ArticleSSM - population health2025
Is objectively measured exposure to built and natural environment associated with population-level cardiovascular disease mortality in Great Britain?
Article in SSM - population health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- A Geospatial Linkage Analysis of Environmental Exposures, Socioeconomic Profile and Health Outcomes Among Women of Reproductive Age in Victoria, Australia.Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Cardiovascular disease (CVD) causes one-third of global mortality, with modifiable risk factors such as unhealthy diet, sedentary behaviour, tobacco/alcohol use contributing to 80 % of CVD deaths. The built environment (BE) can influence CVD risk indirectly by shaping health behaviours and directly through environmental exposures like air pollution. While research has established connections between isolated environmental features and CVD, this study addresses significant research gaps in understanding how multiple BE characteristics influence CVD mortality across socioeconomic contexts, aiming to inform neighbourhood design to reduce both CVD and inequalities. Methods: We modelled, for small areas across GB, tree cover, air pollution, walkability, densities of health-detrimental amenities ('bads') (e.g. fast-food outlets) and health-promoting amenities ('goods') (e.g. gyms), and income deprivation. Generalised linear models were used to assess associations between small area features and (sex-stratified) age-standardised CVD mortality rates (i.e. ICD-10 codes I00-I99), controlling for deprivation, urban-rural, country, and local authority. Combined models (i.e. models mutually adjusted for all BE features) identified the unique contribution of each feature while accounting for those that 'co-located'. Interaction analysis was performed to examine variations by income deprivation. Results: A slight increase in CVD mortality risk was associated with greater 'goods' densities (female mortality ratio (MR):1.005 (CIs:1.003-1.007), p < 0.001, male MR:1.005 (CIs:1.003-1.006), p < 0.001), and higher air pollution (female MR:1.006 (CIs:1.003-1.009), p < 0.001, male MR:1.008 (CIs:1.005-1.009), p < 0.001). A slight decrease in CVD mortality was associated with higher walkability for females (MR:0.996 (CIs:0.992-0.999), p = 0.034) and tree cover for males (MR:0.999 (CIs:0.998-0.999), p = 0.007). Higher air pollution levels and 'bads' were associated with higher male CVD mortality in deprived areas. Conclusion: Findings have clear policy implications, suggesting prioritisation of reductions in air pollution-particularly in deprived areas-while promoting walkability and tree cover to reduce health inequalities. Unexpected positive associations between 'goods' and mortality highlight that complex neighbourhood effects warrant further study.
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