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. 2008 Dec;37(6):1349-58.
doi: 10.1093/ije/dyn136. Epub 2008 Jul 1.

Air pollution and lung function in the European Community Respiratory Health Survey

Thomas Götschi  1 , Jordi Sunyer, Susan Chinn, Roberto de Marco, Bertil Forsberg, James W Gauderman, Raquel Garcia-Esteban, Joachim Heinrich, Bénédicte Jacquemin, Deborah Jarvis, Michela Ponzio, Simona Villani, Nino Künzli
Affiliations

Air pollution and lung function in the European Community Respiratory Health Survey

Thomas Götschi et al. Int J Epidemiol. 2008 Dec.

Abstract

Background: The association of long-term air pollution and lung function has not been studied across adult European multi-national populations before. The aim of this study was to determine the association between long-term urban background air pollution and lung function levels, as well as change in lung function among European adults.

Methods: Forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and the ratio thereof (FEV1/FVC) were assessed at baseline and after 9 years of follow-up in adults from 21 European centres (followed-up sample 5610). Fine particles (PM(2.5)) were measured in 2000/2001 using central monitors.

Results: Despite sufficient statistical power no significant associations were found between city-specific annual mean PM(2.5) and average lung function levels. The findings also do not support an effect on change in lung function, albeit statistical power was insufficient to significantly detect such an association.

Conclusions: The inability to refuse the null hypothesis may reflect (i) no effect of urban air pollution on lung function or (ii) inherent biases due to the study design. Examples of the latter are lack of individual-level air quality assignment, not quantified within-city contrasts in traffic-related pollution, or the heterogeneity of the studied populations and their urban environments. Future studies on long-term effects of air pollution on lung function could increase statistical power and reduce potential misclassification and confounding by characterizing exposure on the level of individuals, capturing contrasts due to local sources, in particular traffic.

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Figures

Figure 1
Figure 1
Distribution of PM2.5 annual mean concentrations across ECRHS centres. Note: Al, Albacete; AC, Antwerp City; AS, Antwerp South; BA, Barcelona; BS, Basel; GA, Galdakao; GN, Grenoble; GO, Gothenburg; HU, Huelva; IP, Ipswich; NO, Norwich; OV, Oviedo; RE, Reykjavik; PA, Pavia; PS, Paris; TA, Tartu; TU, Turin; UM, Umeå; UP, Uppsala; VE, Verona
Figure 2
Figure 2
PM2.5 levels across ECHRS centres by type of spirometer used. Wide grey bars in the background reflect mean PM2.5 levels across centres using the same type of spirometer. Narrow black bars in the foreground represent centre PM2.5 levels. Note: Biomedin, Biomedin Baires water seal volume displacement spirometer; SM Dry, SensorMedics dry seal volume displacement spirometer (changed to Jaeger Masterscope at follow-up); SM Hot Wire, SensorMedics heated wire flow sensing spirometer; Jaeger, Jaeger Pneumotach. Al, Albacete; AC, Antwerp City; AS, Antwerp South; BA, Barcelona; BS, Basel; GA, Galdakao; GN, Grenoble; GO, Gothenburg; HU, Huelva; IP, Ipswich; NO, Norwich; OV, Oviedo; RE, Reykjavik; PA, Pavia; PS, Paris; TA, Tartu; TU, Turin; UM, Umeå; UP, Uppsala; VE, Verona
Figure 3
Figure 3
Lung function levels across ECHRS centres by type of spirometer used. Wide, bright bars in the background reflect mean lung function levels (percentage of predicted values) across centres using the same type of spirometer. Slim, dark bars in the foreground represent centre means of percentage of predicted lung function. Prediction equations used adjusted for sex, height and age. Note: See Figure 2
Figure 4
Figure 4
Distribution of PM2.5 annual means across the 21 centres of ECRHS. The distribution of 2-week NO2 at home outdoor measurements for a sub-sample of participants (N = 1634) is plotted as well (where available) to illustrate potential within city variability of NO2 (Distribution of 2-week NO2 measurements reflects spatial and temporal variability). Note: See Figure 1
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