Proximity of California public schools to busy roads - Children's Health

Environmental Health Perspectives, Jan, 2004 by Rochelle S. Green, Svetlana Smorodinsky, Janice J. Kim, Robert McLaughlin, Bart Ostro

On the basis of these findings, we selected the road segment with the maximum AADT within 150 m of each school as an estimate of exposure to traffic (Figure 2). High exposure to nearby traffic was defined as [greater than or equal to] 50,000 vehicles/day on any road segment within 150 m of the school; medium exposure 25,000-49,999 vehicles; and low exposure < 25,000 vehicles. Schools not having any streets with counted traffic volume within 150 m were assigned to the very-low-exposure category. The school in Figure 2 would be in the low-traffic-exposure category.

[FIGURE 2 OMITTED]

Statistical analyses. We first evaluated associations between variables obtained from the school and traffic databases [e.g., urban/rural location, school demographics (race, ethnicity, economic disadvantage), and type of school] and the four traffic categories (i.e., very low, low, medium, and high exposures). Although there were few measures of economic disadvantage in the school database, several socioeconomic variables were available from the census-tract-level data. Because there were strong associations between the continuous school-level and corresponding census-tract-level demographic variables, as calculated with Pearson's product moment correlations, we also used the following census-tract-level data in our analyses: population density, percentage of households with income below the poverty level, percentage of people 25 years or older with no high school diploma, and percentage of persons born abroad.

To evaluate relationships between traffic categories and both school-level and census-tract-level variables, we performed polytomous logistic regression using SAS software (version 8.2 for Windows; SAS Institute Inc., Cary, NC). Specifically, using race, ethnicity, and socioeconomic status as explanatory variables, we modeled a) the odds of a school belonging to the high traffic category compared with the low and very low categories combined and b) the odds of a school belonging to the medium traffic category compared with the very low and low categories combined. We computed odds ratios (ORs) for a 25% increase in each demographic variable except population density, which was log transformed because of its skewed distribution.

We also examined the association between traffic and race/ethnicity after stratifying schools into two groups: those above and those below the median percentage of students receiving free/reduced-price meals. Finally, we performed a separate analysis of the data after restricting schools to those located in Los Angeles County census tracts with a population density of at least 5,000 people per square mile. In this way, we could examine whether the relationships between exposure to traffic at school and race/ethnicity or economic disadvantage in a highly urbanized area were similar to those seen statewide.

Results

Statewide, 173 schools (2.3%) with a total enrollment of 150,323 students (2.6%) had high exposure to nearby traffic, 536 schools (7.2%) had medium exposure, 4,484 schools (60.1%) had low exposure, and 2,267 schools (30.4%) had very low exposure (Table 1). The percentage of children enrolled in schools in each of the traffic exposure categories, except the category with no attributable traffic, was somewhat larger than the percentage of schools with these levels of traffic (Figure 3). Similar proportions of elementary and middle schools were close to medium or high traffic. A higher percentage of high schools than elementary or middle schools was close to medium traffic (12.2%). Of the 49 schools that combined grades K-12, 20.4% had medium or high exposure to nearby traffic versus 9.5% of schools statewide.