutions. Grounded in Giorgis descriptive phenomenology and employing thematic analysis, this research utilized semi-structured interviews and focus groups to explore the diagnostic process and its impact, experiences with healthcare services, and the caregivers role and daily care activities. Data were analyzed using MAXQDA(R) qualitative software. Findings highlighted the emotional complexity of caring for childre[n]s health. Challenges included late diagnoses, pessimistic views of the children with CCAs condition by healthcare team members; lack of effective support, information, and guidance from health staff; absence of clear care and referral protocols, and limited access to specific adaptations and timely specialized care for children with CCAs. There were also reduced therapeutic services, and a pronounced gendered caregiving burden when responsibilities fall almost exclusively on mothers. System fragmentation, reflected in deficiencies in communication and a lack of clear, coordinated, and timely pathways of care, as well as the absence of adequate psychosocial support for families, emerged as common structural problems in healthcare services in both geographic settings where this research has been conducted. Gender-sensitive strategies focused on alleviating emotional concerns and the burden of caregiving from diagnosis onward within a patient and family-centered care model are decisive. Improving comprehensive CCAs training for healthcare personnel and making adjustments to care pathways are suggested to contribute to the implementation of inclusive health programs that address the diverse needs of children and their families.

BackgroundWhile coal mine fire smoke has been linked to short-term increases in cardiovascular events, there is little evidence on long-term risks. We investigated longer-term risk of major adverse cardiovascular events (MACE) following the 2014 Hazelwood coal mine fire in regional Victoria, Australia. MethodsIn this cohort study, combined administrative data on ambulance attendances, emergency department presentations, hospital admissions, and mortality from March 2014 to June 2022, with survey data from 2016/17. Time-location diaries for the mine-fire period were combined with modelled fire-related particulate matter [&le;]2.5{micro}m in diameter (PM2.5) to estimate individual exposures. We analysed the association between PM2.5 exposure and time to MACE using a recurrent event survival analysis, adjusting for key confounders. Outcomes were examined over 8 years of follow-up and stratified by time. ResultsN = 2,725 cohort members agreed to linking their survey responses to administrative data. There was no detectable effect of fire-related PM2.5 exposure on overall risk of MACE during 8-year follow-up. However, there was weak evidence suggesting increase in MACE risk in the first 3 years post-fire, with hazard ratios ranging from 1.05-1.18 per 10{micro}g/m3 of daily average PM2.5 exposure. Nearly all analyses of cardiovascular death detected an increased risk across the entire follow-up period, with hazard ratios ranging from 1.19-1.25 per 10{micro}g/m3. ConclusionsWe found smoke exposure predicted an increase in cardiovascular health service use in the three years after the mine fire. There was additional evidence that the mine fire increased risk of cardiovascular death over the entire 8-year follow-up. This suggests that cardiovascular screening should be a routine component of planning recovery after landscape fires.

World Health Organization recommendations to reduce road deaths were examined to assess the potential reductions that could be realized in countries that have not adopted them. Data from 72 countries on recommended speeding laws, alcohol laws, and vehicle safety standards were analyzed, controlling statistically for differences in average temperatures and population density per square kilometer. Using regression coefficients, estimates of the reductions that would be realized if each countermeasure were adopted in countries not currently employing it were calculated. The coefficient on alcohol laws was not significant, but deaths in these countries would likely decline by about 23 percent if speeding laws were improved. The road death would have been about 55 percent lower if vehicle safety standards for imported vehicles had been adopted. New and used vehicles that did not adhere to the standards were sold in low-income countries. Better data identifying clusters of specific collision types (pedestrians in the dark, animals, fixed objects) could lead to the adoption of countermeasures known to be effective.

