Monitoring Prevalence, Treatment, and Control Of Metabolic Conditions In New York City Adults Using 2013 Primary Care Electronic Health Records: A Surveillance Validation Study

Abstract

Introduction:Electronic health records (EHRs) can potentially extend chronic disease surveillance, but few EHR-based initiatives tracking population-based metrics have been validated for accuracy. We designed a new EHR-based population health surveillance system for New York City (NYC) known as NYC Macroscope. This report is the third in a 3-part series describing the development and validation of that system. The first report describes governance and technical infrastructure underlying the NYC Macroscope. The second report describes validation methods and presents validation results for estimates of obesity, smoking, depression and influenza vaccination. In this third paper we present validation findings for metabolic indicators (hypertension, hyperlipidemia, diabetes).Methods:We compared EHR-based estimates to those from a gold standard surveillance source - the 2013–2014 NYC Health and Nutrition Examination Survey (NYC HANES) - overall and stratified by sex and age group, using the two one-sided test of equivalence and other validation criteria.Results:EHR-based hypertension prevalence estimates were highly concordant with NYC HANES estimates. Diabetes prevalence estimates were highly concordant when measuring diagnosed diabetes but less so when incorporating laboratory results. Hypercholesterolemia prevalence estimates were less concordant overall. Measures to assess treatment and control of the 3 metabolic conditions performed poorly.Discussion:While indicator performance was variable, findings here confirm that a carefully constructed EHR-based surveillance system can generate prevalence estimates comparable to those from gold-standard examination surveys for certain metabolic conditions such as hypertension and diabetes.Conclusions:Standardized EHR metrics have potential utility for surveillance at lower annual costs than surveys, especially as representativeness of contributing clinical practices to EHR-based surveillance systems increases.

Innovations in Population Health Surveillance: Using Electronic Health Records for Chronic Disease Surveillance

“…19,20 In brief, NYC Macroscope indicators for diabetes, hypertension, smoking, and obesity prevalence performed well according to a priori criteria for agreement (Table 2), and each of these demonstrated high sensitivity and specificity (0.89-1.00) in the chart review study of records from providers contributing to the NYC Macroscope. Although hyperlipidemia prevalence met many of the a priori criteria, sensitivity and specificity were not as good (0.72 and 0.58, respectively).…”

Monitoring Depression Rates in an Urban Community: Use of Electronic Health Records

“…Some researchers and communities have begun work to validate these EHR estimates through neighborhood-level surveys to better assess accuracy of EHR-based estimates. 38 , 41 This process of validation will be important to allay concerns about selection bias for those accessing and represented in an EHR-based estimate.…”

Prevalence estimation by joint use of big data and health survey: a demonstration study using electronic health records in New York city

“…Electronic Health Records (EHR) has been increasingly used as a tool for public health surveillance by local and national jurisdictions [1]. For example, recent studies in New York City (NYC) reported that the prevalence estimates from NYC Macroscope, an EHR-based surveillance system in NYC [2], were comparable to the survey-based estimates for diabetes, hypertension, and smoking [3,4]. EHR often cover more people (n ≥ 100, 000) than traditional population health surveys and, and once the infrastructure is in place, the data collection occurs in near real-time without additional recruitment or interviewing cost.…”

“…It comprises measurement error (e.g., due to the use of non-standardized instruments across sites), extraction error, or the collection of proxy-measurement (e.g., due to the recording without distinction of both self-report and actual measurements). McVeigh et al [3] reported such subjectlevel discrepancies by examining a chart review of participants who both visited NYC Macroscope providers and also participated in the NYC Health and Nutrition Examination Survey (HANES), a population-representative survey with field interviews and biospecimen collection. Assuming NYC HANES measurements as "gold-standard, " the chart review found a 5% subject-level error for obesity, 19% for depression, and 19% for influenza vaccination.…”