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Open Access Highly Accessed Research article

Community-level determinants of obesity: harnessing the power of electronic health records for retrospective data analysis

Caryn Roth1*, Randi E Foraker2, Philip RO Payne1 and Peter J Embi1

Author Affiliations

1 Department of Biomedical Informatics, College of Medicine, 250 Lincoln Tower 1800 Cannon Drive, 43210 Columbus, OH, USA

2 Division of Epidemiology, College of Public Health; The Ohio State University, Columbus, OH, USA

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BMC Medical Informatics and Decision Making 2014, 14:36  doi:10.1186/1472-6947-14-36

Published: 8 May 2014

Abstract

Background

Obesity and overweight are multifactorial diseases that affect two thirds of Americans, lead to numerous health conditions and deeply strain our healthcare system. With the increasing prevalence and dangers associated with higher body weight, there is great impetus to focus on public health strategies to prevent or curb the disease. Electronic health records (EHRs) are a powerful source for retrospective health data, but they lack important community-level information known to be associated with obesity. We explored linking EHR and community data to study factors associated with overweight and obesity in a systematic and rigorous way.

Methods

We augmented EHR-derived data on 62,701 patients with zip code-level socioeconomic and obesogenic data. Using a multinomial logistic regression model, we estimated odds ratios and 95% confidence intervals (OR, 95% CI) for community-level factors associated with overweight and obese body mass index (BMI), accounting for the clustering of patients within zip codes.

Results

33, 31 and 35 percent of individuals had BMIs corresponding to normal, overweight and obese, respectively. Models adjusted for age, race and gender showed more farmers’ markets/1,000 people (0.19, 0.10-0.36), more grocery stores/1,000 people (0.58, 0.36-0.93) and a 10% increase in percentage of college graduates (0.80, 0.77-0.84) were associated with lower odds of obesity. The same factors yielded odds ratios of smaller magnitudes for overweight. Our results also indicate that larger grocery stores may be inversely associated with obesity.

Conclusions

Integrating community data into the EHR maximizes the potential of secondary use of EHR data to study and impact obesity prevention and other significant public health issues.

Keywords:
Electronic health record; Obesity; Data integration; Community data; Clinical research informatics; Prevention; Access; Social determinants of health