Economic impact of using cariprazine as the first versus subsequent adjunctive therapy for Medicaid beneficiaries with major depressive disorder
Bibliographic record
Abstract
Background Timely initiation of appropriate major depressive disorder (MDD) therapy is crucial. Medicaid-insured patients with MDD have higher healthcare resource utilization (HRU) and costs than commercially insured patients, making this group essential to study.Methods Claims in the MerativeTM MarketScan® Medicaid Database (2016 to 2022) were used to determine all‑cause and mental health (MH)‑related HRU and healthcare costs of US adults with MDD and ≥ 1 pharmacy claim for cariprazine adjunctive to antidepressant treatment. Outcomes were evaluated in patients initiating cariprazine as their first adjunctive therapy and those initiating cariprazine as a subsequent adjunctive therapy (e.g. after another atypical antipsychotic [AA], non-AA, antidepressant treatment combination). HRU and costs were compared with rate ratios (RRs) and mean cost differences between weighted cariprazine adjunctive therapy cohorts.Results Among 970 Medicaid beneficiaries meeting inclusion criteria, 392 initiated cariprazine as their first adjunctive therapy and 578 initiated it as a subsequent adjunctive therapy. Patients initiating cariprazine first had significantly lower rates of all‑cause emergency department visits (RR [95% CI] = 0.78 [0.66, 0.94], p < .001) and outpatient (OP) visits (0.80 [0.67, 0.92], p = .012) per patient‑year than those initiating cariprazine subsequently. This translated to lower annual all‑cause medical costs (−$2,101 [−$5,096, −$7], p = .048), driven by lower OP costs (−$2,385, [−$5,251, −$492], p = .016) per patient per year. MH‑related HRU and costs were also significantly lower.Conclusions Findings from this real‑world study of Medicaid beneficiaries indicate that earlier cariprazine use is associated with potential reduction in the substantial humanistic and economic burden of MDD.
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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 | 0.001 | 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) | 0.001 | 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 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".