Underreporting and selection bias of serious road traffic injuries in auto insurance claims and police reports in British Columbia, Canada

Administrative datasets (police reports, insurance claims, medical records), form the basis for road safety research, but suffer from under-reporting and selection bias. Data linkage can provide a fuller picture of road traffic injuries and provide insight into dataset-specific biases. We examined the overlap of serious road traffic injuries involving motor vehicles reported in hospitalization records, police reports, and insurance claims in British Columbia, Canada (2015 – 2019) and assess selection bias within each injury dataset. We probabilistically linked police reports, insurance claims, and hospital admissions to a provincial population directory, identifying distinct persons and injuries across datasets. Injuries were linked to sociodemographic and geographic details from other government data including age, sex, low-income status, neighbourhood income and health authority. We analyzed serious injuries to drivers, cyclists and pedestrians. We assessed the proportion of injuries captured by a database (ascertainment rate) and assessed selection bias based on which sociodemographic groups were more likely to only be captured in hospital admissions. From 2015 to 2019, we estimated 57,097 motor vehicle-involved injuries (48,198 motor vehicle drivers, 2,641 cyclists, 6,258 pedestrians). Insurance claims had the highest ascertainment rate for drivers (95.7%), but lower for cyclists (83.3%) and pedestrians (76.5%). Police records and hospital admissions better captured cyclist and pedestrian injuries compared to driver injuries. Unlinked hospital admission injuries were more likely from low-income and remote populations. The underreporting highlights the need for improved injury data collection especially for pedestrian and cyclists, to better capture the full injury burden, particularly among marginalized sociodemographic groups.