The real-world impact of cariprazine on short- and long-term disability outcomes among commercially insured patients in the United States
Bibliographic record
Abstract
Aim To compare all-cause and mental health (MH)-related short-term and long-term disability leaves and associated costs among patients in the United States with bipolar disorder (BP), major depressive disorder (MDD), or schizophrenia spectrum disorders (SCZ) before versus after cariprazine initiation.Methods Merative MarketScan Commercial and Health and Productivity Management (HPM) databases (January 2016 to December 2021) were utilized to identify adults diagnosed with BP, MDD, or SCZ with ≥2 pharmacy cariprazine claims (first claim = index), ≥3 months of cariprazine use (adjunctively for MDD), and continuous commercial insurance coverage and HPM eligibility during baseline (12 months pre-index) and ≥3 months post-index. Observation continued until cariprazine discontinuation, insurance or HPM eligibility end, 1 year post-index, or HPM data availability end. All-cause and MH-related disability claims, days, and costs were evaluated. Baseline versus post-index rates of disability claims (events) and days were compared using rate ratios (RR); costs were compared using mean cost differences. Comparisons were calculated from generalized estimating equation models. Analyses were replicated separately across indications.Results There were 489 patients overall (BP = 238, MDD = 233, SCZ = 18; mean age = 43.3 years; 60.7% female; mean follow-up = 7.6 months). All-cause rates of disability events and days following cariprazine initiation were 29% (RR = 0.71 [95% CI = 0.57, 0.86]) and 28% (0.72 [0.53, 0.94]) lower than baseline, respectively (both P<.05). MH-related rates of disability events and days were 40% (0.60 [0.43, 0.80]) and 43% (0.57 [0.34, 0.84]) lower, respectively (both P<.01). All-cause disability costs were $2,917 lower and MH-related disability costs were $2,482 lower than baseline (40% and 51% decrease, respectively; both P<.01). Results were similar for indication-specific analyses.Limitations Limited generalizability to patients who are unemployed, uninsured, or have public insurance.Conclusions Rates of disability events, days, and mean costs were significantly lower after versus before cariprazine initiation. These results can help contextualize cariprazine’s role in managing disability for these patients.
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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 | 0.006 |
| 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 | 0.001 |
| 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".