Abstract
BackgroundObesity is a pervasive problem and a popular subject of academic assessment. The ability to take advantage of existing data, such as administrative databases, to study obesity is appealing. The objective of our study was to assess the validity of obesity coding in an administrative database and compare the association between obesity and outcomes in an administrative database versus registry.MethodsThis study was conducted using a coronary catheterization registry and an administrative database (Discharge Database (DAD)). A Body Mass Index (BMI) ≥30 kg/m2 within the registry defined obesity. In the DAD obesity was defined by diagnosis codes E65 – E68 (ICD-10). The sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) of an obesity diagnosis in the DAD was determined using obesity diagnosis in the registry as the referent. The association between obesity and outcomes was assessed.ResultsThe study population of 17380 subjects was largely male (68.8%) with a mean BMI of 27.0 kg/m2. Obesity prevalence was lower in the DAD than registry (2.4% vs. 20.3%). A diagnosis of obesity in the DAD had a sensitivity 7.75%, specificity 98.98%, NPV 80.84% and PPV 65.94%. Obesity was associated with decreased risk of death or re-hospitalization, though non-significantly within the DAD. Obesity was significantly associated with an increased risk of cardiac procedure in both databases.ConclusionsOverall, obesity was poorly coded in the DAD. However, when coded, it was coded accurately. Administrative databases are not an optimal datasource for obesity prevalence and incidence surveillance but could be used to define obese cohorts for follow-up.
Highlights
Obesity is a pervasive problem and a popular subject of academic assessment
The objective of our study was to assess the validity of obesity coding in an administrative database
At the time of catheterization, data are collected on clinical risk factors including age, sex, weight, height, body mass index (BMI, kg/m2), hypertension, hyperlipidemia, diabetes, chronic lung disease, cerebrovascular disease, congestive heart failure, peripheral vascular disease, renal disease, liver or gastrointestinal disease, and malignancy
Summary
Obesity is a pervasive problem and a popular subject of academic assessment. The ability to take advantage of existing data, such as administrative databases, to study obesity is appealing. There are several published ways of measuring obesity, ranging from the simple, such as body mass index (BMI, kg/m2) or waist circumference, to the complex, including body densitometry and more advanced volumetric techniques such as computed tomography (CT) imaging and magnetic resonance imaging (MRI) [7,8]. While the latter methodologies offer more accurate measurements of body composition, the former are more widely employed due to their relatively low cost, ease of use and familiarity. They are, prone to bias: frequently measures of weight and height are taken based on self -report which is rather unreliable, as women tend to underreport weight and men to over report height
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