TRANSPHORM. Deliverable D3.2, type 2

Transcription

1 TRANSPHORM Transport related Air Pollution and Health impacts Integrated Methodologies for Assessing Particulate Matter Collaborative Project, Large-scale Integrating Project SEVENTH FRAMEWORK PROGRAMME ENV Transport related air pollution and health impacts Deliverable D3.2, type 2 Association between PM composition in TRANSPHORM sites of ESCAPE and Chronic Obstructive Pulmonary Disease Prevalence and Incidence in four ESCAPE Cohorts Due date of deliverable: project month 36 Actual submission date: project month 49 Start date of project: 1 January 2010 Duration: 56 months Organisation name of lead contractor for this deliverable: Scientist responsible for this deliverable: Prof. Nino Künzli Dr. Tamara Schikowski

2 Table of Contents Abstract... 3 Introduction... 4 Methods... 4 Definition of COPD... 5 Defining severity of COPD (stages)... 6 Definition of COPD prevalence and incidence... 6 Exposure assessment... 6 Statistical analyses... 7 Discussion... 8 Appendix:... 9

3 Abstract Rationale. Current evidence suggests that ambient air pollution results in adverse acute respiratory effects in populations of all ages. Exposure contrasts related to the different sources are usually poorly represented by the concentrations of PM10 or PM2.5. It is unclear how the chemical composition of PM affects respiratory health. One Aim of TRANSPHORM SP3 was to investigate the association between PM composition and chronic obstructive pulmonary disease (COPD), bronchitis symptoms, as well as asthma among the studies contributing to workpackage 4 (WP4) of the European ESCAPE centres that are also part of the TRANSPHORM multi-center projects. As ESCAPE developed, it became clear that the sample size of the WP4 studies faced some considerable reductions as only subjects with complete data and proper assignment of ESCAPE, thus TRANSPHORM, estimates of exposure were available for the ultimate analyses. Given that TRANSPHORM included only sites with PM measurements, the reduction in sample size affected in particular the investigation of the asthma as well as the bronchitis symptoms hypothesis. It would be inappropriate to use the limited data to investigate these hypotheses in more details, thus, those have not been further pursued. Objectives. To investigate the association between PM composition and COPD among the studies contributing to Workpackage 4 (WP4) of the European ESCAPE and TRANSPHORM multi-center projects. Methods. The analyses are based on subpopulations from four cohort studies included in the ESCAPE analysis. Residential long-term concentrations of PM<10µm (PM 10 ) and <2.5µm (PM 2.5 ) and selected components (copper, iron, potassium, nickel, sulfur, silicon, vanadium, zinc) were estimated with land-use regression models. Component effects were estimated using logistic regression models for each cohort separately. Cohort-specific results were combined using random effects meta-analysis. Main Results. Only three components were available in all cohorts. The meta analysis showed that the prevalence of COPD stage 1+ using the Lower Limit of Normal definition was positively associated with a 20ng/m 3 increase in PM10 Cu (OR 1.11 (95% CI ) and a 100ng/m 3 increase in PM10 K (OR 1.36 (95% CI ), however only PM10 K reached statistical significance. Similar results could be seen for the association of incidence of COPD with a 20ng/m 3 increase in PM10 Cu (OR %CI ) and a 5 ng/m 3 increase in PM2.5 Fe (OR % ), however none of the associations were statistically significant Conclusions. In conclusion, we observed heterogeneous results for the associations between PM composition and COPD prevalence and incidence. The data should be interpreted with caution as the statistical power of the analyses was rather limited. As numerous models were run, the only significant result found for K on PM10 may be due to chance.

