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ORIGINAL ARTICLE
Association of in utero antibiotic exposure on childhood ear
infection trajectories: Results from a national birth cohort study
Yanhong J Hu ,
1,2
Jing Wang,
1,2
Joseph I Harwell
3
and Melissa Wake
1,2
1
Murdoch Children’s Research Institute, The Royal Children’s Hospital,
2
Department of Paediatrics, The University of Melbourne, Melbourne, Victoria,
Australia and
3
Division of Infectious Diseases, The Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States
Aim: Most prescribed medicines during pregnancy are antibiotics, with unknown effects on a fetus and on the infant’s acquired microbiome.
This study investigates associations between in utero antibiotic exposure and ear infection trajectories over the first decade of life, hypothesising
effects on early or persistent, rather than later-developing, ear infections.
Methods: Design and participants: The Longitudinal Study of Australian Children birth cohort recruited a nationally-representative sample of
5107 infants in 2004. Measures: Mothers reported antibiotic use in pregnancy when a child was 3–21 months old (wave 1), and ongoing prob-
lems with ear infection every 2 years spanning ages 0–1to10–11 years (waves 1–6). Analysis: Latent class models identified ear infection trajec-
tories, and univariable and multivariable multinomial logistic regression determined odds of adverse trajectories by antibiotic exposure.
Results: A total of 4500 (88.1% of original sample) children contributed (mean baseline age 0.7 years; 51.3% boys); 10.4% of mothers reported
antibiotic use in pregnancy. Four probability trajectories for ear infection emerged: ‘consistently low’(86.2%), ‘moderate to low’(5.6%), ‘low to
moderate’(6.7%) and ‘consistently high’(1.4%). Antibiotic use in pregnancy was associated with children following ‘consistently high’(adjusted
odds ratio 2.04, 95% confidence interval 1.08–3.88, P= 0.03) and ‘moderate to low’(adjusted odds ratio 1.78, 95% confidence interval 1.25–2.53,
P= 0.001) trajectories.
Conclusions: Antibiotic use in pregnancy is associated with an increased risk of persistent and early childhood ear infections. This highlights
the wisdom of cautious antibiotic use during pregnancy, and the need for the study of potential mechanisms underlying these associations.
Key words: birth cohort; childhood; ear infection; in utero antibiotic exposure; trajectories.
What is already known on this topic
1 Antibiotic use during pregnancy is common.
2 Middle ear infection is a common early childhood disease.
3 Prenatal antibiotics use can change fetal microbiota.
What this paper adds
1 Ten percent of pregnant women reported antibiotic use during
pregnancy (not including antibiotic use during labour).
2 Children of these mothers were more likely to follow trajectories
characterised by early or consistently high rates of ear infections
across the first decade of life.
The consumption of antibiotics is increasing world-wide, leading
to concern around the increasing prevalence of antibiotic resis-
tance and potential long-term adverse environmental and health
effects. One in four women receive at least one antibiotic during
pregnancy and the majority of prescribed medicines are
antibiotics,
1
which could affect the fetus and newborn in under-
appreciated ways that may persist throughout childhood.
2
For
example, antibiotics may perturb maternal bacterial flora from
which a newborn infant’s microbiome is derived.
3
Systematic
reviews, animal models and population studies have shown that
maternal antibiotics exposure changes the gut microbiome
4–9
and
increases the risk of antimicrobial resistance in neonates.
10,11
In
addition to direct impacts on flora, one of the underlying poten-
tial mechanisms is that gut microbiome may influence mucosal
innate and adaptive immunity, which may cause systemic
immune disorders and increase susceptibility.
12,13
Antimicrobial
exposure in utero has previously been shown to have associations
with numerous conditions, including asthma,
14
obesity,
15
atten-
tion deficit hyperactivity disorder
16
and others.
9
Some antibiotics,
such as aminoglycosides, may also have a direct teratogenic effect
on fetal ear development.
17
Middle ear infection (otitis media) is a common early child-
hood disease. More than 80% of children will experience acute
otitis media before 3 years of age, and 40% will have six or more
recurrences by the age of 7 years.
18
Otitis media represents the
most common reason for childhood physician sick visits and for
antibiotic prescription in early childhood.
19
The natural history of
otitis media is dynamic, including early or late onset, periods of
Correspondence: Dr Yanhong J Hu, Murdoch Children’s Research Insti-
tute, The Royal Children’s Hospital, 50 Flemington Road, Parkville, Vic.
3052, Australia. Fax: +61 3 8341 6212; email: jessika.hu@mcri.edu.au
Yanhong J Hu and Jing Wang contributed equally to this study.
