Assessment of obesity prevalence and validity of obesity diagnoses coded in claims data for selected surgical populations: A retrospective, observational study

In many types of surgery, obesity may influence patient selection, prognosis, and/or management. Quantifying the accuracy of the coding of obesity and other prognostic factors is important for the design and interpretation of studies of surgical outcomes based on administrative healthcare data. This study assessed the validity of obesity diagnoses recorded in insurance claims data in selected surgical populations. This was a retrospective, observational study. Deidentified electronic health record (EHR) and linked administrative claims data were obtained for US patients age ≥20 years who underwent a qualifying surgical procedure (bariatric surgery, total knee arthroplasty [TKA], cardiac ablation, or hernia repair) in 2014Q1–2017Q1 (first = index). Patients’ body mass index (BMI) as coded in the claims data (error-prone measure) during the index procedure or 180d pre-index was compared with their measured BMI as recorded in the EHR (criterion standard) to estimate the sensitivity and positive predictive value (PPV) of obesity diagnosis codes. Among patients who underwent bariatric surgery (N = 1422), TKA (N = 8670), cardiac ablation (N = 167), or hernia repair (N = 5450), obesity was present in 98%, 63%, 52%, and 54%, respectively, based on measured BMI. PPVs of obesity diagnosis codes were high: 99.3%, 96.0%, 92.8%, and 94.1% in bariatric surgery, TKA, cardiac ablation, and hernia repair, respectively. The sensitivity of obesity diagnoses was: 99.8%, 46.2%, 41.3%, and 42.3% in bariatric surgery, TKA, cardiac ablation, and hernia repair, respectively. Among false-positive patients diagnosed as obese but with measured BMI <30, the proportion with a BMI ≥28 was 40.0%, 67.6%, 60.7%, and 65.8% for bariatric surgery, TKA, cardiac ablation, and hernia repair, respectively. Our data indicate that obesity is highly prevalent in many surgical populations, obesity diagnosis codes have high PPVs, but also obesity is generally undercoded in claims data. Quantifying the validity of diagnosis codes for obesity and other important prognostic factors is important for the design and interpretation of studies of surgical outcomes based on administrative data. Further research is needed to determine the extent to which undercoding of BMI and obesity can be addressed through the use of proxies that may be better documented in claims data.