Determinants of Economic Costs Following Road Traffic Injuries in Canada: A Quantile Regression Forests Machine Learning Approach.

INTRODUCTION

This study aimed to identify major determinants of the cost of road traffic (RT) injuries, rank their importance, and assess their effects on different quantiles of cost distribution.

METHODS

This study analyzed data collected from 1372 Canadian RT survivors from July 2018 to March 2020. Costs, including healthcare and lost productivity costs over a year following RT injury, were estimated for each participant in 2023 Canadian dollars. Productivity loss was measured using the Institute for Medical Technology Assessment Productivity Cost Questionnaire. We considered 24 potential determinants of costs, which were grouped into five domains: sociodemographic, psychological, health, crash, and injury factors assessed during baseline interview. We employed a quantile regression forests machine learning approach alongside classical quantile regression to analyze costs. These methods were selected to capture heterogeneous effects across cost distribution, which are overlooked by traditional mean-based models, and to inform policy decisions targeting high-cost subgroup.

RESULTS

The results showed that the 10th, 50th, and 90th quantiles of costs were $1,141.9, $7,403.1, and $49,537.5, respectively. ISS, GCS, and age were the top three influential variables among low-cost, medium-cost, and high-cost patients. ISS, GCS, age, sex, employment status, and living situation were common major determinants at all quantiles. Ethnicity was selected as an important determinant at the 50th and 90th quantiles. Education level, years lived in Canada, somatic symptoms severity, psychological distress, HRQoL, road user type, and head, torso, spine/back, and lower extremity injuries were selected only for high-cost patients (90th quantile). Classical quantile regression showed that selected major predictors disproportionately affected low-cost, middle-cost and high-cost patients.

CONCLUSION

High-cost patients were more likely to be older, retired, less educated, and have worse clinical and psychological indicators. These insights can guide targeted prevention and resource allocation strategies to reduce the economic burden of RT injuries.