Impact of smoking on health system costs among cancer patients in a retrospective cohort study in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: Smoking is the main modifiable cancer risk factor. The objective of this study was to examine the impact of smoking on health system costs among newly diagnosed adult patients with cancer. Specifically, costs of patients with cancer who were current smokers were compared with those of non-smokers from a publicly funded health system perspective. METHODS: This population-based cohort study of patients with cancer used administrative databases to identify smokers and non-smokers (1 April 2014-31 March 2016) and their healthcare costs in the 12-24 months following a cancer diagnosis. The health services included were hospitalisations, emergency room visits, drugs, home care services and physician services (from the time of diagnosis onwards). The difference in cost (ie, incremental cost) between patients with cancer who were smokers and those who were non-smokers was estimated using a generalised linear model (with log link and gamma distribution), and adjusted for age, sex, neighbourhood income, rurality, cancer site, cancer stage, geographical region and comorbidities. RESULTS: This study identified 3606 smokers and 14 911 non-smokers. Smokers were significantly younger (61 vs 65 years), more likely to be male (53%), lived in poorer neighbourhoods, had more advanced cancer stage,and were more likely to die within 1 year of diagnosis, compared with non-smokers. The regression model revealed that, on average, smokers had significantly higher monthly healthcare costs ($5091) than non-smokers ($4847), p<0.05. CONCLUSIONS: Smoking status has a significant impact on healthcare costs among patients with cancer. On average, smokers incurred higher healthcare costs than non-smokers. These findings provide a further rationale for efforts to introduce evidence-based smoking cessation programmes as a standard of care for patients with cancer as they have the potential not only to improve patients' outcomes but also to reduce the economic burden of smoking on the healthcare system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".