Momenyan Somayeh, Chan Herbert, Jae Lina, Taylor John A, Staples John A, Harris Devin R, Brubacher Jeffrey R
Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Centre for Clinical Epidemiology & Evaluation, Vancouver, British Columbia, Canada.
Clinicoecon Outcomes Res. 2025 Sep 10;17:639-652. doi: 10.2147/CEOR.S533069. eCollection 2025.
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.
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.
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.
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.
本研究旨在确定道路交通（RT）伤害成本的主要决定因素，对其重要性进行排序，并评估它们对成本分布不同分位数的影响。
本研究分析了2018年7月至2020年3月期间从1372名加拿大RT幸存者收集的数据。以2023年加拿大元为单位，估算了每位参与者RT受伤后一年的成本，包括医疗保健和生产力损失成本。生产力损失使用医学技术评估研究所生产力成本问卷进行衡量。我们考虑了24个潜在的成本决定因素，这些因素被分为五个领域：社会人口统计学、心理、健康、碰撞和在基线访谈中评估的伤害因素。我们采用分位数回归森林机器学习方法以及经典分位数回归来分析成本。选择这些方法是为了捕捉成本分布中的异质性影响，而传统的基于均值的模型忽略了这些影响，并为针对高成本亚组的政策决策提供信息。
结果表明，成本的第10、50和90分位数分别为1141.9美元、7403.1美元和49537.5美元。在低成本、中等成本和高成本患者中，简明损伤定级（ISS）、格拉斯哥昏迷评分（GCS）和年龄是前三大影响变量。ISS、GCS、年龄、性别、就业状况和生活状况是所有分位数上常见的主要决定因素。种族在第50和90分位数被选为重要决定因素。教育水平、在加拿大居住的年限、躯体症状严重程度、心理困扰、健康相关生活质量（HRQoL）、道路使用者类型以及头部、躯干、脊柱/背部和下肢损伤仅在高成本患者（第90分位数）中被选中。经典分位数回归表明，选定的主要预测因素对低成本、中等成本和高成本患者的影响不成比例。
高成本患者更有可能年龄较大、已退休、受教育程度较低，并且临床和心理指标较差。这些见解可以指导有针对性的预防和资源分配策略，以减轻RT伤害的经济负担。
