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Association of in utero antibiotic exposure on childhood ear infection trajectories: Results from a national birth cohort study

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Abstract and Figures

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 problems with ear infection every 2 years spanning ages 0–1 to 10–11 years (waves 1–6). Analysis: Latent class models identified ear infection trajectories, 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.
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Association of in utero antibiotic exposure on childhood ear
infection trajectories: Results from a national birth cohort study
Yanhong J Hu ,
Jing Wang,
Joseph I Harwell
and Melissa Wake
Murdoch Childrens Research Institute, The Royal Childrens Hospital,
Department of Paediatrics, The University of Melbourne, Melbourne, Victoria,
Australia and
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 infants acquired microbiome.
This study investigates associations between in utero antibiotic exposure and ear infection trajectories over the rst 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 321 months old (wave 1), and ongoing prob-
lems with ear infection every 2 years spanning ages 01to1011 years (waves 16). Analysis: Latent class models identied 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% condence interval 1.083.88, P= 0.03) and moderate to low(adjusted odds ratio 1.78, 95% condence interval 1.252.53,
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 rst 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
which could affect the fetus and newborn in under-
appreciated ways that may persist throughout childhood.
example, antibiotics may perturb maternal bacterial ora from
which a newborn infants microbiome is derived.
reviews, animal models and population studies have shown that
maternal antibiotics exposure changes the gut microbiome
increases the risk of antimicrobial resistance in neonates.
addition to direct impacts on ora, one of the underlying poten-
tial mechanisms is that gut microbiome may inuence mucosal
innate and adaptive immunity, which may cause systemic
immune disorders and increase susceptibility.
exposure in utero has previously been shown to have associations
with numerous conditions, including asthma,
tion decit hyperactivity disorder
and others.
Some antibiotics,
such as aminoglycosides, may also have a direct teratogenic effect
on fetal ear development.
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.
Otitis media represents the
most common reason for childhood physician sick visits and for
antibiotic prescription in early childhood.
The natural history of
otitis media is dynamic, including early or late onset, periods of
Correspondence: Dr Yanhong J Hu, Murdoch Childrens Research Insti-
tute, The Royal Childrens Hospital, 50 Flemington Road, Parkville, Vic.
3052, Australia. Fax: +61 3 8341 6212; email:
Yanhong J Hu and Jing Wang contributed equally to this study.
Conict of interest: None declared.
Accepted for publication 13 January 2021.
Journal of Paediatrics and Child Health (2021)
© 2021 Paediatrics and Child Health Division (The Royal Australasian College of Physicians)
recurrence, persistence and complete resolution.
The predomi-
nant bacteria for otitis media in children globally
and in
are Streptococcus pneumoniae and Haemophilus inuenzae
identied 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,
and a
national Australia population study showed that overall antibiotics
use in general increased more than 50% from 2001 to 2012.
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
One way to study this hypothesis is to examine longitudi-
nal trajectories
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
1011 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 rst 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.
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 stratied 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 rst birthday.
The two
cohorts were the Birth cohort (initially aged 01 years) and the
Kindergarten cohort (initially aged 45 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 LSACs ini-
tial study design and recruitment are thoroughly outlined else-
This research draws on data from the rst 6 waves
from 2004 to 2014 for the Birth cohort only, with an initial
response rate of 57.2% (5107/8921).
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
1011 years.
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)
are similar
to surveys in other countries.
After obtaining informed consent, trained professional inter-
viewers conducted biennial 90-min face-to-face interviews in the
childrens 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 parents health, wellbeing and views on things like
family relationships may impact differently on the child.
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 321 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 childrens ongoing ear infections from waves
1 to 6 (ages 01to1011 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)?
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
A childs 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.
This is a composite index based on
ranking postcodes according to relative disadvantage, using data
from the ve-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 reecting less disadvantage. We
created a binary variable of passive smoking exposurefor 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
Identication 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 16, we conducted group-based trajectory
modelling using the trajplug-in in Stata.
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.
We also dropped non-signicant (e.g. P> 0.05) quadratic or
cubic parameters for each trajectory (Tables S1 and S2,
Supporting Information).
Using these criteria, we selected and
named from visual inspection a four-trajectory solution for child
ear infections.
Fig 1 Participant ow 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)
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.