Conflict of interest: None declared.
Accepted for publication 13 January 2021.
doi:10.1111/jpc.15371
Journal of Paediatrics and Child Health (2021)
© 2021 Paediatrics and Child Health Division (The Royal Australasian College of Physicians)
1
recurrence, persistence and complete resolution.
20
The predomi-
nant bacteria for otitis media in children globally
21
and in
Australia
22
are Streptococcus pneumoniae and Haemophilus influenzae
identified through bacterial culture. However, otitis media is usually
caused by viral infections. A recent Australian study showed that
antibiotics are over-prescribed for otitis media for children,
23
and a
national Australia population study showed that overall antibiotics
use in general increased more than 50% from 2001 to 2012.
24
If antibiotics during pregnancy were to exert an impact on the
likelihood of otitis media during childhood, via (for instance)
changes to infant microbiome, then we would expect to see this
additional risk in infancy most proximal to the antibiotic expo-
sure and either persist or slowly decline through, rather than
emerge later in, childhood. This is supported by modelling studies
that suggest that, following antibiotic exposure, the gut micro-
biome may return to stability between 3 months and 5 years
later.
25
One way to study this hypothesis is to examine longitudi-
nal trajectories
26
of propensity to ear infections, requiring the col-
lection of both an indicator of antibiotic use during pregnancy
and repeated measurement of ear infection rates at multiple time
points in population studies.
The national Longitudinal Study of Australian Children
(LSAC) offers this opportunity, with prospective biennial
reporting of ongoing ear infections from infancy to age
10–11 years. Therefore, the objective of this study is to analyse
the association of parent-reported maternal antibiotic use during
pregnancy with risks of different trajectories of middle ear infec-
tion spanning the entire first decade of life. We hypothesised that
antibiotic consumption in pregnancy would be associated with
higher rates of early ear infection that either persist or decline,
but not with a tendency to later-onset ear infections.
Methods
Study design and participants
In 2004, the LSAC was launched to improve understanding of
child development, inform social policy debate, and identify
opportunities for intervention and prevention strategies in policy
areas concerning children and their families. It used a two-stage
random sampling framework stratified by state, urban/rural split
and clustered by postcode to recruit two nationally-representative
samples of approximately 5000 Australian children each from the
Australian Medicare database. Medicare is a core funding mecha-
nism for the Australian universal health-care system into which
98% of children are enrolled by their first birthday.
27
The two
cohorts were the Birth cohort (initially aged 0–1 years) and the
Kindergarten cohort (initially aged 4–5 years), both followed
every 2 years with written and interview-administered question-
naires since enrolment covering many areas, including socio-
demographic information, child functioning, and characteristics
of home, community, relationship, education, health and
childcare. This study is ongoing, with subjects now around
16 and 20 years of age in the two cohorts. Details of LSAC’s ini-
tial study design and recruitment are thoroughly outlined else-
where.
28
This research draws on data from the first 6 ‘waves’
from 2004 to 2014 for the Birth cohort only, with an initial
response rate of 57.2% (5107/8921).
29
Of these, 73.7%
(3764/5107) were retained from wave 1 to wave 6 (the waves
relevant to this paper), when the children were aged
10–11 years.
30
The characteristics of participants more likely and
reasons to drop out (e.g. refused, non-contact, away for the
entire enumeration period and death of study child)
31
are similar
to surveys in other countries.
32
Procedures
After obtaining informed consent, trained professional inter-
viewers conducted biennial 90-min face-to-face interviews in the
children’s homes with their primary caregivers (usually the bio-
logical mother). As well as primary caregivers, other parents/
guardians additionally completed written questionnaires, because
they may have different perspectives on the child, and also
because each parent’s health, wellbeing and views on things like
family relationships may impact differently on the child.
Measures
Use of antibiotics in pregnancy
Mothers reported on their use of antibiotics in pregnancy at base-
line wave (wave 1) when the child was aged 3–21 months in
face-to-face interviews. The use of antibiotics in pregnancy was
recorded using a categorical question. Mothers were asked ‘Did
you take any medicines/tablets during pregnancy?’and, if they
answered yes, ‘What prescribed medicines or tablets were taken?
Antibiotics/penicillin (yes/no)?’, with a single yes/no answer
applying to all antibiotics.
Ear infection
Parents reported on children’s ongoing ear infections from waves
1 to 6 (ages 0–1to10–11 years) at face-to-face interviews. The
presence of ear infection was recorded using the same categorical
question at each wave, with the responding parent asked ‘Does
(child of interest) have any of these ongoing conditions - Ear
infections (yes/no)?’
