美国高峰与非高峰时段道路环境特征与致命碰撞伤害:模型测试与聚类分析

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Spatial and Spatio-Temporal Epidemiology Pub Date : 2023-02-01 DOI:10.1016/j.sste.2022.100562
Oluwaseun Adeyemi , Rajib Paul , Eric Delmelle , Charles DiMaggio , Ahmed Arif
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引用次数: 2

摘要

本研究旨在评估县级致命碰撞伤害与道路环境特征之间的关系,在一天中的所有时间,在高峰和非高峰期间。我们合并了来自病死率分析报告系统的11年(2010 - 2020年)数据。结果变量是县级致命车祸伤害计数。预测变量包括道路类型、路口类型和工作区域以及天气类型。通过对两种嵌套负二项回归模型的预测能力进行检验,得出嵌套空间负二项回归模型优于非空间负二项回归模型。该县一天中所有时间以及高峰和非高峰期间的车祸死亡率中位数分别为每10万人18.4人、7.7人和10.4人。州际公路和高速公路上的致命碰撞伤害率在一天中的所有时间都显著升高,包括高峰和非高峰时段。高速公路上的十字路口、车道和坡道与较高的致命碰撞伤害率有关。在蒙大拿州、内华达州、科罗拉多州、堪萨斯州、新墨西哥州、俄克拉何马州、德克萨斯州、阿肯色州、密西西比州、阿拉巴马州、佐治亚州和内华达州的县都观察到了高致命碰撞伤害率。交通高峰期和非交通高峰期县域交通事故致死性损伤与道路环境因素有关。了解道路环境特征与致命碰撞伤害集群分布之间的关系,可以为需要重点干预的地区提供信息。
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Road environment characteristics and fatal crash injury during the rush and non-rush hour periods in the U.S: Model testing and cluster analysis

This study aims to assess the relationship between county-level fatal crash injuries and road environmental characteristics at all times of the day and during the rush and non-rush hour periods. We merged eleven-year (2010 - 2020) data from the Fatality Analysis Reporting System. The outcome variable was the county-level fatal crash injury counts. The predictor variables were measures of road types, junction types and work zone, and weather types. We tested the predictiveness of two nested negative binomial models and adjudged that a nested spatial negative binomial regression model outperformed the non-spatial negative binomial model. The median county crash mortality rates at all times of the day and during the rush and non-rush hour periods were 18.4, 7.7, and 10.4 per 100,000 population, respectively. Fatal crash injury rate ratios were significantly elevated on interstates and highways at all times of the day – rush and non-rush hour periods inclusive. Intersections, driveways, and ramps on highways were associated with elevated fatal crash injury rate ratios. Clusters of high fatal crash injury rates were observed in counties located in Montana, Nevada, Colorado, Kansas, New Mexico, Oklahoma, Texas, Arkansas, Mississippi, Alabama, Georgia, and Nevada. The built and natural road environment factors are associated with county-level fatal crash injuries during the rush and non-rush hour periods. Understanding the association of road environment characteristics and the cluster distribution of fatal crash injuries may inform areas in need of focused intervention.

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来源期刊
Spatial and Spatio-Temporal Epidemiology
Spatial and Spatio-Temporal Epidemiology PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
5.10
自引率
8.80%
发文量
63
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