Rentsch Christopher T, Kabudula Chodziwadziwa Whiteson, Catlett Jason, Beckles David, Machemba Richard, Mtenga Baltazar, Masilela Nkosinathi, Michael Denna, Natalis Redempta, Urassa Mark, Todd Jim, Zaba Basia, Reniers Georges
Department of Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK.
MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg , 2193, South Africa.
Gates Open Res. 2018 Jan 11;1:8. doi: 10.12688/gatesopenres.12751.2. eCollection 2017.
Linking a health and demographic surveillance system (HDSS) to data from a health facility that serves the HDSS population generates a research infrastructure for directly observed data on access to and utilization of health facility services. Many HDSS sites, however, are in areas that lack unique national identifiers or suffer from data quality issues, such as incomplete records, spelling errors, and name and residence changes, all of which complicate record linkage approaches when applied retrospectively. We developed Point-of-contact Interactive Record Linkage (PIRL) software that is used to prospectively link health records from a local health facility to an HDSS in rural Tanzania. This prospective approach to record linkage is carried out in the presence of the individual whose records are being linked, which has the advantage that any uncertainty surrounding their identity can be resolved during a brief interaction, whereby extraneous information (e.g., household membership) can be referred to as an additional criterion to adjudicate between multiple potential matches. Our software uses a probabilistic record linkage algorithm based on the Fellegi-Sunter model to search and rank potential matches in the HDSS data source. Key advantages of this software are its ability to perform multiple searches for the same individual and save patient-specific notes that are retrieved during subsequent clinic visits. A search on the HDSS database (n=110,000) takes less than 15 seconds to complete. Excluding time spent obtaining written consent, the median duration of time we spend with each patient is six minutes. In this setting, a purely automated retrospective approach to record linkage would have only correctly identified about half of the true matches and resulted in high linkage errors; therefore highlighting immediate benefit of conducting interactive record linkage using the PIRL software.
将健康与人口监测系统（HDSS）与服务于HDSS人群的医疗机构的数据相连接，可生成一个用于直接观察医疗机构服务获取和利用情况数据的研究基础设施。然而，许多HDSS站点位于缺乏独特国家标识符的地区，或存在数据质量问题，如记录不完整、拼写错误以及姓名和住址变更等，所有这些在进行回顾性应用时都会使记录链接方法变得复杂。我们开发了接触点交互式记录链接（PIRL）软件，用于前瞻性地将当地医疗机构的健康记录与坦桑尼亚农村的一个HDSS相链接。这种记录链接的前瞻性方法是在记录被链接的个人在场的情况下进行的，其优点是在简短互动过程中可以解决围绕其身份的任何不确定性，从而可以将外部信息（如家庭成员身份）作为在多个潜在匹配项之间进行裁决的附加标准。我们的软件使用基于费勒吉 - 桑特模型的概率记录链接算法在HDSS数据源中搜索并对潜在匹配项进行排序。该软件的主要优点是能够对同一个人进行多次搜索，并保存患者特定的笔记，这些笔记可在后续门诊就诊时检索。在HDSS数据库（n = 110,000）上进行一次搜索不到15秒即可完成。排除获取书面同意所花费的时间，我们与每位患者相处的中位时间为6分钟。在这种情况下，纯粹自动化的回顾性记录链接方法只能正确识别大约一半的真实匹配项，并导致高链接错误率；因此凸显了使用PIRL软件进行交互式记录链接的直接好处。
