Center for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, USA.
Acad Emerg Med. 2012 Apr;19(4):469-80. doi: 10.1111/j.1553-2712.2012.01324.x.
The objective was to evaluate the process of using existing data sources, probabilistic linkage, and multiple imputation to create large population-based injury databases matched to outcomes.
This was a retrospective cohort study of injured children and adults transported by 94 emergency medical systems (EMS) agencies to 122 hospitals in seven regions of the western United States over a 36-month period (2006 to 2008). All injured patients evaluated by EMS personnel within specific geographic catchment areas were included, regardless of field disposition or outcome. The authors performed probabilistic linkage of EMS records to four hospital and postdischarge data sources (emergency department [ED] data, patient discharge data, trauma registries, and vital statistics files) and then handled missing values using multiple imputation. The authors compare and evaluate matched records, match rates (proportion of matches among eligible patients), and injury outcomes within and across sites.
There were 381,719 injured patients evaluated by EMS personnel in the seven regions. Among transported patients, match rates ranged from 14.9% to 87.5% and were directly affected by the availability of hospital data sources and proportion of missing values for key linkage variables. For vital statistics records (1-year mortality), estimated match rates ranged from 88.0% to 98.7%. Use of multiple imputation (compared to complete case analysis) reduced bias for injury outcomes, although sample size, percentage missing, type of variable, and combined-site versus single-site imputation models all affected the resulting estimates and variance.
This project demonstrates the feasibility and describes the process of constructing population-based injury databases across multiple phases of care using existing data sources and commonly available analytic methods. Attention to key linkage variables and decisions for handling missing values can be used to increase match rates between data sources, minimize bias, and preserve sampling design.
评估使用现有数据源、概率链接和多重插补来创建与结果匹配的大型基于人群的损伤数据库的过程。
这是一项回顾性队列研究，涉及在美国西部七个地区的 122 家医院接受由 94 个紧急医疗服务 (EMS) 机构转运的受伤儿童和成人。所有在特定地理区域内接受 EMS 人员评估的受伤患者均被纳入研究，无论其在现场的处置方式或结局如何。作者对 EMS 记录进行了概率链接，链接到四个医院和出院后数据来源（急诊室 [ED] 数据、患者出院数据、创伤登记处和生命统计文件），然后使用多重插补处理缺失值。作者比较和评估了匹配记录、匹配率（合格患者中的匹配比例）以及各站点和跨站点的损伤结局。
在七个地区，有 381719 名受伤患者接受了 EMS 人员的评估。在转运患者中，匹配率范围为 14.9%至 87.5%，直接受到医院数据源的可用性和关键链接变量缺失值的比例的影响。对于生命统计记录（1 年死亡率），估计的匹配率范围为 88.0%至 98.7%。与完全病例分析相比，使用多重插补（multiple imputation）可减少损伤结局的偏差，尽管样本量、缺失百分比、变量类型以及组合站点与单个站点插补模型都影响了最终估计值和方差。
该项目展示了使用现有数据源和常用分析方法在多个护理阶段构建基于人群的损伤数据库的可行性，并描述了该过程。对关键链接变量的关注以及处理缺失值的决策可以用来提高数据源之间的匹配率，减少偏差，并保留抽样设计。
