Antibiotic Use in Cold and Flu Season and Prescribing Quality
Bibliographic record
Abstract
BACKGROUND: Excessive antibiotic use in cold and flu season is costly and contributes to antibiotic resistance. The study objective was to develop an index of excessive antibiotic use in cold and flu season and determine its correlation with other indicators of prescribing quality. METHODS AND FINDINGS: We included Medicare beneficiaries in the 40% random sample denominator file continuously enrolled in fee-for-service benefits for 2010 or 2011 (7,961,201 person-years) and extracted data on prescription fills for oral antibiotics that treat respiratory pathogens. We collapsed the data to the state level so they could be merged with monthly flu activity data from the Centers for Disease Control and Prevention. Linear regression, adjusted for state-specific mean antibiotic use and demographic characteristics, was used to estimate how antibiotic prescribing responded to state-specific flu activity. Flu-activity associated antibiotic use varied substantially across states-lowest in Vermont and Connecticut, highest in Mississippi and Florida. There was a robust positive correlation between flu-activity associated prescribing and use of medications that often cause adverse events in the elderly (0.755; P<0.001), whereas there was a strong negative correlation with beta-blocker use after a myocardial infarction (-0.413; P=0.003). CONCLUSIONS: Adjusted flu-activity associated antibiotic use was positively correlated with prescribing high-risk medications to the elderly and negatively correlated with beta-blocker use after myocardial infarction. These findings suggest that excessive antibiotic use reflects low-quality prescribing. They imply that practice and policy solutions should go beyond narrow, antibiotic specific, approaches to encourage evidence-based prescribing for the elderly Medicare population.
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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 | 0.001 |
| 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".