Sample characteristics
Figure 1 presents the study ow 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
Ear infection trajectories
Four probability trajectories of parent-reported ear infection
emerged (Fig. 2). The consistently lowgroup 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 lowgroup, 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
moderategroup, 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 lowand 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% condence interval (CI) 1.312.62, P= 0.001)
and consistently high(OR 2.10, 95% CI 1.113.97, P= 0.02)
trajectories, compared to consistently lowtrajectory. 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 1.252.53,
Fig 2 Latent class categories of parent-reported ear infection trajecto-
ries from wave 1 to wave 6 in Longitudinal Study of Australian Childrens
Birth cohort.
Table 1 Sample characteristics; values are mean (standard deviation)
unless specied 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
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Results from a national birth cohort YJ Hu et al.
P= 0.001) and consistently high(OR 2.04, 95% CI 1.083.88,
P= 0.03) trajectories (Fig. 3).
Principal ndings
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
While this association does not prove causality, this is impor-
tant information because parents are the drivers of their childs
health care and highly inuential 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.
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 childs risk,
such as air pollution.
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 rst 3 years of
life (hazard ratio 1.30; 95% CI 1.041.63).
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%. LSACs 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 (0.871.81) 0.22 1.28 (0.891.85) 0.18
Moderate to low 16.6 1.84 (1.312.62)0.0011.78 (1.252.53)0.001
Consistently high 18.5 2.10 (1.113.97)0.022.04 (1.083.88)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, condence interval. ( ),
unadjusted model; ( ), adjusted
Journal of Paediatrics and Child Health (2021)
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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,
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 rst 1011 years of life. The
average posterior probability value for each trajectory (Table S2,
Supporting Information) was above the recommended value of
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 rst 3 years of life. We thus have
a better understanding of patterns of later-developing ear
Our study also has limitations. As in most large population-
based studies,
otitis media events were based on parent report.
Our parent reports of ongoing ear infectionsis a less valid
source of in the momentinformation 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
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.
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 rst trimester exposure to a macrolide
increased the risk of malformation in children.
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 doseresponse effect. A recent
study has shown that antibiotic exposures had a doseresponse
effect, with multiple antibiotic prescriptions having an increased
association with early childhood infection-related hospitaliza-
tions, consistent with the disordered microbiome effect theory.
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.
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 prescribers belief
that antibiotics are harmless, especially when they feel pressured
to ensure patient satiscation.
The nding 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.
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 dene causality, mechanisms and
resulting burden.
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 ndings 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 Childrens Research
Institute (MCRI) is supported by the Victorian Governments
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 ndings 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.
1 Fan H, Gilbert R, OCallaghan 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; 368:110.
2 Rautava S, Luoto R, Salminen S, Isolauri E. Microbial contact during
pregnancy, intestinal colonization and human disease. Nat. Rev.
Gastroenterol. Hepatol. 2012; 9: 56576.
3 Cox LM, Blaser MJ. Antibiotics in early life and obesity. Nat. Rev.
Endocrinol. 2015; 11: 18290.
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: 2018.
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
rst year of infantslife: 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; 69:4251.
7 Raspini B, Porri D, De Giuseppe R et al. Prenatal and postnatal deter-
minants in shaping offsprings microbiome in the rst 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: 17385.
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:
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: 2407.
12 Neish AS. Mucosal immunity and the microbiome. Ann. Am. Thorac.
Soc. 2014; 11 (Suppl. 1): S2832.
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: 1958.
18 Vergison A, Dagan R, Arguedas A et al. Otitis media and its conse-
quences: Beyond the earache. Lancet Infect. Dis. 2010; 10: 195203.
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: 25865.
20 Kong K, Coates HL. Natural history, denitions, risk factors and bur-
den of otitis media. Med. J. Aust. 2009; 191: S3943.
21 Ngo CC, Massa HM, Thornton RB, Cripps AW. Predominant bacteria
detected from the middle ear uid of children experiencing otitis
media: A systematic review. PloS One 2016; 11: e0150949.
22 Massa HM, Cripps AW, Lehmann D. Otitis media: Viruses, bacteria,
biolms and vaccines. Med. J. Aust. 2009; 191: S449.
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:
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: 32937.
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: 184556.
26 Song M. Trajectory analysis in obesity epidemiology: A promising life
course approach. Curr. Opin. Endocr. Metab. Res. 2019; 4:3741.
