Weight gain in the first two years of life is an important predictor of schooling outcomes in pooled analyses from five birth cohorts from low- and middle-income countries.
ABSTRACT Schooling predicts better reproductive outcomes, better long-term health, and increased lifetime earnings. We used data from 5 cohorts (Brazil, Guatemala, India, the Philippines, and South Africa) to explore the relative importance of birthweight and postnatal weight gain for schooling in pooled analyses (n = 7945) that used appropriate statistical methods [conditional weight (CW) gain measures that are uncorrelated with prior weights] and controlled for confounding. One SD increase in birthweight, approximately 0.5 kg, was associated with 0.21 y more schooling and 8% decreased risk of grade failure. One SD increase in CW gain between 0 and 2 y, approximately 0.7 kg, was associated with higher estimates, 0.43 y more schooling, and 12% decreased risk of failure. One SD increase of CW gain between 2 and 4 y, approximately 0.9 kg, was associated with only 0.07 y more schooling but not with failure. Also, in children born in the lowest tertile of birthweight, 1 SD increase of CW between 0 and 2 y was associated with 0.52 y more schooling compared with 0.30 y in those in the upper tertile. Relationships with age at school entry were inconsistent. In conclusion, weight gain during the first 2 y of life had the strongest associations with schooling followed by birthweight; weight gain between 2 and 4 y had little relationship to schooling. Catch-up growth in smaller babies benefited schooling. Nutrition interventions aimed at women and children under 2 y are among the key strategies for achieving the millennium development goal of universal primary education by 2015.
- SourceAvailable from: Camila Corvalán[show abstract] [hide abstract]
ABSTRACT: Pre-natal and post-natal growth are associated with adult body composition, but the relative importance of growth in different periods of childhood is still unclear, particularly in stunted populations. We studied 358 women and 352 men measured as children in 1969-77 in four villages in Guatemala, and re-measured as adults in 2002-04 (mean age 32.7 years). We determined the associations of body mass index (BMI) and length at birth, and changes in BMI and length during infancy (0-1.0 year) and early (1.0-3.0 years) and later (3.0-7.0 years) childhood, with adult BMI ((a)BMI), percentage of body fat ((a)PBF), abdominal circumference ((a)AC) and fat-free mass ((a)FFM). Prevalence of stunting was high (64% at 3 years; HAZ < -2SD). Obesity (WHZ > 2SD) prevalence in childhood was <2%, while overweight prevalence in adulthood was 52%. BMI at birth was positively associated with (a)BMI and (a)FFM while length at birth was positively associated with (a)AC and (a)FFM. Increased BMI in infancy and later childhood were positively associated with all four adult body composition measures; associations in later childhood with fatness and abdominal fatness were stronger than those with (a)FFM. Change in length during infancy and early childhood was positively associated with all four adult body composition outcomes; the associations with (a)FFM were stronger than those with fat mass. Increases in BMI between 3.0 and 7.0 years had stronger associations with adult fat mass and abdominal fat than with (a)FFM; increases in length prior to age 3.0 years were most strongly associated with increases in (a)FFM.International Journal of Epidemiology 07/2007; 36(3):550-7. · 6.98 Impact Factor
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ABSTRACT: Undernutrition in infancy and early childhood is thought to adversely affect cognitive development, although evidence of lasting effects is not well established. With the use of data from the Cebu Longitudinal Health and Nutrition Study, we assesshere the relationship between stunting in the first 2 y of life and later cognitive development, focusing on the significance of severity, timing and persistence of early stunting. The sample included > 2000 Filipino children administered a cognitive ability test at ages 8 and 11 y. Stunting status was determined on the basis of anthropometric data collected prospectively between birth and age 2 y. Children stunted between birth and age 2 y had significantly lower test scores than nonstunted children, especially when stunting was severe. The shortfall in test scores among children stunted in the first 2 y was strongly related to reduced schooling, which was the result of a substantial delay in initial enrollment as well as higher absenteeism and repetition of school years among stunted children. Interactions between stunting and schooling were not significant, indicating that stunted and nonstunted children benefitted similarly from additional schooling. After multivariate adjustment, severe stunting at age 2 y remained significantly associated with later deficits in cognitive ability. The timing of stunting was also related to test performance, largely because children stunted very early also tended to be severely stunted (chi(2) P = 0.000). Deficits in children's scores were smaller at age 11 y than at age 8 y, suggesting that adverse effects