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An investigation of the link between prenatal alcohol exposure and sleep problems across childhood

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Objective To investigate the association between dose and frequency of prenatal alcohol exposure (PAE) and sleep problems in children, after controlling for established risk factors for sleep problems. Methods Data from the birth cohort of the Longitudinal Study of Australian Children (LSAC) was used. Mothers of 3447 children provided information on alcohol consumption during pregnancy, children's sleep problems from 2-to 9-years, and potential confounders associated with sleep problems. Children were classified into PAE groups based on distinct patterns of maternal drinking during pregnancy: abstinent, occasional, low, moderate, and heavy. The effect of PAE on the number and persistence of sleep problems across childhood (2−9 years) was examined. Results After controlling for multiple covariates that impact sleep, children with heavy PAE had 1.13 more sleep problems across childhood (2−9 years) relative to children whose mothers were abstainers, in particular 0.37 more at 2-to 3-years (0.504, 95 % CI 0.053, 0.956), and 0.34 more at 6-to 7-years (0.847, 95 % CI 0.299, 1.396). Compared to children of abstainers, heavy PAE increases the probability of having persistent sleep problems from 2-to 9-years by 22.57 %. No negative associations between moderate or low PAE and sleep were observed. Parenting, family, economic, and child health factors also significantly affected child sleep. Conclusion Heavy PAE was associated with significantly more sleep problems across childhood and a higher probability of reporting persistent sleep problems, relative to children with no PAE. Implications for the understanding and management of sleep in young children with PAE and FASD are discussed.
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An investigation of the link between prenatal alcohol exposure and sleep problems
across childhood
Ned Chandler-Mathera, BPsySc(Hons), Stefano Occhipintia,b, PhD, Caroline Donovana,
PhD, Doug Sheltonc, MBBS, Dip Paed, FRACP, Sharon Dawea,d, BA, MA(Hons), PhD
aSchool of Applied Psychology, Griffith University, Brisbane, Australia
bEPIC Health Systems, Menzies Health Institute Queensland
cGold Coast University Hospital, Southport, Australia
dThe Hopkins Centre, Griffith University
Correspondence:
Ned Chandler-Mather
School of Applied Psychology, Griffith University, Messines Ridge Road, Brisbane, QLD,
4122, Australia.
ned.chandler-mather@griffithuni.edu.au
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
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Abstract
Objective
To investigate the association between dose and frequency of prenatal alcohol exposure
(PAE) and sleep problems in children, after controlling for established risk factors for sleep
problems.
Methods
Data from the birth cohort of the Longitudinal Study of Australian Children (LSAC) was
used. Mothers of 3447 children provided information on alcohol consumption during
pregnancy, children’s sleep problems from 2- to 9-years, and potential confounders
associated with sleep problems. Children were classified into PAE groups based on distinct
patterns of maternal drinking during pregnancy: abstinent, occasional, low, moderate, and
heavy. The effect of PAE on the number and persistence of sleep problems across childhood
(2−9 years) was examined.
Results
After controlling for multiple covariates that impact sleep, children with heavy PAE had 1.13
more sleep problems across childhood (2−9 years) relative to children whose mothers were
abstainers, in particular 0.37 more at 2- to 3-years (0.504, 95 % CI 0.053, 0.956), and 0.34
more at 6- to 7-years (0.847, 95 % CI 0.299, 1.396). Compared to children of abstainers,
heavy PAE increases the probability of having persistent sleep problems from 2- to 9-years
by 22.57 %. No negative associations between moderate or low PAE and sleep were
observed. Parenting, family, economic, and child health factors also significantly affected
child sleep.
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
3
Conclusion
Heavy PAE was associated with significantly more sleep problems across childhood and a
higher probability of reporting persistent sleep problems, relative to children with no PAE.
Implications for the understanding and management of sleep in young children with PAE and
FASD are discussed.
Keywords
Prenatal alcohol exposure (PAE); Alcohol; Fetal alcohol spectrum disorder (FASD), Sleep;
Pregnancy; Child development
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
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An investigation of the link between prenatal alcohol exposure and sleep problems across
childhood
1. Introduction
There is a clear association between prenatal alcohol exposure (PAE) and damage to
the underlying mechanisms that regulate sleep (Inkelis & Thomas, 2018). Preclinical studies
indicate that PAE can lead to the dysregulation of GABAergic neurons throughout the cortex
(Smiley et al., 2015), which are part of inhibitory circuits that support sleep regulation (Luppi
& Fort, 2018). In humans, the neural circuitry involved in arousal and stress control, both
crucial for initiating and maintaining sleep (Dahl, 1996), are potentially affected by PAE
(Fryer et al., 2007; Haley, Handmaker, & Lowe, 2006; Kable & Coles, 2004) leading to over-
activity at bedtime (Keiver, Bertram, Orr, & Clarren, 2015; McLachlan et al., 2016). There is
also emerging evidence that children and adolescents with PAE who meet criteria for Fetal
Alcohol Spectrum Disorder (FASD) have dysfunctional circadian systems, with 79% of
children and adolescents showing abnormal melatonin release across the evening (Goril,
Zalai, Scott, & Shapiro, 2016). In addition, positive genetic correlations between alcohol use
problems and insomnia may provide a separate link between heavy maternal drinking during
pregnancy and sleep problems in their children (Kranzler et al., 2019).
Clinical studies have documented that children with FASD have significantly disrupted sleep
behaviour (Hanlon-Dearman, Chen, & Olson, 2017). Young children with FASD have been
reported to take longer to fall asleep and have higher rates of parent-reported sleep problems,
such as bedtime resistance, short sleep duration, sleep anxiety, night awakenings, and
parasomnias compared to typically developing children (Chen, Olson, Picciano, Starr, &
Owens, 2012; Wengel, Hanlon-Dearman, & Fjeldsted, 2011). In qualitative studies, parents
and caregivers have reported that their child with FASD has significant sleep disturbances
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
5
(Spruyt, Ipsiroglu, Stockler, & Reynolds, 2018) and that these problems are often missed by
clinicians (Ipsiroglu, McKellin, Carey, & Loock, 2013).
Sleep problems during childhood can be disruptive for the child and their parents. They can
lead to greater parental stress and mental health issues (Lam, Hiscock, & Wake, 2003; Quach,
Hiscock, & Wake, 2012) and can have a negative impact on the childs cognitive
development (Astill, Van der Heijden, Van IJzendoorn, & Van Someren, 2012; Bernier,
Beauchamp, Bouvette-Turcot, Carlson, & Carrier, 2013; Turnbull, Reid, & Morton, 2013)
and their regulation of their behaviour (Quach, Nguyen, Williams, & Sciberras, 2018) and
emotions (Williams, Berthelsen, Walker, & Nicholson, 2017). A separate line of research is
investigating how the sleep problems in children with FASD contribute to both cognitive
(Wilson et al., 2016), and emotional difficulties (Mughal, Joyce, Hill, & Dimitriou, 2020).
What remains unclear, is whether PAE has a significant effect on child sleep problems after
potential confounders are controlled for (May et al., 2013). Notably, many children with PAE
are also exposed to a range of other prenatal exposures and postnatal environmental stressors
that, independent of alcohol exposure, have also been associated with sleep problems. These
include a lack of family routines and structure (Whitesell, Crosby, Anders, & Teti, 2018),
lower socioeconomic status and single parent households (El-Sheikh et al., 2013), and
compromised parent-child relationships (Bell & Belsky, 2008; Giallo, Rose, & Vittorino,
2011; Kiel, Hummel, & Luebbe, 2015). Early adversity may also include neglect, parental
separation or divorce, placement in residential or foster care, and physical abuse (Kambeitz,
Klug, Greenmyer, Popova, & Burd, 2019; Streissguth et al., 2004). There is some evidence of
insecure attachment relationships in children of mothers from low socioeconomic
backgrounds who drank during pregnancy (O'Connor, Kogan, & Findlay, 2002). Finally, in
utero exposure to nicotine is common, as many pregnant women who drink also smoke
(McCormack et al., 2018), and parental smoking is associated with sleep difficulties in young
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
6
children, primarily through nicotine’s effects on respiratory and arousal systems (Stéphan-
Blanchard et al., 2008; Stone et al., 2010). Thus, confounding factors could equally explain
higher rates of sleep problems in children with FASD/PAE.
Previous studies investigating the association between PAE and child sleep problems have
led to inconsistent findings. Some studies have found no association (Stone et al., 2010),
others have found an association with fragmented and shorter sleep (Pesonen et al., 2009),
and an increase in night-time parasomnias (Shang, Gau, & Soong, 2006) in children.
