Health Risks of Exposure to Chemical Composition of Fine Particulate Air

Pollution

Francesca DominiciYeonseung Chung

Michelle BellRoger Peng

Department of BiostatisticsSchool of Public Health

Harvard University

PM2.5 PM10

PM10-2.5

Chemical constituents

Size Total mass

SO4=

Si

Ca

Z

Ni

NH4 +

NO3-

Fe

EC

OC

Inorganicfraction of PM

MetalsAl

Groups

Bell Dominici Ebisu Zeger Samet EHP 2007

New Scientific Questionsand 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 PM2.5 chemical

components associated with day-to-day changes in admission rates? (short-term effects of PM2.5 components)– Multi-site time series studies of PM components

• Are the short-term effects of PM2.5 total mass on admission rates modified by long-term averages of PM2.5 chemical components?– Multi-site time series studies of PM total mass and

second stage regression on PM components• Are the long-term effects of PM2.5 total mass on mortality

modified by long-term averages of PM2.5 components?– Spatially varying coefficient models

National Data

The National Medicare Cohort Study, 1999-2009 (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 PM2.5 monitoring network

MCAPS study population: 204 counties with

populations larger than 200,000 (11.5 million people)

Daily time series of hospitalization rates and PM2.5 levels in Los Angeles county (1999-2005)

Exposure data: Chemical composition data on PM2.5 from the STN network

1. Constructed a database of time series data for 52 PM2.5 chemical constituents from over 250 STN monitors for 2000 to 2008

2. Identified a subset of PM2.5 components that substantially contribute and/or co-vary with daily PM2.5 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. EC5. Silicon6. Sodium Ion7. Ammonium

Chemical composition data on PM2.5

PM2.5 chemical components and mortality rates: 1999-2008

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

Ytc ~ Poisson(t

c )

logtc logN t

c kc

k xkt l

c s1(temptc,1) s2(dewt

c,2)

Confounders:•weather variables•seasonality

JASA 2004

s3(temp[1 3],tc ,3) s4 (dew[1 3],t

c ,4 )s5(t,5)

Everson and Morris, JRSSB 2000Dominici Samet Zeger JRSSA 2000R package for TLNISE, released on March 26 2008 by Roger Peng

logtc logN t

c kc

k xkt l

c z tcc

c |0,1,2 ~ N(0 1(h

c h ), 2)

cor( c1 , c2 )exp[ d(c1,c2)]

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)

PM2.5 and Admissions PM10-2.5 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 PM2.5 components, 119 U.S. counties, 2000--2006.

Peng et al submittedPeng et al 2008, EHP

Do the PM2.5 chemical constituents modify the short-term effects of PM2.5 on mortality and morbidity?

% increase in CVD-PM2.5 risk per IQR increase in the fraction of PM2.5 total mass

for each component. Statistically significant associations are shown in bold 101 US counties1999-2005

Bell et al AJRCCM 2009

logtc logN t

c cPM2.5tc z t

cc

c |0,1,2 ~ N(0 1k

k x k ,

2)

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 PM2.5 (Stage I)

Mortality counts in zip codes “close” to monitor “i”

average PM2.5

)(~ ijij PoissonY

iijijijiiijij xxxxaaN **10 ,)log()log(

•“i” is the monitor•“j” is the month• “xij” is the average PM2.5 over the 12 previous months

Bayesian Spatially Varying Coefficient Models for estimating spatially varying long term effects of PM2.5 (Stage II)Investigating whether PM2.5 chemical components explain the

spatial variability in mortality risks

Long-term average of log relative proportion of kth component

,01

*00 i

p

kikki za

,11

*01 i

p

kikki za

€

0 ~ MVN(0,τ 0−1ρ (si,s j;φ0))

€

1 ~ MVN(0,τ 1−1ρ (si,s j;φ1))

iikik zzz *

||)||exp();,( 00 jiji ssss 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 PM2.5 monitoring stations.

• For 241 monitors we have both PM2.5 and composition data • For 277 monitors we have PM2.5 but composition is missing• For 10 monitors we have composition data but PM2.5 is missing.

sparser than 518

251

241

Composition dataavailable

All spatial units in our analysis

Analysis optionsOption 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

-5.39(-5.41,-5.38)

0.0126(0.0088,0.0165)

95.5(78.5,114.7)

4357.8(3141.2 ,5673.8)

17.4(12.8,19.9)

14.1(6.5,19.7)

After the composition is incorporated

-5.41(-5.39,-5.38)

0.0125(0.0088,0.0161)

100.0(82.1,12.1)

4538.6(3319.5805.7)

17.5(12.9,19.8)

14.3(6.5,19.7)

Option 1 : using 241 locations

0 1 0 10 1

1ˆiaPosterior median for slope:0ˆiaPosterior 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).

