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Using Administrative Data to Identify Mental Illness: What Approach Is Best?

¹ VA Palo Alto Health Care System; Division of General Internal Medicine and Center for Primary Care and Outcomes Research, Stanford University, California
² Center for Health Quality, Outcomes & Economic Research, VA Bedford; Boston University School of Public Health, Massachusetts
³ National Center for PTSD, VA Boston Healthcare System, Massachusetts
⁴ Center for Health Care Evaluation, VA Palo Alto Health Care System, Menlo Park; Division of General Internal Medicine, Stanford University, California
⁵ University of Alabama, Birmingham
⁶ Center for Health Quality, Outcomes & Economic Research, VA Bedford, Massachusetts
⁷ Center for Health Care Evaluation and National Center for PTSD, VA Palo Alto Health Care System, Menlo Park, California
⁸ National Center for PTSD; VA Boston Healthcare System; Boston University School of Medicine, Massachusetts

^* To whom correspondence should be addressed. E-mail: sfrayne{at}stanford.edu.

	Abstract

The authors estimated the validity of algorithms for identification of mental health conditions (MHCs) in administrative data for the 133 068 diabetic patients who used Veterans Health Administration (VHA) nationally in 1998 and responded to the 1999 Large Health Survey of Veteran Enrollees. They compared various algorithms for identification of MHCs from International Classification of Diseases, 9th Revision (ICD-9) codes with self-reported depression, posttraumatic stress disorder, or schizophrenia from the survey. Positive predictive value (PPV) and negative predictive value (NPV) for identification of MHC varied by algorithm (0.65-0.86, 0.68-0.77, respectively). PPV was optimized by requiring 2 instances of MHC ICD-9 codes or by only accepting codes from mental health visits. NPV was optimized by supplementing VHA data with Medicare data. Findings inform efforts to identify MHC in quality improvement programs that assess health care disparities. When using administrative data in mental health studies, researchers should consider the nature of their research question in choosing algorithms for MHC identification. (Am J Med Qual XXXX;XX: xx-xx)

First published on October 23, 2009
American Journal of Medical Quality 2009, doi:10.1177/1062860609346347


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