OBJECTIVES: The goal of this study is to explore the relationship between pedestrian injuries and socioeconomic characteristics. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
OBJECTIVES: The goal of this study is to explore the relationship between pedestrian injuries and socioeconomic characteristics. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Craig L Anderson; Kathlynn M Dominguez; Teresa V Hoang; Armaan Ahmed Rowther; M Christy Carroll; Shahram Lotfipour; Wirachin Hoonpongsimanont; Bharath Chakravarthy Journal: Ann Adv Automot Med Date: 2012
Authors: Carolyn DiGuiseppi; Susan E Levy; Katherine R Sabourin; Gnakub N Soke; Steven Rosenberg; Li-Ching Lee; Eric Moody; Laura A Schieve Journal: J Autism Dev Disord Date: 2018-02