may decline over time. Results emphasize the need to prevent early stunting and to provide adequate schooling to disadvantaged children.Journal of Nutrition 08/1999; 129(8):1555-62. · 4.20 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: The risk of type 2 diabetes mellitus is increased in people who have low birth weights and who subsequently become obese as adults. Whether their obesity originates in childhood and, if so, at what age are unknown. Understanding the origin of obesity may be especially important in developing countries, where type 2 diabetes is rapidly increasing yet public health messages still focus on reducing childhood "undernutrition." We evaluated glucose tolerance and plasma insulin concentrations in 1492 men and women 26 to 32 years of age who had been measured at birth and at intervals of three to six months throughout infancy, childhood, and adolescence in a prospective, population-based study. The prevalence of impaired glucose tolerance was 10.8 percent, and that of diabetes was 4.4 percent. Subjects with impaired glucose tolerance or diabetes typically had a low body-mass index up to the age of two years, followed by an early adiposity rebound (the age after infancy when body mass starts to rise) and an accelerated increase in body-mass index until adulthood. However, despite an increase in body-mass index between the ages of 2 and 12 years, none of these subjects were obese at the age of 12 years. The odds ratio for disease associated with an increase in the body-mass index of 1 SD from 2 to 12 years of age was 1.36 (95 percent confidence interval, 1.18 to 1.57; P<0.001). There is an association between thinness in infancy and the presence of impaired glucose tolerance or diabetes in young adulthood. Crossing into higher categories of body-mass index after the age of two years is also associated with these disorders.New England Journal of Medicine 02/2004; 350(9):865-75. · 51.66 Impact Factor
The Journal of Nutrition
Community and International Nutrition
Weight Gain in the First Two Years of Life Is an
Important Predictor of Schooling Outcomes in
Pooled Analyses from Five Birth Cohorts from
Low- and Middle-Income Countries1,2
Reynaldo Martorell,3* Bernardo L. Horta,4Linda S. Adair,5Aryeh D. Stein,3Linda Richter,6
Caroline H. D. Fall,7Santosh K. Bhargava,8S. K. Dey Biswas,9Lorna Perez,10Fernando C. Barros,4
Cesar G. Victora,4and Consortium on Health Orientated Research in Transitional Societies Group11
3Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322;4Universidade Federal de
Pelotas, Pelotas 96090-790, Brazil;5Department of Nutrition, Gillings School of Global Public Health, University of North Carolina,
Chapel Hill, NC 27516-2524;6Birth to Twenty Research Programme, University of the Witwatersrand and the Human Sciences Research
Council, Durban 4014, South Africa;7MRC Epidemiology Resource Centre, University of Southampton, Southampton S016 6YD, UK;
8S.L. Jain Hospital, Delhi 464551, India;9Indian Council of Medical Research, New Delhi 138648, India; and10Office of Population
Studies Foundation, University of San Carlos, Cebu 6000, Philippines
Schooling predicts better reproductive outcomes, better long-term health, and increased lifetime earnings. We used data
from 5 cohorts (Brazil, Guatemala, India, the Philippines, and South Africa) to explore the relative importance of
birthweight and postnatal weight gain for schooling in pooled analyses (n = 7945) that used appropriate statistical
methods [conditional weight (CW) gain measures that are uncorrelated with prior weights] and controlled for
confounding. One SD increase in birthweight, ~0.5 kg, was associated with 0.21 y more schooling and 8% decreased risk
of grade failure. One SD increase in CW gain between 0 and 2 y, ~0.7 kg, was associated with higher estimates, 0.43 y
more schooling, and 12% decreased risk of failure. One SD increase of CW gain between 2 and 4 y, ~0.9 kg, was
associated with only 0.07 y more schooling but not with failure. Also, in children born in the lowest tertile of birthweight,
1 SD increase of CW between 0 and 2 y was associated with 0.52 y more schooling compared with 0.30 y in those in the
upper tertile. Relationships with age at school entry were inconsistent. In conclusion, weight gain during the first 2 y of life
had the strongest associations with schooling followed by birthweight; weight gain between 2 and 4 y had little
relationship to schooling. Catch-up growth in smaller babies benefited schooling. Nutrition interventions aimed at women
and children under 2 y are among the key strategies for achieving the millennium development goal of universal primary
education by 2015.J. Nutr. 140: 348–354, 2010.
Low birthweight and stunting in early childhood are associ-
ated with diminished adult human capital, including poorer
cognitive development, behavioral problems, and lower
schooling attainment, even after controlling for confounding
factors such as parental schooling and socioeconomic status
2Author disclosures: R. Martorell, B. L. Horta, L. S. Adair, A. D. Stein, L. Richter,
C. H. D. Fall, S. K. Bhargava, S. K. Dey Biswas, L. Perez, F. C. Barros, and C. G.
Victora, no conflicts of interest.
