Swiss Tropical and Public Health Institute, 4002 Basel. University of Basel, Switzerland;

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1 Long-term exposure to air pollution and lung function in adults: the ESCAPE project Martin Adam, research associate 1,2 *, Tamara Schikowski, research associate 1,2,3 *, Anne Elie Carsin, research associate 4 *, Yutong Cai, PhD student 5, Benedicte Jacquemin, research associate 4,6,7, Margaux Sanchez, PhD student 6,7, Andrea Vierkötter, research associate 3, Alessandro Marcon, research associate 8, Dirk Keidel, research assistant 1,2, Dorothee Sugiri, research assistant 3, Zaina Al Kanani, PhD student 5, Rachel Nadif, research associate 6,7, Valérie Siroux, research associate 9,10, Rebecca Hardy, professor 11, Diana Kuh, professor 11, Thierry Rochat, professor 12, Pierre-Olivier Bridevaux, research associate 12, Marloes Eeftens, research associate 1,2,13, Ming-Yi Tsai, research associate 1,2, Simona Villani, professor 14, Harish Chandra Phuleria, research associate 1,2, Matthias Birk, research assistant 15, Josef Cyrys, research associate 15,16, Marta Cirach, research assistant 4, Audrey de Nazelle, lecturer 17, Mark J Nieuwenhuijsen, professor 4, Bertil Forsberg, professor 18, Kees de Hoogh, research associate 5, Christophe Declerq, research associate 19, Roberto Bono, professor 20, Pavilio Piccioni, research associate 21, Ulrich Quass, research associate 22, Joachim Heinrich, research associate 15, Deborah Jarvis, professor 5,23, Isabelle Pin, research associate 9,10, 24, Rob Beelen, research associate 13, Gerard Hoek, professor 13, Bert Brunekreef, professor 13,25, Christian Schindler, research associate 1,2, Jordi Sunyer, professor 4#, Ursula Krämer, professor 3#, Francine Kauffmann, professor 6#, Anna L Hansell, senior lecturer 5,26#, Nino Künzli, professor 1,2#, Nicole Probst-Hensch, professor 1,2#. *contributed equally; # Steering Committe of ESCAPE Work Package 4 on Respiratory Health in Adults. Corresponding Author: Prof. Dr. Nicole Probst-Hensch Head Unit Chronic Disease Epidemiology Swiss Tropical and Public Health Institute Socinstrasse 57, P.O. Box, 4002 Basel, Switzerland PHONE: ; Nicole.Probst@unibas.ch 1 Swiss Tropical and Public Health Institute, 4002 Basel 2 University of Basel, Switzerland; 3 Leibniz Research Institute for Environmental Medicine (IUF), Düsseldorf, Germany 1

2 4 Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain 5 MRC-PHE Centre for Environment and Health, Dept of Epidemiology and Biostatistics, School of Public Health, Imperial College London, W2 1PG London, UK 6 Inserm, Centre for research in Epidemiology and Population Health (CESP), U1018, Respiratory and Environmental Epidemiology Team, 94807, Villejuif, France 7 Univ Paris-Sud, UMRS 1018, 94807, Villejuif, France. 8 Unit of Epidemiology and Medical Statistics, Department of Public Health and Community Medicine, University of Verona, Verona, Italy 9 Inserm U823, Environmental Epidemiology Applied to Reproduction and Respiratory Health team, Grenoble, France; 10 Univ Joseph Fourier, Grenoble, France 11 MRC University Unit for Lifelong Health & Ageing at University College London WC1E 6BT, UK 12 Division of Pulmonary Medicine, University Hospitals of Geneva, 1205 Geneva, Switzerland 13 Institute for Risk Assessment Sciences, Utrecht University, 3508 TD Utrecht, The Netherlands 14 Unit of Biostatistics and Clinical Epidemiology Department of Public Health, Experimental and Forensic Medicine University of Pavia, Pavia, Italy. 15 Helmholtz Zentrum, München & German Research Centre for Environmental Health, Institute of Epidemiology I, Neuherberg, Germany 16 Environmental Science Center, University Augsburg, Augsburg, Germany 17 Centre for Environmental Policy, Imperial College London, London SW7 1NA, UK 18 Environmental and Occupational Medicine, Department of Public Health and Clinical Medicine, Umeå University, SE Umeå, Sweden 19 French Institute for Public Health Surveillance, Saint-Maurice, France. 20 Department of Public Health and Pediatrics, University of Turin, Turin, Italy 21 SC Pneumologia CPA ASL 4 Turin, Turin, Italy 22 Air Quality and Sustainable Nanotechnology, IUTA Institut für Energie- und Umwelttechnik e.v., Duisburg, Germany 2

3 23 Department of Respiratory Epidemiology and Public Health, National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK 24 Pédiatrie, CHU de Grenoble, La Tronche, France. 25 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands 26 Public Health and Primary Care Directorate, Imperial College Healthcare NHS Trust, London SW7 2AZ, UK 3

