Fast Food Outlet Density and the Incidence of Overweight and Obesity Across Canadian Metropolitan Areas
Bibliographic record
Abstract
Rationale: The increased incidence of obesity and overweight, particularly in wealthier countries, has been identified as a major public health concern. Access to fast food products has been suggested as a possible culprit. Understanding whether or not such claims have theoretical and empirical support is an important step in informing current policy debates over the use of policy interventions in addressing these dietary concerns. Methodology: We begin by developing and presenting a theoretical model of how the accessibility of fast food may be related to the incidence of overweight and obesity, and also of what might determine differential access to fast food restaurants in different regions. We then apply this framework to an empirical example of overweight and obesity in Canada. Recent evidence from the 2004 Canadian Community Health Survey indicates that there are considerable regional differences in obesity across Canada (Shields and Tjepkema, 2006). For example, although the average rate of adult obesity in Canada is 23%, the estimated incidence in various cities ranges from 11.7% in Vancouver to 36.4% in St. John's. We use data on the location of fast food establishments from the 2005 Business Locations database (compiled by Environics Analytics) to construct various indicies representing the accessbility of fast food across Canadian metropolitan areas, and investigate whether these indices can help explain the variation in obesity and oveweight rates in these areas. Results: The accessibility and composition of fast food varies greatly across Canadian metropolitan areas, when investigated on either a per population or per unit area basis. For example, the population density of the most popular fast food chains nationally ten times greater in Windsor, Ontario than in Quebec City. Furthermore, these measures, when compared to obesity rates across cities, yield insights into how fast food access may impact dietary health. For example, population density measures of fast food accessibility are significant correlates of obesity rates, whereas area density measures or not. Furthermore, the top two fast food population density measure is noteably correlated with the incidence of obesity (Pearson's r = 0.37), but much less so with the incidence of overweight and obesity combined (r = 0.09). Conclusions: Both theoretical and empirical evidence suggests that the incidence of obesity and overweight is related to the accessibility and composition of fast food in Canada, but the causal directions in this relationship are ambiguous. As the theoretical discussion illustrates, the placement of fast food businesses is an endogenous process that is influenced by unobservable factors that warrant further study.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".