Do Pro-Competition Healthcare Reforms Always Bring Health Benefits? Evidence from China
Bibliographic record
Abstract
It is already a common practice for many health care systems in the world to opt for mixed markets where different types of health care facilities compete against each other to offer high-quality health care to patients. Nevertheless, little is known about the effects of the interaction between hospitals of the same or different type on patient health outcomes. This study estimated the impacts of aggregate and specific types of hospital competition by hospital-type on the quality of inpatient care using an analysis dataset comprising 267,183 individuals from China. The Herfindahl-Hirschman index was employed to measure the degree of hospital competition, with length of stay, readmission and mortality being used to measure the quality of inpatient care. The Poisson and binomial logistic models combined with the instrumental variable approach were constructed to estimate the impacts of hospital competition. This study generated three key findings: 1) aggregate hospital competition reduced the quality of inpatient care, as evidenced by a rise in the odds of readmission and length of stay; 2) intra-type hospital competition reduced the quality of inpatient care and in general had larger effects on reducing the quality of inpatient care than inter-type hospital competition; and 3) the only exception was in the way that competition between private nonprofit hospitals contributed to better quality of inpatient care. The overarching suggestion is that instead of treating competition as a panacea for improving health, a flexible plan tailored to specific conditions is needed.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".