However, study limitations include lack of control for the effects of family stress and
parenting (Pesonen et al., 2009) and a failure to examine dose and frequency of PAE
(Pesonen et al., 2009; Shang et al., 2006). Animal model studies demonstrating a detrimental
effect of alcohol exposure early in development on sleep behaviour in mice often administer
doses of ethanol that would result in approximate mean blood alcohol concentrations of
between 300 mg/dL and 500 mg/dL (Ipsiroglu et al., 2019; Stone et al., 1996; Volgin &
Kubin, 2012; Wilson et al., 2016), which would correspond to a heavy dose in humans
(Patten, Fontaine, & Christie, 2014), leaving the effect of lower doses unexplored.
Further, it is not clear whether there are age effects or whether sleep problems are consistent
across childhood. Studies to date have focused on the relationship between PAE and sleep
problems at a single time point, e.g., 8 years (Pesonen et al., 2009), or have averaged parent-
reported sleep problems across birth to early adolescence (Stone et al., 2010). One study that
tracked separate cohorts of children with FASD from age 5.6 years to 9.7 years, 5.7 years to
14.2, and 10.2 years to 13.2 years found that many children had sleep problems that did not
remit (Steinhausen & Spohr, 1998). Persistent sleep problems might reflect lasting damage to
neurobiological structures that underpin the regulation of sleep-wake states as a result of
alcohol exposure in utero (Inkelis & Thomas, 2018; Wilson et al., 2016). However, the study
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
7
by Steinhausen & Spohr (1998) did not control for the potential effects of adversity and
environmental stressors on sleep problems.
The present study examined the relationship between PAE and the number of sleep problems
in a nationally representative cohort of children across childhood (2-9 years), controlling for
prenatal and postnatal factors that might also impact child sleep. Further, the relationship
between prenatal alcohol exposure and the persistence of sleep problems was investigated.
Given the existing evidence for the detrimental effects of heavy PAE on sleep (e.g., Wilson et
al., 2016), we predicted that heavy PAE would predict significantly more sleep problems
across childhood and a higher probability of developing persistent sleep problems relative to
children with no PAE. Since there is a lack of evidence regarding lower levels of PAE, our
analyses at these levels were exploratory.
2. Material and methods
Data were drawn from the birth cohort of the Longitudinal Study of Australian Children
(LSAC), which includes children born between March 2003 and February 2004 (n = 5107).
Parents were interviewed face-to-face and completed questionnaires every two years that
assessed factors with the potential to influence child development. A more detailed
description of the LSAC methodology can be found elsewhere (Soloff, Lawrence, &
Johnstone, 2005). The LSAC was approved by the Australian Institute of Family Studies
Ethics Committee, which is a Human Research Ethics Committee registered with the
National Health and Medical Research Council (NHMRC). Permission to use the data for
research purposes was obtained by Ned Chandler-Mather from the Australian Data Archive
on behalf of the Australian Institute of Family Studies, Department of Social Services, and
the Australian Bureau of Statistics. Demographic and drinking data were extracted at Wave 1
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
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(01 year). Sleep problem and covariate data were extracted at Waves 2 (2 3 years), 3 (45
years), 4 (67 years), and 5 (89 years).
2.1 Participants
We extracted a subsample of children from the 2004 birth cohort (N=3447, 67.50 %) who
had data from their biological mother on marital status, maternal alcohol and cigarette use
during pregnancy, combined family income, highest level of maternal education, child
birthweight, number of weeks of gestation, maternal age, and stressful life events at Wave 1
(n = 1345 excluded), who met one of the PAE group criteria below (n = 7), and were missing
no more than 50 % of their sleep outcome data (n = 308).
Demographic characteristics collected at Wave 1 are detailed in Table 1. This can be
considered to be a representative sample; there were no differences on median maternal age
(32 years in LSAC vs 30.8 years in census) or median household income ($1000$1499 per
week in LSAC vs $1027 per week in census) when compared to 2006 Australian census data
(Australian Bureau Of Statistics, 2006), although more mothers had completed at least a
Bachelors degree or Diploma in our subsample of the LSAC data set than in the general
population (35.5% in LSAC vs 25.6% in census).
2.2 Measures
2.2.1 Maternal alcohol use during pregnancy
Information about alcohol use during pregnancy was collected retrospectively for each
trimester at Wave 1. Mothers were asked to record the frequency (occasional, or number of
days/week) of alcohol consumption per trimester and the average number of drinks they
consumed per drinking occasion over the entire pregnancy (quantity). We used this
information to form PAE categories that capture discrete patterns of alcohol consumption
during pregnancy.
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
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We categorised PAE based on a recently developed method that aims to capture different
patterns of alcohol use during pregnancy (O'Leary, Bower, et al., 2010). There were some
deviations from this composite method owing to the structure of the LSAC survey data that
are described below.
We included an occasional PAE group (defined below) because mothers could choose to rate
their drinking frequency as “occasional” instead of by the number of occasions per week. Our
PAE groups were based on drinking patterns across the entire pregnancy because in the
LSAC survey mothers were asked to estimate quantity of alcohol consumed per occasion
over the entire pregnancy, rather than per trimester. To compute the average number of drinks
per week for the entire pregnancy, we first computed the average number of drinks per week
for each trimester by multiplying the frequency of drinking per week for each trimester
(“occasional” taken as 0.5 drinks/week) by the quantity of drinks per occasion, which were
then averaged to obtain an estimate for the entire pregnancy. Lastly, in addition to O’Leary et
al.’s (2010) criteria for heavy drinking, owing to the low numbers of women who reported
drinking binge-level amounts during pregnancy, we assigned 2 women who reported drinking
11 drinks or more during pregnancy to the “heavy” group regardless of their average daily
intake over the pregnancy as this would constitute a binge event.
Thus, the PAE groups were as follows: (i) abstinent (no alcohol consumption across the
entire pregnancy), (ii) occasional (only frequency of alcohol consumption reported was
“occasionally” across any of the three trimesters and never more than 12 standard drinks per
occasion), (iii) low (drank < 7 drinks per week on average across the entire pregnancy and no
more than 12 drinks per occasion), (iii) moderate (drank < 7 standard drinks/week on
average across the entire pregnancy and < 5 drinks per occasion), and (iv) heavy (drank > 7
standard drinks per week on average, or drank > 5 drinks per occasion and drank on > 2
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
10
occasions/week during any trimester across the entire pregnancy, or drank 11 or more drinks
per occasion regardless of frequency of consumption).
2.2.2 Child sleep problems
Four items that map onto objective measures of sleep (Byars, Yolton, Rausch, Lanphear, &
Beebe, 2012; Sadeh, Mindell, & Rivera, 2011; Williamson, Mindell, Hiscock, & Quach,
2019) and that have been used previously to examine the developmental effects of sleep
problems (Quach et al., 2018) were extracted from the LSAC dataset to measure the number
of child sleep problems at each wave (Waves 25). On each item, the primary caregiver
(almost always the biological mother) was asked to respond “yes” or “no” to whether certain
sleep-related behaviours were a problem “four or more nights per week, that is, more than
half the time”. These behaviours included “getting off to sleep at night”, “not happy to sleep
alone”, “waking during the night”, and “restless sleep”. “Don’t know” responses were coded
as missing. “Yes” responses to each item were summed to create a total count of sleep
problems for each child at each Wave after imputing missing data (see Missing Data section).
Children were categorised as having persistent sleep problems if their parents reported at
least one sleep problem at three of the four Waves (Gregory et al., 2005; Gregory, Caspi,
Moffitt, & Poulton, 2009).
2.2.3 Covariate data from wave 1
Parents provided information about several demographic variables. The mother recorded how
many cigarettes they smoked each day per trimester and these were averaged to compute the
number of cigarettes they smoked (per day) during pregnancy (Stone et al., 2010). Maternal
stress during pregnancy was measured by asking mothers if they had problems with stress,
anxiety or depression during pregnancy (O'Connor et al., 2007). Child birthweight was
converted into four categories: greater than low birthweight (2500g or more), low birthweight
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
11
(15002499g), very low birthweight (1000g1499g) and extremely low birthweight
(<1000g). Weeks of gestation was converted into three categories: Preterm (<37 weeks),
Term (3741.99 weeks), and Post-term (42 weeks or more) (Davis & Thoman, 1987; Rosen
et al., 2003). Combined family income (responses were by categories ranging from Nil or
Negative Income to $2400 per week or $124 800 per year or more), maternal age (years),
maternal education (El-Sheikh et al., 2013) marital status (married, widowed, divorced,
separated, never married), and their child’s sex (Shang et al., 2006). These variables were
included as covariates due to their reported links with child self-regulation and/or sleep
functioning.