4 Outline of Sections: Methods Cohorts Exposure Lung Function Metrics and Outcomes Statistical models Meta-analysis Supplemental Table S1: Description of cohort-specific study populations. Supplemental Table S2: Information on spirometry instruments. Supplemental Table S3. Level of lung function and annual change of lung function of the cohort-specific study populations. Supplemental Table S4. The spatial variance of the applied ESCAPE LUR models. Supplemental Table S5. Cohort-specific distribution of all exposure estimates. Supplemental Table S6. Cohort-specific spearman correlation matrix for all home outdoor exposures. Supplemental Table S7. Results from meta-analyses for the cross-sectional association between the level of lung function and NO 2 exposure (standard contrast of 10 µg/m³) backextrapolated to the time point of the 2 nd spirometry. Supplemental Table S8 and Table S9. Results from meta-analyses for the crosssectional association between the level of lung function and NO 2 exposure (standard contrast of 10 µg/m³) in different subgroups. Supplemental Table S10. Results from meta-analyses for the cross-sectional association between the level of lung function and NO 2 exposure (standard contrast of 10 µg/m³) in the restricted groups included in the sensitivity analysis. Supplemental Figures S1a-e: Flowcharts describing the study specific ESCAPE sampling process. Supplemental Figure S2 and Figure S3: Forest plot displaying the center-specific mixed linear regression models of NO 2 and FVC stratified by obesity status. 4

5 Supplemental Figure S4 and Figure S5: Forest plot displaying the mixed linear regression models of NO 2 and FEV 1 and FVC for women. 5

6 Methods Cohorts ECRHS (European Community Respiratory Health Survey) 1 was initiated in as a cross-sectional study, followed up in The study included 48 centers from 23 countries. In ECRHS I adults aged between 20 and 44 years were selected at random from available population based registers with an oversampling of asthmatics. The baseline (at 1 st spirometry) investigation was based on subjects. The follow up (at 2 nd spirometry) had a response rate of 65.3% of the baseline sample. The main objective of ECRHS I was to estimate the variation in the prevalence of asthma, asthma-like symptoms, asthma sensitization and bronchial reactivity. Further, the identification of risk factors and how these explain variation across Europe was determined as well as the estimation of variation in the treatment for asthma in Europe. Wherever practically possible, 1 st spirometry lung function measures (FEV 1 and FVC) were taken using the same equipment in both, ECRHS I and ECHRS II. In the majority of centers this was a water-sealed bell spirometer (Biomedin, Padova, Italy). Twenty-two ECRHS centers used the same spirometer in both ECRHS I and ECRHS II, with most having updated software on the second occasion. Eighteen centers used the Spiro Medics computerized dry-rolling seal spirometer system 2130 (Sensor Medics, Anaheim, California, USA). The other four centers used other comparable spirometers on both occasions. The use of different equipment did not lead to any heterogeneity in lung function change compared with other centers 1. EGEA (French Epidemiological study on Genetics and Environment of Asthma) 2, 3 is a 12-year follow-up study. It combines a case-control study with a family study of asthma cases (children or adults) conducted between 1991 and 1995 (at 1 st spirometry) in 2047 subjects from five French cities 4, 5. A follow-up (at 2 nd spirometry) of the initial cohort was conducted between 2003 and Among the alive cohort (n = 2002), 92% (n = 1845) and 80% completed a short self-administered questionnaire and among them 1601 had an examination (1414 with lung function test. Spirometry devices were switched between 1 st spirometry to 2 nd spirometry (Biomedin to Spirodyn). 6

7 NSHD (Medical Research Council s National Survey of Health and Development) 6 consists of a socially stratified sample of all births that took place in England, Scotland and Wales during one week in March The original sample of 2547 women and 2815 men have been followed up multiple times during the life course. The main objectives since the 1999 follow-up, taken as the 1 st spirometry for the ESCAPE study, have been the measurement of physical and mental functioning, the study of pathways to those outcomes, and study of morbidity and mortality for multiple health outcomes. Lung function was measured at ages 53 (1999; 1 st spirometry) and ( ; 2 nd spirometry) years using the Micromedical turbine electronic spirometer, administered by a trained nurse. The protocol did not correspond to ATS criteria. Three trials were given at 53 years, and 2 trials were given at years. Where three blows were recorded, the variation in FEV 1 across the best two of these trials was within 5% for 77.5% of the sample. FEV in 1 sec (FEV 1 ) and forced vital capacity (FVC) were measured in the standing position, without nose clips, after instruction and under the supervision of a trained research nurse. Subjects were excluded from subsequent analyses if the best two lung function readings differed by more than 10% from each other and if readings were outside the normal range after adjusting for gender and height (standardized residuals greater than 3 SD units from the mean) 7-9 SALIA (Study on the influence of Air pollution on Lung function, Inflammation and Aging) 10 study was initiated in 1985 as part of Environmental Health surveys, which were an element of the Clean Air Plan initiated by the Government of North-Rhine Westphalia in Germany. Main objective of the baseline investigations was to monitor health effects of outdoor air pollution in the heavily polluted Ruhr Area. A questionnaire follow-up was conducted in 2006 and in 2007 to 2009 (at 2 nd spirometry) health assessments were performed to investigate the long-term effects of outdoor air pollution and changes in pollution on respiratory health. The baseline investigation (at 1 st spirometry) included 4756 women. The geographic regions were chosen to represent a range of polluted areas with high traffic load and steel and coal industries. The regions included parts of the cities of Duisburg, Essen, Gelsenkirchen, Dortmund, Herne and Borken. Sampling included all women of 7

