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AMIA Jt Summits Transl Sci Proc. 2013 Mar 18;2013:226. eCollection 2013.

Augmenting EHR-Derived Clinical Data with Geographic-Level Public Information to Develop Research Hypotheses for Population Obesity Rates.

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1
The Ohio State University, Columbus, OH.

Abstract

Obesity adversely affects not just individuals but populations, and it is a major problem for our society. Environmental and socioeconomic factors influence obesity and are potentially important for research, yet these data are often not readily available in electronic health records (EHRs). We augmented an EHR-derived clinical data set with publicly available data on factors thought to influence obesity rates to examine associations between these factors and the prevalence of overweight and obesity. As revealed by our multinomial logistic model for overweight and obesity in a diverse region, this study demonstrates the potential value for augmenting the secondary use of EHR data with publicly available data.

PMID:
24303269
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