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Objectives: Immigrant adults tend to have better health than native-born adults despite lower incomes, but the health advantage decreases with length of residence. To determine whether immigrant adults have a health advantage over US-born adults in New York City, we compared cardiovascular disease (CVD) risk factors among both groups., Methods: Using data from the New York City Health and Nutrition Examination Survey 2013-2014, we assessed health insurance coverage, health behaviors, and health conditions, comparing adults ages ≥20 born in the 50 states or the District of Columbia (US-born) with adults born in a US territory or outside the United States (immigrants, following the National Health and Nutrition Examination Survey) and comparing US-born adults with (1) adults who immigrated recently (≤10 years) and (2) adults who immigrated earlier (>10 years)., Results: For immigrant adults, the mean time since arrival in the United States was 21.8 years. Immigrant adults were significantly more likely than US-born adults to lack health insurance (22% vs 12%), report fair or poor health (26% vs 17%), have hypertension (30% vs 23%), and have diabetes (20% vs 11%) but significantly less likely to smoke (18% vs 27%) (all P < .05). Comparable proportions of immigrant adults and US-born adults were overweight or obese (67% vs 63%) and reported CVD (both 7%). Immigrant adults who arrived recently were less likely than immigrant adults who arrived earlier to have diabetes or high cholesterol but did not differ overall from US-born adults., Conclusions: Our findings may help guide prevention programs and policy efforts to ensure that immigrant adults remain healthy.

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.

Electronic health records (EHRs) are transforming the practice of clinical medicine, but the extent to which they are being harnessed to advance public health goals remains uncertain. Data extracted from integrated EHR networks offer the potential for almost real-time determination of the health status of populations in care, for targeting interventions to vulnerable populations, and for monitoring the impact of such initiatives over time. This is especially true in ambulatory care settings, which are uniquely suited for monitoring population health indicators including risk factors and disease management indicators associated with chronic diseases. As efforts gather steam to integrate health data across delivery systems, large networks of electronic patient information are increasingly emerging. Few of the national population health surveillance systems that rely on EHR data have progressed beyond laying groundwork to launch and maintain EHR-based surveillance, but a limited number of more focused or local efforts have demonstrated innovation in population health surveillance. Common challenges include incompleteness of population coverage, lack of interoperability across data systems, and variable data quality. This review defines progress, opportunities, and challenges in using EHR data for population health surveillance.