2.2.4 Covariate data from waves 2 to 5
See Table 1 for a covariate information. Analyses were adjusted for variables collected at
each Wave that have been associated with children’s sleep: body mass index (BMI), which
was computed based on direct measures of weight and height taken at home visits (Nixon et
al., 2008), parents report of their child’s asthma (van Maanen et al., 2013) and eczema status
(Reid & Lewis‐Jones, 1995), family stress (Tsai et al., 2018), and the age of the child (weeks)
at each Wave. A count of 21 potential life events was used as an index of family stress (e.g.,
being assaulted, or experiencing a major financial crisis, legal problems, or domestic
violence).
Maternal responses to warm, hostile, and overprotective parenting measures collected via
surveys at each Wave were included to control for the effect of the mother-child relationship
on child sleep behaviour. Their respective psychometric properties have been described in
detail elsewhere (Cooklin, Giallo, D'Esposito, Crawford, & Nicholson, 2013; Zubrick,
Lucas, Westrupp, & Nicholson, 2014). The warm parenting scale contained 5 items that tap
into how often the parent displays affection towards the child, how often they enjoy spending
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
12
time with the child, and how close they feel with the child. The hostile parenting scale
contained 4 items that tap into how often the parent displays anger or shouts at the child,
whether they find their child is crying or upset irritating, and whether they comfort their upset
child. The overprotecting parenting scale contained 3 items that tap into the degree to which
the parent puts their child’s needs before their own, wants to protect the child from life’s
difficulties, and how upset they become when they separate from the child.
2.3 Missing data
There was missing data across Waves 2 to 5. There was complete sleep data for 2918
(84.65%) children across all 4 Waves. There were 351 children (10.18%) missing 4 sleep
data points and 178 children (5.16%) missing 8 data points. There was complete sleep data
for 3365 (97.62%) children at Wave 2, for 3364 (97.59%) at Wave 3, for 3250 (94.28%) at
Wave 4, and for 3102 (89.99%) at Wave 5. Complete covariate data was reported for 2712
(78.68%) children at Wave 2, 2920 (84.71%) at Wave 3, 3165 (91.82%) at Wave 4, and 3001
(87.06%) at Wave 5.
Wilcoxon rank sums tests indicated that those who were missing data reported significantly
more stressful life events (r = -0.04, p = .006), lower incomes (r = -0.09, p = < .001), and
lower levels of education (r = -0.16, p = < .001) at Wave 1 compared to those with complete
data, and therefore the missing data was considered to be Missing At Random (Sterne et al.,
2009). Missing values were imputed using Multiple Imputation by Chained Equations
imputation chained equations (MICE) package (van Buuren et al., 2019) in “R” (R Core
Team, 2019). All the covariates were included as predictors in the imputation model. Family
stress was included at Wave 1 in the imputation model because levels of stress varied
between non-completers and completers. The count of sleep problems was computed after
imputation to reduce estimate bias (Eekhout et al., 2014). Since 48.06% of cases were
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
13
missing at least one data point across Waves 2 to 5 (mode points of missing data = 3), 48
datasets were imputed in order to obtain reliable parameter estimates for the models (van
Buuren, 2018; Von Hippel, 2009). Models were run individually on each imputed data set
and their estimates were pooled to produce the final estimates that are presented in the
Results section.
2.4 Analyses
We used negative binomial regressions to model the effect of PAE on the number of sleep
problems since modelling each of the count outcomes using Poisson distributions lead to
significant over-dispersion (see Table 3, alphas = 0.241.67, ps < .05). We examined the
effect of PAE on the total number of sleep problems parents reported from 29 years (Waves
25), then we examined the effect of PAE on the number of sleep problems reported by
parents at each time point during childhood separately: Wave 2 (23 years), Wave 3 (45
years), Wave 4 (67 years), and Wave 5 (89 years). We also used logistic regression to
examine the effect of PAE on the probability of having persistent sleep problems from 29
years (at least one sleep problems recorded at three of the four Waves).
For the negative binomial models, we estimated the average partial effects of each level of
PAE on the count of sleep problems multiplying each model coefficient by the average count
of sleep problems for the relevant period (Liao, 1994). Similarly, for the logistic regression
models, we estimated the average partial effect of PAE by computing marginal effects using
the “margins” R package (Leeper, Arnold, & Arel-Bundock, 2018) for each level of PAE in
each of the 48 imputed datasets and then averaging them.
Unadjusted (PAE only predictor) and adjusted (PAE + covariates) analyses were run (Table
4). Analyses were adjusted using several covariates: child sex, child birth weight group
(categorical), child prematurity (categorical), child BMI, child eczema and asthma statuses
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
14
(binary), maternal age at Wave 1, maternal marital status at Wave 1 (categorical), maternal
cigarette use during pregnancy, maternal stressful pregnancy (binary), maternal warm, hostile
and overparenting parenting scale scores, maternal education at Wave 1, the family stress
index, and family income. For analyses of sleep problems across childhood (number of sleep
problems and persistent sleep problems), child BMI, the family stress index score and
maternal warm, hostile, and overparenting parenting scale scores were averaged across all
four Waves, while child eczema and asthma statuses were determined by whether eczema and
asthma was present for two or more Waves respectively (binary). For analyses at each Wave,
variables from the given Wave were used in the models.
Lastly, post-hoc propensity score matching was employed to follow-up the unexpected
protective effects of occasional PAE on sleep outcomes. We used the R package
“MatchThem” (Pishgar, 2020) to match children from the occasional group with children
from the abstinent group. Children were matched across the covariates listed above, which
were also used to adjust the original models (see Supplementary Materials for diagnostics).
The original models were then re-run using the matched groups as the only predictor.
3. Results
3.1 Maternal alcohol use during pregnancy
The pattern of drinking across each trimester is displayed in Table 2. Of the 3447 mothers in
the current study, 61.18% reported abstaining from alcohol throughout pregnancy. Of the
39.63% that reported alcohol consumption at any time during pregnancy, 65.52% reported
use in trimester one, 86.75% reported use in trimester two, and 91.22% reported use in
trimester three. Overall, the pattern of consuming alcohol in the current sample reflect the
pattern reported in the total LSAC sample, which are described elsewhere (Hutchinson,
Moore, Breen, Burns, & Mattick, 2013) and similar to patterns of alcohol consumption
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
15
during pregnancy reported by another sample of Australian women during a similar period of
time (Colvin, Payne, Parsons, Kurinczuk, & Bower, 2007).
3.2 The effect of prenatal alcohol exposure on the number of sleep problems across
childhood
Table 3 displays the pooled descriptive statistics of the number of sleep problems across
childhood and at each Wave in the overall sample and by PAE category. At 23 years the
average number of sleep problems (an issue with sleep occurring most of the week) reported
by parents was less than one, and this number declined to almost zero by 89 years.
However, there were clear differences in the mean number of sleep problems across PAE
groups with higher rates among the heavy PAE group.
We examined the effect of PAE on the number of sleep problems across childhood after
adjusting for a range of potential confounders (29 years, see Table 4). Compared to the no-
exposure group, occasional exposure significantly reduced the number of sleep problems by
0.22 (p = 0.029). Low and moderate exposure did not have significant effects on the number
of sleep problems (ps > .05). Heavy exposure significantly increased the number of sleep
problems by 1.13 (p = .011).
Since PAE had a significant effect across childhood, we then examined the effect of PAE on
the number of sleep problems reported at each Wave after adjusting for potential confounders
(see Table 4). At 23 years, occasional exposure significantly lowered the number of sleep
problems by 0.12 (p = .005). Low and moderate exposure did not have significant effects on
the number of sleep problems (ps > .05). Heavy exposure significantly increased the number
of sleep problems by 0.37 (p = .029). At 45 years, no level of exposure had a significant
effect on the number of sleep problems (ps > .05). At 67 years, occasional, low and
moderate exposure did not have significant effects on the number of sleep problems (ps >
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
16
.05). Heavy exposure significantly increased the number of sleep problems by 0.34 (p =
.003). At 89 years, no level of exposure had a significant effect on the number of sleep
problems (ps > .05).