* To whom correspondence should be addressed. E-mail: email@example.com.
11COHORTS group members not included as named authors for this paper:
Pedro Hallal and Denise Gigante (Universidade Federal de Pelotas, Brazil),
Manuel Ramirez-Zea (Institute of Nutrition of Central America and Panama,
Guatemala City, Guatemala), Vinod Kapani (Bureau of Labor Statistics,
Washington, DC), Clive Osmond and Andrew Wills (MRC Epidemiology
Resource Centre, University of Southampton, Southampton, UK), Darren Dahly
(University of Leeds, UK), Christopher Kuzawa (Department of Anthropology,
Northwestern University, IL), Harshpal Singh Sachdev (Sitaram Bhartia Institute
of Science and Research, New Delhi, India), and Shane A. Norris, Mathew
Mainwaring, and Daniel Lopes (Birth to Twenty, Department of Paediatrics,
University of the Witwatersrand, Johannesburg).
1Supported by the Wellcome Trust, UK. Funding sources for each of the
Consortium on Health Orientated Research in Transitional Societies (COHORTS)
sites are as follows: Brazil: Recent phases of the cohort study are funded by the
Wellcome Trust’s Health Consequences of Population Change Programme.
Guatemala: Past and present funding for the study has come from the U.S. NIH
and its Fogarty International Center; the U.S. National Science Foundation, the
Nestle Foundation,the Thrasher Foundation, and the AHA. India: The original cohort
study was funded by the U.S. National Center for Health Statistics and the Indian
Council of Medical Research. The latest phases are supported by the British Heart
Foundation, the Medical Research Council UK, and the Indian Council of Medical
Research. Philippines: Most recent follow-up surveys funded by the U.S. NIH,
Fogarty International Center. South Africa: Funding was provided by the Wellcome
Trust, Human Sciences Research Council, South African Medical Research Council,
the Mellon Foundation, the South-African Netherlands Programme on Alternative
Development, and the University of the Witwatersrand, Johannesburg.
0022-3166/08 $8.00 ã 2010 American Society for Nutrition.
Manuscript received July 28, 2009. Initial review completed September 15, 2009. Revision accepted November 7, 2009.
First published online December 9, 2009; doi:10.3945/jn.109.112300.
(SES)12(1–5). Years of schooling predict earnings (6). The
average rate of return to income of another year of schooling is
10%. Returns are highest for Latin America and the Caribbean
(12.0%) and sub-Saharan Africa (11.7%); the value for Asia is
9.9%. Maternal schooling is also important for child health
and nutrition (7,8) and schooling brings long-term health
benefits to individuals (9,10). For these reasons, achieving
universal primary education by 2015, one of the millennium
development goals, is important (11).
Few studies have investigated the relative importance of
outcomes and even fewer have done so using appropriate
statistical methods. Studies have focused on cognitive develop-
ment or achievement tests (12–16) and few have dealt with
schooling outcomes (17). Studies are particularly needed from
low- and middle-income countries (LMIC), where the impor-
tance of establishing these relationships for policies and
programs is greater due to high rates of growth failure and
We used data from 5 well-described birth cohort studies in
LMIC (1) to study the relative importance of birthweight and
weight gain during 0–24 mo and 24–48 mo for highest grade
attained, ever failed a grade, and age at school entry. Growth
failure in LMIC is substantial during intrauterine life and the
first 2 y of life; conversely, rates of growth after 2 y of age are
generally similar to those found in the WHO reference
population (18). Thus, we expected birthweight and weight
gain from 0 to 24 mo to be more strongly associated with
schooling outcomes than growth from 24 to 48 mo. We used
statistical methods that account for interrelationships in
growth during various periods of life to properly assess relative
importance. We also controlled for maternal schooling and SES
Participants and Methods
Study populations. We used data from 5 cohorts participating in the
Consortium on Health Orientated Research in Transitional Societies
(COHORTS) (1): the 1982 Pelotas (Brazil) Birth Cohort (19); the
Institute of Nutrition of Central America and Panama Nutrition Trial
Cohort (INTC; Guatemala) (20); the New Delhi (India) Study (21,22);
the Cebu Longitudinal Health and Nutrition Survey (CLHNS; Cebu,
Philippines) (23), and the Birth to Twenty (Bt20; South Africa) cohort
(24) (Table 1). For convenience, we refer to these studies below as
Pelotas, Guatemala, New Delhi, Cebu, and Bt20. All studies were
reviewed and approved by an appropriate ethics committee or institu-
tional review board.
