Health & Place

ObjectiveTo determine the extent of inequitable distributions in green space qualities in urban areas of Australia. MethodExisting data from the cities of Sydney, Newcastle, and Wollongong in Australia was used to define green space qualities relating to accessibility, amenities/activities, beaches/coastline, biodiversity, incivilities, landcover and land use. Green space qualities were measured within multiple-scale network distance buffers for residential mesh blocks and linked with the Australian Bureau of Statistics Index of Relative Socio-economic Disadvantage (IRSD). Correlations were analysed using Spearmans rank correlation coefficient between IRSD score (reversed; higher scores are more disadvantaged) and green space qualities aggregated over mesh blocks. Influence of IRSD, population density and random effects of population structures were examined using single-level and multilevel models. Spatial patterns and clusters were identified through choropleth maps and hot spot analyses. ResultsAt the 1600m scale, more disadvantaged areas tended to have green spaces with lower percentages of nearby street trees to roads (Rho=-0.52, p[≤]0.001), lower percentages of slope >6{degrees} (Rho=-0.49), lower likelihood of threatened mammal species/habitat occurrences (Rho=-0.47), and lower percentages of tree canopy (Rho=-0.46). More disadvantaged areas tended to have green spaces with higher percentages of open grass (Rho=0.38, p[≤]0.001) and bare earth (Rho=0.33, p[≤]0.001) and higher densities of robberies (Rho=0.34, p[≤]0.001). For selected qualities, multilevel models tended to support the relationships that were found using Spearmans rank correlation. DiscussionSocioeconomic inequities in tree canopy, biodiversity and incivilities are present for green spaces in large and mid-sized Australian cities.

Greenspace has been associated with a wide range of health outcomes and conditions related to functional limitation and disability. Yet less is known about how the spatial morphology of greenspace relates to disability prevalence across different stages of the life course. This study examines associations between greenspace morphology and disability prevalence among children, working-age adults, and older adults in urban census tracts across the contiguous United States. Using national land-cover data, we quantified morphological metrics at the census-tract level, including greenspace percentage, density, mean size, connectedness, shape complexity, inter-greenspace distance, and diversity. These indicators were linked with age-specific disability prevalence obtained from the American Community Survey. Spatial lag regression models were used to account for spatial dependence while adjusting for socio-demographic and contextual characteristics. Across age groups, higher greenspace percentage was consistently associated with lower disability prevalence (children: {beta} = -0.081, 95% CI: -0.096 to -0.066; adults: {beta} = -0.804, -0.858 to -0.750; older adults: {beta} = -1.132, -1.250 to -1.013). Among children, patch density ({beta} = -0.045, -0.061 to -0.029), mean patch area ({beta} = -0.029, -0.040 to -0.018), connectedness ({beta} = -0.051, -0.069 to -0.032), diversity ({beta} = -0.036, -0.051 to -0.020), and inter-greenspace distance ({beta} = 0.056, 0.039 to 0.073) were all associated with disability prevalence, whereas shape complexity was not ({beta} = 0.004, -0.010 to 0.018). Among working-age adults, associations were observed for mean area ({beta} = -0.023, -0.090 to -0.002), connectedness ({beta} = -0.127, -0.243 to -0.011), shape complexity ({beta} = -0.123, -0.174 to -0.072), diversity ({beta} = -0.146, -0.201 to -0.091), and inter-greenspace distance ({beta} = 0.151, 0.059 to 0.242), whereas patch density was not significantly associated with disability prevalence ({beta} = -0.013, -0.048 to 0.022). In older adults, all examined greenspace morphology metrics showed significant associations with disability prevalence, including patch density ({beta} = -0.445, -0.842 to -0.049), diversity ({beta} = -0.126, -0.188 to -0.065), and inter-greenspace distance ({beta} = 0.455, 0.409 to 0.501). Overall, the findings suggest that higher greenspace percentage, larger patch size, greater connectedness, greater diversity, and more spatially clustered greenspace distributions are associated with lower disability prevalence across the life course, although the strength and consistency of these associations varied across age groups. The study provides national-scale evidence for incorporating greenspace morphology into urban planning and public health strategies to support more inclusive and health-supportive urban environments.

