May 30, Harvard ACE Project 2 May 30, / 34

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1 Harvard ACE Project 2: Air Pollutant Mixtures in Eastern Massachusetts: Spatial Multi-resolution Analysis of Trends, Effects of Modifiable Factors, Climate, and Particle-induced Mortality Brent Coull, Petros Koutrakis, Joel Schwartz, Itai Kloog, Antonella Zanobetti, Joseph Antonelli, Ander Wilson, Jeremiah Zhu, Weeberb Requia, Choong-Min Kang, Marianthi-Anna Kioumourtzoglou, Ce Li, Glen McGee May 30, 2018 Harvard ACE Project 2 May 30, / 34

2 Objectives 1 Decompose high-resolution PM 2.5 mass and ground air temperature data into regional, sub-regional, and local spatial scales. 2 Conduct a spatiotemporal analysis of sub-regional and local variation in PM 2.5 mass and ground air temperature, and local PM 2.5 emissions. 3 Conduct spatial multi-resolution analysis of PM 2.5 mixtures. 4 Conduct air pollution mortality and birth outcome studies in Massachusetts using multi-resolution PM 2.5 mass and species data. Harvard ACE Project 2 May 30, / 34

3 Spatio-temporal Models for Black Carbon Harvard ACE Project 2 May 30, / 34

4 Prediction Models for Black Carbon: Support Vector Machines Abu Awad et al., Environmental International Harvard ACE Project 2 May 30, / 34

5 Spatio-temporal Models for Black Carbon Harvard ACE Project 2 May 30, / 34

6 Spatio-temporal Modeling of PM2.5 Elements from XRF Harvard ACE Project 2 May 30, / 34

7 Spatio-temporal Modeling of PM 2.5 Elements Name Type Sites Time Samples Duration HSPH Outdoor 1 03/ / Daily MAD EPA Outdoor 6 03/ / Daily MPO PPG Outdoor 53 03/ / Daily MPI PPG Indoor 68 10/ / Weekly MNI NAS Indoor / / Weekly Elements Modeled: K, CA, Fe, Zn, Cu, Ti, Al, Pb, V, Ni Harvard ACE Project 2 May 30, / 34

8 Spatio-temporal Modeling of PM 2.5 Elements Outcomes considered 1 Element Concentration 2 Element Concentration / PM PCA Component Scores Modeling frameworks 1 Spatio-temporal Generalized Additive Models (GAMs) 2 Random forests 3 Support Vector Machines 4 GAMs + Gradient Boosting Harvard ACE Project 2 May 30, / 34

9 Spatio-temporal GAMs + Gradient Boosting Validation R 2 Element Data I-95 loop K Fe Cu MAD out in overall MNI out in overall MPI out in overall MPO out in overall Harvard ACE Project 2 May 30, / 34

10 Temporal Distribution of XRF Data, (a) MAD (b) MPI (c) MPO (d) MNI Harvard ACE Project 2 May 30, / 34

11 Spatio-temporal Modeling of PM 2.5 Elements from XRF Metal concentrations (Red: < 3 Uncertainty) Harvard ACE Project 2 May 30, / 34

12 Spatio-temporal Modeling of PM 2.5 Elements: Next Steps MADEP data to be added: 1 7 new locations samples (will triple existing data) Ensemble (averaging) of different models Harvard ACE Project 2 May 30, / 34

13 Model Ensembling: Accounting for Model Uncertainty Spatial distribution of PM 2.5 model CV errors from two exposure models (a) IK (b) QD Harvard ACE Project 2 May 30, / 34

14 Model Ensembling: Accounting for Model Uncertainty Estimated Effect Heterogeneity Using Three Exposure Models Hazard Ratio per 5 µg/m AV IK QD 1.00 ME Q1 Q2 Q3 Q4 ME Q1 Q2 Q3 Q4 ME Q1 Q2 Q3 Q4 Harvard ACE Project 2 May 30, / 34

15 Bayesian Spatially-Varying Model Ensembles y(x): Pollution Spatial Process {ŷ k } K k=1 : Prediction from K base models We seek model ensemble estimates of the form y ens (x) = K k=1 w k(x)ŷ k (x) where spatially varying weight functions are modeled as w k = exp(w k ) K k=1 exp(w k ) where w k (x) GP [ 0, k w,k (x, x l) ] We obtain posterior predictive distribution of y that incorporates both model-specific prediction error model-to-model uncertainty Harvard ACE Project 2 May 30, / 34