4 Introduction Current evidence suggests that ambient air pollution results in adverse acute respiratory effects in populations of all ages [1]. Most studies have reported associations linked to particulate matter (PM) usually of the size smaller than 10µg (PM10) or 2.5 µg (PM2.5). PM consists of a mixture of many different constituents from many different sources. Exposure contrasts related to the different sources are usually poorly represented by the concentrations of PM10 or PM2.5. Traffic and combustion related components such as iron and nickel are considered to be more toxic than other components. Most epidemiological studies have only investigated short-term effects on other health outcomes such as cardiovascular outcomes and mortality [2-6] but not respiratory health outcomes. Studies on long-term effects of PM and its constituents have mainly been conducted in North America and to a lesser extent in Europe [7]. In the ESCAPE study (European Study of Cohorts for Air Pollution Effects, it was assessed whether long-term exposure to air pollution at the home residence was associated with different respiratory outcomes. Within this project it has been shown that higher PM levels were associated with an increased risk in Chronic Obstructive Pulmonary Disease (COPD) (Schikowski, submitted) and a reduction in lung function parameter FEV1 and FVC (Martin, submitted). The results for incidence of Asthma in adults (Jacquemin, submitted) and Chronic Bronchitis symptoms (Yutong, submitted) are inconsistent. A reason might be differences in particle composition [8]. The TRANSPHORM project (Transport related Air Pollution and Health impacts- Integrated Methodologies for Assessing Particulate Matter, provided additional exposure metrics for sites where ESCAPE collected particles. All PM10 and PM2.5 samples were analysed for elemental composition using Energy Dispersive X-ray fluorescence (XRF), and LUR models were developed from these speciation data. The original plan for TRANSPHORM was an assessment of the associations between these elements and COPD as well as asthma among the studies contributing to WP4 of ESCAPE (Respiratory Health in Adults). Since the submission of the TRANSPHORM proposal, the development of ESCAPE revealed a substantial reduction in the number of subjects available for the ESCAPE (and thus TRANSPHORM) analyses with respect to the sample size expected a priori.. This reduction was due in particular to i) the loss of subjects when subject specific variables were defined in all data set ii) the requirement for subjects included in the analyses to have at least a set of default variables available, iii) the limited study sites with PM measurements, thus, elemental composition data, and iv) further loss due to non-convergence of some LUR models and other reasons. In light of the experience with the ESCAPE analyses, where limitations in statistical power due to reduced sample size was already an issue, in particular for asthma incidence, the aim of the current study was to assess the effect of PM composition on COPD only as part of the TRANSPHORM project. If anything, statistical power may possibly be sufficient for COPD while certainly not for asthma. Methods Study populations The analyses are based on subpopulations from four cohort studies. All studies conducted lung function measurements at least at two occasions usually at baseline and at follow-up. To be included in the ESCAPE analyses, participants of the original cohort studies had to be at least 20 years old; have valid lung function data on both occasions; living in geographic areas where ESCAPE derived exposure models; valid non-missing information for the primary co-variates used in the main

5 models age, sex, smoking status, BMI, education (for models see below); and a successfully assigned home outdoor estimate of exposures (with PM). These requirement resulted in sub-population of the original studies being included in the TRANSPHORM analyses The methods used in the original studies are published elsewhere, briefly described below. ECRHS (European Community Respiratory Health Survey) was initiated in as a crosssectional study and had a follow up in The study covered 23countries with 48 centres and subjects were initially investigated at baseline. The follow- up had a response rate of 65.3% of the baseline sample. In ECRHS I adults aged between 20 and 44 years were selected at random from available populations based registers. The main objective of the ECRHS I study 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. SALIA (Study on the influence of Air pollution on Lung function, Inflammation and Aging ) 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. The follow-ups were conducted in 2006 and 2007 to 2010 to investigate the long-term effects of outdoor air pollution and changes in pollution on respiratory health. The baseline investigation included 4757 women. The geographic regions were chosen to represent a range of polluted areas with high traffic load and steel and coal industries. Sampling included all women of German nationality aged 54 to 55 residing in the selected areas. SAPALDIA (Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults) is a multicenter study that was performed 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. This study was initiated in 1991 and continued with a follow-up assessment in 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 results. Definition of COPD Airflow limitation is a hallmark of COPD. Only subjects with valid lung function data, both at baseline and follow-up were included. Reference values are needed to define COPD and to classify severity. Given the age structure of the populations we based the definitions on the third National Health and Nutrition Examination Survey (NHANES III) reference equations REF. Those were based on asymptomatic, lifelong non-smoking subjects with at least two acceptable manoeuvres of NHANES - a representative sample of the general U.S. population. These reference equations were developed for a Caucasian population aged 8-80 years of age. In light of the ongoing debates about the definition of COPD phenotypes, we used the two most frequently used frameworks, namely the GOLD and the LLN definitions. We will then assess whether the effects of air pollution exposure on COPD depend on the definition of COPD. 1) GOLD Definition: The Global Initiative for chronic Obstructive Lung Disease (GOLD) developed spirometric classifications in order to define and stage COPD [9, 10]. It uses a fixed ratio of the postbronchodilator forced expiratory volume in one second FEV1)/ FVC of 0.70 as a threshold to define obstruction. The GOLD criteria classify COPD based on the postbronchdilator lung function 5

6 measurement. Our studies only had pre-bronchodilator spirometers, thus those were used but we excluded subjects with asthma to increase the specificity of our COPD case definitions. The major criticism against the GOLD concept of using the fixed cutoff (FEV1/FVC ratio <0.7) to define obstruction is that the fixed cutoff can misdiagnose cases of airway obstruction, because FEV1/FVC has been shown to vary with age. The fixed ratio FEV1/FVC <0.7 is resulting in an overdiagnosis of milder patients who are elderly as they may have a reduced lung volume as a result of normal aging process. Thus, alternative definitions of COPD are based on the age-specific distribution of lung function in some reference population instead of a fixed cut-off value. 2) Lower Limit of Normal (LLN): we will additionally use the LLN (the statistically defined lower limit of normal), which is the forced expiratory volume expired in the first second divided by the forced vital capacity (FEV 1 /FVC) ratio below the fifth percentile of a large healthy reference group. The LLN definition uses FEV1/FVC below the predicted LLN as a threshold to define obstruction. The LLN will be calculated using the NHANES reference equations. Defining severity of COPD (stages) The severity of COPD is defined by the degree of the obstruction, measured with FEV1. Three cutoffs are usually used to derive four stages of severity, namely FEV1 80%, 50-80%, 30-50% and for the most severe cases <30% of normal. The prevalence of COPD stage 3 and 4 was low in our populations. We will therefore evaluate prevalence and incidence of COPD for only 2 categories, namely COPD in all stages (stage 1 or higher) and the combined group of stage 2, 3 and 4 (stage 2+) in all four cohorts. We do not use the original GOLD stage 0 definition (i.e. normal lung function but symptoms) but restrict to the objectively defined levels of lung function. Definition of COPD prevalence and incidence A prevalent case of COPD is somebody with COPD (i.e. FEV1/FVC ratio 0.70) at baseline and at follow-up. An incident case of COPD was defined as a study participant without COPD (i.e. FEV1/FVC ratio 0.70) nor asthma at baseline, but falling into the categories of a COPD case as defined above at follow-up examination (i.e. FEV1/FVC ratio < 0.70), but not having asthma at follow-up either. Therefore asthmatics who reported ever asthma at baseline or follow-up and/or subjects who reported the diagnose of asthma at either baseline or follow-up are excluded from the analyses. With the three categories (no COPD, stage 1+, stage 2+), the following categories of change in severity of obstruction between baseline and follow-up exam will be defined: 1. Incident case stage all (normal FEV1/FVC ratio at baseline and stage 1 or higher at follow-up), 2. Incident case stage 2 (normal FEV1/FVC at baseline and stage 2 or higher at followup), Exposure assessment The common ESCAPE/TRANSPHORM exposure assessment approaches are published elsewhere. In sum, standardized measurement protocols where used in all geographic sites of ESCAPE/TRANSPHORM. In half of the ESCAPE locations, extensive PM10 and PM2.5 monitoring campaigns were added. All PM10 and PM2.5 samples were analysed for elemental composition using Engery Dispersive X-ray fluorescence (XRF) (De Hoogh, 2013). Analyses were performed by Cooper Environmental Services, Portland USA. Forty-eight elements were measured. However, only 6

