Geographic Variability in Antibiotic Prescribing Rates in Medicaid
Bibliographic record
Abstract
Background: Antibiotic resistance is a persistent and growing concern. There is a lack of data identifying the current state of antibiotic prescription patterns in the Medicaid program. We analyzed temporal and regional trends in antibiotic prescribing data across the United States (U.S.) to identify regional disparities. Methods: We analyzed prescribing rates over the past 2 years in Medicaid Part D for 8 antibiotics. Four were broad spectrum: azithromycin, ciprofloxacin, levofloxacin, and moxifloxacin; and 4 were narrow spectrum: amoxicillin, cephalexin, doxycycline, and trimethoprim/sulfamethoxazole. We identified the geographical distribution of these antibiotics across the U.S. Furthermore, we evaluated total antibiotic prescriptions per state per quarter during 2018 and 2019 collected from the Medicaid State Drug Utilization database. Prescription rates were reported per 1,000 Medicaid enrollees. The states were divided into specific geographic regions according to the U.S. Census to determine which regions have the highest and lowest prescription rates. We analyzed the data and constructed figures using International Business Machine Corporation’s Statistical Package for the Social Sciences (IBM SPSS), Statistical Analysis System’s John’s Macintosh Project (SAS JMP), and GraphPad Prism. Results: Antibiotic prescriptions decreased 9.6% from 2018 to 2019. Amoxicillin was the predominant antibiotic, followed by azithromycin, cephalexin, trimethoprim/sulfamethoxazole, doxycycline, ciprofloxacin, levofloxacin, and moxifloxacin. Substantial geographic and quarterly variation in antibiotic prescribing existed. The South prescribed 52.2% more antibiotics (580/1,000) in 2019 than the West (381/1,000). We identified a significant correlation between the 2018 and 2019 prescription rates (r =0.95, p < 0.001). Conclusions: This study identified the geographical prescribing rates of 8 antibiotics during 2018 and 2019. The south had the highest prescribing rates among all the regions. Areas of high antibiotic prescribing rates may benefit from programs to reduce unnecessary prescribing. Further analysis on state level Medicaid or prescribing policies may be done to identify reasons for such high prescribing rates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".