Out‐of‐pocket payments by end‐stage kidney disease patients on regular hemodialysis: Cost of illness analysis, experience from Sudan
Bibliographic record
Abstract
INTRODUCTION: In Sudan, the number of end-stage kidney disease (ESKD) patients receiving hemodialysis (HD) is growing. Patients and their families incur a high out-of-pocket expenditure (OOPE), given that HD treatment is expensive. There are limited data about OOP spending on HD in the country. This study aims to explore patients' OOP expense on direct medical and nonmedical goods and services and to which extent they can be predicted from sociodemographic characteristics, health insurance status, comorbidity, and accommodation change. METHODS: This is descriptive a cross-sectional study conducted in Ibn Sina Hospital. One hundred and thirty patients undergo regular HD were randomly selected. FINDINGS: Among the study participants (130), the median of the overall total OOP (direct medical and direct nonmedical) spending per patients per year was found to be US$ 3859.1 (interquartile range [IQR]: 2298.1-6261.1). As for the medians OOP expenditure on direct medical and nonmedical costs, they were found to be US$ 2327.6 (IQR: 1421.5-3804.8) and US$ 1096 (IQR: 715.2-2345.2), respectively. The direct medical expenditure (355,586 US$) accounted for 60% of the overall total expenses. DISCUSSION: Medications and investigations were the primary drivers of direct medical spending. Higher OOPE rates were found among those with one or more of these factors; uninsured patients, patient with comorbidity, female gender, and over 40 years aged. The multivariate analysis showed that the significant predictors of direct medical expenditure were health insurance and comorbid conditions, where as the predictors for direct nonmedical expenditure were accommodation change and gender. This study results in a better understanding of OOP spending on direct medical and nonmedical services and its associated predictors among HD patients within the context of Sudan. Further research is needed in this area.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".