Interpregnancy intervals and child development at age 5: a population data linkage study

Abstract

Objective
To investigate the associations between interpregnancy intervals (IPIs) and developmental vulnerability in children’s first year of full-time school (age 5).

Design
Retrospective cohort study using logistic regression. ORs were estimated for associations with IPIs with adjustment for child, parent and community sociodemographic variables.

Setting
Western Australia (WA), 2002–2015.

Participants
34 574 WA born singletons with a 2009, 2012 or 2015 Australian Early Development Census (AEDC) record.

Main outcome measure
The AEDC measures child development across five domains; Physical Health and Wellbeing, Social Competence, Emotional Maturity, Language and Cognitive Skills (school-based) and Communication Skills and General Knowledge. Children with scores <10th percentile were classified as developmentally vulnerable on, one or more domains (DV1), or two or more domains (DV2).

Results
22.8% and 11.5% of children were classified as DV1 and DV2, respectively. In the adjusted models (relative to the reference category, IPIs of 18–23 months), IPIs of <6 months were associated with an increased risk of children being classified as DV1 (adjusted OR (aOR) 1.17, 95% CI 1.08 to 1.34), DV2 (aOR 1.31, 95% CI 1.10 to 1.54) and an increased risk of developmental vulnerability for the domains of Physical Health and Wellbeing (aOR 1.25, 95% CI 1.06 to 1.48) and Emotional Maturity (aOR 1.36, 95% CI 1.12 to 1.66). All IPIs longer than the reference category were associated with and increased risk of children being classified as DV1 and DV2 (aOR >1.15). IPIs of 60–119 months and ≥120 months, were associated with an increased risk of developmental vulnerability on each of the five AEDC domains, with greater odds for each domain for the longer IPI category.

Conclusions
IPIs showed independent J-shaped relationships with developmental vulnerability, with short (<6 months) and longer (≥24 months) associated with increased risks of developmental vulnerability.

Full text

1
DhamraitGK, etal. BMJ Open 2021;11:e045319. doi:10.1136/bmjopen-2020-045319
Open access
Interpregnancy intervals and child
development at age 5: a population data
linkage study
Gursimran Kaur Dhamrait ,1,2 Catherine Louise Taylor ,1,3 Gavin Pereira 1,4,5
To cite: DhamraitGK, TaylorCL,
PereiraG. Interpregnancy
intervals and child development
at age 5: a population data
linkage study. BMJ Open
2021;11:e045319. doi:10.1136/
bmjopen-2020-045319
►Prepublication history and
additional materials for this
paper is available online. To
view these les, please visit
the journal online (http:// dx. doi.
org/ 10. 1136/ bmjopen- 2020-
045319).
Received 28 September 2020
Revised 27 January 2021
Accepted 05 March 2021
1Telethon Kids Institute,
University of Western Australia,
Perth, Western Australia,
Australia
2School of Population and Global
Health, University of Western
Australia, Perth, Western
Australia, Australia
3Centre for Child Health
Research, University of Western
Australia, Perth, Western
Australia, Australia
4Curtin School of Population
Health, Curtin University, Perth,
Western Australia, Australia
5Centre for Fertility and Health
(CeFH), Norwegian Institute of
Public Health, Oslo, Norway
Correspondence to
Gursimran Kaur Dhamrait;
gursimran. dhamrait@
telethonkids. org. au
Original research
© Author(s) (or their
employer(s)) 2021. Re- use
permitted under CC BY- NC. No
commercial re- use. See rights
and permissions. Published by
BMJ.
ABSTRACT
Objective To investigate the associations between
interpregnancy intervals (IPIs) and developmental
vulnerability in children’s rst year of full- time school (age
5).
Design Retrospective cohort study using logistic
regression. ORs were estimated for associations with
IPIs with adjustment for child, parent and community
sociodemographic variables.
Setting Western Australia (WA), 2002–2015.
Participants 34 574 WA born singletons with a 2009,
2012 or 2015 Australian Early Development Census (AEDC)
record.
Main outcome measure The AEDC measures child
development across ve domains; Physical Health and
Wellbeing, Social Competence, Emotional Maturity,
Language and Cognitive Skills (school- based) and
Communication Skills and General Knowledge. Children
with scores <10th percentile were classied as
developmentally vulnerable on, one or more domains
(DV1), or two or more domains (DV2).
Results 22.8% and 11.5% of children were classied as
DV1 and DV2, respectively. In the adjusted models (relative
to the reference category, IPIs of 18–23 months), IPIs of
<6 months were associated with an increased risk of
children being classied as DV1 (adjusted OR (aOR) 1.17,
95% CI 1.08 to 1.34), DV2 (aOR 1.31, 95% CI 1.10 to 1.54)
and an increased risk of developmental vulnerability for
the domains of Physical Health and Wellbeing (aOR 1.25,
95% CI 1.06 to 1.48) and Emotional Maturity (aOR 1.36,
95% CI 1.12 to 1.66). All IPIs longer than the reference
category were associated with and increased risk of
children being classied as DV1 and DV2 (aOR >1.15). IPIs
of 60–119 months and ≥120 months, were associated
with an increased risk of developmental vulnerability on
each of the ve AEDC domains, with greater odds for each
domain for the longer IPI category.
Conclusions IPIs showed independent J- shaped
relationships with developmental vulnerability, with short
(<6 months) and longer (≥24 months) associated with
increased risks of developmental vulnerability.
INTRODUCTION
Interpregnancy interval (IPI), the time from
birth to the conception of the next pregnancy,
has been proposed as an important modifi-
able risk factor for adverse birth and perinatal
outcomes.1 The WHO recommends an IPI
of approximately 2–3 years to reduce infant
and child morbidity and mortality and these
recommendations are informed by several
studies which have reported a strong J- shaped
relationship between various adverse birth
outcomes and IPIs, with the lowest risk of
adverse perinatal outcomes observed for IPIs
of 18–23 months.1–5 Both shorter (<6 months)
and longer IPIs (>60 months) have been
reported to be associated with an increased
risk of adverse birth outcomes however, it is
believed that the pathways governing these
outcomes are different. Associations between
short IPIs and adverse birth outcomes have
been interpreted as evidence in support of the
maternal depletion hypothesis, which proposes
that short IPIs lead to insufficient recovery
time from a pregnancy and the subsequent
period of lactation.1 3 6–8 Associations between
longer IPIs and adverse birth outcomes have
been interpreted as support for the physical
regression hypothesis, such that long IPIs result
Strengths and limitations of this study
►The study is based on a large population- level and
otherwise healthy sample of singleton Australian
children at the time of their rst year of full- time
school.
►The study is the rst to examine associations be-
tween interpregnancy intervals and child devel-
opmental vulnerability in a population of healthy
children in the early childhood period.
►Logistic regression analysis with the calculation of
adjusted ORs was performed to explore the asso-
ciations between multiple interpregnancy interval
categories.
►Important social risk factors including parenting ex-
perience and/or technique, stability and quality of
housing and availability of learning resources within
the household could not be accounted for.
►We did not have information as to whether the preg-
nancies were planned or unplanned and as admin-
istrative records do not include pregnancies ending
before 20 weeks of gestation, we are unable to iden-
tify and account for the effect of miscarriages.
on March 23, 2021 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2020-045319 on 23 March 2021. Downloaded from

