Hydroxychloroquine, Azithromycin, and Chloroquine Prescribing Patterns in Medicaid
Bibliographic record
Abstract
Over the last year, the COVID-19 pandemic has claimed the lives of many people throughout the world. As the virus spreads, affecting millions of patients, there has been a massive movement to discover readily available and effective treatment options (1). Inconsistent information regarding the benefits of hydroxychloroquine/chloroquine and azithromycin in COVID-19 treatment has been an obstacle in the delivery of clinical care during the pandemic (2). Limited data regarding the evolution of these therapies has created a knowledge gap that we aim to address by analyzing the experimental treatment options of COVID-19 using drug prescription patterns. This study used data from the Medicaid State Drug Utilization database and the Micromedex database to gather information on prescribed hydroxychloroquine, chloroquine, and azithromycin in Medicaid from 2016 to 2020. Our results show a decrease in azithromycin (-45.63%) and chloroquine prescription (-18.9%) from 2016 to 2020, and an increase in hydroxychloroquine prescription (+19.8%). Additionally, our results show a decrease in the average cost for hydroxychloroquine (-74.2%) and azithromycin (-20.4) and an increase in the average cost of chloroquine (138.4%). The increase in the number of prescriptions for hydroxychloroquine from quarter 1 of 2020 to quarter 3 of 2020 can be secondary to the COVID-19 pandemic in states, whereas the decrease in azithromycin prescriptions from 2016 to 2020 can be linked to emergence of new antibiotics with stronger function.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".