A Cross-Sectional Study of Patient Out-of-Pocket Costs for Antipsychotics Among Medicaid Beneficiaries with Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Patient affordability is an important nonclinical consideration for treatment access among patients with schizophrenia. OBJECTIVE: This study evaluated and measured out-of-pocket (OOP) costs for antipsychotics (APs) among Medicaid beneficiaries with schizophrenia. METHODS: Medicaid Database (1 January 2018-31 December 2018). OOP AP pharmacy costs ($US 2019) were normalized for a 30-day supply. Results were descriptively reported by route of administration [ROA; orals (OAPs), long-acting injectables (LAIs)], generic/branded status within ROAs, and dosing schedule within LAIs. The proportion of total (pharmacy and medical) OOP costs AP-attributable was described. RESULTS: In 2018, 48,656 Medicaid beneficiaries with schizophrenia were identified (mean age 46.7 years, 41.1% female, 43.4% Black). Mean annual total OOP costs were $59.97, $6.65 of which was AP attributable. Overall, 39.2%, 38.3%, and 42.3% of beneficiaries with a corresponding claim had OOP costs > $0 for any AP, OAP, and LAI, respectively. Mean OOP costs per patient per 30-day claim (PPPC) were $0.64 for OAPs and $0.86 for LAIs. By LAI dosing schedule, mean OOP costs PPPC were $0.95, $0.90, $0.57, and $0.39 for twice-monthly, monthly, once-every-2-months, and once-every-3-months LAIs, respectively. Across ROAs and generic/branded status, projected OOP AP costs per-patient-per-year for beneficiaries assumed fully adherent ranged from $4.52 to $13.70, representing < 25% of total OOP costs. CONCLUSION: OOP AP costs for Medicaid beneficiaries represented a small fraction of total OOP costs. LAIs with longer dosing schedules had numerically lower mean OOP costs, which were lowest for once-every-3-months LAIs among all APs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".