The impact of economic growth on health care utilization: a longitudinal study in rural Vietnam
Bibliographic record
Abstract
INTRODUCTION: In many developing countries, including Vietnam, out-of-pocket payment is the principal source of health financing. The economic growth is widening the gap between rich and poor people in many aspects, including health care utilization. While inequities in health between high- and low-income groups have been well investigated, this study aims to investigate how the health care utilization changes when the economic condition is changing at a household level. METHOD: We analysed a panel data of 11,260 households in a rural district of Vietnam. Of the sample, 74.4% having an income increase between 2003 and 2007 were defined as households with economic growth. We used a double-differences propensity score matching technique to compare the changes in health care expenditure as percentage of total expenditure and health care utilization from 2003 to 2005, from 2003 to 2007, and from 2005 to 2007, between households with and without economic growth. RESULTS: Households with economic growth spent less percentage of their expenditure for health care, but used more provincial/central hospitals (higher quality health care services) than households without economic growth. The differences were statistically significant. CONCLUSIONS: The results suggest that households with economic growth are better off also in terms of health services utilization. Efforts for reducing inequalities in health should therefore consider the inequality in income growth over time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".