Linking Police and EMS Records: An Approach to Strengthen Bicyclist Injury Reporting

Abstract

Bicyclist injuries are underreported in police crash databases. We explored the value of linking police-reported crash data with emergency medical services (EMS) data within the City of Milwaukee, WI. Using data from 2014 to 2016, we linked records by inspection (identical date, similar time of day, and similar roadway location) and found 154 matching records between the two databases (representing 41% of the 375 police crash records and 44% of the 348 EMS injury records). Matched records were more likely to involve fatal and severe injuries according to police-assessed injury ratings. The two datasets also provided different insights into bicyclist injury crashes. Injuries captured only by police reports were significantly more likely than injuries captured only by EMS to be along high-traffic streets and commercial districts and significantly less likely to be near parks. Nearly all police records described the driver and bicyclist movements (e.g., turning) and operating behaviors (e.g., failure to yield) that had contributed to the crash. In addition to capturing more bicyclist injury events, EMS records revealed additional information about their causes. Twenty-three percent of EMS narratives described falls. These falls involved intoxication, striking a curb, swerving to avoid automobiles or other bicyclists, doing tricks, and getting a tire caught in rail tracks. Another 11% described bicyclists striking objects, including a bus stop shelter, stop sign, fire hydrant, and fence. Although there are strengths and limitations to both datasets, linking police-reported crashes with EMS records produced a broader understanding of bicyclist injuries.

EMS injury cause codes more accurate than emergency department visit ICD-10-CM codes for firearm injury intent in North Carolina

“…Next, we calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the manually reviewed dataset to the injury cause codes and for the manually reviewed dataset to the ED visit data. We specifically chose to examine EMS data because precedence [7][8][9][10][11] has shown that EMS narratives can provide rich contextual details on the entire EMS encounter; therefore, the EMS data should provide insights into firearm injury intent.…”