BackgroundDisparities in injury and death indicate that Latinos and Hispanics are disproportionately affected by firearm violence. Understanding the factors that impact Latino and Hispanic engagement with firearm safety is integral to developing nuanced interventions, yet these factors remain largely understudied. This study explores the social ecological factors related to firearm safety engagement among Latino and Hispanic adults residing in New Mexico. MethodsThe study used a convergent mixed-methods design with quantitative and qualitative components. Data were collected from a predominantly Latino-Hispanic community experiencing high rates of firearm violence near Albuquerque, New Mexico. Quantitative data (n=303) were collected using a community-based survey with a non-random sample on firearm safety engagement, collective efficacy, and sociodemographic characteristics. Qualitative data (n=18) included semi-structured interviews from a subset of the survey population who expressed interest in participating. Quantitative data was used to explore descriptive statistics and correlations between reported levels of collective efficacy and firearm safety engagement. Qualitative data were used to explore the firearm safety experiences of Latino and Hispanic participants. AnalysesMultivariate regression analyses examined associations between collective efficacy (exposure) and engagement with firearm safety (outcome). I also explored associations across key domains: collective efficacy, neighborhood characteristics, individual characteristics, and sociodemographic factors. Interviews were analyzed using framework analysis to generate a cohesive thematic structure informed by a social ecological model. The results from the quantitative and qualitative data were then integrated to develop a robust understanding of social ecological factors related to firearm safety engagement using a mixed methods joint display. ResultsThere were 303 survey participants (40.6% male; 55.1% female; 4.3% other gender identity) and 18 interview participants in this study. 57.1% of survey participants reported engaging with at least one firearm safety practice or initiative. Results from multivariate regression indicated that higher collective efficacy ({beta} = 0.082, p = 0.002), higher informal social control ({beta} = 0.174, p = 0.001), stronger endorsement of gun safety principles ({beta} = 0.079, p < 0.001), being married vs. unmarried ({beta} = -0.334, p < 0.001), speaking Spanish in the home vs. English ({beta} = 1.048, p < 0.001), and not owning a gun ({beta} = - 0.638, p = 0.006) were significantly and positively associated with firearm safety engagement. Themes from the qualitative data included barriers (insecure environment; lack of meaningful engagement) and facilitators (location-specific contributors to safety; collective identity and pride) to firearm safety engagement, organized by social ecological domain. Mixed methods findings indicate factors associated with participants individual firearm safety engagement, while providing insights into the perceived barriers and facilitators across social ecological domains. DiscussionFindings from this mixed-methods study suggest that processes of empowerment and collective efficacy may contribute to greater firearm safety engagement within Latino and Hispanic communities. Findings expand injury prevention research by exploring the factors influencing firearm safety engagement among a marginalized and hard-to-reach population who have disproportionate experiences with firearm victimization, perpetration, and injury. ConclusionThis study offers unique methodological approaches by using concurrent mixed methods and collecting complementary data sources to understand firearm safety engagement among Latinos and Hispanics. Findings highlight the need for culturally specific and community-engaged interventions that address social ecological disparities to strengthen safety practices and reduce firearm-related harms.

As climate change intensifies, health risks from extreme heat are rising. Accurate assessment of heat vulnerability at high spatial resolution is crucial for developing effective adaptation strategies, particularly in socioeconomically heterogeneous urban settings. However, the identification of key indicators underlying heat vulnerability remains challenging. Using Chicago, Illinois (USA) as a case study, we systematically compare different variable selection strategies in community-level heat vulnerability assessments. We take the conventional unsupervised principal component analysis (PCA)-based Heat Vulnerability Index (HVI) as a baseline, and compare it with supervised approaches that incorporate variable selection, including machine learning algorithms (Lasso regression, Random Forest, and XGBoost) as well as traditional statistical methods (simple linear regression and polynomial regression). Using the vulnerability indicator subsets identified by each variable selection method, we construct multiple HVIs and evaluate their performance against heat-related excess mortality. Our work indicates that supervised variable selection improves the performance of HVIs in capturing heat-related health risks. Among all methods, the Random Forest-based variable selection algorithm achieves the best overall results, highlighting the potential of machine learning to enhance heat vulnerability assessment tools. Our results demonstrate that poverty rate, lack of air conditioning, and proportion of residents aged 65 and above are robust determinants of heat vulnerability in Chicago.

