Background Childhood obesity remains a public health concern, and tracking local progress may require local surveillance systems. Electronic health record data may provide a cost-effective solution. Purpose To demonstrate the feasibility of estimating childhood obesity rates using de-identified electronic health records for the purpose of public health surveillance and health promotion. Methods Data were extracted from the Public Health Information Exchange (PHINEX) database. PHINEX contains de-identified electronic health records from patients primarily in south central Wisconsin. Data on children and adolescents (aged 2–19 years, 2011–2012, n=93,130) were transformed in a two-step procedure that adjusted for missing data and weighted for a national population distribution. Weighted and adjusted obesity rates were compared to the 2011–2012 National Health and Nutrition Examination Survey (NHANES). Data were analyzed in 2014. Results The weighted and adjusted obesity rate was 16.1% (95% CI=15.8, 16.4). Non-Hispanic white children and adolescents (11.8%, 95% CI=11.5, 12.1) had lower obesity rates compared to non-Hispanic black (22.0%, 95% CI=20.7, 23.2) and Hispanic (23.8%, 95% CI=22.4, 25.1) patients. Overall, electronic health record–derived point estimates were comparable to NHANES, revealing disparities from preschool onward. Conclusions Electronic health records that are weighted and adjusted to account for intrinsic bias may create an opportunity for comparing regional disparities with precision. In PHINEX patients, childhood obesity disparities were measurable from a young age, highlighting the need for early intervention for at-risk children. The electronic health record is a cost-effective, promising tool for local obesity prevention efforts.

Objectives This study compares a statewide telephone health survey and EHR data from a large Wisconsin health system to estimate asthma prevalence in Wisconsin. Methods Frequency tables and logistic regression models were developed for children and adults using Wisconsin Behavioral Risk Factor Surveillance Survey (BRFSS) and University of Wisconsin primary care clinic data. Adjusted odds ratios (OR) from each model were compared. Results Between 2007 and 2009, the EHR database contained 376,000 patients (30,000 with asthma) compared to 23,000 (1,850 with asthma) responding to the BRFSS telephone survey. Adjusted ORs for asthma were similar in magnitude and direction for the majority of covariates, including gender, age, and race between survey and EHR models. The EHR data had greater statistical power to detect associations than survey data, especially in pediatric and ethnic populations, due to larger sample sizes. Conclusions EHRs can be used to estimate asthma prevalence in Wisconsin adults and children. EHR data may improve public health chronic disease surveillance using high quality data at the local level to better identify areas of disparity, risk factors, and guide education and healthcare interventions.

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.

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin’s Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5–50 years over a three-year period. Each patient’s home address was geocoded to one of 3,456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin’s geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors.

The objective of this study was to assess the relationship of community economic hardship index (EHI), race/ethnicity, and childhood obesity using the University of Wisconsin (UW) Public Health Information Exchange (PHINEX) database, which links deidentified electronic health records with census block group data. The EHI is a composite score of community‐level factors (crowded housing, poverty, income, education, unemployment, and number of dependents <18 or >64 years old) that addresses multiple determinants of health. Records from 85,963 children (2‐17 years old) seen at UW clinics from 2007‐2012 were included. The obesity prevalence was 13.4%, and significant disparities were detected (ranging from 11.9% for non‐Hispanic Whites to 23.0% for Hispanics). Childhood obesity was significantly associated with EHI (r=0.489, p<0.001), with mixed‐effects modeling indicating the relationship between EHI and obesity significantly differed among racial/ethnic groups (p=0.01). The magnitude of change in obesity percentage with increasing EHI scores was greater for non‐Hispanic Whites compared to Hispanic and Black children. Our findings demonstrate the utility of linking electronic health records with census data to rapidly and easily identify community‐specific risk factors that allow researchers, practitioners, and public health professionals to tailor community interventions and measure program effectiveness.