Geographic Variation in Outpatient Antibiotic Prescribing Among Older Adults
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".