Outcome variables. The outcomeswere highest gradeattained (y), ever
failed a grade (yes/no), and age at formal school entry (y). Pelotas,
Guatemala, and Cebu contributed data for all schooling outcomes. Most
Bt20 participants were still in school and for this reason we did not
conduct an analysis for highest grade attained for this site. Only highest
grade attained was available for New Delhi. Ever failed was defined as
failing (or being retained for) 1 or more grades and was coded as ever
failed = 1, never failed = 0.
Growth variables. Weight measures were available for all sites; birth
length was not available for Pelotas and Bt20. For this reason, weight
was used as the key exposure. Birthweight (kg) was measured by the
research teams in Pelotas, New Delhi, and Guatemala. In Cebu,
birthweight was measured by birth attendants who had been provided
with mechanical scales for home births (60%) or obtained from hospital
records for the remainder. For Bt20, weight was obtained from birth
records assessed for reliability (25). Weight and length (all but Bt20) or
height (Bt20) were obtained at 24 mo. Mid-childhood weight was
measured at 48 mo in Pelotas, New Delhi, and Guatemala, at 60 mo for
Bt20, and at 102 mo in Cebu. All measurements were converted into Z-
scores (weight-for-age, height-for-age) using the WHO Growth Stan-
dards (26). To make mid-childhood weight comparable across sites, we
imputed 48-mo Z-scores for Bt20 and Cebu participants, assuming a
linearchangeinZ-scorefrom24 to60or 102 mo,respectively, andback-
transformed the resulting Z-scores into weight (in kg). Stunting was
defined as height-for-age Z-score ,2.
Conditional weights. To eliminate statistical problems associated with
modeling highly correlated weight measures, we used conditional weight
(CW) variables to represent weight at a given age, independent of weight
at earlier ages (27,28). CW is the residual derived by regressing weight in
kgateachage onweightatbirthandonanyweight atprioragesandthus
represents a child’s deviation from his or her expected weight in the
context of typical growth in the population. For example, the residual
for CW at 48 mo was obtained by regressing weight at 48 mo on
birthweight and weight at 24 mo. CW variables at 24 and 48 mo can be
interpretedas weightgain intheperiod0–24and 24–48mo,respectively,
that is unexplained by prior weight measures. Models were site- and sex-
specific, included age at measurement, and accounted for nonlinear
relationships by including squared prior weight.
Covariates. Analyses included age at last follow-up, sex, whether the
participant was still in school, SES near birth (5 categories), and years of
maternal schooling (or paternal schooling in India). SES was represented
by family income (Pelotas), father’s occupation (New Delhi), or
ownership of various household assets (elsewhere). A SES score was
created for each site and study participants were allocated to 1 of 5
categories. The lowest group (SES 1) was designated as the reference
category in regression analyses and 4 dummy variables were created to
represent SES 2–5, respectively. More participants in India had schooling
information for fathers than for mothers; the 2 variables were correlated
(r = 0.6; P , 0.0001) and among participants with information for both,
results were similar when using one or the other. For this reason, father’s
schooling was used in India as a proxy of maternal schooling. Site-
specific variables considered as potential confounders included: race/
ethnicity for Pelotas and Bt20, urban-rural residence for Cebu, and
village of birth for Guatemala (to control for village size and nutrition
intervention study design). However, key relationships were not affected
by site-specific variables and, consequently, these variables were omitted.
Missing covariates (0.2% for SES, 1.0% for maternal schooling)
were handled by adding a dummy variable to the model, coded 1 if
missing and 0 otherwise, and recoding the missing values to the site-
specific means for SES and maternal schooling. We tested for systematic
bias due to the inclusion of individuals with 1 or more missing values by
running all models on a data set that included only complete records and
foundthat thedifferencesbetweenfull and restrictedmodels were trivial.
