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Effect of adolescent pregnancy on child mortality in 46 countries

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Effect of adolescent pregnancy on child mortality in 46 countries

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Introduction: Adolescent pregnancy is a known health risk to mother and child. Statements and reports of health outcomes typically group mothers under 20 years old together. Few studies examined this risk at a finer age resolution, none of them comprehensively, and with differing results. Methods: We analysed Demographic and Health Surveys data from 2004 to 2018 in sub-Saharan Africa (SSA) and South Asia, on firstborn children of mothers 25 years old or younger. We examined the association between maternal age and stillbirths, and neonatal mortality rate (NNMR), infant mortality rate (IMR) and under-5 mortality rate (U5MR), using mixed-effects logistic regression adjusting for major demographic variables and exploring the impact of maternal health-seeking. Results: In both regions and across all endpoints, mortality rates of children born to mothers aged <16 years, 16-17 years and 18-19 years at first birth were about 2-4 times, 1.5-2 times and 1.2-1.5 times higher, respectively, than among firstborn children of mothers aged 23-25. Absolute mortality rates declined over time, but the age gradient remained similar across time periods and regions. Adjusting for rural/urban residence and maternal education, in SSA in 2014-2018 having a <16-year-old mother was associated with ORs of 3.71 (95% CI: 2.50 to 5.51) for stillbirth, 1.92 (1.60-2.30) for NNMR, 2.13 (1.85-2.46) for IMR and 2.39 (2.13-2.68) for U5MR, compared with having a mother aged 23-25. In South Asia, in 2014-2018 ORs were 5.12 (2.85-9.20) for stillbirth, 2.46 (2.03-2.97) for NNMR, 2.62 (2.22-3.08) for IMR and 2.59 (2.22-3.03) for U5MR. Part of the effect on NNMR and IMR may be mediated by a lower maternal health-seeking rate. Conclusions: Adolescent pregnancy is associated with dramatically worse child survival and mitigated by health-seeking behaviour, likely reflecting a combination of biological and social factors. Refining maternal age reporting will avoid masking the increased risk to children born to very young adolescent mothers. Collection of additional biological and social data may better reveal mediators of this relationship. Targeted intervention strategies to reduce unintended pregnancy at earlier ages may also improve child survival.
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NooriN, etal. BMJ Global Health 2022;7:e007681. doi:10.1136/bmjgh-2021-007681
Effect of adolescent pregnancy on child
mortality in 46 countries
Navideh Noori,1 Joshua L Proctor,1 Yvette Efevbera,2 Assaf P Oron3
Original research
To cite: NooriN, ProctorJL,
EfevberaY, etal. Effect
of adolescent pregnancy
on child mortality in 46
countries. BMJ Global Health
2022;7:e007681. doi:10.1136/
bmjgh-2021-007681
Handling editor Seye Abimbola
Additional supplemental
material is published online only.
To view, please visit the journal
online (http:// dx. doi. org/ 10.
1136/ bmjgh- 2021- 007681).
Received 11 October 2021
Accepted 27 March 2022
1Institute for Disease Modeling,
Global Health Division, Bill &
Melinda Gates Foundation,
Seattle, Washington, USA
2Gender Equality Division, Bill
& Melinda Gates Foundation,
Seattle, Washington, USA
3University of Washington,
Institute for Health Metrics and
Evaluation, Seattle, Washington,
USA
Correspondence to
Dr Navideh Noori;
navideh. noori@ gatesfoundation.
org
© Author(s) (or their
employer(s)) 2022. Re- use
permitted under CC BY.
Published by BMJ.
ABSTRACT
Introduction Adolescent pregnancy is a known health
risk to mother and child. Statements and reports of
health outcomes typically group mothers under 20 years
old together. Few studies examined this risk at a ner
age resolution, none of them comprehensively, and with
differing results.
Methods We analysed Demographic and Health Surveys
data from 2004 to 2018 in sub- Saharan Africa (SSA) and
South Asia, on rstborn children of mothers 25 years old or
younger. We examined the association between maternal
age and stillbirths, and neonatal mortality rate (NNMR),
infant mortality rate (IMR) and under- 5 mortality rate
(U5MR), using mixed- effects logistic regression adjusting
for major demographic variables and exploring the impact
of maternal health- seeking.
Results In both regions and across all endpoints, mortality
rates of children born to mothers aged <16 years, 16–17
years and 18–19 years at rst birth were about 2–4
times, 1.5–2 times and 1.2–1.5 times higher, respectively,
than among rstborn children of mothers aged 23–25.
Absolute mortality rates declined over time, but the
age gradient remained similar across time periods and
regions. Adjusting for rural/urban residence and maternal
education, in SSA in 2014–2018 having a <16- year- old
mother was associated with ORs of 3.71 (95% CI: 2.50 to
5.51) for stillbirth, 1.92 (1.60–2.30) for NNMR, 2.13 (1.85–
2.46) for IMR and 2.39 (2.13–2.68) for U5MR, compared
with having a mother aged 23–25. In South Asia, in
2014–2018 ORs were 5.12 (2.85–9.20) for stillbirth, 2.46
(2.03–2.97) for NNMR, 2.62 (2.22–3.08) for IMR and 2.59
(2.22–3.03) for U5MR. Part of the effect on NNMR and IMR
may be mediated by a lower maternal health- seeking rate.
Conclusions Adolescent pregnancy is associated with
dramatically worse child survival and mitigated by
health- seeking behaviour, likely reecting a combination
of biological and social factors. Rening maternal age
reporting will avoid masking the increased risk to children
born to very young adolescent mothers. Collection of
additional biological and social data may better reveal
mediators of this relationship. Targeted intervention
strategies to reduce unintended pregnancy at earlier ages
may also improve child survival.
INTRODUCTION
Every year, nearly 12 million adolescent girls
and young women aged 15–19 years and
nearly a million under 15 years give birth.1
The majority of these births are in low- income
and middle- income countries (LMICs).2 The
adolescent fertility rate (birth rate per 1000
girls and young women aged 15–19 years)
over the period 2015–2020 was the highest in
the sub- Saharan Africa (SSA) region at 102.8
births per 1000 person- years, far higher than
the global average (44 per 1000), followed by
South Asia with 26 births per 1000 girls aged
15–19.3
Adolescence is a unique stage of human
development and an important time for
building the foundation of good health;
consequently, pregnancy during this lifestage
can have impacts on both a young woman
and her children. Early pregnancy can lead
to devastating health consequences for
the mother, since adolescent girls may not
yet be physically and biologically ready for
WHAT IS ALREADY KNOWN ON THIS TOPIC
The under- 20 mothers in most previous studies
are treated as a single group when looking at risk
of child health outcomes, and few studies have
assessed the risk gradient versus age within this
group, focusing only on neonatal and infant mortality
rather than broader child survival outcomes.
WHAT THIS STUDY ADDS
Our work, the most comprehensive, multiregional
study to- date, investigated the potential impacts of
adolescent pregnancy, examining multiple child sur-
vival endpoints from stillbirths to under- 5 mortality.
Children of mothers younger than 16 faced 2–4
times higher risk of death at all child mortality stag-
es in both sub- Saharan Africa and South Asia re-
gions, even after controlling for maternal education
and health- seeking risk factors.
HOW THIS STUDY MIGHT AFFECT RESEARCH,
PRACTICE AND/OR POLICY
Our ndings provide evidence on the necessity for
age- disaggregated reporting and additional survey
data on adolescent pregnancy, given the specic bi-
ological and social risks to adolescents.
Improving health- seeking behaviour and quality of
maternal care, as well as targeted interventions to
reduce unintended adolescent pregnancy and miti-
gate its harmful consequences are needed.
