Adaptation of a Canadian culpability scoring tool to Alberta police traffic collision report data

• Published in: Traffic injury prevention 2019-03, Vol.20 (3), p.270-275
• Main Authors: Pitt, Tona M., Aucoin, Janet, Nettel-Aguirre, Alberto, McCormack, Gavin R., Howard, Andrew W., Graff, Paul, Rowe, Brian H., Hagel, Brent E.
• Format: Article
• Language: English
• Subjects: Adaptation; Alcohols; Algorithms; Canadian Culpability Scoring Tool; Collisions; Confidence intervals; Consultation; Culpability; Driving ability; motor vehicle collisions; Motor vehicles; not-at-fault drivers; Police; police collision reports; Police reports; Population (statistical); Regression analysis; responsibility analysis; Risk analysis; Risk factors; Sensitivity analysis; Statistical analysis; Statistical significance; Statistics; Traffic accidents & safety; Transportation
• ISSN: 1538-9588; 1538-957X
• DOI: 10.1080/15389588.2019.1567916

Objective: The objective of this study was to adapt a previously validated Canadian Culpability Scoring Tool (CCST) to Alberta police report data. Methods: Police traffic collision reports from motor vehicle (MV) collisions in Calgary and Edmonton (Alberta, Canada) from 2010 to 2014 were used. Adaptation of the CCST was completed with input from personnel within Alberta Transportation, contributing to face and content validity. Two research assistants, given only the information necessary for scoring, evaluated 175 randomly selected MV-MV collisions. Interrater agreement was estimated using kappa (k) and reported with 95% confidence intervals (CIs). Discussion of disagreements between the research assistants and consultation from Alberta Transportation informed the algorithm used in the Alberta Motor Vehicle Collision Culpability Tool (AMVCCT). The AMVCCT was automated and applied to all motorists involved in collisions. Binary logistic regression was used to examine characteristics of the culpable and nonculpable drivers and their effects were reported using odds ratios (ORs) with 95% CIs. Results: Interrater agreement for the random sample was excellent (k = 0.95; 95% CI, 0.92-0.99). Of those drivers hospitalized, 1,130 (37.54%) were rated not culpable and 1,880 (62.46%) were rated culpable. The odds of being culpable were higher for males than for females (OR = 1.43; 95% CI, 1.23-1.66). The odds of being culpable were higher in those impaired by alcohol than those considered "apparently normal" (OR = 61.10; 95% CI, 22.66-164.75). The odds of being deemed culpable, when compared with drivers >54 years old, were higher for those