The Use of Spatial Exposure Predictions in Health Effects Models: An Application to PM Epidemiology

Transcription

1 The Use of Spatial Exposure Predictions in Health Effects Models: An Application to PM Epidemiology Chris Paciorek and Brent Coull Department of Biostatistics Harvard School of Public Health wwwbiostatharvardedu/ paciorek October 14, 2008 Chris Paciorek and Brent Coull Exposure Predictions and Health 1

2 Eastern Massachusetts Daily Black Carbon ESSEX 95 MIDDLESEX SUFFOLK 90 NORFOLK WORCESTER 90 Boston monitoring sites BRISTOL BC Outdoor & indoor: 30 monitors (APAHRV) BC Indoor: 15 monitors (APAHRV) BC Ambient: 14 monitors EC Outdoor: 23 monitors (EPA) PLYMOUTH : Indoor BC APAHRV study 2 : Outdoor BC APAHRV study 3 : Outdoor EC EPA 4 18 : Outdoor BC NESCAUM study 4/1/99 1/1/00 1/1/01 1/1/02 1/1/03 1/1/04 12/9/04 BC monitors in space (left) and time (right) Chris Paciorek and Brent Coull Exposure Predictions and Health 2

3 Model Published model (Gryparis et al JRSSC 2007): Contains a spatial term, temporal term, and spatial and temporal covariates log BC it = X i β + Z t α + g(s i ) + h(t) + ɛ it Little space-time interaction except for stratification by summer/winter Ongoing work: Build in space-time smoothing and effects of covariates that vary in space and time Additional data from rotating monitors and from 7-day integrated samples Application Acute and chronic health studies in eastern Massachusetts: stroke, hypertension, intermediate cardiovascular markers, mortality, birthweight Chris Paciorek and Brent Coull Exposure Predictions and Health 3

4 Enhancing Exposure Assessment Nationwide Monthly PM PM25 monitors (top) and predictions: northeast US (left) and greater Boston (right) Chris Paciorek and Brent Coull Exposure Predictions and Health 4

5 Model Published model (Yanosky et al Atm Env t 2008, Paciorek et al Annals Appl Stats in press): Contains a spatio-temporal terms (one spatial term for each month) plus spatio-temporal covariates Ongoing work: Combination of land-use regression and spatial smoothing Assessment of use of remotely-sensed AOD to improve spatial coverage (see poster) Consideration of new land use covariates and improved space-time characterization Application Chronic health effects in the Nurses Health Study Chris Paciorek and Brent Coull Exposure Predictions and Health 5

6 Prediction Uncertainty as Measurement Error Spatial smoothing exposure models (kriging, splines, additive modeling) produce a form of regression calibration Result is Berkson-type error in health models Implication of limited bias in health models But, 1) exposure away from home and 2) ambient concentrations vs personal exposure probably adds classical error Chris Paciorek and Brent Coull Exposure Predictions and Health 6

7 Accounting for Prediction Uncertainty Approaches that do not work: Directly weighting by prediction uncertainty Simulating exposures based on prediction uncertainty Approaches with more promise: Bayesian models Using held-out data to calibrate the predictions Application Effect of BC on birthweight in eastern Massachusetts Accounting for uncertainty in large cohort studies in survival analysis such as the Nurses Health Study is an open challenge Chris Paciorek and Brent Coull Exposure Predictions and Health 7

8 References Eastern Massachusetts BC model: Gryparis, Coull, Schwartz and Suh 2007 Latent variable semiparametric regression models for spatio-temporal modeling of mobile source pollution in the greater Boston area Journal of the Royal Statistical Society Series C 56:183 Maynard, Coull, Gryparis, and Schwartz 2007 Mortality risk associated with short-term exposure to traffic particles and sulfates EHP 5:751 National PM modeling Yanosky, Paciorek, Schwartz, Laden, Puett, and Suh 2008 Spatio-temporal modeling of chronic PM 10 exposure for the Nurses Sealth Study Atmospheric Environment 42:4047 Paciorek, Yanosky, Puett, Laden, and Suh 2008 Practical large-scale spatio-temporal modeling of particulate matter concentrations Annals of Applied Statistics, in press (Harvard Biostatistics Technical Report 76) Chris Paciorek and Brent Coull Exposure Predictions and Health 8

9 National PM modeling (cont d) Paciorek and Liu 2008 Limitations of remotely-sensed aerosol as a proxy for fine marticulate matter Harvard Biostatistics Technical Report 89; submitted Puett et al 2008 Chronic particulate exposure, mortality and cardiovascular outcomes in the Nurses Health Study American Journal of Epidemiology in press Measurement Error Gryparis, Paciorek, Zeka, Schwartz, and Coull 2008 Measurement error caused by spatial misalignment in environmental epidemiology Biostatistics, in press (Harvard Biostatistics Technical Report 87) Chris Paciorek and Brent Coull Exposure Predictions and Health 9