Evaluating the Use of Existing Data Sources, Probabilistic Linkage, and Multiple Imputation to Build Population-based Injury Databases Across Phases of Trauma Care

Injury continues to be a major cause of death and disability, particularly among the young.1,2 While the development of trauma centers, trauma systems, injury prevention programs, and public policy have resulted in many strides toward reducing the burden of injury, much work remains. Integral to understanding and further reducing the burden of injury is the ability to measure how injury relates to meaningful health outcomes across broad populations and different phases of care. Trauma registries have traditionally provided the bulk of available injury data. However, trauma registries preferentially target more seriously injured patients treated at trauma centers, are not population-based, and are typically limited to in-hospital outcomes. There is a growing need for broad population-based injury data that effectively span multiple phases of care (out-of-hospital, in-hospital, and postdischarge); include patients with minor and severe injuries; and are not limited by cost, resource, and confidentiality constraints. Such population-based data may be used for trauma quality assurance, improving the effectiveness of field triage protocols and early treatment interventions, and evaluating the outcomes of injured patients not treated at trauma centers and falling outside of traditional quality assurance data sources (e.g., trauma registries). The increasing availability of electronic data combined with certain analytic methods (probabilistic linkage3,4 and multiple imputation5) provide an opportunity to create such unique data resources. Probabilistic linkage is a method for matching disparate datasets when a unique identifier is not available and has been used to match emergency medical systems (EMS) records to hospital outcomes6,7 and validated among injured patients.8 Due to match rates (the proportion of matches among eligible patients) typically less than 100%, and the substantive portion of missing values inherent in EMS and trauma data sources, missing data have been another obstacle in developing population-based injury databases. Handling missing values inappropriately can generate bias, reduce sample size, and lessen study power.9–14 Multiple imputation can effectively mitigate these limitations, provided that certain assumptions are met. We are unaware of any studies evaluating the combined use of probabilistic linkage and multiple imputation in the construction of large population-based databases. While the use and integration of electronic health information is being actively promoted in the United States,15 there is a need for additional literature detailing the methods to effectively link such records across multiple phases of care between different agencies and institutions and appropriately handle missing values. In this article, we describe the methods and evaluate the use of existing data files, probabilistic linkage, and multiple imputation in constructing large population-based injury databases matched to outcomes under a variety of conditions across seven regions in the western United States.