Cost-effectiveness of mammography from a publicly funded health care system perspective
Bibliographic record
Abstract
BACKGROUND: The implementation of population-wide breast cancer screening programs has important budget implications. We evaluated the cost-effectiveness of various breast cancer screening scenarios in Canada from a publicly funded health care system perspective using an established breast cancer simulation model. METHODS: Breast cancer incidence, outcomes and total health care system costs (screening, investigation, diagnosis and treatment) for the Canadian health care environment were modelled. The model predicted costs (in 2012 dollars), life-years gained and quality-adjusted life-years (QALYs) gained for 11 active screening scenarios that varied by age range for starting and stopping screening (40-74 yr) and frequency of screening (annual, biennial or triennial) relative to no screening. All outcomes were discounted. Marginal and incremental cost-effectiveness analyses were conducted. One-way sensitivity analyses of key parameters assessed robustness. RESULTS: The lifetime overall costs (undiscounted) to the health care system for annual screening per 1000 women ranged from $7.4 million (for women aged 50-69 yr) to $10.7 million (40-74 yr). For biennial and triennial screening per 1000 women (aged 50-74 yr), costs were less, at about $6.1 million and $5.3 million, respectively. The incremental cost-utility ratio varied from $36 981/QALY for triennial screening in women aged 50-69 versus no screening to $38 142/QALY for biennial screening in those aged 50-69 and $83 845/QALY for annual screening in those aged 40-74. INTERPRETATION: Our economic analysis showed that both benefits of mortality reduction and costs rose together linearly with the number of lifetime screens per women. The decision on how to screen is related mainly to willingness to pay and additional considerations such as the number of women recalled after a positive screening result.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".