Young children (from birth to 5 years old) are uniquely vulnerable to environmental hazards due to their higher exposure relative to body weight, rapid physiological and neurological development, and strong reliance on caregivers for protection and care. Such risks are often amplified in marginalized communities with socioeconomic disadvantage and limited access to resources. However, widely used indices, such as the Social Vulnerability Index (SVI), the Climate Vulnerability Index (CVI) and the Child Opportunity Index (COI), were not specifically developed for young children and may not capture the combined environmental and socioeconomic risks faced by this age group. To address this critical gap, we developed a county-level Early Childhood Environmental Health Vulnerability Index (EC-EHVI) for the contiguous U.S. using multidimensional indicators within an Exposure-Sensitivity-Adaptive Capacity framework and informed by Bronfenbrenners bioecological model. We identified the underlying drivers and the spatial patterns of the EC-EHVI. Our results showed that the EC-EHVI exhibited the strongest association with county-level young child mortality and explained a larger proportion of spatial heterogeneity compared with the SVI, CVI, and COI. Elevated vulnerability clustered in the Great Plains and Southeastern U.S., where over half of high-risk counties were exposure-driven, and 411 high-high hotspots were identified. The EC-EHVI offers a valuable spatial decision-support tool for designing targeted, place-based interventions and advancing environmental health equity for young children. Plain Language SummaryYoung children (birth to age five) are uniquely vulnerable to environmental hazards. Because their bodies are developing and they consume more air, food, and water relative to their weight, environmental exposures can have severe, lifelong impacts. These risks are often magnified in under-resourced communities. Yet, most existing vulnerability tools were not built with young children in mind, potentially obscuring the combined environmental and social threats they face. To address this gap, we developed a new county-level index to pinpoint where young children are most at risk across the contiguous United States. Our tool integrates data on environmental exposure, community sensitivity, and the resources available to help families cope. When tested, our new index was more strongly linked to young child mortality than several widely used existing measures. We identified major high-risk clusters, particularly in the Great Plains and the Southeastern U.S. This tool can help policymakers and public health officials better target resources and interventions to protect young children and promote environmental health equity. Key PointsO_LIWe developed a county-level Early Childhood Environmental Health Vulnerability Index across the contiguous U.S. C_LIO_LIElevated vulnerability clustered in the Southeast, Great Plains, and Appalachia, with additional hotspots in Michigan and Maine. C_LIO_LIMore than half of high-vulnerability counties were exposure-driven, emphasizing the key role of environmental hazards in child health. C_LI

ObjectiveTo examine whether screen time predicts interindividual variability regarding pubertal development across adolescence. Study designThis longitudinal cohort study included 10786 participants (47.9% female) from the Adolescent Brain Cognitive Development (ABCD) study. First, associations were examined between average daily screen time (hours/day, parent-reported Screen Time Survey) at baseline (mean age = 9.91 {+/-} 0.63 years) and pubertal timing, derived from Pubertal Development Scale (PDS) scores through 4-year follow-up (mean age = 14.08 {+/-} 0.68 years) and standardized by age and sex. Second, associations were examined between screen time groups (very low: 0-1.29 h/day; low: 1.29-2.07 h/day; moderate: 2.07-2.86 h/day; high: 2.86-4.0 h/day; very high: 4.00-12.43 h/day) and age at mid-puberty, defined as the age at first parent report of Pubertal Development Scale (PDS) category at least 3. ResultsIn linear mixed models adjusting for age, sex, race/ethnicity, socioeconomic status, BMI, and physical activity, higher log-transformed screen time at baseline was associated with more advanced pubertal timing at 1-, 2- and 3- year follow-ups, with the strongest effect observed at year 2 (standardized {beta}=0.07 [95%-CI, 0.05 to 0.10]). The associations were more pronounced in girls. The group of participants with very high screen time reached mid-puberty 2.47 months earlier [adjusted effect size, 95%-CI, -3.38 to -1.56) than participants with very low screen time. ConclusionThese findings suggest that screen time in late childhood is linked with earlier pubertal development and highlight its relevance for parental guidance on preadolescents screen media use.

