1. Traffic-related PM 2.5 exposure during the first year of life and asthma incidence in a birth cohort Audrey L. Flak MPH International Society for Environmental Epidemiology Conference September 1, 2015

2. Funding 2 NIH Reproductive, Perinatal, and Pediatric Epidemiology Training Grant 5T32HD052460 NIOSH Environmental Epidemiology Training Grant 5T01OH008609, 2T03OH008609 R834799 The contents of the publication are solely the responsibility of the grantee and do not necessarily represent the official views of the United States Environmental Protection Agency (US EPA). Further, the US EPA does not endorse the purchase of any commercial products or services mentioned in this publication.

3. Traffic-related PM 2.5 and childhood asthma 3  Traffic is a major source of ambient fine particulate matter (PM 2.5 )  Asthma is the most common chronic disease among children  Rapid development of the respiratory and immune systems occurs during early life  PM 2.5 exposure during early life exacerbates pre-existing asthma, and may also play a role in asthma incidence Rowangould et al. 2013 Transportation Research Part D McDaniel et al. 2006 Pediatrics Akinbami et al. 2012 NCHS Data Brief

4. KAPPA: Kaiser Air Pollution and Pediatric Asthma Study 4  Developed to examine the association between first year of life air pollution and childhood asthma incidence  Historical birth cohort of children enrolled in Kaiser Permanente Georgia (n=24,608) – Enrolled in Kaiser from birth until at least age 1 – Date of Birth: January 1, 2000 – December 31, 2010  Access to individual-level health data on children and their mothers (if enrolled) for entirety of enrollment Objective: Examine the association between first year of life residential PM 2.5 from traffic and cumulative asthma incidence by ages 4, 5, 6 in the KAPPA cohort

5. RLINE: A Research LINE-source dispersion model for near-surface releases  Dispersion modeling tool for near roadway assessments  2011 Metropolitan Atlanta Georgia 1. https://www.cmascenter.org/r-line/ 2. Snyder et al. 2013 Atmospheric Environment 0.7 0.4 1 1.3 1.7 PM 2.5 emissions (24h total) g/day/m 2011 RLINE-modeled primary PM 2.5 from traffic emissions (µg/m 3 )

6.  Cumulative Incident Asthma – ≥1 asthma diagnosis (ICD-9 code 493) and ≥1 asthma-related medication dispensing after the first year of life  Binomial linear regression (Risk Differences per 1 µg/m 3 ) – Generalized estimating equations – Each model assesses the association between first year of life PM 2.5 and cumulative asthma incidence among children enrolled in Kaiser Permanente until each age Analysis 6 First year of life primary PM 2.5 (µg/m 3 ) from traffic emissions 0.51.01.52.02.5 2 0 4 6 8 10 Percent 3 rd percentile 97 th percentile

7. Highest SESLowest SES Rural, Average to low SES Suburban & Urban Individual Covariates Contextual SES Covariates sex, race, ethnicity, birth year, maternal age maternal asthma, maternal education Georgia Department of Public Health demographic clusters (18 clusters) –aggregate 25 U.S. Census variables Demographic Clusters of Georgia. Georgia DHP: OHIP; 2012 Spatial Covariates

8. Highest SESLowest SES Rural, Average to low SES Suburban & Urban Individual Covariates Contextual SES Covariates sex, race, ethnicity, birth year, maternal age maternal asthma, maternal education Georgia Department of Public Health demographic clusters (18 clusters) –aggregate 25 U.S. Census variables Demographic Clusters of Georgia. Georgia DHP: OHIP; 2012 Spatial Covariates metro Atlanta (n=2,425) ≤16 km from metro Atlanta (n=9,894) >16 km from metro Atlanta (n=10,781)

9. Highest SESLowest SES Rural, Average to low SES Suburban & Urban Individual Covariates Contextual SES Covariates sex, race, ethnicity, birth year, maternal age maternal asthma, maternal education Georgia Department of Public Health demographic clusters (18 clusters) –aggregate 25 U.S. Census variables Demographic Clusters of Georgia. Georgia DHP: OHIP; 2012 Spatial Covariates metro Atlanta (n=2,425) ≤16 km from metro Atlanta (n=9,894) >16 km from metro Atlanta (n=10,781)

10. Variablen (%) Cohort23,100 Race/Ethnicity Black7,995 (35) White9,034 (39) Other Race 1 2,771 (12) Unknown Race3,300 (14) Hispanic Ethnicity1,839 (8) Maternal Education <12 th grade285 (1) High School/GED2,605 (11) Some College or more13,442 (58) Missing6,768 (29) Contextual Socioeconomic Status 2 Highest SES14,401 (62) Urban/Suburban2,293 (10) Rural, Average to low SES1,130 (5) Lowest SES, Urban5,272 (23) Cumulative Asthma Incidence Age 42,650 (24) Age 52,465 (29) Age 62,149 (32) Cohort Characteristics 10 1 Includes Asian, American Indian, Alaska Native, Native Hawaiian or other Pacific Islander, children identifying with more than one racial group 2 Defined used demographic clusters from Georgia Department of Public Health aggregating income, housing, education, family structure, age, and employment

