TY - JOUR AU - Tomayko, Emily J. AU - Weinert, Bethany A. AU - Godfrey, Liz AU - Adams, Alexandra K. AU - Hanrahan, Lawrence P. PY - 2016 TI - Using Electronic Health Records to Examine Disease Risk in Small Populations: Obesity Among American Indian Children, Wisconsin, 2007-2012 T2 - Preventing Chronic Disease JO - Prev Chronic Dis SP - E29 VL - 13 CY - Centers for Disease Control and Prevention, Atlanta, Georgia 30333, USA. N2 - INTRODUCTION Tribe-based or reservation-based data consistently show disproportionately high obesity rates among American Indian children, but little is known about the approximately 75% of American Indian children living off-reservation. We examined obesity among American Indian children seeking care off-reservation by using a database of de-identified electronic health records linked to community-level census variables. METHODS Data from electronic health records from American Indian children and a reference sample of non-Hispanic white children collected from 2007 through 2012 were abstracted to determine obesity prevalence. Related community-level and individual-level risk factors (eg, economic hardship, demographics) were examined using logistic regression. RESULTS The obesity rate for American Indian children (n = 1,482) was double the rate among non-Hispanic white children (n = 81,042) (20.0% vs 10.6%, P < .001). American Indian children were less likely to have had a well-child visit (55.9% vs 67.1%, P < .001) during which body mass index (BMI) was measured, which may partially explain why BMI was more likely to be missing from American Indian records (18.3% vs 14.6%, P < .001). Logistic regression demonstrated significantly increased obesity risk among American Indian children (odds ratio, 1.8; 95% confidence interval, 1.6-2.1) independent of age, sex, economic hardship, insurance status, and geographic designation. CONCLUSION An electronic health record data set demonstrated high obesity rates for nonreservation-based American Indian children, rates that had not been previously assessed. This low-cost method may be used for assessing health risk for other understudied populations and to plan and evaluate targeted interventions. SN - 1545-1151 UR - http://dx.doi.org/10.5888/pcd13.150479 DO - 12.5888/pcd13.150479 ER -