Cost effectiveness of digital breast tomosynthesis for breast cancer screening in a Swedish setting
Bibliographic record
Abstract
BACKGROUND: In the Malmö Breast Tomosynthesis Screening Trial (MBTST), digital breast tomosynthesis (DBT) increased the detection rate of breast cancer compared to currently used digital mammography (DM). This study evaluated the cost effectiveness of DBT versus DM in Sweden using data from MBTST. MATERIALS AND METHOD: A health economic model was used to compare a breast cancer screening programme using DBT to breast cancer screening using DM in women aged 40-74. Model input values on demographics, breast cancer incidence and mortality were derived from regional registries. The effect of DBT on detection rates and interval breast cancer was based on MBTST. Key information is not yet available and scenario analyses were used to explore the reduction in relative breast cancer mortality required to meet willingness-to-pay thresholds between €10,000 and €50,000/quality-adjusted life year (QALY), assuming three levels of DBT examination cost. RESULTS: Compared with DM, DBT-screening was estimated to detect 56 more invasive cancers and 18 fewer interval cancers per 100,000 invited women. The relative reduction in breast cancer mortality for DBT to be cost effective varied between 2.3 and 14.2% for the lowest versus highest evaluated cost and willingness-to-pay scenarios. The required reduction at a DBT cost and willingness-to-pay threshold of €61 and €50,000/QALY respectively was 3.6%. CONCLUSION: This model analysis shows that the cost effectiveness of DBT-screening is dependent on the expected relative reduction in breast cancer mortality and the willingness-to-pay for screening. Furthermore, the results are driven by the future costs for DBT-screening examinations.
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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 | 0.001 | 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".