11. Cohort (n)RD (95% CI)p-value Age 4 (n=10,999)-3.7% (-6.7%, -0.7%)0.02 Age 5 (n=8,592)-1.8% (-5.5%, 1.9%)0.33 Age 6 (n=6,629)-2.9% (-7.3%, 1.5%)0.20 Crude Asthma Incidence Results (Risk Differences per 1 µg/m 3 PM 2.5 ) 11

12. 12 Age 43.7% (-0.7%, 8.2%)0.10 Age 55.8% (0.4%, 11.2%)0.04 Age 63.6% (-2.9%, 10.1%)0.28 Age 4-3.3% (-6.3%, -0.2%)0.03 Age 5-1.8% (-5.4%, 1.9%)0.35 Age 6-3.1% (-7.4%, 1.3%)0.17 Age 40.7% (-3.3%, 4.6%)0.75 Age 51.5% (-3.3%, 6.3%)0.54 Age 6-0.6% (-6.3%, 5.0%)0.83 Adjusted Asthma Incidence Results (Risk Differences per 1 µg/m 3 PM 2.5 ) Cohort RD(95% CI) p-value Individual Control Individual + Contextual SES Control Individual + Contextual SES + Spatial Control Individual Covariates: sex, race, maternal asthma, birth year Contextual SES Covariates: demographic clusters Spatial Covariates: inside metro ATL, 16 km from metro ATL

13. Parameter RD (95% CI)p-value intercept24.9% (16.4%, 33.4%)<0.01 PM 2.5 3.6% (-2.9%, 10.1%)0.28 male sex8.6% (6.4%, 10.8%)<0.01 black race6.0% (3.0%, 8.9%)<0.01 other/unknown race4.1% (0.8%, 7.4%)0.02 maternal asthma15.2% (11.7%, 18.7%)<0.01 inside metro ATL-8.1% (-13.6%, 2.5%)<0.01 <16 km from metro ATL-3.0% (-6.2%, 0.2%)0.06 13 Age 6 Adjusted Results n=6,628 Model controlled for birth year and demographic cluster (parameter estimates not shown) Reference group: female, white race, no or unknown maternal asthma, >16 km from metro Atlanta

14. 14 Correlation with child PM 2.5 All children Inside metro Atlanta ≤16 km from metro Atlanta >16 km from metro Atlanta Household income Percent families in poverty American Community Survey 2010 pulled via Social Explorer negative Spearman correlation positive Spearman correlation

15. 15 Conclusions  Some evidence for a harmful association between traffic-related PM 2.5 during the first year of life and asthma incidence  In this population, spatial patterns of SES and potential influence are different and more complex than anticipated  In spatial studies of traffic pollution controlling for both contextual SES and city region may be important

16. Strengths, Limitations, Ongoing Work  Strengths – Incidence data – Detailed individual level information – Fine spatial resolution  Limitations – Loss to follow-up – PM 2.5 data for only 2011 – Difficulty of asthma diagnosis  Ongoing Work – Better understanding of confounding by SES – Time resolved PM 2.5 data 21

17. Thank you 22 Emory University, Georgia Institute of Technology, and Kaiser Permanente Georgia Collaborators Lyndsey A. Darrow PhD Matthew J. Strickland PhD, MPH, MA Mitchel Klein PhD, MAT Josephine Bates Xinxin Zhai Howard H. Chang PhD Heather A. Holmes PhD, MS Craig Hansen PhD Carolyn D. Drews-Botsch PhD, MPH Armistead G. Russell PhD, MS Emory SOPHIA group

18. Extra Slides

19. Follow-up 19 Age Children eligible to be followed to each age Number actually followed to each age Number in final dataset Age 422,53711,748 (52.1%)10,999 (48.8%) Age 520,7059,202 (44.4%)8,592 (41.5%) Age 618,4887,103 (38.4%)6,629 (35.9%)

20. Asthma Incidence Trends by birth year 20 Birth year age 4age 5age 6 200026%32%37% 200124%27%30% 200226%29%33% 200324%28%32% 200422%26%29% 200525%29%32% 200625%29%32% 200722%28%33% 200823%30%. 200922%.. 2010...

