School of Environmental Sciences, University of East Anglia, Norwich, Norfolk NR4 7TJ, United Kingdom.
Accid Anal Prev. 2011 Jan;43(1):421-8. doi: 10.1016/j.aap.2010.09.012. Epub 2010 Oct 25.
The annual road traffic fatality (RTF) burden of 43 deaths per 100000 inhabitants in South Africa (SA) is disproportionately high in comparison to the world average of 22 per 100000 population. Recent research revealed strong geographical variations across district councils in the country, as well as a substantial peak in mortality occurring during December. In this study, the factors that explain temporal variations in RTFs in SA are examined. Using weekly data from the period 2002-2006 for the country's nine provinces, non-linear auto-regression exogenous (NARX) regression models were fitted to explain variations in RTFs and to assess the degree to which the variations between the provinces were associated with the temporal variations in risk factors. Results suggest that a proportion of the variations in weekly RTFs could be explained by factors other than the size of the province population, with both temporal and between-province residual variance remaining after accounting for the modelled risks. Policies directed at reducing the effects of the modifiable risks identified in our study will be important in reducing RTFs in SA.
南非每 10 万人中有 43 人死于道路交通,其年度道路交通死亡率(RTF)与世界平均每 10 万人 22 人的水平相比不成比例地高。最近的研究表明,该国各地区议会之间存在明显的地理差异,以及在 12 月死亡率大幅上升的情况。在这项研究中,我们检查了导致南非 RTF 时间变化的因素。使用该国九个省份 2002-2006 年期间的每周数据,我们拟合了非线性自回归外生(NARX)回归模型,以解释 RTF 的变化,并评估各省之间的变化与风险因素的时间变化有多大关联。结果表明,每周 RTF 的变化可以用除了省份人口规模之外的因素来解释,在考虑了模型化风险后,仍存在时间和省际剩余方差。在我们的研究中确定的可修改风险的影响,政策的目标是减少道路交通死亡率(RTF)在南非。