Lives and Economic Loss in Brazil Due to Lack of Radiotherapy Access in Cervical Cancer
Bibliographic record
Abstract
Background: Cervical cancer collects the highest survival benefit from radiotherapy (RT) among all malignancies. A large gap between oncological demand and RT availability exists for cervical cancer in Brazilian Public Health System (BPHS). Aim: To evaluate cost-effectiveness of universal access to RT and chemo-radiation (CRT) for untreated cervical cancer patients in the BPHS. Methods: The incremental cost was calculated based on the direct medical cost from a payer's perspective and the proportion of new cases with unmet RT/CRT needs in 2016. The incremental effectiveness was evaluated by life-year (LY) gain based on life expectancy, cervical cancer incidence and the number of cancer deaths due to lack of RT/CRT access as previously described. The incremental cost-effectiveness ratio (ICER) was calculated from direct medical costs and LYs. The indirect costs from mortality-related productivity loss (MRPL) were estimated based on life expectancy, wage and labor force participation rate. The MRPL was compared with direct medical cost. All costs and effectiveness were age-adjusted based on 2016 Brazilian data and discounted at 3% per year. Costs were adjusted to 2016 U.S. dollars. One-way sensitivity analysis was performed to assess the robustness of the model. Results: The annual cost to close RT gap was $14.3 million, with additional cost of $4.1 million to close the CRT gap. The average years of potential life lost per death was 20.5. Cost per life saved was $10,820 for RT alone (ICER: $528/LY) and $18,919 for CRT (ICER: $584/LY), respectively. The MRPL due to shortage of RT/CRT were 70/81 million respectively. Conclusion: Providing universal access to RT/CRT for cervical cancer patients in the BPHS will incur low cost per life-year saved and provide large economical gain by saving thousands of lives.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".