Analyses. The analyses included 7945 participants with information on
birthweight, weight at 24 and 48 mo, any of 3 schooling outcomes, and
length or height at 24 mo. Sample sizes were 7025, 6287, and 4668 for
highest grade attained, ever failed a grade, and age at school entry,
The key objective was to assess the relative importance of birth-
weight and weight gain from 0 to 24 mo and 24 to 48 mo for schooling
outcomes. We also explored the relationship between stunting at 24 mo,
a widely used summary measure of growth failure, and schooling. For
the former analyses, we used standardized measures (in Z-score units) of
birthweight (ZBWT) and CW at 24 (ZCW0–24) and 48 mo (ZCW24–
48) as the growth variables; these variables, by design, are uncorrelated
12Abbreviations used: Bt20, Birth to Twenty; CLHNS, Cebu Longitudinal Health
and Nutrition Survey; COHORTS, Consortium on Health Oriented Research in
Transitional Societies; CW, conditional weight; INTC, Institute of Nutrition of
Central America and Panama Nutrition Trial Cohort; LMIC, low- and middle-income
countries;SES,socioeconomic status; ZBWT,Z-score units ofbirthweight; ZCW0–
24, Z-score units of conditional weight from 0 to 24 mo; ZCW24–48, Z-score units
of conditional weight from 24 to 48 mo.
Child growth and schooling in 5 cohorts349
with each other. We estimated linear (highest grade attained and age at
school entry) or logistic (ever failed) regression models. Because our
growth variables were expressed in Z-score units, coefficients and CI for
them could be compared to assess relative importance. We developed
2 models. The first model is similar to that used in previous studies and
used stunting at 24 mo as the key exposure. In model 2, we included
ZBWT, ZCW0–24, and ZCW24–48; because the coefficients for ZBWT
were unaffected by CW variables, they could be compared with those in
the literature concerning birthweight alone. In a basic specification of
models 1 and 2, we adjusted for sex and site. In fully adjusted models, we
incorporated SES and maternal schooling. We tested for interactions in
model 2 to explore if relationships between ZCW0–24 and ZCW24–48
and schooling outcomes differed by birthweight. Tertiles of ZBWTwere
based on the distribution: low (,33.3%), medium 33.3–66.7%, and
We tested for heterogeneity by sex and site. We found no evidence of
heterogeneity for highest grade attained and ever failed and thus
undertook pooled analyses. There was significant heterogeneity by site
butnotsex forage atschoolentryand thustheseanalyseswere pooledby
sex and stratified by site.
We used PROC REG and PROC LOGISTIC in SAS version 9.1 (29)
to conduct the analyses. We present coefficients with 95% CI and
P-values. Significance was declared when P , 0.05.
Participants were youngest in Bt20, 15.6 y old, followed by
Pelotas and Cebu (~21–22 y) and then New Delhi and
Guatemala (~29–31 y) (Table 2). There were important differ-
ences in child size across sites. Mean birthweights ranged from
2.8 kg in New Delhi to 3.2 kg in Pelotas. At 4 y, participants
from Guatemala, Cebu, and New Delhi were the lightest and
shortest and those from Bt20 the largest. At follow-up, Bt20
participants were 2.8 kg heavier than those from Cebu and
7.3 cm taller than those from Guatemala. The extent of stunting
at 2 y varied widely, from 86% in Guatemala to 12% in Pelotas.
Schooling varied markedly across sites. Maternal schooling
was lowest in Guatemala and highest in Bt20 and New Delhi.
Most participants in Bt20 were in school and had ~9 y of
schooling at last contact. Whereas 43% were still in school in
Pelotas, most in the remaining sites had completed formal
schooling at follow-up. Levels of schooling were highest for
New Delhi (13.5 y) and lowest for Guatemala (5.0 y). Grade
failure was 69% in Pelotas, 47% in Guatemala and Cebu, and
30% in Bt20. Age at school entry was around 6.7 y in Pelotas,
Guatemala, and Bt20 and 7.2 y in Cebu.
Stunting was associated with a reduction in schooling of 1.8
and 0.9 y before and after controlling for confounding,
respectively (Table 3). In model 2, where ZBWT and the 2
conditional variables were included, we found a significant
relationship with all 3 variables, but this was strongest for
ZCW0–24 and weakest for ZWC24–48. Controlling for SES
and maternal schooling reduced the coefficients by about one-
half, but results remained significant. In fully adjusted models,
1 Z-score of birthweight, ZCW0–24, and ZCW24–48 were
associated with 0.21, 0.43, and 0.07 y more schooling, respec-
tively. Relationships with maternal schooling and SES were
robust and in the expected direction (not shown).We found a
significant interaction between ZBWT and ZCW0–24 (P =
0.002) but not between ZBWT and ZCW24–48 (P = 0.90) in
analyses of highest grade attained. In fully adjusted models, the
relationship between ZCW0–24 and highest grade attained
varied across tertiles of birthweight, with coefficients as follows:
0.50 (CI 0.38, 0.62), 0.47 (CI 0.35, 0.58), and 0.33 (CI 0.22,
0.43), respectively, for tertiles 1, 2, and 3.
