Despite the relevance of road crashes and their impact on social and health care costs, the effects of extreme temperatures on road crashes risk have been scarcely investigated, particularly for those occurring in occupational activities. A nationwide epidemiological study was carried out to estimate the risk of general indistinct and work-related road crashes related with extreme temperatures and to identify crash and occupation parameters mostly involved. Data about road crashes, resulting in death or injury, occurring during years 2013–2015 in Italy, were collected from the National Institute of Statistics, for general indistinct road crashes, and from the compensation claim applications registered by the national workers' compensation authority, for work-related ones. Time series of hourly temperature were derived from the results provided by the meteorological model WRF applied at a national domain with 5 km resolution. To consider the different spatial-temporal characteristics of the two road crashes archives, the association with extreme temperatures was estimated by means of a case-crossover time-stratified approach using conditional logistic regression analysis, and a time-series analysis, using over-dispersed Poisson generalized linear regression model, for general indistinct and work-related datasets respectively. The analyses were controlled for other covariates and confounding variables (including precipitation). Non-linearity and lag effects were considered by using a distributed lag non-linear model. Relative risks were calculated for increment from 75th to 99th percentiles (hot) and from 25 to first percentile (cold) of temperature. Results for general indistinct crashes show a positive association with hot temperature (RR = 1.12, 95 % CI: 1.09–1.16) and a negative one for cold (RR = 0.93, 95 % CI: 0.91−0.96), while for work-related crashes a positive association was found for both hot and cold (RR = 1.06 (95 % CI: 1.01–1.11) and RR = 1.10 (95 % CI: 1.05–1.16). The use of motorcycles, the location of accident (urban vs out of town), presence of crossroads, as well as occupational factors like the use of a vehicle on duty were all found to produce higher risks of road crashes during extreme temperatures. Mitigation and prevention measures are needed to limit social and health care costs.

尽管道路交通事故具有相关性及其对社会和卫生保健成本的影响，但极少对极端温度对道路交通事故风险的影响进行了调查，特别是对于那些从事职业活动的人。进行了一项全国性的流行病学研究，以估计与极端温度相关的一般性模糊和与工作相关的道路交通事故的风险，并确定主要涉及的交通事故和职业参数。从国家统计局收集了关于2013-2015年间意大利发生的交通事故导致死亡或伤害的数据，以处理一般性的不清楚的道路交通事故，并收集了国家工人赔偿当局注册的赔偿要求申请，与工作有关的。每小时温度的时间序列是从在5 km分辨率的国家范围内应用的气象模型WRF提供的结果得出的。为了考虑两个道路事故档案的不同时空特征，使用条件逻辑回归分析和时间序列分析（采用过度分散），通过事例交叉时间分层方法，估计了与极端温度的关联。泊松广义线性回归模型，分别用于一般的模糊数据集和与工作有关的数据集。控制其他协变量和混杂变量（包括降水）的分析。通过使用分布式滞后非线性模型来考虑非线性和滞后效应。计算了从温度的第75个百分点增加到第99个百分点（冷）和从温度的第25个百分点增加到第一个百分数（冷）的相对风险。一般性模糊事故的结果显示与高温呈正相关（RR = 1.12，95％CI：1.09-1.16），与寒冷呈负相关（RR = 0.93，95％CI：0.91-0.96），而与工作相关撞车时，冷热都呈正相关（RR = 1.06（95％CI：1.01-1.11）和RR = 1.10（95％CI：1.05-1.16）。人们发现，在极端温度下，十字路口的存在以及诸如乘车执勤等职业因素都可能增加道路交通事故的风险，因此需要采取缓解措施和预防措施来限制社会和医疗保健费用。