IntroductionAccess to adequate energy in the home is necessary for a healthy and well life, however current energy use, particularly in high-income countries, is unsustainable. Decarbonisation of home energy can benefit climate mitigation and health but there is the potential to create new, or compound existing, inequalities in health if not implemented equitably. Mapping the theoretical causal pathways between home decarbonisation and health will contribute to further understanding of these mechanisms. AimsFirstly, to identify theoretical pathways between decarbonisation of home energy and health and health inequalities in high-income countries, and secondly, to synthesise these into a putative causal evidence map. Inclusion criteriaAll populations in high-income countries are included, as defined by the World Bank in 2023/24. Included concepts are decarbonisation of home energy, and health and health inequalities. Context for this review comprises of the inclusion of a clear theory linking the concepts. All study designs are included. MethodsThis protocol is for a review of theories rather than of intervention effectiveness. Medline/OVID, Scopus, and EconLit will be searched, with no limitation on date. Relevant international policy websites will also be searched. The search is limited to papers in English. Citation tracing may identify further relevant papers. Abstracts and full texts will be screened using Rayyan. At least 10% will be double-screened, and the rest screened by one author, and included full texts will be screened until data saturation is reached. Study inclusion is based on consistency with the inclusion criteria, with some flexibility allowed due to the theoretical nature of this review. Data extracted from papers will be used to develop a diagrammatic map of pathways.

The relationship between access to nature and public health outcomes has been well-studied and established in the literature. However, most studies use simple greenness indices as a proxy for access to nature, which ignores the "quality" of the nature since greenness indices are not able to predict biodiversity. My objective was to investigate the relationship between citizen scientist collected biodiversity data from the eBird platform, urban greenness and four human health outcomes (asthma, coronary heart disease, and self-assessed mental and physical health). I mapped and tested for correlations among eBird record species richness, greenness as NDVI and PLACES human health data in urban census tracts located in three metro areas/ecological zones (Albany, NY: eastern deciduous forest, Kansas City, MO: tallgrass prairie, and Phoenix, AZ: Sonoran Desert). eBird species richness was related to greenness, measures of urbanization and several human health factors; however, the correlations varied by metro area and in strength. Provided confounders are controlled for, eBird data could help to refine models surrounding relationships between public health and nature access.

Ambient temperature is emerging as an environmental factor that may influence cognitive performance in ageing populations. This is particularly relevant in Australia, where people live across diverse climatic regions spanning alpine to tropical conditions. We examined daily temperatures and cognitive performance in 1,873 midlife and older adults (1,297 women, mean age 61.0 years) who completed the Creyos online battery (formerly Cambridge Brain Sciences). Twelve tasks assessed memory, visuospatial processing, language, attention, and executive function. Task scores were linked to postcode-level contemporaneous weather data. The scores were analysed in relation to maximum and minimum air and wet-bulb temperatures and postcode- and month-relative temperature percentiles. Regression models adjusted for age, sex, education, socioeconomic status, and climate zone, with season included for air and wet-bulb measures. Higher minimum, but not maximum, temperature was associated with poorer performance on Paired Associates, a task assessing associative memory. This pattern was observed for air temperature, wet-bulb temperature, and temperature percentile, suggesting poorer memory performance after warmer nights, both in absolute terms and relative to local seasonal norms. Temperature was not significantly associated with performance on any other task, including measures of short-term/working memory, visuospatial processing, language, attention, or executive function. These findings suggest a task-specific association between higher overnight temperature and poorer associative memory performance, rather than a general reduction in cognition. Further studies incorporating personal exposure and sleep measures are needed to clarify whether night-time thermal conditions affect cognitive health in midlife and older populations.