Stunting at 24 mo was a significant predictor of grade failure
(Table 4). Stunting increased the odds by 50 and 16% before and
after controlling for confounding, respectively. In fully adjusted
models, 1 Z-score decreased the odds of failure by 8% for ZBWT
and by 12% for ZCW0–24; the relationship with ZCW24–48
was not significant. Interactions between ZBWTand either of the
2 conditional variables were not significant. Adjustment for
Characteristics of the COHORTS Studies
Age at last
Examined in the
last visit, nComments
Prospective cohort1982 Birth591421–234297 Enrolled all children born in the city's
maternity hospitals (.99% of all births)
Intervention trial of a high-energy and
protein supplement. All children , 7 y
in 1969 and all born 1969–1977 were
enrolled and followed until age 7 or
until the study ended in 1977.
Pregnancies were identified in a population
of married women living in a defined
area of New Delhi, and the newborns were
enrolled and followed. Primarily a
Pregnant women living in 33
randomly selected neighborhoods; 75%
urban. First data collection at 30 wk
gestation. All social classes included.
Pregnant women with a gestational age
of 26–32 wk living in a delimited urban
geographical area. Predominantly poor,
INTCS, GuatemalaCommunity trial 1969–1977Birth–7 y 2392 26–41 1571
New Delhi Birth
Cohort Study, India
Prospective cohort 1969–1972 Before
8181 26–32 1583
Prospective cohort1983–1984 Gestation3080 21.42032
Prospective cohort 1990Gestation 3273 152100
350 Martorell et al.
maternal schooling and SES attenuated relationships between
growth and grade failure appreciably (model 2).
There were significant interactions between child growth and
site in analyses for age at school entry; hence, stratified analyses
were conducted (Table 5). There was considerable heterogeneity
in the results, although all significant relationships indicated an
association between faster weight gain and younger age at
enrollment. In the adjusted analyses, there were no significant
associations with any variable in Guatemala. Stunting was
associated with an older age at enrollment in Pelotas, Cebu, and
Bt20, with the largest coefficient, 0.32 y, for Pelotas. One
Z-score for ZBWT was associated with a decrease in age at
enrollment of about 0.08 y in Pelotas and Cebu. ZCW0–24 was
associated with younger enrollment ages in Pelotas and Cebu
and relationships with ZCW24–48 were in the same direction
and significant for Cebu and Bt20.
In this article, we assess the relative importance of birthweight
and weight gain between 0 and 24 and 24 and 48 mo for
schooling outcomes. Other than an analysis of attained school-
ing in Pelotas (17), we are not aware of other work addressing
the topic. We found that weight gain from 0 to 24 mo had the
strongest relationships with schooling outcomes followed by
birthweight; weight gain from 24 to 48 mo had a weak or no
relationship to schooling outcomes. The magnitudes of the
relationships are of economic and public health importance. In
fully adjusted models, stunting at 2 y, a widely used cumulative
indicator of undernutrition during fetal and postnatal life, was
associated with a reduction in schooling of 0.9 y, with a 16%
increased risk of failing at least 1 grade in school and with older
ages at enrollment in 3 of the sites. Given the estimate of 0.9 y of
Coefficients from linear regression models of highest grade attained (y) according to stunting
at 24 mo, standardized birthweight, and standardized CW gain from birth to 24 and from
24 to 48 mo (n = 7025)
CoefficientP-value95% CICoefficientP-value 95% CI
Stunted at 24 mo (1 = yes, 0 = no)
1Adjusted for site and sex.
2Adjusted for site, sex, SES, maternal schooling, and whether participant is still attending school.