ExtrapnnpExtra

ObspnnpObs

MisspnnpMiss

nzzzzZ

nzzzzZ

nzzzzZ

ExtraExtra

ObsObs

MissMiss

,)',,,,,,(

,)',,,,,,(

,)',,,,,,(

1111

1111

1111

: # of missing locations

: # of observed locations

: # of extra locations

518

251

241

ZMiss

ZObs

ZExtra

277

10

Option 2 : using 518 locationsConstructing a prior for ZMiss

},{),,11

(~ ZZZZn

Zn

Extra

ObsOE

Extra

ObsMVNZZ

Z

},{ ZZ

• 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 ZMiss given Zobs

6. Because the component levels cannot be negative, we use a truncated version of the above multivariate Gaussian process as a prior for ZMiss

Option 2 : using 518 locationsConstructing a prior for ZMiss

}ˆ,ˆ{ˆZZ

)ˆˆˆˆ

,ˆ1ˆ1

(~

OOTMO

MOMM

Zn

Zn

Obs

MissMO

Obs

MissMVNZZ

Z

)ˆˆˆˆ,)ˆ1(ˆˆ]ˆ1([~| 11 TMOOOMOMMZnobsOOMOZnObsMiss ObsMiss

ZMVNZZ

• The hierarchical structure of our full Bayes model is

Option 2 : using 518 locations

))()()()(,,,|())()(ˆ,|(

),,,,,,|(

11001100

21

21

bZZ

bZZXY

ObsMiss

ObsMiss

Prior for ZMiss

Likelihood

Prior for fixed effects

Prior for spatially correlated random effects

OCobs OCobs+pred

SO4obs SO4obs+pred

ECobs ECobs+Pred

Siobs Siobs+pred

NO3obs NO3obs+pred Sodobs Sodobs+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 PM2.5 on mortality by PM2.5 composition

Summary• We used three study designs to address

three related epidemiological questions on the toxicity of PM2.5

• We implemented MCMC algorithms for very large data sets

SummaryWe found that:

– PM10-2.5, (e.g. crustal materials) lead to smaller health risks than PM2.5 (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 PM2.5 chemical constituents

Combustion sources Crustal materials

Region Analysis option

Region 1 Option 1 -5.38( -5.39, -5.35)

0.010 (0.005,0.015)

130 (96,168)

4679(2619, 8589)

18.2(13.1, 19.9)

12.8(2.4, 19.5)

Option 2 -5.38(-5.39, -5.37)

0.009(0.005,0.012)

136(109,163)

6358(3943, 10615)

18.3(13.4,19.9)

9.7(2.2, 19.3)

Region 2 Option 1 -5.38( -5.41, -5.36)

0.010 (0.002, 0.020)

92(58,136)

3404(1589, 6890)

17.7(10.7,19.9)

8.5(0.27,19.3)

Option 2 -5.39(-5.40,-5.38)

0.018(0.011, 0.022)

106(84, 128)

2500(831, 6538)

17.5(12.7, 19.8)

7.1(2.4,15.7)

Region 3 Option 1 -5.44( -5.47, -5.41)

0.018(0.009 , 0.026)

93(66,128)

2787(752,6189)

15.8(8.3, 19.7)

3.7(0.27,18.6)

Option 2 -5.44(-5.45, -5.43)

0.016(0.008, 0.021)

120(97, 156)

4374(2203, 7878)

15.2(10.4, 19.4)

9.2(1.7,19.5)

0 1 0 10 1

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

€

logλ = logN + Xθ1 + Zθ 2

€

(Y | X,Z,θ1,θ 2)

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 PM2.5 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).

},{),,11

(~ ZZZZn

Zn

Extra

ObsOE

Extra

ObsMVNZZ

Z

Option 2 : using 518 locationsConstructing a prior for ZMiss

},{ ZZ

518

251

241

ZMiss

ZObs

ZExtra

277

10

• Using , we specify a multivariate Gaussian process for the component levels for missing+observed locations.

• We derive the conditional distribution for ZMiss given Zobs

)ˆˆˆˆ,)ˆ1(ˆˆ]ˆ1([~| 11 TMOOOMOMMZnobsOOMOZnObsMiss ObsMiss

ZMVNZZ

)ˆˆˆˆ

,ˆ1ˆ1

(~

OOTMO

MOMM

Zn

Zn

Obs

MissMO

Obs

MissMVNZZ

Z

}ˆ,ˆ{ˆZZ

Constructing a prior for ZMiss

• We place a prior for ZMiss and incorporate an imputation procedure in the MCMC iterations.

€

(Y | X,ZMiss,ZObs,θ1,θ 2)(ZMiss | ZObs,ZExtra,Ω)

The prior can be obtained from a multivariate spatial process defined for ZMiss, Zobs, ZExtra (Next Slide).

Option 2 : using 518 locations

We obtain posterior estimates for using a spBayes R package (Finley et al., 2010).

€