Examining Short-Term Air Pollution Exposures and Health Effects: Atlanta as a Case Study
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EXAMINING SHORT-TERM AIR POLLUTION EXPOSURES AND HEALTH EFFECTS: ATLANTA AS A CASE STUDY
Jeremy Sarnat, Emory University
Emory University: Sarnat SE, Darrow L, Flanders D, Kewada P, Klein M, Strickland M, Tolbert PE
Georgia Tech: Mulholland J, Russell AG
EPA: Isakov V, Crooks JL, Touma J, Özkaynak H
CMAS Conference
October 12, 2010
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OUTLINE OF TALK
I. Study designs used to assess short-term exposures, acute health responses
Population-based timeseries analyses Cohort and panel studies
II. Considerations/concerns related to exposure data
Examples: Study of Particles and Health in Atlanta (SOPHIA); Emory-Ga Tech EPA COOP
III. Considerations/concerns related to health data Example: Atlanta Commuters Exposure Study
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I. STUDY DESIGNS 1. Population-based studies Assess relationship between daily or multi-day ambient pollution
concentrations and mortality, ED visits, hospitalization
Data analysis – regression Timeseries, Poisson (using daily counts data) Case-crossover, Logistic (using data on individual visits/deaths)
Relatively inexpensive Can evaluate a single study area, or multiple cities Large N, statistical power to detect subtle changes in endpoint
Atlanta SOPHIA study Data on 10,206,389 ED visits from 41 of 42 hospitals in the 20-county study
area for the period 1993-2004 Objective to assess short-term associations between air pollution and
cardiorespiratory ED visits, hospital admissions, adverse birth outcomes, implanted cardioverter defibrillator (ICD) events
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= Acute care facility = Jefferson St. station
> 8,300 SQ MILES
Data include 10,206,389 ED visits from 41 of 42 hospitals in 20-county Atlanta, 1993-2004
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DATA ANALYSIS
Exposure = daily air pollution measurements Outcome = daily cardiopulmonary emergency department visits Poisson generalized linear models (GLM)
3-day moving average (lags 0, 1, 2) for each pollutant Control for time, day-of-week, holidays, hospital entry/exit,
temperature, dew point
FOR DATASET TWO, 05DEC05
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DATE
01/ 01/ 98 01/ 01/ 99 01/ 01/ 00 01/ 01/ 01 01/ 01/ 02 01/ 01/ 03
Asthma Visits
FOR DATASET TWO, 05DEC05
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DATE
01/ 01/ 98 01/ 01/ 99 01/ 01/ 00 01/ 01/ 01 01/ 01/ 02 01/ 01/ 03
24-hr standard
Annual standard
PM2.5
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PEDIATRIC ED VISITS FOR ASTHMA
Strickland et al., AJRCCM, 2010
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I. STUDY DESIGNS 2. Cohort or panel studies Assess relationship between sub-daily,
daily or multi-day ambient pollution concentrations and sub-clinical, clinical changes in health
Data analysis – regression Linear mixed effect models common Assume that pollution term(s) in
model reflect mean personal exposure of population
Only time-varying factors can confound results
Good exposure/health for small N Relatively expensive and cumbersome
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II. CONSIDERATIONS RELATED TO EXPOSURE DATA
Approach valid if exposure metric accurately captures patterns of pollutant spatiotemporal variability across modeling domain Exposure error, exposure misclassification,
measurement error Varies by pollutant
Concentration ≠ Exposure Lagged exposures and response
Time-varying factors can confound results Confounding by co-pollutants
Disaggregating individual effects vs. effects from mixtures
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II. CONSIDERATIONS RELATED TO EXPOSURE DATA
Approach valid if exposure metric accurately captures patterns of pollutant spatiotemporal variability across modeling domain Exposure error, exposure misclassification,
measurement error Varies by pollutant
Concentration ≠ Exposure Lagged exposures and response
Time-varying factors can confound results Confounding by co-pollutants
Disaggregating individual effects vs. effects from mixtures
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JERRETT ET AL., 2005 22,905 subjects living
in LA area between 1982 – 2000 5,856 deaths
23 PM2.5 and 42 O3 monitors used to create a spatial grid of pollution concentrations
Examine association between long term exposure and excess mortality Compare with Pope et
al., 2002 (ACS)
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EMORY-GA TECH EPA COOP
Objectives Develop and evaluate five alternative exposure metrics
for ambient traffic-related and regional pollutants Apply metrics to two studies examining ambient air
pollution and acute morbidity in Atlanta, GA SOPHIA Atlanta ED & ICD studies
Hypotheses1.Finer spatial resolution in ambient concentrations &
inclusion of exposure factors in analyses changes in estimated distribution of population exposures compared to ambient monitoring data
2.Use of refined estimates reduced exposure error greater power to detect epidemiologic associations of interest
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CURRENT PROJECT
Develop 5 alternative metrics of exposure Traffic: CO, NOX, EC
Regional: O3, SO42-
Mix: PM2.5
Daily, ZIP code level For sub-period, 1999-
2002 For current analysis:
Results using Metrics i, iii, iv, v
(i) Ambient Monitoring
DataEmissions Data
