Out-Of-Pocket Health Expenditure Among the Elderly in Kenya
Bibliographic record
Abstract
Financial risk protection against the burden of out-of-pocket health expenditure (OOPHE) by achieving universal health coverage (UHC) is a key health priority for developing countries. The elderly is a vulnerable demographic group that need this protection. This study sought to analyze how selected social and demographic factors affect OOPHE among the elderly in Kenya. Further, it aimed to determine the distribution of OOPHE among the various wealth quintiles in the elderly using a cross-sectional study. Data was sourced from the Kenya Household Health Expenditure and Utilization Survey (KHHEUS) 2013. The sample size (2,853) consisted of individuals ≥ 60 years who had utilized health services. A multiple regression model and concentration curves were applied. Increasing age, having chronic illnesses, male gender, higher education level, more wealth, possessing health insurance, increased distance, and a higher number of visits to the health facility positively affected OOPHE. These results were statistically significant (P < .050) for presence of chronic illnesses, increasing age, possessing a health insurance cover and being in the richest wealth quintile and insignificant for the rest. Moreover, concentration curves revealed that out-of-pocket (OOP) health payments were concentrated in the richest quintile individuals. Consequently, OOPHE is a regressive way of funding health care among the elderly. In conclusion, elderly persons need financial protection when seeking health care: achievable mainly through health reforms, especially the ones targeting health insurance.
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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 | 0.000 | 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 | 0.001 |
| 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".