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Stunting, age at school entry and academic performance in developing
countries: A systematic review and meta-analysis
Rabi Joël Gansaonré ( rabi-joel.gansaonre.1@ulaval.ca )
Universite Laval https://orcid.org/0000-0002-6199-1771
Lynne Moore
Universite Laval Faculte de medecine
Louis-Philippe Bleau
Université Laval: Universite Laval
Jean-François Kobiané
Université Joseph Ki-Zerbo: Universite Joseph Ki-Zerbo
Slim Haddad
Universite Laval Faculte de medecine
Research
Keywords: stunting, height-for-age, children, school entry, grade repetition, school dropout, schooling level
DOI: https://doi.org/10.21203/rs.3.rs-606866/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
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Abstract
Introduction
Although many studies have examined the associations between growth problems in infancy and age at school entry, grade repetition, school
dropout and schooling level in developing country, no synthesis of the evidence has been conducted. We aim to review evidence of the effects
of stunting, or height-for-age, on schooling level and schooling trajectories, dened as the combination of school entry age, grade repetition,
and school dropouts.
Methods
We conducted a systematic review of studies (last update March 20, 2021) estimating that estimate the association between stunting, or
height-for-age, and at least one component of the school trajectory, or schooling level, using ve databases (PubMed, Embase, Education
Resources Information Center (ERIC), Web of Science and PsycINFO). Study selection and data extraction were performed by two independent
reviewers. Pooled effects were calculated using the generic inverse variance weighting random effect model. The studies’ risk of bias was
assessed using the ROBINS-I tool for non-randomized studies.
Results
We screened 3944 records by titles and abstracts and retained 16 for inclusion in the qualitative and meta-analysis. Meta-analysis showed that
an increase in height-for-age leads to an increase in early enrollment [OR: 1.34 (95% CI: 1.07; 1.67)], a reduction in late enrollment [OR: 0.63
(95% CI: 0.51; 0.78)], an increase in schooling level [MD: 0.24 (95% CI: 0.14; 0.34)], and a reduction of school overage [OR: 0.79 (95% CI: 0.70;
0.90)]. The odds of grade repetition increased by 59% (OR = 1.59; 95% CI: 1.18; 2.14) for stunted children compared to those with no stunting.
Conclusions
This review suggests that stunting in childhood might lead to a delay in school enrollment, grade repetition, school dropout, and low schooling
levels in developing countries. Future research should evaluate the effect of stunting on academic trajectories in the same population and
explore the potential modication effect of socioeconomic status. The current ndings suggest that policy makers need to work more to
prevent stunting and to include health issues in educational policies.
Systematic review registration
: PROSPERO CRD42020198346
Introduction
In many countries, only a minority of children grow up healthy[1]. The 2018 World Nutrition Report indicates that stunting affects 150.8million
children under ve years of age, which represents 22.2% of the world’s children[2, 3]. The vast majority of stunted children come from
developing countries (148.0 of 150.8million)[3]. These countries also have more of out-of-school children or people with low academic
achievement than the global average. The UNESCO Institute for Statistics reports that, in 2018, 17.7% of children of primary school age were
out of school in the least developed countries, compared to only 8.2% globally[4]. In the same year, only 54.0% reached the last grade of
primary education in developing countries compared to 81.7% globally[4].
In this context, many studies have been carried out in developing countries on the effects of early childhood development on future academic
achievement. These studies have shown that stunting in the rst ve years of life leads to cognitive impairment in children[5–8], poor school
performance, fewer years of schooling, and low productivity in adulthood[7, 8]. Children who have been stunted in childhood are therefore more
likely to delay school enrollment, perform poorly in school, repeat a grade, and drop out of school than those who have not been stunted[9, 10].
However, some studies observed no signicant association between childhood stunting and academic performance[11, 12], grade repetition[10,
13], and school dropout[14].
