A Longitudinal Study of Maternal Smoking During Pregnancy and
Child Height Author 1 Author 2 Author 3
Slide 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
Slide 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
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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
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Smoking Categorization Excluded mothers who quit during
pregnancy Self-reported Categories Never smoked 1-9 cigs/day 10-19
cigs/day 20+ cigs/day
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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
Slide 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
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Characteristics of Smokers Smokers are more likely to be: White
Less educated Drinkers Married to smokers Thinner See Table 1
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Crude Plot of Height and Age by Smoking Level
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Mean Height by Age and Child Race
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Mean Height by Age and Birthweight
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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
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Our GEE model Link function: Identity Since outcome variable is
continuous Correlation structure: Independent No correlation
between repeat measurements Standard error estimation: Robust
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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
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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)
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Model Fitting
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Main Effects Output for Exposure Weighted vs. Unweighted
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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
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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)
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Interactions with Age Height (cm) Test for interaction:
chi-square (df=12) = 16.10; p-value = 0.1866
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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
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Strengths Greater age range Longitudinal methods Less recall
bias for sensitive subject
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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
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Directed Acyclic Graph (DAG): Total Effects
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Directed Acyclic Graph (DAG): Time Dependent Confounding
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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
Slide 27
Thank you! Special thanks to Brenda Eskenazi, Houston Gilbert,
Alan Hubbard, Maureen Lahiff, David Lein, and Eric Polley Questions
or comments?