What can longitudinal research tell us about adolescent health and nutrition? Research findings from Young Lives Elisabetta Aurino (with Jere Behrman, Mary Penny and Whitney Schott) Young Lives conference on Adolescence, Youth and Gender 8-9 September 2016

1. What can longitudinal research tell us
about adolescent health?
Research findings from Young Lives
Elisabetta Aurino
(with Jere Behrman, Mary Penny
and Whitney Schott)

2. Why adolescent nutrition?
 Adolescence period of rapid physical and
psychosocial development
 Adolescent health is intrinsically and instrumentally
important for development:
 Possibility of catch-up from earlier nutritional deficiencies
 Reap the “demographic dividend” through better health
and education and increased productivity for the largest
youth population in history
 Achieving better nutrition before conception and improve
the nutritional status of the next generation

4. Despite the growing focus on
adolescent nutrition,
many knowledge gaps exist
 Key data and evidence gaps (van Lieshout et al 2014,
Global Nutrition Report 2015):
 Descriptive evidence on diets/nutritional status (incl.
overnutrition)
 Role of nutritional and dietary factors on puberty and
adolescent health status
 Linkages between maternal nutrition during adolescence
and offspring health
 Summer 2015: Call for proposals on the analysis of
existing datasets on the status of adolescent
nutrition

5. Young Lives study design

6. A “taste” of our research so far
 We have been analysing the Young Lives data with the
following aims:
 Describe nutrition & diets at different ages
 Examine differences by cohort, and gender,
rural/urban, wealth
 Examine how well the Minimum Dietary Diversity
indicator for Women predicts nutrition at different
ages
 Investigate the role of prenatal and childhood
nutrition on menarche
 Examine the relation between maternal nutrition and
her offspring’s health

7. Descriptive stats for 12-years-
olds in 2006 and 2013a
Ethiopia India Peru Vietnam
OC YC OC YC OC YC OC YC
Stunted
(ZHFA < -2 SD)
0.30 0.29 0.34 0.29 0.31 0.19 0.31 0.20
(0.46) (0.45) (0.47) (0.45) (0.46) (0.39) (0.46) (0.40)
Thin
(ZBMI-for-age < -2
SD)
0.36 0.41 0.34 0.33 0.01 0.01 0.17 0.14
(0.48) (0.49) (0.47) (0.47) (0.11) (0.10) (0.38) (0.34)
Overweight
(ZBMI-for-age > 2)
0.00 0.01 0.02 0.02 0.09 0.11 0.02 0.04
(0.06) (0.08) (0.15) (0.12) (0.28) (0.32) (0.13) (0.21)
Minimum Women
Dietary Diversity
(at least 5 groups
out of 9)
N/A 0.09 N/A 0.14 0.56 0.34
(0.29) (0.35) (0.50) (0.47)
a Mean (SD)

8. Gender inequalities in nutrition &
diets vary by context & age (and
girls are not always the
disadvantaged ones)
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Ethiopia India Peru Vietnam
Differences (girls – boys) in selected
nutrition indicators, YC, 12 years (2013)
ZHFA
ZBMI-for-age
Dietary diversity
**
**
***
**
***

9. Diets are changing rapidly for
12-year-olds…
Estimates adjusted for: gender , birth order, caregiver's age, gender, years of schooling,
head of the household gender and age, household size, place of residence, wealth index
Source: Aurino, Fernandes, Penny 2016, Public Health Nutrition
P=0.001
P=0.001

10. …With wide variation in their
composition
Cross-cohort change in sugar consumption, 2006-
2013, rural/urban
Estimates adjusted for: gender , birth order, caregiver's age, gender, years of schooling,
head of the household gender and age, household size, place of residence, wealth index
Source: Aurino, Fernandes, Penny 2016, Public Health Nutrition

11.  2016: FAO/FANTA/USAID launched a new dichotomous
indicator (MDD-W):
 Whether or not women 15-49 years of age have consumed at least
five out of ten defined food groups the previous day or night.
 Indicator developed to proxy micronutrient adequacy
 Does the index predict ZHFA for girls at 12, 15 years and
19 old as well?
What’s the relation between
diets and nutrition in
adolescents?

