Copyright 2008, The Johns Hopkins University and Francesca Dominici. All rights reserved. Use of these materials permitted only in accordance with license rights granted. Materials provided “AS IS”; no representations or warranties provided. User assumes all responsibility for use, and all liability related thereto, and must independently review all materials for accuracy and efficacy. May contain materials owned by others. User is responsible for obtaining permissions for use from third parties as needed. This work is licensed under a Creative Commons Attribution- NonCommercial-ShareAlike License . Your use of this material constitutes acceptance of that license and the conditions of use of materials on this site.
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Copyright 2008, The Johns Hopkins University and Francesca Dominici. All rights reserved. Use of these materials permitted only in accordance with license rights granted. Materials provided “AS IS”; no representations or warranties provided. User assumes all responsibility for use, and all liability related thereto, and must independently review all materials for accuracy and efficacy. May contain materials owned by others. User is responsible for obtaining permissions for use from third parties as needed.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this site.
1. Adjusting for confounding – Semi-Parametric Regression
2. Combining health risk estimates across counties– Bayesian Hierarchical Models
3. Accounting for the uncertainty in the selection of the statistical model– Model averaging for confounding
adjustment
• Compare day-to-day variations in hospital admission rates with day-to-day variations in pollution levels within the same community
• Avoid problem of unmeasured differences among populations
• Key confoundersSeasonal effects of infectious
diseases and weather
Statistical Methods for multi-site time series studies
Statistical Methods
Within city: Semi-parametric regressions for estimating associations between day-to-day variations in air pollution and mortality controlling for confounding factors
Across cities: Hierarchical Models for estimating:– national-average relative rate– exploring heterogeneity of air pollution
effects across the country
Dominici Samet Zeger JRSSA 2000
Confounding
• The association between air pollution and mortality is potentially confounded by:
– Weather– Other pollutants– Seasonality– Long-term trend
1) Semi-parametric regression model for estimating health risk within a
county
logE[Ytc ] logN t
c cx t s(temp) s(time)
# of adverse events on day t
# of people at risk on day t
health riskTime varying confounders:•Weather variables•seasonality
Kelsall Samet Zeger Xu AJE 1997
air pollution series
2)Bayesian hierarchical models for pooling risks
across cities
ˆ c c c
c c
c ~ N(0,v c )
c ~ N(0, 2)
County-specific risk estimate
County-specific true risk
Within-county statistical error
Pooled risk Across-county variance of the true risks
3) Do I have the “right” statistical model?
Explore the sensitivity of the risk estimates to the statistical model
Sensitivity of the national average lag effect of PM10 on mortality to different statistical models to adjust for
confounding (NMMAPS 1987-2000)
Peng Dominici Louis JRSSC 2006
Reported estimate
Different statistical models to adjust for confounding
weak moderate strong
3) Do I have the “right” statistical model?
X
Y
Z1 Z2Z1 is a predictor of YZ2 is a confounder
Regression Models Weights based on prediction(BIC)
Weights based on ability to adjust for confounding
0.9 0.0
0.0 0.9
0.1 0.1
y x 2z2
y x 1z1 2z2
y x 1z1
Estimating risks by averaging across statistical models
3) Model averaging for confounding
adjustment in observational studies • We assign zero weights to models that have
optimal prediction properties but that do not include all the potential confounders
• We identify all the potential confounders by searching for good predictors of the exposure X
• Theoretical results and simulation studies have showed that this approach outperform existing methods to account for model uncertainty
The new challenge:Estimating the toxicity of the PM complex mixture
New Scientific Questionsand Statistical Challenges
What are the mechanisms of PM toxicity?
Size? Chemical components? Sources?
New Methods for estimating health effects of complex mixtures
PM2.5 PM10
PM10-2.5
Chemical constituents
Size Total mass
Cl
OC
SO4
Si
K
EC
NO3
Ca
Al
Fe
Biomassburning
Vehicles
Crustal
Emissionsources
Bell Dominici Ebisu Zeger Samet EHP 2007
Lag
% in
crea
se in
adm
issi
on w
i th
a 10
g/m
3 in
crea
se in
PM
0 1 2 0 1 2 0 1 2 0 1 2
-1.5
-1
-0.5
0
0.5
1
1.5
2
PM10 2.5 PM2.5 PM10 2.5
Adjusted by PM 2.5
PM2.5
Adjusted by PM 10 2.5
% change in CVD hospitalization rate associated with 10 increase in PM10-2.5 on average across 108 US counties (1999-2005)
g /m3
PM10-2.5
alonePM2.5
alone
PM10-2.5
adjusted by PM2.5
PM2.5
adjusted by PM10-2.5
Lag
Peng Bell Chang McDermott Zeger Samet Dominici tech report 2007
Lag Lag Lag
The policy impact
NAAQS: Science has had an Impact
• From US EPA NAAQS Criteria Document 1996: “Many of the time-series epidemiology studies looking for associations between O3 exposure and daily human mortality have been difficult to interpret because of methodological or statistical weaknesses, including the failure to account for other pollutants and environmental effects.”
• From US EPA Criteria Document 2006: “While uncertainties remain in some areas, it can be concluded that robust associations have been identified between various measures of daily O3 concentrations and increased risk of mortality.”
Assessing the Public Health Impact of the Air Quality Regulations
Reproducible research
• We want to reproduce previous findings– “Did you do what you said you did?”
• Test assumptions, robustness of findings; check methodology– “Is what you did any good?”
• Implement and test new methodology– “I can do it better!”
Peng Dominici Zeger AJE 2006
NMMAPSdata package for R
• R is a free software environment for statistical analysis and graphics
• NMMAPSdata package contains the entire updated (1987—2000) NMMAPS database as an add-on module for R
• Supplemental code available online for reproducing canonical NMMAPS analysis and other analyses
• iHAPSS: Internet-based Health and Air Pollution Surveillance System– http://www.ihapss.jhsph.edu/
Peng Welty R news 2004 Zeger Peng McDermott Dominici Samet HEI 2006
Environmental Epidemiology with R: A Case study in Air Pollution and Health
Roger Peng & Francesca Dominici
Pen
g &
Dom
inic
i
Concluding Thoughts
• The weight of the evidence:– Has an explicit role in the Clean Air Act
• New NAAQS process • New Research underway: especially PM Components
and Sources – the cycle begins anew
Policy
Questions Data
Methods
Evidence
Biostatistics in Action!analyses of observational studies
can be used toaddress otherquestions beyondair pollution
Collaboratorsin the BSPH
• Michelle Bell• Patrick Breysse• Ciprian Crainiceanu• Mary Fox• Alyson Geyh• Aidan McDermott• Tom Louis• Giovanni Parmigiani• Roger Peng• Jonathan Samet• Ron White• Scott Zeger
PhD Students
• Howard Chang• Sandy Eckel• Sorina Eftim• Jennifer Feder• Haley Hedlin• Yun Lu• Chi Wang• Yijie Zhou
Medicare data users and
collaboratorsin the BSPH and
SOM
• Gerald Anderson• Emily Smith• Ben Brooke• Lia Clattenburg• Robert Herbert • Peter Pronovost
Funding sources•EPA: PM Research Center (Samet)•NIEHS: Training grant in Environmental Biostatistics (Louis and Dominici)•NIEHS R01: Statistical methods in Environmental Epidemiology (Dominici)