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Submission Type

Empirical Research

Keywords

Electronic health records (EHR), surveillance, chronic diseases, validation, cardiovascular risk factors, metabolic conditions

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.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

DOI

10.13063/2327-9214.1266

Included in

Epidemiology Commons

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