The effect of the National Essential Medicines Policy on health expenditures and service delivery in Chinese township health centres: evidence from a longitudinal study
Bibliographic record
Abstract
OBJECTIVES: The government of China has introduced a National Essential Medicines Policy (NEMP) in the new round of health system reform. The objective of this paper is to analyse whether the NEMP can play a role in curbing the rise of medical expenditures without disrupting the availability of healthcare services at township hospitals in China. DESIGN: This study adopted a pre-post treatment-control study design. A difference-in-differences method and fixed-effects model for panel data were employed to estimate the effect of the NEMP. SETTING: Chongqing, Jiangsu and Henan Province, in China, in 2009 and 2010. PARTICIPANTS: 296 township health centres. OUTCOME MEASURES: Outcomes for health expenditures were average outpatient drug expenses per visit, average inpatient drug expenses per discharged patient, average outpatient expenses per visit and average inpatient expenses per discharged patient. Outcomes for care delivery were the numbers of visits per certified doctor per day and the numbers of hospitalised patients per certified doctor per day. RESULTS: The township health centres that were enrolled in the NEMP reported 26% (p<0.01) lower drug expenditures for inpatient care. An 11% (p<0.05) decrease in average inpatient expenditures per discharged patient was found following the implementation of the NEMP. The impacts of the NEMP on average outpatient expenditures and outpatient drug expenditures were not statistically significant at the 5% level. No statistically significant associations were found between the NEMP and reduction in quantity of health service delivery. CONCLUSIONS: The NEMP was significant in its effect in reducing inpatient medication and health service expenditures. This study shows no evidence that the quantity of healthcare service declined significantly after introduction of the NEMP over the study period, which suggests that if appropriate matching policies are introduced, the side effects of the NEMP can be counteracted to some degree. Further research including a long-term follow-up study is needed.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".