Adherence, Healthcare Resource Utilization, and Costs in Medicaid Beneficiaries with Schizophrenia Transitioning from Once-Monthly to Once-Every-3-Months Paliperidone Palmitate
Bibliographic record
Abstract
OBJECTIVES: The aim was to compare adherence to antipsychotics (APs), healthcare resource utilization (HRU), and costs before and after once-every-3-months paliperidone palmitate (PP3M) initiation in patients with schizophrenia. METHODS: Medicaid data (Iowa, Kansas, and Missouri; 1/2014-3/2017) were used to identify adults with at least one PP3M claim, ≥ 12 months of pre-index enrollment, and at least two schizophrenia diagnoses. Adequate treatment with once-monthly paliperidone palmitate (PP1M) was required pre-PP3M transition. Generalized estimating equations were used to assess linear trends in adherence to APs, HRU, and costs over the four quarters pre-PP3M transition, and to compare monthly HRU and costs 6 months pre- and 12 months post-PP3M transition as well as adherence to APs 12 months pre- and post-PP3M transition. RESULTS: Among 324 patients initiated on PP3M, the mean age was 41.4 years and 36.1% were females. Over the four quarters pre-PP3M transition, the monthly number of emergency room visits, medical costs, and inpatient costs decreased, while pharmacy costs and adherence to APs increased. For patients with ≥ 12 months of follow-up (n = 151), adherence to APs (66.2 vs. 70.2%, p = 0.3758), total (US$3371 vs. US$3456; p = 0.7000), pharmacy (US$1805 vs. US$1870; p = 0.2960), and medical costs (US$1565 vs. US$1586; p = 0.9040) remained similar pre- and post-PP3M transition, while mean monthly number of 1-day mental institute visits (1.71 vs. 1.51; p < 0.01) and associated costs (US$260 vs. US$232, p = 0.01) decreased. CONCLUSIONS: Adherence to APs, HRU, and costs were similar pre- and post-PP3M transition, suggesting that PP3M has no impact on monthly costs for patients adequately treated with PP1M, with the added flexibility of once-every-3-months dosing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".