1. Project 3 Georgia Birth Cohorts Study Characterize pollutant mixture Direct measurements Source apportionment Atmospheric modeling Satellite remote sensing Georgia Cohort: Birth records linked with ED visits Georgia Cohort: Birth records linked with ED visits Kaiser-Permanente Cohort: Children covered by the HMO from birth through early childhood Kaiser-Permanente Cohort: Children covered by the HMO from birth through early childhood Create two birth cohorts Acute and Longer-term Asthma, bronchiolitis, otitis media Preterm delivery, low birth weight During pregnancy M. Strickland, L. Darrow, M. Klein, Y. Liu, L. Waller, H. Chang, K. Gass, A. Flak, C. Hansen, J. Mulholland, A. Russell

2. Specific Aims (abbreviated) 1.Characterize the multi-pollutant atmosphere in GA using several complementary approaches (AQ Core) 2.Estimate associations between short-term changes in air pollution levels and pediatric ED visits, with investigation of potentially-susceptible subgroups 3.Estimate associations between air pollution levels during pregnancy and preterm birth and birth weight 4.Use the Kaiser Permanente birth cohort to investigate associations between longer-term air pollutant levels and incident asthma during childhood

3. Outline Brief highlights from recent publications Lots of results – Statewide preterm birth analyses – Statewide term birth weight analyses – Pediatric asthma & source apportionment analyses Kaiser Air Pollution and Pediatric Asthma (KAPPA) study updates Timeline of major planned activities

4. Lag 0-2 air pollution & asthma ED visits: susceptible subpopulations Strickland et al. Modification of the effect of ambient air pollution on pediatric asthma emergency visits: susceptible subpopulations. Epidemiology 2014; 25: 843-50.

5. Lag 0-2 PM 2.5 components and association with URI ED visits among 0-4 year olds Darrow et al. Air pollution and acute respiratory infections among children 0-4 years: an 18-year time-series study. Am J Epidemiol 2014; 180: 968-77.

6. C&RT methods paper Gass et al. Classification and regression trees for epidemiologic research: an air pollution example. Environ Health 2014; 13: 17.

7. SOM methods paper Pearce JL et al. Using self-organizing maps to develop ambient air quality classifications for use in multipollutant health studies: a time-series example. Environ Health 2014; 13: 56.

8. 1-km MAIAC PM 2.5 estimates Hu et al. Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model. Remote Sensing of Environment 2014; 140: 220-32.

9. Gass et al. Ensemble-based source apportionment of fine particulate matter and emergency department visits for pediatric asthma. In press Am J Epidemiol. (poster) Strickland et al. Gestational age-specific associations between infantile acute bronchiolitis and asthma after age five. Paediatr Perinat Epidemiol 2014; 28: 521-6. Yu et al. Statistical evaluation of the feasibility of satellite-retrieved cloud parameters as indicators of PM 2.5 levels. J Expo Sci Environ Epidemiol. doi:10.1038/jes.2014.49. Hu et al. 10-Year Spatial and Temporal Trends of PM2.5 Concentrations in the Southeastern U.S. Estimated Using High-Resolution Satellite Data. Atmos Chem Phys 2014; 14: 6301-14. Hu et al. Improving Satellite-Driven PM 2.5 Models with MODIS Fire Counts in the Southeastern U.S. J Geophys Res-Atmos 2014. 119: 11375-86 Ma et al. Estimating ground-level PM 2.5 in China using satellite remote sensing. Environ. Sci. Technol 2014. 48(13), 7436-44.