33
Potential confounders were age, sex, birthweight and socio-
economic status (wave 1) and passive smoking, all of which have
been associated with both antibiotic use and ear infections in the
literature.
34–36
A child’s date of birth, sex and birthweight were
taken from LSAC records. Neighbourhood disadvantage was
measured using the disadvantage index from the 2001 Socio-
Economic Indexes for Areas.
37
This is a composite index based on
ranking postcodes according to relative disadvantage, using data
from the five-yearly Census of Population and Housing adminis-
tered by the Australian Bureau of Statistics. Contributing items
include average household education levels, income levels,
employment status and disability for that postcode. The national
mean for this index is standardised to 1000 (standard deviation
(SD) 100), with higher scores reflecting less disadvantage. We
created a binary variable of ‘passive smoking exposure’for chil-
dren if the parent questionnaire recorded any smokers at home
at any LSAC wave from child age 0 to 11 years.
Statistical analysis
LSAC is an Open Science resource. All data are released by the
Australian Data Archive (ADA). Under our ADA licence, we
downloaded the LSAC data in Stata format from the ADA
2Journal of Paediatrics and Child Health (2021)
© 2021 Paediatrics and Child Health Division (The Royal Australasian College of Physicians)
Results from a national birth cohort YJ Hu et al.
website and extracted the relevant variables through Stata.
All statistical analyses were performed in Stata 15.0 (StataCorp
LLC, USA).
Identification of ear infection trajectories (aim 1)
Trajectory modelling was used to identify groups that have simi-
lar patterns of change over time. To examine ear infection tra-
jectories across waves 1–6, we conducted group-based trajectory
modelling using the ‘traj’plug-in in Stata.
38
Only participants
with ear infection data for at least three waves were included in
the trajectories (Fig. 1). For trajectory modelling, ear infection
data were modelled with binary logit distribution which is
designed for the analysis of longitudinal data on a dichotomous
outcome variable. In order to extract the most meaningful and
distinct trajectories, we considered Bayesian information crite-
rion values, average posterior probabilities, the proportion of
the sample in each trajectory and visual graphs of trajectories.
39
We also dropped non-significant (e.g. P> 0.05) quadratic or
cubic parameters for each trajectory (Tables S1 and S2,
Supporting Information).
40
Using these criteria, we selected and
named from visual inspection a four-trajectory solution for child
ear infections.
Fig 1 Participant flow chart birth cohort of Longitudinal Study of Australian Children.
Journal of Paediatrics and Child Health (2021)
© 2021 Paediatrics and Child Health Division (The Royal Australasian College of Physicians)
3
YJ Hu et al. Results from a national birth cohort
Associations between antibiotic use in pregnancy
and ear infection trajectories
We conducted univariable and multivariable multinomial logistic
regression analyses for the associations between antibiotic use in
pregnancy and ear infection trajectories. For multinomial analy-
sis, we adjusted for age, sex, birthweight, type of delivery (vagi-
nal or caesarean), neighbourhood disadvantage (wave 1) and
passive smoking.
Ethics approval and consent to participate section
The research methodology and survey content of Growing Up in
Australia is reviewed and approved by the Australian Institute of
Family Studies Ethics Committee, which is a Human Research
Ethics Committee registered with the National Health and Medi-
cal Research Council (NHMRC). The Ethics Committee ensures
that Growing Up in Australia meets the ethical standards outlined
in the National Statement on Ethical Conduct in Research Involv-
ing Humans. The LSAC study was approved by the Australian
Institute of Family Studies Ethics Committee (AIFS 14-26) in
Jan-Feb 2014; the Ethics Committee also provides ethical review
and approval for LSAC at every wave.
Results
Sample characteristics
Figure 1 presents the study flow from wave 1 of LSAC onward
with the number of children at each wave of the birth cohort of
LSAC. Both antibiotic exposures and ear infection trajectories
data are available for 4500 children (51.2% boys). Table 1 sum-
marises the participant characteristics. The mean age of children
included in analyses was 0.7 years (SD 0.2) at wave 1. The mean
disadvantage index at wave 1 was 1010 (SD 60), indicating our
sample was on average slightly less disadvantaged and more
homogeneous than the general Australian population. A total of
10.4% (n = 467) had parent-reported antibiotic use in
pregnancy.