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pregnancy or childbirth.3 Many adolescents experience
complications during pregnancy and childbirth, which
has become the leading global cause of death among
15–19- year- old females.4 Pregnant adolescents are at a
higher risk of receiving inadequate antenatal care (ANC)
in some settings.5 A significant proportion of adolescents
in SSA do not access nor utilise maternal services during
pregnancy, which is a consequence of several individual,
interpersonal, institutional and systemic factors.6 Early
pregnancy and motherhood for an adolescent girl in
some contexts can also have adverse social consequences
such as stigma and dropping out of school.1 7 They may
not have the opportunity to return to school which jeop-
ardises their economic and employment opportunities
due to their double burden of household maintenance
and child- rearing,7 8 resulting in sustained poverty and
increased vulnerability.
Reduction of adolescent pregnancy has long been the
focus of several organisations and is of current policy
interest. In fact, with only 8 years left to achieve the 2030
Agenda for Sustainable Development, agreed to by more
than 190 countries, there remains a timely commitment
and need to ensure access to sexual and reproductive
healthcare services, particularly for adolescent girls and
young women (Target 3.7), and eliminate child, early
and forced marriage (Target 5.3), given their strong asso-
ciations with adolescent pregnancy and its outcomes.9
Despite these efforts and the recent decline in overall
adolescent mortality10 and global adolescent fertility rate,
prevalence of adolescent pregnancies remains high and a
major public health concern, especially in LMICs.
In standard surveys, reports and WHO statements,
mothers under 20 are usually treated as a single group.11
However, adolescence represents a time of developmental
transition, including physically, cognitively and psycho-
logically, and there are substantial differences across the
10–19 years age range.12 Few studies have looked at the
risk gradient versus age among young mothers. Several
studies have associated early maternal age with neonatal
and infant mortality,2 9 13 14 infant stunting and preterm
birth even after adjustment for sociodemographic
factors.15 In contrast, two recent multicountry studies
did not find a consistent significant association between
adolescent motherhood and stillbirth.16 17 Current find-
ings and studies leave unanswered questions about the
true nature of these relationships.
A meta- analysis of Demographic and Health Surveys
(DHS) showed higher risk of mortality to neonates born
to mothers aged <16 and 16–17 years old than neonates
born to mothers aged 20–29 years in SSA and South and
Southeast Asia,11 even after adjusting for socioeconomic,
demographic and health service utilisation variables.
In LMICs, the infant mortality rate was higher among
mothers with ages of 12–14 and 15–17 years than among
older mothers.13 Finlay et al14 showed in a separate anal-
ysis that the risk of infant mortality in SSA is highest for
high parity young mothers, and short birth intervals
negatively affect infant mortality and stunting outcomes.
A WHO multicountry study divided mother ages into
<16, 16–17, 18–19 and 20–24 years old. They found
stillbirth rates among adolescent mothers to be mildly
higher than 20–24 years old mothers (ORs 1.0–1.3), with
the difference significant only for the 16–17 years old
group.17 A more recent study examined the association
between maternal age, both young and advanced and
risk of neonatal mortality in LMICs using DHS data, and
found the risk of mortality of neonates born to mothers
aged 12–15 and 45+ years was higher than neonates born
to mothers aged 25–29 years.18 A systematic review and
meta- analysis in SSA found that most evidence about the
effects of early childbearing was for mothers 15–19 years
old as a single group, with very few studies providing data
on adolescents aged <18, and concluded that there is a
lack of high- quality observational studies that adjust for
sociodemographic factors.19 Overall, there are limited
number of studies focusing on risk gradient versus
maternal age among young mothers, and majority of
these studies focused on neonatal and infant mortality
rather than broader child survival outcomes.
In our study, the most comprehensive of its kind to date,
we have investigated the potential impacts of adolescent
pregnancy on a substantially broader scope than previous
studies, examining child mortality endpoints from still-
births to under- 5 mortality, and quantifying the risk
gradient as a function of age from adolescence through
young adulthood. In contrast to prior studies which
Figure 1 Neonatal (left), infant (centre) and under- 5 (right) mortality rates and their sampling errors within each age group and
urban (yellow) and rural (green) locations in SSA (top) and South Asia (bottom) by 5- year time period: 2004–2008, 2009–2013
and 2014–2018. The circle size represents number of births within each group.
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Table 1 Mortality rates of different child health outcomes and their sampling errors in sub- Saharan Africa and South Asia
2004–2008 2009–2013 2014–2018
NNMR IMR U5MR NNMR IMR U5MR NNMR IMR U5MR
Sub- Saharan Africa
Maternal age at rst birth (years)
<16 85.1±5 152.8±6.5 238.5±7.5 63.8±4.0 117.7±5.3 186.2±6.2 54.5±4.5 95.9±5.5 156.5±6.6
16–17 58.3±2.3 115.8±3.1 188.3±3.9 49.8±1.9 91.3±2.6 145.2±3.3 44.4±2.0 72.5±2.6 112.8±3.2
