A Geographic Analysis of Collisions Involving Child Pedestrians in a Large Southern California County

Abstract

Objectives: The goal of this study is to explore the relationship between child pedestrian injuries and socioeconomic characteristics of neighborhoods in the context of local traffic volume. Methods: Child pedestrian collisions were identified in the data of the California Statewide Integrated Traffic Records System (SWITRS). Nine hundred sixty crashes over a 5-year period were identified, geocoded, and mapped to Orange County census tracts. Census data from 2000 were used to identify tracts, population, and population characteristics in the county of approximately 3,000,000 individuals. Pedestrian collision maps were merged with census characteristics and analyzed using STATA (Version 10.1, Stata Corp, College Station, IX) to determine correlations between socioeconomic factors and collision rates within census tracts. 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 9 percent of residents with low income and averaged 6 per 100,000 child pedestrian crashes annually. One fourth of the census tracts had more than 32 percent of residents with low income and an average of 56 child pedestrian crashes per 100,000 annually. These data indicate an 8.8-fold increase in collision frequency in the lowest income quartile over the highest income quartile. Other socioeconomic correlates strongly associated with increased child collisions include population density, proportion of population speaking English less than very well, lack of high school education, number of multifamily residences, and Latino ethnicity. Conclusions: Our study showed that child pedestrian collisions are nearly 9 times more frequent in the poorest quartile of neighborhoods than in the richest quartile. Other factors associated with increased pediatric collision risk include increased neighborhood crowding, low levels of education and English speaking ability, and Latino ethnicity.