Cost‐effectiveness of digital mammography screening before the age of 50 in <scp>T</scp>he <scp>N</scp>etherlands
Bibliographic record
Abstract
In the Netherlands, routine mammography screening starts at age 50. This starting age may have to be reconsidered because of the increasing breast cancer incidence among women aged 40 to 49 and the recent implementation of digital mammography. We assessed the cost-effectiveness of digital mammography screening that starts between age 40 and 49, using a microsimulation model. Women were screened before age 50, in addition to the current programme (biennial 50-74). Screening strategies varied in starting age (between 40 and 50) and frequency (annual or biennial). The numbers of breast cancers diagnosed, life-years gained (LYG) and breast cancer deaths averted were predicted and incremental cost-effectiveness ratios (ICERs) were calculated to compare screening scenarios. Biennial screening from age 50 to 74 (current strategy) was estimated to gain 157 life years per 1,000 women with lifelong follow-up, compared to a situation without screening, and cost €3,376/LYG (3.5% discounted). Additional screening increased the number of LYG, compared to no screening, ranging from 168 to 242. The costs to generate one additional LYG (i.e., ICER), comparing a screening strategy to the less intensive alternative, were estimated at €5,329 (biennial 48-74 vs. current strategy), €7,628 (biennial 45-74 vs. biennial 48-74), €10,826 (biennial 40-74 vs. biennial 45-74) and €18,759 (annual 40-49 + biennial 50-74 vs. biennial 40-74). Other strategies (49 + biennial 50-74 and annual 45-49 + biennial 50-74) resulted in less favourable ICERs. These findings show that extending the Dutch screening programme by screening between age 40 and 49 is cost-effective, particularly for biennial strategies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".