Systematic reviews have been undertaken in this eld in developing countries. However, most of them[15–20] are qualitative reviews. To our
knowledge, only one review[21] carried out a meta-analysis on the effect of linear growth or stunting on child development, but it does not
include outcomes on age at school entry, grade repetition, and school dropouts. We aim to review evidence of the effect of stunting or height-
for-age on schooling level and schooling trajectories, dened as the combination of school entry age, grade repetition, and school dropouts.
Methodology
The protocol of the review was designed according to the “Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols
(PRISMA-P) 2015 statement”[22] and registered on the International Prospective Register of Systematic Reviews (PROSPERO) on September
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20, 2020 (#CRD42020198346). This review was conducted according to the “Cochrane collaborative guidelines for systematic reviews”[23]
and is reported according to “The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare
interventions”[24] (see PRISMA check list in Additional le 1).
Eligibility Criteria
The PICOS (Population, Intervention/exposure, Comparator, Outcome and Study design) approach was used to dene inclusion criteria. Our
population of interest was primary school aged children in developing countries. Generally, the legal age of admission to primary school is
between ve and seven years old[25]. Primary school usually lasts six years, although it can range from four to seven years[25], and it usually
ends between ages 10 and 12 years[25]. We considered studies that include children aged between 5 and 12 years from developing countries.
We also included studies on poor child development that resulted in stunting when children were less than ve years old. Studies that use
standardized height-for-age ratio (height-for-age z-scores) as a measure of child growth during infancy were included. We also considered
studies that used height as a marker of stunting. We considered four outcomes: age at school entry, grade repetition, school dropout, and
schooling level. Schooling level was dened as the highest level of education attained by an individual at the time of study. Eligible studies
were observational studies (prospective, retrospective, case-control, case series, and cross-sectional studies). Studies on diverse populations
were included if data on the subgroup of primary school age children related to stunting and outcomes could be extracted or if primary school
aged children constituted more than 80% of the study population.
Research Strategy
We conducted a comprehensive systematic literature search via Pubmed, Embase, ERIC (via Ovid), Web of Science and PsycINFO (via Ovid)
(last updated March 20, 2021). We developed a rigorous search strategy using relevant keywords related to stunting, schooling trajectory, and
geographic area. The search strategy was designed using both free and controlled vocabularies in PubMed, and then translated into other
databases. No restriction was applied on language or date of publication. We consulted an information specialist to validate our search
strategy, and the sensitivity of the strategy was evaluated by verifying the inclusion of ve relevant studies.
Studies management and selection
Data management
The bibliographic reference management software package EndNote was used for citation management. We imported references from
databases into EndNote and then removed duplicates using both automatic and manual screening based on study titles. Then, citations were
transferred to Covidence for selection.
Study Selection process
Two reviewers (JG and LPB) evaluated all studies independently by screening titles, abstracts, and full texts to identify studies that met the
inclusion criteria. We rst evaluated inter-reviewer agreement (Kappa) on eligibility using the rst 300 citations to ensure that reviewers had a
good understanding of inclusion criteria. Inter-reviewer agreements were assessed after each step of selection. Disagreements between JG and
LPB were resolved by consensus or by consulting a third reviewer. At the full text stage, reasons for exclusions were recorded.
Data extraction
An Excel data extraction form and a detailed instruction manual was developed and piloted with a sample of three studies. The same two
reviewers (JG and LPB) extracted data independently from the selected studies. Data extracted include study characteristics (rst author, year
of publication, country), population characteristics (sample size, proportion of girls, age), study design, follow-up duration, exposure (type of
exposure, age at exposition measurement), outcomes (age at school entry, level of education attained, repetition, dropouts), effect measures,
and condence intervals (adjusted measure of effect, condence interval, p-value, standard errors, confounding variables). Study authors were
contacted with up to three email attempts in case of missing information or unclear data. All extracted data from the two reviewers were cross-
checked and disagreements were discussed to reach a consensus or by involving a third reviewer.