2DhamraitGK, etal. BMJ Open 2021;11:e045319. doi:10.1136/bmjopen-2020-045319
Open access
in a loss of the beneficial physiological adaptations from
the previous pregnancy thus, resulting in a state which
resembles a primigravida.9
There is a paucity of research investigating the effects
of IPIs on children’s development during the early child-
hood period. Studies investigating the impacts of IPIs on
child development outcomes have typically focused on
neurodevelopmental morbidities and disabilities such as
autism spectrum disorder or attention- deficit/hyperac-
tivity problems.10–13 In terms of school- based outcomes,
studies have more readily examined the impact of adver-
sities associated with suboptimal IPIs such as preterm
birth and low birth weight. The available studies assessing
the relationship between birth spacing and child devel-
opment outcomes, beyond those observed at birth and
in children without diagnosed developmental disabili-
ties, have primarily examined the impact of birth inter-
vals.14–18 To our knowledge, one study has examined
the associations between IPIs and childhood develop-
ment during the early and middle childhood periods
for children without diagnosed developmental disabil-
ities.19 However, the findings of this study reported no
statistically significant associations between IPI duration
and child development outcomes, after adjustment for
a range of sociodemographic factors.19 Furthermore,
existing studies have reported mixed findings for the
associations between birth intervals and school perfor-
mance.16 20 Compared with IPIs, interdelivery intervals
have an inherent bias, as this measure is conflated by
the gestational length of the subsequent pregnancy.21
Thus, IPIs are a measure of sibling spacing that are not
confounded by gestational age. Furthermore, the first
5 years of a child’s life is recognised as a critical time for
identifying and responding to developmental vulner-
ability. Children’s developmental achievements in the
early childhood period lay the foundation for success at
school. School readiness is a multidimensional concept
that includes the child’s physical health and wellbeing,
social and emotional competence, language and cogni-
tive development and communication skills and general
knowledge22 as well as attitudes towards learning and
classroom skills and behaviours.23 This study aimed to
examine the association between IPIs and child develop-
mental vulnerability in the first year of full- time school in
Australia.
METHODS
Data sources
This study used anonymised individual- level data from
the Midwives Notification System (MNS), which is a
statutory record of all births (stillborn and live- born) in
Western Australia (WA) with either a birth weight >400
g and/or a final gestational length of ≥20 weeks. MNS
variables were cross validated with corresponding records
from WA Birth Registrations. Australian Early Develop-
ment Census (AEDC) records were obtained for all avail-
able years (2009, 2012 and 2015) for all children with WA
birth and perinatal records. WA Register for Develop-
mental Anomalies (WARDA) records were used to iden-
tify children with a diagnosed developmental disability.
Statistical linkage of all records, by matching identifiers
(eg, name, address, date of birth) common to sets of
records,24 was provided by the WA Data Linkage Branch
from the Department of Health WA.
Study population
The study population included all children born in
WA with an AEDC record in either 2009, 2012 or 2015
(n=73 903; figure 1). Children were sequentially excluded
from the study if they, (1) were from a multiple birth
(n=2194 (3.0%)); (2) had a parity equal to zero, that
is, were firstborns (n=29 664 (41.4%)); (3) identified by
their teacher as having ‘special- needs’ based on a diag-
nosed physical and/or intellectual disability (n=1460
(3.5%)); (4) were reported as having any congenital
anomaly in WARDA (n=1828 (4.5%)); (5) had invalid/
Figure 1 Eligible cohort and numbers included for
analyses. AEDC, Australian Early Development Census;
IPIs, Interpregnancy Intervals; WARDA, Western Australian
Register of Developmental Anomalies.
on March 23, 2021 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2020-045319 on 23 March 2021. Downloaded from