8 German nationality aged 54 to 55 residing in the selected areas (near (< 4 km) to governmental measurement stations). SALIA kept one device (Master Scope Jaeger), which was used for most women in Spirometry 1 and 2. During spirometry 1, Vica test was replaced with Master Scope Jaeger. 116 women had an investigation with both devices. From these double measurements a regression equation was established for transforming the values between devices. (FVC jaeger = 1.037*FVC vica ; FEV 1jaeger = *FEV 1vica During spirometry 2 Master Scope Jaeger was replaced with NDD Easy One. 28 persons were investigated with both devices and the following transformation equations were developed: FVC Jaeger = *FVC ndd ; FEV 1Jaeger = *FEV 1ndd The values used in the analysis of this paper were all transformed to Master Scope Jaeger values. Spirometry was performed according to the ATS/ETS recommendations. Forced expiratory volume in 1 second (FEV 1 ) and forced vital capacity (FVC) were measured. Between three to four maneuvers were performed under direction of trained personnel, and the values where the maximal FEV 1 was reached were used. All measuring instruments were calibrated prior to each testing. The technical personnel were trained and all results were reviewed by a pulmonary physician 11. SAPALDIA (Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults) 12 is a multi-center study that was initiated in 1991 in eight geographic areas representing the range of environmental, meteorological and socio-demographic conditions in Switzerland. The main aim of the study was to assess the effect of air pollution (outdoor and indoor) on respiratory and cardiovascular health, with a special focus on how the respiratory and cardio-vascular system interact in this regard, and on the role of lifestyle and genetic background. In 1991, 9'651 subjects, aged 18 to 60 years, were recruited for a detailed interview and more than 90% of them provided valid spirometry (1 st spirometry) results. The follow-up assessment (at 2 nd spirometry) was conducted in 2002 and 8'047 (83%) participants provided health information and 6'528 persons underwent physical reexamination. SAPALDIA will contribute to the study with lung function measurements from the baseline (at 1 st spirometry) examination in 1991 (SAPALDIA1 ) and the first follow-up (at 2 nd spirometry) examination in 2002 (SAPALDIA2). 8

9 The same spirometry protocol was used in SAPALDIA 1 and 2; identical to the protocol in ECRHS and meeting ATS performance standards. Spirometric tests were performed in a sitting position with nose clips. Participants performed at least three and up to eight forced expiratory lung function maneuvers in order to obtain a minimum of two acceptable and reproducible values. Identical spirometers were used by each center and at both, 1 st spirometry SAPALDIA1 and SAPALDIA2 2 nd spirometry examination: each center in SAPALDIA1 and SAPALDIA2 was equipped with an identical computerized spirometer (Sensormedics 2200 SP, Bilthoven, The Netherlands) which uses the successfully evaluated and accepted mass flow anemometer technology. The three best results for forced vital capacity (FVC) and forced expiratory volume in one second (FEV 1 ), and the five-digit error code for ATS criteria were stored on a hard disk and printed on paper, including flow-volume charts for further documentation. The highest values for FVC and FEV 1, of any accepted trial were chosen. SAPALDIA performed detailed quality control measurements between field workers at spirometry 1 ( 13 and between spirometry 1 and spirometry 2 devices (same device) 14. Furthermore the lung function results were also compared between Sensormedics (instrument used in SAPALDIA) and Biomedin (the instrument in ECRHS and EGEA study centers). The quality control studies showed no significant differences between individual technicians or teams. But they showed that although all devices complied with the ATS standards of accurate instruments, and all calibrations were within the required precision, lung function test results taken under biologic conditions did differ significantly between instruments 14. Exposure ESCAPE exposure assessment is described in detail in an online manual ( and in a Lancet Online Supplement associated with Beelen et al Air pollution monitoring campaigns were conducted between 2008 and 2011 in all selected study areas from participating cohorts. In each area, the monitoring campaigns consisted of three 2-week measurements of NO 2 and NO x performed at 40 sites. Simultaneous measurements of PM 2.5 absorbance (marker for black carbon), PM 2.5, PM 10 were performed in a subsample of study areas selected for the NO 2 /NO X measurement campaign, because budgetary constraints prohibited inclusion of all study areas. PM measurements were performed at 20 sites within each study area. The 9

10 ESCAPE exposure assessment in all geographic sites of ESCAPE has followed a standardized measurement protocol 16. Measurement sites were selected to represent the anticipated spatial variation of air pollution in the area. Land use regression (LUR) models specific for the measurement area to explain spatial variation of annual average concentrations as obtained from the measurement campaigns were developed. Predictor variables on nearby traffic intensity, population/household density and land use were derived from Geographic Information Systems (GIS). These LUR models were developed for each cohort following a common protocol ( and assigned annual mean air pollutant concentrations to the baseline residential address of each study participant. Pollution measurements were performed between , but follow-up from 1 st to 2 nd spirometry in all cohorts covered earlier time periods which dated as far back as We therefore had to extrapolate predicted concentrations back in time using the ratio between baseline and periods, based on data from routine background monitoring network site(s) in the study areas. As these background monitoring network only provided historical data for NO 2 and PM 10, we restricted backextrapolation to these two metrics. Historical data on NO 2 and PM 10 was not used for the backextrapolation of additional pollution metrics, as time trends differ by pollutant. Details and examples for the procedure of backextrapolation are provided in the Online Supplement of the paper by Beelen et al 15. ESCAPE NO 2 and PM 10 exposures were backextrapolated to the time point of the 1 st and 2 nd spirometry where possible and appropriate. Backextrapolation of NO 2 and PM 10 exposure to the 1 st spirometry was not possible for ECRHS and EGEA, as historical data was not available from all study areas. NO 2 and PM 10 exposure in NSHD and SALIA were only backextrapolated to 1 st spirometry, as the timepoints for 2 nd spirometry were sufficiently close to the time of the ESCAPE monitoring campaign ( ). In addition to pollutants derived from measurement campaigns and LUR models, traffic intensity on the nearest road (vehicles/day) and total traffic load (intensity*length) on all major roads within a 100 m buffer were used. These variables were obtained using a digital road network linked with traffic intensity data in a GIS. According data was obtained by local cohort experts in the absence of available traffic intensity data on a European-wide scale (for detailed procedures see 10

