Health Risks of Exposure to Chemical Composition of Fine Particulate Air Pollution Francesca Dominici Yeonseung Chung Michelle Bell Roger Peng Department of Biostatistics School of Public Health Harvard University

PM 2.5 PM 10 PM Chemical constituents SizeTotal mass SO4= Si Ca Z Ni NH4 + NO3- Fe EC OC Inorganic fraction of PM Metals Al Groups Bell Dominici Ebisu Zeger Samet EHP 2007

New Scientific Questions and Statistical Challenges What are the mechanisms of PM toxicity?  Size?  Chemical components?  Sources?

Three questions Are day-to-day changes in the levels of the PM 2.5 chemical components associated with day-to-day changes in admission rates? (short-term effects of PM 2.5 components) –Multi-site time series studies of PM components Are the short-term effects of PM 2.5 total mass on admission rates modified by long-term averages of PM 2.5 chemical components? –Multi-site time series studies of PM total mass and second stage regression on PM components Are the long-term effects of PM 2.5 total mass on mortality modified by long-term averages of PM 2.5 components? –Spatially varying coefficient models

National Data

The National Medicare Cohort Study, (MCAPS) Medicare data include: –Billing claims for everyone over 65 enrolled in Medicare (~48 million people), date of service disease (ICD 9) age, gender, and race place of residence (zip code) Approximately 204 counties linked to the PM 2.5 monitoring network

MCAPS study population: 204 counties with populations larger than 200,000 (11.5 million people)

Daily time series of hospitalization rates and PM 2.5 levels in Los Angeles county ( )

Exposure data: Chemical composition data on PM 2.5 from the STN network 1.Constructed a database of time series data for 52 PM 2.5 chemical constituents from over 250 STN monitors for 2000 to Identified a subset of PM 2.5 components that substantially contribute and/or co-vary with daily PM 2.5 concentrations 3.Constructed a database that links by zip code the chemical composition data to human health data Bell et al EHP 2007

Only seven of the 52 components contributed 1% or more to total mass for yearly or seasonal averages 1.OCM 2.Sulfate 3.Nitrate 4.EC 5.Silicon 6.Sodium Ion 7.Ammonium Chemical composition data on PM 2.5

PM 2.5 chemical components and mortality rates:

Short Term Exposures

Multi-site time series data 1.Semi-Parametric Regression for time series data 2.Hierarchical Models for combining health risks across locations 3.Model Uncertainty in effect estimation

Confounders: weather variables seasonality JASA 2004

Everson and Morris, JRSSB 2000 Dominici Samet Zeger JRSSA 2000 R package for TLNISE, released on March by Roger Peng Smooth part

Assessing the sensitivity of the results to model assumptions Sensitivity of the exposure effect estimate to: the number of degrees of freedom in the smooth functions of time to adjust for seasonality the degree of flexibility in the adjustment for weather variables other potential confounders (e.g other pollutants)

PM 2.5 and Admissions PM and Admissions US EPA PM Fact Sheet 2006: To better protect public health EPA issued the Agency most protective suite of national air quality standards for particle pollution ever Dominici et al JAMA 2006 Peng et al JAMA 2008

National average estimates and 95% posterior intervals for the percent increase in hospital admissions for cardiovascular diseases per 1 IQR increase in each of the seven PM 2.5 components, 119 U.S. counties, Peng et al submitted Peng et al 2008, EHP

Do the PM 2.5 chemical constituents modify the short-term effects of PM 2.5 on mortality and morbidity? % increase in CVD-PM 2.5 risk per IQR increase in the fraction of PM 2.5 total mass for each component. Statistically significant associations are shown in bold 101 US counties Bell et al AJRCCM 2009

Long term exposures

Average PM2.5 levels for the period 2000 to 2006 for 518 monitors in the East US

Bayesian Spatially Varying Coefficient Models for estimating spatially varying long term effects of PM 2.5 (Stage I) Mortality counts in zip codes “close” to monitor “i” average PM 2.5 “i” is the monitor “j” is the month “x ij ” is the average PM 2.5 over the 12 previous months

Bayesian Spatially Varying Coefficient Models for estimating spatially varying long term effects of PM 2.5 (Stage II) Investigating whether PM 2.5 chemical components explain the spatial variability in mortality risks Long-term average of log relative proportion of kth component Spatial Coordinate of ith location

Missing data challenge The monitoring network that provides the chemical composition (STN) data is sparser than and does not exactly match with the PM 2.5 monitoring stations. For 241 monitors we have both PM 2.5 and composition data For 277 monitors we have PM 2.5 but composition is missing For 10 monitors we have composition data but PM 2.5 is missing. sparser than Composition data available All spatial units in our analysis

Analysis options Option 1. Using only 241 locations where the chemical composition data are available Option 2. Using all 518 locations with an imputation procedure for missing composition data incorporated in the model

