The Double Burden of Malnutrition among Youth:

Trajectories and Inequalities in Four Emerging Economies

Whitney Schott1,2, Elisabetta Aurino3, Mary E Penny4, Jere R. Behrman1,5

1 Population Studies Center, 3718 Locust Walk, University of Pennsylvania, Philadelphia, PA 19104, wschott@pop.upenn.edu 2 A. J. Drexel Autism Institute, Drexel University, Philadelphia, PA3 Department of Management and Centre for Health Economics and Policy Innovations, Imperial College London and Young Lives, University of Oxford, UK, e.aurino@imperial.ac.uk 4 Instituto de Investigación Nutricional, Av La Molina 1885, La Molina, Lima, Peru, mpenny@iin.sld.pe5 Economics Department, Ronald O. Perelman Center for Political Science and Economics (PCPSE), 133 South 36th Street, University of Pennsylvania, Philadelphia, PA 19104-6297, USA, jbehrman@econ.upenn.edu

Corresponding Author:Whitney SchottPopulation Studies Center 3718 Locust WalkUniversity of PennsylvaniaPhiladelphia, PA 19104-6297wschott@pop.upenn.edu1(215) 886 0865

Abstract

As part of the nutritional transition, undernutrition is globally declining while changes brought by economic development have ushered in increases in overweight and its related economic costs and health consequences around the world. We examine trajectories in stunting and overweight from age one year to mid-adolescence and from mid-childhood to early adulthood among two cohorts from Ethiopia, India, Peru and Vietnam using data from the Young Lives study. We examine descriptive data and then model trajectories in stunting and overweight status over age. Group-based trajectory analysis with five ages of overweight and stunting for each country-cohort reveals (1) trajectories of catch-up growth for a subset of study children between the ages of 12 and 19 in the older cohort in Ethiopia (20.1% of the cohort), India (20.5%), Peru (16.9%), and Vietnam (14.0%); (2) trajectories of increasing probabilities of stunting as children age from 12 to 22 in the older cohort in India (22.2%) and Peru (30.7%); (3) trajectories of early (childhood) increases in overweight probabilities (younger cohort: India, 3.4%, Peru, 19.4%, and Vietnam, 8.1%), and of later (adolescence) increases in overweight probabilities (older cohort: Ethiopia, 0.5%, India, 6.3%, Peru, 40.9%, and Vietnam, 9.4%). Multinomial logit prediction of membership in trajectory categories reveals that higher wealth quartiles and maternal schooling are protective against high stunting probability trajectory group membership, but higher wealth and urban residence predict high overweight probability

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trajectory group membership. This evidence suggests a window of opportunity for interventions to reduce stunting and to avert overweight development in adolescence, in addition to the often-emphasized first 1,000 days after conception. A life-course approach to policies and programs to target both undernutrition and overweight should be considered.

Key Words

Stunting, Overweight/Obesity, BMI, Double Burden of Malnutrition, Nutritional Transition

Classification codes:

I1 Health, I120 Health Behavior, I140 Health and Inequality, J1 Demographic Economics

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1. Introduction

Globally, 150.8 million children under age 5 years were chronically undernourished (stunted) in 2013 (Fanzo et al. 2018), with considerable long-run health and economic consequences over the life cycle (Behrman et al. 2009, Hoddinott et al. 2008, Hoddinott et al. 2013, Maluccio et al. 2009, Victora et al. 2008). At the same time, childhood overweight and diet-related non-communicable disease prevalence are rising substantially in most low- and middle-income countries (LMICs) (Perez-Escamilla et al. 2018, World Health Organization 2017), with over 38.3 million children overweight or obese in 2015 (Fanzo et al. 2018). Economic development has been accompanied by dramatic changes in food consumption patterns towards nutrient-poor, energy-dense foods as well as new household, school and community food environments that encourage these foods and facilitate limited physical activity (Monteiro et al. 2013, Ng and Popkin 2012, Popkin, Adair and Ng 2012, Uauy 2004) . High prevalence of overweight and obesity among children and adolescents threatens to further tax health systems that are already overburdened. As child and adolescent dietary and health behaviors tend to track over the life course (Mikkilä et al. 2005), early overweight status is associated with higher risk of cardiovascular disease, coronary heart disease, and all-cause mortality in adulthood (Baker, Olsen and Sørensen 2007, Bibbins-Domingo et al. 2007, Reilly and Kelly 2011, Thompson et al. 2007, Twig et al. 2016). Economic impacts of excess weight in childhood and adolescence include increased health-care costs, potential productivity costs, and discrimination in labor markets (Chu and Ohinmaa 2016, Lehnert et al. 2013). Thus, overweight and obesity, just like undernutrition, have significant economic as well as broader societal costs.

