TitleUsing Electronic Health Records to Examine Disease Risk in Small Populations: Obesity Among American Indian Children, Wisconsin, 2007-2012.
Publication TypeJournal Article
Year of Publication2016
AuthorsTomayko, EJ, Weinert, BA, Godfrey, L, Adams, AK, Hanrahan, LP
JournalPreventing chronic disease
Volume13
PaginationE29
Date Published2016
Abstract

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.

DOI10.5888/pcd13.150479