18–19 46.9±1.8 89±2.3 146±2.9 39.1±1.4 73.8±1.9 115.5±2.3 34.4±1.4 58±1.8 88.2±2.2
20–22 39.9±1.4 79.3±2.1 127.3±2.6 35.8±1.2 62.4±1.6 98.6±2.0 29.8±1.2 50.3±1.5 74.6±1.8
23–25 40.2±2.1 74.5±2.8 123.5±3.7 33.7±1.8 55.7±2.2 88.8±2.7 28±1.6 44.5±1.9 64.8±2.4
Urban/rural residency
<16 53.2±2.9 115.6±6.1 182.6±10 58.3±16.2 108.1±16.2 159.1±15.5 43.2±3.1 74.1±9 115.9±10.6
92.6±13.7 161.6±13.1 251.9±10.5 65.8±1.0 121.3±1.9 196.1±3.9 58.6±18.7 103.6±18.3 170.9±16.8
16–17 50.2±2.4 97.6±2.3 160.4±2.3 30.2±3.2 63.7±3.2 100.1±3.4 39±13.2 62.4±13.9 90.6±13.6
60.7±2.1 121.2±3.1 196.8±6.9 57.8±2.0 102.5±2.3 163.6±2.8 46.3±1 76.2±1.7 121.2±2.3
18–19 39.2±2.4 75.2±2.5 119.2±2.9 33.5±1.8 59.9±2.0 87.8±2.7 25.9±3.2 45.2±4 67.5±4.1
49.8±2.7 94.1±2.9 156±3.4 41.6±2.0 80.3±2.8 128.4±3.6 37.9±3.1 63.5±3 97.1±3
20–22 30±1.8 60.6±1.8 95.2±1.9 30.7±0.3 49.5±0.7 72.9±1.3 25.5±0.9 41.3±1.5 57.9±4.5
44.2±1.2 87.4±1.3 141.4±1.8 38.7±1.4 69.6±3.3 113.0±4.5 32.2±1.4 55.4±1.5 84±1.5
23–25 30.2±0.5 56.9±2 83.3±2.1 32.4±0.3 47.9±0.9 72.4±1.2 27.6±0.4 40.5±0.5 55.4±0.8
46.4±1.3 85.4±2.3 147.3±4.4 34.7±4.3 62.0±4.1 101.7±4.8 28.4±0.2 48±0.4 72.8±0.6
Maternal education status: any education/no education
<16 69.5±0.9 124.7±1.6 185.3±1.8 59.5±12.8 112.7±12.2 169.8±11.7 55.4±10.8 93±11.9 140.4±12.1
95.2±15.7 170.8±15.7 271.5±13.8 67.6±1.3 122.1±3.4 199.1±5.3 53.4±16.8 99.2±17.3 174.5±15.8
16–17 51.6±0.4 98.6±0.8 150.2±1.2 42.1±0.4 77.4±0.8 121.6±1.3 40.8±8.7 67.3±9.1 97.7±8.8
64.9±2.8 132.6±5.1 224.7±9.4 60.3±3 110±3.3 174.7±3.6 50.6±0.7 81.4±1.1 136.3±1.7
18–19 37.7±2.2 74.3±2.1 117.8±2.2 35±0.3 65.9±2.3 98.5±3.6 31.5±2 52.3±2 76.4±2.3
61.6±4.5 112.1±4 189±4 46.4±3.8 87.8±5 143.3±5.4 41.1±6.7 71.2±6.4 113.4±6.4
20–22 31.8±0.2 64.3±0.4 100.8±0.6 32.5±0.3 56.2±0.5 84.3±1.9 27.8±0.7 45.1±1.1 65.1±1.6
56.4±2.4 109.2±2.4 178.7±3.6 42.2±2.6 74.4±5.8 124.2±7.6 35.1±3 64.2±2.8 97.9±2.6
23–25 35±0.4 65.1±0.7 102.1±0.9 32.1±0.2 51.1±0.4 78±0.5 28.1±0.3 43±0.5 58.7±0.7
52.3±2.3 96.3±4.2 169.9±7.5 37.1±6.7 65.2±6.5 109.6±7.6 27.9±0.1 48.9±0.1 81.1±0.2
Antenatal care visit*: any visit/no visit
<16 33.5±1.6 79.9±4 187.4±15 26.7±18.6 60.7±22.3 109.3±30.8 27.9±3.6 43.9±5.7
38.5±3.6 53.2±5 141.3±16.2 81.9±16.9 122.1±20.7 209.1±47 26.1±1.8 85.2±17.6
Continued
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2004–2008 2009–2013 2014–2018
NNMR IMR U5MR NNMR IMR U5MR NNMR IMR U5MR
16–17 27±1 60.2±2.2 94.2±11.1 24.8±0.5 51.6±1.1 93±2 24.3±0.7 39.8±1.1 64.8±1.3
57.4±2 93.6±10.6 173.1±11.8 28.8±14.7 64.3±14.4 95.8±14.2 41±1.5 79.2±2.8 41±5.2
18–19 25.7±0.7 48.5±1.4 80.8±2.4 23.7±0.4 47.8±3.8 87.9±3.7 19.4±3.7 32.1±3.5 52.4±3.3
0.8±1.7 77.3±3.3 132.6±3.6 36.6±1.3 60.3±7.6 176.2±11.1 38.3±1.3 62.8±2.3 103.9±3.4
20–22 22.8±0.4 48.5±0.7 77.3±1.1 22.6±0.4 41.1±0.6 59.8±0.8 15.9±0.2 27.4±0.3 38.2±0.4
53±2 101.7±4 206.9±18.9 6.2±15.3 74.5±18.2 150.1±19.7 42.8±1.6 82.1±2.9 125.4±4.2
23–25 26.1±0.7 52.3±1.4 82.7±1.9 21.3±0.3 36.8±0.4 65.4±0.5 16.5±0.9 24.9±1 34.1±1.4
24.7±1.2 43.1±15.4 132.2±248.3 20.6±1.2 44.9±2.7 63.1±3.8 15.2±1.2 52.3±4.5 96.1±9.9
Place of delivery*: at home/health facility
<16 88±8.8 161±9.5 253.1±11.6 65.5±2.9 116.9±5.3 214.3±11.9 53±26.4 105.7±25.1 215.4±22.2
59.8±19 94.2±21.5 188.8±31.2 38.6±31.1 73.6±31.3 138.2±29.2 41.1±2.1 78.6±11 140±11.9
16–17 53.8±1.2 108.5±2.5 187.3±5.4 56.3±1.5 90.3±2.5 156.1±5.5 51.2±1.1 80.8±1.7 150.9±3.1
41.7±3 88.6±3.9 138.1±9 32.9±0.6 68.1±1.3 114.7±2.6 34.7±1 60.1±1.6 91.6±1.8
18–19 46.5±5.2 92.5±5 148.6±7.2 41.5±8.4 78.9±7.7 132.1±7 41.2±7.5 72.5±6.7 117±6.1
35±1.1 65.1±2 100.9±2.9 32.2±0.6 59.8±1.1 94.4±1.8 30.9±0.7 46.9±1 68.2±1.7
20–22 43.5±0.7 90.3±1.4 141.8±2.8 44.2±1.3 70.8±2.2 110.4±4.1 38.2±6.7 70.6±6.5 104.9±6.5
33.3±0.6 65.2±1.1 100.7±1.7 31.1±0.6 53.2±1.1 79.6±1.8 27.6±0.3 43.4±0.4 57.9±0.4
23–25 45.9±4.4 83.6±7.1 140.6±6.9 30.3±0.9 60.4±1.7 96.7±21.3 25.4±0.8 48.3±1.6 74.5±2.9
31.7±0.6 60.2±1.2 97.2±1.7 30.7±0.3 47.9±0.4 75.2±0.4 29.5±0.6 41.3±0.8 53.6±1.1
South Asia
Maternal age at rst birth (years)
<16 82.9±1.6 112.6±2.1 145±2.5 55.4±2.7 81.7±3.1 98.9±6.4 59.7±15 84.4±20.8 95.7±22.8
16–17 65.6±2.3 94.8±5 112.1±4.9 50.8±1.1 75±2.8 86.9±2.9 46.4±3.7 64.4±5.1 74±5.7
18–19 53.2±0.7 74.3±1.1 86.5±1.2 45.7±1.1 61.6±1.3 71±2.3 38.7±7.2 51.4±7.3 58.7±8.5
20–22 39.6±1.1 56.4±1 66.9±1 32.5±1.5 45.7±1.5 53.6±1.5 31.3±4.6 41.9±4.6 48.3±4.6
23–25 35.5±0.2 49.6±0.3 57.5±0.4 25.3±0.1 36.4±0.3 45.1±0.3 26.3±1.6 34.4±3.3 40.7±3.3
Urban/rural residency
<16 80.2±4.2 107.3±5.6 132.1±6.5 48.8±6.2 63.5±12 86.4±11.8 56.4±33.5 71.8±42.2 77.1±44.1
83.4±1.6 113.6±2.1 147.6±2.5 57.3±2.8 87.1±4.2 102.6±7.5 60.6±52.8 88±51.4 101.2±50.5
16–17 51.1±1.7 71.5±2.4 87.1±3.2 34.4±3.1 53.8±4.8 60.5±4.8 27.3±4.5 44.4±7.3 54.5±8.6
69±2.2 100.3±4.9 118.1±4.7 55.7±3.7 81.2±4.3 94.4±4.4 52.8±6.1 71.1±6.7 80.5±10.1
Table 1 Continued
Continued
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2004–2008 2009–2013 2014–2018
NNMR IMR U5MR NNMR IMR U5MR NNMR IMR U5MR
18–19 36.5±0.4 53.3±0.5 61±0.6 31±1.6 48±2.1 52.6±3.4 30.4±15.2 40.5±15.3 45.8±17.8
58.2±0.8 80.7±1.2 4.4±1.4 50.7±1.6 66.1±1.8 77.1±2.6 41.5±1.9 55.1±2.5 63.2±2.8
20–22 28±0.2 39.4±0.3 45±0.4 21.3±0.7 30.1±0.7 35.1±1 23.1±8.8 31.2±8.7 35.1±8.7
44.8±1.2 64.1±1.1 76.8±1 37.7±1.7 52.9±1.7 62.4±1.7 34.6±0.9 46.3±1.4 53.6±1.5
23–25 27.1±0.2 37.2±0.2 43.5±0.3 19.2±0.1 28.7±0.5 35.6±0.5 16.6±2.5 21.9±5 26.8±5
41.4±0.3 58.2±0.5 67.3±0.6 9.4±0.2 41.5±0.3 51.5±0.3 31.9±0.4 41.6±0.6 48.8±0.7
Maternal education status: any education/no education
<16 67.4±1.9 95±2.7 120±3.2 42.9±3.2 62.7±3.4 82.6±7.6 67.2±17.9 85.7±22.5 96±24.3
