E J Tomayko1, T L Flood2, A Tandias3, L P Hanrahan3. 1. College of Agricultural & Life Sciences, Department of Nutritional Sciences, University of Wisconsin, Madison, WI, USA. 2. School of Medicine and Public Health, Department of Population Health Sciences, University of Wisconsin, Madison, WI, USA. 3. School of Medicine and Public Health, Department of Family Medicine, University of Wisconsin, Madison, WI, USA.
Abstract
BACKGROUND: Environmental and socioeconomic factors should be considered along with individual characteristics when determining risk for childhood obesity. OBJECTIVES: To assess relationships and interactions among the economic hardship index (EHI) and race/ethnicity, age and sex in regard to childhood obesity rates in Wisconsin children using an electronic health record dataset. METHODS: Data were collected using the University of Wisconsin (UW) Public Health Information Exchange database, which links electronic health records with census-derived community-level data. Records from 53,775 children seen at UW clinics from 2007 to 2012 were included. Mixed-effects modelling was used to determine obesity rates and the interaction of EHI with covariates (race/ethnicity, age, sex). When significant interactions were determined, linear regression analyses were performed for each subgroup (e.g. by age groups). RESULTS: The overall obesity rate was 11.7% and significant racial/ethnic disparities were detected. Childhood obesity was significantly associated with EHI at the community level (r = 0.62, P < 0.0001). A significant interaction was determined between EHI and both race/ethnicity and age on obesity rates. CONCLUSIONS: Reducing economic disparities and improving environmental conditions may influence childhood obesity risk in some, but not all, races and ethnicities. Furthermore, the impact of EHI on obesity may be compounded over time. Our findings demonstrate the utility of linking electronic health information with census data to rapidly identify community-specific risk factors in a cost-effective manner.
BACKGROUND: Environmental and socioeconomic factors should be considered along with individual characteristics when determining risk for childhood obesity. OBJECTIVES: To assess relationships and interactions among the economic hardship index (EHI) and race/ethnicity, age and sex in regard to childhood obesity rates in Wisconsin children using an electronic health record dataset. METHODS: Data were collected using the University of Wisconsin (UW) Public Health Information Exchange database, which links electronic health records with census-derived community-level data. Records from 53,775 children seen at UW clinics from 2007 to 2012 were included. Mixed-effects modelling was used to determine obesity rates and the interaction of EHI with covariates (race/ethnicity, age, sex). When significant interactions were determined, linear regression analyses were performed for each subgroup (e.g. by age groups). RESULTS: The overall obesity rate was 11.7% and significant racial/ethnic disparities were detected. Childhood obesity was significantly associated with EHI at the community level (r = 0.62, P < 0.0001). A significant interaction was determined between EHI and both race/ethnicity and age on obesity rates. CONCLUSIONS: Reducing economic disparities and improving environmental conditions may influence childhood obesity risk in some, but not all, races and ethnicities. Furthermore, the impact of EHI on obesity may be compounded over time. Our findings demonstrate the utility of linking electronic health information with census data to rapidly identify community-specific risk factors in a cost-effective manner.
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