3
DhamraitGK, etal. BMJ Open 2021;11:e045319. doi:10.1136/bmjopen-2020-045319
Open access
incomplete AEDC scores (n=534 (1.4%)); (6) had
missing IPIs (n=3648 (9.5%)); or (7) had missing small
for gestational age data (n=1 (>0.01%)). The final study
sample consisted of 34 574 children.
Patient and public involvement
No patients were involved in the development of the
research question or the outcome measures, or in the
development of the plans for the design or implementa-
tion of the study.
Outcome measure
The AEDC is a national census of early childhood devel-
opment spanning across five developmental domains;
(1) Physical Health and Wellbeing, (2) Social Compe-
tence, (3) Emotional Maturity, (4) Language and Cogni-
tive Skills (school- based) and (5) Communication Skills
and General Knowledge. Based on the Canadian Early
Development Instrument,25 the AEDC is a teacher-
completed instrument collected for all children in the
first year of compulsory schooling, which in WA is pre-
primary (the year level prior to grade 1). The AEDC
is conducted every 3 years, with the first national data
collection conducted in 2009.26 AEDC cut- off scores are
based on the first national AEDC data collection in 2009
and apply to all AEDC data collections.27 Children who
score less than the 10th percentile in a given domain are
classified as ‘developmentally vulnerable.’ A child is clas-
sified as ‘special needs’ if they require special assistance
because of chronic medical, physical or intellectually
disabling condition. Domain scores are not calculated for
those students classified as ‘special needs,’ as these chil-
dren have already been identified as having substantial
developmental needs. Across the 2009, 2012 and 2015
AEDC data collections child participation for the state
of WA ranged between 98.7% and 99.6%.28 We used two
summarised outcome measures; developmentally vulner-
able on one or more AEDC domains (DV1), and devel-
opmentally vulnerable on two or more AEDC domains
(DV2), and assessed developmental vulnerability on each
AEDC domain.
Exposure variables
IPI was derived as the time between the birth of the
older sibling and the estimated start of the pregnancy
(birth date minus gestational age of child, measured in
completed weeks of gestation) of the cohort child. In
line with previous studies,1–4 7 short IPIs were classified
as; <6 months, 6–11 months and 12–17 months, IPIs of
18–23 months formed the reference category and long
IPIs were classified as; 24–59 months, 60–119 months and
≥120 months.
Adjustment variables
Adjustments were made for pregnancy and birth, child
and sociodemographic characteristics, selected on the
basis of availability and findings of previous studies
(table 1).29–32 We conducted univariate analysis for the
association between the background characteristics and
Table 1 Characteristics of the study cohort
Characteristics
Total
population
n (%)
n=34 574
Children classied as developmentally vulnerable on
one or more AEDC domains (DV1)
7899 (22.8)
Children classied as developmentally vulnerable on
two or more AEDC domains (DV2)
3966 (11.5)
Pregnancy and birth
Interpregnancy interval
(months)
<6 1703 (4.9)
6–11 5226 (15.1)
12–17 6451 (18.7)
18–23* 5311 (15.4)
24–59 11 531 (33.4)
60–119 3517 (10.2)
≥120 835 (2.4)
Maternal smoking status
during pregnancy
No* 28 331 (81.9)
Yes 6243 (18.1)
Mode of delivery Vaginal birth* 21 654 (62.6)
Caesarean birth 10 949 (31.7)
All other 1971 (5.7)
Preterm birth Term* 32 488 (94.0)
Preterm 2086 (6.0)
Small for gestational age No* 32 404 (93.7)
Yes 2170 (6.3)
Parity 1* 20 065 (58.0)
2 9086 (26.3)
≥3 5423 (15.7)
Maternal age at time of
child’s birth (years)
<20 508 (1.5)
20–24 4436 (12.8)
25–29* 8969 (25.9)
30–34 12 210 (35.3)
35–39 7101 (20.5)
≥40 1350 (3.9)
Child
Sex Female* 17 229 (49.8)
Male 17 345 (50.2)
Ethnicity All other* 31 736 (91.8)
Indigenous Australian 2838 (8.2)
Child speaks language
other than English at
home
No 31 296 (90.5)
Yes 3278 (9.5)
Age category at time of
AEDC collection
≥3 years and 10 months
to <5 years and 1 month
5982 (17.3)
≥5 years and 1 month to
<5 years and 10 months
25 626 (74.1)
≥5 years and 10 months 2966 (8.6)
Sociodemographic
Continued
on March 23, 2021 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2020-045319 on 23 March 2021. Downloaded from

4DhamraitGK, etal. BMJ Open 2021;11:e045319. doi:10.1136/bmjopen-2020-045319
Open access
the likelihood of children being classified as DV1 (online
supplemental table 1).
Pregnancy and birth variables
Maternal smoking status during pregnancy, mode of
delivery, preterm birth, small for gestational age, parity
and maternal age at time of child’s birth were all obtained
from the MNS and Birth Registrations.
Child variables
Sex and ethnicity33 of child were obtained from the MNS
and Birth Registrations. The age of the child at the time
of the AEDC collection (reported as a categorical vari-
able) and language other than English spoken at home
by the child were obtained from the AEDC. The mean
age category for the study population was ≥5 years and
1 month and <5 years and 4 months. To balance frequen-
cies, the age of children at the time of AEDC completion
was categorised into three groups; (1) ≥3 years and 10
months to <5 years and 1 month, (2) ≥5 years and 1 month
to <5 years and 10 months (reference category) and (3)
≥5 years and 10 months.
Sociodemographic variables
We derived the total number of siblings by calculating
the number of live births each mother had prior to the
year that the cohort child had the AEDC conducted for
them. Siblings of the cohort who died within the neonatal
period were excluded from the calculations. Maternal
marital status at time of child’s birth was obtained from
the MNS and Birth Registrations.
Parental occupational at birth was obtained from Birth
Registrations data and converted to a four- digit standard
code using the Australian and New Zealand Standard
Classification of Occupations. These codes were assigned
a value ranging from 0 to 100 in line with the Australian
Socioeconomic Index 2006 (AUSEI06).34 Low AUSEI06
values represent low- status occupations and high values
represent high- status occupations. Records were assigned
an AUSEI06 value of zero if occupation was reported as
‘unemployed’, ‘stay at home’ parent or ‘pensioner.’ Cases
were classified as missing where parental occupation was
not stated. The AUSEI06 values were categorised into five
groups: [0, 20], (20, 40], (40, 60], (60, 80] and (80, 100].
Remoteness and socioeconomic indices were defined
with the Accessibility and Remoteness Index of Australia
(ARIA)35 and the Index of Relative Socioeconomic Disad-
vantage (IRSD),36 respectively, and were calculated using
the residential address at the time of birth. The ARIA
classifies geographical areas based on access to goods,
services and community resources into five categories
ranging from; 1 (major cities) to 5 (very remote). The
IRSD reflects area- level disadvantage through variables
such as low levels of household income, low educational
attainment and high levels of unemployment. Geograph-
ical areas are given a score from 1 (most disadvantaged)
to 5 (most advantaged).
Characteristics
Total
population
n (%)
n=34 574
Number of siblings 0† 62 (0.2)
1* 13 874 (40.1)
212 374 (35.8)
≥3 8264 (23.9)
Maternal marital status at
time of child’s birth
Married (including de
facto)*
31 776 (91.9)
All other 2561 (7.4)
Missing 237 (0.7)
Maternal occupation
status‡
0–≤20 7828 (22.6)
>20–≤40 5195 (15.0)
>40–≤60 6100 (17.6)
>60–≤80 6405 (18.5)
>80–100* 6635 (19.2)
Missing 2411 (7.0)
Paternal occupation
status‡
0–≤20 5878 (17.0)
>20–≤40 6922 (20.0)
>40–≤60 6422 (18.6)
>60–≤80 5746 (16.6)
>80–100* 7142 (20.7)
Missing 2464 (7.1)
Accessibility and
Remoteness Index of
Australia quintiles§
1* 23 363 (67.6)
2 4140 (12.0)
3 3714 (10.7)
4 1819 (5.3)
5 1008 (2.9)
Missing 530 (1.5)
Index of Relative
Socioeconomic
Disadvantage quintiles¶
1 6255 (18.1)
2 6312 (18.3)
3 6220 (18.0)
4 7381 (21.3)
5* 7766 (22.5)
Missing 640 (1.9)
*Reference group for logistic regression.
†Older sibling died within the neonatal period and therefore each
cohort child has a valid interpregnancy interval but has no additional
siblings born prior to the rst year of school.
‡Maternal and paternal occupation status are classied into ve
categories in line with Australian Socioeconomic Index 2006
(AUSEI06); low AUSEI06 values represent low- status occupations.
§Categorised as nationally dened into ve classes of remoteness;
1=major cities of Australia (least remote) to 5=very remote Australia
(most remote).
¶Categorised as nationally dened quintiles (1=most disadvantaged to
5=least disadvantaged); as quintiles are dened nationally (rather than
within study population), numbers within each category vary from 20%
of total.
AEDC, Australian Early Development Census.
Table 1 Continued
on March 23, 2021 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2020-045319 on 23 March 2021. Downloaded from

5
DhamraitGK, etal. BMJ Open 2021;11:e045319. doi:10.1136/bmjopen-2020-045319
Open access
Multiple imputation
Overall complete covariate information was available
for 86.5% (n=29 911) of the study population. A total
of five covariates had missing data; (1) maternal marital
status at birth, (2) maternal occupation status scale,
(3) paternal occupation status scale, (4) ARIA and (5)
IRSD. All variables used in the analysis had <1.9% missing
data, apart from maternal occupation status (7.0%) and
paternal occupation status (7.1%). Multiple imputation
with chained equations, using 20 imputed data sets, was
applied to minimise bias attributable to missing data,37
and the adjusted analyses presented here were performed
by pooling estimates from these imputed data sets.
Sensitivity analysis
To assess the effect of multiple imputation, we compared
the main results of (1) the study based on the imputed
data (n=34 574) to (2) the results based on the analysis of
the complete cases only (n=29 911; online supplemental
table 2). Although children classified as ‘special needs’
on the AEDC or with a WARDA record lacked outcome
data, it is however, possible that some of the conditions
classified as ‘special needs’ may be related to IPI dura-
tion. Therefore, we also conducted a sensitivity analysis
that assumed a worst- case scenario, whereby all children
with an otherwise complete/valid AEDC record, clas-
sified as either ‘special needs’ on the AEDC or with a
WARDA record were classed as developmentally vulner-
able (online supplemental table 3; n=37 789). Finally, to
assess whether our results were sensitive to IPI categorisa-
tion thresholds, we repeated the analysis by categorising
short IPIs as; <6 months, 6–11 months and 12–17 months,
IPIs of 18–23 months forming the reference category
and long IPIs as; 24–41 months, 42–59 months, 60–119
months and ≥120 months (online supplemental table 4).
Statistical modelling
Logistic regression models were used to estimate the odds
of a child being classified as DV1, DV2 or developmen-
tally vulnerable on an individual AEDC domain. Adjust-
ment variables were added simultaneously to the models.
ORs and the associated 95% CIs were estimated for IPIs
and adjustment variables. All statistical analyses were
conducted in SAS V.9.4.38
RESULTS
Associations between IPIs and developmental vulnerability
22.8% of children were classified as DV1 and 11.5% were
classified as DV2 (table 1). Both unadjusted and adjusted
IPIs exhibited J- shaped associations with developmental
vulnerability (figure 2). In the adjusted models, IPIs of <6
months were associated with an increased risk of children
being classified as DV1 (adjusted OR (aOR) 1.17, 95% CI
1.08 to 1.34) and DV2 (aOR 1.31, 95% CI 1.10 to 1.54),
relative the reference category. All IPIs longer than the
reference category were associated with an increased risk
of children being classified as DV1; 24–59 months (aOR
1.15, 95% CI 1.05 to 1.25), 60–119 months (aOR 1.43,
95% CI 1.28 to 1.60) and ≥120 months (aOR 1.84, 95% CI
1.54 to 2.19), and DV2; 24–59 months (aOR 1.19, 95% CI
1.06 to 1.34), 60–119 months (aOR 1.55, 95% CI 1.35 to
1.79) and ≥120 months (aOR 1.78, 95% CI 1.42 to 2.24).
Figure 2 Unadjusted and adjusted ORs for the association between interpregnancy intervals (IPIs) and developmental
vulnerability on Australian Early Development Census (AEDC) domains. ORs relative to the IPI interval reference category of
18–23 months between IPI and (A) developmental vulnerability on one or more AEDC domains (DV1) and (B) developmental
vulnerability on two or more AEDC domains (DV2). Adjusted for maternal smoking status during pregnancy, mode of delivery,
preterm birth, small for gestational age, parity, mother’s age at time of child’s birth, sex of child, ethnicity, child speaks a
language other than English at home, age of child at time of AEDC completion, number of siblings, mother’s marital status at
time of child’s birth, father’s and mother’s occupational status scale at time of child’s birth, Accessibility and Remoteness Index
of Australia and Index of Relative Socioeconomic Disadvantage category. All data is presented as ORs and 95% CIs; logistic
regression (n=34 574).
on March 23, 2021 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2020-045319 on 23 March 2021. Downloaded from