Ear infection trajectories
Four probability trajectories of parent-reported ear infection
emerged (Fig. 2). The ‘consistently low’group contained the larg-
est number of children (86.2%, n= 3880) and represented a con-
sistently low probability of having ear infections; 5.6% (n= 253)
of children were in the ‘moderate to low’group, which represen-
ted a decreasing probability of having ear infections from age 3 to
11 years. 6.7% (n= 302) of children belonged to the ‘low to
moderate’group, representing the rise in the probability of hav-
ing ear infections from age 0 and 9 years. The ‘consistently high’
group comprised only a small proportion of children (1.4%,
n= 65) and was characterised by a consistently high probability
of having ear infections.
Association between antibiotic use in pregnancy
and ear infection trajectories
The proportion of antibiotic use in pregnancy in each trajectory
was: 9.7% in ‘consistently low’, 11.9% in ‘low to moderate’,
16.6% in ‘moderate to low’and 18.5% in ‘consistently high’
(Table 2). In univariate analysis, antibiotic use in pregnancy was
associated with children following ‘moderate to low’(odds ratio
(OR) 1.84, 95% confidence interval (CI) 1.31–2.62, P= 0.001)
and ‘consistently high’(OR 2.10, 95% CI 1.11–3.97, P= 0.02)
trajectories, compared to ‘consistently low’trajectory. In multi-
variate analysis, adjusting for age, sex, birthweight, type of deliv-
ery, neighbourhood disadvantage and passive smoking, antibiotic
use in pregnancy remained strongly associated with children fol-
lowing ‘moderate to low’(OR 1.78, 95% CI 1.25–2.53,
Fig 2 Latent class categories of parent-reported ear infection trajecto-
ries from wave 1 to wave 6 in Longitudinal Study of Australian Children’s
Birth cohort.
Table 1 Sample characteristics; values are mean (standard deviation)
unless specified otherwise
Characteristics Children (n= 4500)
Age (years)
Baseline wave (wave 1) 0.7 (0.2)
Wave 2 2.8 (0.2)
Wave 3 4.8 (0.2)
Wave 4 6.8 (0.3)
Wave 5 8.9 (0.3)
Wave 6 10.9 (0.3)
Male sex, % 51.3
Neighbourhood disadvantage at wave 1 1010 (60)
Birthweight, kg 3.4 (0.6)
Passive smoking, % 22.3
Antibiotics/penicillin in pregnancy, % 10.4
Ear infection trajectories, %
Consistently low 86.2
Low to moderate 6.7
Moderate to low 5.6
Consistently high 1.4
Type of delivery, %
Caesarean 39.8
Vaginal 60.2
4Journal of Paediatrics and Child Health (2021)
© 2021 Paediatrics and Child Health Division (The Royal Australasian College of Physicians)
Results from a national birth cohort YJ Hu et al.
P= 0.001) and ‘consistently high’(OR 2.04, 95% CI 1.08–3.88,
P= 0.03) trajectories (Fig. 3).
Discussion
Principal findings
This study shows that, compared to those not exposed, children
exposed to parent-reported antibiotics in utero were around twice
as likely to experience high early rates of parent-reported ear
infection that either declined or persisted from 0 to 11 years of
age. However, they were not more likely to have later-onset ear
infections.
While this association does not prove causality, this is impor-
tant information because parents are the drivers of their child’s
health care and highly influential in their diagnoses. A few possi-
ble causal explanations may be considered. One is that maternal
microbiome changes induced through antibiotic use lead to neo-
natal acquisition of a more disordered, higher risk microbiome.
8
Our observation that maternal antibiotic use is associated with a
moderate frequency of otitis media that decreases with time is
consistent with a disordered infant microbiome that is gradually
restored. Second, there may be direct anatomic or structural
impacts from fetal middle ear antibiotic exposures that might not
be reversible and lead to consistently high rates of otitis media. In
addition, a genetic factor that predisposes the mother to infec-
tions could be inherited by the children, or there is an
unmeasured environmental factor that causes both the mother to
be at risk for infection and that also increases the child’s risk,
such as air pollution.
41
In our study we included only passive
smoking in the adjusted model though it had a minimum impact.
To the best of our knowledge there is only one other study of
700 children in the Copenhagen Prospective Study that also
found maternal antibiotic use in third-trimester pregnancy was
associated with the risk of otitis media during the first 3 years of
life (hazard ratio 1.30; 95% CI 1.04–1.63).