3.3 The effect of prenatal alcohol exposure on persistent sleep problems throughout
childhood
We also examined the effect of prenatal alcohol exposure on the probability of having
persistent sleep problems throughout childhood after adjusting for a range of potential
confounders (Table 4). Compared to abstinence, occasional exposure significantly reduced
the probability of having persistent sleep problems by 3.73% (p = .024). Low and moderate
exposure did not have significant effects on the probability of having persistent sleep
problems (ps > .05). Compared to abstinence, heavy exposure significantly increased the
probability of having persistent childhood sleep problems by 22.57% (p = .010).
3.4 Post-hoc propensity score matching occasional and abstinent PAE groups
Post-hoc analyses were conducted to explore the unexpected beneficial effects of occasional
drinking on child sleep behaviour across childhood and at 23 years. We used propensity
scores to match children from the abstainer and occasional PAE groups based on the
covariates used in the original models (see Supplementary Materials). After matching, the
effects of occasional PAE on the number of sleep problems across childhood were non-
significant (-0.105, 95% CI -0.232 0.023, p = 0.109), however the effect at 23 years was
still significant (-0.152, 95% CI -0.296 -0.009, p = 0.037), suggesting that occasional
exposure reduces the number of sleep problems at 23 years by 0.11. The effect of occasional
PAE on the probability of having persistent sleep problems across childhood was not
significant (-0.248, 95% CI 0.5030.007, p = 0.056).
3.5 The effect of covariates on sleep outcomes
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
17
Having parents that have never been married at Wave 1 (relative to married; 0.118, 95% CI
0.0040.231), greater maternal overprotective (0.279, 95% CI 0.1980.360) and hostile
parenting (0.214, 95% CI 0.1540.274), reporting stress, anxiety or depression during
pregnancy (stressful pregnancy; 0.184, 95% CI 0.0790.290), greater maternal age at Wave 1
(0.011, 95% CI 0.0020.020), greater family stress (0.118, 95% CI 0.0850.151), and having
eczema (0.139, 95% CI 0.0300.248) or asthma (0.110, 95% CI 0.0160.204) for at least half
of childhood all predicted significantly more sleep problems reported throughout childhood
(29 years). Conversely, higher family income at Wave 1 predicted significantly fewer sleep
problems across childhood (-0.020, 95% CI -0.038 -0.001). For brevity, further information
about the effects of covariates at each Wave and in the model of persistent sleep problems
can be found in the Supplementary Materials. Of note, maternal cigarette-use during
pregnancy did not have a significant effect on sleep problems at any period during childhood
(p > .05).
4. Discussion
The association between prenatal alcohol exposure (PAE) and sleep problems in early and
middle childhood is of considerable interest given the impact of sleep on neuropsychological
development (Astill et al., 2012). This study assessed the effect of maternal drinking during
pregnancy on a) the number of parent-reported sleep problems and b) the probability of
having persistent sleep problems across early and middle childhood in a nationally
representative sample. Crucially, we controlled for potential confounding variables
independently associated with sleep problems in childhood: presence of stress during
pregnancy, marital status, maternal education, family income, family stress, maternal
parenting styles, prenatal nicotine exposure, child sex, birth weight, gestational age, child
asthma and eczema status, and child BMI. It is important to note that the findings were based
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
18
on a relatively small number of children with moderate and heavy PAE, resulting in wide
confidence intervals around the reported estimates of their respective effects.
We found that children with heavy PAE had 1.13 more sleep problems across childhood (29
years) than children whose mothers who did not drink during pregnancy. Specifically,
children with heavy PAE had 0.37 more sleep problems at 23 years and 0.34 more at 67
years relative to children without PAE. None of the other analyses of heavy PAE at other
periods during childhood reached statistical significance. Further, we found that children in
the heavy PAE group were 22.50% more likely to have sleep problems that persisted across
childhood relative to children with no PAE. We also found that children of occasional
drinkers had a statistically significant drop of 0.11 of a sleep problem at 23 years relative to
children with no PAE after propensity score matching.
Our finding that children with heavy PAE have more sleep problems across childhood is
consistent with previous research into the effects of PAE in both animal models and human
children. Animal models exposed to heavy doses of ethanol, either while in gestation or on
postnatal days 4 to 9 (equivalent to PAE in the third trimester in humans), exhibit
significantly more hyperactivity throughout the day, more transitions in and out of slow wave
sleep, and a reduced proportion of time spent in paradoxical and slow-wave sleep compared
to controls (Stone et al., 1996; Volgin & Kubin, 2012; Wilson et al., 2016). In human studies
children with PAE have been found to have significantly shorter and fragmented sleep
(Pesonen et al., 2009) and significantly more parasomnias (Shang et al., 2006) compared to
those without PAE. We extended these findings by showing that heavy PAE leads to more
parent-reported sleep problems across 2- to 9-years of age whereas lower doses of PAE do
not, even after controlling for the effects of maternal parenting styles and family stress.
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
19
When we examined sleep problems at separate time points during childhood, we found that
children with heavy PAE were most affected at 23 years and 67 years. No previous study
has tracked how PAE affects the number of sleep problems across childhood in the same
cohort. It is important to note that the predicted increases in the number of sleep problems as
a result of heavy PAE at these points in childhood were relatively small, each constituting
less than half of a sleep problem.
Furthermore, none of the other analyses of heavy PAE at the other time points reached
statistical significance. This is potentially at odds with Paavonen et al. (2010), who found an
effect of PAE (they did not examine dosage effects) on sleep duration and consolidation at 8
years, which they measured using actigraphy watches. However, as already noted, our
estimates were based on a small number of children with moderate and heavy PAE, which is
reflected in the wide confidence intervals surrounding each point estimate. We also examined
discrete parent-reported sleep problems rather than continuous dimensions of sleep, like
duration and consolidation which may have meant our analyses were less sensitive to changes
in sleep behaviour. Nevertheless, the estimates at each Wave did follow the expected trend
whereby heavy PAE predicted a greater number of sleep problems compared to abstainers
(see Graphic Abstract and Table 4). The lack of statistical significance might reflect a
genuine absence of an effect or a lack of statistical power to uncover a significant effect of
heavy PAE at these time points (O'Leary, Nassar, et al., 2010). Alternatively, the number of
sleep problems may not be the best metric of how PAE disrupts sleep in children.
In fact, when the trajectory of sleep problems was examined, rather than the number of
different sleep problems, heavy PAE led to a more marked 22.50% increase in the probability
of having sleep problems that persisted throughout childhood (2 to 9 years) relative to no
PAE. Thus, heavy PAE may not lead to a marked increase in the number of separate
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
20
behavioural sleep issues in young children but may instead lead to children developing more
chronic sleep problems.
Children with FASD, the neurodevelopmental disorder that results from PAE, have
previously been reported to have sleep problems that persist across childhood (Steinhausen &
Spohr, 1998). Having chronic sleep problems across childhood can undermine the
development of higher-order cognitive abilities that support self-regulation and emotion
regulation abilities in adolescents and adults without FASD (Friedman, Corley, Hewitt, &
Wright Jr, 2009; Gregory et al., 2005; Gregory et al., 2009). It is well-established that early
childhood is a critical period for the development of executive functions since the neural
structures that support them, such as the prefrontal cortex, do not mature until later in
adolescence (Gogtay et al., 2004). Notably, difficulties with cognitive functions that support
self-regulation, executive functions in particular, and emotion regulation are common in
people with FASD (Mattson, Bernes, & Doyle, 2019; Mattson, Crocker, & Nguyen, 2011).
The degree to which chronic sleep problems across this critical neurodevelopmental period
might compound these impairments in children with FASD should be investigated.
Our finding that children with confirmed heavy PAE had more chronic sleep problems
throughout childhood, after controlling for several potential confounding factors, supports the
view that heavy PAE has a direct teratogenic effect on parts of the nervous system involved
in sleep regulation (Inkelis & Thomas, 2018). However, the mechanisms that explain the
harmful effect of heavy PAE on sleep regulation are unclear. It is possible that PAE might
damage the neurobiological systems that underpin the ability to initiate and maintain a low
arousal state, such as circuitry in the prefrontal cortex (Fryer et al., 2007; O'Hare et al., 2009),
amygdala (Cullen, Burne, Lavidis, & Moritz, 2013; Raineki, Morgan, Ellis, & Weinberg,
2019), and HPA axis (Keiver et al., 2015; McLachlan et al., 2016), along with damage to
GABAergic neurons throughout the cortex (Smiley et al., 2015). Melatonin signalling, which
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
21
regulates the timing of sleep, in the evening might also be impacted by heavy PAE (Goril et
al., 2016). The genetic link between alcohol use problems and insomnia may also play a part
(Kranzler et al., 2019). More research is needed to define the behavioural, neurobiological
and genetic basis for sleep problems observed in children with heavy PAE, which may in turn
lead to the development of more targeted interventions for improving their sleep.