Selected characteristics of participants included in the analyses by study site1
Variables Pelotas, BrazilINTC, Guatemala
Age at follow-up, y
Small for gestational age, %
Stunted 24 mo, %
In school, %
Highest grade attained, y
Age at school entry, y
Ever failed, %
Maternal schooling, y
SES categories, %
22.7 6 0.4 (3602)
31.4 6 1.3 (268)
29.2 6 1.4 (1271)
21.2 6 0.9 (1864)
15.6 6 0.3 (531)
3.2 6 0.5 (3793)
11.1 6 1.6 (3793)
15.6 6 2.3 (3793)
3.0 6 0.5 (268)
9.7 6 1.1 (268)
13.6 6 1.5 (268)
2.8 6 0.4 (1271)
10.1 6 1.3 (1271)
13.7 6 1.6 (1271)
3.0 6 0.4 (2065)
9.8 6 1.2 (2065)
13.1 6 1.7 (2065)
3.1 6 0.5 (548)
11.4 6 1.7 (548)
15.9 6 1.8 (548)
49.2 6 2.4 (197)
76.5 6 3.7 (268)
93.0 6 4.1 (268)
5.0 6 3.5 (268)
6.7 6 1.0 (253)
1.3 6 1.5 (266)
48.6 6 2.1 (1247)
80.5 6 3.7 (1271)
94.8 6 4.3 (1269)
13.5 6 3.3 (1271)
10.8 6 4.9 (1192)
49.1 6 2.0 (2064)
79.3 6 3.5 (2065)
93.1 6 3.9 (2065)
10.8 6 3.2 (1884)
7.2 6 0.6 (2060)
7.4 6 3.7 (2065)
80.8 6 4.9 (3793)
97.5 6 5.1 (3790)
9.5 6 3.1 (3602)
6.6 6 0.7 (1807)
6.6 6 4.2 (3788)
83.0 6 3.9 (548)
100.3 6 3.7 (548)
9.0 6 0.9 (547)
6.7 6 0.8 (548)
9.9 6 2.3 (548)
1Values are means 6 SD (n) or percent (n).
2N/A, Not available.
Child growth and schooling in 5 cohorts351
schooling lost, we would expect stunting to decrease lifetime
income by ~10% in the countries included in our analyses (6).
One SD increase in birthweight, equivalent to ~0.5 kg, is
associated with 0.21 y of additional schooling and 8% decreased
risk of ever failing a grade. One SD increase in CW gain between
birth and 24 mo, equivalent to ~0.7 kg, was associated with
higher estimates of 0.43 y more schooling and 12% decreased
risk of failing. One SD increase of CW gain from 24 to 48 mo,
equivalent to ~0.9 kg, was associated with only 0.07 y more
schooling and was not associated with school failure.
In addition, CW gain from 0 to 24 mo was more important
for schooling in children born small. In children born in the
lowest tertile of birthweight, 1 SD increase of weight gain from
0 to 24 mo was associated with 0.50 y of schooling compared
with 0.33 y in those in the upper tertile. This suggests a beneficial
effect on schooling of catch-up growth in smaller babies.
Our study has numerous strengths. It used prospective data
from 5 well-described cohort studies. Schooling outcomes
included 3 important variables: highest grade attained, ever
failed a grade, and age at school entry. Pooled analyses were
possible for all but age at school entry, thus increasing sample
sizes and power and providing assurance that most of the
relationships we explored were consistent across countries. An
additional strength was the use of weight information at birth
and 24 and 48 mo to assess the relative importance of growth
during specific preschool periods. Further, we used standardized,
CW gain variables to appropriately assess relative importance.
Our growth measures were, by design, uncorrelated with each
other, and the use of SD scores permitted direct comparison of
estimates across weight variables. For example, had we used
initial birthweight and postnatal weight gain, or initial Z-score
at any age and subsequent change in Z-score, as has been done in
studies of mental health (16) and cognitive development (12),
respectively, we would not have removed the phenomenon of
regression to the mean or controlled-for common error terms
(e.g. measurement error will generate a negative correlation
between initial and change values, because larger-than-true
measurements at baseline will lead to smaller change values and
smaller-than-true initial values will lead to larger change values).
Finally, our analyses also controlled for confounding associated
with children’s early environment.
Our study also has weaknesses. Our schooling information
was limited; only highest grade attained was available for New
Delhi andmost Bt20 participants were enrolled in school, so that
analyses of highest grade attained could not be carried out for
this site. We lacked birth length for Pelotas and Bt20 and
therefore relied on analyses using weight. However, we
conducted analyses that used growth in height from 0 to 24
and 24 to 48 mo that was conditioned on birthweight, available
for all 5 sites, and these analyses gave similar results to those
We controlled for maternal schooling and SES. Our analyses
showed that these variables attenuated coefficients substantially
but that relationships with birthweight and weight gain from
0 to 24 mo remained significant. In studies with extensive
control for confounding, the relationship between stunting or
weight faltering with schooling or cognitive development also
remained significant (30,15). Economists have been concerned
with confounding in relating child size to schooling, particularly
regarding unobserved characteristics (31). Use of econometric
techniques that rely upon instrumental variables to control for
confounding show that relationships between birthweight or
Coefficients for site-specific regression models of age at school entry (y) according to stunting at 24 mo and
standardized birthweight and standardized, CW gain from birth to 24 mo and from 24 to 48 mo (n = 4668)1
n = 1807
n = 253
CLHNS, Cebu, Philippines,
n = 2060
n = 548
Stunted at 24 mo
20.42, 0.300.740.090.03, 0.140.0010.18 0.03, 0.320.02
1All regressions adjust for sex, SES, and maternal schooling.
