Improving identification of crash injuries: Statewide integration of hospital discharge and crash report data

Abstract

Objective

The availability of complete and accurate crash injury data is critical to prevention and intervention efforts. Relying solely on hospital discharge data or police crash reports may result in a biased undercount of injuries. Linking hospital data with crash reports may allow for a more robust identification of injuries and an understanding of which populations may be missed in an analysis of one source. We used the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse to examine the share of the entire crash-injured population identified in each of the two data sources, overall and by age, race/ethnicity, sex, injury severity, and road user type.

Methods

We utilized 2016-2017 data from the NJ-SHO warehouse. We identified crash-involved individuals in hospital discharge data by applying the ICD-10-CM external cause of injury matrix. Among crash-involved individuals, we identified those with injury- or pain-related diagnosis codes as being injured. We also identified crash-involved individuals via crash report data and identified injuries using the KABCO scale. We jointly examined the two sources; injuries in the hospital discharge data were documented as being related to the same crash as injuries found in the crash report data if the date of the crash report preceded the date of hospital admission by no more than two days.

Results

In total, there were 262,338 crash-involved individuals with a documented injury in the hospital discharge data or on the crash report during the study period; 168,874 had an injury according to hospital discharge data, and 164,158 had an injury in crash report data. Only 70,694 (26.9%) had an injury in both sources. We observed differences by age, race/ethnicity, injury severity, and road user type: hospital discharge data captured a larger share of those ages 65+, those who were Black or Hispanic, those with higher severity injuries, and those who were bicyclists or motorcyclists.

Conclusions

Each data source in isolation captures approximately two-thirds of the entire crash-injured population; one source alone misses approximately one-third of injured individuals. Each source undercounts people in certain groups, so relying on one source alone may not allow for tailored prevention and intervention efforts.

Keywords:

• Motor vehicle crashes
• data integration
• injuries
• International Classification of Diseases
• hospital discharge data
• police crash reports

Acknowledgments

We are grateful to Mark R. Zonfrillo, MD, MSCE for generating the AIS mapping matrix that we used to classify injuries in hospital discharge data, as well as providing guidance on implementing the matrix. We would like to thank Emma Sartin, PhD, MPH for her assistance with manuscript revisions. We also would like to thank Adrian Diogo, MPH, Elizabeth Borkowski, BA, and Nicole Caputo for their contributions to this project.

Data availability statement

The data that support the findings of this study are available from the NJ Motor Vehicle Commission, NJ Department of Transportation, and NJ Department of Health. Restrictions apply to the availability of these data, which were used under a Memorandum of Agreement and a Data Use Agreement for this study. Data may be available from the authors with the permission of these NJ governmental agencies, a Collaborative Research Agreement with the authors, and with certain restrictions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (R21HD092850 and R21HD098276; Principal Investigator: Allison E. Curry). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The sponsor had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.