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

Abstract

BackgroundObesity 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.MethodsWe 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.Results33, 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.ConclusionsIntegrating 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.

Increasing knowledge of mental illness through secondary research of electronic health records: opportunities and challenges

“…They found that community socioeconomic deprivation and diversity of physical activity establishments were associated with body mass index in all age groupings, whereas the relationship between other environmental variables and obesity differed by age group. More recently, community-level data have been integrated with EHR data to identify additional and unique community-level risk factors for obesity in adults (Roth, Foraker, Payne, & Embi, 2014). Morgan et al (2011Morgan et al ( , 2012 conducted data linkage research to examine the aetiology and risk factors for psychiatric illnesses.…”

The geographic distribution of cardiovascular health in the stroke prevention in healthcare delivery environments (SPHERE) study

“…4,6,10,11 In the context of current public health research, social determinants of health are consistently associated with place of residence. 12–14 …”

“…18–20 While this powerful technology exists, GIS are typically used to visualize historical data rather than to inform medical treatments or interventions at the point-of-care. 14 Meanwhile, the rapid growth of electronic health record (EHR) use across the country presents an abundance of real-time accessible health data which should be utilized for the individual patient and in aggregate. There is much potential for public health impact through EHR use, but fulfilling this promise demands the development of new methods of utilizing these data.…”

Integrating Community-Level Data Resources for Precision Medicine Research

“…These data resources have been used to derive community-level metrics integrated with EHR data, including characteristics of the walkable built environment in Pennsylvania (Nau et al 2015), Massachusetts (D. T. , and Ohio (Roth et al 2014). Similar approaches have been applied to characterize the food environment (Fiechtner et al 2015(Fiechtner et al , 2016.…”