Disparities in modifiable cancer risk factors among Canadian provinces, territories, and health regions
Bibliographic record
Abstract
Data about small area estimates of cancer risk factors are difficult to obtain in Canada. The current study aims to provide an assessment of the prevalence of different behavioral risk factors of cancer at the level of Canadian provinces/territories and sub provincial health regions/units. Canadian Community Health Survey (CCHS) datasets for 2017/2018 were reviewed and adult participants (≥ 18 years old) were included. Baseline demographic data and health behaviors (including ever-smoking, current smoking, alcohol drinking in the past 12 months, below-recommended physical activity, and obesity) were reviewed. Prevalence of each of these behaviors within different provinces/territories as well as within each health region was reviewed. Multivariable logistic regression analysis was then done to examine the association between place of residence and cancer risk factors. A total of 104,636 adult participants were included in the current analysis. For ever-smoking, the highest prevalence was noted in Nunavut (79.7%); for current smoking, the highest prevalence was noted in Nunavut (67.2%); for alcohol drinking in the past 12 months, the highest prevalence was noted in Quebec (89.3%); for below-recommended physical activity, the highest prevalence was noted in Nunavut (51.3%); for obesity, the highest prevalence was noted in Northwest territories (31.5%). Compared to individuals living within a territory, individuals living within a province were less likely to ever smoke (OR: 0.62; 95% CI: 0.54–0.71), currently smoke (OR: 0.51; 95% CI: 0.45–0.59), be obese (OR: 0.82; 95% CI: 0.71–0.95), but more likely to drink alcohol in the past 12 months (OR: 1.41; 95% CI: 1.20–1.65). There is no difference between both categories with regards to physical activity (OR: 1.02; 95% CI: 0.89–1.15). There is a general province/territory disparity in the prevalence of different modifiable cancer risk factors as well as disparity between individual provinces/health regions in 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".