Cost-effectiveness of annual versus biennial screening mammography for women with high mammographic breast density
Bibliographic record
Abstract
OBJECTIVES: The sensitivity of screening mammography is much lower among women who have dense breast tissue, compared with women who have largely fatty breasts, and they are also at much higher risk of developing the disease. Increasing mammography screening frequency from biennially to annually has been suggested as a policy option to address the elevated risk in this population. The purpose of this study was to assess the cost-effectiveness of annual versus biennial screening mammography among women aged 50-79 with dense breast tissue. METHODS: A Markov model was constructed based on screening, diagnostic, and treatment pathways for the population-based screening and cancer care programme in British Columbia, Canada. Model probabilities and screening costs were calculated from screening programme data. Costs for breast cancer treatment were calculated from treatment data, and utility values were obtained from the literature. Incremental cost-effectiveness was expressed as cost per quality adjusted life year (QALY), and probabilistic sensitivity analysis was conducted. RESULTS: Compared with biennial screening, annual screening generated an additional 0.0014 QALYs (95% CI: -0.0480-0.0359) at a cost of $819 ($ = Canadian dollars) per patient (95% CI: 506-1185), resulting in an incremental cost effectiveness ratio of $565,912/QALY. Annual screening had a 37.5% probability of being cost-effective at a willingness-to-pay threshold of $100,000/QALY. CONCLUSION: There is considerable uncertainty about the incremental cost-effectiveness of annual mammography. Further research on the comparative effectiveness of screening strategies for women with high mammographic breast density is warranted, particularly as digital mammography and density measurement become more widespread, before cost-effectiveness can be reevaluated.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".