Geospatial Monitoring of Body Mass Index: Use of Electronic Health Record Data Across Health Care Systems.

OBJECTIVES: The Colorado BMI Monitoring System was developed to assess geographic (ie, census tract) patterns of obesity prevalence rates among children and adults in the Denver-metropolitan region. This project also sought to assess the feasibility of a surveillance system that integrates data across multiple health care and governmental organizations. MATERIALS AND METHODS: We extracted data on height and weight measures, obtained through routine clinical care, from electronic health records (EHRs) at multiple health care sites. We selected sites from 5 Denver health care systems and collected data from visits that occurred between January 1, 2013, and December 31, 2015. We produced shaded maps showing observed obesity prevalence rates by census tract for various geographic regions across the Denver-metropolitan region. RESULTS: We identified clearly distinguishable areas by higher rates of obesity among children than among adults, with several pockets of lower body mass index. Patterns for adults were similar to patterns for children: the highest obesity prevalence rates were concentrated around the central part of the metropolitan region. Obesity prevalence rates were moderately higher along the western and northern areas than in other parts of the study region. PRACTICE IMPLICATIONS: The Colorado BMI Monitoring System demonstrates the feasibility of combining EHRs across multiple systems for public health and research. Challenges include ensuring de-duplication across organizations and ensuring that geocoding is performed in a consistent way that does not pose a risk for patient privacy.

Estimating Childhood Obesity Prevalence in Communities Through Multi-institutional Data Sharing.

CONTEXT: Although local childhood obesity prevalence estimates would be valuable for planning and evaluating obesity prevention efforts in communities, these data are often unavailable. OBJECTIVE: The primary objective was to create a multi-institutional system for sharing electronic health record (EHR) data to produce childhood obesity prevalence estimates at the census tract level. A secondary objective was to adjust obesity prevalence estimates to population demographic characteristics. DESIGN/SETTING/PARTICIPANTS: The study was set in Denver County, Colorado. Six regional health care organizations shared EHR-derived data from 2014 to 2016 with the state health department for children and adolescents 2 to 17 years of age. The most recent height and weight measured during routine care were used to calculate body mass index (BMI); obesity was defined as BMI of 95th percentile or more for age and sex. Census tract location was determined using residence address. Race/ethnicity was imputed when missing, and obesity prevalence estimates were adjusted by sex, age group, and race/ethnicity. MAIN OUTCOME MEASURE(S): Adjusted obesity prevalence estimates, overall, by demographic characteristics and by census tract. RESULTS: BMI measurements were available for 89 264 children and adolescents in Denver County, representing 73.9% of the population estimate from census data. Race/ethnicity was missing for 4.6%. The county-level adjusted childhood obesity prevalence estimate was 13.9% (95% confidence interval, 13.6-14.1). Adjusted obesity prevalence was higher among males, those 12 to 17 years of age, and those of Hispanic race/ethnicity. Adjusted obesity prevalence varied by census tract (range, 0.4%-24.7%). Twelve census tracts had an adjusted obesity prevalence of 20% or more, with several contiguous census tracts with higher childhood obesity occurring in western areas of the city. CONCLUSIONS: It was feasible to use a system of multi-institutional sharing of EHR data to produce local childhood obesity prevalence estimates. Such a system may provide useful information for communities when implementing obesity prevention programs.

A Community-Engaged Approach to Collecting Rural Health Surveillance Data.

PURPOSE: In order for communities to make health-related, data-driven decisions concerning resource allocation, needed services, and intervention priorities, they need an accurate picture of the health status of residents. While state and national health surveillance systems exist to help local communities make data-driven health decisions, rural communities face unique challenges including: (1) limited county-level data; (2) underrepresented segments of the population; and (3) a lack of survey items to address local health concerns. The purpose of this study was to take a community-engaged approach to collecting population-based health status data in a rural area in an effort to address some of these unique challenges. METHODS: Using a community-based participatory research (CBPR) approach, over 1,500 residents from 6 rural and frontier counties were randomly selected with a stratified, multistage cluster study design. Surveys were primarily completed over the phone. FINDINGS: Response rates by county ranged from 59% to 80% (overall = 66%). Males and younger adults (18-24 year olds) were underrepresented in the sample, but Hispanics, low-income residents, and cell phone-only users were adequately represented. Prevalence rates for chronic disease and health behavior varied by county. CONCLUSIONS: The implications of this project are that engaging stakeholders in community surveillance efforts increases the quality, relevance and utility of the information collected and can help reach otherwise difficult-to-reach populations. This can result in a more accurate picture of the health status of residents, which can lead to making health-related, data-driven decisions concerning resource allocation, needed services, and intervention priorities.