Spatially-Resolved Concentrations
Spatially-Resolved Exposures
(v) Exposure Factors
(ii) Spatially- Interpolated Background
Modeling:(iii) AERMOD(iv) Hybrid
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Time Series of Coefficients of Variability: Comparison of Background vs. Hybrid output
NOx
Modeling helps to resolve spatiotemporal variability in pollutant concentrations important for timeseries epi analysis
Slide courtesy of V. Isakov
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Preliminary Results of Epidemiologic Analysis Preliminary Results of Epidemiologic Analysis of ED Visits in Atlantaof ED Visits in Atlanta
CC
CC
CC
CC C
C
CC
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II. CONSIDERATIONS RELATED TO EXPOSURE DATA
Approach valid if central site accurately reflects patterns of pollutant spatiotemporal variability across modeling domain Exposure error, exposure misclassification,
measurement error Varies by pollutant
Concentration ≠ Exposure Lagged exposures and response
Time-varying factors can confound results Confounding by co-pollutants
Disaggregating individual effects vs. effects from mixtures
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MODELING EXPOSURE FACTORS IN EPI ANALYSES OF SHORT-TERM EXPOSURES
Examine whether inclusion of pollutant infiltration (Finf) estimates affect epi results Greater infiltration of ambient pollution
greater signal with ambient-based exposure metric
Consider air exchange rate (AER) surrogates Require readily accessible data and easy to use
in population-based studies Temporal factors = meteorological Stratified analysis by AER category
ED study Zip-code resolved daily estimates of AER
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Fulton
Carroll
Bartow
Cobb
Coweta
Henry
Gwinnett
Walton
Cherokee
Paulding
Newton
De Kalb
Forsyth
Pickens
Fayette
Barrow
Douglas
Spalding
Clayton
Rockdale
0 10 205 Miles
¦ACH Conv + Low
Legend
County borders Major Roads
ACH conv+low
0.264 - 0.332
0.333 - 0.378
0.379 - 0.439
0.440 - 0.572
ZIP CODE RESOLVED AERS IN ATLANTA
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ESTIMATING AER USING LBNL APPROACH
s fs2 T fw
2 v 2
ACH NL
1000H
2.5m
H
0.3
sAER
Chan, W. R.; Price, P. N.; Nazaroff, W. W.; Gadgil, A. J., Distribution of residential air leakage: Implications for health outcome of an outdoor toxic release. Indoor Air 2005: Proceedings of the 10th International Conference on Indoor Air Quality and Climate, Vols 1-5 2005, 1729-1733
where
NL = exp(0 +1yr built + 2floor area + e)H = height of home (m)fS = stack effect estimatefW = wind effect estimateT = temperature (K)V = wind speed (m/sec)
Spatially-varyingTemporally-varying
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0.96
0.97
0.98
0.99
1.00
1.01
1.02
1.03
1.04
1.05
1.06
<0.2
4
0.24
-0.2
8
>0.2
8
<0.2
4
0.24
-0.2
8
>0.2
8
<0.2
4
0.24
-0.2
8
>0.2
8
<0.2
4
0.24
-0.2
8
>0.2
8
CS CS CS BG BG BG AERMODAERMODAERMODHYBRID HYBRID HYBRID
PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25
Rela
tive
Risk
(95
% C
I) p
er IQ
R in
crea
se in
Pol
luta
nt M
etri
c
24-hr PM2.5
CS BG AERMOD HYBRID
PM2.5 – CVD AND RESP ED VISITS BY AER STRATA (+HYBRID METRIC)
0.96
0.97
0.98
0.99
1.00
1.01
1.02
1.03
1.04
1.05
1.06
<0.24
0.24
-0.28
>0.28
<0.24
0.24
-0.28
>0.28
<0.24
0.24
-0.28
>0.28
<0.24
0.24
-0.28
>0.28
CS CS CS BG BG BG AERMODAERMODAERMODHYBRID HYBRID HYBRID
PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25
Relati
ve Ri
sk (95
% CI) p
er IQR
incre
ase in
Pollu
tant M
etric
0.96
0.97
0.98
0.99
1.00
1.01
1.02
1.03
1.04
1.05
1.06
<0.2
4
0.24
-0.2
8
>0.2
8
<0.2
4
0.24
-0.2
8
>0.2
8
<0.2
4
0.24
-0.2
8
>0.2
8
<0.2
4
0.24
-0.2
8
>0.2
8CS CS CS BG BG BG AERMODAERMODAERMODHYBRID HYBRID HYBRID
PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25
Rela
tive
Risk
(95
% C
I) p
er IQ
R in
crea
se in
Pol
luta
nt M
etri
c
AER Strata (hr-1)CVD Visits
AER Strata (hr-1)Resp Visits
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III. CONSIDERATIONS RELATED TO HEALTH DATA Administrative records (e.g., death
certificates, medical billing records) Lack of information about subject location in
time and space Residence only? Mobility pattern throughout
domain? Lack of information about sub-clinical steps
in mechanistic pathway Panel-based design used to address some of
these issues
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ATLANTA COMMUTERS EXPOSURE (ACE) STUDY
•Measure in-vehicle pollutant concentrations and corresponding acute health response for a cohort of health and asthmatic
commuters•Scripted 2h commute during morning rush hour periods in
Atlanta•Highly-speciated in-vehicle particulate exposure measurements•Detailed continuous and pre-post commute health
measurements •Provide means of comparison with modeled estimates, roadside and central site monitoring validation of traffic exposure models
Yij = 0 + b0i + 1 (Exposureij) + (individual level covariatesi) + (confoundersij)+ ij
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AtlantaCommuters Exposure
Study
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SUMMARY
Timeseries and cohort/panel studies constitute complementary approaches to address concerns in examinations of short-term exposures and acute effects
Modeled data may and can provide opportunities to reduce error in population-based timeseries analyses Validity of approaches and interpretation of
results still ongoing Panel studies may serve to validate, highly
spatially-resolved modeled estimates How can models informs cohort and panel
studies?
