Trends in outpatient antibiotic prescribing practice among US older adults, 2011-2015: an observational study
Bibliographic record
Abstract
Structured abstract Objective To identify temporal trends in outpatient antibiotic use and antibiotic prescribing practice among older adults. Design Observational study using United States Medicare administrative claims during 2011-2015. Trends in antibiotic use were estimated using multivariable regression adjusting for beneficiaries’ demographic and clinical covariates. Setting Medicare. Participants 4.6 million Medicare beneficiaries from a nationwide, 20% sample of fee-forservice Medicare beneficiaries ≥65 years old. Main outcome measurements Overall rates of antibiotic prescription claims, rates of appropriate and inappropriate prescribing, rates for each of the most frequently prescribed antibiotics, and rates of antibiotic claims associated with specific diagnoses. Results Antibiotic claims fell from 1362.2 to 1361.6 claims per 1,000 beneficiaries per year during 2011-2015, an overall 0.2% decrease (95% CI 0.07-0.32). Inappropriate antibiotic claims fell from 552 to 533 claims per 1,000 beneficiaries, a 4.1% decrease (CI 3.9-4.3). Individual antibiotics had heterogeneous changes in use. For example, azithromycin claims per beneficiary decreased by 18.4% (CI 18.2-18.7) while levofloxacin claims increased by 28.1% (CI 27.5-28.6). Azithromycin use associated with each of the potentially appropriate and inappropriate respiratory diagnoses we considered decreased, while levofloxacin use associated with each of those diagnoses increased. Conclusion Among US Medicare beneficiaries, overall antibiotic use and inappropriate use declined modestly, but individual drugs experienced divergent changes in use. Trends in drug use across indications were stronger than trends in use for individual indications, suggesting that guidelines and concerns about antibiotic resistance were not major drivers of change in antibiotic 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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".