Obstetrics and gynaecology
Maternal smoking and neonatal mortality
Cande V Ananth and Robert W Platt reexamine the effects of
gestational age, foetal growth and maternal smoking on neonatal
mortality.
Background
Birth weight is arguably one
of the strongest predictors of
infant survival, yet its role as
a causal predictor of
mortality is poorly understood.
This is at least partly
because low birth weight
(<2,500 g) is a construct of
two intricately intertwined
components: preterm
delivery and reduced foetal
growth, or both.
Our lack of understanding
of the complex relationship
among birth weight, gestational
age and perinatal
mortality stems from mixing
etiologically distinct pathways
to mortality, namely
effects chiefly due to foetal
maturity (i.e., gestational
age) versus those related to
foetal growth.
Disentangling the intricate
pathways of gestational age
and foetal growth to
neonatal mortality gets even
more complicated by the
consideration of a third
factor – maternal smoking
during pregnancy.
Smoking
has been clearly associated
with poor reproductive
outcomes, including
increased risk of preterm
birth, stillbirth, and a range
of other outcomes. Recent
studies suggest a more direct
and stronger association
between maternal smoking
and “foetal growth” (birth
weightforgestational age)
than with preterm delivery,
suggesting that the effect of
smoking on mortality may be
largely mediated through
restricted fetal growth rather
than preterm delivery.
To understand the relationship
among these indices of
better foetal wellbeing, we
examined neonatal mortality
in relation to standardised
birth weight (i.e., zscore
birth weight), gestational age,
and smoking during pregnancy.
We applied nonparametric
logistic regression
based on generalised additive
models to examine neonatal
mortality in relation to three
factors.
Methods
Cohort composition of United
States live births
Data for this study were
derived from the 1998–2000
United States vital statistics
data files (live births linked to
infant deaths), assembled by
the National Center for
Health Statistics of the
Centers for Disease Control
and Prevention.
The analysis
was restricted to singleton
live births, with neonatal
mortality defined as deaths
within the first 28 days.
Gestational age assignment
in these data is predominantly
based on selfreported
last menstrual period, with a
small fraction (<5%) based
on the clinical estimate.
Further, the National Center
for Health Statistics imputed missing gestational ages in
these data files prior to
release of the data.
Information on smoking
during pregnancy was available
in two forms on the vital
statistics data: one as an indicator
variable (yes or no), and
the other as a continuous
variable denoting the
number of cigarettes smoked
per day during pregnancy.
Both of these smoking measures
were based on maternal
selfreport.
Information on
smoking patterns across
different trimesters in pregnancy
was not available on
the vital records. Foetal growth was defined
as birth weightforgestational
age, and was expressed
as gestational agespecific
birth weight zscore. This zscore
construct is interpreted
as units of standard deviations
from the populationspecific
mean birth weight at
each gestational age. The zscore
or standardised birth
weight follows a Gaussian
distribution with mean 0 and
variance 1.
In addition to the full
analysis, we also examined in
a subanalysis the impact of
implausible birth
weight/gestational age
combinations on overall
results.
These implausible
birth weight/gestational ages
were identified if infants'
birth weights were outside
the gestational agespecific
birth weight cutoffs. This was
done to examine the impact
of largely apparent gestational
age errors (e.g., infant
delivered at 26 weeks with a
birth weight of 4,000 g) on
neonatal mortality.
Data exclusions
There were 11,677,103
singleton live births from
which we excluded infants
with missing birth weight or
gestational age (n = 237,433),
and birth weight <500 g or
gestational age <24 weeks (n
= 28,732).
Since smoking
data was not reported on
vital statistics in California,
Indiana, New York state, and
South Dakota, births from
these states were also
excluded (n = 1,326,841).
After all exclusions,
6,117,808 singleton live
births remained for analysis.
Statistical analysis
We examined the distributions
of zscore birth weight,
gestational age, and number
of cigarettes smoked per day,
and compared these distributions
between the two groups
of neonatal mortality.