42
In this Danish study,
37% of the mothers received antibiotics during pregnancy which
is much higher than our cohort. The Copenhagen Prospective
Study utilised clinical and pharmacy records to ascertain antibi-
otic use whereas we relied on maternal recall, with a relatively
low rate reported of 10.4%. LSAC’s recall approach may be
expected to underestimate antibiotic use in several ways: (i) a
Table 2 Associations between antibiotics use in pregnancy and ear infection trajectories using multinomial logistic regression
Ear infection trajectories Antibiotics in pregnancy, % OR (95% CI)†POR*(95% CI)‡P
Consistently low 9.7 reference reference
Low to moderate 11.9 1.26 (0.87–1.81) 0.22 1.28 (0.89–1.85) 0.18
Moderate to low 16.6 1.84 (1.31–2.62)∗0.001∗1.78 (1.25–2.53)∗0.001∗
Consistently high 18.5 2.10 (1.11–3.97)∗0.02∗2.04 (1.08–3.88)∗0.03∗
†Unadjusted model.
‡
Adjusted for age, sex, neighbourhood disadvantage, bir thweight and passive smoking.
Fig 3 Associations between antibiotic
use in pregnancy and ear infection tra-
jectories using multinomial logistic
regression. Adjusted for age, sex,
neighbourhood disadvantage,
birthweight, passive smoking and type
of delivery. CI, confidence interval. ( ),
unadjusted model; ( ), adjusted
model.
Journal of Paediatrics and Child Health (2021)
© 2021 Paediatrics and Child Health Division (The Royal Australasian College of Physicians)
5
YJ Hu et al. Results from a national birth cohort
mother may not understand that a medicine given to them is an
antibiotic or may simply forget (recall bias), and (ii) (unlike the
Danish study) the question asked in LSAC did not prompt for
antibiotics during labour (of which many mothers may be
unaware even if prompted). Given that the baseline enrolment
on average occurred when the child was age 0.7 years some
respondents may have forgotten or misreported an antibiotic pre-
scription especially earlier in pregnancy. However, questionnaires
are still a widely used method for many large cohort studies,
43
and the likelihood of recall bias was reduced by asking the ques-
tion in wave 1, soon after the pregnancy. Future study involving
clinical records would increase the reliability and reduce
recall bias.
Strengths and limitations
We were able to examine ear infection trajectories by repeated
biennial reporting throughout the first 10–11 years of life. The
average posterior probability value for each trajectory (Table S2,
Supporting Information) was above the recommended value of
0.70,
44
indicating the model had good assignment accuracy. Our
cohort had a large number of participants more than six times
greater than the Danish cohort. We were also able to follow sub-
jects over a 10-year period to provide a more complete picture of
ear infection events and trends at the population level, while the
Danish study covered only the first 3 years of life. We thus have
a better understanding of patterns of later-developing ear
infections.
Our study also has limitations. As in most large population-
based studies,
41
otitis media events were based on parent report.
Our parent reports of ‘ongoing ear infections’is a less valid
source of ‘in the moment’information than medical assessment,
with one study showing that the diagnostic validity of parent-
reported ear infection is limited (sensitivity 17%, positive predic-
tive value 67%) against tympanograms and pneumatic
otosctopy.
41
However, as our study focused on overall decade-
long trajectories rather than individual event diagnoses, this
repeated biennial report may well give a more complete picture
of ear infection over time than would clinical records. Second,
differential uptake and attrition may limit generalisability; how-
ever, the sample covered a wide social and geographic range and
we adjusted for neighbourhood disadvantage. As our sample
appeared slightly less disadvantaged and more homogeneous
than the general Australian population, these effects may be even
more pronounced in a more disadvantaged population where oti-
tis media is more prevalent. Third, the lack of detailed informa-
tion on which trimester of pregnancy was affected by the
exposure may underestimate or overestimate the actual effect. A
microbiome effect for example might be exaggerated by a late
pregnancy exposure, as was seen in the Copenhagen cohort.
42
An anatomic and/or developmental effect might be more pro-
nounced with early fetal exposures; for example, a study by Fan
et al. highlighted that first trimester exposure to a macrolide
increased the risk of malformation in children.
1
The timing of
pregnancy exposure will be helpful in further investigations of
underlying mechanisms behind this observed increased risk.
Fourth, a lack of antibiotic prescribing information means we
cannot determine if there is a dose–response effect. A recent
study has shown that antibiotic exposures had a dose–response
effect, with multiple antibiotic prescriptions having an increased
association with early childhood infection-related hospitaliza-
tions, consistent with the disordered microbiome effect theory.