Environmental noise is a significant public health concern, ranking among the top environmental risks to citizens health and quality of life. Despite various studies exploring the effects of atmospheric pollution on mental health, spatial investigations into the effects of noise pollution have been notably absent. This study addresses this gap by investigating the association between noise pollution (from road and rail networks) and depression for the first time in England and first explores localised patterns based on area deprivation. Depression prevalence, defined as the percentage of patients with a recorded depression diagnosis was calculated in small areas within Cheshire and Merseyside ICS using the Quality and Outcomes Framework Indicators dataset for 2019. Strategic noise mapping for rail and road noise (LDEN) was employed to quantify noise pollution, indicating a 24-hour annual average noise level with distinct weightings for evening and night periods. The English Index of Multiple Deprivation (IMD) was utilised to represent neighbourhood deprivation. Geographical Weighted Regression and Generalised Structural Equation Spatial Modelling (GSESM) were applied to estimate relationships between transportation noise, depression prevalence, and IMD at the Lower Super Output Area (LSOA) level. While transportation noise showed a low direct effect on depression levels in Cheshire and Merseyside ICS, it significantly mediated other factors linked to depression prevalence. Notably, GSESM revealed that health deprivation and disability was strongly associated (0.62) with depression through the indirect effect of environmental noise, particularly where transportation noise exceeds 55 dB on a 24-hour basis. Comprehending variations in noise exposure across different areas is paramount. This research not only provides valuable insights for informed decision-making but also lays the groundwork for implementing noise mitigation measures. These measures are aimed at addressing mental health inequalities, enhancing the quality of life for the exposed population and supporting a healthier ageing process in urban environments. The findings also carry crucial implications for public health, specifically in tailoring targeted interventions to mitigate noise-related health risks in areas where noise burdens exceed 55 dB, and residents may experience health deprivation and disability.

Urban mobility may provide insight into population health by capturing how residents connect to services, resources, and urban systems. This is relevant for communities facing higher disease burden and limited resources, where reduced connectivity may signal barriers to care, healthy environments, and participation. Mobility patterns are furthermore shaped by socioeconomic position, housing, environmental quality, facilities access, lifestyle patterns, and population composition. Mobility-health associations may therefore reflect underlying social and environmental disadvantage rather than mobility itself, risking misdirected public-health policy responses. This ecological cross-sectional study examined associations between aggregated mobile phone-based mobility and health outcomes in The Hague, Netherlands, from January-July 2019. Mobile phone mobility was measured as mean outgoing mobility distance across eight regions. Contextual and health indicators were available at neighbourhood-level and were aggregated or linked to regions where required. Health outcomes were operationalised as indicators of disease burden, including cardiometabolic medication prescriptions, polypharmacy, and a syndemic-based measure of interacting health conditions. Contextual domains were selected using spatial clustering and ordinary least squares models, after which residual mobility-health associations were assessed. Outgoing mobility varied across regions and was strongly patterned by contextual factors. Lifestyle, housing, physical environment, and income accounted for 73.2% of variance in outgoing mobility. After adjustment, residual mobility showed weak, non-significant associations with cardiometabolic medication prescription, polypharmacy, and the syndemic-based measure. Sensitivity analyses supported these findings. Aggregated mobility should not be interpreted as a straightforward independent determinant of health. Instead, it appears to function as an integrative marker of urban context, spatial structure, and population composition.