10. Statewide preterm birth analysis OBJECTIVE: To investigate associations between 11 ambient air pollutants, estimated by combining Community Multiscale Air Quality model (CMAQ) simulations with measurements from stationary monitors, and risk of preterm birth in Georgia. 511,658 singleton births ≥ 27 weeks gestation complete covariate information date of conception 01/01/2002 - 02/28/2006

12. Epidemiologic model Logistic regression models: 1 st & 2 nd trimester associations Discrete-time survival models: 3 rd trimester associations Control for County Maternal education Race Self-reported smoking Long-term trend (natural spline w/ 5 df total) Survival models include indicators for gestational age (in weeks) Investigate effect modification by education, race, urbanicity, and tertiles of Census tract % below poverty

14. 1-hr max 8-hr max 24-hr ave

18. PM 2.5 and Birth weight, 2001-2006 Air Quality Modeling: 598,230 1km x 1km grid cells 74 monitors Birth Records: 464,329 birth records ≥ 37 weeks gestation same inclusion criteria as the PTB analysis.

19. MESA Air Spatio-temporal Model At point-location s on day t, Two-week average monitoring measurements Spatio-temporal predictors (i.e. meteorology) Splines for temporal trends Location-specific spline coefficients Spatio-temporal residual error

20. Prediction Performance Time ScaleRMSEMAEAverage Bias 95% PI Coverage 2 weeks1.881.39-0.080.98 3 months1.140.91-0.190.94 Leave-one-monitor-out Cross-validation Experiments

21. PM 2.5 Estimates Average PM 2.5 levels (2001-2006) for each Census block group across Georgia and the Atlanta metropolitan area.

22. Health Association Estimation Model: Indicators for gestational weeks Maternal age, education, race Infant sex, indicator for first born Census 2000 block group % population below poverty Smooth function of conception date Indicators for counties # of CountiesTrimester 1Trimester 2Trimester 3 State-wide1591.3 (-3.1, 6.2)-1.3 (-5.7, 3.8)-4.0 (-8.4, 0.53) Counties with monitor53-1.6 (-7.4, 4.0)-2.0 (-7.8, 3.7)-6.4 (-12.3, -0.8) Counties without monitor 1065.4 (-1.3, 12.6)-0.9 (8.5, 6.3)0.4 (-6.7, 7.6) Associations between trimester PM 2.5 exposures (per IQR increase ≈ 4.5 µg/m 3 ) and birth weight (g).

23. Sensitivity Analysis - Time

24. Sensitivity Analysis - Space

25. Comparison to Monitor-only Results Cohort-ExposureTrimester 1Trimester 2Trimester 3 Restricted-Monitoring-3.8 (-9.3, 1.7)1.4 (-6.7, 9.4)-4.1 (-9.9, 1.7) Restricted-Predicted-1.2 (-10.5, 8.0)-2.8 (-11.7, 6.2)-14.4 (-23.3, -5.6) 10km Buffer-Predicted-3.8 (-12.6, 4.9)-4.4 (-13.0, 4.3)-15.7 (-24.6, -6.7) Restricted cohort: Birth within 10km of a monitor. Births with trimester-wide PM 2.5 exposure from monitoring measurements. Lots of missing data in time.

26. Ongoing Work Comparison to other PM 2.5 estimates with complete spatial-temporal coverage: Satellite-derived PM 2.5 estimates. CMAQ-fused estimates Incorporating exposure prediction errors: UW – Bootstrap Emory – Bayesian Harvard – Regression Calibration Examine potential effect modifications

27. Atlanta source apportionment analyses Goal: Estimate the association between short- term changes in PM 2.5 source concentrations and the daily rate of emergency department (ED) visits for pediatric asthma in Atlanta. Gass et al. Ensemble-based source apportionment of fine particulate matter and emergency department visits for pediatric asthma. In press Am J Epidemiol. (poster)

28. Estimating PM 2.5 source contributions Combines 4 independent source apportionment (SA) methods at Jefferson St monitor Chemical mass balance (CMB) w/ gas- based constraints CMB w/ molecular markers Positive matrix factorization CMAQ chemical transport model

29. The root mean squared error between each method and the ensemble average is: The RMSE can be viewed as an average uncertainty of the SA methods, and the inverse can be used as weights to calculate a weighted ensemble average. j = source l = methodk = day