Neonatal mortality was then
modeled using nonparametric
logistic regression
based on generalised additive
models. GAM is one modelling
approach that makes no
assumptions about the functional
form of the exposuredisease
relationship except
for smoothness, i.e., continuity
of the doseresponse
function and its loworder
derivatives.
When combined
with more traditional modelling
approaches, GAMs are
powerful graphical tools that
can provide interesting
insights about complex relationships.
While polynomial
models could be used to the
same end as GAMbased
approaches, such models
result in restricted shapes,
especially at the tail of the
distribution, and may not be
as statistically efficient as
nonparametric models.
Therefore, these models were
not considered.
All regression models were
adjusted for the confounding
effects due to maternal age
and gravidity (i.e., number of
pregnancies).
We examined
the associations between
neonatal mortality and each
of the three factors – zscore
birth weight, gestational age,
and number of cigarettes
smoked per day separately.
We then fit a full model for
mortality after forcing all
three predictors (in addition
to the confounders). The
independent effect of each of
these three factors on
neonatal mortality was
assessed by comparing the
residual deviances between
nested models (i.e.,
comparing the residual
deviances from a full model
to a model without the
predictor). Under the large
sample assumption, the
deviance has an approximate
chisquare distribution, with
degreesoffreedom for the
test being the difference in
the degrees of freedom
between the nested models
being compared.
We also
examined the distribution of
partial residuals from fitting
the model to assess departures
from adequate fit.
In addition, we tested for
all possible twofactor interactions
between the predictors.
Although all interactions
were statistically significant
(owing to the large
study size), none provided any additional insights that
were different from a model
that contained no interaction
terms. Therefore, we did not
consider assessing twoway
interactions in the analysis.
All statistical analyses were
performed in SPlus
(Insightful Corporation,
Seattle WA) version 6.2 on
the UNIX (Sun Microsystems,
Inc: Palo Alto, CA).
Nonparametric logistic
regression models were fit
using the GAM function
based on the it loess scatterplot
smoother, using the
default span of 50%. Given
the large size of the study,
small changes in the span
resulted in statistically significant
improvement in the fit,
while offering very little clinical
insight. Thus, we resorted
to the default span.
Results
The overall neonatal
mortality rate was 2.4 per
1,000 live births. The distribution
of zscore birth weight
among infants that died
during the neonatal period
was shifted more towards
lower standardised birth
weights than among those
that survived the neonatal
period (page 67 Fig 1, left
panel). Infants who died during the neonatal period
were delivered earlier than
those that survived the
neonatal period (Fig 1, right
panel).
Surviving infants
weighed, on average, 1,582 g
more compared with those
who died during the
neonatal period (P < 0.0001;
Table 1). Likewise, infants
who died during the
neonatal period were delivered,
on average, seven weeks
earlier than those who
survived the neonatal period
(P < 0.0001). The proportion
of mothers that smoked
during their pregnancy was
higher among infants that
died during the neonatal
period (19.1%) compared
with those that survived the
neonatal period (16.5%; P <
0.0001).
We first separately examined
the effect of each of the
three covariates standardised
birth weight, gestational age,
and number of cigarettes
smoked per day, on neonatal
mortality. This was done by
fitting nonparametric logistic
regression models (GAM).
The univariable GAM
strongly suggests that the
unadjusted association
between standardised zscore
birth weight and neonatal
mortality is nonlinear (not shown). The association
between gestational age and
neonatal mortality was also
nonlinear, whereas the association
between number of
cigarettes smoked per day
mortality was virtually flat.
The adjusted smooth curves
for these three covariates,
along with their corresponding
95% pointwise confidence
bands are displayed in
Figure 2. These curves were
adjusted for the other two
factors in addition to
maternal age and gravida. It
is interesting to note that the
relationship between standardised
birth weight and
neonatal mortality (adjusted
for gestational age and
smoking and confounders)
was virtually flat at increased
birth weight zscores (i.e., at
zscores ≥ 4.0).
