Simulating the cost-effectiveness of lung cancer screening by low-dose CT scan in Canada.
Bibliographic record
Abstract
6550 Background: The National Lung Screening Trial (NLST) demonstrated that low-dose CT screening diminishes the risk of death among smokers. A cost-effectiveness analysis was undertaken in the context of the Canadian publically funded healthcare system. Methods: Microsimulation of CT screening was undertaken using the Cancer Risk Management Model, which incorporates demographic data, cancer risk factors, cancer registry data, diagnostic and treatment algorithms and health utilities. Simulations were performed at the individual level for a cohort incepted during the period 2012-2032. The criteria for the screen-eligible population, CT scan test characteristics, and screened cohort outcomes were derived from NLST and Canadian data. The baseline screening scenario was annual CT screening for ≥30 pack-year smokers, age 55 to 74. Simulation assumed 60% of the eligible population participates by 10 years, 70% adhere to the screening regimen, and smoking cessation rates are unchanged. One-way sensitivity analyses were performed. Costs and life-years lived were discounted at 3% annually. Results: Compared to no screening, annual screening results in incremental system costs of $2.97 billion (Cdn), 149,000 life-years saved (LYS) or 55,000 quality-adjusted life-years saved (QALYS), an incremental cost-effectiveness ratio (ICER) of $19,900/LYS, and $53,700/QALYS. With participation rates from 40% to 80%, ICER /QALYS remained within the range of $53,700 to $58,200. Increases in screening adherence from 50% through 90% increased the ICER /QALY from $50,400 to $58,800. Higher rates of smoking cessation led to improvements in ICER /QALY (150% of background cessation rate of 3.2-5.3%, $47,000; 200%, $41,500; 300%, $32,900). A system of biennial screening had a net cost of $1.81 billion, resulting in an ICER of $19,600 /LYS, and $54,800 /QALYS. Conclusions: Screening for lung cancer with low-dose CT scans could be cost-effective, but requires substantial system costs. The smoking cessation rate greatly impacts the ICER and a cessation program should be considered if screening is implemented. Compared to annual screening, biennial screening costs less and produces a similar ICER. Further analyses will be detailed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".