Growing old before growing rich: inequality in health service utilization among the mid-aged and elderly in Gansu and Zhejiang Provinces, China
Bibliographic record
Abstract
BACKGROUND: China's recent growth in income has been unequally distributed, resulting in an unusually rapid retreat from relative income equality, which has impacted negatively on health services access. There exists a significant gap between health care utilization in rural and urban areas and inequality in health care access due to differences in socioeconomic status is increasing. We investigate inequality in service utilization among the mid-aged and elderly, with a special attention of health insurance. METHODS: This paper measures the income-related inequality and horizontal inequity in inpatient and outpatient health care utilization among the mid-aged and elderly in two provinces of China. The data for this study come from the pilot survey of the China Health and Retirement Longitudinal Study in Gansu and Zhejiang. Concentration Index (CI) and its decomposition approach were deployed to reflect inequality degree and explore the source of these inequalities. RESULTS: There is a pro-rich inequality in the probability of receiving health service utilization in Gansu (CI outpatient = 0.067; CI inpatient = 0.011) and outpatient for Zhejiang (CI = 0.016), but a pro-poor inequality in inpatient utilization in Zhejiang (CI = -0.090). All the Horizontal Inequity Indices (HI) are positive. Income was the dominant factor in health care utilization for out-patient in Gansu (40.3 percent) and Zhejiang (55.5 percent). The non-need factors' contribution to inequity in Gansu and Zhejiang outpatient care had the same pattern across the two provinces, with the factors evenly split between pro-rich and pro-poor biases. The insurance schemes were strongly pro-rich, except New Cooperative Medical Scheme (NCMS) in Zhejiang. CONCLUSIONS: For the middle-aged and elderly, there is a strong pro-rich inequality of health care utilization in both provinces. Income was the most important factor in outpatient care in both provinces, but access to inpatient care was driven by a mix of income, need and non-need factors that significantly differed across and within the two provinces. These differences were the result of different levels of health care provision, different out-of-pocket expenses for health care and different access to and coverage of health insurance for rural and urban families. To address health care utilization inequality, China will need to reduce the unequal distribution of income and expand the coverage of its health insurance schemes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".