Risk of bias
The risk of bias of the included studies was assessed using the ROBINS-I tool for non-randomized studies [26]. The tool covers confounding
bias, selection bias, classication bias, bias due to missing data, and bias due to measurement of the outcome. Studies were classied as low,
moderate, or high risk. To assess confounding bias, we considered a model well adjusted if it included demographic characteristics (e.g. child
sex, child age), and household characteristics (e.g. socioeconomic status, household size, place of residence), and characteristics of the
mother and father (e.g. education, size, ethnic group). The same two reviewers (JG and LPB) extracted data on risk of bias evaluation
independently. Disagreements were resolved by discussion or by involvement of a third reviewer.
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Statistical analysis and data synthesis
Eligible studies were described in detail according to PICOS parameters. We conducted a meta-analysis to estimate the pooled effect of
stunting on the different outcomes and their 95% condence intervals. Then, all studies with sucient information to estimate the pooled
effect were included in the meta-analysis. Pooled effects were calculated according to the domain of the outcome, study design, and effect
measured. Thus, for a given outcome domain, more than one pooled effect was estimated if the outcomes were measured in different ways or
if different types of effect measures were extracted.
Pooled effects were estimated using generic inverse variance weighting random effect models with Review Manager (RevMan) software [27–
29]. Heterogeneity was evaluated by the Higgins I² statistic which is the proportion of total variation in the pooled effect size attributable to
heterogeneity between studies [30]. Heterogeneity was considered very low, low, moderate, or high if I² were, respectively, ≤ 25 %, > 25 % and ≤
50 %, > 50% and ≤ 75 %, or > 75 % [31, 32]. Sensitivity analyses were performed if heterogeneity was high (I²>50%).
Sensitivity and subgroup analysis
To understand the source of heterogeneity, a sensitivity analysis was performed by removing one study at a time from the pooled effect size
estimation. This allowed us to measure the effect of each selected study on the pooled effect heterogeneity. We were not able to conduct
sensitivity analysis on studies at low risk of bias or subgroup analysis based on age of child stunting assessment (≤ 2 years of age and > 2
years of age) due to the insucient number of studies.
Results
Study selection and characteristics
We identied 4981 studies, of which 3944 were screened by title and abstract after removing duplicates (Fig.1). Eighty-seven (87) studies were
assessed by full text and 16 were considered eligible for the review. From these studies, six were included in the meta-analysis. The inter-
reviewer agreements (Kappa statistics) were 97.5% and 83.5% respectively, for title and abstract screening and full-text selection. All studies
were in English.
Out of the 16 eligible studies, 7 were published before 2006 [14, 33–38], and 7 others between 2006 and 2015 [6, 7, 12, 39–42] (Table1 and
Table2). Only two studies were published after 2015 [10, 43]. Most of the studies (56.2 %) used a prospective observational design [6, 7, 10, 12,
14, 36, 37, 41, 42], while 43.8 % were cross-sectional studies [33–35, 38–40, 43]. The most commonly studied exposure was standardized
height-for-age (56.2 %) [6, 12, 34, 37, 38, 40, 41] followed by stunting (37.4%) [10, 14, 35, 36, 39]. Two studies [42, 43] used both stunting and
height-for-age z-score. Most of the studies analyzed age at school entry [7, 10, 36–42], and schooling level [6, 10, 12, 34, 35, 39, 43]. Relatively
few studies analyzed grade repetition [10, 12, 14, 37, 42] and dropout [14, 33, 38]. One study included four countries [6] and another involved
two countries [35]. One study presented results only by sex [36]. In Sunny, DeStavola (10), the exposition was measured at three time points
(between 0 and 4 months, between 11 and 16 months, and between 4 and 8 years). Sample sizes of included studies ranged from 325 to 2711.
Participants were between 6.2 and 18.0 years old at the time of study (Table2).
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Table 1
Characteristics of studies included in the review and meta-analysis
Characteristics N %
Characteristics of studies
Year of publication
< 2006 7 43.8
2006–2015 7 43.8
> 2015 2 12.4
Study design
Cross-sectional 7 43.8
Prospective 9 56.2
Retrospective
Type of exposure*
Height-for-age z-score 9 56.2
Height 2 12.4
Stunting 6 37.4
Type of outcome domain*
Age at school entry 9 56.2
Schooling level 7 43.8
Grade repetition 5 31.3
School dropout 3 18.8
*Some studies were counted more than once because they use more than one type of exposition or outcome leading to a number of
studies greater than 16 and a total of percentages greater than 100%.
