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- Title
Validation of obesity‐related diagnosis codes in claims data.
- Authors
Suissa, Karine; Schneeweiss, Sebastian; Lin, Kueiyu Joshua; Brill, Gregory; Kim, Seoyoung C.; Patorno, Elisabetta
- 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/m2), and second for obesity or severe obesity (BMI ≥30 kg/m2). 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/m2 and BMI ≥30 kg/m2, 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.
- Subjects
MASSACHUSETTS General Hospital; DIAGNOSIS; TYPE 2 diabetes; NOSOLOGY; OBESITY; BODY mass index; MORBID obesity
- Publication
Diabetes, Obesity & Metabolism, 2021, Vol 23, Issue 12, p2623
- ISSN
1462-8902
- Publication type
Article
- DOI
10.1111/dom.14512