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
OUTLINE OF TALK
Study designs used to assess short-term exposures,
acute health responses
I.
Population-based timeseries analyses
 Cohort and panel studies

Considerations/concerns related to exposure data
II.

Examples: Study of Particles and Health in Atlanta
(SOPHIA); Emory-Ga Tech EPA COOP
Considerations/concerns related to health data
III.

Example: Atlanta Commuters Exposure Study
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
> 8,300 SQ MILES
Data include 10,206,389 ED visits from 41 of 42
hospitals in 20-county Atlanta, 1993-2004
 = Acute care facility
 = Jefferson St. station
DATA ANALYSIS
F OR DA T A S E T
F OR DA T A S E T
T WO,
T WO,
0 5 DE C0 5
0 5 DE C0 5
100
70
PM2.5
60
90
Asthma Visits
80
50
40
70
60
24-hr standard
50
30
40
20
Annual standard
30
10
20
10
0
01/ 01/ 98
01/ 01/ 99
01/ 01/ 00
01/ 01/ 01
DA T E



01/ 01/ 02
01/ 01/ 03
01/ 01/ 98
01/ 01/ 99
01/ 01/ 00
01/ 01/ 01
DA T E
01/ 01/ 02
01/ 01/ 03
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

PEDIATRIC ED VISITS FOR ASTHMA
Strickland et al., AJRCCM, 2010
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
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
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
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)
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
Hypotheses
1. 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
CURRENT PROJECT

Develop 5 alternative
metrics of exposure






Traffic: CO, NOX, EC
(ii) SpatiallyRegional: O3, SO42- Interpolated
Background
Mix: PM2.5
Daily, ZIP code level
For sub-period, 19992002
For current analysis:

Results using Metrics i,
iii, iv, v
(i) Ambient
Monitoring
Data
Emissions Data
Spatially-Resolved
Concentrations
Modeling:
(iii) AERMOD
(iv) Hybrid
(v) Exposure
Factors
Spatially-Resolved
Exposures
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
Preliminary Results of Epidemiologic Analysis of
ED Visits in Atlanta
C
C
C
C
C
C
C
C
C
C
C
C
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
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

ZIP CODE RESOLVED AERS IN ATLANTA
ACH Conv + Low
¦
Pickens
Bartow
Cherokee
Forsyth
Barrow
Paulding
Gwinnett
Cobb
Walton
De Kalb
Douglas
Fulton
Rockdale
Carroll
Clayton
Newton
Henry
Fayette
Coweta
Legend
Spalding
County borders
ACH conv+low
0.264 - 0.332
0.333 - 0.378
0
5
10
20 Miles
0.379 - 0.439
0.440 - 0.572
Major Roads
ESTIMATING AER USING LBNL APPROACH
Temporally-varying
Spatially-varying
0.3
NL 2.5m 
2
2
2
s s f s  T  f w  v
ACH 

AER
1000 H  H 
where
NL = exp(b0 +b1yr built + b2floor area + e)
H = height of home (m)

fS = stack effect estimate
fW = wind effect estimate
T = temperature (K)
V = wind speed (m/sec)
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
PM25
1.03
1.02
1.01
1.00
0.99
0.98
0.97
>0.28
>0.28
<0.24
>0.28
0.24-0.28
0.24-0.28
<0.24
<0.24
>0.28
0.24-0.28
>0.28
<0.24
>0.28
<0.24
0.24-0.28
<0.24
>0.28
BG
HYBRIDAHYBRID
CS AERMOD
BG AERMOD
BG AERMOD
BG AERMOD
ERMOD
AHYBRID
ERMODHYBRID HYBRID HYBRID
0.24-0.28
0.96
0.24-0.28
Relative Risk (95% CI) per IQR increase in Pollutant Metric
1.04
BG
CS
<0.24
0.24-0.28
1.05
>0.28
BG
CS
1.06
0.24-0.28
<0.24
PM2.5 – CVD AND RESP ED VISITS
BY AER STRATA (+HYBRID METRIC)
CS CS CS BG BG BG AERMODAERMODAERMODHYBRIDHYBRIDHYBRID
AERMOD AER
BG
HYBRID
Strata (hr-1) AER Strata (hr-1)
PM2524-hr
PM25 PM
PM252.5PM25 PM25
PM25
PM25
PM25
PM25
PM25PM25
PM25
PM25
PM25 PM25
PM25 PM25
PM25Resp
PM25
PM25
PM25 PM25 PM25
CVD
Visits
Visits
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

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 = b0 + b0i + b1 (Exposureij) + (individual level covariatesi) + (confoundersij)+ ij
Atlanta
Commuters
Exposure
Study
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?
Modeling Approach
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
b- 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
1.06
1.05
1.04
1.03
1.02
1.01
1.00
0.99
0.98
0.97
PM25 PM25 PM25 PM25
>0.28
0.24-0.28
<0.24
>0.28
BG
0.24-0.28
CS
<0.24
<0.24
CS
CS
>0.28
>0.28
CS
0.24-0.28
0.24-0.28
0.96
<0.24
Relative Risk (95% CI) per IQR increase in Pollutant Metric
CVD ED VISITS & PM2.5
BY AER STRATA
BG
BG AERMOD
AERMOD
AERMODHYBRID HYBRID HYBRID
AERMOD
BG
HYBRID
AERMOD
BG
PM2524-hr
PM25 PM
PM252.5PM25 PM25 PM25 PM25 PM25
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?

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
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
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 ZIPspecific ED visits

Spatial and Temporal Characteristics
of Ambient Monitoring Data in Atlanta
• 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
• 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
Time Series of Coefficients of Variability
PM2.5
Modeling helps to resolve spatial scale and provide variability
in pollutant concentrations that is important for the epi analysis
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

SES SURROGATES – ESTIMATED ACH
0.70
0.50
0.40
0.30
0.20
0.10
0.70
0.00
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.60
% of Low Income Households
0.50
ACH [hr-1]
ACH [hr-1]
0.60
0.40
0.30
0.20
0.10
0.00
$0
$20,000
$40,000
$60,000
$80,000
$100,000
Median Income
$120,000
$140,000