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

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.

Disparities in diabetes care: impact of mental illness.

BACKGROUND: Emerging evidence indicates that patients with mental health conditions (MHCs) may receive less intensive medical care. Diabetes serves as a useful condition in which to test for MHC-related disparities in care. We examined whether quality measures for diabetes care are worse for patients with or without MHCs. METHODS: This national, cross-sectional study included 313 586 noninstitutionalized Veterans Health Administration patients with diabetes (identified from diagnostic codes and prescriptions) whose Veterans Health Administration facility transmitted laboratory data to a central database; 76 799 (25%) had MHCs (based on diagnostic codes for depressed mood, anxiety, psychosis, manic symptoms, substance use disorders, personality disorders, and other categories). National data from Veterans Health Administration records, Medicare claims, and a national survey were linked to characterize 1999 diabetes care. RESULTS: Failure to meet diabetes performance measures was more common in patients with MHCs: unadjusted odds ratio (95% confidence interval) was 1.24 (1.22-1.27) for no hemoglobin A(1c) testing, 1.25 (1.23-1.28) for no low-density lipoprotein cholesterol testing, 1.05 (1.03-1.07) for no eye examination, 1.32 (1.30-1.35) for poor glycemic control, and 1.17 (1.15-1.20) for poor lipemic control. Disparities persisted after case mix adjustment and were more pronounced with specific MHCs (psychotic, manic, substance use, and personality disorders). The percentage not meeting diabetes care standards increased with increasing number of MHCs. CONCLUSION: Patients with mental illness merit special attention in national diabetes quality improvement efforts.