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Table 2
Description of included studies and authors’ conclusions
Author, year
Study design
Setting Sample
size Age
(mean) Sex
(% of
girls)
Follow-
up
duration
Exposure Outcome
domain Potential
confounders
considered
Authors’
conclusion
Acharya,
2019 [43]
Cross-
sectional
India 1194 NR 48.6 - Stunting
and
Height-for-
age
between 8
and 11
years
Schooling
level Gender, child’s age,
child had any
chronic illness or
congenital or
perinatal disorder,
mother’s
education, father’s
education, child
lived with both
biological parents
in household,
household income,
scheduled
caste/tribe, English
medium school,
worried about not
having sucient
food during the
past month
Acute
nutritional
status was
associated
with lower
math scores
and lower
educational
attainment,
suggesting
that current
adverse
conditions
are also
important
determinants
of cognitive
achievement
and
educational
attainment.
Alderman,
2006 [41]
Prospective
cohort
Zimbabwe 665 17.7
years 50.8 ≈ 14.4
years Height-for-
age at 39.9
months
Age at
school
entry
Sex, current age,
maternal age,
maternal
education,
maternal height,
year of rst
measurement
(instrumental
variable) exposure
to civil a war,
drought shock
Improved
preschooler
nutritional
status, as
measured by
height given
age, is
associated
with
increased
height as a
young adult,
a greater
number of
grades of
schooling
completed,
and an earlier
age at which
the child
starts school
Alderman,
2009 [7]
Prospective
cohort
Tanzania ≈ 1147 15.7
years 49.0 10
years Percentage
of median
reference
height at
10 years or
less
Age at
school
entry
Residuals %
median, female,
age of child in
years, mother’s
height, father’s
height, parent had
secondary
schooling, number
of teachers per
class, number of
blackboards per
class, urban,
maximum
education in
household, (log)
per capita
household
expenditure,
electricity in the
household,
(instrumental
variables) crop
loss in 2004, and
ood or drought in
2004
Children who
are
malnourished
have lower
schooling
and delay
their school
entry.
NR: Not reported
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Author, year
Study design
Setting Sample
size Age
(mean) Sex
(% of
girls)
Follow-
up
duration
Exposure Outcome
domain Potential
confounders
considered
Authors’
conclusion
Bogin, 1987
[33]
Cross-
sectional
Guatemala 514 Range:
7-
13.99
NR - Height
between 7
and 13.99
years
School
dropout Grade, child’s age,
weight, arm
circumference,
triceps skinfold,
subscapular
skinfold, muscle
area, and fat area
School
continuation
or drop-out
was not
inuenced by
the health or
nutritional
environment.
Crookston,
2013 [6]
Prospective
cohort
Ethiopia
India
Peru
Vietnam
1757
1815
1845
1829
8.1
8.0
7.9
8.1
46.6
46.3
49.8
48.8
7 years Height-for-
age at 1
year
Schooling
level Sex, age of the
mother, years of
schooling of the
mother, years of
schooling of the
father, asset index,
urban residence,
community
population,
community wealth,
and presence of a
community
hospital
Improving
growth in
children who
are stunted in
infancy and
maintaining
nutrition in
children who
otherwise
might falter
may have
signicant
benet for
schooling
and cognitive
achievement.
Daniels, 2004
[36]
Prospective
cohort
Philippines 2198 18.0
years 47,1 ≈ 16
years Stunting at
2 years Age at
school
entry
Parity, maternal
and paternal
education,
maternal height,
index of assets,
index of
environmental
cleanliness,
presence of
electricity, and
deated household
income
Boys and
girls who
were taller at
2 y were
markedly less
likely to drop
out in grade
school or to
be behind in
school and
were
therefore
more likely to
graduate
from high
school on
time.