30. Bayesian Ensemble Initial – unweighted PMF CMAQ CMB-MM CMB-GC Updated –weighted

31. Bayesian-based Source Profiles (BBSPs) N profiles for each day K x N total profiles Day 1, Source j Day K, Source j

32. Daily source contributions (µg/m 3 )

33. Epidemiologic model Poisson regression, scaled of over-dispersion, where: S j0 = the concentration from source j on lag 0 Same covariate control as Strickland et al. Epidemiology (2014), with the addition of max temperature through lag 7. For each source, estimate associations (per 1 µg/m 3 increase) Lag 0-2, controlling for lag 3-7 Lag 0-7 With additional control for ozone With additional control for the other 5 sources

34. Propagation of source apportionment uncertainty Source apportionment model uncertainty propagated through the epidemiologic analysis via multiple imputation 1 – Point estimate (RR) is the average of the RR across the 10 runs – Confidence interval estimate based on the average variance across the 10 runs (within imputation variance) and the variance of the ensemble run coefficients (between imputation variance) 1 Rubin DB. Multiple imputation for nonresponse in surveys. New York: J. Wiley & Sons; 1987.

35. Gass et al. Ensemble-based source apportionment of fine particulate matter and pediatric asthma emergency department visits. Am J Epidemiol [in press]. not apportioned to A thru E

36. Ratio of imputation-corrected SE / average SE

37. KAPPA Study  Kaiser Air Pollution and Pediatric Asthma Study  Historical birth cohort of children born between 2000 and 2010 in Kaiser Permanente Georgia residing in the Atlanta metropolitan area (n≈24,000 enrolled through age 1)  Member-level health data on children and their mothers for entirety of enrollment, additional information from Georgia birth certificates.  First year of life exposure will be estimated using child residence and 250 meter downscaled CMAQ data - focus on traffic-related pollutants Goal: Examine the association between residential ambient air pollution exposure in the first year of life and childhood asthma incidence

38. KAPPA Study Update Fall 2012-Fall 2013  Kaiser subcontract/Data Use Agreement; IRB  State of Georgia approval to use birth certificate data  Worked closely with Kaiser programmers on iterative data pulls  Creation of 250 meter by 250 meter grid for downscaling and geocoding Fall 2013-Fall 2014  Final set of 14 Kaiser datasets received  diagnoses, medications, mother-child linkage, basic history, residential history, HMO enrollment, birth certificate data, mother smoking  Data cleaning/descriptive analyses  Creation/assessment of outcome definitions  Residences geocoded to 250 meter grid  Obtained demographic cluster data from GA Division of Public Health

39. KAPPA Cohort Variablen(%) Sex Female12,059 (49.0) Male12,549 (51.0) Race/Ethnicity Black8,292 (33.7) White9,872 (40.1) Other Race2,886 (11.7) Hispanic Ethnicity1,932 (7.9) Linked to Mothers21,791 (88.6) Linked to Birth Certificates 18,583 (75.5) First Year of Life Residential Information 23,865 (97.0) *Due to missing data percentages do not add up to 100 Variablen(%) Siblings in Cohort (1 or more) 8,003 (32.5) Enrollment Duration Through age 219,169 (77.9) Through age 411,748 (47.7) Through age 67,103 (28.9) Through age 84,075 (16.6) Maternal Education <12 th grade313 (1.3) High School/GED2,854 (11.6) Some College or more14,227 (57.8)