27 Soloff C, Lawrence D, Johnstone R. LSAC technical paper no. 1: Sam-
ple design. Melbourne: Australian Institute of Family Studies; 2005.
28 Sanson A, Johnstone R. Growing up in Australiatakes its rst steps.
Fam. Matters 2004; 67:4653.
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.
31 Daniel G. Patterns of parent involvement: A longitudinal analysis of
familyschool partnerships in the early years of school in Australia.
Australas. J. Early Childhood 2015; 40: 11928.
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: 54856.
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:
35 Petersen I, Gilbert R, Evans S, RidolA, Nazareth I. Oral antibiotic pre-
scribing during pregnancy in primary care: UK population-based
study. J. Antimicrob. Chemother. 2010; 65: 223846.
Journal of Paediatrics and Child Health (2021)
© 2021 Paediatrics and Child Health Division (The Royal Australasian College of Physicians)
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: 14754.
37 Pink B. Socio-Economic Indexes For Areas (SEIFA)Technical Paper.
Commonwealth of Australia; 2006.
38 Jones BL, Nagin DS. A Stata Plugin for Estimating Group-Based Trajec-
tory Models, 4th edn. Pittsburgh, PA: Carnegie Mellon University;
2013; 42.
39 Andruff H, Carraro N, Thompson A, Gaudreau P, Louvet B. Latent
class growth modelling: A tutorial. Tutor. Quant. Methods Psychol.
2009; 5:1124.
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; 23:315.
41 Park M, Han J, Jang MJ et al. Air pollution inuences the incidence of
otitis media in children: A national population-based study. Plos One
2018; 13:111.
42 Pedersen TM, Stokholm J, Thorsen J, Mora-Jensen AC, Bisgaard H.
Antibiotics in pregnancy increase childrens risk of otitis media and
ventilation tubes. J. Pediatr. 2017; 183: 1538 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: 13957.
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: 56171.
46 Krishnakumar J, Tsopra R. What rationale do GPs use to choose a par-
ticular antibiotic for a specic 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
48 Coxeter PD, Del Mar C, Hoffmann TC. Parentsexpectations and expe-
riences of antibiotics for acute respiratory infections in primary care.
Ann. Fam. Med. 2017; 15: 14954.
Supporting Information
Additional Supporting Information may be found in the online
version of this article at the publishers web-site:
Table S1. Criteria for selecting the number and shape of
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.
... Average posterior probability; Methods S1: Dagitty codes for construction of directed acyclic graph; Methods S2: Trajectory analysis. References [5,10,36,[49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65] Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. ...
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Introduction: Early-life antibiotic exposure is common and impacts the development of the child's microbiome and immune system. Information on the impacts of early-life antibiotics exposure on childhood asthma is lacking. Methods: This study examined associations between early-life (0-24 months) antibiotics exposure with childhood (6-15 years) asthma trajectories through the Australian Longitudinal Study of Australian Children (LSAC) and their linked data from the Pharmaceutical Benefits Scheme. Asthma phenotypes were derived by group-based trajectory modeling. Results: Of 5107 LSAC participants, 4318 were included in the final analyses (84.6% retention). Four asthma phenotypes were identified: Always-low-risk (79.0%), early-resolving asthma (7.1%), early-persistent asthma (7.9%), and late-onset asthma (6.0%). Any early-life antibiotic exposure increased risk 2.3-fold (95% CI: 1.47-3.67; p < 0.001) for early-persistent asthma among all children. In subgroup analyses, early-persistent asthma risk increased by 2.7-fold with any second-generation cephalosporin exposure, and by 2-fold with any β-lactam other than cephalosporin or macrolide exposure. Conclusion: We concluded that early-life antibiotic exposure is associated with an increased risk of early-persistent childhood asthma. This reinforces scrutiny of early-life antibiotic use, particularly for common viral infections where no antibiotics are required.
