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

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          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.

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          Most cited references41

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          Uses of electronic health records for public health surveillance to advance public health.

          Public health surveillance conducted by health departments in the United States has improved in completeness and timeliness owing to electronic laboratory reporting. However, the collection of detailed clinical information about reported cases, which is necessary to confirm the diagnosis, to understand transmission, or to determine disease-related risk factors, is still heavily dependent on manual processes. The increasing prevalence and functionality of electronic health record (EHR) systems in the United States present important opportunities to advance public health surveillance. EHR data have the potential to further increase the breadth, detail, timeliness, and completeness of public health surveillance and thereby provide better data to guide public health interventions. EHRs also provide a unique opportunity to expand the role and vision of current surveillance efforts and to help bridge the gap between public health practice and clinical medicine.
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            Validating the 8 CPCSSN case definitions for chronic disease surveillance in a primary care database of electronic health records.

            The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is Canada's first national chronic disease surveillance system based on electronic health record (EHR) data. The purpose of this study was to develop and validate case definitions and case-finding algorithms used to identify 8 common chronic conditions in primary care: chronic obstructive pulmonary disease (COPD), dementia, depression, diabetes, hypertension, osteoarthritis, parkinsonism, and epilepsy. Using a cross-sectional data validation study design, regional and local CPCSSN networks from British Columbia, Alberta (2), Ontario, Nova Scotia, and Newfoundland participated in validating EHR case-finding algorithms. A random sample of EHR charts were reviewed, oversampling for patients older than 60 years and for those with epilepsy or parkinsonism. Charts were reviewed by trained research assistants and residents who were blinded to the algorithmic diagnosis. Sensitivity, specificity, and positive and negative predictive values (PPVs, NPVs) were calculated. We obtained data from 1,920 charts from 4 different EHR systems (Wolf, Med Access, Nightingale, and PS Suite). For the total sample, sensitivity ranged from 78% (osteoarthritis) to more than 95% (diabetes, epilepsy, and parkinsonism); specificity was greater than 94% for all diseases; PPV ranged from 72% (dementia) to 93% (hypertension); NPV ranged from 86% (hypertension) to greater than 99% (diabetes, dementia, epilepsy, and parkinsonism). The CPCSSN diagnostic algorithms showed excellent sensitivity and specificity for hypertension, diabetes, epilepsy, and parkinsonism and acceptable values for the other conditions. CPCSSN data are appropriate for use in public health surveillance, primary care, and health services research, as well as to inform policy for these diseases. © 2014 Annals of Family Medicine, Inc.
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              A comparison of prevalence estimates for selected health indicators and chronic diseases or conditions from the Behavioral Risk Factor Surveillance System, the National Health Interview Survey, and the National Health and Nutrition Examination Survey, 2007-2008.

              To compare the prevalence estimates of selected health indicators and chronic diseases or conditions among three national health surveys in the United States. Data from adults aged 18 years or older who participated in the Behavioral Risk Factor Surveillance System (BRFSS) in 2007 and 2008 (n=807,524), the National Health Interview Survey (NHIS) in 2007 and 2008 (n=44,262), and the National Health and Nutrition Examination Survey (NHANES) during 2007 and 2008 (n=5871) were analyzed. The prevalence estimates of current smoking, obesity, hypertension, and no health insurance were similar across the three surveys, with absolute differences ranging from 0.7% to 3.9% (relative differences: 2.3% to 20.2%). The prevalence estimate of poor or fair health from BRFSS was similar to that from NHANES, but higher than that from NHIS. The prevalence estimates of diabetes, coronary heart disease, and stroke were similar across the three surveys, with absolute differences ranging from 0.0% to 0.8% (relative differences: 0.2% to 17.1%). While the BRFSS continues to provide invaluable health information at state and local level, it is reassuring to observe consistency in the prevalence estimates of key health indicators of similar caliber between BRFSS and other national surveys. Published by Elsevier Inc.
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                Author and article information

                Journal
                EGEMS (Wash DC)
                EGEMS (Wash DC)
                eGEMs
                eGEMs
                AcademyHealth
                2327-9214
                2016
                15 December 2016
                : 4
                : 1
                : 1266
                Affiliations
                [i ]NYU School of Medicine Department of Population Health
                [ii ]New York City Department of Health and Mental Hygiene
                [iii ]Formerly New York City Department of Health and Mental Hygiene
                [iv ]CUNY School of Public Health
                Article
                egems1266
                10.13063/2327-9214.1266
                5226388
                28154836
                73a76604-321e-45fb-936b-e0339a3157e3
                Copyright @ 2016

                All eGEMs publications are licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License http://creativecommons.org/licenses/by-nc-nd/3.0/

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                Categories
                Articles

                electronic health records (ehr),surveillance,chronic diseases,validation,cardiovascular risk factors,metabolic conditions

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