There was no effect of moderate or low levels of PAE in the current study. There are mixed
findings with regards to the effect of low and moderate PAE on developmental outcomes
(Flak et al., 2014; Robinson et al., 2010; Skogerbø et al., 2012). It may be that, despite using
a nationally representative cohort, the present study lacked statistical power to detect a more
modest effect. Alternatively, other physical and psychosocial factors may be more powerful
drivers of sleep problems in children with moderate PAE (May et al., 2013). It may also
suggest a threshold effect of PAE, whereby a sufficiently heavy dose is required before
detrimental effects are observed on developmental outcomes.
There was an unexpected significant protective effect of occasional PAE after adjusting for
potential confounds. The effect of occasional PAE was relatively small, predicting a drop of
0.11 of a sleep problem at 23 years. Previous epidemiological studies of PAE have also
found an apparent protective effect of low level PAE (Kelly et al., 2013; Kelly et al., 2009;
McCormack et al., 2018; Robinson et al., 2010), even after propensity score matching (Kelly
et al., 2013; McCormack et al., 2018). When children with occasional PAE were matched
with children who had no PAE on key covariates that reflect adversity using propensity score
matching, two of these purported protective effects on sleep behaviour were abolished. The
abolishment of two of the three effects in the current study suggests that the positive effect of
occasional drinking stems from residual variance associated with being from a higher
SES/lower adversity background. This residual variance may not have been fully captured in
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
22
previous studies (McCormack et al., 2018) and perhaps was captured better in this study
owing to the inclusion of parenting and family stress factors.
Several covariates contributed significantly to the occurrence of sleep problems across the 2-
to 9-year period. Consistent with previous work, we found significant detrimental effects of
hostile and overprotective parenting, family stress, and lower family income on the number
of sleep problems reported throughout childhood (Bell & Belsky, 2008; El-Sheikh et al.,
2013; Giallo et al., 2011; Kiel et al., 2015; Whitesell et al., 2018). These findings draw
attention to how factors related to the family and material circumstances might contribute to
sleep problems in children with FASD, in addition to the teratogenic effects of PAE (May et
al., 2013). Reporting the presence of problems due to stress during pregnancy also
contributed to the occurrence of sleep problems during childhood, which is in line with
evidence of prenatal programming of the HPA axis (Glover, O’Connor, & O’Donnell, 2010;
Hellemans, Sliwowska, Verma, & Weinberg, 2010) and highlights the need for antenatal
support for families.
Unlike Stone et al. (2010), we did not find an effect of prenatal nicotine exposure on sleep
problems. Differences in outcome measures (parent report of discrete sleep problems vs.
amalgam of questionnaire items), developmental periods assessed (29 years vs 112 years),
and populations assessed (slightly higher educated sample of population vs. at risk mums) in
the current study compared to Stone et al.’s (2010) may underlie these conflicting findings.
They may also reflect the difficulty in fully separating prenatal exposure to different
substances based on retrospective report.
Finally, the pattern of slight increases in drinking across pregnancy reported in the LSAC
may appear counterintuitive as some Australian surveys report that women reduce alcohol
consumption as pregnancy progresses (Cameron, Davey, Kendall, Wilson, & McClure, 2013;
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
23
O'Keeffe et al., 2015). However, this pattern has also been found in another population study
of alcohol use during pregnancy in Australia from a similar period. Hutchinson et al. (2018)
found a similar increase (approximately 10%) in low level alcohol consumption from
Trimester 1 to 3 in a sample of 1534 women recruited from 2009 to 2013. This pattern of
drinking may have been context dependent and may be relate to public health messaging by
the National Health and Medical Research Council in Australia from 2001 to 2009 (National
Health and Medical Research Council, 2001), which suggested that drinking in small
amounts during pregnancy was safe. A stronger abstinence message during pregnancy has
occurred across time in Australia (National Health and Medical Research Council, 2009) with
a notable increase in abstinence-oriented messaging from media reports in particular,
occurring after the data in the above studies were collected (Cook, Leggat, & Pennay, 2020).
This may have influenced the patterns of consumption found in Australian studies prior to
updated abstinence-oriented public health messaging.
4.1 Limitations
Drinking during pregnancy was assessed through retrospective report, which is common
practice in this field (Lange, Shield, Koren, Rehm, & Popova, 2014) and considered reliable
(Robles & Day, 1990). Nonetheless, some women may under-report their alcohol use during
pregnancy, and this may have led to an underestimation of the rate of heavy drinking during
pregnancy. It is also possible that women reported that they abstained when they had not,
potentially resulting in an under-estimate of the effects of PAE at all levels (Lange et al.,
2014). Further efforts were made to ensure reliability by limiting the sample to the birth
cohort, thereby limiting the time between potential drinking episodes and reporting.
Parent-reported sleep problems were used as outcome measures. Parent-report of sleep
problems, while not gold standard, have been found to be consistent with recordings of sleep
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
24
behaviour and neurophysiology using more objective measures (Byars et al., 2012; Sadeh et
al., 2011). While objective measures (e.g., electroencephalography) have been taken on
infants with PAE during sleep revealing disrupted arousal regulation (Scher, Richardson, &
Day, 2000), implementing objective measures in children with PAE or FASD may be
challenging due to their issues with behavioural regulation and polypharmacy, which may in
turn impact protocol adherence and the validity of recordings respectively (Chen et al., 2012).
We used a nationally representative sample of the Australian population. Despite this, our
estimates of the effects of moderate and heavy drinking were based on a relatively small
sample, which is common for population studies of the developmental effects of PAE
(O'Leary, Nassar, et al., 2010; Pesonen et al., 2009). Future research should attempt to further
investigate the effects of heavier PAE in larger samples to obtain more reliable estimates.
5. Conclusion
The present study demonstrates that heavy PAE is associated with 1.13 more parent-reported
child sleep problems across childhood (29 years), at 2- to 3-years and 6- to 7-years of age in
particular. Heavy PAE also significantly increases the probability of having persistent
problems across the 2- to 9-year period by 22.57%. Notably, adjusting for confounding
factors reduced the effect of PAE, suggesting that greater sleep problems in children with
heavy PAE can in part be explained by other factors associated with heavy drinking in
pregnancy. Also, when children with occasional PAE were matched with children who had
no PAE on key covariates that reflect adversity using propensity score matching, most of the
purported protective effects on sleep behaviour were abolished.
Overall, these findings support the argument that sleep problems in children with FASD
might stem partially from the teratogenic effects of exposure to alcohol in utero. Thus,
parents of young children who present with sleep problems should be asked about their
EFFECT OF PRENATAL ALCOHOL EXPOSURE ON CHILD SLEEP PROBLEMS
25
alcohol use during pregnancy. Young children with heavy PAE should be monitored
throughout childhood as they may not simply “grow out” of their sleep problems, since our
findings suggest they are at a significantly higher risk of developing chronic sleep problems.
Instead, they should be assessed for FASD, and may require long-term intervention to
improve their sleep regulation. Future research should investigate the pathways that lead
children with heavy PAE to develop persistent sleep problems and how we can effectively
intervene to improve persistent sleep problems and neuropsychological functioning in this
clinical population.
