Global variation in antibiotic prescribing guidelines and the implications for decreasing AMR in the future
Bibliographic record
Abstract
Introduction: Antimicrobial resistance (AMR) has become a global burden, with inappropriate antibiotic prescribing being an important contributing factor. Antibiotic prescribing guidelines play an important role in improving the quality of antibiotic use, provided they are evidence-based and regularly updated. As a result, they help reduce AMR, which is a critical challenge in low- and middle-income countries (LMICs). Consequently, the objective of this study was to evaluate local, national, and international antibiotic prescribing guidelines currently available-especially among LMICs-and previous challenges, in light of the recent publication of the WHO AWaRe book, which provides future direction. Methodology: Google Scholar and PubMed searches were complemented by searching official country websites to identify antibiotic prescribing guidelines, especially those concerning empiric treatment of bacterial infections, for this narrative review. Data were collected on the country of origin, income level, guideline title, year of publication, development methodology, issuing organization, target population, scope, and coverage. In addition, documentation on implementation strategies, compliance, monitoring of outcome measures, and any associated patient education or counseling efforts were reviewed to assess guideline utilization. Results/findings: A total of 181 guidelines were included, with the majority originating from high-income countries (109, 60.2%), followed by lower-middle-income (40, 22.1%), low-income (18, 9.9%), and upper-middle-income (14, 7.7%) countries. The GRADE methodology was used in only 20.4% of the sourced guidelines, predominantly in high-income countries. Patient education was often underemphasized, particularly in LMICs. The findings highlighted significant disparities in the development, adaptation, and implementation of guidelines across different WHO regions, confirming the previously noted lack of standardization and comprehensiveness in LMICs. Conclusion: Significant disparities exist in the availability, structure, and methodological rigor of antibiotic prescribing guidelines across countries with different income levels. Advancing the development and implementation of standardized, context-specific guidelines aligned with the WHO AWaRe framework-and supported by equity-focused reforms-can significantly strengthen antimicrobial stewardship and help address the public health challenge of AMR.
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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 | 0.016 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".