Interventions to reduce childhood antibiotic prescribing for upper respiratory infections: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Antibiotics are overprescribed for children with upper respiratory infections (URIs), leading to unnecessary expenditures, adverse events and antibiotic resistance. This study assesses whether interventions antibiotic prescription rates (APR) for childhood URIs can be reduced and what factors impact intervention effectiveness. METHODS: MEDLINE, Embase, Google Scholar, Web of Science, Global Health, WHO website, United States CDC website and The Cochrane Central Register of Controlled Trials (CENTRAL) were searched by December 2015. Cluster or individual-patient randomised controlled trials (RCTs) and non-RCTs that examined interventions to change APR for children with URIs were selected for meta-analysis. Educational interventions for clinicians and/or parents were compared with usual care. RESULTS: Of 6074 studies identified, 13 were included. All were conducted in high-income countries. Interventions were associated with lower APR versus usual care (OR 0.63 (95% CI 0.50 to 0.81, p<0.001). A patient-clinician communication approach was the most effective type of intervention, with a pooled OR 0.41 (95% CI 0.20 to 0.83; p<0.001) for clinicians and 0.26 (95% CI 0.08 to 0.91; p=0.04) for parents. Interventions that targeted clinicians and parents were significant, with a pooled OR of 0.52 (95% CI 0.35 to 0.78; p=0.002). Insignificant effects were observed for targeting clinicians and parents alone, with a pooled OR of 0.88 (95% CI 0.67 to 1.16; p=0.37) and 0.50 (95% CI 0.10 to 2.51, p=0.40), respectively. CONCLUSIONS: Educational interventions are effective in reducing antibiotic prescribing for childhood URIs. Interventions targeting clinicians and parents are more effective than those for either group alone. The most effective interventions address patient-clinician communication. Studies in low-income to middle-income countries are 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 | 0.024 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".