Gandhi, 2011
[12]
Prospective
cohort
Malawi 325 12.0 51.0 ≈ 12
years Height-for-
age at 1
month,
residuals
height-for-
age at 6,
18, and 60
months
Schooling
level
Grade
repetition
Gender, gestational
duration, father’s
occupation,
father’s literacy,
mother’s literacy,
and wealth index
Height-for-
age at 1
month and
conditional
height gain
prior to 6
months did
not show
signicant
association
with the
outcome
measures.
NR: Not reported
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Author, year
Study design
Setting Sample
size Age
(mean) Sex
(% of
girls)
Follow-
up
duration
Exposure Outcome
domain Potential
confounders
considered
Authors’
conclusion
Glewwe,
1995 [38]
Cross-
sectional
Ghana 1757 11.0
years 48.2 - Height-for-
age
between 6
and 15
years
Age at
school
entry
School
dropout
Sex, age, zscore
residual, log
expenditure/capita,
expenditure
residual, mother's
schooling, father's
schooling, number
of siblings, ethnic
group, residence
area, travel time to
middle school,
travel time to
primary school,
average teacher
experience,
average teacher
schooling, average
teacher training,
fraction
classrooms with
blackboard,
books/classroom,
fraction
classrooms
leaking, fraction
classrooms
unusable, fraction
classrooms shed
construction, some
children lack
desks, private
school, school has
all six grades,
enrollment fee, and
school denies
admission
Delayed
primary
school
enrollment is
caused by
nutritional
deciencies
in early
childhood,
evidence
which
survives
numerous
robustness
checks.
Glewwe,
2001 [37]
Prospective
cohort
Philippines 1016 11
years 48.0 11
years Height-for-
age Age at
school
entry
Grade
repetition
Sex, age enrolled,
month of birth
effects,
(instruments)
month of birth, the
sibling age
difference, and the
sibling age and sex
differences
interacted with the
barangay level
average grade
repetition rate.
Better
nourished
children
perform
signicantly
better in
school, partly
because they
enter school
earlier and
thus have
more time to
learn but
mostly
because of
greater
learning
productivity
per year of
schooling.
NR: Not reported
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Author, year
Study design
Setting Sample
size Age
(mean) Sex
(% of
girls)
Follow-
up
duration
Exposure Outcome
domain Potential
confounders
considered
Authors’
conclusion
Grira, 2004
[34]
Cross-
sectional
Bangladesh 1338 12.4
years 47.0 - Height-for-
age at 12.4
years
Schooling
level Age, sex, mother’s
schooling, father's
schooling, family
size, agricultural
land, log
income/capita,
school ooring
type, and school
expenses/income
Child health
is not
signicantly
determinant
to the school
enrollment
decision, but,
once enrolled,
nutritional
deciencies
substantially
retard school
progress as
they can
affect a
child's
cognitive
achievement.
Khanam,
2011 [39]
Cross-
sectional
Bangladesh 1441 11.2
years 39.0 - Stunting
between 5
and 17
years
Age at
school
entry,
Schooling
level
Child’s age, gender
of child, total
household
members, log
household
expenditure, father
can read and write,
mother can read
and write, clean
housing condition,
sanitary latrine,
hand washing,
primary school,
secondary girls’
school, secondary
mixed school,
distance to doctor,
and availability of
electricity
Malnourished
children are
signicantly
more likely to
enroll in
school later
than the due
age. It is also
found that,
after
adjusted for
actual
enrolled age,
the grade
attainment of
children is
not affected
by stunting
condition.
McCoy, 2015
[40]
Cross-
sectional
Zambia 2711 6.2
years 48.4 NR Height-for-
age at 6.2
years
Age at
school
entry
Gender, child age,
household size,
regional income,
and urbanicity
Children’s
height-for-
age at age 6
was
signicantly
predictive of
enrollment
both
concurrently
and one year
later,
suggesting
that
caregivers (or
primary
school
teachers)
may be
making
decisions
about school
readiness
largely based
on their
children’s
physical size.
NR: Not reported
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Author, year
Study design
Setting Sample
size Age
(mean) Sex
(% of
girls)
Follow-
up
duration
Exposure Outcome
domain Potential
confounders
considered
Authors’
conclusion
Mendez,
1999 [14]
Prospective
cohort
Philippines 2131 11.0
years NR 10
years Stunting at
2 years Grade
repetition
School
dropout
NR Children
stunted at
age 2 y had a
marked delay
in initial
school
enrollment
and were
much more
likely to
experience
absences
and to drop
out of school
than non-
stunted
children.
Sunny, 2018
[10]
Prospective
cohort
Malawi 1044 NR 47.9 11
years Stunting
between, 0
and 4
months, 11
and 16
months, 4
and 8
years
Age at
school
entry
Schooling
level
Grade
repetition
Sex, father’s
education,
mother’s
education,
household asset
index at birth
Stunting in
early and late
childhood
was
associated
with poor
school
outcomes
(late
enrollment
and poor
progression
through
school).
The
Partnership
for Child
Development,
1999 [35]
Cross-
sectional
Ghana
Tanzania
1566
1390
10.9
years
11.1
years
49.9
52.9
- Ghana:
stunting at
10.9 years
Tanzania:
stunting at
11.1 years
Schooling
level Ghana: age,
Schistosoma
haematobium
(egg/10 ml)
Tanzania: age, sex,
socioeconomic
status score,
school travel time
Children who
are stunted
are more
likely to delay
enrollment in
school.
Delayed
enrollment
may lead to
fewer years
of schooling,
poor
educational
achievement
and poor
employment
prospects.
NR: Not reported
Page 11/18
Author, year
Study design
Setting Sample
size Age
(mean) Sex
(% of
girls)
Follow-
up
duration
Exposure Outcome
domain Potential
confounders
considered
Authors’
conclusion
Satriawan,
2009 [42]
Prospective
cohort
Indonesia 1944 Range:
7–9
years
NR ≈ 7
years Height-for-
age and
non-
stunted
Age at
school
entry
Grade
repetition
Time, mother’s
education, father’s
education, age of
household head,
number of 6 to 14
year old children in
the household,
number of females
adult in the
household, number
of male adults in
the household, per-
capita expenditure,
price of rice, price
of sugar, price of
cooking oil, price
of condensed milk,
community xed
effect.
Reducing
incidence of
poor
childhood
nutrition
reduces also
the
probability of
delayed
enrollment,
but not the
probability of
repeating a
grade. More
importantly,
the estimated
effects when
taking into
account the
endogeneity
of childhood
nutrition are
5 to 7 times
stronger than
when
ignoring the
endogeneity
of childhood
nutrition.
NR: Not reported
Risk of bias
Almost all studies had a moderate (13) or high (2) risk of confounding bias (Table3 in Additional le 2). More than one-fth (4, 25%) of studies
have a high risk of bias due to missing data. Missing information was not reported in ve (31%) studies (Table3 in Additional le 2). Risks of
bias in other domains of bias were low for all studies. Assessment of publication bias by funnel plot was not possible because of low number
of studies by outcome of interest and variation in measure of effects estimated.
Stunting and age at school entry
Nine studies presented associations between height-for-age or stunting and an outcome related to age at school entry[7, 10, 36–42]. These
studies could not be combined because exposures, outcomes or effect measures differed. Among these studies, one estimated the association
between height-for-age at two years and early or late enrollment by sex [36]. The meta-analysis from this study (Fig.2) suggests that one unit
increase in height-for-age is associated with a 34% increase in the odds of early enrollment [OR: 1.33 (95% CI: 1.07; 1.67), I²=0%] and a
reduction of 37% in the odds of late enrollment [OR: 0.63 (95% CI: 0.51; 0.78), I²=0%]. All studies reported an association between height-for-age
or stunting and the age at school entry [7, 10, 36–42] (Table2 and Table4 in Additional le 3).
