Cyclist crash rates and risk factors in a prospective cohort in seven European cities
Cyclist crash rates and risk factors in a prospective cohort in seven European cities
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DOI：
10.1016/j.aap.2020.105540
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发表时间：
2020-06-01
影响因子：
5.9
通讯作者：
Winters, Meghan
中科院分区：
文献类型：
--
作者：
Branion-Calles, Michael;Gotschi, Thomas;Winters, Meghan
增加骑自行车可以改善人口健康，但障碍包括真实的和感知的风险。为了提高安全性和增加骑自行车的人数，了解撞车风险因素很重要。许多自行车碰撞风险的研究都是基于结合不同来源的碰撞和暴露数据，如警方数据库（碰撞）和旅行调查（暴露），基于共享的地理和时间。当合并来自不同来源的碰撞和暴露数据时，可以量化的风险因素仅是两个数据集共有的变量，这些变量往往限于地理（例如国家，省，市）和一些一般道路使用者特征（例如性别和年龄层）。通过可持续交通方法开展身体活动（PASTA）项目是一项前瞻性队列研究，收集了来自七个欧洲城市（安特卫普、巴塞罗那、伦敦、厄勒布鲁、罗马、维也纳和苏黎世）的碰撞和暴露数据。本研究的目的是使用PASTA项目的数据来量化保险调整的碰撞率和模型调整的碰撞风险因素，包括详细的社会人口特征，对交通的态度，社区建筑环境特征和城市位置。我们使用负二项回归来模拟独立于暴露的风险因素的影响。在4,180名骑自行车的人中，10. 2%的人报告了535起撞车事故。我们发现，伦敦的总体撞车率是撞车率最高的城市的6.7倍，而奥雷布罗是撞车率最低的城市。城市之间总体车祸率的差异主要是由不需要医疗的车祸和涉及机动车的车祸造成的。在一个简约的碰撞风险模型中，我们发现，对于不太频繁的骑自行车者，男性，那些认为骑自行车在他们的社区中不受重视的人，以及那些居住在建筑密度非常高的地区的人，碰撞风险更高。纵向收集碰撞和暴露数据可以为碰撞风险的个体差异提供重要的见解。城市、社区和人口群体之间的碰撞风险存在巨大差异，这表明改善自行车安全的潜力很大。
Increased cycling uptake can improve population health, but barriers include real and perceived risks. Crash risk factors are important to understand in order to improve safety and increase cycling uptake. Many studies of cycling crash risk are based on combining diverse sources of crash and exposure data, such as police databases (crashes) and travel surveys (exposure), based on shared geography and time. When conflating crash and exposure data from different sources, the risk factors that can be quantified are only those variables common to both datasets, which tend to be limited to geography (e.g. countries, provinces, municipalities) and a few general road user characteristics (e.g. gender and age strata). The Physical Activity through Sustainable Transport Approaches (PASTA) project was a prospective cohort study that collected both crash and exposure data from seven European cities (Antwerp, Barcelona, London, Orebro, Rome, Vienna and Zurich). The goal of this research was to use data from the PASTA project to quantify exposure-adjusted crash rates and model adjusted crash risk factors, including detailed sociodemographic characteristics, attitudes about transportation, neighbourhood built environment features and location by city. We used negative binomial regression to model the influence of risk factors independent of exposure. Of the 4,180 cyclists, 10.2 % reported 535 crashes. We found that overall crash rates were 6.7 times higher in London, the city with the highest crash rate, relative to Orebro, the city with the lowest rate. Differences in overall crash rates between cities are driven largely by crashes that did not require medical treatment and that involved motor-vehicles. In a parsimonious crash risk model, we found higher crash risks for less frequent cyclists, men, those who perceive cycling to not be well regarded in their neighbourhood, and those who live in areas of very high building density. Longitudinal collection of crash and exposure data can provide important insights into individual differences in crash risk. Substantial differences in crash risks between cities, neighbourhoods and population groups suggest there is great potential for improvement in cycling safety.