BackgroundAir pollution is a potentially modifiable risk factor for dementia with a population attributable risk fraction of 3%. Little is known about the causal mechanisms behind the association, so we aimed to investigate this. MethodsData from the UK Biobank were used to investigate the association between six measures of air pollution (NO2, NOx, PM2{middle dot}5-10, PM2{middle dot}5, PM2{middle dot}5 absorbance and PM10) and dementia incidence. Indirect pathways through four mediators (cardiovascular conditions, mental health treatment, insufficient exercise and social isolation) were explored. Logistic regression was used to model the associations between air pollution, mediators and dementia. Casual mediation analysis implemented using the g-formula was used to investigate the joint indirect effect through the mediators. FindingsExposure to the highest quintile of PM2{middle dot}5 (Rte:1{middle dot}14, 95% CI:1{middle dot}06-1{middle dot}23), NOx (Rte:1{middle dot}11, 95% CI:1{middle dot}03-1{middle dot}20) or NO2 (Rte:1{middle dot}08, 95% CI:0{middle dot}99-1{middle dot}16), compared to the lowest quintile, was associated with higher dementia risk. Most of the observed association resulted from the direct effect of air pollution, consisting of pathways not captured through considered mediators. Amongst those in the highest PM2{middle dot}5 quintile, jointly intervening on the four mediators would result in a 1% reduction in risk of dementia (Rpnie:1{middle dot}01, 95% CI: 1{middle dot}01-1{middle dot}02). The randomised pure natural indirect effect was similar for NO2 (Rpnie:1{middle dot}01, 95% CI: 1{middle dot}00-1{middle dot}01) and NOx (Rpnie:1{middle dot}01, 95% CI: 1{middle dot}01-1{middle dot}02). InterpretationMost of the association between dementia and PM2{middle dot}5, NO2 and NOx occurs through the direct effect of air pollution, or other unmeasured mediators, and not pathways through these four mediators. FundingMedical Research Council (Grant MR/W006774/1).

Background Heatwaves increasingly threaten public health in the Mediterranean region, and Greece is among the hardest hit countries. Yet evidence on long-term adaptation, spatial vulnerability, and the contribution of human-induced climate change to heatwave-related mortality in Greece remains limited. Methods We analysed 2,144,957 all cause deaths in Greece during 2000 and 2019 using a time stratified case crossover design. We derived population weighted daily maximum temperatures at NUTS3 level from ERA5 reanalysis and WorldPop. We applied six heatwave definitions (HD1-HD6) varying by duration (2 or 3 consecutive days or more) and thresholds (90th, 95th, 99th percentiles). We fitted Bayesian hierarchical Poisson models to estimate heatwave-mortality associations varying by space and time. We additionally adjusted for relative humidity and national. We then combined these estimates with probabilistic climate attribution methods to quantify the number and proportion of heatwave-related deaths attributable to human induced climate change. Results Heatwaves raised mortality consistently, with relative risks from 1.08 (95% CrI (Credible Interval): 1.07- 1.09; HD1) to 1.15 (1.11- 1.20; HD6). Risks increased with heatwave intensity and duration and peaked among females and adults aged 85 years and older. We did not detect a consistent temporal decline in risk or marked spatial heterogeneity. Human induced climate accounted for 51-94% of heatwave related deaths across definitions. The proportion attributable to climate change rose over time. Conclusions Heatwaves already impose a major mortality burden in Greece, with more than half driven by anthropogenic climate change and little evidence of population level adaptation. These findings call for rapid emissions reductions and targeted adaptation, including stronger heat health warning systems and protection of vulnerable groups.

