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本文引用的文献

1
Enhancing Electronic Health Record Data with Geospatial Information.利用地理空间信息增强电子健康记录数据
AMIA Jt Summits Transl Sci Proc. 2017 Jul 26;2017:123-132. eCollection 2017.
2
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Am J Epidemiol. 2017 Aug 1;186(3):289-296. doi: 10.1093/aje/kwx096.
3
Association of environmental indicators with teen alcohol use and problem behavior: Teens' observations vs. objectively-measured indicators.环境指标与青少年酒精使用及问题行为的关联:青少年的观察结果与客观测量指标的比较
Health Place. 2017 Jan;43:151-157. doi: 10.1016/j.healthplace.2016.12.004. Epub 2017 Jan 4.
4
Association of Drug and Alcohol Use With Adolescent Firearm Homicide at Individual, Family, and Neighborhood Levels.在个体、家庭和社区层面,药物和酒精使用与青少年枪支杀人案的关联。
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Acad Pediatr. 2017 Mar;17(2):127-134. doi: 10.1016/j.acap.2016.11.011. Epub 2016 Nov 19.
6
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将地理空间上变化的社会、经济和环境因素与电子健康记录数据相联系以更好地理解哮喘急性加重的方法。

Approaches to Link Geospatially Varying Social, Economic, and Environmental Factors with Electronic Health Record Data to Better Understand Asthma Exacerbations.

作者信息

Xie Sherrie, Himes Blanca E

机构信息

Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.

出版信息

AMIA Annu Symp Proc. 2018 Dec 5;2018:1561-1570. eCollection 2018.

PMID:30815202
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6371292/
Abstract

Electronic health record (EHR)-derived data has become an invaluable resource for biomedical research, but is seldom used for the study of the health impacts of social and environmental factors due in part to the unavailability of relevant variables. We describe how EHR-derived data can be enhanced via linking of external sources of social, economic and environmental data when patient-related geospatial information is available, and we illustrate an approach to better understand the geospatial patterns of asthma exacerbation rates in Philadelphia. Specifically, we relate the spatial distribution of asthma exacerbations observed in EHR-derived data to that of known and potential risk factors (i.e., economic deprivation, crime, vehicular traffic, tree cover). Areas of highest risk based on integrated social and environmental data were consistent with an area with increased asthma exacerbations, demonstrating that data external to the EHR can enhance our understanding of negative health-related outcomes.

摘要

电子健康记录(EHR)衍生的数据已成为生物医学研究的宝贵资源,但部分由于相关变量难以获取,很少用于研究社会和环境因素对健康的影响。我们描述了在有患者相关地理空间信息时,如何通过链接社会、经济和环境数据的外部来源来增强EHR衍生的数据,并举例说明了一种更好地理解费城哮喘加重率地理空间模式的方法。具体而言,我们将EHR衍生数据中观察到的哮喘加重的空间分布与已知和潜在风险因素(即经济贫困、犯罪、车辆交通、树木覆盖)的分布相关联。基于综合社会和环境数据的高风险区域与哮喘加重增加的区域一致,表明EHR外部的数据可以增强我们对与健康相关的负面结果的理解。