The impact of different benefit packages of Medical Financial Assistance Scheme on health service utilization of poor population in Rural China
Bibliographic record
Abstract
BACKGROUND: Since 2003 and 2005, National Pilot Medical Financial Assistance Scheme (MFA) has been implemented in rural and urban areas of China to improve the poorest families' accessibility to health services. Local governments of the pilot areas formulated various benefit packages. Comparative evaluation research on the effect of different benefit packages is urgently needed to provide evidence for improving policy-making of MFA. This study was based on a MFA pilot project, which was one component of Health VIII Project conducted in rural China. This article aimed to compare difference in health services utilization of poor families between two benefit package project areas: H8 towns (package covering inpatient service, some designated preventive and curative health services but without out-patient service reimbursement in Health VIII Project,) and H8SP towns (package extending coverage of target population, covering out- patient services and reducing co-payment rate in Health VIII Supportive Project), and to find out major influencing factors on their services utilization. METHODS: A cross-sectional survey was conducted in 2004, which used stratified cluster sampling method to select poor families who have been enrolled in MFA scheme in rural areas of ChongQing. All family members of the enrolled households were interviewed. 748 and 1129 respondents from two kinds of project towns participated in the survey. Among them, 625 and 869 respondents were included (age>/=15) in the analysis of this study. Two-level linear multilevel model and binomial regressions with a log link were used to assess influencing factors on different response variables measuring service utilization. RESULTS: In general, there was no statistical significance in physician visits and hospitalizations among all the respondents between the two kinds of benefit package towns. After adjusting for major confounding factors, poor families in H8SP towns had much higher frequency of MFA use (beta = 1.17) and less use of hospitalization service (OR = 0.7 (H8SP/H8), 95%CI (0.5, 1.0)) among all the respondents. While calculating use of hospital services among those who needed, there was significant difference (p = 0.032) in percentage of hospitalization use between H8SP towns (46%) and H8 towns (33%). Meanwhile, the non-use but ought-to-use hospitalization ratio of H8SP (54%) was lower than that of H8 (67 %) towns. This indicated that hospitalization utilizations had improved in H8SP towns among those who needed. Awareness of MFA detailed benefit package and presence of physician diagnosed chronic disease had significant association with frequency of MFA use and hospitalizations. There was no significant difference in rate of borrowing money for illness treatment between the two project areas. Large amount of medical debt had strong association with hospitalization utilization. CONCLUSIONS: The new extended benefit package implemented in pilot towns significantly increased the poor families' accessibility to MFA package in H8SP than that of H8 towns, which reduced poor families' demand of hospitalization services for their chronic diseases, and improved the poor population's utilization of out-patient services to some degree. It can encourage poor people to use more outpatient services thus reduce their hospitalization need. Presence of chronic disease and hospitalization had strong association with the presence of large amount of medical debt, which indicated that: although establishment of MFA had facilitated accessibility of poor families to this new system, and improved service utilization of poor families to some degree, but its role in reducing poor families' medical debt resulted from chronic disease and hospitalization was still very limited. Besides, the following requirements of MFA: co-payment for in-patient services, ceiling and deductibles for reimbursement, limitations on eligibility for diseases reimbursement, also served as most important obstacles for poor families' access to health care. Therefore, there is great need to improve MFA benefit package design in the future, including extending to cover out-patient services, raising ceiling for reimbursement, removing deductibles of MFA, reducing co-payment rate, and integrating MFA with New Rural Cooperative Medical Scheme more closely so as to provide more protection to the poor families.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".