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National examination surveys provide trend information on diabetes prevalence, diagnoses, and control. Few localities have access to such information. Using a similar design as the National Health and Nutrition Examination Survey (NHANES), two NYC Health and Nutrition Examination Surveys (NYC HANES) were conducted over a decade, recruiting adults ≥ 20 years using household probability samples (n = 1808 in 2004; n = 1246 in 2013–2014) and physical exam survey methods benchmarked against NHANES. Participants had diagnosed diabetes if told by a health provider they had diabetes, and undiagnosed diabetes if they had no diagnosis but a fasting plasma glucose ≥ 126 mg/dl or A1C ≥ 6.5%. We found that between 2004 and 2014, total diabetes prevalence (diagnosed and undiagnosed) in NYC increased from 13.4 to 16.0% (P = 0.089). In 2013–2014, racial/ethnic disparities in diabetes burden had widened; diabetes was highest among Asians (24.6%), and prevalence was significantly lower among non-Hispanic white adults (7.7%) compared to that among other racial/ethnic groups (P 9%), decreased from 26.9 to 18.0% (P = 0.269), though both were non-significant. While local racial/ethnic disparities in diabetes prevalence persist, findings suggest modest improvements in diabetes diagnosis and management.

The objective of this study was to measure change in obesity prevalence among New York City (NYC) adults from 2004 to 2013–2014 and assess variation across sociodemographic subgroups. We used objectively measured height and weight data from the NYC Health and Nutrition Examination Survey to calculate relative percent change in obesity (≥ 30 kg/m(2)) between 2004 (n = 1987) and 2013–2014 (n = 1489) among all NYC adults and sociodemographic subgroups. We also examined changes in self-reported proxies for energy imbalance. Estimates were age-standardized and statistical significance was evaluated using two-tailed T tests and multivariable regression (p

Purpose Capacity to monitor non-communicable diseases (NCDs) at state or local levels is limited. Emerging approaches include using biomeasures and electronic health record (EHR) data. In 2004, New York City (NYC) performed a population-based health study on adult residents using biomeasures (NYC Health and Nutrition Examination Study, or NYC HANES), modeled after NHANES. A second NYC HANES was launched in 2013 to examine change over time, evaluate municipal policies, and validate a proposed EHR-based surveillance system. We describe the rationale and methods of NYC HANES 2013–2014. Methods NYC HANES was a population-based, cross-sectional survey of NYC adults using three-stage cluster sampling. Between August 2013 and June 2014, selected participants completed a health interview and physical exam (blood pressure, body mass index, and waist circumference). Fasting biomeasures included diabetes, lipid profiles, kidney function, environmental biomarkers, and select infectious diseases. Results Of the 3065 households approached, 2742 were eligible and 1827 were successfully screened (67%). A total of 1524 of eligible participants completed the survey (54%), for an overall response rate of 36%. Conclusion Completing a second NYC HANES a decade after the first study affords an opportunity to understand changes in prevalence, awareness and control of NCDs and evaluate municipal efforts to manage them., Highlights • NYC HANES describes chronic conditions and allows evaluation of municipal policies. • Innovations in survey research methods inform chronic disease surveillance. • Emerging directions include using EMRs for population surveillance purposes.

With 87% of providers using electronic health records (EHRs) in the United States, EHRs have the potential to contribute to population health surveillance efforts. However, little is known about using EHR data outside syndromic surveillance and quality improvement. We created an EHR-based population health surveillance system called the New York City (NYC) Macroscope and assessed the validity of diabetes, hyperlipidemia, hypertension, smoking, obesity, depression, and influenza vaccination indicators. The NYC Macroscope uses aggregate data from a network of outpatient practices. We compared 2013 NYC Macroscope prevalence estimates with those from a population-based, in-person examination survey, the 2013–2014 NYC Health and Nutrition Examination Survey. NYC Macroscope diabetes, hypertension, smoking, and obesity prevalence indicators performed well, but depression and influenza vaccination estimates were substantially lower than were survey estimates. Ongoing validation will be important to monitor changes in validity over time as EHR networks mature and to assess new indicators. We discuss NYC’s experience and how this project fits into the national context. Sharing lessons learned can help achieve the full potential of EHRs for population health surveillance.