Stunting and schooling level
Two of the four cross-sectional studies[34, 35] assessing the association between height-for-age and school overage by mean difference
observed that an increase of one unit of height-for-age in a child was associated with an increase in schooling level for their age. The pooled
effect of the studies led to similar results [MD: 0.24 (95% CI: 0.14; 0.34), I²=92%] (Fig.3), but was characterized by high heterogeneity. Two
other cross-sectional studies were not used in this pooled effect estimation [39, 43]. One [39] found that stunted children were more likely to be
overage, and the other reported that stunted children were more likely to be in a low grade; a one unit increase in height-for-age was associated
with an increase in grade attainment [43]. In meta-analysis of longitudinal studies, an increase in height-for-age was associated with a
reduction in the odds of school overage [OR: 0.79 (95% CI: 0.70; 0.90), I²=76%] with high heterogeneity. Gandhi, Ashorn (12) and Sunny,
DeStavola (10) were not included in the pooled effect estimation because of the analysis methods they used and their outcome
measurements. Gandhi, Ashorn (12) reported a non-signicant association between height-for-age and schooling level, and Sunny, DeStavola
(10) found a signicant association (Table2 and Table4 in Additional le 3).
Stunting and grade repetition
Figure 4 shows that grade repetition is associated with stunting or height-for-age. All included studies used a longitudinal design. The pooled
estimates suggests that the odds of grade repetition increase by 59 % [OR: 1.59 (95% CI: 1.18; 2.14), I²=51%) for stunted children compared to
non-stunted children with moderate heterogeneity. Two studies, which are not include in the meta-analysis, report an association between
stunting and grade repetition[12, 37], and one other study did not nd an association[42] (see Table2 and Table4 in Additional le 3).
Stunting and school dropout
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Discussion
This systematic review suggests that stunting determines age at school entry, schooling level, and grade repetition. An increase one unit in
standard deviation in height-for-age is associated with an increase in the odds of early enrollment and delayed enrollment. Children with
greater height-for-age were less likely to be overage for their grade. We also found that stunted children were more likely to repeat a grade than
nonstunted children. Results from this study do not allow conclusions to be drawn regarding the relationship between stunting or height-for-
age and dropping out of school.
Childhood stunting can be associated with diculties learning the school curriculum. Children with high height-for-age z-scores, or those who
were non-stunted started school earlier than those with low height-for-age z-score or those who were stunted. The latter are considered unready
to start school at the minimum enrollment age [7, 37, 44]. Delayed enrollment could also reect a lter imposed by schools if administrators
use height as a sign of school readiness [7]. The high probability of grade repetition for stunted children is due to low school performance.
Grade repetition occurs when children’s academic performance is deemed unsatisfactory. Schooling levels can be seen as the reection of age
at school enrollment and grade repetition, and are thus dependent on academic performance. Several studies have highlighted that stunted
growth and height-for-age are associated, respectively, negatively and positively with test results in mathematics, reading, communication and
motor development [6, 21, 45–49]. Results showed that impaired growth and development in infancy negatively affects later academic
performance and therefore academic trajectory, which leads, overall to low school levels. This may explain why stunted children are more likely
to be unemployed, less productive, and to have low social status than non-stunted children[50–53].
Stunting could lead to a delay in the development of cognitive functions and permanent cognitive impairments, which improve little with
age[54]. This relationship between stunting and cognitive abilities is particularly important in the rst years of life when vital human
development occurs in all domains, including the brain formation[16, 55]. When stunting occurs in this early stage of life, it severely affects
attention development, executive functions such as cognitive exibility, working memory, and visuospatial functions like visual
construction[54]. Experimental research on animals has also shown that nutrition deciencies negatively affect brain development and
measure of performance[55–58], but it is dicult to extrapolate this to human cognition[57]. Thus, to establish causality between nutritional
status and performances, intervention studies has been undertaken, and they have shown that early intervention on health and nutrition
increase child probability to be enrolled on time in primary school, and improve cognitive development [15, 59].
