Data linkage for crash outcome assessment: Linking police-reported crashes, emergency response data, and trauma registry records

Aryan Hosseinzadeh, Abolfazl Karimpour, Robert Kluger, Raymond Orthober

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Introduction: Traffic crash reports lack detailed information about emergency medical service (EMS) responses, the injuries, and the associated treatments, limiting the ability of safety analysts to account for that information. Integrating data from other sources can enable a better understanding of characteristics of serious crashes and further explain variance in injury outcomes. In this research, an approach is proposed and implemented to link crash data to EMS run data, patient care reports, and trauma registry data. Method: A heuristic framework is developed to match EMS run reports to crashes through time, location, and other indicators present in both datasets. Types of matches between EMS and crashes were classified. To investigate the fidelity of the match approach, a manual review of a sample of data was conducted. A comparative bias analysis was implemented on several key variables. Results: 72.2% of EMS run reports matched to a crash record and 69.3% of trauma registry records matched with a crash record. Females, individuals between 11 and 20 years old, and individuals involved in single vehicle or head on crashes were more likely to be present in linked data sets. Using the linked data sets, relationships between EMS response time and reported injury in the crash report, and between police-reported injury and injury severity score were examined. Conclusion: Linking data from other sources can greatly enhance the information available to address road safety issues, data quality issues, and more. Linking data has the potential to result in biases that must be investigated as they relate to the use-case for the data. Practical implications: This research resulted in a transferable heuristic approach that can be used to link data sets that are commonly collected by agencies across the world. It also provides guidance on how to check the linked data for biases and errors.

Original languageEnglish (US)
Pages (from-to)21-35
Number of pages15
JournalJournal of Safety Research
StatePublished - Jun 2022


  • Crash outcomes
  • Data linkage
  • Emergency medical services
  • Linkage bias
  • Selectivity bias
  • Trauma registry

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality


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