Highlights- Nation-wide study with data for nearly all adult Dutch inhabitants. - Ethnic minorities face higher physico-chemical exposures than Dutch-origin inhabitants. - Food & physical activity environments better for ethnic minorities than Dutch-origin. - Socio-economic characteristics less favorable for ethnic minorities than Dutch-origin. IntroductionEthnic minority populations may be disproportionally affected by unhealthy environmental exposures, increasing health inequities. This study aims to identify whether residential neighborhood exposures differ between ethnic groups in the Netherlands. MethodsThis cross-sectional study included all adult residents of the Netherlands registered in the national population register on 01/01/2022 (N=13,926,871). Exposure data (physico-chemical, food and physical environment, socio-economic characteristics, health and social well-being) were obtained from Statistics Netherlands, GECCO and the Dutch Health Monitor, and linked to individuals based on geocoded home addresses. Ethnicity was based on country of birth of individuals and their parents. Estimated marginal means were calculated and ethnic differences in exposure determined using multiple linear and logistic regression, adjusted for age and sex, stratified by socio-economic status (SES) and population density. ResultsCompared to Dutch-origin, ethnic minority populations had less favorable physico-chemical exposures (e.g. 0.87{micro}g/m3 [95%-CI: 0.86;0.88] higher PM2.5 exposure for Moroccans in "high SES-high population density"). Conversely, the food and physical activity environment was more favorable for ethnic minorities (e.g. 1.82km/ha [95%-CI 1.80;1.83] higher bike path density among Turks in the "low SES-low population" density category). Socio-economic characteristics of the environment were generally less favorable for ethnic minorities (E.g. difference between Dutch Caribbeans and Dutch-origin -4.23% [95%-CI -4.35;-4.11] in "high income-high population density". Ethnic differences in health and social well-being varied. Neighborhood-level smoking was most prevalent among ethnic minorities, while excessive drinking was most prevalent among Dutch-origin. Exposure to vandalism and (sexual)violence was lowest among Dutch-origin and highest among Dutch Caribbean, Moroccans, Turks and Surinamese. ConclusionPhysico-chemical exposure, socio-economic characteristics of the environment and safety from crime were less favorable among ethnic minority populations compared to Dutch-origin. The food and physical activity environment was more favorable for ethnic minorities. Ethnic inequalities were most pronounced among Moroccans, Turks, Surinamese and Dutch Caribbeans compared to Dutch-origin.

ObjectiveTo identify factors associated with local variation in the time course of COVID-19 case burden in England. MethodsWe analyzed laboratory-confirmed COVID-19 case data for 150 upper tier local authorities, from the period from January 30 to May 6, 2020, as reported by Public Health England. Using methods suitable for time-series data, we identified clusters of local authorities with distinct trajectories of daily cases, after adjusting for population size. We then tested for differences in sociodemographic, economic, and health disparity factors between these clusters. ResultsTwo clusters of local authorities were identified: a higher case trajectory that rose faster over time to reach higher peak infection levels, and a lower case trajectory cluster that emerged more slowly, and had a lower peak. The higher case trajectory cluster (79 local authorities) had higher population density (p<0.001), higher proportion of Black and Asian residents (p=0.03; p=0.02), higher multiple deprivation scores (p<0.001), a lower proportions of older adults (p=0.005), and higher preventable mortality rates (p=0.03). Local authorities with higher proportions of Black residents were more likely to belong to the high case trajectory cluster, even after adjusting for population density, deprivation, proportion of older adults and preventable mortality (p=0.04). ConclusionAreas belonging to the trajectory with significantly higher COVID-19 case burden were more deprived, and had higher proportions of ethnic minority residents. A higher proportion of Black residents in regions belonging to the high trajectory cluster was not fully explained by differences in population density, deprivation, and other overall health disparities between the clusters. What is already known on this subject?Emerging evidence suggests that the burden of COVID-19 infection is falling unequally across England, with provisional data suggesting higher overall infection and mortality rates for Black, Asian, and mixed race/ethnicity individuals. What does this study add?We found that regions with greater socioeconomic deprivation and poorer population health measures showed a faster rise in COVID-19 cases, and reached higher peak case levels. Areas with a higher proportion of Black residents were more likely to show this kind of time course, even after adjusting for multiple co-occurring factors, including population density. This finding merits further investigation in terms of the intersecting vulnerability factors Black and other minority ethnic individuals face in England (e.g. proportion of people working in service and caring roles, and the role of structural discrimination), and has implications for the ongoing allocation of public health resources, in order to better mitigate such inequalities.

