Estimating Public Economic Gains from Early Breast Cancer and Curative Treatment: A Case Study in Human Epidermal Growth Factor Receptor (HER-2) Positive Targeted Therapies
Bibliographic record
Abstract
INTRODUCTION: Cancer diagnosis influences the choices that patients make regarding current and future labor market activity. These choices have implications for governments based on resulting changes in taxes paid and benefits received. In this analysis we explore how human growth receptor 2 (HER2)-positive residual invasive breast cancer and different treatments influence government accounts excluding health costs. METHODS: HER2-positive early breast cancer (eBC) health states from a published disease model were used to establish likelihood of working and wage impact at different stages of disease. The indirect productivity losses for an average woman aged 49 years were translated into fiscal consequences to government by applying an established government perspective-modeling framework. The fiscal projections (discounted) include gross tax revenue by disease stage, government transfer costs related to time off work and early retirement ,and net fiscal balance (e.g., gross taxes-transfers) in three countries Canada, Portugal, and Brazil. RESULTS: The net fiscal balance in Canada for a healthy woman was C$109,551 compared with a HER2-positive eBC woman treated with trastuzumab emtansine (C$69,767) or trastuzumab (C$62,971). A similar pattern was observed in the three countries but reflecting the overall tax burden in each country, labor force activity, and available public benefits. Age at diagnosis was an important determinant of the likely net fiscal balance, as this influences the remaining working years. DISCUSSION: Women diagnosed with HER2-positive eBC were estimated to pay less lifetime gross taxes and receive more in sickness benefits compared with healthy women. Treatments that improve outcomes are likely to offer fiscal gains for government from improved work force participation.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".