11 ( A default of 500 vehicles/day was assigned to minor roads missing from the local road networks or to roads lacking information on traffic intensity. Epidemiological evidence on the association between traffic indicators and health outcomes and their interpretation are discussed in detail in the Health Effect Institute Report on Traffic-Related Air Pollution: A Critical Review of the Literature on Emissions, Exposure, and Health Effects 17. Lung Function Metrics and Outcomes Applied spirometry instruments for pulmonary function testing in the ESCAPE centers included in this analysis, differed by cohort and in some study areas by assessment round (1 st spirometry and 2 nd spirometry) (Supplemental Table 2). As spirometric outcome metrics we assessed FEV 1 and FVC cross-sectionally (level of lung function) and longitudinally (change in lung function) in this study. For level of lung function, the time point of spirometry closest to the actual ESCAPE exposure measurement provided the outcome metrics. For change in lung function we assessed the annual lung function decline (ml/year) which was calculated as (lung function at 2 nd spirometry minus lung function at 1 st spirometry)/years of follow-up, thus a negative value indicates that lung function declined during follow-up. In addition, we calculated the percent change in lung function (%), calculated as (annual decline/lung function at 1 st spirometry*100), thus a negative percent value indicates that lung function declined during follow-up. Statistical models It was a strategic decision in the ESCAPE project to aim for local analyses, following strict and harmonized protocols, followed by meta-analysis. Privacy issues and limited centralized resources precluded centralized analyses. The association between lung function and air pollution was analyzed in each cohort separately according to a common statistical protocol, codebook and STATA script used by the local analysts. A statistical working group developed general ESCAPE wide guidelines for the statistical analyses ( 11

12 Consistent with other ESCAPE projects, we applied a staged modeling approach to choose the main analytic model. Model 1 was the unadjusted crude analyses. Model 2 was a simple model with adjustment only for age, age squared, height, and sex. Model 3 (Main Model of reference) is in particular used for all assessments of interactions and for the sensitivity analyses and the meta-analyses. The model was adjusted for a common set of potential confounders, which were available in all studies in a standardized form, based on evidence from previous studies and the assessment and quality of available data within the ESCAPE cohorts. Confounders in the models were selected a priori based on current knowledge on determinants of lung function and the potential association with air pollution. The Main Model does not include variables that might be on the pathway linking air pollution with the specific health outcome (e.g. chronic bronchitis). Thus, Model 3 included in the cross-sectional analyses all variables of Model 2 plus BMI, highest educational level, and smoking status and, in the longitudinal analyses, all variables of Model 2 plus BMI, BMI change, highest educational level, smoking status at 1st spirometry and quitting smoking during follow-up. Study-specific models with additional covariates (e.g. packyears smoked, occupational exposures to gas / dust / fumes) were also tested on smaller numbers of subjects, to assess sensitivity of the associations to these additional adjustments. As results from models with these additional adjustments did not differ materially from model 3 adjustments (or the number of subjects on whom information on these additional variables was available, were too few to be informative), all results presented were derived from model 3. Meta-analysis Cohort specific overall and stratum-specific effect estimates obtained by mixed linear regression models were meta-analyzed. The heterogeneity of the effect estimates between the studies was assessed using X 2 test. In the absence of heterogeneity between studies (i.e., if the p-value of heterogeneity is larger than 0.1), fixed-effect models were used to calculate the summary effect estimates. In presence of heterogeneity, random-effect models were used instead. In addition, the I 2 statistic for quantifying heterogeneity was calculated. We assessed the contribution from each cohort to the overall effect estimate. 12

13 13

14 Online Supplement Supplemental Table S1. Description of cohort-specific study populations. Characteristics a are presented for the larger subgroup of participants included in the analysis of NO 2 and NO x, traffic indicators and for the smaller subgroup of participants included in the PM metrics analysis. ECRHS EGEA NSHD SALIA SAPALDIA Cohort Ntotal=3859 Ntotal=1831 Ntotal=568 Ntotal=342 Ntotal=844 Ntotal=751 Ntotal=580 Ntotal=580 Ntotal=1764 Ntotal=729 NO population PM population NO population PM population NO population PM population NO population PM population NO population PM population 1 st spirometry nd spirometry Characteristics N/mean %/SD N/mean %/SD N/mean %/SD N/mean %/SD N/mean %/SD N/mean %/SD N/mean %/SD N/mean %/SD N/mean %/SD N/mean %/SD Female % % % % % % % % Age BMI [kg/m 3 ] Height [in cm] Exsmoker % % % % % % % % % Current smoker % % % % % % % % % Pack years at 1 st spirometry b Pack years from 1 st spirometry to 2 nd spirometry b Medium educational level b % % % % % % % % % High educational level b % % % % % % % % % % Environmental tobacco exposure at home or at work b % % % % % % % % % % Occupational exposure to dust/fumes or gases b % % % % % % % % % % Ever asthma b,c % % % % % % % % % % The table shows the amount of observations (N, and % of total N) for categorical variables, and the mean value (and standard deviation (SD)) in case of continuous variables. a Characteristics refer to time point of 2 nd spirometry. b Information missing on a limited number of subjects. c Asthma diagnosed by a physician at 1 st and/or at 2 nd spirometry. 14

