Jones A P, Haynes R, Kennedy V, Harvey I M, Jewell T, Lea D
School of Environmental Sciences, University of East Anglia, Norwich, Norfolk NR4 7TJ, UK.
Health Place. 2008 Sep;14(3):519-35. doi: 10.1016/j.healthplace.2007.10.001. Epub 2007 Oct 10.
Data on road traffic fatalities, serious casualties and slight casualties in each local authority district England and Wales were obtained for 1995-2000. District-level data were assembled for a large number of potential explanatory variables relating to population numbers and characteristics, traffic exposure, road length, curvature and junction density, land use, elevation and hilliness, and climate. Multilevel negative binomial regression models were used to identify combinations of risk factors that predicted variations in mortality and morbidity. Statistically significant explanatory variables were the expected number of casualties derived from the size and age structure of the resident population, road length and traffic counts in the district, the percentage of roads classed as minor, average cars per capita, material deprivation, the percentage of roads through urban areas and the average curvature of roads. This study demonstrates that a geographical approach to road traffic crash analysis can identify contextual associations that conventional studies of individual road sections would miss.
获取了1995 - 2000年英格兰和威尔士各地方当局辖区内道路交通死亡、重伤和轻伤的数据。收集了地区层面大量与人口数量和特征、交通暴露、道路长度、曲率和路口密度、土地利用、海拔和坡度以及气候相关的潜在解释变量的数据。使用多层负二项回归模型来识别预测死亡率和发病率变化的风险因素组合。具有统计学意义的解释变量包括常住人口规模和年龄结构、地区道路长度和交通流量、被归类为次要道路的百分比、人均汽车数量、物质匮乏程度、穿过城市地区的道路百分比以及道路平均曲率所衍生的预期伤亡人数。这项研究表明,道路交通碰撞分析的地理方法能够识别传统的单个路段研究可能遗漏的背景关联。