16 Bayesian Spatially-Varying Model Ensembles: Simulation Simulation: Residual process from individual base models Harvard ACE Project 2 May 30, / 34

17 Bayesian Spatially-Varying Model Ensembles: Simulation Ensemble weight flexibility v.s. Cross-validated RMSE Harvard ACE Project 2 May 30, / 34

18 Critical Windows of Exposure for Children s Health Evidence supports associations between maternal exposure to air pollution during pregnancy and children s health outcomes. Recent interest focuses on critical windows of vulnerability. We have estimated daily exposures from multiple pollutants: NO 2, OC, EC, Sulfate, O 3, NH 4 Recent work has shown distributed lag modeling (DLM) can outperform models based on trimester-averaged exposures (TAE). No corresponding methods for air pollution mixtures. Goal: Develop DLM methods for air pollution mixtures. Harvard ACE Project 2 May 30, / 34

19 Distributed Lag Model for Single Exposure Exposure z i (t) is time-specific (week of pregnancy) Y i = α + γ z i (t)w(t)dt + x T i β + ɛ i w(t) identifies critical windows of vulnerability γ is the within-window effect Harvard ACE Project 2 May 30, / 34

20 Bayesian Kernel Machine Regression for Mixtures 1 Y i is a health endpoint, x i contains potential confounders. 2 z i = (z i1,..., z im ) T are M (univariate) pollutant concentrations. Y i = h (z i1,..., z im ) + x T i β + ɛ i, i = 1,..., n h ( ) is an unknown function β: effects of the confounders; ɛ i iid N ( 0, σ 2 ) Harvard ACE Project 2 May 30, / 34

21 Bayesian Kernel Machine Regression - DLM Ultimately interest focuses on z mi (t), m = 1,..., M Y i = h(e 1i,..., E Mi ) + x T i β + ɛ i. E mi = z mi (t)w m (t)dt. Model fitting estimates the critical windows w m (t) and the mixture effect h() simultaneously. Harvard ACE Project 2 May 30, / 34

22 ACCESS Prospective Birth Cohort ORIGINAL ARTICLE Study participants (i): 191 Boston-area births between 8/2002 and 1/2007 traditional LUR predictors to yield residence-specific estimates of daily PM 2.5 as detailed previously (22). The model was run using day-specific calibrations of AOD data using ground PM 2.5 measurements from 78 monitoring stations covering New England and LUR and meteorologic variables (temperature, wind speed, visibility, elevation, distance to major roads, percent open space, point emissions, and area emissions). This approach incorporates highly resolved spatial information from the LUR data and important spatiotemporal data from the remote sensing satellite data. The AOD-PM 2.5 relationship was calibrated for each day using data from grid cells with both monitor and AOD values using mixed models with random slopes for day, nested within region. For days without AOD data (because of cloud coverage, snow, and so forth), the model was fit with a smooth function of latitude and longitude and a random intercept for each cell (similar to universal Kriging). The out of sample 10-fold cross validation R 2 for daily values was 0.83 and 0.81 for days with and without available AOD data, respectively. For use in the health effect models, to reduce potential noise caused by day-to-day PM 2.5 variation, daily levels were averaged into weekly exposure profiles. Predicted overall prenatal PM 2.5 levels at participant s residence in Exposure (Z mit ): NO 2, OC, EC, S, O 3, NH 4 at maternal residence for each week (t) of pregnancy Outcomes (Y i ): FEV 1 at age 8 relation to the km grids for which AOD data were available are shown in Figure 1. Although levels were higher around major roadways as anticipated, there was reasonable heterogeneity. Asthma Maternal-reported clinician-diagnosed asthma was ascertained from birth up to age 6 years through telephone and face-to- A validation analysis on a subset of 121 ACCESS women showed no difference in the level of agreement/disagreement for height and weight when comparing values PM 2.5 estimates over pregnancy (µg/m 3 ) Major road ways 10 10km prediction grid Kilometers Baseline covariates (X i ): child sex, maternal pre-pregnancy BMI, age, education, race/ethnicity, atopy, self reported smoking during pregnancy, stress index, neighborhood disadvantage index [figure source: Hsu et al. Am. J. Respir. Crit. Care Med. 2015] Figure 1. Predicted daily particulate matter with a diameter less than or equal to 2.5 mm (PM2.5) levels for Asthma Coalition on Community, Environment and Social Stress participants averaged over pregnancy. This figure demonstrates predicted daily PM2.5 levels for study participants based on residence and averaged throughout the gestation period. The km aerosol optical depth grid used to predict daily PM2.5 levels is also depicted. a confounder. We measured stress using the Crisis in Family Systems-Revised survey administered prenatally within 2 weeks of enrollment (37, 38). This survey assesses life events experienced across 11 domains (e.g., financial, relationships, violence, housing, discrimination/prejudice). Mothers endorsed events experienced in the past 6 months and rated each as measured early in pregnancy (,10 wk) with self-report (34). Women were asked about smoking at enrollment and in the third trimester and classified as prenatal smokers if smoking at either visit. Mothers Harvard ACEreported Project postnatal 2 smoking and whether positive, May negative, 30, or 2018 neutral. The number 22 / 34 N

