
Psychiatr Serv 49:1043-1048, August 1998
© 1998 American Psychiatric Association
Agreement Between Seriously Mentally Ill Veterans and Their Clinicians About Medication Compliance
Marcia Valenstein, M.D., M.S.,
Kristen Lawton Barry, Ph.D.,
Frederic C. Blow, Ph.D.,
Laurel Copeland, M.P.H. and
Esther Ullman, M.S.W.
Objectives: Levels of agreement about medication compliance in a large cohort of seriously mentally ill veterans and their clinicians were examined to determine whether agreement increased with exposure to enhanced treatment programs emphasizing compliance and whether compliance reports were associated with hospitalization. METHODS: A total of 1,369 seriously mentally ill patients and their treating clinicians at 14 Veterans Affairs medical centers rated medication compliance at enrollment in enhanced programs or comparison programs offering standard care. Patients and clinicians reassessed compliance one and two years after enrollment. Overall agreement, agreement about compliance and noncompliance, and kappa statistics were determined for concurrent assessments. RESULTS: Overall, patients rated themselves as significantly more compliant with medication than did clinicians at enrollment. Cohen's kappa at enrollment was .095, indicating little patient-clinician agreement beyond that expected by chance. Kappa values increased significantly at one and two years for patients in the enhanced programs but continued to indicate poor-to-modest levels of agreement. Patient-clinician pairs in enhanced programs did not differ from those in comparison programs in overall agreement. Reports of good compliance by both patients and clinicians were associated with significantly decreased odds of hospital admission in the 30 days after the report was made. CONCLUSIONS: Seriously mentally ill patients and their clinicians showed little agreement about medication use beyond that expected by chance. Intensive programming appeared to have little effect on agreement. Both patients' and clinicians' compliance assessments predicted hospitalization and thus can be used in research models that attempt to predict relapse and readmission.
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