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Claims-based proxies of patient instability among commercially insured adults with schizophrenia

Authors Ruetsch C, Un H, Waters HC

Received 19 August 2017

Accepted for publication 28 March 2018

Published 7 May 2018 Volume 2018:10 Pages 259—267


Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Amy Norman

Peer reviewer comments 2

Editor who approved publication: Professor Dean Smith

Charles Ruetsch,1 Hyong Un,2 Heidi C Waters3

1Health Analytics, LLC, Columbia, MD, USA; 2Aetna, Inc, Blue Bell, PA, USA; 3Otsuka Pharmaceutical Development & Commercialization, Inc., Princeton, NJ, USA

Objective: Schizophrenia (Sz) patients are among the highest utilizers of hospital-based services. Prevention of relapse is in part a treatment goal in order to reduce hospital admissions. However, predicting relapse is a challenge, particularly for payers and disease management firms with only access to claims data. Understandably, such organizations have had little success predicting relapse. A tool that allows payers to identify patients at elevated risk of relapse could facilitate targeted interventions prior to relapse and avoid rehospitalization. In this study, a series of proxy measures of patient instability, calculated from claims data were examined for their utility in identifying Sz patients at elevated risk of relapse.
Methods: Aetna claims were used to assess the relationship between instability of Sz patients and valence and magnitude of antipsychotic (AP) medication change during a 2-year period. Six proxies of instability including hospital admissions, emergency department visits, medication utilization patterns, and use of outpatient services were identified. Results were replicated using claims data from Truven MarketScan®.
Results: Patients who switched AP ingredient had the highest overall instability at the point of switch and the second steepest decline in instability following switch. Those who changed to a long-acting injectable AP showed the second highest level of instability and the steepest decrease in instability following the change. Patients augmented with a second AP showed the smallest increase in instability, up to the switch. Results were directionally consistent between the two data sets.
Conclusion: Using claims-based proxy measures to estimate instability may provide a viable method to better understand Sz patient markers of change in disease severity. Also, such proxies could be used to identify those individuals with the greatest need for treatment modification preventing relapse, improving patient outcomes, and reducing the burden of illness.

Keywords: schizophrenia, relapse, algorithm, claims data

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