Outdoor Air Concentrations of Nitrogen Dioxide and Sulfur Dioxide and Prevalence of Wheezing in School Children

Abstract

We report analysis of data on outdoor air pollution and respiratory symptoms in children collected in the Czech part of the international Small Area Variations in Air pollution and Health (SAVIAH) Project, a methodological study designed to test the use of geographical information systems (GIS) in studies of environmental exposures and health at small area level. We collected the following data in two districts of Prague: (1) individual data on 3,680 children (response rate 88%) by questionnaires; (2) census-based socio-demographic data for small geographical units; (3) concentrations of nitrogen dioxide (NO2) and sulfur dioxide (SO2) measured by passive samplers in three 2-week surveys at 80 and 50 locations, respectively. We integrated all data into a geographical information system. Modeling of NO2 and SO2 allowed estimation of exposure to outdoor NO2 and SO2 at school and at home for each child. We examined the associations between air pollution and prevalence of wheezing or whistling in the chest in the last 12 months by logistic regression at individual level, weighted least squares regression at small area (ecological) level and multilevel modeling. The results varied by the level of analysis and method of exposure estimation. In multilevel analyses using individual data, odds ratios per 10 microg/m3 increase in concentrations were 1.16 (95% CI = 0.95-1.42) for NO2, and 1.08 (95% CI = 0.97-1.21) for SO2. While mapping of spatial distribution of NO2 and SO2 in the study area appeared valid, the interpolation from outdoor to personal exposures requires consideration.

Long-term ambient air pollution levels in four Chinese cities: inter-city and intra-city concentration gradients for epidemiological studies

“…In each study, exposure -effects relationships can be derived for the pollution range encountered in the study and thus, a wide pollution range is favorable. The eight districts of this study exhibited a wide range for each concerned pollutant, as shown clearly in Figure 5 where the ranges from major previous US /Europe studies are also shown for comparison purposes (Ware et al, 1986;Dockery et al, 1989Dockery et al, , 1996Spengler et al, 1996;Braun -Fahrlander et al, 1997;Peters et al, 1999b;Pikhart et al, 2000 ). The comparison indicates that the data on pollution levels collected in this study substantially extended the upper end of the pollution ranges of major published studies.…”

“…Health effects of exposure to ambient air pollution have been extensively investigated in epidemiological studies in North American and European cities ( Chapman et al, 1985;Ware et al, 1986;Dockery et al, 1993Dockery et al, , 1996Pope et al, 1995;Peters et al, 1999a,b;Pikhart et al, 2000 ). Although these studies have found, more or less, evidence of adverse health effects of air pollution exposure, the available results remain subject to significant uncertainties in several key areas including: (1 ) the community health effects of longterm exposure; ( 2) the relative health importance of different air pollutants and different size fractions and constituents of atmospheric particles; (3 ) the ethnic, demographic, and other factors that influence susceptibility to air pollution effects.…”

On ecological fallacy, assessment errors stemming from misguided variable selection, and the effect of aggregation on the outcome of epidemiological study

“…Contrary to initial expectations, the research indicated no significant differences in the average values of the PF tests across the townships, despite their distinctively different air pollution levels and the wealth of evidence accumulated to date on the link between air pollution and PF development in children (see inter alia Peters et al, 1999a, b;Pikhart et al, 2000;Gauderman et al, 2000Gauderman et al, , 2004Schwartz, 2004).…”

Retrospective assessment of exposure to traffic air pollution using the ExTra index in the VESTA French epidemiological study

“…In these conditions, the current address cannot be representative of the whole life living places. All of the teams who have used such dispersion models, with the exception of Pikhart et al (2000), only estimated outdoor concentrations at home without considering the exposure in front of work places or day-care centers, and therefore run the risk of misclassification. Our approach highlights significant disparities in time-weighted NO x levels in front of the children's living places, whereas the use of urban background pollution data provided by the air quality networks would not have described such diversity.…”

“…Only four teams Pikhart et al, 2000;Hoek et al, 2001;Brauer et al, 2002;Gehring et al, 2002;Hoek et al, 2002;Brauer et al, 2003) had recourse to statistical regression models to estimate exposure to traffic air pollutants (TAP). Moreover, pollutant dispersion models have been developed to improve the estimate of exposure levels (Hertel and Berkowicz, 1989;Benson, 1992;Eerhens et al, 1993;Berkowicz et al, 1994), but they have rarely been used in epidemiology (Larssen et al, 1993;Pershagen et al, 1995;Bartonova et al, 1999;Clench-Aas et al, 1999a, b;Briggs et al, 2000;Clench-Aas et al, 2000;Nyberg et al, 2000;Pikhart et al, 2000;Raaschou-Nielsen et al, 2000;Bellander et al, 2001;Raaschou-Nielsen et al, 2001;Zmirou et al, 2002).…”