Cost-Effectiveness of Three Rounds of Mammography Breast Cancer Screening in Iranian Women
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the most common cancer in Iranian women as is worldwide. Mammography screening has been introduced as a beneficial method for reducing mortality and morbidity of this disease. OBJECTIVES: We developed an analytical model to assess the cost effectiveness of an organized mammography screening program in Iran for early detection of the breast cancer. PATIENTS AND METHODS: This study is an economic evaluation of mammography screening program among Iranian woman aged 40 - 70 years. A decision tree and Markov model were applied to estimate total quality adjusted life years (QALY) and lifetime costs. RESULTS: The results revealed that the incremental cost effectiveness ratio (ICER) of mammography screening in Iranian women in the first round was Int. $ 37,350 per QALY gained. The model showed that the ICER in the second and third rounds of screening program were Int. $ 141,641 and Int. $ 389,148 respectively. CONCLUSIONS: Study results identified that mammography screening program was cost-effective in 53% of the cases, but incremental cost per QALY in the second and third rounds of screening are much higher than the accepted payment threshold of Iranian health system. Thus, evaluation of other screening strategies would be useful to identify more cost-effective program. Future studies with new national data can improve the accuracy of our finding and provide better information for health policy makers for decision making.
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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 | 0.003 | 0.011 |
| 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) | 0.003 | 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".