OBJECTIVE: To identify relapse in schizophrenia and the main cost drivers of relapse using a cost-based algorithm. METHODS: Multi-state Medicaid data (1997-2010) were used to identify adults with schizophrenia receiving atypical antipsychotics (AP). The first schizophrenia diagnosis following AP initiation was defined as the index date. Relapse episodes were identified based on (1) weeks during the ≥2 years post-index associated with high cost increase from baseline (12 months before the index date) and (2) high absolute weekly cost. A compound score was then calculated based on these two metrics, where the 54% of patients associated with higher cost increase from baseline and higher absolute weekly cost were considered relapsers. Resource use and costs of relapsers during baseline and relapse episodes were compared using incidence rate ratios (IRRs) and bootstrap methods. RESULTS: In total, 9793 relapsers were identified with a mean of nine relapse episodes per patient. Duration of relapse episodes decreased over time (mean [median]; first episode: 34 [4] weeks; remaining episodes: 8 [1] weeks). Compared with baseline, resource utilization during relapse episodes was significantly greater in pharmacy, outpatient, and institutional visits (hospitalizations, emergency department visits), with IRRs ranging from 1.9-2.4 (all p < 0.0001). Correspondingly, relapse was associated with a mean (95% CI) incremental cost increase of $2459 ($2384-$2539) per week, with institutional visits representing 53% of the increase. LIMITATIONS: Relapsers and relapse episodes were identified using a cost-based algorithm, as opposed to a more clinical definition of relapse. In addition, their identification was based on the assumption from literature that ~54% of schizophrenia patients will experience at least one relapse episode over a 2-year period. CONCLUSIONS: Significant cost increases were observed with relapse in schizophrenia, driven mainly by institutional visits.
OBJECTIVE: To identify relapse in schizophrenia and the main cost drivers of relapse using a cost-based algorithm. METHODS: Multi-state Medicaid data (1997-2010) were used to identify adults with schizophrenia receiving atypical antipsychotics (AP). The first schizophrenia diagnosis following AP initiation was defined as the index date. Relapse episodes were identified based on (1) weeks during the ≥2 years post-index associated with high cost increase from baseline (12 months before the index date) and (2) high absolute weekly cost. A compound score was then calculated based on these two metrics, where the 54% of patients associated with higher cost increase from baseline and higher absolute weekly cost were considered relapsers. Resource use and costs of relapsers during baseline and relapse episodes were compared using incidence rate ratios (IRRs) and bootstrap methods. RESULTS: In total, 9793 relapsers were identified with a mean of nine relapse episodes per patient. Duration of relapse episodes decreased over time (mean [median]; first episode: 34 [4] weeks; remaining episodes: 8 [1] weeks). Compared with baseline, resource utilization during relapse episodes was significantly greater in pharmacy, outpatient, and institutional visits (hospitalizations, emergency department visits), with IRRs ranging from 1.9-2.4 (all p < 0.0001). Correspondingly, relapse was associated with a mean (95% CI) incremental cost increase of $2459 ($2384-$2539) per week, with institutional visits representing 53% of the increase. LIMITATIONS: Relapsers and relapse episodes were identified using a cost-based algorithm, as opposed to a more clinical definition of relapse. In addition, their identification was based on the assumption from literature that ~54% of schizophreniapatients will experience at least one relapse episode over a 2-year period. CONCLUSIONS: Significant cost increases were observed with relapse in schizophrenia, driven mainly by institutional visits.
Authors: Wallace Breno Barbosa; Juliana de Oliveira Costa; Lívia Lovato Pires de Lemos; Rosângela Maria Gomes; Helian Nunes de Oliveira; Cristina Mariano Ruas; Francisco de Assis Acurcio; Corrado Barbui; Marion Bennie; Brian Godman; Augusto Afonso Guerra Journal: Appl Health Econ Health Policy Date: 2018-10 Impact factor: 2.561