Smoking-attributable morbidity: acute care hospital diagnoses and days of treatment in Canada, 2002
Bibliographic record
Abstract
BACKGROUND: Smoking is one of the most important risk factors for burden of disease. Our objective was to estimate the number of hospital diagnoses and days of treatment attributable to smoking for Canada, 2002. METHODS: Distribution of exposure was taken from a major national survey of Canada, the Canadian Community Health Survey. For chronic diseases, risk relations were taken from the published literature and combined with exposure to calculate age- and sex-specific smoking-attributable fractions (SAFs). For fire deaths, SAFs were taken directly from available statistics. Information on morbidity, with cause of illness coded according to the International Classification of Diseases version 10, was obtained from the Canadian Institute for Health Information. RESULTS: For Canada in 2002, 339,179 of all hospital diagnoses were estimated to be attributable to smoking and 2,210,155 acute care hospital days. Ischaemic heart disease was the largest single category in terms of hospital days accounting for 21 percent, followed by lung cancer at 9 percent. Smoking-attributable acute care hospital days cost over $2.5 billion in Canada in 2002. CONCLUSION: Since the last major project produced estimates of this type, the rate of hospital days per 100,000 population has decreased by 33.8 percent. Several possible factors may have contributed to the decline in the rate of smoking-attributable hospital days: a drop in smoking prevalence, a decline in overall hospital days, and a shift in distribution of disease categories. Smoking remains a significant health, social, and economic burden 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".