Exposure to World Health Organization's AWaRe antibiotics and isolation of multidrug resistant bacteria: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Antibiotic use drives antibiotic resistance. OBJECTIVES: To systematically review the literature and estimate associations between prior exposure to antibiotics across World Health Organization's (WHO) AWaRe categories (Access, Watch, Reserve) and isolation of critical and high-priority multidrug resistant organisms (MDROs) on the WHO priority pathogen list. DATA SOURCES: Embase, Ovid Medline, Scopus, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov (from inception to 20/08/2020). STUDY ELIGIBILITY CRITERIA: Case-control, cohort, or experimental studies that assessed the risk of infection/colonization with MDROs. PARTICIPANTS: Inpatients or outpatients of any age and sex. INTERVENTIONS: Prior exposure to antibiotics that could be categorized into the AWaRe framework. DATA ANALYSIS: Tailored design-specific checklists applied to each included study. For each antibiotic/class, crude odds ratios (ORs) were pooled through random-effects meta-analyses, both overall and by MDRO. Heterogeneity was examined. RESULTS: We identified 349 eligible studies. All were observational, prone to bias due to design and lack of adjustment for confounding, and not primarily designed to compare associations across AWaRe categories. We found statistically significant associations between prior exposure to almost all antibiotics/classes across AWaRe categories and colonization/infection with any MDRO. We observed higher ORs for Watch and Reserve antibiotics than with Access antibiotics. First generation cephalosporins (Access) had the least association with any MDRO colonization/infection (58 studies; OR = 1.2 [95% CI: 1.0-1.4]), whereas strongest associations were estimated for linezolid (Reserve) (22 studies; OR = 2.6 [95% CI: 2.1-3.1]), followed by carbapenems (Watch) (237 studies; OR = 2.3 [95% CI: 2.1-2.5]). There was high heterogeneity for all antibiotic/MDRO associations. CONCLUSIONS: Optimising use of Access antibiotics is likely to reduce the selection of MDROs and global antibiotic resistance. Despite data limitations, our study offers a strong rationale for further adoption of AWaRe as an important tool to improve antibiotic use globally.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".