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Electronic health records identify community‐specific risk factors for childhood obesity (130.2)
Author(s) -
Tomayko Emily,
Flood Tracy,
Tandias Aman,
Arndt Brian,
Buckingham William,
Hanrahan Lawrence
Publication year - 2014
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.28.1_supplement.130.2
Subject(s) - obesity , ethnic group , medicine , childhood obesity , public health , demography , poverty , health equity , community health , psychological intervention , american community survey , body mass index , census , unemployment , gerontology , environmental health , population , overweight , political science , nursing , pathology , psychiatry , sociology , law , economics , economic growth
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.