The coexistence of contrasting forms of malnutrition at the individual, household, and population levels is often referred to as the "double burden" of malnutrition (Turner 2017). This phenomenon presents a challenge to health systems across much of the world. Recent multilateral policy initiativesa have highlighted the need for multisectoral “double-duty actions” (Hawkes, Demaio and Branca 2017, World Health Organization 2017), or interventions and policies with the potential to simultaneously reduce the risk of both undernutrition (including stunting) and of overweight. Examples of such interventions include protection and promotion of exclusive breastfeeding in the first six months, maternal nutrition programs, school policies and programs, and marketing regulations (Perez-Escamilla et al. 2018, World Health Organization 2017).

Double-duty actions call for a life-course perspective on nutrition and growth. However, much of the previous literature on the nutritional status of children focuses on children under five years old and women of reproductive age, perhaps due to limited data on other groups, such as adolescents. Studies on adolescents in LMICs often rely on cross-sectional data (Caleyachetty et al. 2018, Galloway 2017). Longitudinal data on the anthropometric trajectories of children transitioning to adolescence and early adulthood in LMICs, and the inequalities in these trajectories within and across countries, are limited (Lobstein et al. 2015). Also, cross-sectional evidence is not able to discern whether observed differences in the double burden of malnutrition across age groups are age effects or cohort effects (Aurino, Fernandes and Penny

a Examples include the United Nations (UN) decade of Action on Nutrition and Sustainable Development Goal 2 on food security and nutrition.

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2017, Lobstein et al. 2015). In rapidly developing countries amid nutritional transitions, younger cohorts are exposed to more obesogenic food environments, but also to increasing social protection and nutrition-sensitive policy measures, while their older peers were more likely to have had exposure to more traditional food environments and less likely to have been exposed to extensive social protection systems. Data from longitudinal studies on the double burden of malnutrition among cohorts of young people in LMICs can support the design of effective double-duty actions focusing on specific nutritional needs at different stages of the life course, in differing environmental contexts, for vulnerable populations (Galloway, 2015).

This paper documents longitudinal trajectories in stunting and overweight for two birth cohorts of children growing-up in four diverse LMICs over two periods critical to nutrition in the lifecycle. Relying on unique longitudinal data collected over a span of 14 years from the Young Lives study, we show patterns over time in children’s stunting and overweight trajectories from age one to 15 years for the younger cohort, and from age eight to 22 years for the older cohort, in Ethiopia, India, Peru and Vietnam. We reveal patterns in the double burden of malnutrition across cohorts and countries, as well as within countries, with respect to gender, place of residence, socio-economic status, and cohort.

We extend the literature by: (i) documenting longitudinal trajectories in stunting and overweight over 14 years in four countries in differing stages of development, (ii) uncovering latent growth patterns in longitudinal trajectories of stunting and overweight over five measurements of height and weight (from ages one to 15 for the younger cohort and from ages eight to 22 for the older cohort) in four countries, and (iii) examining the associations of trajectory group membership in low, medium, or high probabilities of stunting or overweight with initial child characteristics (child sex) and socioeconomic conditions (wealth, mothers’ schooling, and urban residence).

