Why is There a Ten-Fold Variation between States in Clozapine Usage among Medicaid Enrollees in the United States?
Bibliographic record
Abstract
Background: Clozapine was the first atypical antipsychotic for treating schizophrenia, with a long history of controversy over its usage. Guidelines currently recommend clozapine for patients diagnosed with refractory schizophrenia. However, prescribers are underutilizing clozapine because of the costs associated with close monitoring of its adverse effects, particularly agranulocytosis. This is unfortunate because clozapine has demonstrated greater effectiveness compared with other antipsychotics. It is essential to examine clozapine usage to determine if it is being adequately utilized in the treatment of schizophrenia in the United States. Methods: Medicaid data, including the number of quarterly clozapine prescriptions and the number of Medicaid enrollees in each state from 2015 to 2019, was collected and used to evaluate clozapine use over time. Data analysis and figures were prepared with Excel. Results: The number of prescriptions, corrected for the number of enrollees in Medicaid, was generally consistent over time. However, average prescriptions per quarter were markedly lower in 2017 compared with other years, decreasing by 44.4% from 2016 average prescriptions per quarter. From 2015 to 2019, states from the upper Midwest and Northeast regions of the country had the highest average clozapine prescriptions per 10,000 Medicaid enrollees (ND: 190.0, SD: 176.6, CT: 166.2). States from the Southeast and Southwest had much lower average rates (NV: 17.9, KY: 19.3, MS: 19.7). Overall, there was a 10-fold difference in clozapine prescriptions between states from 2015-2019 (2015 = 19.9-fold, 2016=11.4 fold, 2017=11.6 fold, 2018=13.3 fold, and 2019=13.0 fold). There was a moderate correlation of (r(48)=0.50, p < 0.05) between prescriptions per 10,000 enrollees and the Medicaid spending per enrollee in each state in 2019. Conclusion: Clozapine is an important pharmacotherapy for refractory schizophrenia. Overall, clozapine use tends to be highest among the upper Midwest and Northeast states. Further research is ongoing to better understand the origins of the 10-fold regional disparities in clozapine use.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".