Strengths and limitations
This review is one step towards better understanding the effects of growth in early childhood on subsequent school trajectories. It is the rst
review to highlight the components of the academic trajectory that are inuenced by stunting. This review does have some weaknesses,
however. First, almost all studies were identied as having moderate risk of confounding bias, even though some of them used advanced
methods to control for confusion. This is due to the tool of bias assessment. Second, outcomes and measures of effect varied widely across
studies, which limited our ability to estimate pooled effects. However, this diversity allowed us to explore multiple facets of academic
performance. Third, we did not obtain sucient data to estimate pooled effects of stunting or height-for-age on dropouts, which suggests that
this outcome has not been suciently studied in the literature. Fourth, due to the low number of studies, we were not able to perform subgroup
analysis which may have shown an effect of the timing of stunting (e.g., stunting before 2 years vs stunting after 2 years).
Conclusion
The results show that stunting in childhood might lead to a delay school enrollment, grade repetition, school dropout and low schooling levels.
This study is a step towards understanding the overall effect of stunting or height-for-age on academic trajectory. Results showed that
impaired growth and development in infancy is associated with a delay of school age entry, an increased risk of grade repetition, and increased
school dropout, which, in turn, lead to children’s low levels of education. Although this review provides an overall picture of the educational
trajectory of children from developing countries who experienced stunting in childhood, further research is needed on the effect of stunting on
educational trajectories among this population. Since stunting affects more children from poor communities than from wealthy communities,
Pooled effects were not estimated for school dropout because no two studies used the same effect measures. Nevertheless, results from these
studies were mitigated. Mendez and Adair (14) reported that stunted children were more likely to drop out of school than non-stunted children.
But Glewwe and Jacoby (38) found that taller children tended to leave school earlier, while Bogin and MacVean (33) reported that school
continuation or dropout was not inuenced by health or nutritional environment (Table2 and Table4 in Additional le 3).
Sensitivity analysis
The number of studies was not sucient to conduct sensitivity analyses according to risk of bias or subgroup analyses by age. We performed
a sensitivity analysis on schooling level by removing one study or estimated effect at a time from the pooled effect size. When we removed the
effect size of Tanzania from The Partnership for Child Development (35) study, the Higgin’s I² decreased from 90–0% and the magnitude of the
pooled effect increased from 0.24 (Fig.3) to 0.29 (Fig.5 in Additional le 4).
Page 13/18
future research should also explore the effect modication of socioeconomic status on the relationship between stunting and school
trajectories to inform the development of effective interventions. The current results imply the need for leaders of developing countries to work
more for the prevention of stunting through programs and projects focused on nutrition and health problems in childhood. Similarly, health
issues should be integrated into education policies to allow for specic care of stunted children in order to improve their school performance.
Abbreviations
PROSPERO
International Prospective Register of Systematic Reviews
PRISMA-P
Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols
OR
Odds Ratio
MD
Mean Difference
RevMan
Review Manager
PICOS
Population, Intervention/exposure, Comparator, Outcome and Study Design
Declarations
Ethics approval and consent to participate
Not Applicable
Consent for publication
Not Applicable
Availability of data and materials
Not Applicable
Competing interests
The authors declare that they have no competing of interests
Funding
This research has no source of funding.
Authors' contributions
RJG, SH, and LM conceived the study. RJG drafted the protocol and LM revised it. RJG and LPB selected studied and extracted data. SH
intervened to settle disagreements. RJG wrote the rst draft of the manuscript, which was revised by LM and JFK. The nal manuscript was
read and approved by all authors.
Acknowledgements
Not Applicable
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Figures
Figure 1
Study selection process for the review * Kappa for screening by title and abstract = 97.5% ** Kappa for selection by full text = 83.5%
Page 17/18
Figure 2
Meta-analysis of the association between height-for-age and early or late enrollment
Figure 3
Meta-analysis of the association between height-for-age and schooling level
Figure 4
Meta-analysis of the association between height-for-age and grade repetition
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