Abstract
BackgroundElectronic Health Records (EHR) has been increasingly used as a tool to monitor population health. However, subject-level errors in the records can yield biased estimates of health indicators. There is an urgent need for methods to estimate the prevalence of health indicators using large and real-time EHR while correcting the potential bias.MethodsWe demonstrate joint analyses of EHR and a smaller gold-standard health survey. We first adopted Mosteller’s method that pools two estimators, among which one is potentially biased. It only requires knowing the prevalence estimates from two data sources and their standard errors. Then, we adopted the method of Schenker et al., which uses multiple imputations of subject-level health outcomes that are missing for the subjects in EHR. This procedure requires information to link some subjects between two sources and modeling the mechanism of misclassification in EHR as well as modeling inclusion probabilities to both sources.ResultsIn a simulation study, both estimators yielded negligible bias even when EHR was biased. They performed as well as health survey estimator when EHR bias was large and better than health survey estimator when EHR bias was moderate. It may be challenging to model the misclassification mechanism in real data for the subject-level imputation estimator. We illustrated the methods analyzing six health indicators from 2013 to 14 NYC HANES and the 2013 NYC Macroscope, and a study that linked some subjects in both data sources.ConclusionsWhen a small gold-standard health survey exists, it can serve as a safeguard against potential bias in EHR through the joint analysis of the two sources.
Highlights
Electronic Health Records (EHR) has been increasingly used as a tool to monitor population health
For the public health surveillance system using EHR records, there is an urgent need for methods to estimate the prevalence of health indicators using large and real-time EHR while correcting the potential bias using external sources
We aim to demonstrate that the joint analysis of a large EHR with a much smaller goldstandard health survey can improve the accuracy of the prevalence estimates
Summary
Electronic Health Records (EHR) has been increasingly used as a tool to monitor population health. There is an urgent need for methods to estimate the prevalence of health indicators using large and real-time EHR while correcting the potential bias. The patient population from NYC Macroscope under-represents young men, over-represents patients living in high poverty neighborhoods It only includes patients who visit primary care doctors connected to a particular EHR system [2]. McVeigh et al [3] reported such subjectlevel discrepancies by examining a chart review of participants who both visited NYC Macroscope providers and participated in the NYC Health and Nutrition Examination Survey (HANES), a population-representative survey with field interviews and biospecimen collection. For the public health surveillance system using EHR records, there is an urgent need for methods to estimate the prevalence of health indicators using large and real-time EHR while correcting the potential bias using external sources
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