23 BKMR using pregnancy-average exposure Harvard ACE Project 2 May 30, / 34

24 BKMR-DLM: Exposure-response of each exposure at low/high levels of another Harvard ACE Project 2 May 30, / 34

25 BDLM analyses of NH4, by low/high NO2, EC, Sulfate, O 3 Harvard ACE Project 2 May 30, / 34

26 Delay of Onset and Exposure Error in Case-Crossover Designs Time-stratified case-crossover studies often used to estimate effect of air pollution on acute events For each event, create set of reference times based on same year, month, DOW, and hour Delayed recording of event onset yields exposure error. Can yield severe attenuation of effect estimates (Lokken et al. 2009). Goal: Develop a method that corrects for this bias based on a validation sample of delay times. Harvard ACE Project 2 May 30, / 34

27 (A) Distribution of delay times and (B) Resulting Exposure Error in 24-hour PM 2.5 in Boston Stroke Study Harvard ACE Project 2 May 30, / 34

28 Subtle problem: Matched sets change with error. None of the typical measurement error assumptions hold. We developed the following error corrections: Marginal likelihood estimator Regression calibration estimator Conditional score estimator Each of these + second-stage parametric bootstrap Harvard ACE Project 2 May 30, / 34

29 Comparative analysis of Boston stoke study using true and delayed event times and measurement error corrections. Method True Error CS RC B.Error B.CS B.RC ndelays= Effect Estimate Harvard ACE Project 2 May 30, / 34

30 Spatial multi-resolution analysis: Conceptual Framework Goal: Decompose daily pollution surfaces into different scales, which are representative of different sources of pollution Examine health effects, predictors, and trends at different scales Figure taken from HSPH class EH521 Notes (Annette Peters) Harvard ACE Project 2 May 30, / 34

31 Applied to Satellite PM 2.5 Predictions Two-dimensional wavelet decomposition: All panels are averaged over days in 2006 Harvard ACE Project 2 May 30, / 34

32 Multi-resolution Work - Integration within Larger Center Currently using wavelet decompositions for: mortality and scale-specific PM 2.5 in New England, source-receptor mapping of power-plant emissions (Project 4) emissions modeling (Project 1) Reproducible Software Harvard ACE Project 2 May 30, / 34

33 Project 2 Posters 1 Abu Awad et al. A spatio-temporal prediction model for Black Carbon based on ensemble machine learning. 2 Liu et al. Spatio-temporal modeling of ambient PM 2.5 elemental concentrations in eastern Massachusetts. 3 Liu et al. Adaptive Bayesian spatio-temporal ensemble of air pollution predictive models. 4 Wilson et al. Distributed lag models for assessing critical windows of exposure to air pollution mixtures. 5 Coull et al. Corrections for measurement error due to delayed onset of illness for case-crossover designs. Harvard ACE Project 2 May 30, / 34

34 Harvard ACE SAC Review we encourage the Center to also consider the potential for confounding and/or effect modification by the broader pollutant mixture... BC, XRF modeling BKMR-DLM Each project should try to quantify the uncertainties, how they propagate, and identify which are the major uncertainties of concern. Model ensembling Consider birth cohorts in addition to administrative records ACCESS, VIVA Multi-resolution analysis of PM 2.5 -mortality relationship Data and computational infrastructure complete Results being generated now Harvard ACE Project 2 May 30, / 34