BackgroundAlthough the number of new HIV diagnoses among men who have sex with men (MSM) in the Netherlands has declined considerably, the recent plateau suggests ongoing transmission. In 2024, 29% of new diagnoses among MSM were in a late HIV stage, showing that the time between infection and diagnosis can still be substantially reduced. In low-incidence settings, infections introduced through immigration are increasingly important in sustaining transmission, highlighting the need to re-evaluate current testing guidelines. We assess targeted testing strategies among MSM in the Netherlands addressing these considerations. MethodsWe used an agent-based model of HIV transmission among MSM in the Netherlands, incorporating infections acquired domestically and abroad. For 2024 - 2040, we simulated testing interventions targeting different subgroups, including offering an HIV test to immigrants upon entry, increasing testing rates among MSM residing in the Netherlands, and combinations of these approaches. ResultsOffering HIV testing to immigrating MSM at the entry averted up to 94 (95-th % quantile interval, 95% QI -128 - 328) new infections over 15 years if at least 50% take the test. Increasing testing to every 7 months in the general MSM population achieved the largest reduction, with up to 508 (95% QI 292 - 900) infections averted. The same testing rate in MSM with more than 5 partners within the previous six months resulted in 340 (95% QI 132-592) infections averted. Combining testing at entry with 7-months testing among general resident MSM averted the most infections, 534 (95% QI 308 - 884). ConclusionsCombination of offering HIV test to immigrating MSM at the entry with 7-month testing frequency in the general resident MSM population can substantially reduce HIV infections. The difference in impact between targeting general MSM and those with relatively high recent partner numbers suggests that criteria for being at risk of having HIV need to expand. 1 Author summaryWhile HIV transmission among MSM in the Netherlands has decreased substantially over the last decade, it is still ongoing. In 2024, 29% of new HIV diagnoses in MSM were in individuals in late-stage of HIV infection, suggesting that the time between HIV acquisition and diagnosis should be shortened further. Additionally, in a low-incidence setting such as MSM in the Netherlands, introduction of HIV infections through immigration becomes more important. We evaluated several HIV testing strategies for this context, considering both immigrating MSM and resident MSM. While offering HIV test at entry point can avert many HIV infections, increasing testing rate in resident MSM to on average every seven months can avert substantially more HIV infections. The greatest impact is achieved when these approaches are combined: targeting both immigrating MSM and those already living in the country. This combined strategy requires the fewest additional tests per infection averted. Importantly, our simulations show that there are MSM living with undiagnosed HIV who do not necessarily meet the traditional criteria for being at risk. Improved testing strategies can help reach these individuals earlier, benefiting both public and their personal health.

BackgroundThe rapid growth of the worlds urban population has contributed to the expansion of informal urban settlements in many cities across the world. In these settings, lack of safe sanitation combined with high population density and poverty contributes to heightened health risks for often vulnerable populations. The aim of this study was to evaluate the effect of a shared, onsite sanitation intervention on the nutritional status of children in Maputo, Mozambique. MethodsThe Maputo Sanitation (MapSan) trial was a controlled before-and-after study to evaluate the effect of a shared, onsite sanitation intervention on child health in Maputo, Mozambique. Here, we report the effects on childhood stunting, wasting and underweight, and height-for-age, weight-for-height and weight-for-age z-scores. Children were enrolled aged 1-48 months at baseline and outcomes were measured before and 12 and 24 months after the intervention, with concurrent measurement among children in a comparable control arm. The primary analysis was intention-to-treat. The trial was registered at ClinicalTrials.gov, number NCT02362932. ResultsWe enrolled 757 and 852 children in the intervention and control groups respectively. There was no evidence for an effect of the intervention on any outcome at 12 or 24 months of follow-up except for wasting where there was very weak evidence for an effect (adjusted prevalence ratio: 0.497; 95% CI: 0.22-1.11; p=0.09). In two exploratory analyses - one including only those children born into compounds post-intervention and a second excluding children in control compounds which had independently improved their sanitation facilities during follow-up - we found that stunting increased in the intervention group whilst wasting decreased. ConclusionsThis study contributes to the growing evidence on the role of sanitation in shaping child health outcomes in informal urban settlements. We found no evidence for an effect on stunting and weak evidence for an effect on wasting. More research is needed to understand how sanitation can reduce childhood undernutrition in complex urban environments.