samsi workshop: september 15, 2009 discussion on spatial epidemiology: with focus on chronic effects...
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SAMSI Workshop: September 15, 2009SAMSI Workshop: September 15, 2009
USC UNIVERSITY
OF SOUTHERN
CALIFORNIA Discussion on Spatial Epidemiology:Discussion on Spatial Epidemiology:with focus on Chronic Effects of Air with focus on Chronic Effects of Air
Pollution Pollution
Kiros Berhane, Ph.D.
(with Duncan Thomas, Jim Gauderman and the CHS Team)
Department of Preventive MedicineKeck School of Medicine
University of Southern CaliforniaLos Angeles, CA, USA
(e-mail: [email protected])
SAMSI Workshop: September 15, 2009SAMSI Workshop: September 15, 2009
USC UNIVERSITY
OF SOUTHERN
CALIFORNIAOutlineOutline
• 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, 2009SAMSI Workshop: September 15, 2009
USC UNIVERSITY
OF SOUTHERN
CALIFORNIAChildren’s Health Study Children’s Health Study
BackgroundBackground• 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 whether air pollution (regional and/or local) is associated with chronic health effects in children?
SAMSI Workshop: September 15, 2009SAMSI Workshop: September 15, 2009
USC UNIVERSITY
OF SOUTHERN
CALIFORNIA
LLLL
LLLL
LLLL
LMLH
HMLM
HHHH
HHHH
LHMH
HHHL HMHL
MMMM
MLLL
O3, PM10, NO2, H+: L = low M = Medium H = High
SAMSI Workshop: September 15, 2009SAMSI Workshop: September 15, 2009
USC UNIVERSITY
OF SOUTHERN
CALIFORNIA
– Level I: Between times (k) within subjects (i )
ycik = aci + bci tcik + zcik + (xcik–xci)1 + ecik
– Level II: Between subjects within community (c)
bci = Bc + zci + (xci – Xc)2 + eci
– Level III: Between communities
Bc = 0 + Zc + Xc 3 + ec
Fitted simultaneously as a mixed effects model
Linear Multi-level ModelLinear Multi-level Model
Spatio-temporal effects could be assessed at any of the levels Berhane et al, Berhane et al, Statist Sci Statist Sci 2004; 19: 414-4402004; 19: 414-440
SAMSI Workshop: September 15, 2009SAMSI Workshop: September 15, 2009
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OF SOUTHERN
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Accounting for Intra-Accounting for Intra-Community Variation 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, 2009SAMSI Workshop: September 15, 2009
USC UNIVERSITY
OF SOUTHERN
CALIFORNIA
WW
YY
ZZ
XX
Traffic, Traffic, Land UseLand Use
Local ExposureLocal ExposureMeasurementsMeasurements
HealthHealthOutcomeOutcome
True True ExposureExposure
LLLocationsLocations
PPRegionalRegional
BackgroundBackground
Molitor et al, Molitor et al, AJEAJE 2506;164:69-76 (nonspatial) 2506;164:69-76 (nonspatial)Molitor et al, Molitor et al, EHPEHP 2507:1147-53 (spatial) 2507:1147-53 (spatial)
Bayesian Spatial Bayesian Spatial Measurement Error ModelMeasurement Error Model
Subsample Subsample S | Y, L, WS | Y, L, W
SAMSI Workshop: September 15, 2009SAMSI Workshop: September 15, 2009
USC UNIVERSITY
OF SOUTHERN
CALIFORNIA
Spatial Regression ModelSpatial Regression Model• Exposure model
E(Xi) = WiW = land use covariates, dispersion model predictions
cov(Xi,Xj) = 2Iij + 2 exp(– Dij)
MESA Air spatio-temporal model:
x(s,t) = X0(s) + k Xk(s) Tk(t)
• Measurement model E(Zi) = Xi
• Disease model g[E(Yi)] = Xi
• Multivariate exposure model (“co-kriging”)
SAMSI Workshop: September 15, 2009SAMSI Workshop: September 15, 2009
USC UNIVERSITY
OF SOUTHERN
CALIFORNIA
ASSIGNMENT OF LOCAL ASSIGNMENT OF LOCAL EXPOSURESEXPOSURES
• 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´ ji (xcj Zci´) Ccij / Ccii
SAMSI Workshop: September 15, 2009SAMSI Workshop: September 15, 2009
USC UNIVERSITY
OF SOUTHERN
CALIFORNIA
Spatial Model: for Full CohortSpatial Model: for Full Cohort• Fit subsample data, regressing measurements Z on
predictors WE(Zi) = Wi cov(Zi,Zj) = 2Iij + 2 exp(–Dij)
• Impute exposures X to all subjects based on W and mean of residuals for neighbors
Xi = Zi + iNj (Zj – Xj) wij
• Fit full cohort, regressing health outcomes Y on imputed X, weighted by uncertainties of imputations
E(Yi) = Xi var(Yi) = 2 + 2 var(Xi)
Thomas, LDA 2007; 13: 565-81
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SAMSI Workshop: September 15, 2009SAMSI Workshop: September 15, 2009
USC UNIVERSITY
OF SOUTHERN
CALIFORNIA
Multivariate CAR ModelMultivariate CAR Model
• Structured covariance matrix with submatrices for each pollutant (p,q) and their correlations
cov(Xpi,Xqj) = pq exp(pq Dij)
• 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, 2009SAMSI Workshop: September 15, 2009
USC UNIVERSITY
OF SOUTHERN
CALIFORNIA
Sampling StrategiesSampling 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 S that 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, 2009SAMSI Workshop: September 15, 2009
USC UNIVERSITY
OF SOUTHERN
CALIFORNIA
Additional SubstudiesAdditional Substudies• 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
SAMSI Workshop: September 15, 2009SAMSI Workshop: September 15, 2009
USC UNIVERSITY
OF SOUTHERN
CALIFORNIA
Further Extensions of the Integrated Further Extensions of the Integrated Research programResearch program
SAMSI Workshop: September 15, 2009SAMSI Workshop: September 15, 2009
USC UNIVERSITY
OF SOUTHERN
CALIFORNIA Discussion PointsDiscussion 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, 2009SAMSI Workshop: September 15, 2009
USC UNIVERSITY
OF SOUTHERN
CALIFORNIA
SAMSI Workshop: September 15, 2009SAMSI Workshop: September 15, 2009
USC UNIVERSITY
OF SOUTHERN
CALIFORNIA
THANK YOU!THANK YOU!
Contact me [email protected]