40. KAPPA Asthma Classification How accurately do different early life asthma classifications predict school-age asthma in the KAPPA cohort? (manuscript in preparation) Asthma Classification [using information in the medical record between ages 0 and 3] Percent of cohort meeting classification Percent correctly classified SensitivitySpecificity 1.1 asthma diagnosis 22.5%77.5%49.9%86.2% 2.2 asthma diagnoses 13.3%79.8%35.4%93.7% 3.3 asthma diagnoses 9.0%79.9%26.8%96.6% 4.2 asthma diagnoses OR 1 acute asthma diagnosis 14.2%79.5%36.8%93.0% 5.1 asthma diagnosis OR 2 medication dispensings 35.2%71.4%63.8%73.8% 6.1 asthma diagnosis AND 1 medication dispensing 21.7%77.9%49.2%87.0% 7.1 asthma diagnosis AND 2 medication dispensings 19.8%78.6%46.7%88.7% 8.1 asthma diagnosis OR 2 medication dispensings one of which must be a steroid 24.1%77.0%52.2%84.8% 9.1 asthma diagnosis AND (2 reliever dispensings OR 1 controller dispensing) 19.9%78.7%47.0%88.7% Predicting an asthma diagnosis between ages 5 and 8 (considered here the gold standard)

41. Incident asthma: ≥1 asthma diagnosis and ≥1 asthma-related medication after the first year of life Prevalent asthma: children meeting the incident asthma classification, who also have ≥1 asthma diagnosis and/or ≥1 asthma-related medication in the past year KAPPA Asthma Burden Follow-up ageCohort sizeIncident Asthma n (%) Prevalent Asthma n (%) 219,1692,121 (11.1) 411,7482,814 (24.0)2,085 (17.7) 67,1032,291 (32.3)1,443 (20.3) 84,0751,441 (35.4)747 (18.3)

42. KAPPA Children Residences in Metropolitan Atlanta

43. Exposure Assignment AQ Core: estimation of pollutant concentrations for each day of study at a 250 meter grid resolution – Bayesian hierarchical model used to downscale 4-km CMAQ estimates and integrate fine-scale traffic emissions data (Atlanta Regional Commission) – Model is calibrated using measurements from monitoring stations – Uncertainty in the exposure predictions can be propagated through the epidemiologic models Exposure assigned to each child: average pollutant concentrations during the child's first year of life in the 250m grid of residence (time-weighted average if > 1 residence during first year)

44. KAPPA Socioeconomic (SES) Characterization  Concern about confounding of the relationship between spatially-assigned ambient air pollution and asthma incidence motivates thorough assessment of both individual and contextual measures of SES  Individual measures of child SES —Child race/ethnicity —Maternal education —Paternal education  Neighborhood-level SES —Georgia Department of Public Health demographic characterizations —American Community Survey poverty data

45. Demographic Clusters in Metropolitan Atlanta Household income Rent Number of vehicles Resident ages Education Occupation  Each color represents one of 18 unique demographic characterizations  These clusters were created by Georgia Department of Public Health at block group spatial resolution using data from the 2010 U.S. Census  Variables used in cluster creation: Marriage status Children Owner occupied housing Vacant housing Age of occupied housing Length of residency

46. KAPPA Modeling Approach  Model cumulative incidence of asthma at subsequent follow-up ages: 1-2 year risk, 1-4 year risk, 1-6 year risk, 1-8 year risk  Binomial generalized linear regression models  Directly model risk, estimation of risk differences  Generalized Estimating Equations (GEE) used to account for correlation between siblings in the cohort Average air pollution concentration(s) in 250 grid at residence(s) during first year of life

47. Risk Differences as Measure of Association  Most studies use multiplicative scale (i.e., ratio measures - OR, RR) instead of additive scale (differences)  In the KAPPA study, we have a well-defined cohort and can model absolute risks directly  We have interest in exploring interaction between pollution and various susceptibility factors in our study  can choose additive or multiplicative model  Example: multiplicative vs. additive interaction between race and pollution  Some would argue that an assessment of additive interaction has more relevance for public health impact  Other theoretical justifications for focusing on additive interaction  Additive interaction is consistent with sufficient component cause model interaction, which is said to correspond to biologic interaction (e.g., Rothman, Greenland, Lash RaceBaseline riskRisk among exposed (high pollution) No additive interaction (RD pollution =0.05) No multiplicative interaction (RR pollution =1.5) white0.100.15 black0.200.250.30

