Luke M Funk1, Ying Shan, Corrine I Voils, John Kloke, Lawrence P Hanrahan. 1. *William S. Middleton VA Hospital †Department of Surgery, Wisconsin Surgical Outcomes Research Program (WiSOR), University of Wisconsin-Madison, Madison, WI ‡Center for Health Services Research in Primary Care, Veterans Affairs Medical Center §Department of Medicine, Duke University Medical Center, Durham, NC Departments of ∥Biostatistics and Medical Informatics ¶Family Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI.
Abstract
BACKGROUND: Estimating population-level obesity rates is important for informing policy and targeting treatment. The current gold standard for obesity measurement in the United States-the National Health and Nutrition Examination Survey (NHANES)-samples <0.1% of the population and does not target state-level or health system-level measurement. OBJECTIVE: To assess the feasibility of using body mass index (BMI) data from the electronic health record (EHR) to assess rates of overweight and obesity and compare these rates to national NHANES estimates. RESEARCH DESIGN: Using outpatient data from 42 clinics, we studied 388,762 patients in a large health system with at least 1 primary care visit in 2011-2012. MEASURES: We compared crude and adjusted overweight and obesity rates by age category and ethnicity (white, black, Hispanic, Other) between EHR and NHANES participants. Adjusted overweight (BMI≥25) and obesity rates were calculated by a 2-step process. Step 1 accounted for missing BMI data using inverse probability weighting, whereas step 2 included a poststratification correction to adjust the EHR population to a nationally representative sample. RESULTS: Adjusted rates of obesity (BMI≥30) for EHR patients were 37.3% [95% confidence interval (95% CI), 37.1-37.5] compared with 35.1% (95% CI, 32.3-38.1) for NHANES patients. Among the 16 different obesity class, ethnicity, and sex strata that were compared between EHR and NHANES patients, 14 (87.5%) contained similar obesity estimates (ie, overlapping 95% CIs). CONCLUSIONS: EHRs may be an ideal tool for identifying and targeting patients with obesity for implementation of public health and/or individual level interventions.
BACKGROUND: Estimating population-level obesity rates is important for informing policy and targeting treatment. The current gold standard for obesity measurement in the United States-the National Health and Nutrition Examination Survey (NHANES)-samples <0.1% of the population and does not target state-level or health system-level measurement. OBJECTIVE: To assess the feasibility of using body mass index (BMI) data from the electronic health record (EHR) to assess rates of overweight and obesity and compare these rates to national NHANES estimates. RESEARCH DESIGN: Using outpatient data from 42 clinics, we studied 388,762 patients in a large health system with at least 1 primary care visit in 2011-2012. MEASURES: We compared crude and adjusted overweight and obesity rates by age category and ethnicity (white, black, Hispanic, Other) between EHR and NHANES participants. Adjusted overweight (BMI≥25) and obesity rates were calculated by a 2-step process. Step 1 accounted for missing BMI data using inverse probability weighting, whereas step 2 included a poststratification correction to adjust the EHR population to a nationally representative sample. RESULTS: Adjusted rates of obesity (BMI≥30) for EHR patients were 37.3% [95% confidence interval (95% CI), 37.1-37.5] compared with 35.1% (95% CI, 32.3-38.1) for NHANES patients. Among the 16 different obesity class, ethnicity, and sex strata that were compared between EHR and NHANES patients, 14 (87.5%) contained similar obesity estimates (ie, overlapping 95% CIs). CONCLUSIONS: EHRs may be an ideal tool for identifying and targeting patients with obesity for implementation of public health and/or individual level interventions.
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