Inequality of opportunity in healthcare expenditures: evidence from China
Bibliographic record
Abstract
BACKGROUND: The theory of equality of opportunity attributes total inequality to effort levels and circumstance factors. Inequality attributable to circumstance is defined as inequality of opportunity (IOp), namely inequity. Many studies have been pursued in this area but few concerning health care, especially in China. Despite Chinese health system reforms, healthcare inequity remains. This study explores the extent and sources of IOp in outpatient and inpatient expenditures in China. METHODS: We used three waves (2011, 2013 and 2015) of data from the China Health and Retirement Longitudinal Study that offer a nationally representative sample of Chinese residents aged 45 and older. Based on a pooled regression model, we estimated the contribution of circumstance factors to the inequality in outpatient and inpatient expenditures by defining a counterfactual distribution. The "circumstance-free effort" was introduced to deal with the correlation between circumstance and effort. RESULTS: We report a decline in inequity from 2011 to 2015, and the IOp ratio to total inequality in outpatient and inpatient expenditures decreased 9.4% (from 28.6 to 25.9%) and 3.3% (from 49.1 to 47.5%), respectively. Social background, medical supply-side factors, including the type of basic medical insurance, region and community medical resources were important sources of IOp in outpatient and inpatient expenditures. CONCLUSIONS: These findings provide information on which to base policies designed to reduce inequity in healthcare expenditures. It is necessary to transfer more subsidies to the New Co-operative Medical System, and to address the uneven regional distribution of medical resources. Additionally, increasing access to quality primary community clinics may be a pro-poor policy to alleviate inequity in the use of outpatient care. Compared to outpatient services, policies protecting vulnerable populations need to pay more attention to the financing and design of inpatient services.
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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".