Description
Background: Schizophrenia is a chronic mental disorder that requires lifelong treatment. Medication nonadherence contributes to poor medical outcomes; the ability to predict a patient‚Äôs adherence trajectory cannot be understated in forestalling negative effects of nonadherence. This study aims to identify antipsychotic adherence latent classes among patients diagnosed with schizophrenia or schizoaffective disorder and evaluate predictors of adherence latent classes. Healthcare utilization, including hospitalization and emergency department (ED) visits, by latent class were also evaluated. Methods: This study utilized South Carolina Medicaid claims for beneficiaries diagnosed with schizophrenia or schizoaffective disorder from 2012 to 2020. Eligible patients were observed with baseline characteristics (i.e., sex, age, race, and type of drug administration route), and adherence measured quarterly using the proportion of days covered over a 2-year follow-up period. Using latent class mixture modeling framework (LCMM), we obtained two submodels: a membership model that categorized patients into an adherence class and a longitudinal model that characterized the adherence trajectory over time within each adherence class. Patient characteristics and adherence measurements were used as predictors, and the outcome was latent adherence classes of patient medication adherence. Descriptive statistics for healthcare utilization were calculated by latent class. Results: A total of 2,870 eligible patients were included in the study, with 1,707 males and 1,163 females. Four distinct adherence trajectories were identified: most adherent (56% of the population), early drop-off (12%), initial nonadherence followed by an increase (8%), least adherent (24%). The impact of predictors and frequency of healthcare utilization outcomes varied across classes. Time consistently demonstrated significant impacts on trajectory shapes. Healthcare outcomes for hospitalizations and Ed visits followed similar patterns. Early drop-off and initial nonadherence followed by an increase had higher averages, followed by least adherent, then most adherent. Conclusion: Four distinct trajectories for adherence were found among SC‚ Medicaid beneficiaries. Significant patient characteristics that determined class membership and adherence trajectory varied across classes. While limitations to the generalizability of these findings exist, LCMM can be applied to other populations and inform strategies that seek to improve medication adherence.
Publication Info
2026.