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 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) | 0.000 | 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".