Residential greenness has been associated with reduced heat-related illness, yet the specific role of greenspace morphology at the neighborhood scale remains insufficiently understood. This study quantified the relationship between heat-related illness and multiple dimensions of greenspace morphology using an eight year (2016-2023) unbalanced panel dataset comprising 19,021 block group year observations across 2,427 census block groups in Arizona, USA. One meter high resolution National Agricultural Imagery Program aerial imagery was classified to calculate greenspace percentage, number of greenspaces, average size, shape complexity, connectedness, and distantness, at the block group level. We applied conditional spatial autoregressive models with a negative binomial distribution to estimate associations between each morphology metric and yearly heat-related illness counts, adjusting for sociodemographic and geographic covariates. We found higher greenspace percentage, aggregation, shape complexity, connectedness, and density were consistently associated with lower heat-related illness risk. A one standard deviation increases in shape complexity corresponded to a 12.4% decrease in expected heat-related illness counts (IRR=0.876, 95% CI: 0.834-0.921). Similarly, increases in greenspace percentage (14.6% decrease; IRR=0.855, 95% CI: 0.827-0.885), number of greenspace patches (3.7% decrease; IRR=0.963, 95% CI: 0.937-0.990), average size (4.5% decrease; IRR=0.955, 95% CI: 0.923-0.989), and connectedness (5.5% decrease; IRR=0.945, 95% CI: 0.918-0.972) were all protective. In contrast, larger inter greenspace distances were associated with increased heat-related illness risk (6.1% increase; IRR=1.061, 95% CI: 1.033-1.091). Our findings highlight the critical importance of multiple dimensions of greenspace morphology in mitigating heat-related health risks. These results suggest that heat reduction planning with greening initiatives should consider not only the amount of greenspace but also its spatial configuration to maximize cooling and result in health benefits.

Gentrification is a complex and controversial process, where the influx of new, wealthier residents to previously run-down neighbourhoods brings change such as economic development, infrastructure investments and lower crime rates, but can be to the detriment of the original lower-income residents, who are either displaced, or stay but cannot take advantage of the new opportunities. Understanding how neighbourhood change affects food environments can shed light on the possible causal pathways between gentrification and urban health inequalities. This rapid evidence assessment reviewed evidence on the impact of gentrification on the healthfulness of food environments globally. Ten studies were identified through a systematic keyword search and assessed. We found limited evidence of an effect, with a small, albeit consistent, body of evidence mostly comprised of low- to medium-quality observational studies, all from high-income countries. Most studies examined effects on availability or affordability of food, finding an association between gentrification and increased availability of unhealthy foods, or reduced affordability for original low-income residents.

The association between multi-dimensional deprivation and public health is well established, and many area-based indices have been developed to measure or account for socioeconomic status in health surveillance. The Yost Index, developed in 2001, has been adopted in the US for cancer surveillance and is based on the combination of two heavily weighted (household income, poverty) and five lightly weighted (rent, home value, employment, education and working class) indicator variables. Our objectives were to 1) update indicators and find a more parsimonious version of the Yost Index by examining potential models that included indicators with more balanced weights/influence and reduced redundancy and 2) test the statistical consistency of the factor upon which the Yost Index is based. Despite the usefulness of the Yost Index, a one-factor structure including all seven Yost indicator variables is not statistically reliable and should be replaced with a three-factor model to include the true variability of all seven indicator variables. To find a one-dimensional alternative, we conducted maximum likelihood exploratory factor analysis on a subset of all possible combinations of fourteen indicator variables to find well-fitted one-dimensional factor models and completed confirmatory factor analysis on the resulting models. One indicator combination (poverty, education, employment, public assistance) emerged as the most stable unidimensional model. This model is more robust to extremes in local cost of living conditions, is comprised of ACS variables that rarely require imputation by the end-user and is a more parsimonious solution than the Yost index with a true one-factor structure.

BackgroundDrowning remains a major global public health challenge. This study examined whether the timing and trajectories of urbanisation--beyond the current built environment--are associated with subnational drowning mortality. MethodsWe linked satellite-derived measures of built-environment change (GHSL), population crowding (WorldPop), surface water exposure (JRC Global Surface Water), and infrastructure proxies (VIIRS/DMSP nighttime lights) to GBD 2021 drowning mortality estimates across 203 ADM1 regions in 12 countries (2006-2021; 3,248 region-year observations). Temporal predictors captured recent expansion, development "newness" ([&le;]10-year built share), acceleration/volatility, and a crowdingxgrowth interaction. We screened predictors using LASSO (10-fold cross-validation) and fitted mixed-effects models with region random intercepts. Distributed-lag models tested temporal precedence and development age, and income-stratified models assessed heterogeneity. ResultsAdding temporal predictors improved fit beyond contemporaneous built-environment measures ({Delta}AIC=177; {Delta}BIC=147). In adjusted models, crowdingxgrowth was strongly positively associated with drowning mortality, and a higher share of recent development was associated with higher mortality. Lag models showed a development age gradient: older built environment was most protective. Associations differed by income group, with several key coefficients reversing sign across strata. DiscussionDrowning mortality appears shaped by development histories as well as present-day conditions, with risk concentrated in rapidly changing, dense settings and the newest built environments. Cross-context heterogeneity suggests mechanisms and prevention priorities are unlikely to be uniform. ConclusionsDevelopment timing and trajectories help explain subnational drowning mortality beyond current built form alone. Prevention and planning should prioritise transition-period safety strategies in newly developing and rapidly densifying areas.