45
However, this may not apply to ear infection if antibiotic use
affects the ear structure during a narrow window in early fetal
development. Fifth, we lack information on potentially con-
founding cross-generational variables, both behavioural (tenden-
cies for mothers to seek antibiotics for themselves and their child,
and for prescribers to provide) and genetic/environmental predis-
position to infection. Sixth, we also acknowledge the maternal
infection for which the antibiotic was given rather than antibiotic
exposure itself may contribute independently to the association,
and our survey questionnaire data do not indicate whether the
antibiotics were taken as prescribed or the type and severity of
the maternal infection. Early antibiotic use by the infants and
children themselves were not analysed in this study. However,
antibiotics use for otitis media is increasing.
23
This might further
exacerbate infant microbiome disruption. In any case, should
infants require antibiotic treatment this would support the under-
lying hypothesis that prenatal exposure increases the risk for
infections like otitis.
Much larger studies with biological sampling and detailed
individual-level data on antibiotic class, duration and diagnoses
would further clarify and explain these observations and the
underlying mechanisms, causal or otherwise. This population-
based study was not designed to answer a causal question but
nonetheless emphasises the wisdom of appropriate and cautious
antibiotic use during pregnancy. Previous studies have found that
inappropriate antibiotic use may be linked to a prescriber’s belief
that antibiotics are harmless, especially when they feel pressured
to ensure patient satisfication.
46–48
The finding of this study
(i.e. that there may be under-appreciated harms) could perhaps
help doctors to limit their prescriptions during pregnancy and
reduce antibiotics demanding from parents.
Conclusions
Parent-reported used of prescription antibiotics during pregnancy
is associated with an increased risk of persistent or early ear
infection in childhood. This emphasises the importance of appro-
priate antibiotic use during pregnancy. Further studies with
detailed information on antibiotic exposure timing in relation to
pregnancy as well as assessments of maternal and infant micro-
biome will be needed to define causality, mechanisms and
resulting burden.
Acknowledgements
This study uses unit record data from Growing Up in Australia,
the LSAC. The study is conducted in partnership between the
Department of Social Services, the Australian Institute of Family
Studies, and the Australian Bureau of Statistics. The findings and
views reported in this paper are solely those of the authors. YJ
Hu and J Wang had full access to all the data in the study and
take responsibility for the integrity of the data and the accuracy
of the data analysis. We thank the LSAC study participants and
staff for their contributions and Australian Data Archive for data
management. Research at the Murdoch Children’s Research
Institute (MCRI) is supported by the Victorian Government’s
6Journal of Paediatrics and Child Health (2021)
© 2021 Paediatrics and Child Health Division (The Royal Australasian College of Physicians)
Results from a national birth cohort YJ Hu et al.
Operational Infrastructure Support Program. The funding bodies
did not play any role in the study. JW was supported by the
MCRI Lifecourse Postdoctoral Fellowship. M Wake was supported
by the National Health and Medical Research Council (NHMRC)
Senior Research Fellowship (1046518) and (Principal Research
Fellowship 1160906) in this work.
Data Availability Statement
The data that support the findings of this study are available from
Australian Data Archive (ADA) but restrictions apply to the avail-
ability of these data, which were used under license for the current
study, and so are not publicly available. Data are however avail-
able from the authors upon reasonable request and with permis-
sion from Australian Data Archive and corresponding author.
References
1 Fan H, Gilbert R, O’Callaghan F, Li L. Associations between macrolide
antibiotics prescribing during pregnancy and adverse child outcomes
in the UK: Population based cohort study. Br. Med. J. 2020; 368:1–10.
2 Rautava S, Luoto R, Salminen S, Isolauri E. Microbial contact during
pregnancy, intestinal colonization and human disease. Nat. Rev.
Gastroenterol. Hepatol. 2012; 9: 565–76.
3 Cox LM, Blaser MJ. Antibiotics in early life and obesity. Nat. Rev.
Endocrinol. 2015; 11: 182–90.
4 Zimmermann P, Curtis N. Effect of intrapartum antibiotics on the
intestinal microbiota of infants: A systematic review. Arch. Dis. Child.
Fetal Neonatal Ed. 2020; 105: 201–8.
5 Rutayisire E, Huang K, Liu Y, Tao F. The mode of delivery affects the
diversity and colonization pattern of the gut microbiota during the
first year of infants’life: A systematic review. BMC Gastroenterol.
2016; 16: 86.
6 Torres J, Hu J, Seki A et al. Infants born to mothers with IBD present
with altered gut microbiome that transfers abnormalities of the adap-
tive immune system to germ-free mice. Gut 2020; 69:42–51.
7 Raspini B, Porri D, De Giuseppe R et al. Prenatal and postnatal deter-
minants in shaping offspring’s microbiome in the first 1000 days:
Study protocol and preliminary results at one month of life. Ital.
