Motor vehicle crash data linkage has emerged as a vital tool to better understand the injury outcomes and the factors contributing to crashes. This systematic review and meta-analysis aims to explore the existing knowledge on data linkage between motor vehicle crashes and hospital-based datasets, summarize and highlight the findings of previous studies, and identify gaps in research. A comprehensive and systematic search of the literature yielded 54 studies for a qualitative analysis, and 35 of which were also considered for a quantitative meta-analysis. Findings highlight a range of viable methodologies for linking datasets, including manual, deterministic, probabilistic, and integrative methods. Designing a linkage method that integrates different algorithms and techniques is more likely to result in higher match rate and fewer errors. Examining the results of the meta-analysis reveals that a wide range of linkage rates were reported. There are several factors beyond the approach that affect the linkage rate including the size and coverage of both datasets and the linkage variables. Gender, age, crash type, and roadway geometry at the crash site were likely to be associated with a record's presence in a linked dataset. Linkage rate alone is not the only important metric and when linkage rate is used as a metric in research, both police and hospital rates should be reported. This study also highlights the importance of examining and accounting for population and bias introduced by linking two datasets.

机动车碰撞数据联动已成为更好地了解伤害结果和导致碰撞的因素的重要工具。这项系统回顾和荟萃分析旨在探索机动车碰撞事故与医院数据集之间数据关联的现有知识，总结和强调先前研究的结果，并找出研究差距。对文献进行全面、系统的检索，得出 54 项研究进行定性分析，其中 35 项研究也考虑进行定量荟萃分析。研究结果强调了一系列用于链接数据集的可行方法，包括手动方法、确定性方法、概率方法和综合方法。设计一种集成不同算法和技术的联动方法更有可能获得更高的匹配率和更少的错误。检查荟萃分析的结果表明，报告了广泛的连锁率。除了该方法之外，还有几个因素会影响链接率，包括数据集和链接变量的大小和覆盖范围。事故现场的性别、年龄、事故类型和道路几何形状可能与链接数据集中记录的存在相关联。连锁率本身并不是唯一重要的指标，当连锁率用作研究指标时，应报告警察和医院的比率。这项研究还强调了检查和解释人口以及通过链接两个数据集引入的偏差的重要性。