文章基本信息

标题：Determining Chronic Disease Prevalence in Local Populations Using Emergency Department Surveillance
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作者：David C. Lee ; Judith A. Long ; Stephen P. Wall 等
期刊名称：American journal of public health
印刷版ISSN：0090-0036
出版年度：2015
卷号：105
期号：9
页码：e67-e74
DOI：10.2105/AJPH.2015.302679
语种：English
出版社：American Public Health Association
摘要：Objectives. We sought to improve public health surveillance by using a geographic analysis of emergency department (ED) visits to determine local chronic disease prevalence. Methods. Using an all-payer administrative database, we determined the proportion of unique ED patients with diabetes, hypertension, or asthma. We compared these rates to those determined by the New York City Community Health Survey. For diabetes prevalence, we also analyzed the fidelity of longitudinal estimates using logistic regression and determined disease burden within census tracts using geocoded addresses. Results. We identified 4.4 million unique New York City adults visiting an ED between 2009 and 2012. When we compared our emergency sample to survey data, rates of neighborhood diabetes, hypertension, and asthma prevalence were similar (correlation coefficient = 0.86, 0.88, and 0.77, respectively). In addition, our method demonstrated less year-to-year scatter and identified significant variation of disease burden within neighborhoods among census tracts. Conclusions. Our method for determining chronic disease prevalence correlates with a validated health survey and may have higher reliability over time and greater granularity at a local level. Our findings can improve public health surveillance by identifying local variation of disease prevalence. In its 2012 report on measures for population health, the Institute of Medicine prioritized understanding local population health to improve health care for populations with the highest need. 1 Generally, health care providers have used the term “population health” when referring to patients linked to a specific health care provider or insurance group. 2 However, the discipline of public health more broadly defines population health as the health of all individuals living in specific geographic regions. 3 To estimate disease burden, traditional methods include performing population-based telephone health surveys. 4 Unless large numbers of individuals are surveyed, it is difficult to determine prevalence in small geographic areas such as census tracts, and yearly estimates have significant noise because of small sample sizes. 5 Low response rates can lead to errors in estimating disease prevalence, and larger surveys can be costly and difficult to perform. 6 With increasing use of big data in the form of large administrative data sets with clinical data, 7 there is an opportunity to create more precise measures of population health by reducing the variance associated with small sample sizes. 8–10 These methods may be biased as they only track individuals who register a medical claim, which makes for a type of convenience sample. Nevertheless, a significant proportion of all individuals, regardless of insurance type, interact with the health care system, especially through emergency services. Nearly 1 in 5 individuals report having gone to an emergency department (ED) in the past year. 11 Previous studies have demonstrated the promise of using emergency claims data for tracking acute illnesses; however, there is potential to extend these methods to the surveillance of chronic disease. 12,13 One of the advantages of using administrative claims data is the achievement of large sample sizes without the need to conduct large surveys. 14,15 In this study, we have introduced a novel geographic method of public health surveillance and determined whether we could use ED administrative claims to estimate chronic disease prevalence at a local level over time. As the ED is generally a place where all individuals can access care regardless of socioeconomic or insurance status, it offers an ideal environment for public health surveillance among all types of individuals within a heterogeneous population. 16