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J Biomed Inform. 2016 Apr;60:95-103. doi: 10.1016/j.jbi.2016.01.013. Epub 2016 Jan 29.

The geographic distribution of cardiovascular health in the stroke prevention in healthcare delivery environments (SPHERE) study.

Author information

1
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.
2
Comprehensive Cancer Center, James Cancer Hospital and Solove Research Institute, The Ohio State University, Columbus, OH, USA; Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH, USA.
3
Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA.
4
Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH, USA.
5
Department of Health Services, School of Public Health, University of Washington, Seattle, WA, USA.
6
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA; Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH, USA. Electronic address: rforaker@cph.osu.edu.

Abstract

BACKGROUND:

Community-level factors have been clearly linked to health outcomes, but are challenging to incorporate into medical practice. Increasing use of electronic health records (EHRs) makes patient-level data available for researchers in a systematic and accessible way, but these data remain siloed from community-level data relevant to health.

PURPOSE:

This study sought to link community and EHR data from an older female patient cohort participating in an ongoing intervention at the Ohio State University Wexner Medical Center to associate community-level data with patient-level cardiovascular health (CVH) as well as to assess the utility of this EHR integration methodology.

MATERIALS AND METHODS:

CVH was characterized among patients using available EHR data collected May through July of 2013. EHR data for 153 patients were linked to United States census-tract level data to explore feasibility and insights gained from combining these disparate data sources. Analyses were conducted in 2014.

RESULTS:

Using the linked data, weekly per capita expenditure on fruits and vegetables was found to be significantly associated with CVH at the p<0.05 level and three other community-level attributes (median income, average household size, and unemployment rate) were associated with CVH at the p<0.10 level.

CONCLUSIONS:

This work paves the way for future integration of community and EHR-based data into patient care as a novel methodology to gain insight into multi-level factors that affect CVH and other health outcomes. Further, our findings demonstrate the specific architectural and functional challenges associated with integrating decision support technologies and geographic information to support tailored and patient-centered decision making therein.

KEYWORDS:

Applied clinical informatics; Cardiovascular health; Data integration; Electronic health records; Geographic information systems

PMID:
26828957
PMCID:
PMC4836963
DOI:
10.1016/j.jbi.2016.01.013
[Indexed for MEDLINE]
Free PMC Article

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