Popul Health Manag 13:201-207.
Division of General Internal Medicine, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
Obesity is underdiagnosed, hampering system-based health promotion and research. Our objective was to develop and validate a claims-based risk model to identify obese persons using medical diagnosis and prescription records. We conducted a cross-sectional analysis of de-identified claims data from enrollees of 3 Blue Cross Blue Shield plans who completed a health risk assessment capturing height and weight. The final sample of 71,057 enrollees was randomly split into 2 subsamples for development and validation of the obesity risk model. Using the Johns Hopkins Adjusted Clinical Groups case-mix/predictive risk methodology, we categorized study members’ diagnosis (ICD) codes. Logistic regression was used to determine which claims-based risk markers were associated with a body mass index (BMI) > or = 35 kg/m(2). The sensitivities of the scores > or =90(th) percentile to detect obesity were 26% to 33%, while the specificities were >90%. The areas under the receiver operator curve ranged from 0.67 to 0.73. In contrast, a diagnosis of obesity or an obesity medication alone had very poor sensitivity (10% and 1%, respectively); the obesity risk model identified an additional 22% of obese members. Varying the percentile cut-point from the 70(th) to the 99(th) percentile resulted in positive predictive values ranging from 15.5 to 59.2. An obesity risk score was highly specific for detecting a BMI > or = 35 kg/m(2) and substantially increased the detection of obese members beyond a provider-coded obesity diagnosis or medication claim. This model could be used for obesity care management and health promotion or for obesity-related research.
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