ArticleBMJ open2016
Using kernel density estimation to understand the influence of neighbourhood destinations on BMI.
Article in BMJ open, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Geo-demographic and socioeconomic determinants of diagnosed hypertension among urban dwellers in Ibadan, Nigeria: a community-based study.Journal of health, population, and nutrition · 2025Article
- Interplay between genetic risk and built neighborhood conditions as predictor of BMI across the transition into adulthood.Obesity (Silver Spring, Md.) · 2025Article
- Built environment and physical activity in adolescents: Use of the kernel density estimation and the walkability index.PloS one · 2024Article
- Geo-Demographic and Socioeconomic Determinants of Diagnosed Hypertension among Urban Dwellers in Ibadan, Nigeria: A Community-based Study.Research square · 2023Article
- A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation.BMC medical research methodology · 2023Article
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- Where we eat is who we are: a survey of food-related travel patterns to Singapore's hawker centers, food courts and coffee shops.The international journal of behavioral nutrition and physical activity · 2020Article
- Geographic mapping of Enterobacteriaceae with extended-spectrum β-lactamase (ESBL) phenotype in Pereira, Colombia.BMC infectious diseases · 2020Article
- Validating a GPS-based approach to detect health facility visits against maternal response to prompted recall survey.Journal of global health · 2020Article
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesLittle is known about how the distribution of destinations in the local neighbourhood is related to body mass index (BMI). Kernel density estimation (KDE) is a spatial analysis technique that accounts for the location of features relative to each other. Using KDE, this study investigated whether individuals living near destinations (shops and service facilities) that are more intensely distributed rather than dispersed, have lower BMIs. STUDY DESIGN AND
settingA cross-sectional study of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia.
methodsDestinations were geocoded, and kernel density estimates of destination intensity were created using kernels of 400, 800 and 1200 m. Using multilevel linear regression, the association between destination intensity (classified in quintiles Q1(least)-Q5(most)) and BMI was estimated in models that adjusted for the following confounders: age, sex, country of birth, education, dominant household occupation, household type, disability/injury and area disadvantage. Separate models included a physical activity variable.
resultsFor kernels of 800 and 1200 m, there was an inverse relationship between BMI and more intensely distributed destinations (compared to areas with least destination intensity). Effects were significant at 1200 m: Q4, β -0.86, 95% CI -1.58 to -0.13, p=0.022; Q5, β -1.03 95% CI -1.65 to -0.41, p=0.001. Inclusion of physical activity in the models attenuated effects, although effects remained marginally significant for Q5 at 1200 m: β -0.77 95% CI -1.52, -0.02, p=0.045.
conclusionsThis study conducted within urban Melbourne, Australia, found that participants living in areas of greater destination intensity within 1200 m of home had lower BMIs. Effects were partly explained by physical activity. The results suggest that increasing the intensity of destination distribution could reduce BMI levels by encouraging higher levels of physical activity.
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