J. Pediatr. 2020; 46: 45.
8 Zhang MY, Differding MK, Benjamin-Neelon SE, Ostbye T, Hoyo C,
Mueller NT. Association of prenatal antibiotics with measures of infant
adiposity and the gut microbiome. Ann. Clin. Microbiol. Antimicrob.
2019; 18: 18.
9 Milliken S, Allen RM, Lamont RF. The role of antimicrobial treatment
during pregnancy on the neonatal gut microbiome and the develop-
ment of atopy, asthma, allergy and obesity in childhood. Expert Opin.
Drug Saf. 2019; 18: 173–85.
10 Wright AJ, Unger S, Coleman BL, Lam P-P, McGeer AJ. Maternal anti-
biotic exposure and risk of antibiotic resistance in neonatal early-
onset sepsis: A case-cohort study. Pediatr. Infect. Dis. J. 2012; 31:
1206–8.
11 Stoll BJ, Hansen N, Fanaroff AA et al. Changes in pathogens causing
early-onset sepsis in very-low-birth-weight infants. N. Engl. J. Med.
2002; 347: 240–7.
12 Neish AS. Mucosal immunity and the microbiome. Ann. Am. Thorac.
Soc. 2014; 11 (Suppl. 1): S28–32.
13 Neish AS, Denning TL. Advances in understanding the interaction
between the gut microbiota and adaptive mucosal immune
responses. F1000 Biol Rep 2010; 2: 27.
14 Loewen K, Monchka B, Mahmud SM, Jong G, Azad MB. Prenatal anti-
biotic exposure and childhood asthma: A population-based study.
Eur. Respir. J. 2018; 52: 1702070.
15 Leong KSW, McLay J, Derraik JGB et al. Associations of prenatal and
childhood antibiotic exposure with obesity at age 4 years. JAMA
Netw. Open 2020; 3: e1919681.
16 Hamad AF, Alessi-Severini S, Mahmud SM, Brownell M, Kuo IF. Prena-
tal antibiotics exposure and the risk of autism spectrum disorders: A
population-based cohort study. Plos One 2019; 14: e0221921.
17 Scheinhorn DJ, Angelillo VA. Antituberculous therapy in pregnancy.
Risks to the fetus. West J. Med. 1977; 127: 195–8.
18 Vergison A, Dagan R, Arguedas A et al. Otitis media and its conse-
quences: Beyond the earache. Lancet Infect. Dis. 2010; 10: 195–203.
19 Brouwer CN, Rovers MM, Maille AR et al. The impact of recurrent
acute otitis media on the quality of life of children and their care-
givers. Clin. Otolaryngol. 2005; 30: 258–65.
20 Kong K, Coates HL. Natural history, definitions, risk factors and bur-
den of otitis media. Med. J. Aust. 2009; 191: S39–43.
21 Ngo CC, Massa HM, Thornton RB, Cripps AW. Predominant bacteria
detected from the middle ear fluid of children experiencing otitis
media: A systematic review. PloS One 2016; 11: e0150949.
22 Massa HM, Cripps AW, Lehmann D. Otitis media: Viruses, bacteria,
biofilms and vaccines. Med. J. Aust. 2009; 191: S44–9.
23 Clay-Williams R, Stephens JH, Williams H et al. Assessing the appro-
priateness of the management of otitis media in Australia: A
population-based sample survey. J. Paediatr. Child Health 2020; 56:
215–23.
24 Teoh L, Stewart K, Marino R, McCullough M. Part 1. Current prescrib-
ing trends of antibiotics by dentists in Australia from 2013 to 2016.
Aust Dent. J. 2018; 63: 329–37.
25 Shaw LP, Bassam H, Barnes CP, Walker AS, Klein N, Balloux F. Model-
ling microbiome recovery after antibiotics using a stability landscape
framework. ISME J. 2019; 13: 1845–56.
26 Song M. Trajectory analysis in obesity epidemiology: A promising life
course approach. Curr. Opin. Endocr. Metab. Res. 2019; 4:37–41.
27 Soloff C, Lawrence D, Johnstone R. LSAC technical paper no. 1: Sam-
ple design. Melbourne: Australian Institute of Family Studies; 2005.
https://growingupinaustralia.gov.au/research-findings/annual-statistical-
report-2016/introduction-and-overview-lsac-data
28 Sanson A, Johnstone R. ’Growing up in Australia’takes its first steps.
Fam. Matters 2004; 67:46–53.
29 Edwards B. Growing up in Australia: The Longitudinal Study of
Australian Children: Entering adolescence and becoming a young
adult. Fam. Matters 2014; 95:5.