Since smoking was weakly
associated with neonatal
mortality, we examined if the
effect of smoking on
mortality was mediated
through either standardised
birth weight or gestational
age (or both). We therefore
modeled neonatal mortality
in relation to these two
covariates (and adjusted for
confounders) within broad
categories of smokers and
nonsmokers (Fig 3).
Compared with nonsmokers,
neonatal mortality among
women that smoked during
their pregnancy was higher
among infants that were
between 5 and 1, and
between 1 and 5 standard
deviation units of the birth
weight distribution among
smokers. Infants with birth
weight zscores between 1
and 1 had mortality rates that
were similar regardless of
maternal smoking status.
When neonatal mortality
rates were examined by gestational
age, the mortality
curve was consistently higher
at every gestational age
among smokers than among
nonsmokers (P < 0.001). In
order to better understand
whether smoking affects foetal growth, we examined
the distributions of gestational
agespecific standardised
birth weight zscores
between the two groups of
smokers (Fig 4).
The results
indicate that the adjusted
mean zscore birth weight
among nonsmokers is fairly
constant across gestational
age. However, among women
that smoked during pregnancy,
the adjusted mean zscore
is higher that those of
nonsmokers between 22 and
28 weeks, and begins to drop precipitously with increasing
gestational age. This pattern
indicates that smoking results
in more growth restricted
infants, and that the effect of
smoking on reduced foetal
growth appears to get
stronger at gestational ages 32
weeks and beyond.
The logistic regression
models discussed thus far are
based on the implicit
assumption that the
combined effects of standardised
birth weight and gestational
age are multiplicative
on a logistic scale. We examined
the sensitivity of this
assumption by modelling
neonatal mortality by
allowing an interaction term
between these two factors
based on nonparametric
smooth fit. The joint effect of
standardised birth weight
and gestational age on
neonatal mortality reveals
that both reduced foetal
growth and early delivery
result in increasing mortality
risk, with the mortality plane
progressively diminishing
with increasing standardised
birth weight and gestational
age (Fig 5).
Discussion
For decades, several
researchers have focused on
trying to understand the
complex biological relationship
among pregnancy duration,
infant size, and
neonatal mortality. Not only
are gestational age and birth
weight highly correlated, but
both are powerful predictors
of neonatal mortality. The
chief findings from our study
include (i) zscore birth
weight and preterm delivery
(independent of birth
weight) exert strong influences
on neonatal mortality;
(ii) the effect of maternal
smoking is mediated largely
through reduced foetal
growth and, to a smaller
extent, through shortened
gestation; and (iii) mortality
among babies born to
smoking mothers is virtually
higher at every zscore birth
weight (independent of
gestational age) than those
born to nonsmoking
mothers.
The inverted Jshaped relationship
between birth
weight and mortality essentially
holds for analyses
relating to gestational age
and mortality. While birth
weight is considered a marker
for foetal size, gestational age
is thought of as an indicator
of foetal maturity. Almost
three decades ago, Susser and
colleagues proposed that
gestational age is causally
precedent to birth weight
(implying that birth weight is
in the causal pathway of the
gestational agemortality
relationship). Wilcox and
Skjaerven examined close to
400,000 singleton births
from Norway in an effort to
separate the influences of
birth weight and gestational
age on neonatal mortality.
They showed that, comparisons
using the “relative birth
weight” scale, there were two strong and separable factors
related to mortality: gestational
age independent of
birth weight, and relative
birth weight at any given
gestational age.
On these similar lines,
Herman and Hastie examined
neonatal mortality in
relation to (absolute) birth
weight and gestational age.
They initially speculated that
among preterm (<37 weeks)
babies, maturity would serve
as a strong predictor of
mortality, while among term
babies, the increased
mortality was probably due
to growth restriction.
However, their analysis
showed that mortality was
associated only with birth
weight and not with gestational
age. Their approach to
analysis may have suffered
from collinearity (between
birth weight and gestational
age), perhaps leading to the
attenuated gestational agemortality
relationship. Coory
analysed neonatal mortality
in relation to birth weight
and gestational age. He
concluded that both birth
weight and gestational age
have independent effects on
mortality, and that both are
fundamental riskadjusting
variables.