15 Supplemental Table S2: Information on instruments used at 1 st and 2 nd spirometry in ESCAPE centers included in this analysis. Study Study center ECRHS Belgium/Antwerp ECRHS France/Grenoble ECRHS France/Paris ECRHS Germany/Erfurt ECRHS Italy/Pavia ECRHS Italy/Turin ECRHS Italy/Verona ECRHS Spain/Albacete ECRHS Spain/Barcelona ECRHS Spain/Galdakoa ECRHS Spain/Huelva ECRHS Spain/Oviedo ECRHS Sweden/Umea ECRHS UK/Norwich ECRHS UK/Ipswich SAPALDIA Basel 1991/92 SAPALDIA Geneva 1991/92 SAPALDIA Lugano 1991/92 NSHD UK 1999 SALIA Ruhr Area EGEA Grenoble EGEA Lyon EGEA Marseille Year of 1 st spirometry for this paper Instrument(s) used at 1 st spirometry: Instrument / N Sensormedics N=440 Biomedin spir. N=329 Biomedin spir. N=322 Jaeger pneum N=254 Biomedin spir. N=147 Biomedin spir. N=149 Biomedin spir. N=184 Biomedin spir. N=338 Biomedin spir. N=161 Biomedin spir. N=332 Biomedin spir. N=266 Biomedin spir. N=230 Sensormedics N=392 Biomedin spir. N=245 Biomedin spir. N=283 Sensormedics 2200 N=643 Sensormedics 2200 N=394 Sensormedics 2200 N=728 Micro Medical Plus (MS03s ) N=844 Master Scope Jaeger / (partly VICAest 4 transformed) N=580 Biomedin/ N=210 Jaeger pneumotach (Lyon)/ N=154 Biomedin/ N=75 Year of 2 nd spirometry for this paper 15 Instrument(s) used at 2 nd spirometry: Instrument / N Jaeger pneum N= % Biomedin spir. N=329 0% Biomedin spir. N=322 0% Jaeger pneum N=254 0% Biomedin spir. N=147 0% Biomedin spir. N=149 0% Biomedin spir. N=184 0% Biomedin spir. N=338 0% Biomedin spir. N=161 0% Biomedin spir. N=332 0% Biomedin spir. N=266 0% Biomedin spir. N=230 0% Sensormedics N= Biomedin spir. N=245 0% Biomedin spir. N=283 0% Sensormedics 2200 N=643 Sensormedics 2200 N=394 Sensormedics 2200 N=728 Micro Medical Plus (MS03s) N=844 Master Scope Jaeger / (partly Easy One transformed) N= SPIRODYN R N= % SPIRODYN R N=154 SPIRODYN R N=75 Participants switching instruments during 2 nd spirometry (N/%) 0% 0% 0% 0% 0% (0%) (after transformation) 100% 100%

16 EGEA Paris Biomedin/ N= SPIRODYN R N= % 16

17 Supplemental Table S3 Level of lung function and annual change of lung function of the cohort specific study populations. Presented are the number of observations (N), the means and the standard deviations (sd) of level and change in FEV 1 and FVC (in liters per year) during follow up for all five study populations stratified by sex, smoking status (never vs. ever) and asthma status (never vs. ever) for the larger subgroup of participants included in the analysis of NO 2, NOx and traffic indicators, and for the smaller subgroup of participants included in the analysis of the PM metrics, respectively. ECRHS All Female Male Never smoker Ever smoker No asthma Asthma NO population N= 3859 N= 1981 N= 1878 N= 1664 N= 2195 N= 3423 N= 430 mean Sd mean sd mean sd mean sd mean sd mean sd mean sd FEV 1 at 2 nd spirometry up [L] FVC at 2 nd spirometry [L] change of FEV 1 [l] change of FVC [l] All Female Male Never smoker Ever smoker No asthma Asthma PM population N= 1831 N= 967 N= 864 N= 858 N= 973 N= 1588 N= 237 mean Sd mean sd mean sd mean sd mean sd mean sd mean sd FEV 1 at 2 nd spirometry [L] FVC at 2 nd spirometry [L] change of FEV 1 [l] change of FVC [l] EGEA All Female Male Never smoker Ever smoker No asthma Asthma NO population N= 568 N= 303 N= 265 N= 282 N= 286 N= 383 N= 158 mean Sd mean sd mean sd mean sd mean sd mean sd mean sd FEV 1 at 2 nd spirometry [L] FVC at 2 nd spirometry [L] change of FEV 1 [L] change of FVC [L] PM population All Female Male Never smoker Ever smoker No asthma Asthma N= 342 N= 182 N= 160 N= 175 N= 167 N= 227 N=

18 mean Sd mean sd mean sd mean sd mean sd mean sd mean sd FEV 1 at 2 nd spirometry [L] FVC at 2 nd spirometry [L] change of FEV 1 [L] change of FVC [L] NSHD All Female Male Never smoker Ever smoker No asthma Asthma NO population N= 844 N= 471 N= 373 N= 270 N= 574 N= 774 N= 44 mean Sd mean sd mean sd mean sd mean sd mean sd mean sd FEV 1 at 2 nd spirometry [L] FVC at 2 nd spirometry [L] change of FEV 1 [L] change of FVC [L] All Female Male Never smoker Ever smoker No asthma Asthma PM population N= 751 N= 418 N= 333 N= 230 N= 521 N= 690 N= 37 mean Sd mean sd mean sd mean sd mean sd mean sd mean sd FEV 1 at 2 nd spirometry [L] FVC at 2 nd spirometry [L] change of FEV 1 [L] change of FVC [L] SALIA All Female Male Never smoker Ever smoker No asthma Asthma NO population N= 580 N= 580 N= 0 N= 459 N= 121 N= 558 N= 9 mean Sd mean sd mean sd mean sd mean sd mean sd mean sd FEV 1 at 2 nd spirometry [L] NA NA FVC at 2 nd spirometry [L] NA NA change of FEV 1 [L] NA NA change of FVC [L] NA NA PM population All Female Male Never smoker Ever smoker No asthma Asthma 18

