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Record W3213467308 · doi:10.1016/j.envint.2021.106959

Chronic disease and where you live: Built and natural environment relationships with physical activity, obesity, and diabetes

2021· article· en· W3213467308 on OpenAlexafffundabout
Lawrence D. Frank, Binay Adhikari, Katherine White, Trevor Dummer, Ellen Demlow, Yumian Hu, Andy Hong, Matilda van den Bosch

Bibliographic record

VenueEnvironment International · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsVancouver Coastal HealthCanadian Centre for Applied Research in Cancer ControlUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsWalkabilityObesityEnvironmental healthBody mass indexMedicineBuilt environmentMediationDemographyDiabetes mellitusGerontologyPhysical activityEcologyPhysical therapyBiologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Diabetes is among the most prevalent non-communicable diseases causing significant morbidity and mortality globally. The aetiology and disease development of diabetes are influenced by genetic, lifestyle, and environmental factors. Due to an increasing number of cases each year, it is imperative to improve the understanding of modifiable environmental risk and protective factors. In this study we aimed to analyse associations between built and natural environment features and diabetes prevalence; and two major risk factors: physical activity and obesity and their mediation effects. METHODS: We analysed relationships between walkability and park availability with physical activity, obesity, and diabetes, using self-reported data from a large cross-sectional survey in British Columbia, Canada (n = 22,418). We validated results with an independent cohort (n = 11,972) in a subset of the analyses. The outcome measures included walking, moderate to vigorous physical activity (MVPA), body mass index (BMI), and diabetes. Built and natural environment features within a 1 km road network buffer around residential postal code centroids were assessed using validated indicators of walkability and park availability. We used general linear multivariable models (GLM) to examine the direct relationship between environmental features, physical activity, obesity, and diabetes respectively. Path models were developed to analyse mediation effects of physical activity and obesity on the association between environmental indicators and diabetes. The relative contribution of direct versus indirect effects was assessed. All models were adjusted for age, gender, income. ethnicity, years lived in neighbourhood and regional accessibility. RESULTS: Walkable neighbourhoods and areas with greater park availability were associated with lower rates of diabetes. There was a direct association of walkability and park availability on physical activity (highest vs. lowest quintile OR = 1.15; 95% CI: 1.00, 1.33 and OR = 1.28, 95% CI: 1.13, 1.45 respectively), obesity (highest vs. lowest quintile OR = 0.58, 95% CI: 0.49, 0.70 and OR = 0.57, 95% CI: 0.48, 0.68 respectively) and diabetes (highest vs. lowest quintile OR = 0.62, 95% CI: 0.45, 0.85, and OR = 0.63, 95% CI: 0.47, 0.84 respectively). Results were similar in the independent cohort. The associations between diabetes and walkability and park availability respectively were partly mediated by obesity (41% of total association for walkability and 53% of total association for park availability). The mediating effect of physical activity was negligible. CONCLUSION: Results support investments in walkability through active transportation and transit infrastructure. Changes in zoning and subdivision regulations governing land use actions are required to enable compact mixed-use environments with access to parks and high quality transit service. Future studies including cost-benefit analyses of health-related economic impacts of such investments can contribute to evidence-based decisions for healthier cities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.253
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations105
Published2021
Admission routes3
Has abstractyes

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