title: Community-level determinants of obesity: harnessing the power of electronic health records for retrospective data analysis.
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.
authors.affiliation: Department of Biomedical Informatics, College of Medicine, 250 Lincoln Tower 1800 Cannon Drive, 43210 Columbus, OH, USA. roth.369@osu.edu.
authors.forename: Caryn
authors.initials: C
authors.lastname: Roth
authors.forename: Randi E
authors.initials: RE
authors.lastname: Foraker
authors.forename: Philip R O
authors.initials: PR
authors.lastname: Payne
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Embi
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D015992:Body Mass Index; D003625:Data Collection / Q000706:statistics & numerical data; D057286:Electronic Health Records / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008490:Medical Informatics / Q000379:methods; D008875:Middle Aged; D009765:Obesity / Q000453:epidemiology* / Q000517:prevention & control; D009820:Ohio / Q000453:epidemiology; D050177:Overweight / Q000453:epidemiology / Q000517:prevention & control; D012111:Residence Characteristics / Q000706:statistics & numerical data; D012189:Retrospective Studies; D064890:Social Determinants of Health / Q000706:statistics & numerical data; D055815:Young Adult