Identifying Physician-Recognized Depression from Administrative Data: Consequences for Quality Measurement

• Published in: Health services research Vol. 38; no. 4; pp. 1081 - 1102
• Main Authors: Spettell, Claire M., Wall, Terry C., Allison, Jeroan, Calhoun, Jaimee, Kobylinski, Richard, Fargason, Rachel, Kiefe, Catarina I.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing 01-08-2003; Health Research and Educational Trust; Blackwell Publishing Ltd; Blackwell Science Inc
• Subjects: Algorithms; Antidepressive Agents - therapeutic use; Data Collection; Depression; Depression (Mood disorder); Depression, Mental; Depressive Disorder - diagnosis; Depressive Disorder - drug therapy; Depressive Disorder - epidemiology; Diagnosis; Family Practice - standards; Family Practice - statistics & numerical data; Health Benefit Plans, Employee - standards; Health care industry; Health Services Research - methods; HEDIS; Humans; International Classification of Diseases; Managed Care Programs - standards; Medical diagnosis; Mental depression; Mid-Atlantic Region - epidemiology; New England - epidemiology; Original; Patient Compliance; predictive value; Prevalence; Primary Health Care - standards; Quality Assurance, Health Care; quality of care; Quality of service; Sensitivity and Specificity; Studies; Mid-Atlantic Region; New England; United States; United States > US
• ISSN: 0017-9124; 1475-6773
• DOI: 10.1111/1475-6773.00164

Background. Multiple factors limit identification of patients with depression from administrative data. However, administrative data drives many quality measurement systems, including the Health Plan Employer Data and Information Set (HEDIS®).
Methods. We investigated two algorithms for identification of physician‐recognized depression. The study sample was drawn from primary care physician member panels of a large managed care organization. All members were continuously enrolled between January 1 and December 31, 1997. Algorithm 1 required at least two criteria in any combination: (1) an outpatient diagnosis of depression or (2) a pharmacy claim for an antidepressant. Algorithm 2 included the same criteria as algorithm 1, but required a diagnosis of depression for all patients. With algorithm 1, we identified the medical records of a stratified, random subset of patients with and without depression (n=465). We also identified patients of primary care physicians with a minimum of 10 depressed members by algorithm 1 (n=32,819) and algorithm 2 (n=6,837).
Results. The sensitivity, specificity, and positive predictive values were: Algorithm 1: 95 percent, 65 percent, 49 percent; Algorithm 2: 52 percent, 88 percent, 60 percent. Compared to algorithm 1, profiles from algorithm 2 revealed higher rates of follow‐up visits (43 percent, 55 percent) and appropriate antidepressant dosage acutely (82 percent, 90 percent) and chronically (83 percent, 91 percent) (p<0.05 for all).
Conclusions. Both algorithms had high false positive rates. Denominator construction (algorithm 1 versus 2) contributed significantly to variability in measured quality. Our findings raise concern about interpreting depression quality reports based upon administrative data.