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Background: Fetal programming during in utero life defines the set point of physiological and metabolic responses that lead into adulthood; events happening in "the first 1,000 days" (from conception to 2-years of age), play a role in the development of non-communicable diseases (NCDs). The infant gut microbiome is a highly dynamic organ, which is sensitive to maternal and environmental factors and is one of the elements driving intergenerational NCDs' transmission. The A.MA.MI (Alimentazione MAmma e bambino nei primi MIlle giorni) project aims at investigating the correlation between several factors, from conception to the first year of life, and infant gut microbiome composition. We described the study design of the A.MA.MI study and presented some preliminary results. Methods: A.MA.MI is a longitudinal, prospective, observational study conducted on a group of mother-infant pairs (n = 60) attending the Neonatal Unit, Fondazione IRCCS Policlinico San Matteo, Pavia (Italy). The study was planned to provide data collected at T0, T1, T2 and T3, respectively before discharge, 1,6 and 12 months after birth. Maternal and infant anthropometric measurements were assessed at each time. Other variables evaluated were: pre-pregnancy/gestational weight status (T0), maternal dietary habits/physical activity (T1-T3); infant medical history, type of feeding, antibiotics/probiotics/supplements use, environment exposures (e.g cigarette smoking, pets, environmental temperature) (T1-T3). Infant stool samples were planned to be collected at each time and analyzed using metagenomics 16S ribosomal RNA gene sequence-based methods. Results: Birth mode (cesarean section vs. vaginal delivery) and maternal pre pregnancy BMI (BMI < 25 Kg/m2 vs. BMI ≥ 25 Kg/m2), significant differences were found at genera and species levels (T0). Concerning type of feeding (breastfed vs. formula-fed), gut microbiota composition differed significantly at genus and species level (T1). Conclusion: These preliminary and explorative results confirmed that pre-pregnancy, mode of delivery and infant factors likely impact infant microbiota composition at different levels. Trial registration: identifier: NCT04122612.
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Objective To assess the association between macrolide antibiotics prescribing during pregnancy and major malformations, cerebral palsy, epilepsy, attention deficit hyperactivity disorder, and autism spectrum disorder in children. Design Population based cohort study. Setting The UK Clinical Practice Research Datalink. Participants The study cohort included 104 605 children born from 1990 to 2016 whose mothers were prescribed one macrolide monotherapy (erythromycin, clarithromycin, or azithromycin) or one penicillin monotherapy from the fourth gestational week to delivery. Two negative control cohorts consisted of 82 314 children whose mothers were prescribed macrolides or penicillins before conception, and 53 735 children who were siblings of the children in the study cohort. Main outcome measures Risks of any major malformations and system specific major malformations (nervous, cardiovascular, gastrointestinal, genital, and urinary) after macrolide or penicillin prescribing during the first trimester (four to 13 gestational weeks), second to third trimester (14 gestational weeks to birth), or any trimester of pregnancy. Additionally, risks of cerebral palsy, epilepsy, attention deficit hyperactivity disorder, and autism spectrum disorder. Results Major malformations were recorded in 186 of 8632 children (21.55 per 1000) whose mothers were prescribed macrolides and 1666 of 95 973 children (17.36 per 1000) whose mothers were prescribed penicillins during pregnancy. Macrolide prescribing during the first trimester was associated with an increased risk of any major malformation compared with penicillin (27.65 v 17.65 per 1000, adjusted risk ratio 1.55, 95% confidence interval 1.19 to 2.03) and specifically cardiovascular malformations (10.60 v 6.61 per 1000, 1.62, 1.05 to 2.51). Macrolide prescribing in any trimester was associated with an increased risk of genital malformations (4.75 v 3.07 per 1000, 1.58, 1.14 to 2.19, mainly hypospadias). Erythromycin in the first trimester was associated with an increased risk of any major malformation (27.39 v 17.65 per 1000, 1.50, 1.13 to 1.99). No statistically significant associations were found for other system specific malformations or for neurodevelopmental disorders. Findings were robust to sensitivity analyses. Conclusions Prescribing macrolide antibiotics during the first trimester of pregnancy was associated with an increased risk of any major malformation and specifically cardiovascular malformations compared with penicillin antibiotics. Macrolide prescribing in any trimester was associated with an increased risk of genital malformations. These findings show that macrolides should be used with caution during pregnancy and if feasible alternative antibiotics should be prescribed until further research is available. Trial registration NCT03948620
