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

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

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

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

Air pollution exposure framework Hypothesized personal exposure model: Personal exposure: E P = ambient source (E A ) + non-ambient source (E N ) E A = ambient concentration (C A ) * α Ambient concentration occurs both outdoors and indoors due to the infiltration of ambient pollution into indoor environments α = [f o +(1-f o )F inf ] is the ambient exposure attenuation factor Ambient attenuation is a weighted average of infiltration (F inf ), weighted by time spent outdoors (f o ) Exposure of interest: long-term ambient source (E A )

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

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

Nugget ( ) Partial sill ( ) 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

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

AQS Monitor in Azusa ( ) AQS Monitor in Long Beach ( ) Log NOx (ppb) Need For Spatio-Temporal Model Space-time interaction and temporally sparse data suggest spatio-temporal model to predict long-term averages Home Outdoor Monitor in Long Beach (notional)

Examples of spatial surfaces ■ Spatial surface of five exposure models (lighter = higher concentration):

“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

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

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

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

Application example Relative risk of detectable aortic calcium for a 10 ug/m 3 increase in PM 2.5 (Allen et al 2009): Kriged exposure: 1.06 (.96, 1.16) Nearest monitor: 1.05 (.96, 1.15)

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 + bias 2 )

Health effect estimates example True exposure Fitted exposure (R 2 ) Bias 2 Variance Mean square error Coverage probability of 95% confidence interval LeastTrue predictableNearest Kriging (0) True Nearest Kriging (.20) True Nearest Kriging (.40) MostTrue predictableNearest Kriging (.47)

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

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

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

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