Evidence From the China Family Panel Studies Survey on the Effect of Integrating the Basic Medical Insurance System for Urban and Rural Residents on the Health Equity of Residents: Difference-in-Differences Analysis
Bibliographic record
Abstract
BACKGROUND: The fragmentation of the medical insurance system is a major challenge to achieving health equity. In response to this problem, the Chinese government is pushing to establish the unified Urban and Rural Resident Basic Medical Insurance (URRBMI) system by integrating the New Rural Cooperative Medical Scheme and the Urban Resident Basic Medical Insurance. By the end of 2020, URRBMI had been implemented almost entirely across China. Has URRBMI integration promoted health equity for urban and rural residents? OBJECTIVE: This study aims to examine the effect of URRBMI integration on the health level of residents and whether the integration can contribute to reducing health disparities and promoting health equity. METHODS: We used the staggered difference-in-differences method based on the China Family Panel Studies survey from 2014 to 2018. Our study had a nationally representative sample of 27,408 individuals from 98 cities. We chose self-rated health as the measurement of health status. In order to more accurately discern whether the sample was covered by URRBMI, we obtained the exact integration time of URRBMI according to the official documents issued by local governments. Finally, we grouped the sample by urban and rural areas, regions, and household income to examine the impact of the integration on health equity. RESULTS: We found that overall, the URRBMI integration has improved the health level of Chinese residents (B=0.066, 95% CI 0.014-0.123; P=.01). In terms of health equity, the results showed that first, the integration has improved the health level of rural residents (B=0.070, 95% CI 0.012-0.128; P=.02), residents in western China (B=0.159, 95% CI 0.064-0.255; P<.001), and lower-middle-income groups (B=0.113, 95% CI 0.004-0.222, P=.04), so the integration has played a certain role in narrowing the health gap between urban and rural areas, different regions, and different income levels. Through further mechanism analysis, we found that the URRBMI integration reduced health inequity in China by facilitating access to higher-rated hospitals and increasing reimbursement rates for medical expenses. However, the integration did not improve the health of the central region and low-income groups, and the lack of access to health care for low-income groups was not effectively reduced. CONCLUSIONS: The role of URRBMI integration in promoting health equity among urban and rural residents was significant (P=.02), but in different regions and income groups, it was limited. Focusing on the rational allocation of medical resources between regions and increasing the policy tilt toward low-income groups could help improve the equity of health insurance integration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".