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Health data analysis based on Poisson models to examine the association between ambient pollutant concentrations and counts of cardiovascular and respiratory emergency department visits
Epidemiological statistical models: log(E(Ykt)) = α + β exposure metrickt + kγkakt+ …other covariates
k: 225 Zip codes
t: 365 days x 4 years
risk ratio for increments of one interquartile range (IQR) in corresponding pollutant concentrations
Where Ykt = daily deaths, ED visits or hospital admission counts in area k on day t for outcome chosen (e.g., respiratory or cardiovascular)
Exposure Metrics are Monitored or Modeled Ambient Pollution concentrations for area k on day t
Modeling Approach
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CVD ED VISITS & PM2.5 BY AER STRATA
0.96
0.97
0.98
0.99
1.00
1.01
1.02
1.03
1.04
1.05
1.06
<0.2
4
0.24
-0.2
8
>0.2
8
<0.2
4
0.24
-0.2
8
>0.2
8
<0.2
4
0.24
-0.2
8
>0.2
8
<0.2
4
0.24
-0.2
8
>0.2
8
CS CS CS BG BG BG AERMODAERMODAERMODHYBRID HYBRID HYBRID
PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25 PM25
Rela
tive
Risk
(95
% C
I) p
er IQ
R in
crea
se in
Pol
luta
nt M
etri
c
24-hr PM2.5
CS BG AERMOD HYBRIDCS BG AERMOD HYBRID
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VALIDITY OF APPROACH?
What if we have better means of assigning exposure? (e.g., spatiotemporal models)
Will this improve estimates of magnitude of effect, strength of effect?
Is there a way to compare whether a given assignment approach is ‘better’ than another?
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SUMMARY
Varying degrees of spatial and temporal variability observed for different exposure metrics Variability more pronounced for traffic-related (CO,
NO2) vs. regional (SO42-) pollutants
Similar magnitudes of association across metrics observed for CVD outcome Robust results for spatially heterogeneous pollutants
as well
Hybrid metric strongest associations for respiratory outcome Significant for CO, PM2.5; CS non-significant
Suggestive evidence of AER as a modifier of effect for models using hybrid metric
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CHALLENGES - FUTURE DIRECTIONS
Magnitude and strength of association affected by numerous factors RRs from spatiotemporal ambient pollutant do
not necessarily reflect exposure
Future work will incorporate both exposure factors and spatially-resolved ambient concentrations for epi models Metric V, SHEDS
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TEMPORAL ASSOCIATIONS
Exposure contrast in time-series studies Temporal differences One daily pollutant value daily ED visits
With spatially-resolved daily data Let variation over time within each ZIP code
provide exposure contrast Daily ZIP-specific pollutant values daily ZIP-
specific ED visits
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• For PM2.5, temporal variability between the days dominates, while spatial patterns of concentrations between the monitoring sites vary only by 10-30% within a given day. This is as expected because PM2.5 is a regional pollutant and the day to day variability reflects the movement of various air masses and the influence of photochemical transformations
Spatial and Temporal Characteristics of Ambient Monitoring Data in Atlanta
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• For NOx, the pattern is different; both temporal and spatial variability exists. Unlike PM2.5, NOx concentrations can vary by a factor of 3 for any given day. This pollutant is highly influenced by local sources of emissions and thus the concentrations do not change unless there is a shift in meteorological conditions within the day
Spatial and Temporal Characteristics of Ambient Monitoring Data in Atlanta
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PM2.5
Time Series of Coefficients of Variability
Modeling helps to resolve spatial scale and provide variability in pollutant concentrations that is important for the epi analysis
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DEFINING TERMS
We estimated several air tightness parameters: Infiltration surrogates
Home age Home size
# of rooms, home area, home value Normalized Leakage (NL) = describes relative
leakage for a range of building types Unitless (leakage area per exposed envelope area) Most single-family homes have NL values between 0.2
– 2 Air Exchange Rate (AER)
Expressed in hr-1
AER > 1 well-ventilated
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SES SURROGATES – ESTIMATED ACH
0.00
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0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70
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$0 $20,000 $40,000 $60,000 $80,000 $100,000 $120,000 $140,000
Median Income
AC
H [h
r-1]
% of Low Income Households
AC
H [h
r-1]