12. Minimum dietary diversity
predicts to adolescent girls’
height-for-age z-scores
12 years old
(YC)
15 years old
(OC)
19 years old
(OC)
Bivariate
correlation
Adjusted
coefficienta
Bivariate
correlation
Adjusted
coefficienta
Bivariate
correlation
Adjusted
coefficienta
Minimum dietary
diversity (5 out of 9
food groups)
0.173*** 0.104*** 0.13*** 0.04 0.16*** 0.05
(0.043) (0.040) [0.04] [0.04] [0.04] [0.04]
a Estimates adjusted for: age in months, number of meals, maternal age,
overweight, height, household wealth index, urban residence , country

13. Does childhood nutrition predict
the timing of puberty?
 Early puberty predicts some cancers, CVD, adolescent
health risk behaviours, and psychosocial disorders
 Hypothesis of in utero programming of timing of
menarche, but later nutrition may mediate this relation
 Direction of association between prenatal nutrition and onset
of puberty not always consistent
 Absence of comparative evidence, particularly from high-
malnutrition contexts
 At 12 years old, about one third of the sample in India,
Peru and Vietnam has reached puberty

14. Prenatal and subsequent
changes in nutrition influence
menarche
60%
50%
60%
30%
-35%
Fastest growth between one and 8 years
Fastest weight change between one and 8
years
Fastest growth between birth and one
year
Fastest weight change between birth and
one year
Low birthweight (<2500 gr)
Hazard of early menarche
Multivariate Weibull model, pooled sample (N=1,858)
P<0.05
P<0.01
P<0.01
P<0.1
P<0.01
Estimates adjusted for: BMI at 8 years, first child, mother’s height, urban location at Round 1,
wealth index at 8 years, consumption of: fruits and vegetables, meat & fish, eggs, legumes, milk &
dairy at 8 years, country dummy

15. How about the new
generation?
At 19 years, 15% OC girls have
become mothers.
We can examine the relation
between their pre-pubertal and
adolescent nutrition and their
children’s health
We can also control for the
adolescent’s mother nutrition,
hence we have a three-generation
model of nutrition

16. Nutrition during puberty is linked to
offspring’s outcomes
Birthweight Low Birthweighta
HAZ age 8 lowest tercile 26.34 [110.33] -0.06 [0.08]
HAZ age 8 highest tercile 85.6 [114.68] -0.01[0.08]
BAZ age 8 lowest tercile -94.31 [105.74] 0.05 [0.08]
BAZ age 8 highest tercile 208.92** [104.45] -0.07 [0.07]
Positive change in height ranking, 8-12 years 268.47** [119.26] -0.07 [0.08]
Positive change in BMI ranking, 8-12 years -20.48 [120.26] -0.06 [0.09]
Negative change in height ranking, 8-12 years 12.17 [107.55] 0.04 [0.08]
Negative change in BMI ranking, 8-12 years -296.59** [129.95] 0.01 [0.09]
YL adolescent met minimum dietary diversity (ate 5 of 9 food groups),
age 16
122.64 [98.24] -0.07 [0.07]
YL adolescent received prenatal care during pregnancy 378.78** [189.58] -0.12 [0.13]
YL adolescent enrolled in school at age 16 213.26** [97.99] -0.05 [0.07]
R-squared 0.34 0.17
Notes: Regressions also control for: positive and negative percentile change in height and BMI between ages 12 and 16 and
between ages 16 and 20, YL adolescent's age in months at first birth, an indicator for YL adolescent having entered puberty
early, number of meals eaten at 16 years old, YL adolescent’s child is first born, YL adolescent prenatal health good (of 3
categories), YL adolescent child difficult labour, YL adolescent's mother's height, age, education and overweight, YL adolescent
father’s completed grades of schooling, HH wealth index at 16, urban residence at 16, country dummies.
Standard errors in brackets; *** p<0.01, ** p<0.05, * p<0.1.
a
Indicates coefficients are from linear probability model.

17. We are still “in progress”!
 Limitations of YL for this type of research:
 Unobserved factors
 Difficult to provide “causal estimates”
 YL multi-purpose and not nutrition study
 A lot of work still needs to be done:
 Longitudinal research can illuminate on physical, social and
economic trajectories of adolescent health in different
settings
 Thanks for your comments/feedback!
 e.aurino@imperial.ac.uk