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Introduction Increasing adoption of electronic health record (EHR) systems by health care providers presents an opportunity for EHR-based population health surveillance. EHR data, however, may be subject to measurement error because of factors such as data entry errors and lack of documentation by physicians. We investigated the use of a calibration model to reduce bias of prevalence estimates from the New York City (NYC) Macroscope, an EHR-based surveillance system. Methods We calibrated 6 health indicators to the 2013-2014 NYC Health and Nutrition Examination Survey (NYC HANES) data: hypertension, diabetes, smoking, obesity, influenza vaccination, and depression. We classified indicators into having low measurement error or high measurement error on the basis of whether the proportion of misclassification (ie, false-negative or false-positive cases) was greater than 15% in 190 reviewed charts. We compared bias (ie, absolute difference between NYC Macroscope estimates and NYC HANES estimates) before and after calibration. Results The health indicators with low measurement error had the same bias after calibration as before calibration (diabetes, 2.5 percentage points; smoking, 2.5 percentage points; obesity, 3.5 percentage points; hypertension, 1.1 percentage points). For indicators with high measurement error, bias decreased from 10.8 to 2.5 percentage points for depression, and from 26.7 to 8.4 percentage points for influenza vaccination. Conclusion The calibration model has the potential to reduce bias of prevalence estimates from EHR-based surveillance systems for indicators with high measurement errors. Further research is warranted to assess the utility of the current calibration model for other EHR data and additional indicators.

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 Racial/ethnic minority adults have higher rates of hypertension than non-Hispanic white adults. We examined the prevalence of hypertension among Hispanic and Asian subgroups in New York City. Methods Data from the 2013-2014 New York City Health and Nutrition Examination Survey were used to assess hypertension prevalence among adults (aged ≥20) in New York City (n = 1,476). Hypertension was measured (systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg or self-reported hypertension and use of blood pressure medication). Participants self-reported race/ethnicity and country of origin. Multivariable logistic regression models assessed differences in prevalence by race/ethnicity and sociodemographic and health-related characteristics. Results Overall hypertension prevalence among adults in New York City was 33.9% (43.5% for non-Hispanic blacks, 38.0% for Asians, 33.0% for Hispanics, and 27.5% for non-Hispanic whites). Among Hispanic adults, prevalence was 39.4% for Dominican, 34.2% for Puerto Rican, and 27.5% for Central/South American adults. Among Asian adults, prevalence was 43.0% for South Asian and 39.9% for East/Southeast Asian adults. Adjusting for age, sex, education, and body mass index, 2 major racial/ethnic minority groups had higher odds of hypertension than non-Hispanic whites: non-Hispanic black (AOR [adjusted odds ratio], 2.6; 95% confidence interval [CI], 1.7-3.9) and Asian (AOR, 2.0; 95% CI, 1.2-3.4) adults. Two subgroups had greater odds of hypertension than the non-Hispanic white group: East/Southeast Asian adults (AOR, 2.8; 95% CI, 1.6-4.9) and Dominican adults (AOR, 1.9; 95% CI, 1.1-3.5). Conclusion Racial/ethnic minority subgroups vary in hypertension prevalence, suggesting the need for targeted interventions.

INTRODUCTION Electronic health records (EHRs) from primary care providers can be used for chronic disease surveillance; however, EHR-based prevalence estimates may be biased toward people who seek care. This study sought to describe the characteristics of an in-care population and compare them with those of a not-in-care population to inform interpretation of EHR data. METHODS We used data from the 2013-2014 New York City Health and Nutrition Examination Survey (NYC HANES), considered the gold standard for estimating disease prevalence, and the 2013 Community Health Survey, and classified participants as in care or not in care, on the basis of their report of seeing a health care provider in the previous year. We used χ(2) tests to compare the distribution of demographic characteristics, health care coverage and access, and chronic conditions between the 2 populations. RESULTS According to the Community Health Survey, approximately 4.1 million (71.7%) adults aged 20 or older had seen a health care provider in the previous year; according to NYC HANES, approximately 4.7 million (75.1%) had. In both surveys, the in-care population was more likely to be older, female, non-Hispanic, and insured compared with the not-in-care population. The in-care population from the NYC HANES also had a higher prevalence of diabetes (16.7% vs 6.9%; P < .001), hypercholesterolemia (35.7% vs 22.3%; P < .001), and hypertension (35.5% vs 26.4%; P < .001) than the not-in-care population. CONCLUSION Systematic differences between in-care and not-in-care populations warrant caution in using primary care data to generalize to the population at large. Future efforts to use primary care data for chronic disease surveillance need to consider the intended purpose of data collected in these systems as well as the characteristics of the population using primary care.