Validation of obesity‐related diagnosis codes in claims data
Karine Suissa, Sebastian Schneeweiß, Kueiyu Joshua Lin, Gregory Brill, Seoyoung C. Kim, Elisabetta Patorno
Abstract
Abstract Aim To determine whether body mass index (BMI) can be accurately identified in epidemiological studies using claims databases. Materials and methods Using the Mass General Brigham Research Patient Data Repository‐Medicare‐linked database, we identified a cohort of patients with a BMI measurement for the periods January 1 to June 31, 2014 or January 1 to June 31, 2016, to capture both the International Classification of Disease (ICD)‐9 and ICD‐10 eras. Patients were divided into two groups, with or without an obesity‐related ICD code in the 6 months before or after the BMI measurement date. We created two binary measures, first for composite overweight, obesity, or severe obesity (BMI ≥25 kg/m 2 ), and second for obesity or severe obesity (BMI ≥30 kg/m 2 ). We calculated accuracy measures (sensitivity, specificity, positive predictive value [PPV] and negative predictive value [NPV]) for each obesity category for the overall cohort, and stratified by type 2 diabetes and ICD‐code era. Results The cohort included 73 644 patients with a BMI measurement in 2014 or 2016, of whom 16 280 had an obesity‐related ICD code. The specificity of obesity‐related ICD codes (ICD‐9 and ICD‐10) was 99.7% for underweight/normal weight, 97.4% for overweight, 99.7% for obese and 98.9% for severely obese. For binary categories capturing BMI ≥25 kg/m 2 and BMI ≥30 kg/m 2 , specificity was 97.0% and 98.2%, and PPV was 86.9% and 97.3%. Sensitivity was low overall (<40%). Codes for patients with type 2 diabetes and codes in the ICD‐10 era had higher sensitivity, PPV and NPV. Conclusion Obesity‐related ICD codes can accurately identify patients with obesity in epidemiological studies using claims databases.