BackgroundEmerging evidence has demonstrated that nature-based interventions (NBIs) can improve mental and physical health. Considering that the global burden of poor mental health continues to rise, such interventions could be a cost-effective means to improve mental health, as well as reconnect individuals with the natural world, and thus aid efforts. However, the effectiveness of NBIs as a prescriptive intervention is, in part, a function of access to blue and green spaces. Accordingly, this scoping review will explore how structural inequalities influence the effectiveness of nature-based interventions as treatment options for mental and physical ill health. MethodsA scoping review will be conducted to identify the barriers and facilitators associated with the utilisation of green and blue spaces. The review will follow the PRISMA-ScR guidelines, in addition to the associated Cochrane guidelines for scoping reviews. A literature search will be performed across five databases, and articles will be selected based on key inclusion/ exclusion criteria. All data will be extracted to a pre-defined charting table. The primary and secondary outcomes will be mental and physical health respectively. DiscussionThis review will better inform relevant stakeholders of the potential enablers and barriers of nature-based interventions, and thereby improve provision and implementation of NBIs as public health initiatives. Ethics and DisseminationAll data rely on secondary, publicly available data sources; therefore no ethical clearance is required. Upon completion, the results of this study will be disseminated via the Imperial College London Community and published in an open access, peer-reviewed journal. Article SummaryO_ST_ABSStrengths and Limitations of this StudyC_ST_ABSO_LIThis scoping review protocol is the first to focus on the accessibility to green and blue spaces in the context of mental and physical health. C_LIO_LIThis protocol and subsequent review benefit from increased transparency, a systematised strategy (PRISMA-ScR), and a reduction in the risk of bias, through publication in an open access journal. C_LIO_LIThis review will also capture grey literature - studies published outside peer-reviewed journals. C_LIO_LIDue to the broad nature of the review, the research may unearth more questions than solutions. C_LI Registration NumberOpen Science Framework: 10.17605/OSF.IO/8J5Q3

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.

BackgroundYoung 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.