100.1±0 132±0 170.9±0.4 100.2±4.6 149.4±6.7 159.5±6.9 45.1±8.3 81.5±14.4 94.6±16.9
16–17 52.7±3.9 76.8±3.7 88.3±3.6 40.3±0.9 61±3.1 68±3.1 42.1±3.2 57.4±4.4 65±4.8
83.1±0.8 118.8±5.2 142.5±5 94.5±1.6 132.8±2.2 160.5±2.6 56.8±7.1 80.8±9.9 94.3±11.1
18–19 44.2±0.7 61.6±1.4 70.7±1.4 39.8±1.1 52.6±1.3 61.5±2.6 35.4±1.3 45.7±1.6 51.6±6.7
68.9±0.5 96.6±0.7 113.2±0.8 72.6±0.8 102.5±1.1 113.6±1.2 47.7±20.4 66.8±20.5 77.4±20.4
20–22 32.4±1.5 46.3±1.4 53.5±1. 28.2±1.7 40.3±1.7 47.3±1.7 28.7±0.9 37.2±1.2 42±1.3
59.1±0.2 83.8±0.3 102.1±2.9 57.5±0.3 76.7±0.4 89.5±0.5 39.9±16 56.9±15.8 67.4±15.7
23–25 29±0.2 39.3±0.3 45.7±0.4 22.4±0.1 31.8±0.4 39.3±0.4 22.8±1.9 28.7±1.9 33.8±1.9
63.8±0.8 93.8±1 107.2±1 46±0.6 68.9±1 85.4±1.2 40.8±2.3 57.1±6.4 67.5±6.6
Antenatal care visit: any visit/no visit
<16 23.4±0.8 56.3±2 67.1±2.4 – – – 29.2±2 29.2±2 55.7±7.8
62.4±5.6 63.8±5.7 65.8±5.9 40.3±30.9 40.3±30.9 40.3±31
16–17 26.4±0.5 38.9±0.7 56±1.3 19.3±2.1 23.7±2.2 27.1±7 19±2 25.9±2.8 28.4±3.2
29.1±0.7 65.2±11.9 69.8±11.9 27.1±1.4 32±5.1 39.2±5.3 39.8±3.2 67±5.6 83±7.2
18–19 26.8±0.4 35.5±0.5 40.3±0.6 19.9±0.4 24.1±0.4 27±0.5 17.8±1.2 25±1.7 27.3±1.9
25.6±0.4 31.6±7.9 34.7±7.8 20.7±0.8 39.6±1.5 47.8±1.8 38.4±2.7 59.4±4.1 64.8±4.7
20–22 23.3±0.2 31.4±0.2 36.3±0.2 12.9±3.6 16±3.6 17.5±3.6 16.6±0.7 22.9±0.9 25.8±1
38.2±0.9 52.4±1.3 75.1±2.1 24.1±6.5 37.9±6.5 39.3±8.3 34.6±6 46.6±8.1 54.7±8.4
23–25 16.8±0.2 22.7±0.2 23.2±0.2 11.9±0.1 18.5±0.7 24.9±0.7 15.7±3.4 20.4±3.4 24±3.5
56.6±3.2 81.8±4.5 89±5.1 39.2±4.3 44.9±5 44.9±5 36±2 52±2.9 70.2±3
Place of delivery: at home/health facility
<16 57.3±2.2 87.3±3.5 120.7±5.1 28.1±1.8 34.9±2.3 50.3±35.8 53.7±14.9 79.4±24.1 112.5±24.2
74.1±6.1 109.3±8.7 109.3±8.7 40.7±40.4 65.7±39.7 65.7±39.7 55.7±2.6 75.3±3.6 141.1±9.2
Table 1 Continued
Continued
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focused mostly on survival endpoints around birth, we
hypothesised that since adolescent mothers face greater
physical, emotional and social challenges, the impact on
their offspring’s survival might be felt throughout early
childhood. In addition, to examine whether observed
associations between maternal age and child survival
may be caused by confounding variables that affect both,
we explored adjustment for key demographic variables
such as urban versus rural residence. We also investigated
whether the association between mother’s age and child
mortality endpoints might be mediated by maternal
health- seeking. We focused on SSA, the region with
the highest adolescent pregnancy and child mortality
burdens, as well as South Asia, the second- highest region
in child mortality burden where adolescent pregnancy
rates fell rapidly in recent years. The comprehensive-
ness and multiregionality of our analysis helps frame
the inconsistent findings from previous studies9 13–17 on
the relationship between maternal age and child health
outcomes. Disaggregation of the adolescent age group
helps highlight the increased risk of younger adolescents
and the potential benefits of providing health services for
these girls.
METHODS
Data source and study population
We analysed DHS data collected between 2004 and 2018
from countries in SSA and South Asia. DHS are cross-
sectional nationally representative large- scale household
surveys that collect and analyse demographic, health and
nutrition data, in a manner that enables comparisons
across countries and over time.20 The women’s question-
naire, used in this study, invites all women aged 15–49 in
a surveyed household to respond. In a few surveys, the
target group was women and girls aged 10–49 years old.
We defined three time periods: 2004–2008, 2009–2013
and 2014–2018, to assess the variation in each outcome
over time. We estimated the risk gradient versus age at
first birth among adolescent and young adult mothers.
We considered only first births in order to avoid the
various confounders associated with parity, and also
because most adolescent births are first births. Since the
vast majority of first births in the two regions take place
by women’s mid- 20s, we restricted the analysis to mothers
25 years old or younger. In total, 35 countries with 80
surveys in SSA and 11 countries with 27 surveys in South
Asia were included in the analysis (online supplemental
material).
Endpoints and risk factors
Maternal age at first birth within the 10 years preceding
the survey was divided into five groups: <16, 16–17,
18–19, 20–22 and 23–25 years old. For stillbirth, DHS
only collects data from the 5 years preceding the survey.
The age group <16 includes mothers aged 10–15 years
old, or 15 years old in surveys restricted to women aged
15–49. Risk factors accounted for in this analysis are
2004–2008 2009–2013 2014–2018
NNMR IMR U5MR NNMR IMR U5MR NNMR IMR U5MR
16–17 61.2±3.3 91.9±5.4 103.9±5.3 36.3±0.8 56.2±1.1 64.3±1.1 53.9±4 80.4±5.9 88.2±6.1
62±1.8 83.6±2.4 94.3±2.6 38±0.8 38±0.8 47.7±1.2 41.7±3.5 56.9±4.9 67.4±6.5
18–19 50.4±1.2 69.9±3.6 89±3.5 48.5±0.9 58.5±1.1 68.7±6.3 43.4±2 64.4±3.2 70.1±3.3
47.2±0.6 60±0.7 66.8±0.8 32.6±0.9 40.9±1.2 46.2±1.4 34.7±3 46.5±4.1 52±4.7
20–22 43.4±2.6 61±2.5 78.1±3.8 30.4±0.5 41.5±0.7 49.7±0.9 46±4.7 60.9±6.4 79.1±10.1
30±0.3 38.3±0.4 43.1±0.4 26±0.2 32.3±0.2 38±0.2 27.2±1.9 37.1±2.6 43.7±3
23–25 46.9±0.6 68.2±0.8 76.6±0.9 29.2±0.5 43.5±0.7 53.8±0.9 42.4±1.5 58.1±9.2 73.2±9.2
29.3±0.4 36.7±0.5 41.8±0.5 19.2±0.1 27.2±1.4 33.2±1.4 24.4±3 31.3±3.2 38.2±3.3
*Births that occurred in the 3/5 years preceding the survey.
IMR, infant mortality rate; NNMR, neonatal mortality rate; U5MR, under- 5 mortality rate.
