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Medical Care:
April 2007 - Volume 45 - Issue 4 - pp 363-369
doi: 10.1097/01.mlr.0000254574.23418.f6
Brief Report

Adherence to Antidepressant Treatment Among Privately Insured Patients Diagnosed With Depression

Akincigil, Ayse PhD; Bowblis, John R. MA; Levin, Carrie PhD; Walkup, James T. PhD; Jan, Saira PharmD; Crystal, Stephen PhD

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Abstract

Background: Antidepressants are effective in treatment of depression, but poor adherence to medication is a major obstacle to effective care.

Objective: We sought to describe patient and provider level factors associated with treatment adherence.

Methods: This was a retrospective, observational study using medical and pharmacy claims from a large health plan, for services provided between January 2003 and January 2005. We studied a total of 4312 subjects ages 18 or older who were continuously enrolled in the health plan with a new episode of major depression and who initiated antidepressant treatment. Treatment adherence was measured by using pharmacy refill records during the first 16 weeks (acute phase) and the 17-33 weeks after initiation of antidepressant therapy (continuation phase). Measures were based on Health Plan Employer Data and Information Set (HEDIS) quality measures for outpatient depression care.

Results: Fifty-one percent of patients were adherent through the acute phase; of those, 42% remained adherent in the continuation phase. Receipt of follow-up care from a psychiatrist and higher general pharmacy utilization (excluding psychotropics) were associated with better adherence in both phases. Younger age, comorbid alcohol or other substance abuse, comorbid cardiovascular/metabolic conditions, use of older generation antidepressants, and residence in lower-income neighborhoods were associated with lower acute-phase adherence. Continuation-phase adherence was lower for HMO participants than for others.

Conclusion: In an insured population, many patients fall short of adherence to guideline recommended therapy for depression. Information from existing administrative data can be used to predict patients at highest risk of nonadherence, such as those with substance abuse, and to target interventions.

© 2007 Lippincott Williams & Wilkins, Inc.

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