examining short-term air pollution exposures and health effects: atlanta as a case study

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

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

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

= 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

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

10

20

30

40

50

60

70

80

90

100

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

0

10

20

30

40

50

60

70

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

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

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

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

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

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 Preliminary Results of Epidemiologic Analysis of ED Visits in Atlantaof ED Visits in Atlanta

CC

CC

CC

CC C

C

CC

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

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

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

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



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


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

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 = 0 + b0i + 1 (Exposureij) + (individual level covariatesi) + (confoundersij)+ ij

AtlantaCommuters 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?

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

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



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

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 ZIP-

specific ED visits

• 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

Spatial and Temporal Characteristics of Ambient Monitoring Data in Atlanta

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

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

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

$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]

examining short-term air pollution exposures and health effects: atlanta as a case study

Documents

exposure misclassification

exposure metric

exposure dataexamples

exposure dataapproach

health effects

cardiorespiratory ed

pollution terms

shortterm exposures