Cost-sharing effects on adherence and persistence for second-generation antipsychotics in commercially insured patients.
Bibliographic record
Abstract
PURPOSE: To assess the relationship between patient cost-sharing (e.g., copayments or coinsurance) and adherence and persistence to second-generation (atypical) antipsychotic (SGA) medications. DESIGN AND METHODOLOGY: A retrospective, observational study of adults aged 18-64 years with schizophrenia or bipolar disorder (n = 7,910) who initiated SGA medications with employer-sponsored insurance in the 2003-2006 MarketScan Commercial Claims and Encounters Database. Adherence was defined as percent of days covered in each calendar quarter. Persistence was defined as days from initiation of SGA to the first 90-day gap in medication on-hand. Generalized Estimating Equations were used to determine the effects of cost-sharing on adherence to SGA medications based on patient-quarter data. A Cox proportional hazards model with patient cost-sharing as a time-varying covariate estimated the effects on persistence with SGA medication. PRINCIPAL FINDINGS: Higher cost-sharing was associated with a lower likelihood of adherence. When compared to plans with cost-sharing below $10, adherence rates were approximately 27% lower for patients in plans with SGA cost-sharing of $50 and above and about 10% lower for patients in plans with cost-sharing between $30 and $50. In both cases, the reduction in adherence was significant. Higher cost-sharing was also associated with a shorter time to discontinuation (HR: 1.028; 95% CI [1.006-1.051]). CONCLUSION: High SGA cost-sharing appears to be a financial barrier to SGA medication compliance, especially when cost-sharing levels exceeded $30. Our findings have implications for health plans, employers, and policymakers who have, or are, contemplating establishing cost-sharing tiers for SCA medications for commercially insured patients with serious mental illnesses.
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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 | 0.000 | 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".