1. A Longitudinal Study of Maternal Smoking During Pregnancy and Child Height Author 1 Author 2 Author 3

2. Our Study What is the total effect of prenatal smoking on child height from birth to adolescence? Prospective cohort study Longitudinal methods Height deficits through adolescence may lead to increased disease risk in later life.  evidence for maternal anti-smoking campaigns

3. Summary of Literature Older cohort studies, some case-control Few longitudinal methods Stat. significance often not stated Children at birth - age 5 Little to no height deficits after 1 year No evidence of interaction with alcohol

4. Height measurements Restricted to children with birth length Recumbent height measured under age 2 Standing height measured over age 2 Most height measurements under age 2 In final analysis, only include children with height measured at age 8 or older

5. Smoking Categorization Excluded mothers who quit during pregnancy Self-reported Categories – Never smoked – 1-9 cigs/day – 10-19 cigs/day – 20+ cigs/day

6. Confounders Child age Child sex Child race Child birth weight Birth order Gestational age Paternal smoking during pregnancy Mother ’ s marital status during pregnancy Mother ’ s alcohol consumption Mother ’ s total number of prenatal visits Maternal age during pregnancy Maternal pre-pregnancy weight Maternal height Maternal education

7. Dataset restricted to: Singleton births No severe congenital abnormalities Live births First pregnancies only Has maternal smoking variable Mother did not quit smoking during pregnancy Children age 9 and under Birth length measured Children with height measurements at or after age 8 See Figure 1

8. Characteristics of Smokers Smokers are more likely to be: White Less educated Drinkers Married to smokers Thinner See Table 1

9. Crude Plot of Height and Age by Smoking Level

10. Mean Height by Age and Child Race

11. Mean Height by Age and Birthweight

12. Statistical Model Longitudinal data – Individuals ’ repeat measurements are correlated – Ignoring correlation affects precision of parameter estimates (  ) Generalized estimating equations (GEE) – Must specify link function, covariance structure, standard error estimation – Covariance structure accounts for covariance due to repeated measurements – Estimates of SE (  ) are not affected by misspecification of the correlation model

13. Our GEE model Link function: Identity – Since outcome variable is continuous Correlation structure: Independent – No correlation between repeat measurements Standard error estimation: Robust

14. Missing data Data was missing for many covariates Assumed to be missing at random (MAR) Weighting – Uses complete cases only – Up-weights children with covariate distributions similar to people dropped due to missingness – Increases precision of SE estimates

15. Model Fitting: Transformations of Age Linear Log-linear Linear spline (knots between age 0.5 and 1.5) Quadratic spline Cubic spline  Compare using graphs and quasi-likelihood criterion (QIC)

16. Model Fitting

17. Main Effects Output for Exposure Weighted vs. Unweighted

18. Covariates ’ Effects on Height Increase height – African American/Black, older maternal age, taller mothers, and higher birthweight, male sex Decrease height – Older gestational age and later birth order No statistically significant effect – Maternal alcohol use, education, paternal smoking during pregnancy, marital status, maternal pre- pregnancy BMI and # of maternal prenatal visits See Regression Table

19. Interactions Explored Alcohol (  2 = 3.54, df = 3, p = 0.3154 ) Paternal smoking during pregnancy (  2 = 0.89, df = 1, p = 0.3448) Child age (  2 = 16.10, df = 12, p = 0.1866)

20. Interactions with Age Height (cm) Test for interaction: chi-square (df=12) = 16.10; p-value = 0.1866

21. Summary of Our Results Interaction model confirms crude smoking trend Main effects model suggests dubious lack of dose response relationship Growth rate does not differ between smoking levels

22. Strengths Greater age range Longitudinal methods Less recall bias for sensitive subject

23. Limitations Limited external generalizability Potential selection bias due to restriction on primary exposure Correlation structure assumes no relationship between repeat height measurements Model for weights could be misspecified Self-reported primary exposure Mother reported paternal smoking No control of time-dependent confounding Unable to explore relationship through adolescence Insufficient number observations for caffeine

24. Directed Acyclic Graph (DAG): Total Effects

25. Directed Acyclic Graph (DAG): Time Dependent Confounding

26. Future Steps Explore unexpected trend across smoking levels Investigate direct effect of maternal smoking during pregnancy on child height – Use more statistically advanced methods to control for time-dependent confounding – Measure smoking and other covariates during childhood Examine older ages and effect of caffeine

27. Thank you! Special thanks to Brenda Eskenazi, Houston Gilbert, Alan Hubbard, Maureen Lahiff, David Lein, and Eric Polley Questions or comments?