The Impact of WHO Essential Medicines Policies on Inappropriate Use of Antibiotics
Bibliographic record
Abstract
BACKGROUND: Inappropriate overuse of antibiotics contributes to antimicrobial resistance (AMR), yet policy implementation to reduce inappropriate antibiotic use is poor in low and middle-income countries. AIMS: To determine whether public sector inappropriate antibiotic use is lower in countries reporting implementation of selected essential medicines policies. MATERIALS AND METHODS: Results from independently conducted antibiotic use surveys in countries that did, and did not report implementation of policies to reduce inappropriate antibiotic prescribing, were compared. Survey data on four validated indicators of inappropriate antibiotic use and 16 self-reported policy implementation variables from WHO databases were extracted. The average difference for indicators between countries reporting versus not reporting implementation of specific policies was calculated. For 16 selected policies we regressed the four antibiotic use variables on the numbers of policies the countries reported implementing. RESULTS: Data were available for 55 countries. Of 16 policies studied, four (having a national Ministry of Health unit on promoting rational use of medicines, a national drug information centre and provincial and hospital drugs and therapeutics committees) were associated with statistically significant reductions in antibiotic use of ≥20% in upper respiratory infection (URTI). A national strategy to contain antibiotic resistance was associated with a 30% reduction in use of antibiotics in acute diarrheal illness. Policies seemed to be associated with greater effects in antibiotic use for URTI and diarrhea compared with antibiotic use in all patients. There were negative correlations between the numbers of policies reported implemented and the percentage of acute diarrhoea cases treated with antibiotics (r = -0.484, p = 0.007) and the percentage of URTI cases treated with antibiotics (r = -0.472, p = 0.005). Major study limitations were the reliance on self-reported policy implementation data and antibiotic use data from linited surveys. CONCLUSIONS: Selected essential medicines policies were associated with lower antibiotic use in low and middle income countries.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".