Descriptively, in all four countries, we find overall trends towards decreases in stunting, either as children grow older (Ethiopia), from one cohort to the next (India and Peru), or both (Vietnam). Over the period of the study there has also been the emergence (India and Vietnam) and increase (Peru) of overweight prevalence. Group-based trajectory analysis reveals important subgroups in each country with trajectories showing (i) decreases in the probability of stunting during adolescence, (ii) persistently high probabilities of stunting, (iii) increases in the probability of overweight in early childhood, and (iv) increases in the probability of overweight during adolescence.

1.1. A life-course approach to nutrition

It is important to examine growth and nutritional trajectories from various stages of the life course in order to inform broad-based policies that may confront the double burden. Nutrition in the first few years of life has implications for child survival, health, cognitive development, adult wages, adult mental health, and next-generation anthropometrics (Behrman et al. 2009, Black, Morris and Bryce 2003, Caulfield et al. 2006, Crookston et al. 2010, Hoddinott et al. 2008, Huang et al. 2013, Kimani-Murage et al. 2010, Kowalski et al. 2018) . While nutrition in the first 1,000 days of life is crucial, there is increasing evidence that

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nutritional improvements in later childhood to early adolescence may also have longer-term consequences, particularly for cognitive development (Crookston et al. 2013, Kowalski et al. 2018). Cognitive and socioemotional skill accumulation may be more plastic than physical processes, as the brain continues to develop through early adulthood and possibly beyond (Alderman et al. 2017, Borghans et al. 2008, Grantham-McGregor et al. 2007) . There is also concern that stunting may predispose children to becoming overweight (Lobstein et al. 2015), though initial evidence from longitudinal studies has not found this to be the case (Andersen et al. 2016). A few studies, however, have found associations between stunting and the development of non-communicable diseases (Barker 2002, Ezzati et al. 2002, Forsen et al. 2000).

Adolescence, in addition to early childhood, has been proposed as a key life-course stage for nutrition. The period of adolescent growth is suggested as a “second window of opportunity” to promote better nutrition, recover from previous nutritional disadvantages, and set healthy dietary behaviors that can reduce the risk of non-communicable disease later in life (Black et al. 2013, Bundy et al. 2017, Van den Berg et al. 2014). Adolescence is a stage of high nutrient and energy demand. Adolescent girls are especially at risk of poor nutrition due to their higher iron needs after menstruation onset and the possibility of early pregnancy, as 11% of all births globally are from adolescent mothers (Christian and Smith 2018, The World Health Organization 2018). Children born to malnourished adolescent mothers are at higher risk of poor developmental outcomes (Benny, Dornan and Georgiadis 2017, Schott et al. 2018), and the consequences of concurrent stunting and overweight may compound risks to both the adolescents and their offspring (Caleyachetty et al. 2018). At the other nutritional extreme, with changes in lifestyle and dietary habits that accompany economic development, adolescents are highly vulnerable to consumption of energy-rich but nutrient-poor foods and beverages (Cunha et al. 2018, Lobstein et al. 2015). Furthermore, studies have identified a relationship between obesity and early sexual maturation (Adair and Gordon-Larsen 2001, Aurino et al. 2018, Wang 2002), putting girls at risk of pregnancy at earlier ages.

2. Data

We use data from Young Lives, a study of childhood poverty with longitudinal data on children in Ethiopia, India (Andhra Pradesh and Telangana), Peru and Vietnam (Barnett et al. 2012) (www.younglives.org.uk). The study followed two cohorts of children over 14 years; the younger cohort (n~2,000 per country) was born in 2001-02 and surveyed at roughly (including about 6 months before and 6 months after ages) 1, 5, 8, 12 and 15 years and the older cohort (n~1,000 per country) was born in 1994-95 and surveyed at ages roughly 8, 12, 15, 19, and 22 years. Within each country, children and their families were randomly selected from 20 sentinel sites. Sentinel site selection was semi-purposive. As the Young Lives study was designed to help study issues of poverty in childhood, children from poor families were oversampled. While the samples selected are not nationally-representative, they do represent the geographic, ethnic, social, and economic variation of each of the four countries (Barnett et al., 2013). This analysis utilizes data from both cohorts of children in all four countries.

