Cost-benefit analysis of haemodialysis in patients with end-stage kidney disease in Abuja, Nigeria
Bibliographic record
Abstract
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 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 | 0.002 |
| 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) | 0.198 | 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".