Seasonal Variation in Penicillin Use in Mexico and Brazil: Analysis of the Impact of Over-the-Counter Restrictions
Bibliographic record
Abstract
During 2010, Mexico and Brazil implemented policies to enforce existing laws of restricting over-the-counter sales of antibiotics. We determined if the enforcement led to more appropriate antibiotic use by measuring changes in seasonal variation of penicillin use. We used retail quarterly sales data in defined daily doses per 1,000 inhabitant-days (DDD/TID) from IMS Health from the private sector in Mexico and Brazil from the first quarter of 2007 to the first quarter of 2013. This database contains information on volume of antibiotics sold in retail pharmacies using information from wholesalers. We used interrupted time-series models controlling for external factors with the use of antihypertensives with interaction terms to assess changes in trend, level, and variation in use between quarters for total penicillin use and by active substance. The most used penicillin was amoxicillin, followed by amoxicillin-clavulanic acid and ampicillin (minimal use in Brazil). Before the restrictions, the seasonal variation in penicillin use was 1.1 DDD/TID in Mexico and 0.8 DDD/TID in Brazil. In Mexico, we estimated a significant decrease in the seasonal variation of 0.4 DDD/TID after the restriction, mainly due to changes in seasonal variation of amoxicillin and ampicillin. In Brazil, the seasonal variation did not change significantly, overall and in the breakdown by individual active substances. For Mexico, inappropriate penicillin use may have diminished after the restrictions were enforced. For Brazil, increasing use and no change in seasonal variation suggest that further efforts are needed to reduce inappropriate penicillin use.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".