The Economic Burden of Cancers Attributable to Tobacco Smoking, Excess Weight, Alcohol Use, and Physical Inactivity in Canada
Bibliographic record
Abstract
OBJECTIVES: The purpose of the present study was to calculate the proportion of cancers in Canada attributable to tobacco smoking (ts), alcohol use (au), excess weight (ew), and physical inactivity (pia); to explore variation in the proportions of those risk factors (rfs) over time by sex and province; to estimate the economic burden of cancer attributable to the 4 rfs; and to calculate the potential reduction in cancers and economic burden if all provinces achieved rf prevalence rates equivalent to the best in Canada. METHODS: We used a previously developed approach based on population-attributable fractions (pafs) to estimate the cancer-related economic burden associated with the four rfs. Sex-specific relative risk and age- and sex-specific prevalence data were used in the modelling. The economic burden was adjusted for potential double counting of cases and costs. RESULTS: In Canada, 27.7% of incident cancer cases [95% confidence interval (ci): 22.6% to 32.9%] in 2013 [47,000 of 170,000 (95% ci: 38,400-55,900)] were attributable to the four rfs: ts, 15.2% (95% ci: 13.7% to 16.9%); ew, 5.1% (95% ci: 3.8% to 6.4%); au, 3.9% (95% ci: 2.4% to 5.3%); and pia, 3.5% (95% ci: 2.7% to 4.3%). The annual economic burden attributable to the 47,000 total cancers was $9.6 billion (95% ci: $7.8 billion to $11.3 billion): consisting of $1.7 billion in direct and $8.0 billion in indirect costs. Applying the lowest rf rates to each province would result in an annual reduction of 6204 cancers (13.2% of the potentially avoidable cancers) and a reduction in economic burden of $1.2 billion. CONCLUSIONS: Despite substantial reductions in the prevalence and intensity of ts, ts remains the dominant risk factor from the perspective of cancer prevention in Canada, although ew and au are becoming increasingly important rfs.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".