Out‐of‐pocket expenditures, catastrophic household finances, and quality of life among hemodialysis patients in Kerala, India
Bibliographic record
Abstract
INTRODUCTION: Kidney replacement therapy in chronic kidney disease patients can result in catastrophic health costs, pushing them into poverty in lower middle-income countries. There are only limited studies from India focusing on the financial hardship of these patients. Data on direct nonmedical and indirect cost of hemodialysis (HD) are also limited. This study aims to find the different components of cost for HD and its association with quality of life (QOL) among HD patients. METHODS: Primary objective was to find the cost of HD, which include direct medical, direct nonmedical and indirect cost. Secondary objective was to study whether the ratio of out-of-pocket (OOP) payments for HD to household income can affect the QOL. The QOL was assessed using EQ-5D-5L instrument. Catastrophic health care expenditure was defined as OOP health care expenditure exceeding 40% of the household income and distress financing as borrowing money or selling assets to meet the OOP expenditure for treatment. FINDINGS: Of the 152 patients enrolled for the study, 103 (67.8%) were males. Mean age was 60.9 ± 12.5 years. Monthly OOP expenditure for dialysis was USD 478.4 (362.6-663.6) of which direct nonmedical and indirect expenses constitute USD 115.6 (88.4-292.4). Median percentage of household income spent for dialysis was 194.5 (IQR 128-297). One hundred and forty-two (93.4%) had catastrophic healthcare expenditure and 76 (50%) had distressing health care expenditure. On multivariable linear regression, proportion of total household income spend for dialysis was associated with poor QOL in patients undergoing HD; coefficient = -0.04 (95% CI -0.008 - 0.092), p = 0.039. DISCUSSION: Nonmedical direct and indirect cost is substantial among patients undergoing HD. Nine of 10 patients had catastrophic health care expenditure, which pushed 50% of the patients to distress financing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".