However, he was
cautious in not interpreting
the effects of gestational age,
but focused his interpretations
almost entirely on birth
weight. Our construction of standardised birth weight zscore
was developed conditional
on gestational age.
Thus, this birth weight zscore
(independent of gestational
age) enabled us to
assess the effects of shortened
gestation and foetal growth
restriction on mortality.
It is widely acknowledged
that smoking mothers give
birth to infants that are
lighter compared with those
born to nonsmoking
mothers. This reduction in
birth weight is thought
mainly to result in foetal
growth restriction, as well as
to shortened gestation.
Although the precise mechanism
by which smoking
during pregnancy affects the
foetus is unclear, two possible
pathways have been
proposed.
Smoking results in
increased capillary fragility
and vasoconstriction of arterial
walls, leading to reduced
blood flow to the uterus and
eventually to the placenta.
The second is the “foetal
hypoxia” hypothesis,
whereby smoking leads to a
villous shrinkage due to an
alteration in the thickness of
the villous membrane,
thereby reducing oxygen
transfer to the foetus. Both
mechanisms are likely to
increase the risk of uteroplacental
bleeding in pregnancy,
which, in turn, increases the
risk of not only neonatal
deaths, but also preterm
delivery and growth restriction.
Our study provides
circumstantial evidence that
after the general effects of
(shortened) gestational age
and (reduced) foetal growth are accounted for, smoking
has little direct impact on
neonatal mortality.
Our study has some limitations.
First, errors in the estimation
of gestational age are
likely to affect our results to
some extent. Our study was
based on gestational age
largely determined from the
date of last menstrual period
as opposed to one based on
early ultrasound.
Sonographically estimated
gestational age is likely to
shift the overall gestational
age distribution to lower
gestational ages sometimes by
as much as a full menstrual
cycle, possibly due to delayed
ovulation or amenorrhea.
Second, the impact of
congenital malformations
and chromosomal abnormalities
on the risk of neonatal
death could have been partly
responsible for the findings
noted here.
Although data on
malformations are contained
on the vital statistics files,
they are recorded poorly.
Third, although we adjusted
all the analysis for maternal
age and gravidity, the study
does not take into account
other known or suspected risk
factors for neonatal mortality.
These risk factors may
account for a part of the associations
noted here, but is
unlikely that these factors
could explain the powerful
effects of foetal growth
restriction and preterm
delivery on neonatal
mortality. Finally, nondifferential
misclassification of
smoking data on vital records
is likely and may have attenuated
the smokingmortality
association to some extent.
Application of generalised
additive regression models to
examine neonatal mortality
appears useful towards understanding
the complex biological
relationship among the
predictors. However, we make
no claim that GAMs serve as
adjuncts to other modelling
approaches; on the contrary,
we believe that GAMs can
provide the first step toward
modelling complex exposuredisease
relationships.
Conclusions
Our study provides important
insights about the
combined effects of gestational
age, foetal growth, and
smoking during pregnancy
on neonatal mortality. Both
standardised zscore birth
weight and preterm delivery
are strongly associated with
neonatal mortality, and the
effect of maternal smoking
appears largely mediated
largely through reduced
foetal growth and, to a
smaller extent, through
shortened gestation.
References available here:
www.biomedcentral.com/14712393/4/22
Credit
BMC Pregnancy and Childbirth
2004 4:22
The electronic version of this
article is the complete one
and can be found here:
www.biomedcentral.com/14712393/4/22
Authors
Cande V Ananth is a
researcher at the Division of
Epidemiology and
Biostatistics, Department of
Obstetrics, Gynecology, and
Reproductive Sciences,
UMDNJRobert Wood
Johnson Medical School, New
Brunswick, New Jersey, USA.
Robert W Platt is a
researcher at the
Departments of Pediatrics,
and of Epidemiology and
Biostatistics, McGill
University, Montreal,
Canada.