19 N= 580 N= 580 N= 0 N= 459 N= 121 N= 558 N= 9 mean Sd mean sd mean sd mean sd mean sd mean sd mean sd FEV 1 at 2 nd spirometry [L] NA NA FVC at 2 nd spirometry [L] NA NA change of FEV 1 [L] NA NA change of FVC [L] NA NA SAPALDIA All Female Male Never smoker Ever smoker No asthma Asthma NO population N= 1764 N= 980 N= 784 N= 766 N= 998 N= 1658 N= 106 mean Sd mean sd mean sd mean sd mean sd mean sd mean sd FEV 1 at 2 nd spirometry [L] FVC at 2 nd spirometry [L] change of FEV 1 [L] change of FVC [L] All Female Male Never smoker Ever smoker No asthma Asthma PM population N= 729 N= 422 N= 307 N= 323 N= 406 N= 693 N= 36 mean Sd mean sd mean sd mean sd mean sd mean sd mean sd FEV 1 at 2 nd spirometry [L] FVC at 2 nd spirometry [L] change of FEV 1 [L] change of FVC [L] NA, indicates not applicable. 19

20 Supplemental Table S4 The spatial variance of the applied ESCAPE LUR models by study center. Center/Area R 2 in cohorts Number of sites Mid-East Spain: Albacete- Valencia, Spain R 2 cross validation (LOOCV R2) RMSE a cross validation (µg/m 3 ) NO x Moran s I (p-value) Umea, Region Sweden 87% 82% (0.12) London/Oxford, UK 91% 88% (0.78) Netherlands/Belgium 87% 82% region (0.06) Ruhr area, Germany 88% 81% (0.95) Erfurt, Germany 87% 84% (0.95) Paris, France 75% 67% (0.77) Grenoble, France 82% 74% (0.76) Lyon, France 75% 65% (0.88) Marseilles, France 53% 39% (0.65) Basel, Switzerland 61% 52% (0.71) Geneva, Switzerland 81% 73% (0.23) Lugano, Switzerland 87% 82% (0.28) Turin, Italy 78% 72% (0.36) Pavia, Italy 88% 80% (0.46) Verona, Italy 64% 54% (0.91) Barcelona, Spain 73% 65% (0.26) 88% 84% (0.46 Huelva, Spain 56% 31% (0.08) NO 2 Umea, Region 87% 83% Sweden (0.43) London/Oxford, UK 89% 87% (0.71) Netherlands/Belgium 86% 81% region (0.09) Ruhr area, Germany 89% 84% (0.08) 20 Measured concentration (µg/m 3 ) 18.9 [ ] 69.3 [ ] 51.8 [ ] 60.0 [ ] 28.8 [ ] 80.3 [ ] 48.2 [ ] 61.7 [ ] 70.1 [ ] 53.1 [ ] 55.9 [ ] 47.8 [ ] [ ] 50.9 [ ] 91.8 [ ] [ ] 42.7 [ ] 33.8 [ ] 9.3 [ ] 37.9 [ ] 30.9 [ ] 33.2 [ ]

21 Erfurt, Germany 89% 87% (0.16) Paris, France 77% 67% (0.71) Grenoble, France 83% 78% (0.82) Lyon, France 90% 72% (0.08) Marseilles, France 59% 46% (0.87) Basel, Switzerland 67% 58% (0.45) Geneva, Switzerland 87% 81% (0.25) Lugano, Switzerland 87% 82% (0.51) Turin, Italy 78% 70% (0.10) Pavia, Italy 92% 87% (0.99) Verona, Italy 64% 55% (0.33) Barcelona, Spain 75% 68% (0.98) 90% 87% (0.37) Mid-East Spain: Albacete- Valencia, Spain Huelva, Spain 55% 31% (0.10) PM 10 London/Oxford, UK 90% 88% (0.42) Netherlands/Belgium region 68% 60% (0.28) Ruhr area, Germany 69% 63% (0.99) Paris, France 87% 77% (0.91) Lugano, Switzerland 87% 80% (0.10) Turin, Italy 78% 69% (0.70) Barcelona, Spain 87% 82% (0.88) PM 2.5 London/Oxford, UK 82% 77% (0.20) Netherlands/Belgium region 67% 61% (0.77) Ruhr area, Germany 88% 79% (0.64) Paris, France 89% 73% (0.83) [ ] 39.8 [ ] 27.2 [ ] 35.0 [ ] 36.1 [ ] 31.0 [ ] 29.3 [ ] 28.6 [ ] 53.3 [ ] 25.9 [ ] 41.6 [ ] 57.7 [ ] 26.1 [ ] 21.9 [ ] 18.6[ ] 27.1 [ ] 27.9 [ ] 25.6 [ ] 23.9 [ ] 43.1 [ ] 37.4 [ ] 11.2 [ ] 17.1 [ ] 18.5 [ ] 16.0 [ ]

