Linkage of people experiencing homeless using two consent models.

IF 1.6 Q3 HEALTH CARE SCIENCES & SERVICES International Journal of Population Data Science Pub Date : 2022-08-25 DOI:10.23889/ijpds.v7i3.1865
R. Trubey, I. Thomas, R. Cannings‐John, Peter Mackie
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Abstract

ObjectivesAdministrative data linkage is relatively under-utilised as a way of generating evidence to guide homelessness policy and service delivery in the UK. Our objective is to contribute insight into the ethical, legal, and practical challenges of using data linkage with data from people experiencing homelessness (PEH). ApproachWe outline the data collection and linkage methodologies for two UK-based studies related to PEH. The first design aimed to explore the acceptability and feasibility of consented linkage of trial data (‘Moving On’ trial) to NHS Digital records in a cohort of recruited PEH in two English local authorities (n=50). The second design used administrative data originating from a local authority homelessness service in Wales (n=17,000 cases) to explore educational outcomes of children in homeless households. The resultant data linkage rates are contrasted and discussed in relation to the mechanisms for obtaining and linking personal data. ResultsThe Moving On trial demonstrated high rates of consent for data linkage and the ability to collect sufficient personal identifiable data to increase the chance of successful matching. Aggregate match rates will be discussed. Of the roughly 17,000 cases included in the local authority administrative data, 75% could be linked to unique individuals using probabilistic matching and were therefor ‘useable’ in linkage research. The proportion of useable cases rapidly decreased as the cut-off for matching quality was increased, to roughly 50% of cases being useable when a 99% match probability cut-off was used. Matching rates were higher amongst priority need homeless cases, possibly reflecting business need to identify and work closely with these people. ConclusionWhere homelessness administrative data systems are not designed to enable data linkage, low matching rates can result, reducing study sample sizes and potentially leading to bias towards more extreme cases of homelessness if missed-matches are not random. Consented linkage within large-scale trials offers one possibility for generating long-term evidence.
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使用两种同意模型将无家可归者联系起来。
目的在英国,作为一种生成证据来指导无家可归政策和服务提供的方式,行政数据链接的利用率相对较低。我们的目标是深入了解将数据链接与无家可归者(PEH)的数据结合使用的道德、法律和实际挑战。方法我们概述了两项英国PEH相关研究的数据收集和联系方法。第一个设计旨在探索在两个英国地方当局(n=50)招募的PEH队列中,将试验数据(“继续”试验)与NHS数字记录进行同意连接的可接受性和可行性。第二个设计使用了来自威尔士地方当局无家可归服务机构的行政数据(n=17000例)来探索无家可归家庭中儿童的教育结果。将由此产生的数据链接率与获取和链接个人数据的机制进行对比和讨论。结果Moving On试验表明,数据链接的同意率很高,并且能够收集足够的个人身份数据来增加成功匹配的机会。将讨论总匹配率。在地方当局行政数据中包括的大约17000个病例中,75%的病例可以通过概率匹配与独特的个体联系起来,因此在联系研究中“可用”。可用案例的比例随着匹配质量截止值的增加而迅速下降,当使用99%的匹配概率截止值时,大约50%的案例可用。优先需求无家可归者的匹配率更高,这可能反映了企业需要识别这些人并与他们密切合作。结论如果无家可归者管理数据系统的设计不能够实现数据链接,则可能会导致匹配率低,从而减少研究样本量,如果错过匹配不是随机的,则可能导致对更极端的无家可归案例的偏见。大规模试验中的同意联系为产生长期证据提供了一种可能性。
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
386
审稿时长
20 weeks
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