Pattern in the Antibiotic Prescribing Practices at Primary Health Settings in India: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is one of the main public health concerns in India, and it is stimulated through the uncontrolled overprescription of antibiotics within primary health care (PHC) centers. This systematic review and meta-analysis evaluate the usage pattern of antibiotics among Indian PHCs for prevalence, antibiotic type, and World Health Organization (WHO) access, watch, reserve (AWaRe) guidelines compliance. Following PRISMA 2020 guidelines, we systematically searched PubMed, Embase, Scopus, Web of Science, and Google Scholar (January 2000-July 2025) for Indian PHC antibiotic prevalence studies. Observational and intervention studies with reported types and rates of prescriptions were considered for inclusion. Extraction was done by a standardized tool, and quality was evaluated by the Newcastle-Ottawa Scale and Cochrane Risk of Bias tools. Pooled prevalence of prescribing was estimated by random-effects meta-analysis, with subgroup analyses by region and setting. Eight studies incorporating more than 28,000 patient encounters reported a combined antibiotic prescribing prevalence of 65% (95% CI: 54-75%; I² = 92%). Broad-spectrum "Watch" antibiotics (e.g., fluoroquinolones, cephalosporins) prevailed, and there was suboptimal usage of "Access" (31.6%) antibiotics. Appropriate overprescribing occurred for the wrong infections, such as the upper respiratory tract infections (70-80%). Higher usage prevailed among northern compared with southern states (72% vs. 62%). Indian PHCs' overprescription of antibiotics because of system drivers necessitates urgent stewardship interventions, enhanced diagnosis, and AWaRe guideline adherence for addressing 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 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.008 | 0.010 |
| 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) | 0.003 | 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".