Utilization, Spending, and Price Trends for Quinolones in the US Medicaid Programs: 25 Years’ Experience 1991–2015
Bibliographic record
Abstract
BACKGROUND: Given that the quinolones is one of the antibacterial classes most frequently used to treat patients with bacterial infections in the United States, any change in prescribing patterns of quinolones will impact Medicaid medical expenditures. OBJECTIVES: This study was undertaken to examine trends in utilization, reimbursement, and prices of quinolone antibacterials for the US Medicaid population. METHODS: The publicly available Medicaid State Drug Utilization outpatient pharmacy files were used for this study. Quarterly and annual prescription counts and reimbursement amounts were calculated for each of the quinolones reimbursed by Medicaid from quarter 1, 1991 through quarter 2, 2015. Average per-prescription reimbursement, as a proxy for drug price, was calculated as the drug reimbursement divided by the number of prescriptions. RESULTS: The total annual number of quinolone prescriptions increased 402%, from 247,395 in the first quarter of 1991 to 1.2 million in the second quarter of 2015, peaking at 1.3 million in the first quarter of 2005. Similarly, the total reimbursement for quinolone agents increased by 245.5% over the same period. More than 80% of quinolone prescriptions reimbursed by Medicaid were for the second-generation agent, ciprofloxacin, and the third-generation agent, levofloxacin. The average payment per prescription for quinolones increased from US$43.8 in the first quarter of 1991 to US$87.6 in the second quarter of 2015. CONCLUSIONS: A substantial rise in Medicaid expenditures on quinolones was observed during the 25-year study period, which was mainly because of rising utilization. Therefore, there is a need for additional research that has access to clinically relevant data with which to measure the rate of inappropriate quinolone use among the Medicaid population and associated clinical outcomes and healthcare costs.
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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 | 0.001 | 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 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".