Cost/DALY Averted in a Small Hospital in Sierra Leone: What Is the Relative Contribution of Different Services?
Bibliographic record
Abstract
BACKGROUND: A cost-effective analysis (CEA) can be a useful tool to guide resource allocation decisions. However, there is a dearth of evidence on the cost/disability-adjusted life year (DALY) averted by health facilities in the developing world. METHODS: We conducted a study to calculate the costs and the DALYs averted by an entire hospital in Sierra Leone, using the method suggested by McCord and Chowdhury (Int J Gynaecol Obstet 2003;81:83-92). RESULTS: For the 3-month study period, total costs were calculated to be dollar 369,774. Using the approach of McCord and Chowdhury, we calculated that 11,282 DALYs were averted during the study period, resulting in a cost/DALY averted of dollar 32.78. This figure compares favorably to other non-surgical health interventions in developing countries. We found that while surgery accounts for 63% of total caseload, it contributes to 38% of the total DALYs averted. CONCLUSIONS: Surgical treatment of some common pathologies in developing countries may be more cost-effective than previously thought, and our results provide evidence for the inclusion of surgery as part of the basic public health armamentarium in developing countries. However, these results are highly context-specific, and more research is needed from developing countries to further refine the methodology and analysis.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".