Health care inequality under different medical insurance schemes in a socioeconomically underdeveloped region of China: a propensity score matching analysis
Bibliographic record
Abstract
BACKGROUND: Since economic inequality is often accompanied by health inequalities, health care inequalities are increasingly becoming a hot issue on a global scale. As a developing country, China is still facing the same problems as other countries in the world. Especially in underdeveloped regions, owing to the relatively backward economy, health care inequality may be more serious. The objective of this study was to explore health care inequality in a socioeconomically underdeveloped city, thus providing a certain theoretical basis for further development and reform of the medical insurance schemes. METHODS: We mainly extracted relevant insurance information of 628,952 insured enrollees, as well as consumption of outpatient visit and hospitalization. The propensity score matching had been used to estimate different urban medical insurance schemes effect on healthcare utilization, the choice of hospital types and healthcare cost. RESULTS: Insured enrollees spent most hospitalization expenses in tertiary-level hospitals, which had lowest hospitalization compensation ratios. Healthcare utilization and cost vary significantly by different insurance schemes. Urban employees had significantly higher outpatient visit rates in all hospital types than urban residents. Urban employees preferred to receive hospitalization treatment in tertiary-level hospitals, while those who receive hospitalization treatment in first-level hospitals are more likely to be enrolled in Urban Residents Basic Medical Insurance. Hospitalization expenses and hospitalization compensation ratios of urban employees were also significantly higher than urban residents in all hospital types. CONCLUSIONS: Health care inequality is mainly reflected in the imbalance between hospitalization expenses and hospitalization compensation ratios, as well as inequalities under different medical insurance schemes in healthcare utilization, the choice of hospital types and healthcare cost in socioeconomically underdeveloped regions of China. We should conduct a targeted medical insurance reform for the socioeconomically underdeveloped regions, rather than applying templates of ordinary regions. Further efforts are needed in the future to provide equal health care for every patient.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".