6DhamraitGK, etal. BMJ Open 2021;11:e045319. doi:10.1136/bmjopen-2020-045319
Open access
Associations between IPIs and domain-specic
developmental vulnerability
A total of 3691 (10.7%) children were classified as devel-
opmentally vulnerable for the domains of; Physical Health
and Wellbeing, 2794 (8.1%) for Social Competence, 2817
(8.1%) for Emotional Maturity, 3381 (9.8%) for Language
and Cognitive Skills (school- based) and 2856 (8.3%) for
Communication Skills and General Knowledge (table 2).
In the adjusted models, IPIs of <6 months were associated
with an increased risk of developmental vulnerability for
the domains of Physical Health and Wellbeing (aOR 1.25,
95% CI 1.06 to 1.48; table 2) and Emotional Maturity (aOR
1.36, 95% CI 1.12 to 1.66) and IPIs of 6–11 months were asso-
ciated with an increased risk of developmental vulnerability
on the domain of Emotional Maturity (aOR 1.27, 95% CI
1.09 to 1.48), only. IPIs of 60–119 and ≥120 months were
associated with an increased risk of developmental vulnera-
bility on each of the five AEDC domains, with greater odds
for each domain for the longer IPI category. IPIs of 24–59
months were associated with an increased risk of develop-
mental vulnerability for all AEDC domains, except Phys-
ical Health and Wellbeing, and Communication Skills and
General Knowledge.
Sensitivity analysis
Sensitivity analysis revealed that the overall associations
between IPIs and developmental vulnerability at age 5
were not substantially different between complete cases
and the imputed cases (online supplemental table 2).
DISCUSSION
We found that short IPIs of <6 months and all longer
IPIs (≥24 months) were associated with increased odds
of developmental vulnerability, compared with an IPI of
18–23 months. These results were obtained from a large
population of >34 000 children and associations were
not fully explained by pregnancy, birth, child and socio-
demographic characteristics. Thus, the results indicate
the potential for the adverse effects of short and longer
IPIs to persist beyond the perinatal period and into early
childhood.
Few studies have examined the associations between
pregnancy spacing and school readiness or academic
performance.16 20 A US study of 5339 children using
data from the National Longitudinal Survey of Youth
1979 (NLSY79) assessed the associations between IPIs
and child cognitive ability and externalising behavioural
symptoms (as assessed by the Behaviour Problem Index)
in early and middle childhood.19 This study reported that
after controlling for the sex of the child and birth order,
short IPIs (≤12 months) were associated with scores lower
than the mean for performance in the Peabody Picture
Vocabulary Test and the maths, reading and reading
recognition subtests of the Peabody Individual Achieve-
ment Test- Revised (PIAT- R).19 Furthermore, this study
also reported that long IPIs (>36 months) were associated
with scores lower than the mean on the maths component
of the PIAT- R.19 However, after controlling for several
additional factors including, maternal age, ethnicity and
family income this study reported no statistically signif-
icant association between suboptimal IPI duration and
child development outcomes.19 Furthermore, this study
reported no statistically significant associations between
short or long IPIs and externalising behavioural problems
in children.19 Our results also indicate that very short IPIs
(<6 months) and long IPIs (≥24 months) are associated
with increased odds of developmental vulnerability in the
adjusted models. Likewise, a cohort study of 6915 chil-
dren from South Carolina (USA) concluded that chil-
dren born after short birth intervals (<24 months) were
more likely to fail a school readiness test when compared
with children born with longer birth intervals (24–120
months), after controlling for maternal risk factors
including education level, ethnicity and marital status.16
Our results also indicate that very short IPIs (<6 months)
are associated with increased odds of developmental
vulnerability. However, we also reported that IPIs ≥24
months were associated with developmental vulnerability.
Differences in findings between these studies and our
study may be attributed to differences in the definition of
the reference categories. The birth interval reference cate-
gory of 24–120 months used in the South Carolina study
would equate to an IPI of roughly 15–111 months (based
on a term pregnancy), while the reference category for the
NLSY79 study was 12–36 months. These reference catego-
ries are wide and overlaps with several of the IPI categories
used in our study, for which the direction of associations
were not consistent.16 Taken together the results of both
US studies and our study provide preliminary evidence to
suggest that short IPIs may be associated with an increased
likelihood of developmental vulnerability in children and
that the effects of the maternal depletion hypothesis may
extend beyond birth outcomes. It is estimated that approxi-
mately half of the pregnancies in Australia are unplanned.39
Decreasing the frequency of suboptimal IPIs may improve
birth outcomes and as a result, may further improve overall
school readiness in the population. However, further
research is needed to establish if this relationship is causal.
Alternatively, a longitudinal study of 1154 French chil-
dren, assessing the relationship between language skills
of children aged between 5 and 6 years of age and the age
gap between their immediately older sibling, reported
that more closely spaced siblings were more likely to
have higher language scores.17 The results of our study
however, reported an increased, although statistically
insignificant odds of developmental vulnerability the
AEDC domains of Language and Cognitive Skills (school-
based) and Communication Skills and General Knowl-
edge. It should be noted that the French study modelled
age gap as a continuous variable and thus this may account
for variations in the findings of the association between
birth spacing and child development outcomes between
this study and our study. Furthermore, the French study
concluded that a larger study would be required to deter-
mine if the negative age- gap effect was genuine.
on March 23, 2021 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2020-045319 on 23 March 2021. Downloaded from