Table 1 Continued
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sociodemographic factors: urban/rural residency and
maternal education status (dichotomised as any education
vs none); economic factors: wealth quintile (a country-
specific measure of the household wealth compared with
other households in each survey and grouped as poorest,
poorer/middle/richer and richest in our analysis); and
health- seeking factors: place of delivery (at home vs
health facility) and ANC utilisation (no ANC visit vs any
ANC visit). When we used literacy instead of education,
we found similar results and thus we did not include it
in our analysis. All risk factors were coded as categorical
variables in our analysis.
We examined the following outcomes in our study:
stillbirth (pregnancies that lasted 7 or more months and
terminated in fetal death), neonatal mortality (death
after a live birth within the first 28 days of life), infant
mortality (death within the first year of life), child
mortality (death after the first year and before reaching
the age of 5 years), 1–59 month mortality (death after the
first month and before reaching the age of 5 years) and
under- 5 mortality (death before reaching the age of 5
years). Of these, stillbirths, neonatal, infant and under- 5
mortality are reported in the main article, and the
remaining endpoints in online supplemental material.
Statistical analysis
Descriptive analyses included calculating the neonatal,
infant, child and under- 5 and 1–59 month mortality
rates per 1000 live births and their sampling errors based
on the DHS mortality rates estimation methodology, a
synthetic cohort life table.21 We calculated the mortality
rates for three time periods in each region and for each
maternal age group combined with selected risk factors.
Among multiple births (twins, triplets, etc), only those
with the birth assigned order number of 1 by DHS were
considered in the analysis. While excluding the remaining
multiple- birth siblings reduces the sample size by about
1%, it helps simplify and stabilise the analysis by avoiding
the need to account for another level of dependence.
A mixed- effect logistic regression model was applied to
each time period and each region separately, to examine
the association between each of the outcome variables
and the risk factors. The sample size for the maternal
age group and sociodemographic factors are the same
and therefore, these models are layered stepwise and
comparable. A model including only age group and the
random effects was developed, called here Model 0. The
model was further adjusted for different combinations of
risk factors, based on prior knowledge of common risk
factors for LMIC child mortality, as well as to assess the
impact of leading healthcare access and socioeconomic
indicators on the risk gradient. In Model 1, maternal
age at first birth, urban/rural residency and maternal
education status were included. Questions about place of
delivery and ANC utilisation were available for the last
births in the 3/5 years preceding the survey. A model
adjusting for maternal age and healthcare accessibility
factors was developed, called here Model 2. All the first
births that happened within the 3/5 years preceding the
survey were included in Model 2. Given that the primary
sampling units, year of survey and country name were
used as nested random effects in the model, the wealth
index represents the deviation of household wealth from
its own country’s mean wealth at the time of interview11
and Model 1 was further adjusted for the wealth index,
called here Model 3. We also combined the two health-
care variables into one and grouped the outcomes as
having either ANC visit or facility birth; both ANC visit
and facility birth; and no ANC visit and home delivery,
and further adjusted Model 1 for this variable, called here
Model 4. Note that despite including Model 1’s variables,
Model 4 is not nested within Model 1 as reported here,
because the health- seeking variables were only available
for each mother’s last birth. Together with the first- birth
constraint of all models, this reduces the sample size by
5–7 times, as well as weights the sample more towards
recent first births. Models 0–2 are reported in the main
article, and Models 3 and 4 in the online supplemental
material. Models 2 and 4 are layered stepwise and compa-
rable, separately from Models 0, 1 and 3. Each survey’s
sample weights were used as the model prior weights in
the fitting process. Mothers of age 23–25 years old were
considered the reference age group. All the analyses were
performed in R V.4.0.22
Patient and public involvement statement
Study participants or the public were not involved in the
design, or conduct, or reporting, or dissemination plans
of our research.
RESULTS
Univariate maternal-age risk gradient
The numbers of first births and neonatal, infant, child
and under- 5 deaths to women aged under 25 years old
that occurred within 10 years preceding the survey,
grouped by region, survey period,and age, are given in
online supplemental tables 1 and 2. Within each time
period, about 20%–23% of first births to mothers 25
years old or younger in SSA were attributed to mothers
under 18 years old. In South Asia, this proportion was
lower and decreased from 15.7% in 2004–2008 to 8.1%
in 2014–2018.
The majority of women across all ages and time periods
lived in rural areas. About 60% and 43% of mothers aged
<16 years old, respectively, in SSA and South Asia, in the
2004–2008 period had no formal education. This rate
decreased with age and time in SSA, but a similar trend
was not observed in survey data from South Asia (online
supplemental table 2). In both regions, about 80% of
women had at least one ANC visit. About 45%–48% of
pregnant women aged <16 years old in both regions gave
birth at home during the 2014–2018 period, two to three
times more often than mothers over 20 years old. The
gap has not narrowed substantially between 2004–2008
and 2014–2018 (online supplemental tables 1 and 2).
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The mortality rates for different child outcomes and
their sampling errors are given in table 1 for SSA and South
Asia and online supplemental tables 3 and 4, respectively.
In SSA, neonates, infants and children under- 5 born to
mothers aged <16 were at about two times higher risk of
death (54.5±4.5, 95.9±5.5 and 156.5±6.6 deaths per 1000
live births, respectively, in 2014–2018) than those born
to mothers aged 23–25 years old (28±1.6, 44.5±1.9 and
64.8±2.4, respectively). The mortality rates for children
of the <16 age group in South Asia were two to three
times higher (59.7±15, 84.4±20.8 and 95.7±22.8, respec-
tively) than the oldest age group (26.3±1.6, 34.4±3.3 and
40.7±3.3, respectively), however the uncertainty intervals
are wide for the youngest age group. Overall, mortality
rates decreased with time, but the risk gradient versus age
has remained similar.
In both regions, the risk gradient versus age appears
in both rural and urban locations (figure 1). Similar
age gradients were observed when dividing mothers by
maternal education status and other variables (table 1).
Multivariate analysis adjusting for risk factors
Estimates from Models 0, 1 and 2 for the period 2014–
2018 are shown in figures 2–4. According to these models,
adjusted maternal- age effects are consistent with the patterns
of table 1 and figure 1. Mortality risk for all endpoints
increases with younger age, and children in both regions
born to mothers aged <16 faced 2–4 times higher mortality
risk than those born to mothers aged 23–25 years old at all
stages, from stillbirth to under- 5 mortality (figure 2), even
after adjustment for demographic factors (figure 3). The
OR for stillbirth is particularly high with under- 16 mothers,
around 4 or more in both regions and models. Adjusting
for health- seeking variables reduced the age effect for
neonatal and infant mortality but not for under- 5 mortality
in both regions (figure 4 and online supplemental figure
4). However, for stillbirths, the risk gradient versus age was
stronger after adjustment for health- seeking, suggesting
that some health- seeking recorded in the survey could be
related to pregnancy complications or even to the stillbirths
themselves. It should be emphasised that the dataset used
in figure 4 and online supplemental figure S4 is smaller
than the one used in figures 1–3 since the healthcare vari-
ables were available only for the last birth in the 3/5 years
preceding the survey. Further adjustment for wealth quintile
did not modify the age effect significantly (Model 3, online
supplemental figure 3). For the 2009–2013 time period in
SSA, similar patterns were observed (online supplemental
figures 5–9), and in South Asia where this time period had
a particularly small sample size, the age effect was reduced.
For the 2004–2008 time period, the risk gradient versus age
appeared in both regions for the majority of child survival
outcomes (online supplemental figures 10–14).
DISCUSSION
Among young mothers in SSA and South Asia, there was
a consistent risk gradient versus maternal age at all stages
of child mortality and all survey periods. Compared with
other known risk factors, young maternal age appears
to be among the strongest risk factors of child mortality.
