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

Abstract

Summary Background Environmental and socioeconomic factors should be considered along with individual characteristics when determining risk for childhood obesity. Objective To assess relationships and interactions among economic hardship index and race/ethnicity, age, and sex in regards to childhood obesity rates in Wisconsin children using an electronic health record dataset. Methods Data were collected using the University of Wisconsin Public Health Information Exchange (PHINEX) database, which links electronic health records with census-derived community-level data. Records from 53,775 children seen at UW clinics from 2007–2012 were included. Mixed effects modeling 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.

Circannual variation in relative weight of children 5 to 16 years of age

“…poverty, economic hardship, built environment, fresh fruit and vegetable consumption, etc.) from other public records including census information . The database used for this analysis is for all primary care visits of children from South‐central Wisconsin (Counties‐Dane, Sauk, Columbia, Dodge, Jefferson, Iowa, Rock, Green Marquette; also includes widely dispersed sampling of patients in Eau Claire, Augusta, Wausau and Appleton counties) from a multicenter healthcare system (family medicine, paediatrics and internal medicine).…”

“…from other public records including census information. (19,20) The database used for this analysis is for all primary care visits of children from South-central Wisconsin (Counties-Dane, Sauk, Columbia, Dodge, Jefferson, Iowa, Rock, Green Marquette; also includes widely dispersed sampling of patients in Eau Claire, Augusta, Wausau and Appleton counties) from a multicenter healthcare system (family medicine, paediatrics and internal medicine). All PHINEX data were derived from the Epic EHR Clarity Database (EpicCare Electronic Medical Record, Epic Systems Corp., Verona WI).…”

The Social Determinants of Chronic Disease

“…There are numerous other examples of social factors acting as a powerful determinant of health outcomes, as seen in studies of end-stage renal disease, 24 breast cancer, 25 childhood obesity, 26 coronary heart disease, 27 and cardiometabolic health. 28 Research on the social determinants of health has evolved into an increasingly larger field of investigation, as seen in Table 1 listing the number of papers indexed in PubMed (MEDLINE) using the term social determinants.…”

Electronic Health Record Data Versus the National Health and Nutrition Examination Survey (NHANES)

“…Several studies using EHR data have focused on childhood obesity 9 and its association with socioeconomic factors. [10][11][12] Studies on adults have targeted improvement in obesity documentation in the EHR, [13][14][15][16][17] community-level determinants of obesity, 18 and the likelihood of weight loss maintenance over time. 19 One criticism of these analyses has been that selection bias may exist because the sample of patients represented in an EHR dataset may be different than the general population.…”