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Importance Although antibiotics are associated with obesity in animal models, the evidence in humans is conflicting. Objective To assess whether antibiotic exposure during pregnancy and/or early childhood is associated with the development of childhood obesity, focusing particularly on siblings and twins. Design, Setting, and Participants This cross-sectional national study included 284 211 participants (132 852 mothers and 151 359 children) in New Zealand. Data analyses were performed for 150 699 children for whom data were available, 30 696 siblings, and 4188 twins using covariate-adjusted analyses, and for 6249 siblings and 522 twins with discordant outcomes using fixed-effects analyses. Data analysis was performed November 2017 to March 2019. Exposure Exposure to antibiotics during pregnancy and/or early childhood. Main Outcomes and Measures The main outcome is odds of obesity at age 4 years. Anthropometric data from children born between July 2008 and June 2011 were obtained from the B4 School Check, a national health screening program that records the height and weight of 4-year-old children in New Zealand. These data were linked to antibiotics (pharmaceutical records) dispensed to women before conception and during all 3 trimesters of pregnancy and to their children from birth until age 2 years. Results The overall study population consisted of 132 852 mothers and 151 359 children (77 610 [51.3%] boys) who were aged 4 to 5 years when their anthropometrical measurements were assessed. Antibiotic exposure was common, with at least 1 course dispensed to 35.7% of mothers during pregnancy and 82.3% of children during the first 2 years of life. Results from covariate-adjusted analyses showed that both prenatal and early childhood exposures to antibiotics were independently associated with obesity at age 4 years, in a dose-dependent manner. Every additional course of antibiotics dispensed to the mothers yielded an adjusted odds ratio (aOR) of obesity in their children (siblings) of 1.02 (95% CI, 0.99-1.06), which was similar to the odds across pregnancy for the whole population (aOR, 1.06; 95% CI, 1.04-1.07). For the child’s exposure, the aOR for the association between antibiotic exposure and obesity was 1.04 (95% CI, 1.03-1.05) among siblings and 1.05 (95% CI, 1.02-1.09) among twins. However, fixed-effects analyses of siblings and twins showed no associations between antibiotic exposure and obesity, with aORs of 0.95 (95% CI, 0.90-1.00) for maternal exposure, 1.02 (95% CI, 0.99-1.04) for child’s exposure, and 0.91 (95% CI, 0.81-1.02) for twins’ exposure. Conclusions and Relevance Although covariate-adjusted analyses demonstrated an association between antibiotic exposure and odds of obesity, further analyses of siblings and twins with discordant outcomes showed no associations. Thus, these discordant results likely reflect unmeasured confounding factors.
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Background: Many studies have investigated the ways in which physicians decide whether to prescribe antibiotics, but very few studies have focused on the reasons for which general practitioners (GPs) choose to prescribe a particular antibiotic in a specific clinical situation. Improvements in our understanding of the rationale behind GPs' decisions would provide insight into the reasons for which GPs do not always prescribe the antibiotic recommended in clinical practice guidelines and facilitate the development of appropriate interventions to improve antibiotic prescription. The objective of the study was to understand the rationale used by GPs to decide which antibiotic to prescribe in a specific clinical situation, and to propose a model representing this rationale. Methods: We used a three-step process. First, data were collected from interviews with 20 GPs, and analysed according to the grounded theory approach. Second, data were collected from publications exploring the factors used by GPs to choose an antibiotic. Third, data were used to develop a comprehensive model of the rationale used by GPs to decide which antibiotic to prescribe. Results: The GPs considered various factors when choosing antibiotics: factors relating to microbiology (bacterial resistance), pharmacology (adverse effects, efficacy, practicality of the administration protocol, antibiotic class, drug cost), clinical conditions (patient profile and comorbid conditions, symptoms, progression of infection, history of antibiotic treatment, preference), and personal factors (GP's experience, knowledge, emotion, preference). Conclusions: Various interventions, targeting all the factors underlying antibiotic choice, are required to improve antibiotic prescription. GP-related factors could be improved through interventions aiming to improve the GPs' knowledge of antibiotics (e.g. continuing medical education). Factors relating to microbiology, pharmacology and clinical conditions could be targeted through the use of clinical decision support systems in everyday clinical practice.