Odds ratio for ever failed (1 = yes, 0 = no) according to stunting at 24 mo, standardized birthweight, and standardized, CW
gain from birth to 24 mo and from 24 to 48 mo (n = 6281)
Odds ratioP-value 95% CIOdds ratioP-value 95% CI
Stunted at 24 mo
,0.00011.34, 1.781.20.02 1.04, 1.39
1Adjusted for site and sex.
2Adjusted for site, sex, SES, and maternal schooling.
352Martorell et al.
child height for age and educational outcomes remain significant
and can actually increase in magnitude after controlling for
unobserved characteristics (32,33,5). Although economists of-
ten attribute causality in analyses of nonexperimental data with
instrumental variables, they recognize that experimental data
offer the best possibility for doing so. The nutrition intervention
to which the Guatemalan cohort was exposed (Table 1)
improved diets and reduced stunting at 3 y of age (34) and
also had long-term effects on schooling (women), cognitive
development (men and women), and wages (men) (35–37), thus
providing support for a causal effect. In addition, the specificity
of our finding of a much larger role for growth in the first 2 y of
life than for later growth is in agreement with the physiology of
brain growth, also supporting a causal association.
Few would argue that it is child size or growth per se that
causes impaired cognitive development and low educational
achievement. Rather, growth failure in early childhood should
be viewed as a marker of lack of nutrients at the cellular level
that has systemic effects on growth and development in general,
including the brain and neurological development.
From a public health perspective, it is important to place
these findings in the context of what is known about the timing
of weight gain and chronic disease outcomes. Other analyses
from the 5 cohorts showed that rapid weight gain in every age
range from birth to mid-childhood is associated with increased
blood pressure (38), but studies of body composition suggest
that weight gains after 2 y of age are more closely associated
with adult fat mass than earlier weight gains (39–42). Further
analyses are needed for other chronic disease outcomes, but, to
date, there is a good case for promoting weight gain in early life
in low- and middle-income populations in light of its positive
association with human capital outcomes such as schooling.
We showed important associations of prenatal and postnatal
growth to 2 y with schooling outcomes. There are many other
influences, such as school quality, that also determine schooling
outcomes and achieving the millennium development goal of
universal primary education by 2015 will require action along
a broad front. Our results suggest that designing effective
nutrition interventions and targeting them to mothers and child-
ren ,2 y of age should be among these actions.
Special thanks to other contributors to the 5 studies: Brazil:
Rosangela Lima (Universidade Cato ´lica de Pelotas); India:
Lakshmi Ramakrishnan, Nikhil Tandon (All-India Institute of
Medical Sciences, New Delhi), Dorairaj Prabhakaran (Centre
for the Control of Chronic Diseases, New Delhi), Siddharth
Ramji (Maulana Azad Medical College, New Delhi), and
Srinath Reddy (Public Health Foundation of India); Guatemala:
Ann DiGirolamo (Institute for Community Health, Cambridge
Health Alliance, Boston, MA), Rafael Flores (US CDC), Usha
Ramakrishnan, Kathryn Yount, Meng Wang (Emory Univer-
sity), Ruben Grajeda (PAHO), Paul Melgar, Humberto Mendez,
Luis Fernando Ramirez (Institute of Nutrition of Central
America and Panama), Jere Behrman (University of Pennsylva-
nia), John Hoddinott, Agnes Quisumbing, Alexis Murphy
(IFPRI), John Maluccio (Middlebury College); Philippines:
Barry Popkin (University of North Carolina at Chapel Hill),
Socorro Gultiano, Judith Borja, Josephine Avila (Office of
Population Studies Foundation, University of San Carlos,
Cebu), Thomas McDade (Northwestern University); South
Africa: Noel Cameron (Loughborough University, UK) and
John Pettifor (University of Witwatersrand). The COHORTS
group was responsible for the development of the concept and
methods for this paper; R.M., B.H., L.S.A., and A.D.S
conducted the data analyses. R.M. prepared the first and last
drafts of the manuscript. Each of the 5 studies is represented
by 1 or more authors who participated in the design and/or
implementation of the original study or follow-up surveys. All
authors participated in the interpretation of the results and
contributed to the revision of the manuscript. All authors read
and approved the final version of the paper.
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