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Critical Issues of Exposure Assessment for Human Health Studies of Air Pollution. SAMSI September 15, 2009. Michelle L. Bell Yale University. Outline. Basic health effects model Methods of measuring exposure Key challenges in assessing exposure Spatial misalignment

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critical issues of exposure assessment for human health studies of air pollution

Critical Issues of Exposure Assessment for Human Health Studies of Air Pollution

SAMSI

September 15, 2009

Michelle L. Bell

Yale University

outline
Outline
  • Basic health effects model
  • Methods of measuring exposure
  • Key challenges in assessing exposure
    • Spatial misalignment
    • Multiple pollutant exposures
    • Special case of particulate matter
  • Current and upcoming approaches to estimating exposure
  • Other challenges
exposure assessment for studies of air pollution and health
Exposure Assessment for Studies of Air Pollution and Health
  • Basic health effects model
  • Methods of measuring exposure
  • Key challenges in assessing exposure
    • Spatial misalignment
    • Multiple pollutant exposures
    • Special case of particulate matter
  • Current and upcoming approaches to estimating exposure
  • Other challenges
slide4
Example Air Pollution and Health Effects Model
  • Time-series model / Acute exposure
  • Community-aggregated health data
  • Community-aggregated exposure data
slide5
Example Air Pollution and Health Effects Model

Estimated Air Pollution Exposure

exposure assessment for studies of air pollution and health1
Exposure Assessment for Studies of Air Pollution and Health
  • Basic health effects model
  • Methods of measuring exposure
  • Key challenges in assessing exposure
    • Spatial misalignment
    • Multiple pollutant exposures
    • Special case of particulate matter
  • Current and upcoming approaches to estimating exposure
  • Other challenges
traditional approaches to exposure assessment
Traditional Approaches to Exposure Assessment
  • Use of ambient monitors

+ cost-effective

+ can provide large sample size

    • not present in all times or locations of interest
    • locations based on regulatory, not scientific, purposes
    • obscures between-person differences
  • Personal monitors

+ individualized data

    • short timeframe
    • small population
    • logistical concerns

Source: Louisville, KY government

Jiang and Bell EHP 2008

traditional approaches to exposure assessment1
Traditional Approaches to Exposure Assessment
  • Use of ambient monitors

+ cost-effective

+ can provide large sample size

    • not present in all times or locations of interest
    • locations based on regulatory, not scientific, purposes
    • obscures between-person differences
  • Personal monitors

+ individualized data

+ measure pollutant characteristics of interest

    • short timeframe and small population
    • logistical concerns and expensive
exposure assessment for studies of air pollution and health2
Exposure Assessment for Studies of Air Pollution and Health
  • Basic health effects model
  • Methods of measuring exposure
  • Key challenges in assessing exposure
    • Spatial misalignment
    • Multiple pollutant exposures
    • Special case of particulate matter
  • Current and upcoming approaches to estimating exposure
  • Other challenges
spatial misalignment
Spatial Misalignment
  • Spatially heterogeneity in the concentration surface field
  • Mismatch between data used to estimate exposure and actual subjects’ locations
correlation of pm 2 5 components by distance
Correlation of PM2.5 components by distance

Peng and Bell Biostatistics Accepted

spatial disconnect in data
Spatial Disconnect in Data

Pollution data measured at a single or multiple fixed locations

Health data aggregated over a spatial area

Weather data measured at a single or multiple fixed locations, that likely differ from the pollution monitor locations

error model
Error Model

Pollution data measured at a single or multiple fixed locations

True pollution level over a spatial area

Peng and Bell Biostatistics Accepted

spatial misalignment adjustment
Spatial Misalignment Adjustment
  • Monitor average ( ) good proxy for true value ( xt) with good monitor coverage and/or low spatial heterogeneity
  • Spatial misalignment adjustments useful when:
    • Pollutant very spatially heterogeneous (e.g., EC)
    • Poor monitor coverage within area of interest, but monitors elsewhere
multi pollutant concepts
Multi-Pollutant Concepts
  • Physiologically we respond to a complex mixture of air pollutants
  • Many studies focus on the effects of a single pollutant
  • Additional pollutants typically considered with respect to confounding, not complex effects
  • Air pollution policy set for single pollutants
    • Based on single pollutant science
major air pollution emissions sources
SO2