21.  Created from EASI Demographics data (2011) available at the census block group level of the 2010 census geographies, containing 25 variables relating to age, income, family structure, housing value and type, education attainment and employment type.  Classification model composed of Two-Step-Clustering and Discriminant Analysis was used to classify census block groups based on the 25 selected variables. The census block groups were first classified into four major groups, which were further partitioned into a total of 18 distinct demographic clusters. 21 Demographic Clusters of Georgia Demographic Clusters of Georgia. Georgia DHP: OHIP; 2012 Variables Used to Create Demographic Clusters Median household income, median rent, median number of vehicles, percent population aged <18 years, percent population aged 18-24 years, percent population aged 25-34 years, percent population aged 35-44 years, percent population aged 45-54 years, percent population aged 55-64 years, percent less than high school education, percent high school education, percent some college education, percent management and professional occupation, percent sales occupation, percent service occupation, percent production and construction occupation, percent divorced of population 16 years and older, percent married family households with children <18, percent married family households with no children <18, percent single family households with children <18, percent single family households with no children <18, percent owner occupied housing, percent vacant housing, median age of occupied housing in years, median length of residence in years

22. 22 KAPPA Residences by Race black race white race

23. Asthma Diagnoses 18 Characteristicn (%) All asthma diagnoses32,991 Encounter Type Ambulatory visit28,381 (86.0) Emergency1,566 (4.8) Radiology only963 (2.9) Telephone802 (2.4) Inpatient625 (1.9) Other381 (1.2) E-mail273 (0.8) Diagnosis Type Primary3,943 (12.0) Secondary (non-primary)1,366 (4.1) Unable to classify27,682 (83.9)

24. Asthma Medications OutcomeDefinition Asthma controller 1 Any of the following medication dispensings: 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 relieverAny of the following medication dispensings: albuterol, albuterol sulfate, ipratropium bromide, ipratropium/albuterol sulfate, levalbuterol, metaproterenol sulfate Asthma-related medicationDispensing of any asthma controller or reliever 1 Underlined medications contain a steroid 19

25. 25 Asthma Incidence Results Stratified by Race (Risk Differences) Controlling for: sex, maternal asthma, minor cluster, city region, birth year White Black RD (95% CI)p p Age 4Does not converge3.6% (-3.6%, 10.9%)0.33 Age 58.8% (0.7%, 16.9%)0.037.0% (-1.8%, 15.7%)0.12 Age 65.5% (-4.4%, 15.3%)0.283.3% (-7.0%, 13.5%)0.53 Adjusted Models Stratified by race White Black RD (95% CI)p p Age 4-6.5% (-11.0%, -2.0%)<0.01 0.7% (-4.7%, 6.1%)0.80 Age 5-5.9 (-11.4%, -0.5%)0.033.7% (-2.8%, 10.1%)0.26 Age 6-7.4% (-13.7%, -1.0%)0.021.1% (-6.4%, 8.6%)0.77 Crude Models Stratified by race

26. 26 Asthma Incidence Results Stratified by Location (Risk Differences) Inside metro Atlanta <16 km from metro Atlanta >16 km from metro Atlanta RD (95% CI)p p p Age 4-1.6% (-12.3%, 9.1%) a 0.774.6% (-2.1%, 11.4%)0.182.9% (-4.1%, 9.8%)0.42 Age 51.9% (-11.8%, 15.7%)0.783.1% (-5.2%, 11.4%)0.478.3% (-0.1%, 16.7%)0.05 Age 6-11.7% (-25.9%, 2.6%)0.11-0.8% (-10.7%, 9.0%)0.8710.1% (0.3%, 19.9%)0.04 Controlling for: sex, race, maternal asthma, minor cluster (combining some categories for convergence), birth year a Dropped birth year in order to get model to converge Adjusted Models Stratified by location Inside metro Atlanta <16 km from metro Atlanta >16 km from metro Atlanta RD (95% CI)p p p Age 4-12.5% (-24.2%, -0.7%)0.04-0.2% (-6.0%, 5.6)0.950.6 (-5.4, 6.5)0.86 Age 5-15.7% (-29.6%, -1.7%)0.030.1% (-7.0%, 7.2%)0.977.3% (-0.0%, 14.5%)0.05 Age 6-22.6% (-41.1%, -4.1%)0.02-2.0% (-10.5%, 6.5%)0.658.5% (-0.1%, 17.1%)0.05 Crude Models Stratified by location

27. 27 Crude Models Stratified by Location & Race (Risk Differences) Inside metro Atlanta <16 km from metro Atlanta >16 km from metro Atlanta RD (95% CI)p p p Age 41.5% (-20.9%, 23.8%)0.904.8% (-8.2%, 17.8%)0.472.8% (-5.2%, 10.7%)0.49 Age 511.8% (-14.4%, 38.0%)0.38-7.9% (-24.3%, 8.4%)0.348.8% (-0.7%, 18.3%)0.07 Age 6-4.5% (-33.0%, 24.1%)0.76-16.0% (-33.7%, 1.7%)0.087.8% (-3.6%, 19.1%)0.18 White Crude Models Inside metro Atlanta <16 km from metro Atlanta >16 km from metro Atlanta RD (95% CI)p p p Age 4-9.0% (-29.1%, 11.2%)0.386.2% (-2.1%, 14.4%)0.147.8% (-6.1%, 21.8%)0.27 Age 5-14.3% (-37.3%, 8.7%)0.2211.0% (1.0%, 21.0%)0.0317.8% (1.2%, 34.4%)0.04 Age 6-14.7% (-44.9%, 15.5%)0.346.3% (-5.5%, 18.1%)0.3016.7% (-2.6%, 36.0%)0.09 Black Crude Models