7 the PM2.5 and PM10 constituents copper (CU), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (si), vanadium (V) and zinc (Z) were used for further analysis as these components were detected in >80% of the samples. Land use regression models derived the spatial distributions of long-term mean concentrations for all exposure markers. These models were used to assign estimates to each residential address of all study participants. Statistical analyses Data of the studies were not pooled but analysed separately in each study centre following an identical analytic code, applied to datasets which all used the identical code book to define and name variables (ESCAPE code book). Models were defined a priori, based on the ESCAPE COPD analysis. Results were then combined using meta analytic approaches. All models were run a) for the default exposure and for the primary COPD outcomes, namely i) prevalence of COPD using GOLD and LLN in severity stages 1+ and 2+ at follow-up ii.) incidence of COPD using GOLD and LLN in severity stages 1+ and 2+. A hierarchical modelling approach was used. We first applied logistic regression models in each study separately to obtain study-specific estimates. MODEL 1 were the unadjusted crude analyses, MODEL 2 was a simple model with adjustment only for age, age squared, height, and sex (both in the prevalence and incidence analyses). 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 standardised form, based on evidence form previous studies and the assessment and quality of available data within the ESCAPE cohorts. The Main Model did not include variables that might be on the pathway linking air pollution with COPD. Thus, Model 3 included in the prevalence analyses all variables of Model 2 plus BMI, education, smoking and packyears and in the incidence analyses all variables of Model 2 plus BMI, BMI change, education, smoking, quitting, and packyears. All models included the PM constituent as well as PM2.5 or PM10. In a second step a meta-analysis to provide overall estimates was conducted. All meta-analyses were conducted from the Main Model results (model 3) only. 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 was used to calculate the summary effect estimates; in the instance that heterogeneity is found however, random-effects models were used instead. In addition, the I 2 statistic for quantifying heterogeneity was calculated. We carefully assessed the contribution from each cohort to the overall effect estimate. Results Altogether we included all the three cohorts in the analysis for which PM constituent measurements were available for at least one study region. A brief description of the study regions and participants of each cohort is shown in table 1. The mean age varied between 43.5 (ECRHS) and 73.3 years (SALIA). SAPALDIA where only one of the eight sites got included in the PM / TRANSPHORM protocol (namely Lugano) had the highest percentage of current smokers and SALIA the lowest. Table 2 shows the description of the estimated PM components for each cohort as well as the corresponding model fit (R2) of the LUR models. S, Ni, V and Si of PM10 and PM2.5 were not available for SAPALDIA. CU, FE and K were available in all cohorts. For all cities, t6he LUR model R2s were on average >0.50, with the exception of the a few cases where the R2 was below < All cities showed high R2 for CU for both PM10 and PM2.5. Our meta-analysis showed the overall pooled estimates for the associations between constituents, which were available in all cohorts, and COPD prevalence and incidence. The prevalence of COPD 7

8 stage 1+ using the LLN was positively associated with PM10 CU (OR 1.11 (95% CI ) and PM10 K (OR 1.36 (95% CI ), however only PM10 K reached statistical significance (figure 1). Similar results could be seen for the association of incidence of COPD with PM10 CU (OR %CI ) and PM2.5 FE (OR % ), however none of the associations were statistically significant (see Table 3 and figure 2). There was evidence for heterogeneity between the cohort-specific effect estimates for PM2.5 K and prevalence of COPD and PM10 K and incidence of COPD, but not for the other constituents. The models for COPD LLN stage 2+ did not converge due to limited power in some studies. Discussion In this European multicity study we could observe that COPD prevalence and incidence were positively associated with PM10 CU, PM10 K and PM2.5 FE outdoor concentrations estimated at the residence. With the exception of K on PM10, none of the associations were statistically significant, and results were also heterogeneous across the cohorts. Due to power issues and limited availability of constituents in all study areas, interpretation of these findings are a challenge. Only three components were available in all three studies, which made a meta-analysis only possible for these three components. The presented results have to be interpreted with caution as the more pronounced result for potassium might be a chance finding. In the SAPALDIA study only one city out of eight areas originally chosen to investigate long-term effects of air pollution in Switzerland, was included. In case of ECRHS, only 3 out of 21 sites were included. This reduced the study sample to 1924 subjects only. We were only able to include three of the initial 6 studies in the current analysis, because two studies (namely EGEA and E3N) were not included in the COPD analysis (different stud design and no lung function data) and for NSHD the analysis of the component analysis was not suitable for inclusion in this analysis, which further jeopardized statistical power. For the current analysis we used the PM measures from the ESCAPE project, which did not measure concentrations of PM components at residence but used land-use regression models. Therefore the estimated components might also reflect different sources in the different study areas. Differences in the cohort specific LUR models might be the reason for the large heterogeneity in the study results. Some LUR models were additionally adjusted for industry-related sources, whereas other cohorts only included traffic indicators. It is not advisable to conduct analyses for asthma incidence or for chronic bronchitis as statistical power problems would be further amplified and results would again be based on a highly selected number of sites where elemental composition may mark rather different sources or characteristics of pollution. In conclusion, we observed heterogeneous results for the effect of PM composition on COPD prevalence and incidence, which should be interpreted with caution. 8

9 Appendix: Table 1 Description of study populations of all three studies as used in the analyses. Presented are the subpopulations of the original studies with individually assigned PM measures, respectively. The table shows the N (and % of total N) for categorical variables, and the mean value (and standard deviation) in case of continuous variables. ECRHS SALIA SAPALDIA N=1541 N=580 N=729 PM population PM population PM population N sd/% N sd/% N sd/% Female Age at baseline Age at follow-up BMI at follow-up [kg/m 3 ] Smoking status at follow-up: Neversmoker Exsmoker Current smoker Pack years smoked at baseline Change in pack years from baseline to follow-up Educational level at follow-up: Low Medium High Environmental tobacco exposure at home or at work at follow-up Occupational exposure to dust/fumes or gases at follow-up