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32
Overall
Prenatal Alcohol Exposure Category
Abstainer
Occasional
Low
Moderate
Heavy
N (%)
3447 (100)
2081 (60.37)
906 (26.28)
402 (11.66)
33 (0.96)
25 (0.73)
Wave 1 Variables (0-1 year)
Child sex = Male (%)
1763 (51.1)
1048 (50.4)
445 (49.1)
237 (59.0)
20 (60.6)
13 (52.0)
Child birth weight (%)
Extremely low birthweight (< 1000g)
8 (0.2)
7 (0.3)
1 (0.1)
0 (0.0)
0 (0.0)
0 (0.0)
Very low birthweight (1000g-1499g)
13 (0.4)
9 (0.4)
2 (0.2)
2 (0.5)
0 (0.0)
0 (0.0)
Low birthweight (1500g-2499g)
159 (4.6)
111 (5.3)
32 (3.5)
13 (3.2)
1 (3.0)
2 (8.0)
Greater than low birthweight (2500g or more)
3267 (94.8)
1954 (93.9)
871 (96.1)
387 (96.3)
32 (97.0)
23 (92.0)
Child gestational age (%)
Pre-term (<37 weeks)
216 (6.3)
139 (6.7)
46 (5.1)
26 (6.5)
2 (6.1)
3 (12.0)
Term (37-41.99 weeks)
3082 (89.4)
1863 (89.5)
822 (90.7)
344 (85.6)
31 (93.9)
22 (88.0)
Post-term (42 weeks or more)
149 (4.3)
79 (3.8)
38 (4.2)
32 (8.0)
0 (0.0)
0 (0.0)
Ave daily cigarettes during pregnancy (mean (SD))
1.10 (3.64)
1.09 (3.46)
0.88 (3.30)
0.80 (2.95)
6.17 (9.02)
7.27 (10.99)
Maternal age Wave 1 (mean (SD))
31.41 (5.08)
30.74 (5.31)
32.13 (4.42)
33.23 (4.55)
29.36 (5.44)
34.04 (3.62)
Maternal marriage status Wave 1 (%)
Married
2668 (77.4)
1568 (75.3)
751 (82.9)
322 (80.1)
13 (39.4)
14 (56.0)
Divorced
92 (2.7)
54 (2.6)
22 (2.4)
13 (3.2)
1 (3.0)
2 (8.0)
Separated
47 (1.4)
37 (1.8)
4 (0.4)
4 (1.0)
2 (6.1)
0 (0.0)
Widowed
8 (0.2)
6 ( 0.3)
1 (0.1)
0 (0.0)
0 (0.0)
1 (4.0)
Never Married
632 (18.3)
416 (20.0)
128 (14.1)
63 (15.7)
17 (51.5)
8 (32.0)
Family income category (%)
Up to $25 999
492 (14.3)
369 (17.7)
69 (7.6)
34 (8.5)
10 (30.3)
10 (40.0)
Between $26 000 and $41 599
927 (26.9)
618 (29.7)
223 (24.6)
74 (18.4)
9 (27.3)
3 (12.0)
$41 600 and over
2028 (58.8)
1094 (52.6)
614 (67.8)
294 (73.1)
14 (42.4)
12 (48.0)
33
Maternal education (%)
Completed up to Year 11
917 (26.6)
620 (29.8)
194 (21.4)
77 (19.2)
19 (57.6)
7 (28.0)
Completed up to Year 12
1259 (36.5)
798 (38.3)
317 (35.0)
124 (30.8)
11 (33.3)
9 (36.0)
Completed a Tertiary Degree or Diploma
1271 (36.9)
663 (31.9)
395 (43.6)
201 (50.0)
3 (9.1)
9 (36.0)
Wave 2 Variables (2-3 years)
Child BMI Wave 2 (mean (SD))
16.81 (1.58)
16.82 (1.63)
16.78 (1.49)
16.80 (1.46)
17.11 (2.14)
17.00 (1.13)
Child age (months) Wave 2 (mean (SD))
149.03
(12.51)
149.69
(12.73)
147.59
(11.80)
148.99
(12.71)
147.00
(12.34)
149.96
(12.51)
Stressful life index score Wave 2 (mean (SD))
1.27 (1.35)
1.26 (1.40)
1.29 (1.32)
1.23 (1.18)
1.50 (1.65)
1.41 (1.53)
Child asthma status Wave 2 = Yes (%)
449 (13.4)
288 (14.3)
108 (12.2)
44 (11.3)
5 (16.1)
4 (16.0)
Child eczema status Wave 2 = Yes (%)
608 (18.1)
351 (17.3)
168 (18.9)
80 (20.3)
4 (12.9)
5 (20.0)
Mother warm parenting Wave 2 (mean (SD))
4.62 (0.41)
4.64 (0.40)
4.60 (0.41)
4.58 (0.42)
4.69 (0.33)
4.59 (0.34)
Mother hostile parenting Wave 2 (mean (SD))
3.21 (1.34)
3.15 (1.33)
3.25 (1.30)
3.39 (1.41)
3.11 (1.59)
3.36 (1.50)
Mother overprotective parenting Wave 2 (mean (SD))
3.64 (0.68)
3.72 (0.69)
3.53 (0.66)
3.52 (0.62)
3.69 (0.75)
3.53 (0.60)
Wave 3 Variables (4-5 years)
Child BMI Wave 3 (mean (SD))
16.31 (1.72)
16.34 (1.81)
16.25 (1.60)
16.26 (1.55)
16.26 (1.51)
16.53 (0.99)
Child age (months) Wave 3 (mean (SD))
252.05
(12.24)
252.46
(12.49)
250.90
(11.54)
252.62
(12.30)
250.55
(13.12)
252.22
(11.46)
Stressful life index score Wave 3 (mean (SD))
0.98 (1.25)
0.98 (1.24)
0.99 (1.30)
0.89 (1.14)
1.52 (1.29)
1.09 (1.44)
Child asthma status Wave 3 = Yes (%)
682 (20.3)
423 (21.0)
171 (19.2)
74 (18.7)
8 (25.8)
6 (26.1)
Child eczema status Wave 3 = Yes (%)
486 (14.4)
278 (13.7)
136 (15.3)
64 (16.1)
4 (12.9)
4 (17.4)
Mother warm parenting Wave 3 (mean (SD))
4.50 (0.47)
4.52 (0.48)
4.48 (0.45)
4.46 (0.47)
4.65 (0.37)
4.40 (0.57)
Mother hostile parenting Wave 3 (mean (SD))
3.17 (1.26)
3.13 (1.28)
3.20 (1.19)
3.30 (1.28)
3.33 (1.53)
3.18 (1.33)
Mother overprotective parenting Wave 3 (mean (SD))
3.56 (0.69)
3.65 (0.70)
3.47 (0.65)
3.34 (0.61)
3.71 (0.65)
3.58 (0.76)
Wave 4 Variables (6-7 years)
Child BMI Wave 4 (mean (SD))
16.49 (2.16)
16.57 (2.35)
16.37 (1.97)
16.32 (1.60)
16.82 (1.68)
16.36 (1.35)
Child age (months) Wave 4 (mean (SD))
357.73
(15.31)
358.28
(15.51)
356.86
(15.07)
357.22
(14.92)
354.79
(14.93)
356.92
(12.73)
Stressful life index score Wave 4 (mean (SD))
2.64 (2.30)
2.62 (2.33)
2.70 (2.29)
2.49 (2.13)
3.55 (2.76)
3.17 (2.59)
34
Child asthma status Wave 4 = Yes (%)
802 (24.8)
490 ( 25.4)
204 (23.5)
95 (25.0)
6 (21.4)
7 (28.0)
Child eczema status Wave 4 = Yes (%)
430 (13.2)
233 (12.0)
130 (14.9)
62 (16.2)
2 (6.9)
3 (12.0)
Mother warm parenting Wave 4 (mean (SD))
4.54 (0.49)
4.56 (0.50)
4.52 (0.47)
4.51 (0.48)
4.58 (0.57)
4.54 (0.55)
Mother hostile parenting Wave 4 (mean (SD))
1.94 (0.52)
1.91 (0.52)
1.97 (0.52)
2.00 (0.52)
2.06 (0.55)
1.81 (0.58)
Mother overprotective parenting Wave 4 (mean (SD))
3.43 (0.68)
3.53 (0.70)
3.30 (0.63)
3.22 (0.61)
3.47 (0.78)
3.54 (0.72)
Wave 5 Variables (8-9 years)
Child BMI Wave 5 (mean (SD))
17.52 (2.79)
17.69 (3.01)
17.25 (2.50)
17.23 (2.24)
18.09 (2.20)
17.85 (2.63)
Child age (months) Wave 5 (mean (SD))
465.57
(15.93)
465.66
(16.01)
465.01
(15.54)
466.28
(16.28)
464.50
(16.21)
468.24
(18.10)
Stressful life index score Wave 5 (mean (SD))
2.52 (2.40)
2.50 (2.45)
2.58 (2.36)
2.43 (2.20)
2.75 (2.52)
3.79 (2.43)
Child asthma status Wave 5 = Yes (%)
810 (26.2)
493 (26.9)
203 (24.3)
102 (27.6)
5 (17.9)
7 (28.0)
Child eczema status Wave 5 = Yes (%)
379 (12.2)
215 ( 11.7)
113 ( 13.5)
43 ( 11.7)
4 ( 14.3)
4 ( 16.0)
Mother warm parenting Wave 5 (mean (SD))
4.44 (0.54)
4.45 (0.55)
4.41 (0.52)
4.42 (0.52)
4.42 (0.61)
4.41 (0.55)
Mother hostile parenting Wave 5 (mean (SD))
1.96 (0.52)
1.93 (0.52)
2.00 (0.51)
1.99 (0.52)
2.02 (0.51)
1.78 (0.41)
Mother overprotective parenting Wave 5 (mean (SD))
3.39 (0.69)
3.50 (0.70)
3.25 (0.65)
3.17 (0.61)
3.50 (0.72)
3.36 (0.63)
35
Alcohol Consumption
Overall
Prenatal Alcohol Exposure Category
Abstainers
Occasional
Low
Moderate
Heavy
Quantity per drinking occasion (%)
None
2081 (60.4)
2081 (100.0)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
1 or 2 drinks per occasion
1320 (38.3)
0 (0.0)
906 (100.0)
402 (100.0)
0 (0.0)
12 (48.0)
3 or 4 drinks per occasion
41 (1.2)
0 (0.0)
0 (0.0)
0 (0.0)
33 (100.0)
