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

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 > 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.

Racial/Ethnic Differences in Primary Care Experiences in Patient-Centered Medical Homes among Veterans with Mental Health and Substance Use Disorders

“…Therefore, results may not generalize to civilians, to Veterans receiving primary care in other integrated healthcare systems, or to Veterans without MHSUDs. Moreover, while our survey response rate of 45 % is better than CAHPS PCMH response rates in other outpatient settings, 40,41 it does limit the generalizability of this study. Fourth, with the data available, we were unable to identify causes of racial/ethnic differences in experiences with care, such as patient expectations or provider biases.…”

“…Veterans were eligible for the current study if, in the year prior to the survey, they experienced one inpatient visit or two outpatient visit days with an International Classification of Diseases version 9 (ICD-9) diagnosis for common MHSUDs, including depression, post-traumatic stress disorder, other anxiety disorders, bipolar disorder, any psychotic disorder, or alcohol or drug use disorder. 41 To focus on patients with probable mental health service needs, we ignored disorders where remission was indicated (i.e., depression, bipolar disorder, or schizophrenia in remission). In addition, we excluded patients who reported two or more races, because this group is heterogeneous and their findings would be difficult to interpret.…”

Outcomes of Anticoagulation Therapy in Patients with Mental Health Conditions

“…While our database allowed us to study a large population of patients, we could not validate the completeness and accuracy of administrative data. Because our method of categorization differed from other studies 31 , direct comparison of studies might be difficult. While our algorithm 30 for identifying major hemorrhage had face validity, it clearly could not be as precise as a chart review.…”

Fibromyalgia syndrome care of Iraq- and Afghanistan-deployed Veterans in Veterans Health Administration

“…Researchers have cautioned that a single ICD-9-CM FMS diagnosis or diagnoses in nonrheumatology settings may have limited specificity to identify true FMS cases [1,[14][15]. To improve the specificity of our FMS case definition and to be consistent with prior research of VHA administrative data [16], we only included Veterans who received ≥1 FMS diagnosis in a rheumatology specialty care setting (identified by corresponding clinic stop code of 314) or ≥2 FMS diagnoses on separate dates within 12 mo, regardless of outpatient care setting. There were 5,963 male and 2,245 female Veterans who met our FMS case definition.…”