Validity of a Claims-Based Diagnosis of Obesity Among Medicare Beneficiaries. (December 2015)

• Record Type: Journal Article
• Authors: Lloyd, Jennifer T.; Blackwell, Steve A.; Wei, Iris I.; Howell, Benjamin L.; Shrank, William H.
• Is Part Of: Evaluation & the health professions. Volume 38:Number 4(2015:Dec.)
• Page Start: 508
• Page End: 517
• Publication Date: 2015-12
• Subjects: aging -- body mass index -- Medicare -- NHANES -- epidemiology; Medical care -- Evaluation -- Periodicals; Medicine -- Ability testing -- Periodicals; 362.105
• DOI: 10.1177/0163278714553661
• Languages: English
• ISSNs: 0163-2787

Abstract

Population-level data on obesity are difficult to obtain. Claims-based data sets are useful for studying public health at a population level but lack physical measurements. The objective of this study was to determine the validity of a claims-based measure of obesity compared to obesity diagnosed with clinical data as well as the validity among older adults who suffer from chronic disease. This study used data from the National Health and Nutrition Examination Survey 1999–2004 for adults aged ≥65 successfully linked to 1999–2007 Medicare claims ( N = 3, 554). Sensitivity, specificity, positive and negative predictive values, κ statistics as well as logistic regression analyses were computed for the claims-based diagnosis of obesity versus obesity diagnosed with body mass index. The claims-based diagnosis of obesity underestimates the true prevalence in the older Medicare population with a low sensitivity (18.4%). However, this method has a high specificity (97.3%) and is accurate when it is present. Sensitivity was improved when comparing the claim-based diagnosis to Class II obesity (34.2%) and when used in combination with chronic conditions such as diabetes, congestive heart failure, chronic obstructive pulmonary disease, or depression. Understanding the validity of a claims-based obesity diagnosis could aid researchers in understanding the feasibility of conducting research on obesity using claims data.