Exposure Assessment for Health Effect Studies: Insights from Air Pollution Epidemiology

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Exposure Assessment for Health Effect Studies
Insights from Air Pollution Epidemiology
Lianne Sheppard University of Washington Special
thanks to Sun-Young Kim, Adam Szpiro
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Background

• Most epidemiological studies assess the effects
  of an exposure on a disease outcome by estimating
  a regression parameter (e.g. relative risk)
• Models condition on exposure
• A complete set of pertinent exposure measurements
  typically are not available
• gt Need to use an approach to estimate (predict)
  exposure
• Health results are affected by the quality of the
  exposure estimate
• Exposure assessment for epidemiology should be
  evaluated in the context of the health effect
  estimation goal

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Typical approach

• Estimate or predict exposure as accurately as
  possible
• Plug in exposure estimates into a health model
  estimate health effects
• Challenges
• Health effect estimate is affected by the nature
  and quality of the exposure assessment approach
• Health effect estimate may be
• Biased
• More variable
• Typical analysis does not account for uncertainty
  in exposure prediction gt inference not correct

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Goals

• Advance understanding of environmental and
  occupational exposure assessment for use in
  epidemiological research
• Focus on the air pollution epidemiology
  application because certain features of exposure
  may be better understood than other applications

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Air pollution exposure framework

• Hypothesized personal exposure model
• Personal exposure EP ambient source (EA)
  non-ambient source (EN)
• EA ambient concentration (CA) a
• Ambient concentration occurs both outdoors and
  indoors due to the infiltration of ambient
  pollution into indoor environments
• a f o(1-f o)Finf is the ambient exposure
  attenuation factor
• Ambient attenuation is a weighted average of
  infiltration (Finf), weighted by time spent
  outdoors (f o)
• Exposure of interest long-term ambient source
  (EA)

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Air pollution exposure assessment for
ambient-source long-term exposure

• Individual time-activity and building specific
  infiltration information typically not available
• Ambient concentration data are readily available
  from EPA
• Limited number of fixed locations
• Rich in time (often daily or hourly measurements)
• Monitor siting criteria are pollutant dependent
  for some pollutants monitors are sited away from
  sources
• Even with rich data, models are needed to predict
  concentrations at locations without monitors
• Collection of additional concentration data to
  better predict spatially varying concentrations
  should focus on representing
• Design space
• Geographic space

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Air pollution concentration prediction

• Spatially varying concentrations are typically
  predicted using
• Land use regression
• Kriging or other spatial smoothing approach
• Nearest monitor
• Air pollution concentration modeled using
  universal kriging includes
• Mean model (design space)
• Geographically defined (spatially varying)
  covariates Land use regression
• New covariates derived from physically-based
  deterministic models
• Variance model (geographic space)
• Spatial smoothing

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Spatial Correlation Structure

• Variance model recognizes that nearby residuals
  are correlated
• Example Exponential geostatistical variogram
  model

Incorporating spatial correlation into the model
will improve spatial predictions
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Concentration prediction comments

• With limited concentration data, a
  spatio-temporal model is needed for air pollution
  concentration data
• The success of a prediction model depends upon
• The structure in the underlying exposure surface
• The availability of data to capture this
  structure

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Need For Spatio-Temporal Model
AQS Monitor in Azusa (060370002)
AQS Monitor in Long Beach (060371301)
Log NOx (ppb)
Home Outdoor Monitor in Long Beach (notional)
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Examples of spatial surfaces

• Spatial surface of five exposure models (lighter
  higher concentration)

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Plug-in exposure health effect estimates

• Predicted exposure is used as the covariate in
  the health effect regression model
• The quality of the exposure model affects the
  quality of health effect estimates
• Exposures that can be predicted well (e.g. those
  with large-scale spatial structure) yield health
  effect estimates with good properties regardless
  of prediction approach
• Less predictable exposure surfaces yield health
  effect estimates with poorer properties
• Attenuation bias
• Large standard errors

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Plug-in exposure health effect estimates
Exposure with structure captured by the
predictions
True exposure vs. nearest monitor
True exposure vs. kriged
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Plug-in exposure health effect estimates
Exposure with little structure captured by the
predictions
True exposure vs. nearest monitor
True exposure vs. kriged
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Health effect estimates

• Applications Note that comparing results from
  different exposure predictions gives only one
  realization of the relationship between health
  effect estimates
• This is very limited information

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Application example

• Relative risk of detectable aortic calcium for a
  10 ug/m3 increase in PM2.5 (Allen et al 2009)
• Kriged exposure 1.06 (.96, 1.16)Nearest
  monitor 1.05 (.96, 1.15)

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Comments about health effect estimates

• Even with true (known) exposures, health effect
  estimates have uncertainty
• Uncertainty of health effect estimates increases
  as predicted exposure becomes more smooth (less
  variable)
• Predictions (modeled exposures) only represent a
  fraction of the variation in true exposure
• Health effect estimates can be evaluated by
  assessing their
• Bias
• Variance
• Coverage Percent of 95 confidence intervals
  that cover the true value

• or Mean square error (variance bias2)

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Health effect estimates example
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Conclusions

• Capture as much of the pertinent underlying
  exposure variation as possible in the exposure
  model
• Health effect estimate is affected by the nature
  and quality of the exposure assessment approach

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Health effect estimates for exposures other than
air pollution

• Is the underlying exposure framework clear?
• Challenges predicting exposure
• Less data (no existing regulatory monitoring
  network)
• Cant capture complex structures (such as
  spatio-temporal variation)
• How well do exposure data represent individuals
  with no data?
• Many sources of variation, often without
  measurable determinants

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Comments

• Study design is a critical feature
• Linkage between the design, the key aspects of
  exposure, and the pertinent health outcome?
• Does the design focus on spatial variation
  (cohort studies) or temporal variation (time
  series studies)?
• Multiple testing and potential for reporting bias
• Evaluation of multiple exposure prediction
  approaches is yet another opportunity for
  epidemiologists to cherry-pick results
• Predictions are more smooth than data
• gt decreased exposure variation in health analyses

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Research needs

• What are the important exposure features to
  capture for health effect estimates? Consider
• Sources of variation in underlying true exposure
  and their relevance for the health outcome
• Study design
• Exposure data that are feasible to collect
• Alignment of these features
• How many exposure measurements are needed?
• Exposure data are often much more limited than
  health data
• What are the best inputs to the exposure models?
• Approaches to health effect estimation to give
  good inference
• Good coverage 95 CI covers the true value 95
  of the time