Willingness to pay for haemodialysis among patients with chronic kidney disease in Abuja, Nigeria
Bibliographic record
Abstract
BACKGROUND: Evidence of willingness to pay for kidney replacement therapy is scarce in low-middle-income countries, including Nigeria's Formal Sector Social Health Insurance Programme. The study, therefore, assessed the willingness to pay for haemodialysis among chronic kidney disease patients in Abuja, Nigeria. METHODS: The study adopted a cross-sectional survey design. We used the contingent valuation method to estimate the maximum stated willingness to pay (WTP) for haemodialysis among end-stage kidney disease (ESKD) patients. We obtained informed written consent from respondents before data collection. The socio-demographic characteristics and willingness to pay data were summarized using descriptive statistics. We evaluated the mean differences in respondents' WTP using Mann-Whitney and Kruskal-Wallis tests. All variables that had p < 0.25 in the bivariate analysis were included in the Generalized Linear Model (gamma with link function) to determine the predictors of the WTP for one's and another's haemodialysis. The level of significance in the final model was ρ < 0.05. RESULTS: About 88.3% and 64.8% of ESKD patients were willing to pay for personal and altruistic haemodialysis, correspondingly. The mean annual WTP for haemodialysis for one's and altruistic haemodialysis was USD25,999.06 and USD 1539.89, respectively. Private hospital patients were likelier to pay for their haemodialysis (β = 0.39, 95%CI: 0.21 to 0.57, p < 0.001). Patients attending public-private partnership hospitals were less likely to pay for altruistic haemodialysis than those attending public hospitals (β = -1.65, 95%CI: -2.51 to -0.79, p < 0.001). CONCLUSIONS: The willingness to pay for haemodialysis for themselves and others was high. The type of facility ESKD patients attended influenced their willingness to pay for haemodialysis. The findings highlight the need for policies to enhance affordable and equitable access to haemodialysis in Nigeria through pre-payment mechanisms and altruistic financing strategies.
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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".