Impact of China's essential medicines scheme and zero‐mark‐up policy on antibiotic prescriptions in county hospitals: a mixed methods study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of the national essential medicines scheme and zero-mark-up policy on antibiotic prescribing behaviour. METHODS: In rural Guangxi, a natural experiment compared one county hospital which implemented the policy with a comparison hospital which did not. All outpatient and inpatient records in 2011 and 2014 were extracted from the two hospitals. Primary outcome indicator was antibiotic prescribing rate (APR) among children aged 2-14 presenting in outpatients with a primary diagnosis of upper respiratory tract infection (URTI). We organised independent physician reviews to determine inappropriate prescribing for inpatients. Difference-in-difference analyses based on multivariate regressions were used to compare APR over time after adjusting potential confounders. We conducted 12 in-depth interviews with paediatricians, hospital directors and health officials. RESULTS: A total of 8219 and 4142 outpatient prescriptions of childhood URTIs were included in the intervention and comparison hospitals, respectively. In 2011, APR was 30% in the intervention and 88% in the comparison hospital. In 2014, the intervention hospital significantly reduced outpatient APR by 21% (95% CI:-23%, -18%), intravenous infusion by 58% (95% CI: -64%, -52%) and prescription cost by 31 USD (95% CI: -35, -28), compared with the controls. We collected 251 inpatient records, but did not find reductions in inappropriate antibiotic use. Interviews revealed that the intervention hospital implemented a thorough antibiotics stewardship programme containing training, peer review of prescriptions and restrictions for overprescribing. CONCLUSION: The national essential medicines scheme and zero-mark-up policy, when implemented with an antimicrobial stewardship programme, may be associated with reductions in outpatient antibiotic prescribing and intravenous infusions.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".