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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

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Discussion on spatial epidemiology with focus on chronic effects of air pollution

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: [email protected])

SAMSI Workshop: September 15, 2009


Outline
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


Children s health study background
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


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


Linear multi level model
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


Accounting for intra community variation
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


Bayesian spatial measurement error model
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


Spatial regression model
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”)


Assignment of local exposures
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


Spatial model for full cohort
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


Multivariate car model
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


Sampling strategies
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


Additional substudies
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


Further extensions of the integrated research program
Further Extensions of the Integrated Research program

SAMSI Workshop: September 15, 2009


Discussion points
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



Thank you

THANK YOU!

Contact me at

[email protected]

SAMSI Workshop: September 15, 2009


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