Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information.
Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information.
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DOI：
10.1371/journal.pone.0278672
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Dulin, Akilah J.
中科院分区：
文献类型：
--
作者：
Nassel, Ariann;Wilson-Barthes, Marta G.;Howe, Chanelle J.;Napravnik, Sonia;Mugavero, Michael J.;Agil, Deana;Dulin, Akilah J.
在对居住地址信息与社区级数据进行地理编码和链接时维护患者隐私可能会在研究过程中带来挑战。当研究人员在地理编码和链接数据方面接受的培训有限，或者具有适当专业知识的非研究人员的可用性有限，不熟悉研究的人群或目标，或者研究团队负担不起时，可能会出现挑战。与不在现场的非研究人员合作时，也可能会出现数据泄露的机会。我们详细介绍了一个免费、用户友好的协议，用于在依赖参与者受保护的健康信息的多地点、基于临床的队列研究中构建社区风险环境指数。该协议可以由没有接受过地理信息系统 (GIS) 先前培训的研究人员来实施，并且可以帮助最大限度地减少将地理数据集成到公共卫生项目中的运营成本。该协议演示了如何：(1) 在诊所环境中对患者的居住地址进行安全地理编码，并使用地理信息系统软件（Esri，雷德兰兹，加利福尼亚州）将地理编码地址与人口普查区进行匹配； (2) 通过美国社区调查和 ArcGIS 业务分析师（Esri，雷德兰兹，加利福尼亚州）确定风险环境的背景变量； (3) 使用地理标识符将邻里风险数据链接到包含地理编码地址的人口普查区域； (4) 将随机生成的标识符分配给人口普查区，并剥离人口普查区的地理标识符，以维护患者的机密性。完成该协议将为患者的编码人口普查区位置生成三个邻里风险指数（即邻里劣势指数、谋杀率指数和袭击率指数）。没有 GIS 经验的研究人员可以使用该协议轻松创建邻里风险环境的客观指数，同时维护患者的机密性。未来的研究可以调整该协议以适应其特定的患者群体和分析目标。
Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study’s population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants’ protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects. This protocol demonstrates how to: (1) securely geocode patients’ residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality. Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients’ coded census tract locations. This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.
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