Effects of over-the-counter sales restriction of antibiotics on substitution with medicines for symptoms relief of cold in Mexico and Brazil: time series analysis
Bibliographic record
Abstract
We evaluated changes in the use of non-steroidal anti-inflammatory drugs (NSAIDs), non-opioid analgesics and cough and cold medicines and its relation with the use of antibiotics after the over-the-counter (OTC) antibiotic sales restrictions in Mexico and Brazil. IMS Health provided retail quarterly data from the private sectors in Mexico and Brazil from the first quarter of 2007 to the first quarter of 2013. Data of each active substance of antibiotics, easily accessible medicines perceived as antibiotics substitutes (cough and cold medicines, analgesics and NSAIDs-the latter two being combined in the analyses), and medicines to control for external factors that can affect the medicines usage trend (antihypertensives) were converted from kilograms to defined daily doses per 1000 inhabitants days (DDD/TID). Interrupted time series were used to estimate changes in level of medicines use at the intervention point and slope after the regulation. The Gregory-Hansen cointegration test was used to explore the relation between the use of antibiotics and perceived substitutes. After the regulation in Mexico NSAIDs-analgesics usage level increased by 1.1 DDD/TID with a slope increase of 0.2 DDD/TID per quarter and the cough and cold medicines usage level increased by 0.4 DDD/TID. In Brazil NSAIDs-analgesics usage level increased by 1.9 DDD/TID, and cough and cold medicines did not change. In the two countries, NSAIDs-analgesics usage changes were related with antibiotic usage changes; in Mexico cough and cold medicines usage changes had a relation with the antibiotics usage changes. These results showed a substitution effect on the use of other medicines, especially NSAIDs and analgesics, after reinforcement of OTC antibiotics sales restrictions. These regulations aimed to improve the antibiotics use and as a consequence reduce antimicrobial resistance; however, this type of policies should be comprehensive and take into account the potential substitution effects on the use of other medicines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".