Cost-effectiveness analysis of prosthesis provision for patients with transfemoral amputation in Tanzania
Bibliographic record
Abstract
BACKGROUND: Limb loss leads to significant disability. Prostheses may mitigate this disability but are not readily accessible in low- and middle-income countries (LMICs). Cost-effectiveness data related to prosthesis provision in resource-constrained environments such as Tanzania is greatly limited. OBJECTIVES: This study aimed to compare the cost-effectiveness of a prosthesis intervention compared with that of no prosthesis for persons with transfemoral amputations in an LMIC. STUDY DESIGN: This is a prospective cohort study. METHODS: Thirty-eight patients were prospectively followed up. Clinical improvement with prosthesis provision was measured using EuroQuol-5D, represented as quality-adjusted life years gained. Direct and indirect costs were measured. The primary outcome was incremental cost per quality-adjusted life year, measured at 1 year and projected over a lifetime using a Markov model. Reference case was set as a single prosthesis provided without replacement from a payer perspective. Additional scenarios included the societal perspective and replacement of the prosthesis. Uncertainty was measured with one-way probabilistic sensitivity analysis. RESULTS: From the payer perspective, the incremental cost-effectiveness ratio (ICER) was $242 for those without prosthetic replacement over a lifetime, and the ICER was $390 for those with prosthetic replacement over a lifeime. From the societal perspective, prosthesis provision was both less expensive and more effective. One-way sensitivity analysis demonstrated the ICER remained below the willingness to pay threshold up to prosthesis costs of $763. CONCLUSIONS: These findings suggest prosthesis provision in an LMIC may be cost-effective, but further studies with long-term follow up are needed to validate the results.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| 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".