10 Table 2. Description of the PM components for each city with the corresponding model fit (R2). XRF based elements on PM10 Model adjusted R2 Model adjusted R2 TRANSPHORM Cities (study) N participants N of sites XRF PM2.5 N of sites Antwerp (ECRHS) 414 PM10_CU PM25_CU Ruhr (SALIA) 580 PM10_CU PM25_CU Torino (ECRHS) 142 PM10_CU PM25_CU Barcelona (ECRHS) 119 PM10_CU PM25_CU Lugano (SAPALDIA) 729 PM10_CU PM25_CU Antwerp (ECRHS) 414 PM10_FE PM25_FE Ruhr (SALIA) 580 PM10_FE PM25_FE Torino (ECRHS) 142 PM10_FE PM25_FE Barcelona (ECRHS) 119 PM10_FE PM25_FE Lugano (SAPALDIA) 729 PM10_FE PM25_FE Antwerp (ECRHS) 414 PM10_K PM25_K Ruhr (SALIA) 580 PM10_K PM25_K Torino (ECRHS) 142 PM10_K PM25_K Barcelona (ECRHS) 119 PM10_K PM25_K Lugano (SAPALDIA) 729 PM10_K PM25_NI Antwerp (ECRHS) 414 PM10_NI PM25_NI Ruhr (SALIA) 580 PM10_NI PM25_NI Torino (ECRHS) 142 PM10_NI PM25_NI Barcelona (ECRHS) 119 PM10_NI PM25_S Antwerp (ECRHS) 414 PM10_S PM25_S Ruhr (SALIA) 580 PM10_S PM25_S Torino (ECRHS) 142 PM10_S PM25_S Barcelona (ECRHS) 119 PM10_S PM25_SI Antwerp (ECRHS) 414 PM10_SI PM25_SI Ruhr (SALIA) 580 PM10_SI PM25_SI Torino (ECRHS) 142 PM10_SI PM25_SI Barcelona (ECRHS 119 PM10_SI PM25_V Antwerp (ECRHS) 414 PM10_V PM25_V Ruhr (SALIA) 580 PM10_V PM25_V Torino (ECRHS) 142 PM10_V PM25_V Barcelona (ECRHS) 119 PM10_V PM25_ZN Antwerp (ECRHS) 414 PM10_ZN PM25_ZN Ruhr (SALIA) 580 PM10_ZN PM25_ZN Torino (ECRHS) 142 PM10_ZN PM25_ZN Barcelona (ECRHS) 119 PM10_ZN PM25_CU Lugano (SAPALDIA) 729 PM10_ZN PM25_CU PM 10 : particulate matter with an aerodynamic diameter <10µm, PM 2.5 : particulate matter with an aerodynamic diameter <2.5µm, S: sulfur, K: potassium, CU: cupper, FE: iron, NI: nickel, V: vanadium, ZN: zink, SI: silicon, SD: standard deviation, a R² of the land use regression models based on PM measurements and geographic information system (GIS)-derived predictor variables, b amount of PM component on PM total. 10

11 Table 3 Adjusted association between PM constituents measured on PM10 and PM2.5 with both, the prevalence and incidence of COPD (all stages) using the lower limit of normal (LLN): Results from the random effect meta-analysis from single pollutant models (OR and 95%CIs) and I 2 (p-value) test for heterogeneity of effect estimates between cohorts Prevalence COPD stage 1plus Exposure aor (95%CI) I 2 p-value het Incidence COPD stage 1plus aor (95%CI) I 2 p-value het PM10 CU 1.11 (0.83, 1.49) 0.0 p = FE 0.99 (0.62, 1.58) 54.3 p = K 1.36 (1.03, 1.80) 0.0 p = PM2.5 CU 0.91 (0.47, 1.76) 45.6 p = FE 0.79 (0.52, 1.19) 0.0 p = K 0.92 (0.43, 1.99) 78.8 p = (0.77, 1.90) 12.9 p = (0.63, 1.43) 0.0 p = (0.23, 3.37) 97.1 p = (0.32, 2.71) 57.5 p = (0.50, 2.22) 51.9 p = (0.64, 2.16) 55.9 p = *PM10: particulate matter with an aerodynamic diameter <10µm; PM2.5: particulate matter with an aerodynamic diameter <2.5 µm; S: sulfur; K:potassium; Cu:copper; pvalue het: p-value of heterogeneity; Assocations are presented for the following increments in exposure: 20ng/m 3 for Cu; 5 ng/m 3 for FE; 100ng/m 3 for K Adjusted for sex at baseline, smoking at follow-up, maximum educational level, age at follow-up, height at baseline, BMI at follow-up of all participants Adjusted for sex at baseline, smoking at baseline, smoking cessation, maximum educational level, age at baseline, height at baseline, BMI at baseline, change in BMI of all participants; associations with traffic intensity and traffic load were additionally adjusted for background NO 2 concentrations 11

12 Figure 1. Cohort-specific and meta-analytically pooled association between Potassium (K) on PM10 and PM2.5 and COPD prevalence stage 1plus additionally adjusted for PM10 and PM2.5 mass concentration. Figure 2. Cohort-specific and pooled PM component Copper effects on COPD prevalence stage 1plus additionally adjusted for PM10 and PM

13 Figure 3. Cohort-specific and pooled PM component Iron effects on COPD incidence stage 1plus additionally adjusted for PM10 and PM

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