Lung Cancer–Related Clinical and Economic Impacts of Achieving a 5% Smoking Prevalence Rate by 2035 in Canada
Bibliographic record
Abstract
Background: Smoking is responsible for nearly 85% of lung cancer cases and 30% of all cancer-related deaths. Canada has set an ambitious target to reduce tobacco use to 5% by 2035 in alignment with a world-wide tobacco endgame initiative. Aim: We project the impact of achieving a national 5% smoking prevalence rate by 2035 on population-level lung cancer outcomes and costs. Methods: OncoSim-Lung (version 2.5), led by the Canadian Partnership Against Cancer with model development by Statistics Canada, is a microsimulation model that incorporates Canadian demographics, risk factors, registry data, resource utilization and other data to project clinical and economic impacts of cancer control measures. Smoking cessation parameters were modified to reduce the current average national smoking prevalence rate of 18% over time to 5% in 2035. Impacts were compared with those in a reference scenario, which maintained the current prevalence rate. Outputs of interest included lung cancer incidence, mortality, treatment costs, and quality-adjusted life-years (QALYs). Costs and QALYs are undiscounted and reported in 2016 CAD. Results: Achieving a 5% smoking rate by 2035 would result in a 2017-2035 cumulative total of 31,000 fewer lung cancer cases, 21,000 fewer lung cancer-related deaths, and 457,000 additional QALYs compared with projections based on a constant smoking prevalence rate of ∼20%. When stratified by sex, there would be 15,600 and 15,700 fewer lung cancer diagnoses and 11,000 and 10,000 fewer lung cancer-related deaths for males and females respectively. Furthermore, treatment-related costs would be reduced by $680 million dollars. On average there would be 4,500 fewer lung cancer cases, 3,500 fewer deaths, and $35 million in cost savings annually. If a 5% smoking rate is sustained until 2050, then there would be a 15% reduction in lung cancer cases and a 13% reduction in deaths from 2017-2050. Conclusion: Reducing Canada's smoking prevalence to 5% by 2035 could result in a significant reduction in lung cancer cases, deaths and treatment costs. Like Canada, other countries with relatively high smoking prevalence could use averted treatment costs to offset costs of aggressive smoking prevention and cessation programs or redirect them to other healthcare services.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".