BackgroundAssessments of climate change health impacts often rely on ambient temperature, often neglecting the role of humidity and wind speed. This can be problematic in countries with high climate diversity and pronounced climate warming as it is the case in many South-East Asian nations. This study compared trends in multiple heat metrics (i.e., actual heat, Heat Index, Humidex and Apparent Temperature) across Thailands provinces and evaluated their respective associations with province-level mortality. MethodsWe analysed daily meteorological data (2006-2024) to calculate trends across heat metrics for 71 provinces (6 missing). Then, using negative binomial mixed-effects regression models, we assessed the 1-year lagged associations between each annual heat indicator and all-cause mortality from 2008 to 2023, adjusting for year and province. ResultsComposite heat indices increased at a faster rate than ambient temperature in the majority of provinces. All composite heat metrics surpassed actual heat in predicting mortality. Specifically, our analyses revealed that a 1{degrees}C increase in one-year lagged Apparent Temperature was associated with 47,196 Excess Deaths (ED) (95% CI: 13,287 to 82,704) over the study period, representing the strongest association among the metrics tested. However, the strength of the association varied across regions with different climate trajectories. Stratified analyses by death in- and out-of-hospital settings show strong association of Humidex and Apparent Temperature with hospital deaths at 1-year lag but out-of-hospital deaths show no associations and broad confidence intervals. ConclusionHeat stress is increasing faster than Apparent Temperature across Thailand. While composite measures like the Apparent Temperature and Humidex are stronger predictors of national-level mortality, regional variations in these relationships underscores the need to develop local heat metric that account for both local characteristics and climate change patterns to accurately tailor public health responses. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/25337088v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@11f61a5org.highwire.dtl.DTLVardef@1439c50org.highwire.dtl.DTLVardef@1cb7f34org.highwire.dtl.DTLVardef@10c4148_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundGreen and blue spaces can promote good physical and mental health and prevent the development of long-term conditions. Evidence suggests that not all green spaces affect health equally, and that certain types and properties of green spaces are stronger predictors of health than others. However, research into the causal mechanisms is limited in large cohorts due to lack of objective and comparable data on green space type, accessibility, and usage. MethodsWe used data from Urban Atlas to compute measures of urban park accessibility, street trees availability, and total green and blue space availability for 300,000 UK Biobank participants. Exposure metrics were computed using circular buffers with radii of 100 m to 3000 m. Pearson correlation coefficients and other descriptive statistical parameters were used to test agreement between variables and explore the utility of indictors in capturing different types of green spaces. ResultsStrong positive correlations were observed between variables of the same indicator with different buffer sizes. The presence of park and proportion of street tree canopy variables were negatively correlated with amount of total green space variables. This signifies distinct differences in type of green spaces captured by these variables. ConclusionsOverall, five distinct indicators of park accessibility, street trees availability, and total green and blue space availability have been integrated into a large sample of the UK Biobank. Our method is replicable to settings across Europe and facilitates evidence-based research on the roles of different green and blue spaces in health promotion and ill-health prevention. Key MessagesO_LIDifferent types of green spaces and their position in the neighbourhood can promote and protect health by mitigating pollution and increasing physical activity and socialisation. C_LIO_LIWe present the methods of constructing and linking data on urban green spaces, street trees and natural vegetation into a large health cohort, the UK Biobank. C_LIO_LIThe ability to distinguish between types of green spaces and their intended use can help inform public health interventions, influence urban policy, and aid urban planning in building sustainable and healthy cities. C_LIO_LIOur methods are transferable and will allow others to explore the links between environment and health in UK Biobank and other health cohorts. C_LI

Environmental exposures are increasingly examined in relation to mental health, yet large-scale epidemiological analyses remain constrained by fragmented geospatial data, heterogeneous spatial and temporal resolutions, and privacy-preserving linkage requirements, limiting systematic investigation of multiple environmental domains at the population level. We present environMAP, a harmonised set of analysis-ready environmental exposure layers derived from open, global sources. environMAP spans the built environment, green and blue spaces, light exposure (solar radiation and night-time light), terrain, weather and extremes, and air pollution. We document data provenance, spatial buffers, preprocessing, projection alignment, and metadata, and provide a reproducible workflow for privacy-preserving linkage to cohort residential locations. To demonstrate utility, we linked environMAP to >200,000 adults in the German National Cohort (NAKO) and summarised self-reported lifetime doctor-diagnosed depression across exposure gradients using sex-stratified descriptive analyses. Gradients were interpretable and broadly consistent with prior evidence, supporting feasibility, scalability, and hypothesis generation. The framework is adaptable to other outcomes, cohorts, and regions.

In the United States and the United Kingdom COVID-19 has disproportionately affected Black, Indigenous and People of Colour (BIPOC) and Black, Asian and Minority Ethnic (BAME) people respectively. Multiple studies identify environmental factors such as overcrowded housing and poor workplace conditions as contributing factors for the disproportionate COVID-19 rates amongst BAME and BIPOC communities. This paper will show that to fully understand the phenomenon, both an ecological and biological approach is needed. An ecological approach highlights how a persons habitat and the experiences within it mediate their susceptibility to disease. Moreover, to understand how this mediation works, this paper will use allostatic load as a biological pathway to link a person to their habitat and the poor health outcomes that contributed to COVID-19 susceptibility. In introducing this new approach, the paper will serve as an anti-racist framework for understanding how COVID-19 affected BAME and BIPOC communities. It is anti-racist by centring poor health outcomes on the habitats people are forced to live in due to structural racism rather than the physiology of a persons race or ethnicity. This is important in order to avoid similar crises in the future and to improve the health of marginalised communities.