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Background: Prenatal antibiotic exposure induces changes in infants' gut microbiota composition and is suggested as a possible contributor in the development of autism spectrum disorders (ASD). In this study, we examined the association between prenatal antibiotic exposure and the risk of ASD. Methods: This was a population-based cohort study utilizing the Manitoba Population Research Data Repository. The cohort included 214 834 children born in Manitoba, Canada between April 1, 1998 and March 31, 2016. Exposure was defined as having filled one or more antibiotic prescription during pregnancy. The outcome was autism spectrum disorder diagnosis. Multivariable Cox proportional hazards regression was used to estimate the risk of developing ASD in the overall cohort and in a sibling cohort. Results: Of all subjects, 80 750 (37.6%) were exposed to antibiotics prenatally. During follow-up, 2965 children received an ASD diagnosis. Compared to children who were not exposed to antibiotics prenatally, those who were exposed had a higher risk of ASD: (adjusted HR 1.10 [95% CI 1.01, 1.19]). The association was observed in those exposed to antibiotics in the second or third trimester (HR 1.11 [95% CI 1.01, 1.23] and 1.17 [95% CI 1.06, 1.30], respectively). In the siblings' cohort, ASD risk estimate remained unchanged (adjusted HR 1.08 [95% CI 0.90, 1.30], although it was not statistically significant. Conclusions: Prenatal antibiotic exposure is associated with a small increase in the risk of ASD. Given the potential of residual confounding beyond what it was controlled through our study design and because of possible confounding by indication, such a small risk increase in the population is not expected to be clinically significant.
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Abstract Background Prenatal antibiotic exposure has been associated with an altered infant gut microbiome composition and higher risk of childhood obesity, but no studies have examined if prenatal antibiotics simultaneously alter the gut microbiome and adiposity in infants. Method In this prospective study (Nurture: recruitment 2013–2015 in North Carolina, United States), we examined in 454 infants the association of prenatal antibiotic exposure (by any prenatal antibiotic exposure; by trimester of pregnancy; by number of courses; by type of antibiotics) with infant age- and sex-specific weight-for-length z score (WFL-z) and skinfold thicknesses (subscapular, triceps, abdominal) at 12 months of age. In a subsample, we also examined whether prenatal antibiotic exposure was associated with alterations in the infant gut microbiome at ages 3 and 12 months. Results Compared to infants not exposed to prenatal antibiotics, infants who were exposed to any prenatal antibiotics had 0.21 (95% confidence interval [CI] 0.02, 0.41) higher WFL-z at 12 months, and 0.28 (95% CI 0.02, 0.55) higher WFL-z if they were exposed to antibiotics in the second trimester, after adjustment for potential confounders, birth weight, and gestational age. We also observed a dose-dependent association (P-value for trend = 0.006) with infants exposed to ≥ 3 courses having 0.41 (95% CI 0.13, 0.68) higher WFL-z at 12 months. After further adjustment for delivery method, only second-trimester antibiotic exposure remained associated with higher infant WFL-z (0.27, 95% CI 0.003, 0.54) and subscapular skinfold thickness (0.49 mm, 95% CI 0.11, 0.88) at 12 months. Infants exposed to second-trimester antibiotics versus not had differential abundance of 13 bacterial amplicon sequence variants (ASVs) at age 3 months and 17 ASVs at 12 months (false discovery rate adjusted P-value
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Treatment with antibiotics is one of the most extreme perturbations to the human microbiome. Even standard courses of antibiotics dramatically reduce the microbiome’s diversity and can cause transitions to dysbiotic states. Conceptually, this is often described as a ‘stability landscape’: the microbiome sits in a landscape with multiple stable equilibria, and sufficiently strong perturbations can shift the microbiome from its normal equilibrium to another state. However, this picture is only qualitative and has not been incorporated in previous mathematical models of the effects of antibiotics. Here, we outline a simple quantitative model based on the stability landscape concept and demonstrate its success on real data. Our analytical impulse-response model has minimal assumptions with three parameters. We fit this model in a Bayesian framework to data from a previous study of the year-long effects of short courses of four common antibiotics on the gut and oral microbiomes, allowing us to compare parameters between antibiotics and microbiomes, and further validate our model using data from another study looking at the impact of a combination of last-resort antibiotics on the gut microbiome. Using Bayesian model selection we find support for a long-term transition to an alternative microbiome state after courses of certain antibiotics in both the gut and oral microbiomes. Quantitative stability landscape frameworks are an exciting avenue for future microbiome modelling.