Our findings confirm and substantially expand the
Figure 2 Ratios associated with neonate, infant, child, 1–59 months, under- 5 years and stillbirth in SSA and South Asia for
the 2014–2018 survey period. Risk factors reducing the probability of death have ORs lower than 1 to the left of the vertical
red line. ORs (blue points) and 95% CIs (horizontal blue lines) are given. P values are shown with the asterisk signs (***0.001;
**0.01; *0.05; ‘.’ 0.1 ‘ ’ 1). Reference group is mothers aged 23–25 years old (Model 0). SSA, Sub- Saharan Africa.
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conclusions from previous studies about the association
between early childbearing and adverse child health
outcomes,2 23 24 and suggest that the increased risk to
children of younger mothers continues to linger even in
regions with dropping adolescent pregnancy rates such
as South Asia. Even in adjusted analyses, after controlling
for several risk factors, the associations between adoles-
cent pregnancy and child survival remained similar,
except for neonatal and infant mortality where the effect
was reduced after adjustment for health- seeking varia-
bles. This suggests that ensuring young mothers receive
quality maternal care could reduce some of the early
Figure 3 Ratios associated with neonate, infant, child, 1–59 months, under- 5 years and stillbirth in SSA and South Asia for
the 2014–2018 survey period. Risk factors reducing the probability of death have ORs lower than 1 to the left of the vertical
red line. ORs (blue points) and 95% CIs (horizontal blue lines) are given. P values are shown with the asterisk signs (***0.001;
**0.01; * 0.05; ‘.’ 0.1 ‘ ’ 1). Reference group is mothers aged 23–25 years old who live in rural areas and have no formal
education (Model 1). SSA, Sub- Saharan Africa.
Figure 4 Ratios associated with neonate, infant, child, 1–59 months, under- 5 years and stillbirth in SSA and South Asia for
the 2014–2018 survey period. Risk factors reducing the probability of death have ORs lower than 1 to the left of the vertical red
line. ORs (blue points) and 95% CIs (horizontal blue lines) are given. P values are shown with the asterisk signs (‘**0.001; **0.01;
* 0.05; ‘.’ 0.1 ‘ ’ 1). Reference group is mothers aged 23–25 years old who delivered at home, and had no ANC visit (Model 2).
ANC, antenatal care; SSA, Sub- Saharan Africa.
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childbearing effects. Provision of antenatal and postnatal
health services to adolescents can further be improved by
recognising their biological and social needs and vulner-
abilities,25 considering that adolescents may experience
social stigma from healthcare providers, besides the soci-
oeconomic limitations they deal with.26 The overall risk
of death is higher among neonates, infants and children
of mothers in the poorest wealth quintile living in rural
areas with no formal education, across all age groups.
The heterogenous and limited progress in reducing
adolescent pregnancies among these vulnerable groups
emphasises the inequity as well as inadequate distribution
of resources and health services.12 27 However, the risk
trend among younger mothers was evident across all soci-
oeconomic status (SES) groups, suggesting that beyond
external social factors placing children of younger
mothers at a higher level of disadvantage than other
ages, underlying biological or behavioural immaturity of
the mother was likely also at play.16 27 Past studies have
found that coinciding pregnancy with growth in young
adolescents may lead to maternal–fetal competition for
nutrients and consequently, to increased risk of low birth
weight, neonatal mortality and preterm delivery.28 29
The high mortality risk of children under- 5 highlights the
likely long- term impact of limited education and livelihood
opportunities for adolescent mothers.30 This can initiate a
poverty cycle in their families, in addition to mental health
and psychological challenges from social stigma that young
mothers may deal with.31 32 To reduce neonatal and under- 5
mortality rates towards Sustainable Development Goals
(SDG) targets, it is necessary to focus efforts on reducing
unintended pregnancies in adolescents in countries where
they are prevalent, and ensure adolescent girls and young
women have access to sexual and reproductive health
services to both increase choice and regulate spacing in their
fertility decisions. Our analyses highlight where the risk is
the highest and where we need to learn more in order to
ensure girls, young women, and their children have more
favourable health outcomes.
Previous studies have found strong associations between
adolescent motherhood and child or early marriage, partic-
ularly in African and Asian contexts where marriage usually
precedes childbearing.18 Moreover, early marriage before
age 18 has been positively associated with higher fertility,
poorer maternal and reproductive health and poorer
health and developmental outcomes among their chil-
dren, through pathways including biological factors, social
risks and maternal behaviour.31 32 Despite existing laws and
human rights frameworks calling to eliminate marriage of
girls before age 18, 650 million girls and women alive today
married before their 18th birthday; 40% of those women
live in South Asia, while 18% live in SSA.33 Strengthening
measures to delay age at marriage may help reduce adoles-
cent pregnancies in regions where both are strongly linked.
Our work has some limitations. There is likely under-
reporting of mortality at early stages of child life, especially
neonatal death and stillbirth. Further, survey responses rely
on recall data,2 and respondents may overstate their ages at
births during the interview due to social pressure. In addition,
the survey data represent the sociodemographic characteris-
tics of respondents at the time of interview, and not during
the birth events. Besides the risk factors we accounted for in
our study, other covariates related to the pregnancy- induced
complications such as maternal nutrition status,18 which
are not available in the DHS datasets, could help better
explain the observed patterns. In addition, for the children
under- 5 mortality endpoint, other risk factors such as infec-
tious diseases along with preterm birth complications, birth
asphyxia and trauma, and congenital anomalies which are
the leading causes of death for children under- 5, were not
included in this analysis due to lack of data.34 The limited
sample size of the group suffering the greatest disparities,
under- 16 mothers, constrained the number of variables we
could consider in any single model, particularly in South
Asia, and even more so with respect to health- seeking vari-
ables. The limited sample size also required us to group all
mothers under 16 in the main analysis. In sensitivity anal-
ysis, we divided the under- 16 age group into two roughly
equal groups of 10–14 and 15 years old in SSA for the unad-
justed Model 0 as well as Model 1 and the period 2014–2018
(online supplemental figures 15 and 16). Results showed
a strong age effect for all child health outcomes with chil-
dren of 10–14 years old mothers doing even worse than the
combined under- 16 group. However, the small sample size
did not allow for further analysis. Similarly, after examining
the maternal mortality DHS module, we decided to not
include maternal mortality in our analysis, due to the small
sample size and lack of risk factors in this module, whose data
are based on interviews with siblings of deceased mothers. All
the abovementioned limitations in retrospective analyses of
cross- sectional survey data restrain the ability to disentangle
the underlying biological, behavioural and environmental
mechanisms, and to rule out residual confounding factors.
There is a need for longitudinal studies and follow- up data
in diverse contexts to help tease apart the drivers of adverse
child outcomes for young mothers.
CONCLUSIONS
Our study highlights the strong differences in child health
outcomes within the under- 20 maternal age group, and
provides quantitative evidence on the necessity for age-
disaggregated reporting and survey data on adolescent
pregnancy, given the specific biological and social risks to
adolescent mothers and their babies,25 to better under-
stand its associations with child outcomes and how their
nature, scale and impact vary by age. Revising the future
classification of maternal age, and reporting of adoles-
cent reproductive health will help better develop and
monitor the progress of age- specific programmes aimed
at achieving the SDGs of reducing adolescent preg-
nancy.35 By building on previous studies and policies, our
work is more cognizant of empirical and health- seeking
contexts, and suggests the path forward with respect to
policy modification, while recognising that adolescents
biological and social needs and vulnerabilities should
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be accounted for when improving health services and
developing age- specific policies. Building the capacity of
adolescents to make their own decision and choices on
their reproductive health, which is shaped by numerous
social, cultural and economic circumstances,36 is vital.
