Discussion on Spatial Epidemiology: with focus on Chronic Effects of Air Pollution

1. Discussion on Spatial Epidemiology:with focus on Chronic Effects of Air Pollution Kiros Berhane, Ph.D. (with Duncan Thomas, Jim Gauderman and the CHS Team) Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles, CA, USA (e-mail: kiros@usc.edu) SAMSI Workshop: September 15, 2009

2. Outline • Long Term Cohort Studies • The Children’s Health Study • The multi-Level modeling Paradigm • Spatio-temporal Issues • Integrated modeling • Discussion points SAMSI Workshop: September 15, 2009

3. Children’s Health Study Background • Designed to take advantage of existing air monitoring data to choose optimal sites • Exploits temporal, spatial, and individual comparisons • Extensive exposure and health assessment to support all three levels of comparison • Study Goal: To assess whetherair pollution (regional and/or local) is associated with chronic health effects in children? SAMSI Workshop: September 15, 2009

4. LLLL LLLL LMLH LLLL HMLM HHHH HHHH HMHL HHHL O3, PM10, NO2, H+: L = low M = Medium H = High LHMH MMMM MLLL SAMSI Workshop: September 15, 2009

5. Linear Multi-level Model • Level I: Between times (k) within subjects (i) ycik = aci + bci tcik + zcikg+ (xcik–xci)b1+ ecik • Level II: Between subjects within community (c) bci = Bc + zcid+ (xci – Xc)b2 + eci • Level III: Between communities Bc = b0 + Zch+ Xcb3 + ec Fitted simultaneously as a mixed effects model Spatio-temporal effects could be assessed at any of the levels Berhane et al, Statist Sci 2004; 19: 414-440 SAMSI Workshop: September 15, 2009

6. Accounting for Intra-Community Variation Goals: • To build a model for personal exposure combining spatio-temporal model for ambient concentrations with time-activity data from questionnaires and measurements • To optimize the design of time/activity sampling SAMSI Workshop: September 15, 2009

7. Bayesian Spatial Measurement Error Model L Y Health Outcome Locations P X Regional Background Subsample S | Y, L, W True Exposure Z W Traffic, Land Use Local Exposure Measurements • Molitor et al, AJE 2506;164:69-76 (nonspatial) • Molitor et al, EHP 2507:1147-53 (spatial) SAMSI Workshop: September 15, 2009

8. Spatial Regression Model SAMSI Workshop: September 15, 2009 • Exposure model E(Xi) = Wia W = land use covariates, dispersion model predictions cov(Xi,Xj) = s2Iij + t2 exp(– rDij) MESA Air spatio-temporal model: x(s,t) = X0(s) + SkXk(s) Tk(t) • Measurement model E(Zi) = Xi • Disease model g[E(Yi)] = bXi • Multivariate exposure model (“co-kriging”)

9. ASSIGNMENT OF LOCAL EXPOSURES For all homes in cohort, we can assign an estimated exposure based on fitted parameters Systematic component depends on community ambient level and traffic density Random component is weighted mean of measurements at other homes, using estimated covariance matrix E(xci) = Zci´bSji (xcj  Zci´b) Ccij / Ccii SAMSI Workshop: September 15, 2009

10. Spatial Model: for Full Cohort ^ ^ ^ ^ ^ ^ SAMSI Workshop: September 15, 2009 • Fit subsample data, regressing measurements Z on predictors W E(Zi) = aWi cov(Zi,Zj) = s2Iij + t2 exp(–rDij) • Impute exposures X to all subjects based on W and mean of residuals for neighbors Xi = aZi + SiNj (Zj – Xj) wij • Fit full cohort, regressing health outcomes Y on imputed X, weighted by uncertainties of imputations E(Yi) = bXi var(Yi) = w2 + b2 var(Xi) Thomas, LDA 2007; 13: 565-81

11. Multivariate CAR Model Structured covariance matrix with submatrices for each pollutant (p,q) and their correlations cov(Xpi,Xqj) = spq exp(- rpqDij) Hope is to incorporate atmospheric chemistry and dispersion theory in means and covariance models We have currently spatial measurements on samples of homes for NO2 and O3, but not the same homes Plans to measure NO2, NO, and O3 in a larger sample of homes SAMSI Workshop: September 15, 2009

12. Sampling Strategies Case-control: choose S to be set of asthma cases and their town-matched controls Surrogate diversity: choose S that maximizes the variance of traffic density Spatial diversity: choose Sthat maximizes the geographic spread of measurements Maximize total distance from all other points Maximize minimum distance from nearest point Maximize the informativeness of sample for predicting non-sample points Hybrid: First measure cases and controls; then add additional subjects that would be most informative for refining E(X |Z,P,W ) Thomas, LDA 2007; 13: 565-81 SAMSI Workshop: September 15, 2009

13. Additional Substudies SAMSI Workshop: September 15, 2009 • Personal exposure measurements • Biomarkers of latent disease processes • Time-activity data • Have “usual” times and subjective activity levels in various locations (home, school, playgrounds, in transit, etc.) • Plan to obtain GPS measurements of actual time-resolved locations on a subsample for short periods • Also plan to obtain step-counts and/or accelerometry on a subsample for short periods

14. Further Extensions of the Integrated Research program SAMSI Workshop: September 15, 2009

15. Discussion Points • Issues with exposure modeling for Intra-community variation • Measurement error? • Implications of using snapshots in space/time to assess long term exposure? • Implications of sampling strategies? • Differences in spatio-temporal resolution of data: Outcome vs. Exposure • Implications for health effects analysis? • Integrated Modeling approaches vs. Compartmentalized modeling • Which way to go? • Issues in Chronic vs. Acute effects analysis • Are they really different? SAMSI Workshop: September 15, 2009

16. SAMSI Workshop: September 15, 2009

17. THANK YOU! Contact me at kiros@usc.edu SAMSI Workshop: September 15, 2009