8 (32.0)
5 or 6 drinks per occasion
1 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
1 (4.0)
7 to 10 drinks per occasion
2 (0.1)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
2 (8.0)
11 or more drinks per occasion
2 (0.1)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
2 (8.0)
Frequency Trimester 1 (%)
Never
2552 (74.0)
2081 (100.0)
333 (36.8)
135 (33.6)
3 (9.1)
0 (0.0)
Occasionally
650 (18.9)
0 (0.0)
573 (63.2)
57 (14.2)
18 (54.5)
2 (8.0)
1-3 days per week
212 (6.2)
0 (0.0)
0 (0.0)
193 (48.0)
11 (33.3)
8 (32.0)
4-7 days per week
33 (1.0)
0 (0.0)
0 (0.0)
17 (4.2)
1 (3.0)
15 (60.0)
Frequency Trimester 2 (%)
Never
2262 (65.6)
2081 (100.0)
141 (15.6)
35 (8.7)
4 (12.1)
1 (4.0)
Occasionally
839 (24.3)
0 (0.0)
765 (84.4)
55 (13.7)
18 (54.5)
1 (4.0)
1-3 days per week
316 (9.2)
0 (0.0)
0 (0.0)
296 (73.6)
11 (33.3)
9 (36.0)
4-7 days per week
30 (0.9)
0 (0.0)
0 (0.0)
16 (4.0)
0 (0.0)
14 (56.0)
36
Frequency Trimester 3 (%)
Never
2201 (63.9)
2081 (100.0)
78 (8.6)
27 (6.7)
13 (39.4)
2 (8.0)
Occasionally
882 (25.6)
0 (0.0)
828 (91.4)
40 (10.0)
13 (39.4)
1 (4.0)
1-3 days per week
324 (9.4)
0 (0.0)
0 (0.0)
308 (76.6)
7 (21.2)
9 (36.0)
4-7 days per week
40 (1.2)
0 (0.0)
0 (0.0)
27 (6.7)
0 (0.0)
13 (52.0)
37
Sleep
Outcome
Overall
Prenatal Alcohol Exposure Category
Abstainers
Occasional
Low
Moderate
Heavy
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Persistent
0.20
0.40
0.22
0.41
0.17
0.38
0.19
0.39
0.15
0.36
0.50
0.51
Count 2-
9yrs
2.03
2.45
2.13
2.53
1.82
2.33
1.82
2.11
2.1
1.88
4.00
3.68
Count 2-
3yrs
0.73
1.00
0.78
1.04
0.64
0.93
0.69
0.93
0.75
1.01
1.32
1.18
Count 4-
5yrs
0.50
0.85
0.52
0.87
0.46
0.82
0.43
0.78
0.51
0.69
0.84
1.07
Count 6-
7yrs
0.40
0.75
0.41
0.76
0.37
0.73
0.37
0.69
0.47
0.75
1.08
1.32
Count 8-
9yrs
0.40
0.77
0.42
0.79
0.35
0.75
0.33
0.69
0.37
0.67
0.76
0.93
38
Prenatal alcohol exposure category
Outcome
time point
Occasional
Low
Moderate
Heavy
Unadjusted
Adjusted
Unadjusted
Adjusted
Unadjusted
Adjusted
Unadjusted
Adjusted
Negative binomial regression model estimates (95% confidence intervals)
Childhood
2-9 years
-0.158 (-
0.256, -0.059)
-0.109 (-
0.207, -0.011)
-0.156 (-
0.291, -0.020)
-0.077 (-
0.212, 0.058)
-0.017 (-
0.449, 0.415)
-0.186 (-
0.604, 0.232)
0.628 (0.180,
1.076)
0.557 (0.127,
0.988)
2-3 years
-0.187 (-
0.301, -0.074)
-0.162 (-
0.277, -0.048)
-0.113 (-
0.267, 0.040)
-0.073 (-
0.228, 0.082)
-0.035 (-
0.534, 0.463)
-0.206 (-
0.702, 0.291)
0.533 (0.071,
0.995)
0.504 (0.053,
0.956)
4-5 years
-0.135 (-
0.275, 0.005)
-0.091 (-
0.232, 0.050)
-0.189 (-
0.385, 0.006)
-0.109 (-
0.306, 0.089)
-0.031 (-
0.645, 0.582)
-0.243 (-
0.842, 0.356)
0.468 (-0.132,
1.068)
0.419 (-0.166,
1.005)
6-7 years
-0.102 (-
0.254, 0.052)
-0.049 (-
0.203, 0.104)
-0.109 (-
0.323, 0.105)
-0.027 (-
0.243, 0.190)
0.130 (-0.529,
0.789)
-0.099 (-
0.740, 0.542)
0.965 (0.402,
1.529)
0.847 (0.299,
1.396)
8-9 years
-0.188 (-
0.353, -0.024)
-0.122 (-
0.290, 0.045)
-0.246 (-
0.478, -0.013)
-0.131 (-
0.366, 0.104)
-0.153 (-
0.931, 0.625)
-0.381 (-
1.142, 0.381)
0.585 (-0.061,
1.232)
0.470 (-0.172,
1.112)
Logistic regression model estimates (95% confidence intervals)
39
Persistent
Problems
2-9 years
-0.330 (-
0.536, -0.123)
-0.251 (-
0.470, -0.033)
-0.195 (-
0.474, 0.084)
-0.059 (-
0.356, 0.238)
-0.512 (-
1.555, 0.529)
-0.916 (-
2.015, 0.183)
1.265 (0.458,
2.072)
1.130 (0.274,
1.986)
... Results of this studies illustrate that the sleep disorders are more prevalent in patients with FASD as compared to typically developing children [13,[22][23][24][25]. ...
... The reported frequency is similar to our findings, moreover, in our study the sleep onset delay was also more common among FASD patients than in the control group. Increased odds of sleep problems reported by us corroborates with the findings presented by Chandler-Mather at al. [25] who assessed sleep problems with a yes-no, 4question questionnaire ("getting off to sleep at night", "not happy to sleep alone", "waking during the night", and "restless sleep"). Polysomnography data on FASD patients are scarce. ...
Article
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Background Fetal alcohol spectrum disorders (FASD) is a group of conditions resulting from prenatal alcohol exposure (PAE). Patients with FASD experience a variety of neuropsychological symptoms resulting from central nervous system impairment. Little is known about sleep disorders associated with PAE. The objective of this study was to investigate sleep problems related to FASD. Methods Forty patients (median age 8 years (6; 11)) diagnosed with FASD and forty typically developing children (median age 10 years (8; 13)) were recruited for the 1st phase of the study. In the 1st phase, the screening of sleep problems was performed with Child Sleep Habit Questionnaire (CSHQ) filled in by a caregiver. Those of the FASD group who scored above 41 points were qualified to the 2nd phase of the study and had an in-lab attended polysomnography (PSG) performed. The measurements consisted of electroencephalogram, electrooculograms, chin and tibial electromyogram, electrocardiogram, ventilatory monitoring, breathing effort, pulse oximetry, snoring and body position. Their results were compared to PSG laboratory reference data. Results The number of participants with sleep disturbances was markedly higher in the FASD group as compared to typically developing children (55% vs. 20%). The age-adjusted odds ratio for a positive result in CSHQ was 4.31 (95% CI: 1.54–12.11; p = 0.005) for FASD patients as compared to the control group. Significant differences between the FASD as compared to the typically developing children were observed in the following subscales: sleep onset delay, night wakings, parasomnias, sleep disordered breathing, and daytime sleepiness. Children from the FASD group who underwent PSG experienced more arousals during the sleep as compared with the PSG laboratory reference data. The respiratory indices in FASD group appear higher than previously published data from typically developing children. Conclusion The results support the clinical observation that sleep disorders appear to be an important health problem in individuals with FASD. In particular distorted sleep architecture and apneic/hypopneic events need further attention.