INTRODUCTION Electronic health record (EHR) systems provide an opportunity to use a novel data source for population health surveillance. Validation studies that compare prevalence estimates from EHRs and surveys most often use difference testing, which can, because of large sample sizes, lead to detection of significant differences that are not meaningful. We explored a novel application of the two one-sided t test (TOST) to assess the equivalence of prevalence estimates in 2 population-based surveys to inform margin selection for validating EHR-based surveillance prevalence estimates derived from large samples. METHODS We compared prevalence estimates of health indicators in the 2013 Community Health Survey (CHS) and the 2013-2014 New York City Health and Nutrition Examination Survey (NYC HANES) by using TOST, a 2-tailed t test, and other goodness-of-fit measures. RESULTS A ±5 percentage-point equivalence margin for a TOST performed well for most health indicators. For health indicators with a prevalence estimate of less than 10% (extreme obesity [CHS, 3.5%; NYC HANES, 5.1%] and serious psychological distress [CHS, 5.2%; NYC HANES, 4.8%]), a ±2.5 percentage-point margin was more consistent with other goodness-of-fit measures than the larger percentage-point margins. CONCLUSION A TOST with a ±5 percentage-point margin was useful in establishing equivalence, but a ±2.5 percentage-point margin may be appropriate for health indicators with a prevalence estimate of less than 10%. Equivalence testing can guide future efforts to validate EHR data.

Introduction: Electronic health records (EHRs) have the potential to offer real-time, inexpensive standardized health data about chronic health conditions. Despite rapid expansion, EHR data evaluations for chronic disease surveillance have been limited. We present design and methods for the New York City (NYC) Macroscope, an EHR-based chronic disease surveillance system.This methods report is the first in a three part series describing the development and validation of the NYC Macroscope. This report describes in detail the infrastructure underlying the NYC Macroscope; indicator definitions; design decisions that were made to maximize data quality; characteristics of the population sampled; completeness of data collected; and lessons learned from doing this work. The second report describes the methods used to evaluate the validity and robustness of NYC Macroscope prevalence estimates; presents validation results for estimates of obesity, smoking, depression and influenza vaccination; and discusses the implications of our findings for NYC and for other jurisdictions embarking on similar work. The third report applies the same validation methods to metabolic outcomes, including the prevalence, treatment and control of diabetes, hypertension and hyperlipidemia.Methods: We designed the NYC Macroscope for comparison to a local “gold standard,” the 2013-14 NYC Health and Nutrition Examination Survey, and the telephonic 2013 Community Health Survey. NYC Macroscope indicators covered prevalence, treatment, and control of diabetes, hypertension, and hyperlipidemia; and prevalence of influenza vaccination, obesity, depression and smoking. Indicators were stratified by age, sex, and neighborhood poverty, and weighted to the in-care NYC population and limited to primary care patients. Indicator queries were distributed to a virtual network of primary care practices; 392 practices and 716,076 adult patients were retained in the final sample.Findings: The NYC Macroscope covered 10% of primary care providers and 15% of all adult patients in NYC in 2013 (8-47% of patients by neighborhood). Data completeness varied by domain from 98% for blood pressure among patients with hypertension to 33% for depression screening.Discussion: Design and validation efforts undertaken by NYC are described here to provide one potential blueprint for leveraging EHRs for population health monitoring. To replicate a model like NYC Macroscope, jurisdictions should establish buy-in; build informatics capacity; use standard, simple case definitions; establish documentation quality thresholds; restrict to primary care providers; and weight the sample to a target population.

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.

Health departments need the capacity to rapidly expand and modify surveillance during a changing outbreak., On April 23, 2009, the New York City Department of Health and Mental Hygiene (DOHMH) was notified of a school outbreak of respiratory illness; 2 days later the infection was identified as pandemic (H1N1) 2009. This was the first major outbreak of the illness in the United States. To guide decisions on the public health response, the DOHMH used active hospital-based surveillance and then enhanced passive reporting to collect data on demographics, risk conditions, and clinical severity. This surveillance identified 996 hospitalized patients with confirmed or probable pandemic (H1N1) 2009 virus infection from April 24 to July 7; fifty percent lived in high-poverty neighborhoods. Nearly half were

Electronic health records (EHRs) are increasingly being adopted to improve quality of care in health care systems, but they also have potential to monitor health at the population level. Enhancing existing surveillance methods with innovative EHR use allows for strategic targeting of health resources, and data to guide and evaluate public health initiatives and policies. New York City is currently developing the NYC Macroscope, aggregating EHR data into a surveillance tool to inform public health decisions. We have outlined key issues in developing an EHR-based surveillance system, and proposed indicators for the NYC Macroscope.