Barbosa Estela Capelas, Blom Niels, Bunce Annie
Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.
University of Manchester, Manchester, United Kingdom.
PLoS One. 2025 Jan 14;20(1):e0301155. doi: 10.1371/journal.pone.0301155. eCollection 2025.
Violence has been analysed in silo due to difficulties in accessing data and concerns for the safety of those exposed. While there is some literature on violence and its associations using individual datasets, analyses using combined sources of data are very limited. Ideally data from the same individuals would enable linkage and a longitudinal understanding of experiences of violence and their (health) impacts and consequences. This paper aims to provide proof of concept to create a synthetic dataset by combining data from the Crime Survey for England and Wales (CSEW) and administrative data from Rape Crisis England and Wales (RCEW), pertaining to victim-survivors of sexual violence in adulthood. Intuitively, the idea was to impute missing information from one dataset by borrowing the distribution from the other. In our analyses, we borrowed information from CSEW to impute missing data in the RCEW administrative dataset, creating a combined synthetic RCEW-CSEW dataset. Using look-alike modelling principles, we provide an innovative and cost-effective approach to exploring patterns and associations in violence-related research in a multi-sectorial setting. Methodologically, we approached data integration as a missing data problem to create a synthetic combined dataset. Multiple imputation with chained equations were employed to collate/impute data from the two different sources. To test whether this procedure was effective, we compared regressions analyses for the individual and combined synthetic datasets on binary, continuous and categorical variables. We extended our testing to an outcome measure and, finally, applied the technique to a variable fully missing in one data source. Our results show that the effect sizes for the combined dataset reflect those from the dataset used for imputation. The variance is higher, resulting in fewer statistically significant estimates. Our approach reinforces the possibility of combining administrative with survey datasets using look-alike methods to overcome existing barriers to data linkage.
由于获取数据存在困难以及担心数据提供者的安全，暴力行为一直是孤立地进行分析的。虽然有一些关于暴力行为及其关联的文献使用了单个数据集，但使用综合数据源进行的分析非常有限。理想情况下，来自同一人群的数据能够实现数据关联，并对暴力经历及其（健康）影响和后果进行纵向了解。本文旨在通过合并英格兰和威尔士犯罪调查（CSEW）的数据以及英格兰和威尔士强奸危机组织（RCEW）的行政数据，为创建一个综合数据集提供概念验证，这些数据涉及成年人性暴力的受害者幸存者。直观地说，想法是通过借鉴另一个数据集的分布来填补一个数据集中缺失的信息。在我们的分析中，我们从CSEW中借用信息来填补RCEW行政数据集中缺失的数据，创建一个综合的RCEW - CSEW数据集。利用相似性建模原则，我们提供了一种创新且具有成本效益的方法，用于在多部门环境中探索暴力相关研究中的模式和关联。从方法论上讲，我们将数据整合视为一个缺失数据问题，以创建一个综合的数据集。我们采用链式方程的多重插补法来整理/插补来自两个不同来源的数据。为了测试这个过程是否有效，我们比较了单个数据集和综合数据集对二元、连续和分类变量的回归分析。我们将测试扩展到一个结果指标，最后，将该技术应用于一个在一个数据源中完全缺失的变量。我们的结果表明，综合数据集的效应大小反映了用于插补的数据集的效应大小。方差更高，导致具有统计学意义的估计值更少。我们的方法强化了使用相似性方法将行政数据集与调查数据集相结合以克服现有数据关联障碍的可能性。
