Geographic Variation in Outpatient Antibiotic Prescribing Among Older Adults
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Consequences of antibiotic overuse are substantial, especially among older adults, who are more susceptible to adverse reactions. Findings about variation in antibiotic prescribing can target policy efforts to focused areas; however, little is known about these patterns among older adults. METHODS: Using Medicare Part D data from January 1, 2007, through December 31, 2009 (comprising 1.0-1.1 million patients per year), we examined geographic variation in antibiotic use among older adults in 306 Dartmouth Atlas of Health Care hospital referral regions, 50 states and the District of Columbia, and 4 national regions (South, West, Midwest, and Northeast). In addition, we examined the quarterly change in antibiotic use across the 4 regions. Differences in patient demographics, insurance status, and clinical characteristics were adjusted for across regions. RESULTS: Substantial geographic and quarterly variation in outpatient antibiotic prescribing existed across regions after adjusting for population characteristics. This variation could not be explained by differences in the prevalences of the underlying conditions. For example, the ratios of the 75th percentile to the 25th percentile of adjusted annual antibiotic spending were 1.31 across states and 1.32 across regions. The highest antibiotic use was in the South, where 21.4% of patients per quarter used an antibiotic, whereas the lowest antibiotic use was in the West, where 17.4% of patients per quarter used an antibiotic (P < .01). Regardless of region, the rate of antibiotic use was highest in the first quarter (20.9% in January through March) and was lowest in the third quarter (16.9% in July through September) (P < .01). CONCLUSIONS: Areas with high rates of antibiotic use may benefit from targeted programs to reduce unnecessary prescription. Quality improvement programs can set attainable targets using the low-prescribing areas as a reference, particularly targeting older adults.
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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 it