Divisions of Epidemiology (Ms Chan and Perlman and Dr Lim) and Prevention and Primary Care (Ms Smolen), New York City Department of Health and Mental Hygiene, Long Island City, New York; and Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine, New York, New York (Dr Lee).
J Public Health Manag Pract. 2022;28(1):E109-E118. doi: 10.1097/PHH.0000000000001142.
Disease burden may vary substantively across neighborhoods in an urban setting. Yet, data available for monitoring chronic conditions at the neighborhood level are scarce. Large health care data sets have potential to complement population health surveillance. Few studies have examined the utility of health care data for neighborhood-level surveillance.
We examined the use of primary care electronic health records (EHRs) and emergency department (ED) claims for identifying neighborhoods with higher chronic disease burden and neighborhood-level prevalence estimation.
Comparison of hypertension and diabetes estimates from EHRs and ED claims with survey-based estimates.
Forty-two United Hospital Fund neighborhoods in New York City.
The EHR sample comprised 708 452 patients from the Hub Population Health System (the Hub) in 2015, and the ED claim sample comprised 1 567 870 patients from the Statewide Planning and Research Cooperative System in 2015. We derived survey-based estimates from 2012 to 2016 Community Health Survey (n = 44 189).
We calculated hypertension and diabetes prevalence estimates by neighborhood from each data source. We obtained Pearson correlation and absolute difference between EHR-based or claims-based estimates and survey-based estimates.
Both EHR-based and claims-based estimates correlated strongly with survey-based estimates for hypertension (0.91 and 0.72, respectively) and diabetes (0.83 and 0.82, respectively) and identified similar neighborhoods of higher burden. For hypertension, 10 and 17 neighborhoods from the EHRs and ED claims, respectively, had an absolute difference of more than 5 percentage points from the survey-based estimate. For diabetes, 15 and 4 neighborhoods from the EHRs and ED claims, respectively, differed from the survey-based estimate by more than 5 percentage points.
Both EHRs and ED claims data are useful for identifying neighborhoods with greater disease burden and have potential for monitoring chronic conditions at the neighborhood level.
在城市环境中，疾病负担可能在不同社区之间有很大差异。然而，用于监测社区层面慢性疾病的数据却很少。大型医疗保健数据集有可能补充人群健康监测。很少有研究探讨利用医疗保健数据进行社区层面监测的效用。
我们检验了使用初级保健电子健康记录（EHRs）和急诊部（ED）的索赔记录来识别慢性病负担更高的社区以及进行社区层面患病率估计。
EHR 和 ED 索赔记录中的高血压和糖尿病估计值与基于调查的估计值进行比较。
纽约市 42 个联合健康基金社区。
EHR 样本由 2015 年 Hub 人群健康系统（Hub）中的 708452 名患者组成，ED 索赔样本由 2015 年全州规划和研究合作系统中的 1567870 名患者组成。我们从 2012 年到 2016 年的社区健康调查（n=44189）中得出基于调查的估计值。
我们根据每个数据源计算了社区层面的高血压和糖尿病患病率估计值。我们获得了 EHR 或索赔估计值与调查估计值之间的 Pearson 相关系数和绝对差异。
EHR 或索赔估计值与调查估计值在高血压方面高度相关（分别为 0.91 和 0.72），在糖尿病方面也高度相关（分别为 0.83 和 0.82），并确定了相似的高负担社区。在高血压方面，EHR 和 ED 索赔记录分别有 10 个和 17 个社区的绝对差值超过基于调查的估计值 5 个百分点。在糖尿病方面，EHR 和 ED 索赔记录分别有 15 个和 4 个社区的差值超过基于调查的估计值 5 个百分点。
EHR 和 ED 索赔数据都可用于识别慢性病负担较高的社区，并且有可能用于监测社区层面的慢性疾病。