PM

Pb

NOx

CO

VOCs

Major Air Pollution Emissions Sources

Industrial Sources

Diesel

Domestic Sources

Gasoline

Power

Generation

VEHICLE

SOURCES

STATIONARY

SOURCES

particulate matter
Particulate Matter
  • What is represented by an exposure estimate for PM?
  • Only pollutant regulated without regard to chemical form
  • May vary in:
    • Size
    • Shape
    • Chemical structure
    • Water content
    • Acidity
    • Age
    • Etc.
slide18
Different chemical components by PM size

Different sources by PM size

slide19
<1.0 mg/m3

1.0 to 2.5

2.5 to 4.0

4.0 to 5.5

>5.5

PM as a Pollutant Mixture

PM2.5 sulfate (2000-2002)

Bell et al. EHP 2007

slide20
Spring

Winter

Fall

Summer

<2 mg/m3

2 to 3

Seasonal sulfate PM2.5 averages for 187 U.S. counties, 2000-2005

3 to 4

4 to 5

>5

Bell et al. EHP 2007

pm 2 5 and medicare hospital admissions
PM2.5 and Medicare Hospital Admissions

3

2

1

0

-1

West

East

-2

% Change in Hospital Admissions per 10 mg/m3 PM2.5

Ischemic Heart Disease

Heart Failure

Heart Rhythm

Respiratory Infection

COPD

Peripheral Vascular Disease

Cerebrovascular Disease

Dominici et al. JAMA 2006

season interaction model
Season Interaction Model
  • Allows different effect estimates by season

Harmonic Model

  • Allows effect estimates to differ throughout the year

Bell et al. Am J Epidemiol 2008

slide24
Seasonal Variation in PM Total Mass

Health Effect Estimates

% increase in CVD hospitalizations per 10 mg/m3 L0 PM2.5

Day of the year

Bell et al. Am J Epidemiol 2008

exposure assessment for studies of air pollution and health3
Exposure Assessment for Studies of Air Pollution and Health
  • Basic health effects model
  • Methods of measuring exposure
  • Key challenges in assessing exposure
    • Spatial misalignment
    • Multiple pollutant exposures
    • Special case of particulate matter
  • Current and upcoming approaches to estimating exposure
  • Other challenges
example air quality modeling to estimate exposure
Domain 1

Domain 2

Domain 3

Domain 4

Example: Air Quality Modeling to Estimate Exposure
  • CMAQ / MM5
  • Aug 15 – 18, 1995
  • 2,168 cells with 4 km horizontal resolution
  • 8 monitors for ozone

Bell Environ Int 2006

slide28
> 180

170 to 180

160 to 170

150 to 160

140 to 150

130 to 140

120 to 130

110 to 120

100 to 110

<100

County-level Exposure Estimates

1-Hour max O3 (ppb)

Monitor in county

Modeling estimates

Spatial interpolation of monitors

Bell Environ Int 2006

slide29
180+

170 to 180

160 to 170

150 to 160

140 to 150

130 to 140

120 to 130

110 to 120

100 to 110

<100

Modeling estimates

Individual-Level Exposure Estimates

1-Hour max O3 (ppb)

Nearest monitor

Bell Environ Int 2006

ozone monitors in georgia 2000
< 50

51 to 75

75 to 150

151 to 250

251 to 500

501 to 1000

>1001

Ozone monitors in Georgia 2000

Persons / Sq. Mile

example traffic modeling to estimate exposure
Example: Traffic Modeling to Estimate Exposure

Holford et al. Statistics in Medicine Accepted

estimated no 2 traffic levels for new haven county area 2002
Estimated NO2 (traffic) Levels for New Haven County Area (2002)

Holford et al. Statistics in Medicine Accepted

exposure assessment for studies of air pollution and health4
Exposure Assessment for Studies of Air Pollution and Health
  • Basic health effects model
  • Methods of measuring exposure
  • Key challenges in assessing exposure
    • Spatial misalignment
    • Multiple pollutant exposures
    • Special case of particulate matter
  • Current and upcoming approaches to estimating exposure
  • Other challenges
other challenges
Other Challenges
  • Other factors affecting certainty of monitor values
    • Detection limits of monitors
    • Measurement error (see co-located monitors)
  • Other factors that affect exposure and variation of exposure
    • Movement through the community
    • Indoor/outdoor activity patterns
    • Behaviors and activities (e.g., AC, jogging)
  • Differences between exposure and dose
thank you
Thank you
  • Key Collaborators
    • Francesca Dominici, Harvard University
    • Roger D. Peng, Johns Hopkins University
    • Keita Ebisu, Yale University
  • Sponsors
    • National Institute for Environmental Health Sciences (NIEHS)
    • Health Effects Institute
    • U.S. Environmental Protection Agency-sponsored Johns Hopkins Particulate Matter Research Center
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