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

Our finding that about 721,363 California students (12.4% of the total statewide) attend public schools located within 150 m of a busy road has important health implications. Several European studies have found adverse health effects in children residing or attending school in close proximity to roads with AADT of 25,000 or more vehicles. A study of German school children (Wjst et al. 1993) found a decrease in peak expiratory flow of 0.71% (95% CI, 0.33-1.08%) per increase of 25,000 cars daily on roads with the highest traffic volume passing through the school district. That study also found that increasing traffic volume was associated with recurrent wheezing (adjusted OR/25,000 cars = 1.08; 95% CI, 1.01-1.16) and recurrent dyspnea (adjusted OR/25,000 cars = 1.10; 95% CI, 1.00-1.20). A case-control study of hospital admissions for asthma in Birmingham (U.K.; Edwards et al. 1994) found that cases were more likely to live near a road with high traffic (> 24,000 cars/day) than were community controls (OR- 1.4; 95% CI, 1.13-1.74). Furthermore, they found a significant linear trend in hospital admissions with increasing traffic for subjects living less than 500 m from a major road (p < 0.006). However, only unadjusted results were reported. A study conducted in San Diego, California (English et al. 1999), found an almost 3-fold increase (OR = 2.91; 95% CI, 1.23-6.91) in medical care visits for asthmatic children for whom the traffic flow on the nearest street within 168 m was 41,000 vehicles per day and a moderately increased OR (1.85, 95% CI; 0.92-3.71) for 21,200 or more vehicles per day. Because children spend many hours per day at school, both in the classroom and outside at play or participating in rigorous sports, school proximity to large traffic volumes may increase children's risk of adverse respiratory and other health outcomes.

The traffic volume and distance cut points used in this analysis are based on the results of both epidemiologic studies and studies monitoring the air dispersion of traffic-related pollutants, and they represent a reasonable basis for an initial categorization of California public schools in relation to traffic. Other traffic metrics, such as the sum of traffic within a buffer, distance-weighted traffic density, or the traffic count at the nearest road have been used in epidemiologic studies (English et al. 1999; Wilhelm and Ritz 2003). However, we felt that the maximum traffic within 150 m would be a reasonable, easy-to-visualize metric for a descriptive study of proximity to traffic and would be a more practical metric for school site planning. To date, no good studies have compared the relation of different traffic metrics to actual exposure to traffic-related pollutants. Most of the epidemiologic studies have been conducted in Europe, where fleet composition and other factors affecting traffic emissions differ, and further studies are needed to quantify traffic volumes that may be associated health effects in children in the United States.

Several studies suggest that residential proximity to truck traffic may be more strongly associated with adverse health effects compared with total vehicular traffic (Brunekreef et al. 1997; Duhme et al. 1996; van Vliet et al. 1997; Weiland et al. 1994). Unfortunately, the CalTrans database did not include complete data on truck or bus traffic. Additionally, we were not able to take into account prevailing wind direction, topography, or climate, all of which can affect exposure to traffic-related pollutants.

Although the development of GIS software allowed us to determine the traffic volume nearby for more than 98% of schools that were eligible for study, this method does have some limitations. First, the street linework layer used to geocode the school addresses did not align perfectly with the street linework from CalTrans, which contains the traffic volume data. This would cause some incorrect distance calculations. The misalignment is difficult to quantify on a statewide basis because it varies by region of the state with no consistent pattern. It would have been too labor intensive to actually determine the degree of misalignment between the CalTrans street linework and the street linework provided with Maptitude for each of the nearly 8,000 schools in this study. If this error were randomly distributed, it would not cause differential misclassification. The associations we saw between traffic and race/ethnicity statewide were similar to those we saw when we restricted our analysis to schools in Los Angeles County census tracts with population density of at least 5,000 people per square mile. This suggests that there were no systematic errors in calculating distances to roads in urban areas. However, random errors could have biased toward the null the associations we found between traffic and factors such as ethnicity and socioeconomic status.