The influence of ambient coarse particulate matter on asthma hospitalization in children: case-crossover and time-series analyses

Abstract

In this study, we used both case-crossover and time-series analyses to assess the associations between size-fractionated particulate matter and asthma hospitalization among children 6-12 years old living in Toronto between 1981 and 1993. Specifically, we used exposures averaged over periods varying from 1 to 7 days to assess the effects of particulate matter on asthma hospitalization. We calculated estimates of the relative risk of asthma hospitalization adjusted for daily weather conditions (maximum and minimum temperatures, and average relative humidity) for an incremental exposure corresponding to the interquartile range in particulate matter. Both bidirectional case-crossover and time-series analyses revealed that coarse particulate matter (PM10-2.5) averaged over 5-6 days was significantly associated with asthma hospitalization in both males and females. The magnitude of this effect appeared to increase with increasing number of days of exposure averaging for most models, with the relative risk estimates stabilizing at about 6 days. Using a bidirectional case-crossover analysis, the estimated relative risks were 1.14 [95% confidence interval (CI), 1.02, 1.28] for males and 1.18 (95% CI, 1.02, 1.36) for females, for an increment of 8.4 microg/m(3) in 6-day averages of PM10-2.5. The corresponding relative risk estimates were 1.10 and 1.18, respectively, when we used time-series analysis. The effect of PM10-2.5 remained positive after adjustment for the effects of the gaseous pollutants carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). We did not find significant effects of fine particulate matter (PM2.5) or of thoracic particulate matter (PM10) on asthma hospitalizations using either of these two analytic approaches. For the most part, relative risk estimates from the unidirectional case-crossover analysis were more pronounced compared with both bidirectional case-crossover and time-series analyses.