A Neighborhood Analysis of Underage Tobacco Sales within the Serving Area of a Canadian Public Health Unit
Bibliographic record
Abstract
Despite the fact that the sale of tobacco to minors is illegal in Ontario, youth are still able to purchase tobacco. This study aims to determine the geographic variations of underage tobacco sales at the neighborhood level within the Windsor-Essex County Health Unit. Data were collected on all inspections of tobacco retail stores from 2007 to 2011 in the Windsor-Essex County Health Unit. Data were split into season 1 (September-February) and season 2 (March-August) to assess a possible seasonal effect. Relative risks were calculated for each dissemination area (DA) by modeling the risks in a hierarchical Bayesian fashion, incorporating appropriate random effects terms for both spatially correlated and uncorrelated random errors with adjustments for neighborhood income. The association between violation rate and proximity to a school was assessed through a buffer analysis. Elliptical analysis detected a significant cluster of high risk DAs in season 1 in Windsor (p-value = 0.022) but no significant cluster in season 2. Some DAs exhibited higher relative risks of tobacco sales to minors, however after adjusting the model for neighborhood income no excess risk was observed. The results of the buffer analysis showed that in season 1 there was a significantly higher probability (p-value = 0.045) of tobacco vendors located closer to schools to sell tobacco to minors. This analysis demonstrates the utility of a systematic approach to identifying neighborhoods with higher risks of tobacco sales to minors. The insights provided by this exploratory, ecologic study are valuable for program planning and directing tobacco enforcement efforts to high risk areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".