Some of the contributing factors to this problem are
beliefs, attitudes and norms in the community as well as
healthcare providers about adolescent sexuality that put
them at risk for poor health outcomes.26 Among women
who would want to avoid pregnancy in LMICs, the unmet
need for modern contraception is much higher for
adolescents than for all women aged 15–49. About 44% of
adolescent women in LMICs who want to avoid pregnancy
have an unmet need for modern contraception.36 Inter-
ventions focused on expanding contraceptive access and
use are key towards shifting social and gender norms at
family and community levels,31 addressing early pregnan-
cy,and subsequently improving child outcomes.37 Some
age- specific programmes could be related to increasing
awareness on the importance of interventions, laws and
enforcement, and advocacy and outreach addressing
individual and community barriers to delaying first preg-
nancy, including delaying marriage through establishing
and enforcing laws,38 addressing underlying social and
economic drivers and norms, empowering young women
to choose if, when, and whom they marry, and enabling
young women to continue and attain higher levels of
education and to reduce unintended adolescent preg-
nancies as well as rapid repeat pregnancies.
Acknowledgements We would like to thank Dr Guillaume Chabot- Couture and Dr
Edward Wenger for their useful and constructive comments and insights.
Contributors JLP and APO helped develop the research concept and approach.
NN analysed the data and wrote the initial draft of the manuscript. YE and JLP
provided subject- matter expertise and guidance. All authors contributed to the
interpretation of results and writing of the manuscript and have read and approved
the nal manuscript. NN is responsible for the overall content of the manuscript as
the guarantor.
Funding This work was supported, in whole or in part, by the Bill & Melinda Gates
Foundation.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in
the design, or conduct, or reporting, or dissemination plans of this research.
Patient consent for publication Not applicable.
Ethics approval This study was based on secondary analysis of Demographic and
Health Surveys (DHS) data. The ethical clearance was provided by the Institutional
Review Board of ICF International. Therefore, this secondary analysis was exempt
from ethical review approval, since it used publicly available, deidentied data.
Participants gave informed consent to participate in the study before taking part.
Provenance and peer review Not commissioned; externally peer reviewed.
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... increase in the risk of stillbirth, and 2.62-fold (95% CI: 2.22-3.08) increase in the risk of neonatal mortality (4). A combination of biological and social factors ranging from immature reproductive systems and low socioeconomic status (5), to unhealthy lifestyle behaviors (i.e., alcohol/tobacco misuse during pregnancy) (4), and unplanned pregnancy may affect the health of teenage mothers. ...
... increase in the risk of neonatal mortality (4). A combination of biological and social factors ranging from immature reproductive systems and low socioeconomic status (5), to unhealthy lifestyle behaviors (i.e., alcohol/tobacco misuse during pregnancy) (4), and unplanned pregnancy may affect the health of teenage mothers. Unplanned pregnancy will also place pregnant adolescents at greater risk than women of average maternal age with severe maternal morbidity (SMM) or maternal mortality (6). ...
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Most studies examining contraceptive use among women focus on their own fertility desires and family planning attitudes and do not incorporate the desires and attitudes of their partner. Using Demographic and Health Survey data from young couples (wife is aged 15–24) from six countries, we use descriptive and multivariate analyses to examine the association between couple-level fertility desires and current contraceptive use and women’s future intention to use contraception. Results demonstrate that young couples want to have children immediately or may want to delay having children for two or more years; very few do not want (more) children. Discordant fertility desires were found in all countries. Compared to couples where both partners want a child soon, young couples that want to delay childbearing or where the husband wants a child, and the wife wants to delay or avoid childbearing are significantly more likely to use contraception. Similar results are found for women’s intention to use contraception. When discordant fertility desires are associated with the outcomes, the young wife’s fertility desire matters more than her husband’s. Among young couples, promoting communication and positive social norms for delaying a first or second birth can lead to positive health outcomes for mothers and babies.
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Background: Single aggregate figures for adolescent pregnancy may fail to demonstrate particular population groups where rates are very high, or where progress has been slow. In addition, most indicators fail to separate younger from older adolescents. As there is some evidence that the disadvantages faced by adolescent mothers are greatest for those at the younger end of the spectrum, this is an important omission. This paper provides information on levels and trends of adolescent first births in 22 countries (at national and regional level) disaggregated by age (< 16 years, 16/17 years and 18/19 years), socio-economic status and place of residence. It highlights differences and similarities between countries in the characteristics of women who experience first birth during adolescence, as well as providing information on trends to identify groups where progress in reducing adolescent first births is poor. Methodology: In this descriptive and trend analysis study we used data from 22 low- and middle-income countries from sub-Saharan Africa that have at least three Demographic and Health Surveys (DHS) since 1990, with the most recent carried out after 2005. Adolescent first births from the most recent survey are analysed by age, wealth, and residence by country and region for women aged 20-24 years at time of survey. We also calculated annual percentage rates of change (using both short- and longer-term data) for adolescent first births disaggregated by age, family wealth and residence and examined changes in concentration indices (CI). Findings: Overall percentages of adolescent first births vary considerably between countries for all disaggregated age groups. The burden of first birth among adolescents is significant, including in the youngest age group: in some countries over 20% of women gave birth before 16 years of age (e.g. Mali and Niger). Adolescent first births are more common among women who are poorer, and live in rural areas; early adolescent first births before 16 years of age are particularly concentrated in these disadvantaged groups. Progress in reducing adolescent first births has also been particularly poor amongst these vulnerable groups, leading to increasing inequity. Conclusions: Findings from this study show that adolescent births are concentrated among vulnerable groups where progress is often poorest. Strategies and programmes need to be developed to reduce adolescent pregnancies in marginalised young women in low- and middle-income countries.
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Importance: Understanding causes and correlates of health loss among children and adolescents can identify areas of success, stagnation, and emerging threats and thereby facilitate effective improvement strategies. Objective: To estimate mortality and morbidity in children and adolescents from 1990 to 2017 by age and sex in 195 countries and territories. Design, setting, and participants: This study examined levels, trends, and spatiotemporal patterns of cause-specific mortality and nonfatal health outcomes using standardized approaches to data processing and statistical analysis. It also describes epidemiologic transitions by evaluating historical associations between disease indicators and the Socio-Demographic Index (SDI), a composite indicator of income, educational attainment, and fertility. Data collected from 1990 to 2017 on children and adolescents from birth through 19 years of age in 195 countries and territories were assessed. Data analysis occurred from January 2018 to August 2018. Exposures: Being under the age of 20 years between 1990 and 2017. Main outcomes and measures: Death and disability. All-cause and cause-specific deaths, disability-adjusted life years, years of life lost, and years of life lived with disability. Results: Child and adolescent deaths decreased 51.7% from 13.77 million (95% uncertainty interval [UI], 13.60-13.93 million) in 1990 to 6.64 million (95% UI, 6.44-6.87 million) in 2017, but in 2017, but aggregate disability increased 4.7% to a total of 145 million (95% UI, 107-190 million) years lived with disability globally. Progress was uneven, and inequity increased, with low-SDI and low-middle-SDI locations experiencing 82.2% (95% UI, 81.6%-82.9%) of deaths, up from 70.9% (95% UI, 70.4%-71.4%) in 1990. The leading disaggregated causes of disability-adjusted life years in 2017 in the low-SDI quintile were neonatal disorders, lower respiratory infections, diarrhea, malaria, and congenital birth defects, whereas neonatal disorders, congenital birth defects, headache, dermatitis, and anxiety were highest-ranked in the high-SDI quintile. Conclusions and relevance: Mortality reductions over this 27-year period mean that children are more likely than ever to reach their 20th birthdays. The concomitant expansion of nonfatal health loss and epidemiological transition in children and adolescents, especially in low-SDI and middle-SDI countries, has the potential to increase already overburdened health systems, will affect the human capital potential of societies, and may influence the trajectory of socioeconomic development. Continued monitoring of child and adolescent health loss is crucial to sustain the progress of the past 27 years.
