The Association Between the Neighbourhood Food Environment and Prevalence of Three Chronic Diseases in Urban Canada: A Cross-Sectional Analysis
Bibliographic record
Abstract
Research on the association between the neighbourhood food environment and prevalence of chronic diseases is very limited in Canada. The objective of this thesis was to investigate: (i) the associations between the neighbourhood food environment and prevalence of type II diabetes, cardiovascular disease and hypertension among Canadian adults living in urban areas; and (ii) whether or not dietary patterns, obesity and physical activity mediate such associations. Self-reported diagnosis of three chronic diseases, and individual-level socio-demographic and lifestyle variables were taken from the 2009-2010 Canadian Community Health Survey; neighbourhood-level socio-economic data were taken from the 2011 National Household Survey; and the locations of all restaurants and grocery stores in Canada were taken from the 2011 CFM Leads Business Dataset. The associations between prevalence of three chronic diseases and the density of various restaurant and food outlets (density is defined as the number of outlets per 10,000 people and per square kilometer in the respondent’s Forward Sortation Area) were analyzed using a modified Poisson regression. The mediation analyses were conducted using the Baron & Kenny method. I found that fast-food restaurant density is positively associated with the prevalence of type II diabetes but statistically non-significant for cardiovascular disease and hypertension. I also find that non-chain restaurants density is negatively associated with the prevalence of type II diabetes. Obesity, fruits & vegetables consumption, and physical activity were found to be partial mediators of these associations. The main implication of this study is that fast-food restaurant density is an important factor for the prevalence of type II diabetes in urban Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".