Why the study?
Does electronic health record data provide comparable cardiovascular disease risk factor prevalence estimates to community screenings in a rural population?
Does electronic health record data provide comparable cardiovascular disease risk factor prevalence estimates to community screenings in a rural population?
EHR data provides sufficiently precise prevalence estimates for CVD risk factors compared to community screenings, though differences suggest some selection bias depending on the method used.
EHR data may misestimate rural CVD risk factor prevalence; leaves open the extent of selection bias versus community screening.
The objective of this study was to examine the utility of using electronic health record (EHR) data for periodic community health surveillance of cardiovascular disease (CVD) risk factors through 2 research questions. First, how many years of EHR data are needed to produce reliable estimates of key population-level CVD health indicators for a community? Second, how comparable are the EHR estimates relative to those from community screenings? The study takes place in the context of the Heart of New Ulm Project, a 10-year population health initiative designed to reduce myocardial infarctions and CVD risk factor burden in a rural community. The community is served by 1 medical center that includes a clinic and hospital. The project screened adult residents of New Ulm for CVD risk factors in 2009. EHR data for 3 years prior to the heart health screenings were extracted for patients from the community. Single- and multiple-year EHR prevalence estimates were compared for individuals ages 40-79 years (N=5918). EHR estimates also were compared to screening estimates (N=3123). Single-year compared with multiyear EHR data prevalence estimates were sufficiently precise for this rural community. EHR and screening prevalence estimates differed significantly-systolic blood pressure (BP) (124.0 vs. 128.9), diastolic BP (73.3 vs. 79.2), total cholesterol (186.0 vs. 201.0), body mass index (30.2 vs. 29.5), and smoking (16.6% vs. 8.2%)-suggesting some selection bias depending on the method used. Despite differences between data sources, EHR data may be a useful source of population health surveillance to inform and evaluate local population health initiatives.
No takes yet. Share an insight, caveat, or question.
Sidebottom et al. (2014) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: