UBC Faculty Research and Publications

Satellite-based estimates of ambient air pollution and global variations in childhood asthma prevalence Anderson, H. Ross; Butland, Barbara K.; van Donkelaar, Aaron; Brauer, Michael; Strachan, David P.; Clayton, Tadd; van Dingenen, Rita; Amann, Marcus; Brunekreef, Bert; Cohen, Aaron; Dentener, Frank; Lai, Christopher; Lamsal, Lok N.; Martin, Randall V.; ISAAC Phase One study group; ISAAC Phase Three study group Sep 30, 2012

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata


52383-Anderson_H_et_al_Satellite_estimates_ambient.pdf [ 436.63kB ]
JSON: 52383-1.0074688.json
JSON-LD: 52383-1.0074688-ld.json
RDF/XML (Pretty): 52383-1.0074688-rdf.xml
RDF/JSON: 52383-1.0074688-rdf.json
Turtle: 52383-1.0074688-turtle.txt
N-Triples: 52383-1.0074688-rdf-ntriples.txt
Original Record: 52383-1.0074688-source.json
Full Text

Full Text

Environmental Health Perspectives • volume 120 | number 9 | September 2012 1333Research | Children’s HealthThere is substantial evidence from short-term exposure studies that ambient air pollution plays a role in the exacerbation of asthma symp-toms [World Health Organization (WHO) 2006]. In contrast, the evidence concerning asthma and long-term exposure to outdoor air pollution is not coherent. Individual-level studies conducted within communities suggest that traffic-related air pollution is associated with both the incidence and the prevalence of asthma (Health Effects Institute 2010), whereas between-community studies (i.e., those that compare communities) do not observe associa-tions between community-average levels of pol-lution and asthma prevalence (Anderson et al. 2011; WHO 2006; WHO European Centre for Environment and Health 2005).The International Study of Asthma and Allergies in Childhood (ISAAC) has obtained, using standardized protocols, data on the prevalence of asthma symptoms in > 2 million primary and secondary school–age children from > 200 communities in nearly 100 coun-tries throughout the world (ISAAC 2011). A study of Phase One ISAAC asthma prevalence data from the mid-1990s restricted to cities with > 100,000 population found no evidence of associations between various measures of asthma prevalence and city-level concentra-tions of particulate matter (with aerodynamic diameter ≤ 10 µm; PM10) estimated using an econometric model (Anderson et al. 2010). Recent developments in the application of sat-ellite remote sensing to ground-level air pol-lution (Hoff and Christopher 2009; Martin 2008) provide important new opportunities for investigating associations between air pol-lution and health outcomes on a global scale. To our knowledge, satellite-based estimates of particulate matter have been used to investigate associations with respiratory disease on a local scale (in Hong Kong) only (Lai et al. 2010).In this study we investigated, on a global scale, associations between the community-level prevalence of children’s asthma symptoms and satellite-based estimates of particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5), nitrogen dioxide (NO2), and mod-eled ozone (O3). In contrast with a previous analysis of Phase One ISAAC data (Anderson et al. 2010), the present analysis uses the Phase Three ISAAC data set, which is more recent and includes more centers. In addition, we investigated PM2.5 rather than PM10, as well as NO2 and O3. Finally, we examined for the first time associations between trends in air pollution and changes in prevalence within centers between Phases One and Three.MethodsAsthma data. Detailed ISAAC protocols are available on the ISAAC Website (ISAAC 2011). For the cross-sectional analysis we used Address correspondence to H.R. Anderson, Division of Population Health Sciences and Education, St George’s, University of London, Cranmer Terrace, London, SW17 0RE United Kingdom. Telephone: 442087255424. Fax: 442087253584. E-mail: r.anderson@sgul.ac.ukSupplemental Material is available online (http://dx.doi.org/10.1289/ehp.1104724).We thank the children and parents who participated in ISAAC Phase One and Phase Three, the school staff for their assistance and help with coordination, the ISAAC principal investigators and their colleagues, and the many funding bodies throughout the world that supported the individual ISAAC centers and collabora-tors and their meetings. We also thank M. Field-Smith for her assistance with the preparation of this paper. All investigators in the Phase One and Phase Three study groups are listed in Supplemental Material, p. 3. M.A. is employed by the International Institute for Applied Systems Analysis, Austria. A.C. is employed by the Health Effects Institute (HEI), Boston, MA, USA. The HEI receives about half of its core funds from the U.S. Environmental Protection Agency and half from the worldwide motor industry. The views expressed in this paper are those of the authors and do not necessar-ily reflect the views of the HEI or its sponsors.The authors declare they have no actual or potential competing financial interests.Received 9 November 2011; accepted 1 May 2012.Satellite-based Estimates of Ambient Air Pollution and Global Variations in Childhood Asthma PrevalenceH. Ross Anderson,1,2 Barbara K. Butland,2 Aaron van Donkelaar,3 Michael Brauer,4 David P. Strachan,2 Tadd Clayton,5 Rita van Dingenen,6 Marcus Amann,7 Bert Brunekreef,8 Aaron Cohen,9 Frank Dentener,6 Christopher Lai,10 Lok N. Lamsal,3,11,12 Randall V. Martin,3,13 and the ISAAC Phase One and Phase Three study groups1MRC-HPA Centre for Environment and Health, King’s College London, United Kingdom; 2MRC-HPA Centre for Environment and Health, St Georges, University of London, United Kingdom; 3Dalhousie University, Halifax, Nova Scotia, Canada; 4University of British Columbia, Vancouver, British Columbia, Canada; 5University of Auckland, Auckland, New Zealand; 6European Commission, Joint Research Centre, Ispra, Italy; 7International Institute for Applied Systems Analysis, Laxenburg, Austria; 8Institute of Risk Assessment Sciences and Julius Center for Health Sciences and Primary Care, Utrecht, the Netherlands; 9Health Effects Institute, Boston, Massachusetts, USA; 10The Chinese University of Hong Kong, Hong Kong, People’s Republic of China; 11Goddard Earth Sciences Technology and Research, Universities Space Research Association, Columbia, Maryland, USA; 12NASA Goddard Space Flight Center, Greenbelt, Maryland, USA; 13Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts, USABackground: The effect of ambient air pollution on global variations and trends in asthma  prevalence is unclear.oBjectives: Our goal was to investigate community-level associations between asthma prevalence data from the International Study of Asthma and Allergies in Childhood (ISAAC) and satellite-based estimates of particulate matter with aerodynamic diameter < 2.5 µm (PM2.5) and nitrogen dioxide (NO2), and modelled estimates of ozone.Methods: We assigned satellite-based estimates of PM2.5 and NO2 at a spatial resolution of 0.1° × 0.1° and modeled estimates of ozone at a resolution of 1° × 1° to 183 ISAAC centers. We used center-level prevalence of severe asthma as the outcome and multilevel models to adjust for gross national income (GNI) and center- and country-level sex, climate, and population density. We examined associations (adjusting for GNI) between air pollution and asthma prevalence over time in centers with data from ISAAC Phase One (mid-1900s) and Phase Three (2001–2003). results: For the 13- to 14-year age group (128 centers in 28 countries), the estimated average within-country change in center-level asthma prevalence per 100 children per 10% increase in center-level PM2.5 and NO2 was –0.043 [95% confidence interval (CI): –0.139, 0.053] and 0.017 (95% CI: –0.030, 0.064) respectively. For ozone the estimated change in prevalence per parts per billion by volume was –0.116 (95% CI: –0.234, 0.001). Equivalent results for the 6- to 7-year age group (83 centers in 20 countries), though slightly different, were not significantly positive. For the 13- to 14-year age group, change in center-level asthma prevalence over time per 100 children per 10% increase in PM2.5 from Phase One to Phase Three was –0.139 (95% CI: –0.347, 0.068). The corresponding association with ozone (per ppbV) was –0.171 (95% CI: –0.275, –0.067).conclusion: In contrast to reports from within-community studies of individuals exposed to traf-fic pollution, we did not find evidence of a positive association between ambient air pollution and asthma prevalence as measured at the community level.key words: air pollution, asthma prevalence, children, epidemiology, global, nitrogen  dioxide, ozone, particulate matter, satellite observations. Environ Health Perspect 120:1333–1339 (2012). http://dx.doi.org/10.1289/ehp.1104724 [Online 1 May 2012]Anderson et al.1334 volume 120 | number 9 | September 2012 • Environmental Health Perspectivespreviously published 12-month period preva-lence estimates for severe asthma symptoms obtained in ISAAC Phase Three (2000–2003) for children 13–14 years of age in 233 cen-ters in 97 countries, and children 6–7 years of age in 144 centers in 61 countries (Lai et al. 2009). Estimates were based on responses to self-completed questionnaires (ages 13–14 years) and parental report questionnaires (ages 6–7 years). The asthma outcome used through-out our analyses was severe wheezing in the preceding 12 months, defined as at least four attacks of wheeze or at least one episode of speech-limiting wheeze, or sleep disturbance due to wheeze at least once a week, during the preceding 12 months (Lai et al. 2009). For the time trend analysis we used published prevalence data for severe asthma symptoms among children 13–14 years of age from 106 ISAAC centers that participated in both Phase One (mid-1990s) and Phase Three (median time between surveys, 7 years) (Pearce et al. 2007). All collaborating centers obtained eth-ics approval for their study from their local ethics committee or board. Letters describing the survey were sent to parents of all children. Parental completion of the questionnaire for 6- to 7-year-olds implied informed consent. For the 13- to 14-year-olds, passive consent for the child to complete their own questionnaire at school was used by the great majority of centers.Pollution data. For this analysis, we esti-mated annual ground-level PM2.5 concentra-tions standardized to 50% relative humidity using satellite-based observations by combin-ing aerosol vertical profiles obtained from the global chemical transport model GEOS-Chem with total column aerosol depth obtained from two spectroradiometers (MODIS and MISR) on the satellite Terra, as discussed and vali-dated in detail elsewhere (Brauer et al. 2012; van Donkelaar et al. 2010). The relation between aerosol optical depth and ground-level PM2.5 is complex (Paciorek and Liu 2009). However, a comparison of  satellite-based PM2.5 estimates with ground-level measure-ments indicated significant agreement for North America (r = 0.77; slope = 1.07; n = 1,057) and sites from other parts of the world (r = 0.83; slope = 0.86; n = 244) (Brauer et al. 2012). The 1 SD of uncertainty in satel-lite-based PM2.5 was 25% as inferred through error propagation of uncertainty in satellite remote sensing of aerosol optical depth, in satellite sampling, and in aerosol vertical pro-file (Holben et al. 1998; Winker et al. 2010). The inferred 25% uncertainty was validated by comparison with in situ measurements over North America (van Donkelaar et al. 2010). Concentrations averaged over 2001–2006 were provided at 0.1° × 0.1° geographic grids (Atmospheric Composition Analysis Group 2010b; van Donkelaar et al. 2010). For the time trend analysis we estimated concentrations for 1990 by scaling 2001–2006 estimates using a GEOS-Chem simulation with anthropogenic emissions for 1990.We estimated annual mean ground-level NO2 concentrations, averaged for 2005, by combining GEOS-Chem NO2 profiles with tropospheric NO2 columns obtained from the Ozone Monitoring Instrument on the satel-lite Aura (Atmospheric Composition Analysis Group 2010a; Lamsal et al. 2008).We modeled 3-month running averages of daily 1-hr maximum O3 concentrations for the years 1990 and 2005 using the two-way nested TM5 Global Chemical Transport Model (de Meij et al. 2006; Huijnen et al. 2010; Krol et al. 2005) first at a resolution of 1° × 1° at the source regions and then con-verted to 0.1° × 0.1° grids using mathematical linear interpolation.Climate and other covariates.  We obtained daily mean temperature, monthly precipitation, and water vapor pressure data averaged over the period 1991–2000 for 0.5° × 0.5° grids from the International Panel on Climate Change Data Distribution Centre (Mitchell 2004; Mitchell and Jones 2005).Gross national incomes (GNI) per capita for 2001 (Atlas method) were provided by the World Bank (World Bank 2009) and where missing (five countries), were imputed using data from the Central Intelligence Agency (2003). We obtained population densities for 2005 from the Center for International Earth Science Information Network (2005) and processed them onto the 0.1° × 0.1° pollution grids.Assignment of environmental variables to centers. Our analyses were restricted to ISAAC Phase Three centers with respiratory data for 13- to 14-year-olds and complete pollu-tion data, which were contained (n = 177) or almost contained (n = 6) within a 1,000-km2 square. We used the geographic center of the study population, identified from a map, to obtain a starting 0.1° × 0.1° grid square and the eight surrounding 0.1° × 0.1° grid squares. To confirm that the starting grid square captured the center of population, we com-pared its population density with that of the eight surrounding 0.1° × 0.1° grid squares. The square with the highest population den-sity was designated the center grid and used for mapping prevalence estimates to climate,  altitude, population, and pollution variables.Statistical methods. The Spearman correla-tion coefficient was used to investigate unad-justed associations between the center-level variables. Our adjusted analysis focused on the regression slopes between asthma preva-lence and pollutant variables across centers within country (cross-sectional analysis: esti-mating the center-level slope) and across time points within centers (trend analysis: estimat-ing the temporal slope). We investigated these associations using multilevel linear regression models (Langford et al. 1998; Leckie 2010) to account for the clustered nature of the data (i.e., centers within countries, and time points within centers within countries) with explana-tory variables parameterized as suggested by Begg and Parides (2003) in order to facilitate the separate estimation of effects at different levels of the data hierarchy (e.g., country level, center level).Based on histograms (data not shown) both PM2.5 and NO2 appeared to have positively skewed distributions and were therefore log-transformed before modeling. We used multi-level linear regression rather than multilevel logistic regression because there was evidence of a linear association between asthma prevalence and log PM2.5 in both older [Pearson correla-tion coefficient (r) = –0.443; slope = –1.839; n = 183; p < 0.001] and younger (r = –0.341; slope = –1.715; n = 85; p = 0.001) age groups [Figure 1; see also Supplemental Material, Figure S1 (http://dx.doi.org/10.1289/ehp.1104724)] and because of the problems of overdispersion and scaling associated with the choice of a binomial error structure (Gelman and Hill 2007; Steele 2009).Country-level variables. Before modeling and for each center-level explanatory vari-able, we calculated a country-level variable X—i defined simply by the formula ,X nXi iijjn1i==/ [1]where Xij is the value of the explanatory variable for center j in country i and ni is the number of centers in country i (Begg and Parides 2003). Cross-sectional analyses. Models for analy ses of cross-sectional data included coun-try as a random intercept and fixed effects of the pollutant (log PM2.5, log NO2, or O3), sex (percent boys), the climate variables, and popu lation density in 2005 (including both the center-level and country-level representa-tion of each variable), and GNI per capita in 2001 (available only at country level). These models were used to estimate the absolute change in center-level asthma prevalence (per 100 children) associated with a 10% relative increase in center-level PM2.5 or NO2 or a 1-ppbV (parts per billion volume) absolute increase in O3 (i.e., the center-level regres-sion slope) adjusted for unmeasured effects of country, the fixed country-level effect of the pollutant, and fixed center- and country-level effects of the other explanatory variables.In addition, for the subset of centers for which there was at least one other center in the same country, we modeled country as both a random intercept and a random slope, thus allowing the estimated center-level effects of pollutants (i.e., center-level regression slopes) to vary among countries. The random Air pollution and global asthma prevalenceEnvironmental Health Perspectives • volume 120 | number 9 | September 2012 1335intercept and random intercept/random slope models were fitted using XTMIXED in STATA10 (StataCorp, College Station, TX, USA). Significance tests and 95% confidence intervals (CIs) for fixed-effect estimates were based on the standard normal distribution.For PM2.5 and NO2, we also added indi-vidual cross-level interaction terms to fully adjusted random intercept/random slope mod-els to investigate potential modifying effects of country-level variables on the center-level effects (slopes) of air pollutants. The country-level variables investigated included altitude, latitude, prevalence of current rhinoconjuncti-vitis, log PM2.5, and log NO2 (all calculated as in Equation 1) and GNI per capita.Trend analyses. Models of temporal trend included both center and country as random intercepts, as well as fixed effects of study (i.e., ISAAC phase), the pollutant (log PM2.5 or O3), and GNI per capita. For Phase One we used GNI per capita for 1992 provided by the World Bank (World Bank 2009), but where these data were missing (five countries) Phase One GNI was set equal to Phase Three GNI. NO2 was not available for the trend analysis. The pollutant was represented in models by three variables: Xijk, the value of the explanatory variable for study k in center j in country i; X—ij, the mean value of X across studies in center j and country i (as defined in Equation 2); and X—i, the mean value of X across centers and studies in country i (as in Equation 3, where ni is the number of centers in country i). GNI was represented by two variables: Gik, GNI per capita for study k in country i, and G—i, the mean value of GNI across studies in country i (as in Equation 4). This facilitated the separation of temporal effects from center-level and country-level effects. Small numbers precluded any meaningful trend analy-sis for the 6- to 7-year age group. XX2ijijkk 12= =/ [2] X nXiijjni1i==/ [3] GG2iikk 12= =/ [4]ResultsCross-sectional analysis, ages 13–14 years. For the cross-sectional analysis of 13- to 14-year-olds, 183 of the 233 centers in 83 of the 97 countries satisfied our inclusion criteria [see Supplemental Material, Figure S2 (http://dx.doi.org/10.1289/ehp.1104724)]. For this population, all three pollutants were positively correlated with popu-lation density and negatively correlated with water vapor pressure and rainfall (Table 1). NO2 was positively correlated with GNI per capita. Asthma prevalence was negatively correlated with all three pollutants (Table 1, Figure 1) and positively correlated with the three climate variables. The three pollutants were correlated positively with one another, the strongest being PM2.5 with O3 and the weakest being NO2 with O3. The Spearman correlation coefficient (rS) between the one standard deviation of uncer-tainty in the satellite-based estimate of PM2.5 (expressed as percent) and asthma prevalence was rS = 0.084 (p = 0.260).Cross-sectional analysis of PM2.5. The fully adjusted random intercept model estimate (model 3, Table 2)—the estimated change Figure 1. Scatterplots of the association of asthma prevalence at 13–14 years of age with (A) PM2.5 (µg/m3) and (B) NO2 (ppbV). 151050151050Asthma prevalence per 100 childrenAsthma prevalence per 100 children1.25 2.5 5 10 20 40 80PM2.5 (µg/m3)0.01 0.04 0.2 1 5 25 100NO2 (ppbV)Single-center countryMulticenter countryTable 1. Descriptive statistics for the main analytic data set ages 13–14 years (n = 183 centers).aMedian (interquartile range)Spearman correlation withVariable Time period  Asthma prevalence PM2.5 NO2 O3Sex (ISAAC Phase Three, ages 13–14 years)     % boys in sample ≈ 2000–2003 49.2 (47.1–51.5) –0.082 0.134 0.296# 0.201**Disease (ISAAC Phase Three, ages 13–14 years)Asthma prevalence (%) ≈ 2000–2003 5.05 (3.34–8.04) — –0.412# –0.198** –0.489#Climate/altitudeDaily temperature (°C) 1991–2000 18.7 (12.7–24.9) 0.159* –0.033 –0.358# –0.060Water vapor pressure (hPa) 1991–2000 14.5 (10.8–22.5) 0.209** –0.183* –0.372# –0.196**Precipitation (mm/month) 1991–2000 81.5 (50.3 –125.1) 0.217** –0.347# –0.248# –0.355#Altitude (m) NA 85 (22–458) 0.006 0.099 –0.019 0.102Economic/populationGNI per capita (US$) 2001 1,960 (1,020–9,800) –0.023a –0.022a 0.537a,# –0.132aPopulation density (thousands per 0.1° × 0.1° grid square) 2005 167 (47.0–514) –0.038 0.302# 0.408# 0.218**PollutionPM2.5 (µg/m3) 2001–2006 14.6 (8.2–19.4) — — 0.468# 0.617#NO2 (ppbV) 2005 0.77 (0.36–2.00) — — — 0.332#O3 (ppbV) 2005 53.2 (40.4–61.7) — — — —NA, not applicable. aCorrelations for GNI per capita (available only at country level) are with country-level variables (defined as in Equation 1). There are 83 countries. All other correlations are across all 183 centers. *p < 0.05, **p < 0.01, #p < 0.001.Anderson et al.1336 volume 120 | number 9 | September 2012 • Environmental Health Perspectivesin center-level asthma prevalence associated with a 10% relative increase in center-level PM2.5—was small and nonsignificant (–0.016 per 100 children; 95% CI: –0.095, 0.063). The estimated change in center-level asthma preva-lence associated with a 10% relative increase in country-level PM2.5—the country-level effect—indicates that the association between asthma and PM2.5 may differ at different levels of geographical aggregation, and highlights the importance of fitting models that allow for this difference. In this case, the additional esti-mated effect of country-level PM2.5 (defined as in Equation 1) on center-level prevalence was estimated as –0.172 (95% CI: –0.306, –0.038) (Table 2).Whereas all 183 centers contributed to the estimation of the country-level effect, only data from those countries with at least two centers could contribute to the estima-tion of the center-level effect. When the data set was restricted to the 28 countries with at least two centers (n = 128) the model fit was improved significantly by allowing the center-level regression slope to vary between countries (model 5), although this had little effect on the overall center-level effect esti-mate (now the estimated center-level slope for the average country), which was still small and nonsignificant (–0.043; 95% CI: –0.139, 0.053). We found no evidence of any modify-ing effect on the  center-level slope by GNI per capita (p = 0.440) or country-level altitude (p = 0.664), latitude (p = 0.971), prevalence of current rhinoconjunctivitis (p = 0.224), log PM2.5 (p = 0.489), or log NO2 (p = 0.280).Figure 2A displays country-specific  center-level effect estimates for PM2.5 based on model 5 (Table 2). The estimates are sorted by country-level asthma prevalence (defined as in Equation 1) from low (China) to high (Channel Islands) and suggest an inverse asso-ciation between the gradient of the center-level slope and country-level prevalence.When we expanded model 5 to include adjustment for NO2 and O3 (Table 2, models 6–8) the overall center-level effect estimate for PM2.5 moved further toward the null.Cross-sectional analysis of NO2. The fully adjusted estimated change in center-level asthma prevalence per 10% increase in  center-level NO2 (Table 2, models 3 and 4) was small and nonsignificant (0.012; 95% CI: –0.031, 0.055). When we allowed the center-level slope to vary Table 2. The association of asthma prevalence ages 13–14 years with PM2.5 and NO2.Estimated change in center-level asthma prevalence (95% CI) per 100 children per 10% increasePM2.5 NO2No. Model type Adjustment Country-levela Center-levelb Country-levela Center-levelbUsing data from 183 centers in 83 countries1 Random intercept Unadjusted –0.128 (–0.248, –0.009)* –0.032 (–0.101, 0.037) –0.032 (–0.092, 0.027) –0.005 (–0.040, 0.029)2 Random intercept Sex, climate, GNI –0.160 (–0.282, –0.037)* –0.028 (–0.100, 0.043) –0.062 (–0.133, 0.009) –0.002 (–0.037, 0.032)3 Random intercept Sex, climate, GNI, population density –0.172 (–0.306, –0.038)* –0.016 (–0.095, 0.063) –0.068 (–0.149, 0.013) 0.012 (–0.031, 0.055)Restricted to two or more centers per country (128 centers in 28 countries)4 Random intercept Sex, climate, GNI, population density –0.293 (–0.445, –0.140)# –0.016 (–0.095, 0.063) –0.253 (–0.391, –0.114)# 0.012 (–0.031, 0.055)5 Random intercept/random slopecSex, climate, GNI, population density –0.232 (–0.359, –0.105)# –0.043 (–0.139, 0.053) –0.262 (–0.391, –0.133)# 0.017 (–0.030, 0.064)6 Random intercept/random slopeSex, climate, GNI, population density, O3 –0.068 (–0.193, 0.058) –0.020 (–0.132, 0.092) –0.142 (–0.265, –0.019)* 0.022 (–0.025, 0.069)7 Random intercept/random slopeSex, climate, GNI, population density, log(PM2.5) or log(NO2) as appropriate–0.116 (–0.264, 0.032) –0.026 (–0.133, 0.081) –0.163 (–0.309, –0.018)* 0.020 (–0.032, 0.072)8 Random intercept/random slopeSex, climate, GNI, population density, O3, log(PM2.5) or log(NO2) as appropriate–0.004 (–0.139, 0.131) –0.008 (–0.121, 0.105) –0.130 (–0.262, 0.002) 0.027 (–0.025, 0.079)Sex, climate (i.e., temperature, precipitation, water vapor pressure), population density, and pollutants, if included in models were included both as country level (defined as in Equation 1) and center-level variables. GNI per capita was only available at country level.aCountry-level effect: estimate of the association between center-level asthma prevalence and country-level pollutant (defined as in Equation 1). bCenter-level effect: estimate of the within country association between center-level asthma prevalence and center-level pollutant. cTest (likelihood ratio test) for a random slope in PM2.5 (model 5), χ2 = 10.76 (degrees of freedom = 2), p < 0.01; test for a random slope in NO2 (model 5), χ2 = 6.64 (degrees of freedom = 2), p < 0.05. *p < 0.05, #p < 0.001. Figure 2. Country-specific estimates of change in center-level prevalence per 100 children 13–14 years of age per 10% increase in center-level PM2.5 (A) and per 10% increase in center-level NO2 (B). Estimates were obtained from model 5 (Table 2) and sorted by country-level asthma prevalence (defined as in Equation 1) from low (China) to high (Channel Islands). Change in prevalence per 100 children Change in prevalence per 100 childrenChinaLithuaniaIndonesiaMalaysiaIndiaSerbia and MontenegroItalyJapanSyrian Arab RepublicPalestineMexicoPortugalSpainPolandMoroccoIranArgentinaKyrgyzstanChileThailandEcuadorPakistanColombiaBrazilTunisiaKenyaNew ZealandChannel IslandsChinaLithuaniaIndonesiaMalaysiaIndiaSerbia and MontenegroItalyJapanSyrian Arab RepublicPalestineMexicoPortugalSpainPolandMoroccoIranArgentinaKyrgyzstanChileThailandEcuadorPakistanColombiaBrazilTunisiaKenyaNew ZealandChannel Islands–0.3 –0.2 –0.1 0.1 0.2 0.30 –0.3 –0.2 –0.1 0.1 0.2 0.30Air pollution and global asthma prevalenceEnvironmental Health Perspectives • volume 120 | number 9 | September 2012 1337between countries (model 5), the model fit improved significantly although the estimated center-level effect for the average country was still small and nonsignificant (0.017; 95% CI: –0.030, 0.064). Figure 2B shows some sug-gestion of an inverse association between the gradient of the center-level slope and country-level asthma prevalence. However, we found no evidence of any modifying effects on the cen-ter-level slope of other country-level variables including GNI per capita (p = 0.944), altitude (p = 0.751), latitude (p = 0.302), prevalence of rhinoconjunctivitis (p = 0.541), log PM2.5 (p = 0.199), or log NO2 (p = 0.563).When we expanded model 5 to include adjustment for PM2.5 and O3 (Table 2,  models 6–8), the center-level effect estimate was little changed.Cross-sectional analysis of O3. Using the random intercept/random slope model with full adjustment (model 5) we estimated that for the average country the change in center-level prevalence (per 100 children) associated with a 1-ppbV increase in center-level ozone was –0.116 (95% CI: –0.234, 0.001) (data not shown).Cross-sectional analysis ,  ages  6–7 years. The results for children 6–7 years of age are shown in Supplemental Material, Tables S1 and S2, Figure S1 (http://dx.doi.org/10.1289/ehp.1104724). In contrast to our findings for those 13–14 years of age, asthma prevalence among younger children was negatively associated with the percentage of boys in the sample.In the random intercept model with full adjustment, the estimated change in center-level asthma prevalence per 100 children per 10% increase in center-level PM2.5 and NO2 was 0.026 (95% CI: –0.116, 0.168) and 0.004 (95% CI: –0.059, 0.067) respectively—both positive and nonsignificant. For O3 the esti-mated change in prevalence per ppbV was –0.128 (95% CI: –0.247, –0.009)—negative and statistically significant. There was no evi-dence that associations with pollutants in this age group differed among countries (i.e., no significant improvement in the fit of mod-els from allowing center-level slopes to vary between countries).Trend analysis. Eighty-five centers were eligible for the trends analysis of 13- to 14-year-olds [see Supplemental Material, Figure S2 (http://dx.doi.org/10.1289/ehp.1104724)] but these are not representa-tive of the whole sample (see Table 3, notes). Pollution data for the early period were avail-able only for PM2.5 and O3. A scatterplot (Figure 3) of absolute change in asthma preva-lence between phases versus the ratio of PM2.5 (Phase Three/Phase One) suggests a weak nonsignificant negative association (Spearman correlation coefficient: rS = –0.182, p = 0.095). Using a random intercept model we estimated the absolute change in asthma prevalence asso-ciated with a 10% relative increase in PM2.5 over time within center, having adjusted for center (including any effects of center-average PM2.5, defined as in Equation 2) and change in GNI per capita. The adjusted estimate was negative but nonsignificant (–0.139; 95% CI: –0.347, 0.068) (Table 3). Using the same approach, the estimated change in asthma prevalence associated with a 1-ppbV increase in O3 between phases, adjusted for center (including center-average O3, defined as in Equation 2) and change in GNI per capita, was negative and statistically significant (–0.171; 95% CI: –0.275, –0.067). When we attempted to allow these temporal asso-ciations to vary between centers using random intercept/ random slope models, the model for PM2.5 failed to converge, and for O3 there was no evidence of any improvement in fit (p > 0.05).DiscussionThe central aim of this study was to investi-gate the potential role of community-average levels of PM2.5, NO2, and O3 in explaining worldwide variations in childhood asthma prevalence. Neither the cross-sectional nor longitudinal analyses provided any support for a positive association with any of these measures of ambient air pollution. This con-clusion is robust to the statistical modeling Table 3. The association between pollutants and asthma prevalence ages 13–14 years: trend analysis based on 85 centers in 50 countries.Pollutant (increment)Estimated change in center-level asthma prevalence (95% CI) per 100 children per increment in pollutantModel Adjustment Country levela Center levelbCenter level over time (Phase Three – Phase One)cRandom intercept PM2.5 (10%) Unadjusted –0.184 (–0.369, 0.001) 0.155 (–0.092, 0.402) –0.145 (–0.351, 0.060)GNI per capita –0.200 (–0.379, –0.022)* 0.149 (–0.100, 0.398) –0.139 (–0.347, 0.068)Random intercept O3 (1 ppbV) GNI per capita 0.201 (0.051, 0.351)** –0.092 (–0.259, 0.075) –0.171 (–0.275, –0.067)**Each pollutant when included in models was included as 3 variables, Xijk, the value of the explanatory variable for study k in center j in country i ; X—ij (as defined in Equation 2); and X—i (as defined in Equation 3). GNI per capita, which was available only at country-level was included as two variables, Gik (GNI for study k in country i ) and G—i (as defined in Equation 4). A cross-sectional Phase Three analysis for PM2.5 restricted to the 85 centers with both Phase One and Phase Three data and based on a random intercept model with full adjustment, yielded a center-level estimate of 0.21 (0.072, 0.348)** and a country-level estimate of –0.461 (–0.645, –0.277).# aEstimate of the association between center-level asthma prevalence and country-average pollutant (defined as in Equation 3). bEstimate of the within-country association between center-level asthma prevalence and center-average pollutant (defined as in Equation 2). cEstimate of the within-center association between change in center-level asthma prevalence over time (Phase Three – Phase One) and change in center-level pollutant over time (Phase Three – Phase One). *p < 0.05, **p < 0.01.Figure 3. Scatterplot illustrating the association between absolute change (Phase Three – Phase One) in asthma prevalence per 100 children 13–14 years of age and relative change (Phase Three/Phase One) in PM2.5.Absolute change in asthma prevalence per 100 childrenRatio of PM2.5 (Phase Three/Phase One)1050–5–100.5 1.0 2.01.5Anderson et al.1338 volume 120 | number 9 | September 2012 • Environmental Health Perspectivesand control at country level for GNI and at country level and center level for sex, climate, and population density.Being based on ecological data, our analy-ses were not designed to investigate asso-ciations between air pollution and asthma prevalence at an individual level within  centers. An individual-level analysis of ISAAC data found a convincing association between asthma prevalence and proximity to truck traffic (Brunekreef et al. 2009). The disparity between within-community associations at the individual level and between- community associations at the ecological level thus mir-rors disparities between individual- and  community-level data in the wider literature (Anderson et al. 2011; Health Effects Institute 2010; WHO 2006; WHO European Centre for Environment and Health 2005). In further analyses we plan to investigate this apparent paradox by extending our multilevel models to include individual-level data on proximity to truck traffic, secondhand smoke, and cook-ing fuels, which are available for a subset of ISAAC Centres.As part of our current analyses, we esti-mated country-level effects of pollution, some of which were negative and statistically sig-nificant (Table 2). We do not interpret these associations as causally related to ambient air pollution but as representing important clues in the investigation of causes of variations in asthma prevalence between countries. These might be elucidated by identifying potential causal factors that are negatively associated with air pollution such as lifestyle and eco-nomic