Non-prescribed antibiotic use for children at community levels in low- and middle-income countries: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Non-prescribed antibiotic use is an emerging risky practice around the globe. An inappropriate use involving nonprescription access is one cause of the rapid increase in antibiotic resistance. Children commonly encounter many self-limiting illnesses for which they frequently use antibiotics without prescription. However, no specific and conclusive evidence exists to inform actions against this unsafe practice. We thus aimed to estimate the pooled proportion of non-prescribed antibiotic use for children at community levels in low- and middle-income countries. METHODS: A systematic search of records was conducted from PubMed/Medline, Embase, Scopus, CINAHL, and Google scholar. Eligible English-language publications were original articles which reported on community-based non-prescribed antibiotic use for children and conducted in low- and middle-income countries. Study features and the number of antibiotics used without prescriptions were extracted and pooled for effect sizes employing a random-effects model. The pooled proportion of non-prescribed antibiotic use was estimated as a percentage. RESULTS: In this analysis, we included a total of 39 articles consisting of 40,450 participants. Of these, 16,315 participants used non-prescribed antibiotics. The pooled percentage for this use of non-prescribed antibiotics was 45% (95% CI: 40-50%). The estimate was considerably higher in studies involving simulated patient methods (56%; 95% CI: 49-62%) than those studies with community surveys (40%; 95% CI: 34-46%) (P = 0.001). It was also varied by the recall period of antibiotics use-56% (95% CI: 50-62%) for instantly observed practice, 36% (95% CI: 22-50%) for within two week recall, 35% (95% CI: 26-45%) for 1-6 months recall, and 46% (95% CI: 37-54%) for more than six months recall (P = 0.001). Primary access points for the non-prescribed antibiotic uses were retail drug outlets. CONCLUSIONS: We found that nearly half of the antibiotics used for children in community settings were without prescriptions. For these unsafe practices, caregivers accessed antibiotics mainly from drug outlets. Hence, context-specific educational and regulatory interventions at these outlets and the community levels are the first steps to improving antibiotic usage for children in low- and middle-income countries. TRIAL REGISTRATION NUMBER: CRD42021288971 (PROSPERO). https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021288971 .
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.040 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".