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

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

2. 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 • => 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

3. 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 => inference not correct

4. 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

5. Air pollution exposure framework Hypothesized personal exposure model: • Personal exposure: EP = ambient source (EA) + non-ambient source (EN) • EA = ambient concentration (CA) * α • Ambient concentration occurs both outdoors and indoors due to the infiltration of ambient pollution into indoor environments • α = [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)

6. 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

7. 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

8. Partial sill ( ) Nugget ( ) Range ( ) 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

9. 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

10. Space-time interaction and temporally sparse data suggest spatio-temporal model to predict long-term averages 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)

11. Examples ofspatial surfaces • Spatial surface of five exposure models (lighter = higher concentration):

12. “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

13. “Plug-in exposure” health effect estimates: Exposure with structure captured by the predictions True exposure vs. nearest monitor True exposure vs. kriged

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

15. 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

16. 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)

17. 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)

18. Health effect estimates example

19. 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

20. 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) • Can’t 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

21. 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 • => decreased exposure variation in health analyses

22. 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