development.The health hazard of ambient NO2 is debated, with most authorities tending to regard it as an indicator of more toxic com-ponents of the pollution mixture rather than as toxic per se (WHO 2006). The value of including NO2 in our analyses was primar-ily as a more precise marker of combustion-related pollution than PM2.5, which may include noncombustion sources such as dust (Veefkind et al. 2011). We found, however, that NO2 was strongly and positively corre-lated with PM2.5. And like PM2.5, the within-country center-level association of NO2 with asthma symptoms, though more precisely esti-mated than the estimate for PM2.5, was weak and nonsignificant; furthermore, its inclusion in the PM2.5 model did not materially influ-ence the center-level estimates for PM2.5.O3 is a secondary pollutant which is toxic to the respiratory system at ambient or near ambient concentrations (WHO 2006). It is plausibly linked to asthma prevalence through effects on severity of exacerbations and longer-term airways damage. Our O3 concentrations were estimated by the global chemical trans-port model TM5 but with less confidence than our estimates for PM2.5 because the spatial resolution was lower and allowance for the urban titration effect could not be made. The unadjusted correlation for O3 across all centers was strongly positive with PM2.5 and moderately strongly negative with asthma prevalence; in the main analysis however, the influence of O3 on the center-level estimates for PM2.5 was small. When it was considered as an explanatory variable, there was some evidence that O3 was negatively correlated with childhood asthma both at the center-level (within countries) and over time (within centers). This result is consistent with recent reviews of multicommunity studies of O3 and asthma (Anderson et al. 2011).Our methods for measuring asthma in large populations were limited to question-naires that attempt to summarize symptoms experienced over a prior period, in this case 12 months. The ISAAC questionnaire has been validated in terms of physician assess-ment (Jenkins et al. 1996), comparison between surveys of 6- to 7-year-olds and 13- to 14-year-olds carried out independently in the same center, comparisons with inde-pendent adult asthma surveys in the same country (Pearce et al. 2000), and comparisons with national hospital admission and mortal-ity rates (Anderson et al. 2008). However, we cannot be certain that the results of this questionnaire would be sufficiently sensitive to reflect any marginal effect of air pollution on asthma exacerbations.The importance of the trend analysis is that, being within center and by using the same methods, it controls for unknown sources of bias and for unknown or unmea-sured confounding factors that do not vary substantially over time. Nevertheless it is even more prone than our cross-sectional analyses to the adverse effects of measurement error. Nonetheless, the nonsignificant negative asso-ciations with changes in PM2.5 over time are consistent with the nonsignificant negative association with PM2.5 obtained in the cross-sectional analysis.ConclusionIn this ecological study we did not find evi-dence of positive community-level associations between the prevalence of asthma and satellite-based estimates of PM2.5 and NO2 and mod-eled estimates of O3, either cross-sectionally or over time. It is possible that an underlying positive community-level association may have been obscured by insufficient precision in our measures of exposure and outcome and by lim-ited statistical power. Nonetheless, our findings do not support an association between ambi-ent air pollution and asthma prevalence at the community level. The disparity between these findings and those of within-community stud-ies of individuals exposed to traffic pollution remains to be explained.RefeRencesAnderson HR, Favarato G, Atkinson RW. 2011. Long-term expo-sure to outdoor air pollution and the prevalence of asthma: meta-analysis of multi-community prevalence studies. Air Qual Atmos Health; doi:10.1007/s11869-011-0145-4 [Online 19 April 2011].Anderson HR, Gupta R, Kapetanakis V, Asher MI, Clayton T, Robertson CF, et al. 2008. International correlations between indicators of prevalence, hospital admissions and mortality for asthma in children. Int J Epidemiol 37:573–582.Anderson HR, Ruggles R, Pandey KD, Kapetanakis V, Brunekreef B, Lai CK, et al. 2010. Ambient particulate pollution and the world-wide prevalence of asthma, rhi-noconjunctivitis and eczema in children: Phase One of the International Study of Asthma and Allergies in Childhood (ISAAC). Occup Environ Med 67:293–300.Atmospheric Composition Analysis Group, Dalhousie University. 2010a. Annual Global Mean Ground-level NO2 Mixing Ratio at OMI Overpass Time (~1:30 PM), 2005. Available: http://fizz.phys.dal.ca/~atmos/g47.swf [accessed 11 October 2010].Atmospheric Composition Analysis Group, Dalhousie University. 2010b. Annual Mean Satellite-Derived PM2.5, 2001–2006, at 50% RH. Available: http://fizz.phys.dal.ca/~atmos/g47.swf [accessed 6 December 2010].Begg MD, Parides MK. 2003. Separation of individual-level and cluster-level covariate effects in regression analysis of correlated data. Stat Med 22:2591–2602.Brauer M, Amann M, Burnett RT, Cohen A, Dentener F, Ezzati M, et al. 2012. Exposure assessment for estimation of the global burden of disease attributable to outdoor air pollution. Environ Sci Technol 46 (2):652–660. Brunekreef B, Stewart AW, Anderson HR, Lai CK, Strachan DP, Pearce N. 2009. Self-reported truck traffic on the street of residence and symptoms of asthma and allergic disease: a global relationship in ISAAC Phase 3. Environ Health Perspect 117:1791–1798.Center for International Earth Science Information Network (CIESIN), Columbia University; and Centro Internacional de Agricultura Tropical (CIAT). 