How much do I save if I use my health insurance card when seeking outpatient care? Evidence from a low-income country
Bibliographic record
Abstract
Much of the existing literature on the financial protection of health insurance focuses on the impact of insurance status on total out-of-pocket expenditure on all sorts of care sought, regardless of whether the insured patients use their health insurance cards. Using Vietnam's 2006 Household Living Standard Survey data and an appropriate multivariate regression model, this article assesses the influence of Vietnam's three health insurance schemes on out-of-pocket expenditures with and without controlling for the actual use of the health insurance card when seeking outpatient care. Vietnam's experience suggests that insurance provides some financial protection, provided that insurance benefits are actually accessed. Compared with private fee-paying patients, the use of the insurance card reduces out-of-pocket expenditures, on average, by as much as 50-56%. In contrast, failure to control for the use of the health insurance card reduces the financial protection of insurance to 26-37%. However, the financial protection benefits afforded by Vietnam's insurance schemes are distributed rather inequitably. Insurance reduces out-of-pocket expenditures by as much as 71-75% for contacts at the major state hospitals, as compared with 26-38% for contacts at the community health centres. The overall financial protection provided by insurance is also found to be larger for the higher-income individuals than the middle- and low-income individuals. Efforts to ensure that all enrollees receive equitable and good-quality health services according to the benefits package appear warranted. Improving the quality of care provided by the community health centres-the main access point for medical care for many enrollees with health insurance for the poor coverage-and a more effective referral system may also be a cost-effective way of channelling outpatient service contact to the lower-level health facilities, away from the overcrowded higher-level health facilities.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".