7
DhamraitGK, etal. BMJ Open 2021;11:e045319. doi:10.1136/bmjopen-2020-045319
Open access
Table 2 Unadjusted and adjusted ORs1 for the association between developmental vulnerability for each AEDC domain and interpregnancy interval
AEDC domain and model
Interpregnancy interval (months)
OR (95% CI)†
<6 6–11 12–17 18–23 24–59 60–119 ≥120
Physical health and
Wellbeing (n=3691)‡
Unadjusted 2.01 (1.72 to 2.35)*** 1.12 (0.98 to 1.27) 1.02 (0.90 to 1.15) 1 (referent) 1.13 (1.01 to 1.26)* 1.56 (1.36 to 1.78)*** 1.58 (1.27 to 1.96)***
Adjusted§ 1.25 (1.06 to 1.48)** 0.97 (0.84 to 1.10) 0.98 (0.86 to 1.11) 1 (referent) 1.03 (0.92 to 1.16) 1.35 (1.17 to 1.55)*** 1.44 (1.14 to 1.82)**
Social Competence (n=2794) Unadjusted 1.78 (1.47 to 2.14)*** 1.23 (1.06 to 1.43)** 1.11 (0.96 to 1.28) 1 (referent) 1.29 (1.13 to 1.46)*** 1.68 (1.44 to 1.96)*** 2.10 (1.66 to 2.64)***
Adjusted 1.19 (0.98 to 1.45) 1.11 (0.95 to 1.30) 1.08 (0.93 to 1.25) 1 (referent) 1.20 (1.06 to 1.37)** 1.51 (1.28 to 1.77)*** 2.01 (1.57 to 2.58)***
Emotional Maturity (n=2817) Unadjusted 1.91 (1.58 to 2.30)*** 1.36 (1.17 to 1.58)*** 1.18 (1.02 to 1.37) 1 (referent) 1.31 (1.15 to 1.49)*** 1.71 (1.46 to 1.99)*** 2.04 (1.61 to 2.59)***
Adjusted 1.36 (1.12 to 1.66)** 1.27 (1.09 to 1.48)** 1.16 (1.00 to 1.35) 1 (referent) 1.23 (1.08 to 1.41)** 1.55 (1.32 to 1.83)*** 1.90 (1.47 to 2.45)***
Language and Cognitive
Skills (school- based)
(n=3381)
Unadjusted 2.05 (1.73 to 2.42)*** 1.22 (1.06 to 1.40)** 1.03 (0.90 to 1.18) 1 (referent) 1.35 (1.20 to 1.52)*** 1.92 (1.67 to 2.21)*** 1.82 (1.45 to 2.28)***
Adjusted 1.15 (0.96 to 1.38) 1.01 (0.87 to 1.17) 0.98 (0.85 to 1.13) 1 (referent) 1.25 (1.10 to 1.42)*** 1.71 (1.47 to 1.99)*** 1.87 (1.46 to 2.39)***
Communication Skills and
General Knowledge (n=2856)
Unadjusted 1.94 (1.63 to 2.32)*** 1.19 (1.03 to 1.38)* 1.08 (0.93 to 1.24) 1 (referent) 1.24 (1.10 to 1.41)** 1.55 (1.33 to 1.80)*** 1.51 (1.18 to 1.93)**
Adjusted 1.20 (1.00 to 1.45) 1.02 (0.88 to 1.19) 1.03 (0.89 to 1.19) 1 (referent) 1.14 (1.00 to 1.30) 1.35 (1.15 to 1.58)*** 1.51 (1.16 to 1.98)**
Interpregnancy interval was dened as the time between the birth of the older sibling and the estimated start of the pregnancy (birth date minus gestational age of child, measured in completed weeks
of gestation) of the cohort child.
***p<0.001, **p<0.01, *p<0.05.
†All data are presented as ORs (95% CIs) (total population: n=34 574).
‡Number of children classied as vulnerable for each AEDC domain for each cohort.
§Adjusted for maternal smoking status during pregnancy, mode of delivery, preterm birth, small for gestational age, parity, mother’s age at time of child’s birth, sex of child, ethnicity, child speaks a
language other than English at home, age of child at time of AEDC completion, number of siblings, mother’s marital status at time of child’s birth, father’s and mother’s occupational status scale at time
of child’s birth, Accessibility and Remoteness Index of Australia and Index of Relative Socioeconomic Disadvantage category.
AEDC, Australian Early Development Census.
on March 23, 2021 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2020-045319 on 23 March 2021. Downloaded from

8DhamraitGK, etal. BMJ Open 2021;11:e045319. doi:10.1136/bmjopen-2020-045319
Open access
The available studies assessing associations between
birth spacing and academic performance have been
primarily conducted in children older than those assessed
in our study. One of the earliest studies to investigate the
relationship between school performance conducted
in Singapore reported that children born after a birth
interval of ≥24 months performed better at school, at
age 9 than children born after a birth interval of <24
months.20 Alternatively, a cross- sectional study of Saudi
boys, aged between 9 and 10 years, reported that chil-
dren born after short birth intervals of <17 months were
more likely to be classified as below average on teacher-
assessed school performance in comparison to children
born after long birth intervals (>31 months).14 Again,
differences in findings between these studies and those
of our study may be attributed to the fact that both of
these studies used binary birth interval categories, which
overlap with several of the IPI categories used in our study
and with the current WHO recommendations of an IPI of
approximately 2–3 years.40 Given these mixed results and
the relatively small number of studies on the topic, the
association between IPIs and developmental vulnerability
beyond the perinatal period remains not well- established.
Furthermore, as prior research assessing the associations
between IPIs and child development outcomes has largely
been confined to the perinatal period, further research
is required to assess the relationship between IPIs and
developmental vulnerability in early childhood and to
determine causality.
Limitations
Our study had several limitations. First, important social
risk factors including parenting experience and/or tech-
nique, stability and quality of housing and availability of
learning resources within the household could not be