Aim: Acute otitis media (AOM) is the most common infectious disease for which antibiotics are prescribed; its management is costly and has the potential to increase the antimicrobial resistance of this infection. This study measured the levels of adherence to the clinical practice guidelines (CPGs) of AOM and otitis media with effusion (OME) management in Australian children. Methods: We searched for national and international CPGs relating to AOM and OME in children and created 37 indicators for assessment. We reviewed medical records for adherence to these indicators in 120 locations, across one inpatient and three ambulatory health-care settings. Our review sample was obtained from three Australian states that contain 60% of the nation's children. Results: We reviewed the records of 1063 children with one or more assessments of CPG adherence for otitis media. Of 22 indicators with sufficient data, estimated adherence ranged from 7.4 to 99.1%. Overuse of treatment, particularly overprescribing of antibiotics, was more common than underuse. A frequent lack of adherence with recommended care was observed for children aged between 1 and 2 years with AOM. Adherence varied by health-care setting, with emergency departments and inpatient settings more adherent to CPGs than general practices. Conclusions: Our assessment of a number of indicators in the common settings in which otitis media is treated found that guideline adherence varied widely between individual indicators. Internationally agreed standards for diagnosis and treatment, coupled with clinician education on the existence and content of CPGs and clinical decision support, are needed to improve the management of children presenting with AOM and OME.
Introduction The use of intrapartum antibiotic prophylaxis (IAP) has become common practice in obstetric medicine and is used in up to 40% of deliveries. Despite its benefits, the risks associated with exposing large numbers of infants to antibiotics, especially long-term effects on health through changes in the microbiota, remain unclear. This systematic review summarises studies that have investigated the effect of IAP on the intestinal microbiota of infants. Methods A systematic search in Ovid MEDLINE was used to identify original studies that investigated the effect of IAP on the intestinal microbiota in infants. Studies were excluded if: they included preterm infants, the antibiotic regimen was not specified, antibiotics were used for indications other than prophylaxis, probiotics were given to mothers or infants, or antibiotics were given to infants. Results We identified six studies, which investigated a total of 272 infants and included 502 stool samples collected up to 3 months of age. In all the studies, IAP was given for group B streptococcus (GBS) colonisation. Infants who were exposed to GBS IAP had a lower bacterial diversity, a lower relative abundance of Actinobacteria, especially Bifidobacteriaceae , and a larger relative abundance of Proteobacteria in their intestinal microbiota compared with non-exposed infants. Conflicting results were reported for the phyla Bacteroidetes and Firmicutes. Conclusions GBS IAP has profound effects on the intestinal microbiota of infants by diminishing beneficial commensals. Such changes during the early-life ‘critical window’ during which the intestinal microbiota and the immune response develop concurrently may have an important influence on immune development. The potential long-term adverse consequences of this on the health of children warrant further investigation.
Background and aims Prenatal and early life bacterial colonisation is thought to play a major role in shaping the immune system. Furthermore, accumulating evidence links early life exposures to the risk of developing IBD later in life. We aimed to assess the effect of maternal IBD on the composition of the microbiome during pregnancy and on the offspring’s microbiome. Methods We prospectively examined the diversity and taxonomy of the microbiome of pregnant women with and without IBD and their babies at multiple time points. We evaluated the role of maternal IBD diagnosis, the mode of delivery, antibiotic use and feeding behaviour on the microbiome composition during early life. To assess the effects of IBD-associated maternal and infant microbiota on the enteric immune system, we inoculated germ-free mice (GFM) with the respective stool and profiled adaptive and innate immune cell populations in the murine intestines. Results Pregnant women with IBD and their offspring presented with lower bacterial diversity and altered bacterial composition compared with control women and their babies. Maternal IBD was the main predictor of the microbiota diversity in the infant gut at 7, 14, 30, 60 and 90 days of life. Babies born to mothers with IBD demonstrated enrichment in Gammaproteobacteria and depletion in Bifidobacteria . Finally, GFM inoculated with third trimester IBD mother and 90-day infant stools showed significantly reduced microbial diversity and fewer class-switched memory B cells and regulatory T cells in the colon. Conclusion Aberrant gut microbiota composition persists during pregnancy with IBD and alters the bacterial diversity and abundance in the infant stool. The dysbiotic microbiota triggered abnormal imprinting of the intestinal immune system in GFM.