22 Lugano, Switzerland 83% 77% (0.10) Turin, Italy 71% 59% (0.45) Barcelona, Spain 83% 71% (0.46) PM 2.5absorbance London/Oxford, UK 96% 92% (0.16) Netherlands/Belgium 92% 89% region (0.42) Ruhr area, Germany 97% 95% (0.65) Paris, France 91% 81% (0.97) Lugano, Switzerland 79% 71% (0.09) Turin, Italy 88% 81% (0.82) Barcelona, Spain 86% 80% (0.64) PM coarse London/Oxford, UK 68% 57% (0.29) Netherlands/Belgium 51% 38% region (0.75) Ruhr area, Germany 66% 57% (0.73) Paris, France 81% 73% (0.82) Lugano, Switzerland 77% 65% (0.18) Turin, Italy 65% 58% (0.30) Barcelona, Spain 75% 70% (0.61) 17.2 [ ] 29.3 [ ] 16.3 [ ] 1.6 [ ] 1.7 [ ] 1.6 [ ] 2.0 [ ] 2.0 [ ] 3.0 [ ] 2.7 [ ] 7.4 [ ] 9.3 [ ] 9.4 [ ] 9.6 [ ] 6.8 [ ] 13.8 [ ] 21.0 [ ] a RMSE indicates Root-mean squared error 22

23 Supplemental Table S5 Distribution of all cohort-specific exposure estimates (annual averages of ambient air pollutants and traffic variables), at participants address in each cohort. ECRHS Exposures N Mean SD Min P25 P50 P75 Max IQR PM 2.5 [µg/m 3 ] PM 2.5absorbance [10-5 m -1 ] PM 10 [µg/m 3 ] PM (coarse) [µg/m 3 ] NO 2 [µg/m 3 ] NO x [µg/m 3 ] Traffic intensity on nearest road [cars/day] Traffic load on nearest major road [cars-km/day; in thousand] a NO 2 (backextrapolated to BL) [µg/m 3 ] No complete exposure backextrapolation to 1 st spirometry available PM 10 (backextrapolated to BL) [µg/m 3 ] No complete exposure backextrapolation to 1 st spirometry available NO 2 (backextrapolated to FU) [µg/m 3 ] PM 10 (backextrapolated to FU) [µg/m 3 ] EGEA Exposures N Mean SD Min P25 P50 P75 Max IQR PM 2.5 [µg/m 3 ] PM 2.5absorbance [10-5 m -1 ] PM 10 [µg/m 3 ] PM (coarse) [µg/m 3 ] NO 2 [µg/m 3 ] NO x [µg/m 3 ] Traffic intensity on nearest road [cars/day] Traffic load on nearest major road [cars-km/day; in thousand] a NO 2 (backextrapolated to BL) [µg/m 3 ] No complete exposure backextrapolation to 1 st spirometry available PM 10 (backextrapolated to BL) [µg/m 3 ] No complete exposure backextrapolation to 1 st spirometry available NO 2 (backextrapolated to FU) [µg/m 3 ]

24 PM 10 (backextrapolated to FU) [µg/m 3 ] NSHD Exposures N Mean SD Min P25 P50 P75 Max IQR PM 2.5 [µg/m 3 ] PM 2.5absorbance [10-5 m -1 ] PM 10 [µg/m 3 ] PM (coarse) [µg/m 3 ] NO 2 [µg/m 3 ] NO x [µg/m 3 ] Traffic intensity on nearest road [cars/day] Traffic load on nearest major road [cars-km/day; in thousand] a NO 2 (backextrapolated to BL) [µg/m 3 ] PM 10 (backextrapolated to BL) [µg/m 3 ] NO 2 (backextrapolated to FU) [µg/m 3 ] ESCAPE exposure measurements were conducted at time of second spirometry PM 10 (backextrapolated to FU) [µg/m 3 ] ESCAPE exposure measurements were conducted at time of second spirometry SALIA Exposures N Mean SD Min P25 P50 P75 Max IQR PM 2.5 [µg/m 3 ] PM 2.5absorbance [10-5 m -1 ] PM 10 [µg/m 3 ] PM (coarse) [µg/m 3 ] NO 2 [µg/m 3 ] NO x [µg/m 3 ] Traffic intensity on nearest road [cars/day] Traffic load on nearest major road [cars-km/day; in thousand] a NO 2 (backextrapolated to BL) [µg/m 3 ] PM 10 (backextrapolated to BL) [µg/m 3 ] NO 2 (backextrapolated to FU) [µg/m 3 ] ESCAPE exposure measurements were conducted at time of second spirometry PM 10 (backextrapolated to FU) [µg/m 3 ] ESCAPE exposure measurements were conducted at time of second spirometry 24

25 SAPALDIA Exposures N Mean SD Min P25 P50 P75 Max IQR PM 2.5 [µg/m 3 ] PM 2.5absorbance [10-5 m -1 ] PM 10 [µg/m 3 ] PM (coarse) [µg/m 3 ] NO 2 [µg/m 3 ] NO x [µg/m 3 ] Traffic intensity on nearest road [cars/day] Traffic load on nearest major road [cars-km/day; in thousand] a NO 2 (backextrapolated to BL) [µg/m 3 ] PM 10 (backextrapolated to BL) [µg/m 3 ] NO 2 (backextrapolated to FU) [µg/m 3 ] PM 10 (backextrapolated to FU) [µg/m 3 ] BL, indicates Baseline; FU, Follow-up. PM 2.5 : particulate matter with a diameter of 2.5 micrometers or less; PM 2.5 abs: absorbance of particulate matter with a diameter of 2.5 micrometers; PM 10 : particulate matter with a diameter of 10 micrometers or less; PM coarse : coarse fraction of PM 2.5 to PM 10 ; NO 2 : nitrogen dioxide; NO x : nitrogen oxides. a Traffic load on nearest major road in a 100m buffer presented in thousand. 25