accounted for due to the nature of administrative data.
Second, we did not have information as to whether the
pregnancies were planned or unplanned and as adminis-
trative records do not include pregnancies ending before
20 weeks of gestations, we are unable to identify and
account for the effect of miscarriages. Third, as the birth-
dates of older siblings were obtained from Birth Registra-
tions and MNS records in the state of WA, we were not
able to calculate IPIs for children with an older sibling
born in another state. Finally, there are a wide range of
reproductive health behaviours and physiological factors
governing the length of IPIs including, fertility levels, the
use of contraception, time- until- conception and breast-
feeding durations,41 which we could not control for as
they are not recorded in administrative data.
Implications of ndings
The behaviours, emotions and physical and cognitive
capacities that develop in the first 5 years of life assist
in the facilitation of learning and are predictive of later
school achievement and behavioural outcomes.25–27 The
cumulative nature of school- based learning means that
children who begin school with poor school readiness
often fail to catch up with their peers and tend to fall
further behind as they progress through schooling.28 In
particular, research has indicated that children classified
as DV1 are more likely to score in the bottom 20% of all
students on the National Assessment Program Literacy
and Numeracy assessments in grades 3, 5 and 7.42 This
study supports the hypothesis that IPIs are a predictor of
developmental vulnerability and poor school readiness.
The concept of optimising birth spacing has been widely
discussed in the literature, and the findings of our study
imply that the adverse impacts of IPIs may extend beyond
birth outcomes. Decreasing the frequency of suboptimal
IPIs may improve birth outcomes and as a result, may
further improve overall child development outcomes in
the population.
CONCLUSIONS
IPIs exhibited independent J- shaped associations with
developmental vulnerability. For our cohort, very short
IPIs of less than 6 months and longer IPIs of 2 years or
longer were associated with increased risk of develop-
mental vulnerability on one or more and two or more
AEDC domains. IPIs of 5 years or longer were associ-
ated with developmental vulnerability on all five AEDC
domains. The results provide empirical support for the
association between long IPIs of 2 years or longer and
developmental vulnerability at age 5. Although further
research is required to establish causality, the results of
this study add to the current body of literature suggesting
the potential for optimising IPIs as a means for improving
child development outcomes.
Acknowledgements We gratefully acknowledge the WA Data Linkage Branch
and Data Custodians who provided data for this study and the people of Western
Australia for the use of their administrative data. This study does not necessarily
reect their views. This study uses data from the Australian Early Development
Census (AEDC). The AEDC is funded by the Australian Government Department of
Education and Training. The ndings and views reported are those of the authors
and should not be attributed to the Department or the Australian Government. We
thank Helen Bailey for assistance in the use of these data and Scott Sims and
Daniel Christensen for statistical advice and support.
Contributors GKD led study conceptualisation and design, conducted the literature
review, performed data manipulation, analysis and interpretation of ndings,
drafted the initial manuscript and reviewed and revised the manuscript critically
for important intellectual content. GP and CLT contributed to conceptualising and
designing the study, interpreting the results and writing the manuscript. All authors
approved the nal manuscript as submitted and agree to be accountable for all
aspects of the work.
Funding This work was supported by the National Health and Medical Research
Grants (grant numbers GNT1173991 and GNT1099655 to GP), the Australian
Research Council Centre of Excellence for Children and Families over the Life
Course (grant number CE140100027 to CLT). GKD was supported by the ARC
Centre of Excellence for Children and Families over the Life Course Scholarship,
the ARC Centre of Excellence for Children and Families over the Life Course Top- Up
Scholarship and the Stan and Jean Perron Top- Up Scholarship. GP was supported
with funding from the National Health and Medical Research Council Project and
Investigator (grant numbers 1099655 and 1173991) and the Research Council of
Norway through its Centres of Excellence funding scheme (grant number 262700).
Competing interests None declared.
Patient consent for publication Not required.
Ethics approval Ethics approval for this study was granted by the Western
Australian Department of Health Human Research Ethics Committee (2016/51)
on March 23, 2021 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2020-045319 on 23 March 2021. Downloaded from