Before the composition is incorporated (-5.41,-5.38) (0.0088,0.0165) 95.5 (78.5,114.7) (3141.2,5673.8) 17.4 (12.8,19.9) 14.1 (6.5,19.7) After the composition is incorporated (-5.39,-5.38) (0.0088,0.0161) (82.1,12.1) ( ) 17.5 (12.9,19.8) 14.3 (6.5,19.7) Option 1 : using 241 locations Posterior median for slope: Table 1. Posterior median for each parameter with 95% credible intervals

We propose a prior for the missing composition data and incorporate an imputation procedure in the MCMC iterations. 1.We denote the component levels for 3 different locations as 2.We assume the component levels for observed + extra locations come from a multivariate Gaussian spatial process as 3.We obtain posterior estimates for using a spBayes R package (Finley et al., 2010). : # of missing locations : # of observed locations : # of extra locations Z Miss Z Obs Z Extra Option 2 : using 518 locations Constructing a prior for Z Miss

We propose a prior for the missing composition data and incorporate an imputation procedure in the MCMC iterations. 4.Using, we specify a multivariate Gaussian process for the component levels for missing+observed locations. 5.We derive the conditional distribution for Z Miss given Z obs 6.Because the component levels cannot be negative, we use a truncated version of the above multivariate Gaussian process as a prior for Z Miss Option 2 : using 518 locations Constructing a prior for Z Miss

The hierarchical structure of our full Bayes model is Option 2 : using 518 locations Prior for Z Miss Likelihood Prior for fixed effects Prior for spatially correlated random effects

OC obs OC obs+pred SO4 obs SO4 obs+pred EC obs EC obs+Pred Si obs Si obs+pred NO3 obs NO3 obs+pred Sod obs Sod obs+pred

(Using 241 locations)(Using 518 locations) Dot is posterior median and line indicates 95% credible interval. Effect modification of the long term effects of PM 2.5 on mortality by PM 2.5 composition

Summary We used three study designs to address three related epidemiological questions on the toxicity of PM 2.5 We implemented MCMC algorithms for very large data sets

Summary We found that: –PM , (e.g. crustal materials) lead to smaller health risks than PM 2.5 (e.g. combustion-related constituents) –EC and OCM, which are generated typically from vehicle emissions, diesel, and wood burning, lead to the largest risk of emergency hospital admissions for cardiovascular and respiratory diseases compared to the other PM 2.5 chemical constituents Combustion sourcesCrustal materials

RegionAnalysis option Region 1Option ( -5.39, -5.35) (0.005,0.015) 130 (96,168) 4679 (2619, 8589) 18.2 (13.1, 19.9) 12.8 (2.4, 19.5) Option (-5.39, -5.37) (0.005,0.012) 136 (109,163) 6358 (3943, 10615) 18.3 (13.4,19.9) 9.7 (2.2, 19.3) Region 2Option ( -5.41, -5.36) (0.002, 0.020) 92 (58,136) 3404 (1589, 6890) 17.7 (10.7,19.9) 8.5 (0.27,19.3) Option (-5.40,-5.38) (0.011, 0.022) 106 (84, 128) 2500 (831, 6538) 17.5 (12.7, 19.8) 7.1 (2.4,15.7) Region 3Option ( -5.47, -5.41) (0.009, 0.026) 93 (66,128) 2787 (752,6189) 15.8 (8.3, 19.7) 3.7 (0.27,18.6) Option (-5.45, -5.43) (0.008, 0.021) 120 (97, 156) 4374 (2203, 7878) 15.2 (10.4, 19.4) 9.2 (1.7,19.5) Table 1. Posterior median for other parameters with 95% credible intervals Sub-region analysis

Our model can be written as Main effects interactions The likelihood function is Option 2 : using 518 locations

References Chung Y, Dominici F, Bell M Bayesian Spatially Varying Coefficients Models of Long term effects of PM2.5 and PM 2.5 composition (in progress) Papers in blue have been presented in these slides

We denote the component levels for 3 different locations as We assume that the component levels for observed + extra locations come from a multivariate Gaussian spatial process as We obtain posterior estimates for using a spBayes R package (Finley et al., 2010). Option 2 : using 518 locations Constructing a prior for Z Miss Z Miss Z Obs Z Extra

Using, we specify a multivariate Gaussian process for the component levels for missing+observed locations. We derive the conditional distribution for Z Miss given Z obs Constructing a prior for Z Miss

We place a prior for Z Miss and incorporate an imputation procedure in the MCMC iterations. The prior can be obtained from a multivariate spatial process defined for Z Miss, Z obs, Z Extra (Next Slide). Option 2 : using 518 locations We obtain posterior estimates for using a spBayes R package (Finley et al., 2010).