加拿大道路交通伤害后的经济成本的决定因素:量子回归森林机器学习方法
Somayeh Momenyan1, Herbert Chan1,2, Lina Jae1
1Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
ClinicoEconomics and outcomes research : CEOR
|September 16, 2025
概括
道路交通 (RT) 伤害成本差异很大. 伤害严重性评分 (ISS),格拉斯哥昏迷量表 (GCS) 和年龄是关键的成本驱动因素,特别是对于高成本的幸存者. 有针对性的干预措施可以减少经济负担.
科学领域:
- 卫生经济学 卫生经济学
- 伤害预防 预防伤害
- 医疗保健中的机器学习
背景情况:
- 道路交通 (RT) 伤害对全球的健康和经济造成重大负担.
- 了解损害成本的决定因素对于有效的资源配置和政策制定至关重要.
- 传统的成本分析模型往往无法捕捉幸存者之间的成本异质性.
研究的目的:
- 识别和排名道路交通 (RT) 伤害成本的主要决定因素.
- 评估这些决定因素对不同成本量度 (低,中,高) 的影响.
- 为高成本的RT伤害幸存者提供有针对性的干预信息.
主要方法:
- 来自1372名加拿大RT幸存者的数据分析 (2018-2020).
- 估计伤害后一年的医疗保健和生产力损失成本.
- 应用量子回归森林和经典量子回归来分析24个潜在成本决定因素.
- 将决定因素分为社会人口统计学,心理,健康,撞车和受伤因素.
主要成果:
- 第10,第50和第90个成本量分别为1,141.9美元,7,403.1美元和49,537.5美元.
- 伤害严重程度评分 (ISS),格拉斯哥昏迷量表 (GCS) 和年龄是成本水平的首要决定因素.
- 共同的决定因素包括性别,就业和生活状况;种族对成本较高的量有意义.
- 教育,心理困扰和特定伤害类型等因素对于高成本患者来说尤为重要.
结论:
- 高成本的RT伤害幸存者往往是年长的,退休的,受教育程度较低的,临床和心理指标较差.
- 量子回归方法揭示了预测因素在成本分布上的不成比例影响.
- 研究结果支持有针对性的预防和资源分配战略,以减轻RT伤害对经济的影响.
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