Assessing the Contribution of National Health Insurance Scheme to Reduce Household Out-of-Pocket Payments for Healthcare in Nepal
Bibliographic record
Abstract
Nepal launched National Health Insurance Scheme (NHIS) in 2016 aiming to reduce out-of-pocket (OOP) payments for healthcare and to ensure financial protection of households in healthcare and increase healthcare access. The systematic impact analysis of the NHIS in Nepal is sparse. The objective of the study was to analyze the contribution of NHIS to reduce OOP payments for healthcare and increase healthcare access and ensure financial protection. The primary data for this study were collected from nine wards of Tikapur Municipality of Kailali district using a structured questionnaire during first quarter of 2025. The sample of the study was 120, consisting of 62 within the experimental group and 58 in the control group from nine wards using proportionate stratified sampling based on the number of enrollee in each ward, with purposive sampling applied within strata. The Eviews software was applied for statistical analysis of the data. The estimated result of multiple regression analysis shows that the households enrolled in insurance scheme are able to reduce OOP payments for healthcare by 6.7 percent (p <0.01). Moreover, the analysis revealed that higher healthcare utilization and the presence of chronic disease in household significantly increase household OOP payments. Conversely, the higher education levels of household heads are associated with the lower OOP payments for healthcare, indicating educated households head may be opting for cost effective healthcare thereby emphasizing preventive care. The findings highlight the increased contribution of NHIS of Nepal for the financial protection of households in healthcare expenditure and also provided valuable insight for policy implication for reforming the scheme expanding benefit package and improving administrative procedures to increase effectiveness of NHIS of Nepal.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".