Using administrative data to identify mental illness: what approach is best?

Am J Med Qual. Jan-Feb 2010;25(1):42-50. doi: 10.1177/1062860609346347. Epub 2009 Oct 23.


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 > or =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.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Aged
  • Algorithms*
  • Databases as Topic
  • Female
  • Health Services Research / methods
  • Humans
  • International Classification of Diseases
  • Male
  • Mental Disorders / diagnosis*
  • Middle Aged
  • Quality of Health Care
  • Statistics as Topic
  • United States
  • United States Department of Veterans Affairs