48. Planned analyses For each age of follow-up calculate risk (and prevalence) differences for given increase in pollutant concentration – consideration of joint effects and interaction between pollutants where possible Explore differential susceptibility (effect modification) – Measures of SES – Sex – Race/ethnicity – Evidence of maternal asthma/allergy – Atopic comorbidities in the child (atopic dermatitis, allergic rhinitis)

49. SCAPE Project 3 goals for next year Late 2014 / early 2015 paper submissions: – Statewide preterm birth epi (poster) – Statewide birth weight epi – Statewide pediatric ED visits epi (poster) – Three city C&RT models and pediatric asthma ED visits – Atlanta SOM classifications and pediatric asthma ED visits – Kaiser asthma classifications Ongoing: DTT modeling and epidemiologic analyses in Atlanta ~January 2015: Downscaled 250m CMAQ estimates ready; intense focus on Kaiser birth cohort epidemiologic analyses will follow ~Mid-2015: Statewide hybrid CMAQ and CMB estimate ready; statewide epidemiologic analyses of pediatric ED visits will follow

50. KAPPA Extra Slides

51. Asthma Classification  Asthma ICD-9 Code 493.XX  Asthma Medication Categories Asthma Controllers Aminophylline Beclomethasone Diproprionate* Budesonide* Budesonide/Formoterol Fumarate* Cromolyn Sodium Fluticasone Propionate* Fluticasone/Sameterol* Mometasone Furoate* Montelukast Sodium Salmeterol Xinafoate Theophylline Anhydrous Tiotropium Bromide Triamcinolone Acetonide* Asthma Relievers Albuterol Albuterol Sulfate Ipratropium Bromide Ipratropium/Albuterol Sulfate Levalbuterol Metaproterenol Sulfate *indicates contains a steroid

52. KAPPA Residences KAPPA children residences, by percent of children in each 250 m grid of black race 0-33% 34-67% 68-100%

53. Demographic Clusters A.1 Georgia’s wealthiest cluster is primarily populated by “new money” executives and professionals living in tract mansions of metropolitan suburbs and exurbs. Predominantly White with an above average index for Asians, this highly educated cluster is composed of married couples in their middle adulthood ages (45-64) with young and adolescent children. A.2 This well-educated, suburban cluster, dominated by professionals and managers, has the second highest level of affluence in the state. Mostly White with a high percentage in their middle or late adulthood (55+), they have adolescent and grown children. A.3 Found in the metro suburbs, this mixed- ethnicity with majority of Whites and high index for African-Americans, more youthful cluster is populated by married couples in their late 20’s through early 40’s with young children. The majority has some college degree or are college graduates. Most are employed in sales and other white-collar jobs, while some are high-earning blue-collar families. This cluster has a median household income well above the state average. D.5 This is a mixed-ethnicity cluster with a high index of Hispanics and Multiracial groups. Most have high school diploma or less; they mainly work in low-paying blue collar jobs in production and construction industries. The cluster’s housing is half owner-occupied and half renter- occupied with a high percentage of vacant housing. D.6 This cluster is predominantly populated by African-Americans with high percentage of population in their 60’s and over. Most have a high school diploma or less; they mainly work in service industries. Their median income is second lowest in the state. D.7 This cluster is predominantly composed of very young African-Americans with more females than males. The cluster has the highest percentage of population less than 18 years of age in non-military clusters in the state, of whom most live in female- headed households. Most have a high school diploma or less; they work in low-paying jobs and live in rental units. The median household income in this cluster is the lowest in the state. 18 Clusters Total: A1, A2, A3, B1, B2, B3, B4, C1, C2, C3, C4, D1, D2, D3, D4, D5, D6, D7 Cluster Descriptions for the highest and lowest clusters: