Integrating population- and patient-level data for secondary use of electronic health records to study overweight and obesity.
We combined patient-level clinical data derived from the Electronic Health Record (EHR) with area-level environmental and socioeconomic data to study factors independently associated with overweight and obesity. Our multinomial logistic regression model showed that area-level factors such as farmers' markets, grocery stores and percent college-educated at the zip code level were significantly associated with the outcomes. However, mismatch in the granularity of community and clinical data limited us in creating a discriminatory model. While these results are promising, they reveal challenges that must be overcome in order to maximize secondary use of EHR data to further explore population health status.
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus OH, USA.
Caryn
C
Roth
Chaitanya P
CP
Shivade
Randi E
RE
Foraker
Peter J
PJ
Embi
D057225:Data Mining / Q000706:statistics & numerical data*; D016208:Databases, Factual; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D055991:Health Records, Personal; D006801:Humans; D008498:Medical Record Linkage / Q000379:methods*; D009765:Obesity / Q000453:epidemiology* / Q000517:prevention & control; D009820:Ohio / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods*; D015995:Prevalence; D018511:Systems Integration