Healthcare resource utilization and costs associated with first versus subsequent use of cariprazine for bipolar I disorder
Bibliographic record
Abstract
Aims To evaluate the healthcare resource utilization (HRU) and costs of patients who initiated cariprazine as their first versus subsequent atypical antipsychotic (AA) following a bipolar I disorder (BP-I) diagnosis.Methods Adults with a BP-I diagnosis (first claim = index), commercial, Medicare Supplemental, or Medicaid insurance, and ≥1 outpatient cariprazine dispensing were identified from Merative MarketScan database. Cohorts included patients who initiated cariprazine as either their first or subsequent AA after initial BP-I diagnosis. Characteristics were balanced between cohorts using inverse probability of treatment weighting (IPTW). Outcomes evaluated post-index included all-cause and mental health (MH)–related HRU (hospitalizations, emergency department [ED] visits, outpatient visits), total healthcare costs (medical + pharmacy), and treatment patterns. HRU and healthcare costs were reported per patient-year (PPY) and compared between cohorts using rate ratios and 95% CIs estimated using nonparametric bootstrap procedures. Treatment patterns were analyzed descriptively, with standardized differences ≥10% considered important.Results After IPTW, cohorts included 1,409 patients who initiated cariprazine first and 1,621 patients who initiated cariprazine subsequently; the average (standard deviation, SD) observation period was 678 (373) and 758 (389) days for first and subsequent initiators, respectively. Patients who initiated cariprazine first had 23% fewer all-cause hospitalizations and 28% fewer MH-related hospitalizations PPY (each comparison, p < 0.001). Rates of all-cause and MH-related outpatient visits were significantly lower in patients who initiated cariprazine first versus subsequently (each comparison, p < 0.001), while rates of ED visits were similar. Relative to subsequent initiators, first initiators incurred $2,587 and $2,130 lower all-cause and MH-related total healthcare costs PPY, respectively (each comparison, p < 0.05). Before starting cariprazine, first initiators used fewer BP-I–related medications on average than subsequent initiators (2.6 vs 3.9; standardized difference = 23.9%).Limitations Potential coding inaccuracies and residual confounding.Conclusions In this real-world database analysis, patients with BP-I who initiated cariprazine as their first AA had lower rates of HRU and incurred lower costs than patients who initiated cariprazine as a subsequent AA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".