Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, United States.
Department of Sociology, College of Arts and Sciences, American University, Washington, DC, United States.
J Med Internet Res. 2022 Mar 17;24(3):e30619. doi: 10.2196/30619.
Clinical epidemiology and patient-oriented health care research that incorporates neighborhood-level data is becoming increasingly common. A key step in conducting this research is converting patient address data to longitude and latitude data, a process known as geocoding. Several commonly used approaches to geocoding (eg, ggmap or the tidygeocoder R package) send patient addresses over the internet to web-based third-party geocoding services. Here, we describe how these approaches to geocoding disclose patients' personally identifiable information (PII) and how the subsequent publication of the research findings discloses the same patients' protected health information (PHI). We explain how these disclosures can occur and recommend strategies to maintain patient privacy when studying neighborhood effects on patient outcomes.
临床流行病学和以患者为中心的医疗保健研究越来越多地结合了社区层面的数据。进行这项研究的一个关键步骤是将患者地址数据转换为经纬度数据,这个过程被称为地理编码。地理编码的几种常用方法(例如 ggmap 或 tidygeocoder R 包)将患者地址通过互联网发送到基于网络的第三方地理编码服务。在这里,我们描述了这些地理编码方法如何披露患者的个人身份信息(PII),以及随后发表的研究结果如何披露同一患者的受保护健康信息(PHI)。我们解释了这些披露是如何发生的,并建议在研究邻里效应对患者结果的影响时维护患者隐私的策略。