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Showing content from https://pubmed.ncbi.nlm.nih.gov/25559099/ below:

Linking electronic health records with community-level data to understand childhood obesity risk

doi: 10.1111/ijpo.12003. Epub 2015 Jan 5. Linking electronic health records with community-level data to understand childhood obesity risk

Affiliations

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.

PMID: 25559099
PMCID: PMC4492911
DOI: 10.1111/ijpo.12003

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Linking electronic health records with community-level data to understand childhood obesity risk

E J Tomayko et al. Pediatr Obes. 2015 Dec.

doi: 10.1111/ijpo.12003. Epub 2015 Jan 5. Affiliations

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.

PMID: 25559099
PMCID: PMC4492911
DOI: 10.1111/ijpo.12003

Item in Clipboard

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.

Keywords: Childhood obesity; economic hardship; electronic health records; social determinants.

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Conflict of interest statement

Conflict of Interest Statement

The authors have no conflicts of interest to report.

Figures
Figure 1

Economic hardship index is associated…

Figure 1

Economic hardship index is associated with childhood obesity rate at the census block…

Figure 1

Economic hardship index is associated with childhood obesity rate at the census block group level (n=241 block groups with a minimum patient denominator of 100) as determined by Pearson correlation coefficients.

Figure 2

Race/ethnicity significantly modifies the relationship…

Figure 2

Race/ethnicity significantly modifies the relationship between economic hardship index and childhood obesity rate…

Figure 2

Race/ethnicity significantly modifies the relationship between economic hardship index and childhood obesity rate at the block group level. A significant interaction was determined for race/ethnicity in the mixed effects model, so simple linear regression analyses were conducted for each subgroup (e.g., Hispanics). Block groups with a minimum of 20 patients for the group under consideration were included in the analysis. NH, non-Hispanic.

Figure 3

Age significantly modifies the relationship…

Figure 3

Age significantly modifies the relationship between economic hardship and childhood obesity at the…

Figure 3

Age significantly modifies the relationship between economic hardship and childhood obesity at the block group level. A significant interaction was determined for age in the mixed effects model; therefore, simple linear regression analyses were conducted for each subgroup (e.g., 2–4 year old age group). Block groups with a minimum of 20 patients for the group under consideration were included in the analysis.