Cost‐effectiveness analysis of a multi‐dimensional intervention to reduce inappropriate antibiotic prescribing for children with upper respiratory tract infections in China
Bibliographic record
Abstract
BACKGROUND: We developed a multifaceted intervention to reduce antibiotic prescription rate for children with upper respiratory tract infections (URTIs) among primary care doctors in township hospitals in China. The intervention achieved a 29% (95% CI 16-42) absolute risk reduction in antibiotic prescribing. This study was to assess the cost-effectiveness of our intervention at reducing antibiotic prescribing in rural primary care facilities as measured by the intervention's effect on the antibiotic prescription rates for childhood URTIs. METHODS: We took a healthcare provider perspective, measuring costs of consultation (time cost of doctor), prescription monitoring process and peer-review meetings (time cost of participants) and medication costs. Costs on provider side were collected through a bespoke questionnaire from all 25 township hospitals in December 2016, while medication costs were collected prospectively in the trial. Incremental cost-effectiveness ratios were calculated by dividing the mean difference in cost of the two trial arms by the mean difference in antibiotic prescribing rate. RESULTS: This showed an incremental cost of $0.03 per percentage point reduction in antibiotic prescribing. In addition to this incremental cost, the cost of implementing the intervention, including training and materials delivered by township hospitals, was $390.65 (SD $145.68) per healthcare facility. CONCLUSIONS: This study shows that a multifaceted intervention programme, when embedded into routine practice, is very cost-effective at reducing antibiotic prescribing in primary care facilities and has the potential of scale up in similar resource limited settings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".