Antibiotic prescribing trends among Iranian GPs during COVID-19: a longitudinal analysis of antimicrobial resistance risks
Bibliographic record
Abstract
BACKGROUND: Antimicrobial resistance (AMR) is a major global health threat, exacerbated by inappropriate antibiotic use. The COVID-19 pandemic disrupted prescribing behaviors worldwide. No previous study has comprehensively analyzed longitudinal antibiotic prescribing trends among Iranian general practitioners (GPs) across pre-, during-, and post-COVID periods. This study addresses this gap by examining 1,431,004 prescriptions from 73 GPs in Iran. METHODS: We conducted a retrospective cohort analysis using anonymized data from Iran’s National Hospital Information System (HIS), focusing on a central public clinic (representing ~ 7% of national GP prescriptions). Three periods were analyzed: pre-COVID (January 2019–December 2019), COVID (January 2020–May 2021), and post-COVID (June 2021–May 2022). Descriptive and inferential statistics (chi-square, Poisson regression, ANOVA) were used; p < 0.05 was considered significant. RESULTS: Antibiotic prescriptions rose from 107,365 (pre-COVID) to 200,433 (COVID), an 87% increase (95% CI: 82–92%; p < 0.001). Post-COVID, prescribing remained elevated (208,040; 94% above baseline). The antibiotic-to-total drug ratio doubled during COVID (7.6% to 14.4%, p < 0.001). Azithromycin use surged by 120% (p < 0.001), mainly for respiratory infections per national guidelines. Injectable penicillin G prescriptions dropped by 100% post-pandemic. Prescriptions per GP fell during COVID but rebounded after. CONCLUSIONS: The sustained rise in broad-spectrum antibiotic prescribing by Iranian GPs during and after COVID-19 is likely to accelerate AMR in Iran. Urgent, locally tailored stewardship programs, GP education, and expanded rapid diagnostic testing are needed to curb unnecessary prescribing and protect public health.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".