Cost-benefit analysis of haemodialysis in patients with end-stage kidney disease in Abuja, Nigeria
Bibliographic record
Abstract
BACKGROUND: Significant gaps in scholarship on the cost-benefit analysis of haemodialysis exist in low-middle-income countries, including Nigeria. The study, therefore, assessed the cost-benefit of haemodialysis compared with comprehensive conservative care (CCC) to determine if haemodialysis is socially worthwhile and justifies public funding in Nigeria. METHODS: The study setting is Abuja, Nigeria. The study used a mixed-method design involving primary data collection and analysis of secondary data from previous studies. We adopted an ingredient-based costing approach. The mean costs and benefits of haemodialysis were derived from previous studies. The mean costs and benefits of CCC were obtained from a primary cross-sectional survey. We estimated the benefit-cost ratios (BCR) and net benefits to determine the social value of the two interventions. RESULTS: The net benefit of haemodialysis (2,251.30) was positive, while that of CCC was negative (-1,197.19). The benefit-cost ratio of haemodialysis was 1.09, while that of CCC was 0.66. The probabilistic and one-way sensitivity analyses results demonstrate that haemodialysis was more cost-beneficial than CCC, and the BCRs of haemodialysis remained above one in most scenarios, unlike CCC's BCR. CONCLUSION: The benefit of haemodialysis outweighs its cost, making it cost-beneficial to society and justifying public funding. However, the National Health Insurance Authority requires additional studies, such as budget impact analysis, to establish the affordability of full coverage of haemodialysis.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.002 | 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".