9
DhamraitGK, etal. BMJ Open 2021;11:e045319. doi:10.1136/bmjopen-2020-045319
Open access
and the University of Western Australia Human Research Ethics Committee
(RA/4/20/4776).
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement All data relevant to the study are included in the
article or uploaded as supplementary information. We received data from WA
Department of Health through the Data Linkage Branch. The data are not publicly
available, and privacy and legal restrictions apply to the provision of the data to
third parties.
Supplemental material This content has been supplied by the author(s). It has
not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been
peer- reviewed. Any opinions or recommendations discussed are solely those
of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and
responsibility arising from any reliance placed on the content. Where the content
includes any translated material, BMJ does not warrant the accuracy and reliability
of the translations (including but not limited to local regulations, clinical guidelines,
terminology, drug names and drug dosages), and is not responsible for any error
and/or omissions arising from translation and adaptation or otherwise.
Open access This is an open access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY- NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non- commercially,
and license their derivative works on different terms, provided the original work is
properly cited, appropriate credit is given, any changes made indicated, and the use
is non- commercial. See:http:// creativecommons. org/ licenses/ by- nc/ 4. 0/.
ORCID iDs
Gursimran KaurDhamrait http:// orcid. org/ 0000- 0002- 5191- 211X
Catherine LouiseTaylor http:// orcid. org/ 0000- 0001- 9061- 9162
GavinPereira http:// orcid. org/ 0000- 0003- 3740- 8117
REFERENCES
1 Ball SJ, Pereira G, Jacoby P, etal. Re- evaluation of link
between interpregnancy interval and adverse birth outcomes:
retrospective cohort study matching two intervals per mother. BMJ
2014;349:g4333.
2 Shachar BZ, Lyell DJ. Interpregnancy interval and obstetrical
complications. Obstet Gynecol Surv 2012;67:584–96.
3 Grisaru- Granovsky S, Gordon E- S, Haklai Z, etal. Effect of
interpregnancy interval on adverse perinatal outcomes- a national
study. Contraception 2009;80:512–8.
4 Conde- Agudelo A, Belizán JM, Norton MH, etal. Effect of the
interpregnancy interval on perinatal outcomes in Latin America.
Obstet Gynecol 2005;106:359–66.
5 Hanley GE, Hutcheon JA, Kinniburgh BA, etal. Interpregnancy
interval and adverse pregnancy outcomes: an analysis of successive
pregnancies. Obstet Gynecol 2017;129:408–15.
6 Conde- Agudelo A, Rosas- Bermudez A, Norton MH. Birth spacing
and risk of autism and other neurodevelopmental disabilities: a
systematic review. Pediatrics 2016;137 doi:10.1542/peds.2015-
3482
7 Zhu B- P. Effect of interpregnancy interval on birth outcomes: ndings
from three recent US studies. Int J Gynaecol Obstet 2005;89:S25–33.
8 Miller JE. Birth intervals and perinatal health: an investigation of three
hypotheses. Fam Plann Perspect 1991;23:62–70.
9 Habimana- Kabano I, Broekhuis A, Hooimeijer P. The effects of
Interpregnancy intervals and previous pregnancy outcome on fetal
loss in Rwanda (1996-2010). Int J Reprod Med 2015;2015:10.
10 Carlsson T, Molander F, Taylor MJ, etal. Early environmental risk
factors for neurodevelopmental disorders - a systematic review of
twin and sibling studies. Dev Psychopathol 2020:1–48.
11 Elhakham D, Wainstock T, Sheiner E, etal. Inter- pregnancy interval
and long- term neurological morbidity of the offspring. Arch Gynecol
Obstet 2020 doi:10.1007/s00404-020-05788-9
12 Cheslack- Postava K, Sourander A, Suominen A. Increased risk of
ADHD at short and long Interpregnancy intervals in a national birth
cohort. Paediatr Perinat Epidemiol 2020
13 Class QA, Rickert ME, Oberg AS, etal. Within- family analysis of
Interpregnancy interval and adverse birth outcomes. Obstet Gynecol
2017;130:1304–11.
14 Bella H, Al- Almaie SM. Do children born before and after adequate
birth intervals do better at school? J Trop Pediatr 2005;51:265–70.
15 Bella H, Khalil MS, Al- Almaie SM, etal. The effects of birth interval
on intellectual development of Saudi school children in eastern Saudi
Arabia. Saudi Med J 2005;26:741–5.
16 Hayes H, Luchok K, Martin AB, etal. Short birth intervals and the
risk of school unreadiness among a Medicaid population in South
Carolina. Child Care Health Dev 2006;32:423–30.
17 Havron N, Ramus F, Heude B, etal. The effect of older siblings on
language development as a function of age difference and sex.
Psychol Sci 2019;30:1333–43.
18 Nathens AB, Neff MJ, Goss CH, etal. Effect of an older sibling and
birth interval on the risk of childhood injury. Inj Prev 2000;6:219.
19 Sujan AC, Class QA, Rickert ME, etal. Risk factors and child
outcomes associated with short and long interpregnancy intervals.
Early Child Dev Care 2019;34:1–12.
20 Martin C. A study of inuences on birth interval in Singapore. J Trop
Pediatr Env Child Health 1979;25:49–76.
21 Cofer FG, Fridman M, Lawton E, etal. Interpregnancy interval and
childbirth outcomes in California, 2007-2009. Matern Child Health J
2016;20:43–51.
22 Guhn M, Gadermann AM, Almas A, etal. Associations of teacher-
rated social, emotional, and cognitive development in kindergarten to
self- reported wellbeing, peer relations, and academic test scores in
middle childhood. Early Child Res Q 2016;35:76–84.
23 Domitrovich C, Moore J, Thompson R. Interventions that promote
social- emotional learning in young children. In: Pianta RC, Barnett
SW, Justice LM, eds. Handbook of early childhood education. New
York, NY: Guilford Press, 2012: 393–415.
24 Holman C D'Arcy J, Bass AJ, Rosman DL, etal. A decade of
data linkage in Western Australia: strategic design, applications
and benets of the WA data linkage system. Aust Health Rev
2008;32:766–77.
25 Guthridge S, Li L, Silburn S, etal. Early inuences on developmental
outcomes among children, at age 5, in Australia’s Northern Territory.
Early Child Res Q 2016;35:124–34.
26 Australian Government Department of Education & Training.
Australian Early Development Census (AEDC) data guidelines
department of education & training 2017.
27 Department of Education Employment and Workplace Relations for
the Council of Australian Governments (DEEWR). Belonging, being
and becoming: the early years learning framework for Australia.
Canberra: DEEWR, 2009.
28 Department of Education and Training. Australian early development
census national report 2015. Canberra: Department of Education and
Training, 2016.
29 Chen X- K, Wen SW, Fleming N, etal. Teenage pregnancy and
adverse birth outcomes: a large population based retrospective
cohort study. Int J Epidemiol 2007;36:368–73.
30 Crosnoe R, Wildsmith E. Nonmarital fertility, family structure, and
the early school achievement of young children from different Race/
Ethnic and immigration groups. Appl Dev Sci 2011;15:156–70.
31 Boyle MH, Georgiades K, Racine Y, etal. Neighborhood and family
inuences on educational attainment: results from the Ontario child
health study follow- up 2001. Child Dev 2007;78:168–89.
32 Dhamrait GK, Christensen D, Pereira G, etal. Associations
between biological and sociodemographic risks for developmental
vulnerability in twins at age 5: a population data linkage study in
Western Australia. BMJ Open 2020;10:e038846.
33 Christensen D, Davis G, Draper G, etal. Evidence for the use of
an algorithm in resolving inconsistent and missing Indigenous
status in administrative data collections. Aust J Soc Issues
2014;49:423–43.
34 McMillan J, Beavis A, Jones F. The AUSEI06:A new socioeconomic
index for Australia. J Sociol 2009;45:123–49.
35 Australian Bureau of Statistics. The Australian statistical geography
standard (ASGS) remoteness structure, 2018. Available: https://www.
abs. gov. au/ websitedbs/ D3310114. nsf/ home/ remoteness+ structure
[Accessed 10 Sep 2019].
36 Australian Bureau of Statistics. Index of relative socio- economic
disadvantage, 2018. Available: https://www. abs. gov. au/ ausstats/
abs@. nsf/ Lookup/ by% 20Subject/ 2033. 0. 55. 001~ 2016~ Main%
20Features~ IRSD~ 19 [Accessed 10 Sep 2019].
37 Royston P, White I. Multiple Imputation by Chained Equations (MICE):
Implementation in Stata. J Stat Softw 2011;45:1–20.
38 SAS Institute. SAS (program). Cary, NC: SAS Institute, 2013.
39 Marie Stopes International Australia. Real choices: women,
contraception and unplanned pregnancy. Melbourne: Marie Stopes
International Australia, 2008.
40 World Health Organization. Report of a technical consultation on birth
spacing. Geneva: WHO, 2005.
41 Wendt A, Gibbs CM, Peters S, etal. Impact of increasing inter-
pregnancy interval on maternal and infant health. Paediatr Perinat
Epidemiol 2012;26 Suppl 1:239–58.
42 Brinkman S, Gregory T, Harris J, etal. Associations between the early
development instrument at age 5, and reading and Numeracy skills
on March 23, 2021 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2020-045319 on 23 March 2021. Downloaded from

10 DhamraitGK, etal. BMJ Open 2021;11:e045319. doi:10.1136/bmjopen-2020-045319
Open access
at ages 8, 10 and 12: a prospective linked data study. Child Indic Res 2013;6:695–708.
on March 23, 2021 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2020-045319 on 23 March 2021. Downloaded from