Department of Emergency Medicine, University of California, Irvine School of Medicine, Orange, California, USA.
Traffic Inj Prev. 2010 Oct;11(5):508-13. doi: 10.1080/15389588.2010.497546.
The goal of this study is to explore the relationship between pedestrian injuries and socioeconomic characteristics.
Pedestrian collisions were identified in the data of the California Statewide Integrated Traffic Records System (SWITRS), which is assembled from police crash reports by the California Highway Patrol Information Services Unit. Four thousand crashes were identified and geocoded within the census tracts in a county population of 2,846,289 over a 5-year period. Population and population characteristics for census tracts were obtained from the 2000 U.S. Census.
The percentage of the population living in households with low income (less than 185% of the federal poverty level) was the strongest predictor of pedestrian injuries. One fourth of census tracts had less than 8.7 percent of residents with low income and averaged 11 per 100,000 pedestrian crashes annually. One fourth of the census tracts had more than 32.2 percent of residents with low income and an average of 44 pedestrian crashes per 100,000 annually. Negative binomial regression showed that with each 1 percent increase in the percentage of residents with low income was associated with a 2.8 percent increase in pedestrian crashes. The percentage of residents age 14 years or less, adult residents who had not completed high school, residents who spoke English less than "very well" and spoke another language at home, and the population density were each associated with a higher frequency of pedestrian crashes. However, when low income was added to these 4 regression models, the relationship between low income and pedestrian crashes increased.
Our study showed that pedestrian crashes are 4 times more frequent in poor neighborhoods and that neither age of the population, education, English language fluency, nor population density explained the effect of poverty.
本研究旨在探讨行人伤害与社会经济特征之间的关系。
行人碰撞事故是从加利福尼亚州公路巡逻队信息服务部从警察事故报告中汇编的加利福尼亚州全州综合交通记录系统（SWITRS）的数据中确定的。在 5 年期间，在一个拥有 2846289 人口的县的普查区内确定并地理编码了 4000 起事故。普查区的人口和人口特征是从 2000 年美国人口普查中获得的。
生活在低收入家庭（低于联邦贫困线的 185%）的人口比例是行人受伤的最强预测因素。四分之一的普查区的居民中低收入者不到 8.7%，平均每年每 10 万行人中发生 11 起事故。四分之一的普查区有超过 32.2%的居民收入低，每年每 10 万行人中平均发生 44 起事故。负二项回归表明，居民中低收入者的比例每增加 1%，行人事故就会增加 2.8%。年龄在 14 岁或以下的居民比例、未完成高中学业的成年居民比例、英语说得不太好的居民比例以及在家说另一种语言的居民比例以及人口密度都与行人事故的发生频率较高相关。然而，当将低收入纳入这 4 个回归模型时，低收入与行人事故之间的关系增强了。
我们的研究表明，在贫困社区，行人事故的发生率是其他地区的 4 倍，而人口的年龄、教育程度、英语流利程度或人口密度都无法解释贫困的影响。
