Cutuli J J, McDuffie Mary Joan, Nescott Erin
Senior Research Scientist, Nemours Children's Health.
Policy Scientist, Center for Community Research & Service, Joseph R. Biden, Jr. School of Public Policy and Administration, University of Delaware.
Dela J Public Health. 2023 Jun 12;9(2):24-29. doi: 10.32481/djph.2023.06.006. eCollection 2023 Jun.
This study investigates different approaches to integrating evictions data with Medicaid and homeless shelter utilization records at the individual level for the state of Delaware. We especially focus on evaluating the feasibility of creating an integrated dataset focused on children and adolescents through different approaches to matching.
We attempt to link existing statewide records on evictions, Medicaid, and shelter from 2017-2019. We first compare direct match and probabilistic match approaches to linking evictions and Medicaid records, and then incorporate shelter records. Finally, we consider a limited set of characteristics relevant to potential future public health research among children who experienced eviction, had a shelter stay, and were enrolled in Medicaid.
Direct matching resulted in a lower match (14%) rate than probabilistic matching (22%) of eviction records to Medicaid data. Homeless shelter records had a high match rate to Medicaid records, even when using a direct match (75%). A sizeable subset of children (n=216) were linked across the three data sources, though this was from a small percentage of cases in the evictions data. Among this subset of children, most (71%) were enrolled in Medicaid in all three years considered by this study and Black children were greatly overrepresented (75%).
Integrating evictions records with other health and human service data involves a number of challenges. Probabilistic matching yielded a considerably higher number of matches after manual review, resulting in a possible study sample of children who have experienced eviction, a homeless shelter stay, and were enrolled in Medicaid. Strategies to increase the match rate for eviction records through using records from other, more universal services may be necessary for investigations that require more comprehensive coverage of the population.
本研究调查了特拉华州在个体层面将驱逐数据与医疗补助及无家可归者收容所使用记录相整合的不同方法。我们特别关注通过不同匹配方法创建一个聚焦于儿童和青少年的综合数据集的可行性。
我们尝试将2017 - 2019年该州现有的驱逐、医疗补助和收容所记录相链接。我们首先比较直接匹配和概率匹配方法来链接驱逐和医疗补助记录，然后纳入收容所记录。最后，我们考虑了一组与经历过驱逐、在收容所居住且参加医疗补助的儿童未来潜在公共卫生研究相关的有限特征。
直接匹配导致驱逐记录与医疗补助数据的匹配率（14%）低于概率匹配（22%）。即使使用直接匹配，无家可归者收容所记录与医疗补助记录的匹配率也很高（75%）。相当一部分儿童（n = 216）在三个数据源之间建立了链接，尽管这仅占驱逐数据中一小部分案例。在这部分儿童中，大多数（71%）在本研究考虑的所有三年中都参加了医疗补助，且黑人儿童的占比过高（75%）。
将驱逐记录与其他健康和人类服务数据相整合涉及诸多挑战。经过人工审核后，概率匹配产生的匹配数量显著更多，从而形成了一个可能的研究样本，该样本包含经历过驱逐、在无家可归者收容所居住且参加医疗补助的儿童。对于需要更全面覆盖人群的调查，可能有必要采取通过使用来自其他更普遍服务的记录来提高驱逐记录匹配率的策略。