30 Norton A, Monahan K. LSAC technical paper no. 15: Wave 6 weighting
and non-response. Australian Institute of Family Studies; 2015.
https://growingupinaustralia.gov.au/data-and-documentation/technical-
papers
31 Daniel G. Patterns of parent involvement: A longitudinal analysis of
family–school partnerships in the early years of school in Australia.
Australas. J. Early Childhood 2015; 40: 119–28.
32 Stoop I, Billiet J, Koch A, Fitzgerald R. Improving Survey Response:
Lessons Learned from the European Social Survey. ISBN: 978-0-470-
51669-0. Wiley; 2010.
33 Liu T, Lingam R, Lycett K et al. Parent-reported prevalence and persis-
tence of 19 common child health conditions. Arch. Dis. Child. 2018;
103: 548–56.
34 Gultekin E, Develioglu ON, Yener M, Ozdemir I, Kulekci M. Prevalence
and risk factors for persistent otitis media with effusion in primary
school children in Istanbul, Turkey. Auris Nasus Larynx 2010; 37:
145–9.
35 Petersen I, Gilbert R, Evans S, RidolfiA, Nazareth I. Oral antibiotic pre-
scribing during pregnancy in primary care: UK population-based
study. J. Antimicrob. Chemother. 2010; 65: 2238–46.
Journal of Paediatrics and Child Health (2021)
© 2021 Paediatrics and Child Health Division (The Royal Australasian College of Physicians)
7
YJ Hu et al. Results from a national birth cohort
36 Lieu JE, Feinstein AR. Effect of gestational and passive smoke expo-
sure on ear infections in children. Arch. Pediatr. Adolesc. Med. 2002;
156: 147–54.
37 Pink B. Socio-Economic Indexes For Areas (SEIFA)—Technical Paper.
Commonwealth of Australia; 2006. https://www.abs.gov.au/ausstats/
abs@.nsf/mf/2039.0
38 Jones BL, Nagin DS. A Stata Plugin for Estimating Group-Based Trajec-
tory Models, 4th edn. Pittsburgh, PA: Carnegie Mellon University;
2013; 42. https://doi.org/10.1177/0049124113503141.
39 Andruff H, Carraro N, Thompson A, Gaudreau P, Louvet B. Latent
class growth modelling: A tutorial. Tutor. Quant. Methods Psychol.
2009; 5:11–24.
40 Helgeson VS, Snyder P, Seltman H. Psychological and physical adjust-
ment to breast cancer over 4 years: Identifying distinct trajectories of
change. Health Psychol. 2004; 23:3–15.
41 Park M, Han J, Jang MJ et al. Air pollution influences the incidence of
otitis media in children: A national population-based study. Plos One
2018; 13:1–11.
42 Pedersen TM, Stokholm J, Thorsen J, Mora-Jensen AC, Bisgaard H.
Antibiotics in pregnancy increase children’s risk of otitis media and
ventilation tubes. J. Pediatr. 2017; 183: 153–8 e1.
43 Teague S, Youssef GJ, Macdonald JA et al. Retention strategies in lon-
gitudinal cohort studies: A systematic review and meta-analysis. BMC
Med. Res. Methodol. 2018; 18: 151.
44 Nagin DS. Analyzing developmental trajectories: A semiparametric,
group-based approach. Psychol. Methods 1999; 4: 139–57.
45 Miller JE, Wu C, Pedersen LH, de Klerk N, Olsen J, Burgner DP. Mater-
nal antibiotic exposure during pregnancy and hospitalization with
infection in offspring: A population-based cohort study. Int.
J. Epidemiol. 2018; 47: 561–71.
46 Krishnakumar J, Tsopra R. What rationale do GPs use to choose a par-
ticular antibiotic for a specific clinical situation? BMC Fam. Pract.
2019; 20: 178.
47 Bettering the evaluation and care of health (BEACH) 2001-2002. Aust.
Fam. Physician 2003; 32:59
–63.
48 Coxeter PD, Del Mar C, Hoffmann TC. Parents’expectations and expe-
riences of antibiotics for acute respiratory infections in primary care.
Ann. Fam. Med. 2017; 15: 149–54.
Supporting Information
Additional Supporting Information may be found in the online
version of this article at the publisher’s web-site:
Table S1. Criteria for selecting the number and shape of
trajectories.
Table S2. Average posterior probability value for ear infection
trajectory groups.
8Journal of Paediatrics and Child Health (2021)
© 2021 Paediatrics and Child Health Division (The Royal Australasian College of Physicians)
Results from a national birth cohort YJ Hu et al.