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Introduction Adolescent motherhood (childbearing below 18 years of age) is a major global health and social problem. Understanding the impact of early motherhood on maternal and child health indices is important to community and population health promotion in developing countries. This study examined the association between adolescent motherhood and maternal and child health indices in Maiduguri, Nigeria. Methods A cross-sectional design method was used to recruit 220 mothers (age=14–25 years) from four communities in the city of Maiduguri, Northeastern Nigeria. Participants were surveyed using a self-developed interviewer-administered questionnaire that assesses maternal and child health indices and sociodemographic characteristics. Logistic regression analysis was used to compute adjusted OR and 95% CI of the associations between motherhood in adolescence (mothers below 18 years old) and maternal and child health indices. Results Compared to adult mothers, adolescent mothers were more likely to experience fistula (OR=5.01, 95% CI=3.01 to 14.27), to have postpartum haemorrhage (OR=6.83, 95% CI=2.93 to 15.92), to have sexually transmitted infections (OR=6.29, 95% CI=2.26 to 17.51) and to lose a child within 5 years of birth (OR=3.52, 95% CI=1.07 to 11.60). Children born to adolescent mothers were less likely to have normal weight at birth (OR=0.34, CI=0.15 to 0.73) than those born to adult mothers. Conclusion Adolescent motherhood was associated with negative maternal and child health indices. The findings can be used by public health professionals including physiotherapists in this role to inform effective primary healthcare practice and community health advocacy to improve maternal and child health indices among adolescent mothers in Maiduguri. Future studies are needed to confirm the evidence at the regional or national level including the rural population in Nigeria.
Article
Background: Adequate antenatal care (ANC) utilization is recognized as one of the important drivers of safe childbirth and positive birth outcomes. The usage of ANC services fluctuates with various personal, socio-economic, and cultural characteristics and in resource-poor settings, adolescent mothers are at a particularly high risk of insufficient ANC utilization. Objectives: This paper investigates whether the usage of ANC services and institutional delivery as well as newborn birth weight differ systematically between adolescent and adult mothers in West and Central Africa. Moreover, we explore to what extent differences in birth weight are explained by ANC usage, adolescence, and select socio-economic characteristics of the mother. Methods: We pooled cross-sectional data from all Demographic and Health Surveys (DHS) and Multi Indicator Cluster Surveys (MICS) conducted in countries in West and Central Africa region between 1986 and 2017 to estimate measures of ANC usage and qualified delivery assistance (along with a combined measure of "adequate maternal healthcare" aggregating these two factors) and newborn birth weight by maternal age group. We estimated various regression models to analyze a) the association between adolescence and adequate prenatal and maternal health care controlling for select socio-economic maternal characteristics as well as the local environment and b) between adolescence, adequate maternal health care, and newborn birth weight outcomes, also controlling for maternal characteristics and the local environment. All regressions were linear probability models for binary outcomes and simple linear models for continuous outcomes. Results: Adequate maternal health care provision was lowest among adolescent mothers: 23.0% among adolescents vs an average of 29.2% across all other age groups. Moreover, we found maternal education and wealth to be positively and significantly associated with receiving adequate maternal health care. Adolescent mothers had the highest risk of low infantile birth weight with 14.5% (95% confidence interval (CI) = 13.6%-15.5%), which is roughly 1.5-2 times higher than in older mothers. We found that adolescence is still strongly associated with low birth weight even when adequate maternal health care and various socio-economic factors as well as the local environment are controlled for. Conclusions: Our findings suggest that ANC supply in resource-poor settings should be particularly tailored to adolescent mothers' needs and that further research is necessary to explore what individual maternal characteristics beyond socio-economic and physical (eg, BMI) factors drive the prevalence of low birth weight. Moreover, the currently used measures of maternal care quality are heavily dependent on pure quantitative measures (number of ANC visits). New indicators incorporating measures of factual quality and scope ought to be developed and incorporated into large routine household surveys such as DHS and MICS.
Article
Background Documenting trends and inequalities in the prevalence of adolescent motherhood across low-income and middle-income countries (LMICs) is important to support the adolescent sexual and reproductive health target in the UN Sustainable Development Goals (SDGs). We aimed to examine time trends and sociodemographic inequalities in the prevalence of adolescent motherhood in LMICs. Methods We analysed data from 747 137 young women (aged 15–19 years) from 74 LMICs, using 254 nationally representative Demographic and Health Surveys done between 1990 and 2018. We estimated the population-weighted prevalence of adolescent motherhood among women aged aged 15–19 years (defined as having had a livebirth or being pregnant at the time of the survey). Trends in the prevalence were calculated at the national level using the average annual rate of change (AARC) in a subset of 61 countries with at least two surveys from different timepoints during the study period. Sociodemographic inequalities (eg, wealth quintile, level of education, and rural or urban residence) in adolescent motherhood were described using the normalised concentration index. Findings The highest prevalence of adolescent motherhood was observed in sub-Saharan African countries, for example it was 36·00% (95% CI 33·98–38·08) in Mali (which had recent survey data; 2018). Examining AARC, countries such as Nigeria (AARC −1·35%; 1990–2018) and India (−4·62%; 1992–2015) experienced a steady decline in the prevalence of adolescent motherhood during the study period. However, several high-burden countries experienced little change in prevalence over time (−0·60%; Bangladesh, 1993–2014), and 16 countries, such as Cambodia (2·42%; 2000–14) and Philippines (1·59%; 1993–2017), had an increase in the prevalence of adolescent motherhood over time. Sociodemographic inequalities in the prevalence of adolescent motherhood persist in most countries in this study. Interpretation Many of the countries in this study experienced either a slow rate of reduction or an increase in the prevalence of adolescent motherhood during the study period, and sociodemographic inequalities within countries persist. These results indicate that efforts to reduce adolescent motherhood and the associated health burden need to be improved within many LMICs. These findings can assist policy makers to target the rollout of interventions on the basis of observed geographic and sociodemographic inequalities to reduce adolescent motherhood among the disadvantaged, and accelerate progress towards adolescent sexual and reproductive health targets in the UN SDGs. Funding None.
Article
Background Both young and advanced maternal age have been associated with higher risks of neonatal mortality, but most studies are from high‐income countries and the evidence from low‐ and middle‐income countries (LMICs) is scarce. Objective To investigate the association between maternal age at delivery and neonatal mortality in LMICs. Methods This is a cross‐sectional study using data from 159 Demographic and Health Surveys in 67 LMICs between 2000 and 2018. Maternal age at the time of the birth was the exposure variable, and neonatal mortality was the outcome. Multivariable logistic regression model taking into consideration complex survey design was performed with adjustments for maternal education level, paternal education level, rural/urban residence, country, and survey year. Subgroup analyses were performed by time of death, sex, the country's World Bank income classification, the World Health Organization region, and survey year. Results A total of 1 395 746 mother‐neonate pairs were included. Overall, compared with neonates born to mothers aged 25‐29 years, those born to younger mothers aged 20‐24, 16‐19 and 12‐15 years were at an increased risk of mortality (adjusted odds ratio [aOR] 1.24, 95% confidence interval [CI] 1.17, 1.30; aOR 1.81, 95% CI 1.71, 1.93; aOR 2.29, 95% CI 1.96, 2.67, respectively). Neonates born to mothers aged 30‐34, 35‐39, 40‐44, and ≥45 years were also at an increased risk of mortality (aOR 1.09, 95% CI 1.03, 1.15; aOR 1.30, 95% CI 1.21, 1.39; aOR 1.50, 95% CI 1.38, 1.64; aOR 1.84, 95% CI 1.54, 2.20, respectively). The results were consistent across most subgroup analyses. Conclusions Neonates born to younger (<25 years) and older mothers (≥30 years) are at increased risk of neonatal death in LMICs.