2005. Gridded Population of the World, Version 3 (GPWv3): Population Density Grid. Palisades, NY:Socioeconomic Data and Applications Center (SEDAC), Columbia University. Available: http://sedac.ciesin.columbia.edu/gpw. [accessed 3 April 2010].Central Intelligence Agency. 2003. The World Factbook 2002. Washington, DC:Central Intelligence Agency. Available: https://www.cia.gov/library/publications/download/download-2002/index.html [accessed 10 November 2010].de Meij A, Krol M, Dentener F, Vignati E, Cuvelier C, Thunis P. 2006. The sensitivity of aerosol in Europe to two different emission inventories and temporal distribution of emis-sions. Atmos Chem Phys 6:4287–4309.Gelman A, Hill J. 2007. Analytical Methods for Social Research: Data Analysis Using Regression and Multilevel/Hierarchical Models. New York:Cambridge University Press. Health Effects Institute. 2010. Traffic-Related Air Pollution: A Critical Review of the Literature on Emissions, Exposure, and Health Effects. HEI special report 17. Boston:Health Effects Institute.Hoff RM, Christopher SA. 2009. Remote sensing of particulate pollution from space: have we reached the promised land? J Air Waste Manag Assoc 59:645–675.Holben BN, Eck TF, Slutsker I, Tanre D, Buis JP, Setzer A, et al. 1998. AERONET – a federated instrument network and data archive for aerosol characterization. Remote Sens Environ 66:1–16.Huijnen V, Williams J, van Weele M, van Noije T, Krol M, Dentener F, et al. 2010. The global chemistry transport model TM5: description and evaluation of the tropospheric chemistry version 3.0. Geosci Model Dev 3:445–473.ISAAC (The International Study of Asthma and Allergies in Childhood). 2011. Available: http://isaac.auckland.ac.nz [accessed November 2011].Jenkins M, Clarke J, Carlin J, Robertson C, Hopper J, Dalton M, et al. 1996. Validation of questionnaire and bronchial hyper-responsiveness against respiratory physician assessment in the diagnosis of asthma. Int J Epidemiol 25:609–616.Krol M, Houweling S, Bregman B, van den Broek M, Segers A, van Velthoven P, et al. 2005. The two-way nested global chemistry-transport zoom model TM5: algorithm and appli-cations. Atmos Chem Phys 5:417–432.Lai CK, Beasley R, Crane J, Foliaki S, Shah J, Weiland S. 2009. Global variation in the prevalence and severity of Air pollution and global asthma prevalenceEnvironmental Health Perspectives • volume 120 | number 9 | September 2012 1339asthma symptoms: phase three of the International Study of Asthma and Allergies in Childhood (ISAAC). Thorax 64:476–483.Lai HK, Ho SY, Wong CM, Mak KK, Lo WS, Lam TH. 2010. Exposure to particulate air pollution at different living loca-tions and respiratory symptoms in Hong Kong—an appli-cation of satellite information. Int J Environ Health Res 20:219–230.Lamsal LN, Martin RV, van Donkelaar A, Steinbacher M, Celarier EA, Bucsela E, et al. 2008. Ground-level nitrogen dioxide concentrations inferred from the satellite-borne Ozone Monitoring Instrument. J Geophys Res 113:D16308.Langford IH, Bentham G, McDonald AL. 1998. Multi-level mod-elling of geographically aggregated health data: a case study on malignant melanoma mortality and UV exposure in the European Community. Stat Med 17:41–57.Leckie G. 2010. LEMMA (Learning Environment for Multilevel Methods and Applications). Module 5: Introduction to Multilevel Modelling Stata Practical. LEMMA VLE, University of Bristol, Centre for Multilevel Modelling. Available: http://www.cmm.bris.ac.uk/lemma/mod/resource/view.php?id=657 [accessed 30 July 2012].Martin RV. 2008. Satellite remote sensing of surface air quality. Atmos Environ 42:7823–7843.Mitchell TD. 2004. High Resolution Observational Climatologies, version 2.1. University of East Anglia:Climatic Reseach Unit. Available: http://www.ipcc-data.org/obs/cru_ts2_1.html [accessed 9 November 2010].Mitchell TD, Jones PD. 2005. An improved method of construct-ing a database of monthly climate observations and asso-ciated high-resolution grids. Int J Climatol 25:693–712.Paciorek CJ, Liu Y. 2009. Limitations of remotely sensed aero-sol as a spatial proxy for fine particulate matter. Environ Health Perspect 117:904–909.Pearce N, it-Khaled N, Beasley R, Mallol J, Keil U, Mitchell E, et al. 2007. Worldwide trends in the prevalence of asthma symptoms: phase III of the International Study of Asthma and Allergies in Childhood (ISAAC). Thorax 62:758–766.Pearce N, Sunyer J, Cheng S, Chinn S, Björkstén B, Burr ML, et al. 2000. Comparison of asthma prevalence in the ISAAC and the ECRHS. Eur Resp J 16:420–426.Steele F. 2009. Module 7: Multilevel Models for Binary Responses: Concepts. LEMMA VLE, University of Bristol, Centre for Multilevel Modelling. Available: http://www.cmm.bris.ac.uk/lemma/mod/resource/view.php?id=565 [accessed 30 July 2012].van Donkelaar A, Martin RV, Brauer M, Kahn R, Levy R, Verduzco C, et al. 2010. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ Health Perspect 118:847–855.Veefkind JP, Boersma KF, Wang J, Kurosu TP, Krotkov N, Chance K, et al. 2011 Global satellite analysis of the rela-tion between aerosols and short-lived trace gases. Atmos Chem Phys 11:1255–1267.WHO (World Health Organization). 2006. Air Quality Guidelines: Global Update 2005, Particulate Matter, Ozone, Nitrogen Dioxide and Sulphur Dioxide. Copenhagen:WHO Regional Office for Europe.WHO European Centre for Environment and Health. 2005. Effects of Air Pollution on Children’s Health and Development – A Review of The Evidence. Bonn:WHO Regional Office for Europe.Winker DM, Pelon J, Coakley JA, Ackerman SA, Charlson RJ, Colarco PR, et al. 2010. The CALIPSO Mission: A Global 3D View of Aerosols and Clouds. B Am Meteorol Soc 91:1211–1229.World Bank. 2009. GNI per Capita, Atlas Method (current US$). Available: http://data.worldbank.org/indicator/NY.GNP.PCAP.CD [accessed 15 June 2009].


Citation Scheme:


Citations by CSL (citeproc-js)

Usage Statistics



Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            async >
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:


Related Items