26 Supplemental Table S6. Cohort-specific spearman correlation matrix for all individually assigned markers of home outdoor exposures, by cohort. ECRHS Exposures PM 2.5 PM 2.5 absorbance PM 10 PM coarse NO 2 NO x Traffic intensity Traffic load NO 2 (back to BL) PM 10 (back to BL) NO 2 (back to FU) PM 10 (back to FU) N rho rho rho Rho rho rho rho rho rho rho rho rho PM 2.5 [µg/m 3 ] NA NA PM 2.5absorbance [10-5 m -1 ] NA NA PM 10 [µg/m 3 ] NA NA PM (coarse) [µg/m 3 ] NA NA NO 2 [µg/m 3 ] NA NA NO x [µg/m 3 ] NA NA Traffic intensity on nearest road [cars/day] NA NA Traffic load on nearest major road in a 100m buffer [cars-km/day] NA NA NO 2 (backextrapolated to BL) [µg/m 3 ] NA NA NA NA NA NA NA NA NA NA NA NA NA PM 10 (backextrapolated to BL) [µg/m 3 ] NA NA NA NA NA NA NA NA NA NA NA NA NA NO 2 (backextrapolated to FU) [µg/m 3 ] NA NA PM 10 (backextrapolated to FU) [µg/m 3 ] NA NA EGEA Exposures PM 2.5 PM 2.5 absorbance PM 10 PM coarse NO 2 NO x Traffic intensity Traffic load NO 2 (back to BL) PM 10 (back to BL) NO 2 (back to FU) PM 10 (back to FU) N rho rho rho Rho rho rho rho rho rho rho rho rho PM 2.5 [µg/m 3 ] NA NA PM 2.5absorbance [10-5 m -1 ] NA NA PM 10 [µg/m 3 ] NA NA PM (coarse) [µg/m 3 ] NA NA NO 2 [µg/m 3 ] NA NA NO x [µg/m 3 ] NA NA

27 Traffic intensity on nearest road [cars/day] NA NA Traffic load on nearest major road in a 100m buffer [cars-km/day] NA NA NO 2 (backextrapolated to BL) [µg/m 3 ] NA NA NA NA NA NA NA NA NA NA NA NA NA PM 10 (backextrapolated to BL) [µg/m 3 ] NA NA NA NA NA NA NA NA NA NA NA NA NA NO 2 (backextrapolated to FU) [µg/m 3 ] NA NA PM 10 (backextrapolated to FU) [µg/m 3 ] NA NA NSHD Exposures PM 2.5 PM 2.5 absorbance PM 10 PM coarse NO 2 NO x Traffic intensity Traffic load NO 2 (back to BL) PM 10 (back to BL) NO 2 (back to FU) PM 10 (back to FU) N rho rho rho Rho rho rho rho rho rho rho rho rho PM 2.5 [µg/m 3 ] PM 2.5absorbance [10-5 m -1 ] PM 10 [µg/m 3 ] PM (coarse) [µg/m 3 ] NO 2 [µg/m 3 ] NO x [µg/m 3 ] Traffic intensity on nearest road [cars/day] Traffic load on nearest major road in a 100m buffer [cars-km/day] NO 2 (backextrapolated to BL) [µg/m 3 ] PM 10 (backextrapolated to BL) [µg/m 3 ] NO 2 (backextrapolated to FU) [µg/m 3 ] NA NA NA NA NA NA NA NA NA NA NA NA NA PM 10 (backextrapolated to FU) [µg/m 3 ] NA NA NA NA NA NA NA NA NA NA NA NA NA SALIA Exposures PM 2.5 PM 2.5 absorbance PM 10 PM coarse NO 2 NO x Traffic intensity Traffic load NO 2 (back to BL) PM 10 (back to BL) NO 2 (back to FU) PM 10 (back to FU) N rho rho rho Rho rho rho rho rho rho rho rho rho PM 2.5 [µg/m 3 ]

28 PM 2.5absorbance [10-5 m -1 ] PM 10 [µg/m 3 ] PM (coarse) [µg/m 3 ] NO 2 [µg/m 3 ] NO x [µg/m 3 ] Traffic intensity on nearest road [cars/day] Traffic load on nearest major road in a 100m buffer [cars-km/day] NO 2 (backextrapolated to BL) [µg/m 3 ] PM 10 (backextrapolated to BL) [µg/m 3 ] NO 2 (backextrapolated to FU) [µg/m 3 ] NA NA NA NA NA NA NA NA NA NA NA NA NA PM 10 (backextrapolated to FU) [µg/m 3 ] NA NA NA NA NA NA NA NA NA NA NA NA NA SAPALDIA Exposures PM 2.5 PM 2.5 absorbance PM 10 PM coarse NO 2 NO x Traffic intensity Traffic load NO 2 (back to BL) PM 10 (back to BL) NO 2 (back to FU) PM 10 (back to FU) N rho rho rho rho rho rho rho rho rho rho rho rho PM 2.5 [µg/m 3 ] PM 2.5absorbance [10-5 m -1 ] PM 10 [µg/m 3 ] PM (coarse) [µg/m 3 ] NO 2 [µg/m 3 ] NO x [µg/m 3 ] Traffic intensity on nearest road [cars/day] Traffic load on nearest major road in a 100m buffer [cars-km/day] NO 2 (backextrapolated to BL) [µg/m 3 ] PM 10 (backextrapolated to BL) [µg/m 3 ] NO 2 (backextrapolated to FU) [µg/m 3 ] PM 10 (backextrapolated to FU) [µg/m 3 ]