Gender and urban-rural influences on antibiotic purchasing and prescription use in retail drug shops: a one health study
Bibliographic record
Abstract
Abstract Introduction Few studies have reported antibiotic purchases from retail drug shops in relation to gender in low and middle-income countries (LMICs). Using a One Health approach, we aimed to examine gender dimensions of antibiotic purchases for humans and animals and use of prescriptions in retail drug shops in Bangladesh. Methods We conducted customer observations in 20 drug shops in one rural and one urban area. Customer gender, antibiotic purchases, and prescription use were recorded during a four-hour observation (2 sessions of 2 hours) in each shop. We included drug shops selling human medicine (n = 15); animal medicine (n = 3), and shops selling both human and animal medicine (n = 2). Results Of 582 observations, 31.6% of drug shop customers were women. Women comprised almost half of customers (47.1%) in urban drug shops but only 17.2% of customers in rural drug shops (p < 0.001). Antibiotic purchases were more common in urban than rural shops (21.6% versus 12.2% of all transactions, p = 0.003). Only a quarter (26.0%) of customers who purchased antibiotics used a prescription. Prescription use for antibiotics was more likely among women than men (odds ratio (OR) = 4.04, 95% CI 1.55, 10.55) and more likely among urban compared to rural customers (OR = 4.31 95% CI 1.34, 13.84). After adjusting for urban-rural locality, women remained more likely to use a prescription than men (adjusted OR = 3.38, 95% CI 1.26, 9.09) but this was in part due to antibiotics bought by men for animals without prescription. Customers in drug shops selling animal medicine had the lowest use of prescriptions for antibiotics (4.8% of antibiotic purchases). Conclusion This study found that nearly three-quarters of all antibiotics sold were without prescription, including antibiotics on the list of critically important antimicrobials for human medicine. Men attending drug shops were more likely to purchase antibiotics without a prescription compared to women, while women customers were underrepresented in rural drug shops. Antibiotic stewardship initiatives in the community need to consider gender and urban-rural dimensions of drug shop uptake and prescription use for antibiotics in both human and animal medicine. Such initiatives could strengthen National Action Plans.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".