... Further, O'Connor and colleagues hypothesized that greater prenatal alcohol and tobacco use would be associated with greater child sleep disturbances at ages 18 and 30 months. While they failed to find a significant association between these variables, recent work indicates significant associations between greater prenatal substance use and poorer early childhood sleep health [50]. Powell and colleagues also included prenatal tobacco use as a covariate in their models [14]. ...
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Greater exposure to racial/ethnic discrimination among pregnant Black American women is associated with elevated prenatal depressive symptomatology, poorer prenatal sleep quality, and poorer child health outcomes. Given the transdiagnostic importance of early childhood sleep health, we examined associations between pregnant women’s lifetime exposure to racial/ethnic discrimination and their two-year-old children’s sleep health. We also examined women’s gendered racial stress as a predictor variable. In exploratory analyses, we examined prenatal sleep quality and prenatal depressive symptoms as potential mediators of the prior associations. We utilized data from a sample of Black American women and children (n = 205). Women self-reported their lifetime experiences of discrimination during early pregnancy, their sleep quality and depressive symptoms during mid-pregnancy, and their children’s sleep health at age two. Hierarchical linear multiple regression models were fit to examine direct associations between women’s experiences of discrimination and children’s sleep health. We tested our mediation hypotheses using a parallel mediator model. Higher levels of gendered racial stress, but not racial/ethnic discrimination, were directly associated with poorer sleep health in children. Higher levels of racial/ethnic discrimination were indirectly associated with poorer sleep health in children, via women’s prenatal depressive symptomatology, but not prenatal sleep quality. Clinical efforts to mitigate the effects of discrimination on Black American women may benefit women’s prenatal mental health and their children’s sleep health.
Chapter
In 2004, when we asked FAS in Australia: Fact or Fiction? we lacked sufficient data on fetal alcohol syndrome (FAS) to answer that question. Despite coming relatively late to the issue in Australia, research has shifted FAS from the realm of fiction to fact in two decades. We now have good Australian data on patterns of prenatal alcohol exposure (PAE) and women’s and health professionals’ knowledge, attitudes, and practice regarding alcohol use in pregnancy and fetal alcohol spectrum disorder (FASD). Indigenous-led research provides population-based data on FASD prevalence in high-risk remote communities and its impacts at home and school; and prevalence data are available for FASD in a juvenile detention center. Data linkage studies estimate the frequency of PAE effects in infancy and in the long-term, while pregnancy cohorts allow exploration of biological markers, including epigenetics and three-dimensional (3D) facial imaging, to inform pathological understanding, screening, and early diagnosis of FASD. Importantly, using a model that could be adopted elsewhere, we have taken a systematic approach to FASD, established collaborative national research and clinical networks, consulted with caregivers, engaged and supported health professionals, and worked closely with governments and non-government organizations, advocates, politicians, and policymakers to ensure that research is translated into clinical and public health practice and policy. Challenges remain but we look to a future that includes early diagnosis, effective treatment, and prevention of FASD, underpinned by a sound, current, and contextual evidence base.Key wordsFetal alcohol spectrum disorderFetal alcohol syndromeAustraliaAlcohol during pregnancy
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The text theoretically defines fetal alcohol spectrum disorders (FASD) and their causes, describing FASD as a spectrum including specific diagnoses with typical symptomatology and impacts on the individual's school functioning and eco-system. This study investigates inclusive conditions in school environments for children with FASD. Based on qualitative research through the research method of "thematic analysis", it provides an in-depth analytical probe into the situation of children with fetal alcohol syndrome (FASD) and their families in the process of compulsory school attendance in Slovakia. The qualitative research aimed to find out where guardians/parents of children with FASD see positive sources of support and opportunities for their children's inclusive acceptance in the school environment and, on the other hand, where they identify barriers and obstacles). Positive sources of school inclusion for the child with FASD in according to caregivers/parents of these children included: The importance of knowing the FASD diagnosis; The use of alternative teaching approaches for the child with FASD in the school setting; Finding positive reserves in the child with FASD; and A welcoming attitude from educators in the school setting. These factors significantly helped the children function in the school environment and participate in school life. On the contrary, we from interviews identified the following as barriers to school inclusion of the child with FASD: Developmental difficulties in the child with FASD; Unproportional academic performance in the child with FASD; Eco-systemic barriers to the child with FASD; Institutional exclusion of the child with FASD. This paper is a partial output of the Kega project 002UK-4/2020 Supporting a child with sensory processing disorder through a multisensory environment. Keywords: Fetal Alcohol Spectrum Disorders - FASD, school inclusion, narrative interview
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An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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The dense expression of glucocorticoid receptors (GR) within the amygdala, medial prefrontal cortex (mPFC) and paraventricular nucleus of hypothalamus (PVN) mediates many aspects of emotional and stress regulation. Importantly, both prenatal alcohol exposure (PAE) and adolescent stress are known to induce emotional and stress dysregulation. Little is known, however, about how PAE and/or adolescent stress may alter the expression of GR in the amygdala, mPFC, and PVN. To fill this gap, we exposed PAE and control adolescent male and female rats to chronic mild stress (CMS) and assessed GR mRNA expression in the amygdala, mPFC, and PVN immediately following stress or in adulthood. We found that the effects of PAE on GR expression were more prevalent in the amygdala, while effects of adolescent stress on GR expression were more prevalent in the mPFC. Moreover, PAE effects in the amygdala were more pronounced during adolescence and adolescent stress effects in the mPFC were more pronounced in adulthood. GR expression in the PVN was affected by both PAE and adolescent stress. Finally, PAE and/or adolescent stress effects were distinct between males and females. Together, these results suggest that PAE and adolescent CMS induce dynamic alterations in GR expression in the amygdala, mPFC, and PVN, which manifest differently depending on the brain area, age, and sex of the animal. Additionally, these data indicate that PAE-induced hyperresponsiveness to stress and increased vulnerability to mental health problems may be mediated by different neural mechanisms depending on the sex and age of the animal.
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In utero alcohol exposure can disrupt the development of the fetal brain and result in a wide‐range of neurobehavioral outcomes collectively known as fetal alcohol spectrum disorders (FASD). This paper provides a comprehensive review of the cognitive and behavioral outcomes of prenatal alcohol exposure, including domains of general intelligence, executive functioning, language development, learning and memory, adaptive functioning, academic performance, and concurrent psychopathology. In addition, the current status of the neurobehavioral profile of FASD and its potential as a diagnostic tool will be discussed. This article is protected by copyright. All rights reserved.
Article
Introduction: Clinical research and studies using animal models have revealed a complex and relatively under-explored interaction between prenatal alcohol exposure (PAE) and alterations in sleep-wake behaviors. Objectives: To utilize a structured naturalistic observation-based methodology, consisting of descriptive elements, to provide insight into possible links between altered sleep and disruptive daytime presentations in children and adolescents with fetal alcohol spectrum disorder (FASD). To apply a similar structured behavioral observation protocol in a PAE animal model to compare outcomes from the experimental and clinical studies utilizing naturalistic observational methodology. Methods: Forty pediatric patients with FASD (1.8-17.5 yrs, median age 9.4 yrs) and chronic sleep problems were assessed. In the PAE animal model, male offspring from PAE, Pair-Fed (PF), and ad libitum-fed Control (C) groups (n = 8/group) were assessed in the juvenile/preadolescent (23-25 days of age) and adolescent/pubertal (35-36 days of age) periods. Results: In the clinical setting, we found that 95% of children with FASD showed disruptive or externalizing behaviors, 73% showed internalizing behaviors, 93% had circadian rhythm sleep disorders, all had chronic insomnia, and 85% had restless sleep, often with tossing/turning/kicking movements indicative of non-restorative sleep with hypermotor events. In the daytime, individuals showed excessive daytime sleepiness as well as hyperactive/hyperkinetic behaviors, an urge-to-move, and involuntary movements suggestive of hyperarousability. Alterations in sleep/wake behaviors in the PAE animal model paralleled the clinical data in many aspects, demonstrating greater sleep latencies, less total time asleep, more total time awake and longer awake bouts, more position changes, more time in transition, and longer transition bouts in PAE compared to PF and/or control animals. Conclusions: Thus, our findings provide support for the power and validity of naturalistic observational paradigms in revealing dysregulated sleep-wake behaviors and their association and/or exacerbating relationship with day and nighttime behavioral problems, such as disruptive behaviors, externalizing and internalizing disorders, and daytime sleepiness.