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Surveys were conducted over five rounds and include data on a broad range of household and community characteristics as well as child-specific data including anthropometrics. Attrition in the survey was low due to extensive efforts by local study teams to follow families even when they moved from the original sampling areas. Attrition was 5.3%, 3.7%, 8.4%, and 2.5% in the younger cohort for Ethiopia, India, Peru, and Vietnam, respectively, from round one to round five. For the older cohort, 17.7%, 7.6%, 14.1%, and 8.6%, of the initial sample respectively for Ethiopia, India, Peru, and Vietnam were not successfully re-interviewed in round five.b Higher attrition for the older cohort was likely due to greater mobility as children became young adults (Young Lives 2018). The Young Lives study received institutional review board approval from the University of Oxford and local ethics review boards at each participating country’s lead institution.

2.1 Study settings

The four Young Lives countries differ widely in their level of development. Peru is classified as upper-middle income by the World Bank, with a per capita gross domestic product (GDP) adjusted for purchasing power parity (PPP) of $13,434. Ethiopia, in contrast, is classified as a low-income country, with a PPP-adjusted GDP of $1,899. India and Vietnam had PPP-adjusted GDPs of $7,055 and $6,776, respectively (all data from 2017) (The World Bank 2018c). Inequality is high in each of these countries, with Gini coefficient values of 0.39 (Ethiopia), 0.35 (India), 0.44 (Peru), and 0.35 (Vietnam) (The World Bank 2018c).

These countries also vary in terms of stages of the nutritional transition and food environments (Aurino, Fernandes and Penny 2017). Despite being one of the poorest nations in Africa, Ethiopia experienced substantial economic growth between 2006 and 2016, averaging over 10 percent per year, and growing faster than the average of the region (The World Bank 2018d). These economic changes have been accompanied by a decrease in the percentage of the population living below the poverty line (The World Bank 2018d). However, a recent review of nutritional trends in Ethiopia found an overall stabilization of stunting and underweight prevalence over the period 1996-2010 followed by increases from 2010-2014 (Abdulahi et al. 2017). At the same time, overweight and obesity prevalence has been documented as a growing health issue among adolescents, associated with being female and of higher socioeconomic status (Gali, Tamiru and Tamrat 2017)

In India, undernutrition is persistent despite the rapid economic growth of the last few decades (Deaton and Drèze 2009). Although stunting prevalence has declined from 32.6% in 2000 to 22.2% in 2017, India is home to the largest number of stunted children globally (Development Initiatives 2018). Malnutrition among adolescents is also widespread: undernutrition and anemia are major public health issues for adolescents, as about one in two Indian girls aged 15–19 years is anaemic and has low BMI (Aguayo, Paintal, & Singh, 2013). At the same time, overweight and obesity are rising rapidly, together with the prevalence of diet-related diseases such as Type-2 diabetes, among all income groups (Development Initiatives 2018). Gender inequalities in nutrition are present, with women being more likely to be bear

b In Peru for example, geographic mobility was a challenge to follow up – while in round one, the study visited 27 districts in 13 regions, this number increased to 300 districts in all 24 regions by round five.

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the double burden of malnutrition at all stages of the life course (Aurino 2017, Calvi 2017, Jayachandran and Kuziemko 2011, Lee et al. 2015, Milazzo 2014).

Peru has been one of the fastest growing economies in Latin America in recent years, with GDP growing at 6.1% per year from 2002-2013, cutting the poverty rate in half over this period (The World Bank 2018a). Rapid changes have accompanied declines in malnutrition, with stunting prevalence among children under five years of age falling from 19.5% in 2011 to 14.4% in 2015 (Instituto Nacional de Estadística e Informática 2017). Meanwhile, along with rapid development came an increase in the prevalence of overweight and obesity that has been particularly acute among adolescent girls and women. The prevalence of overweight at ages 15-19 years reached 25.6% by 2015, while the prevalence among adult women was 22%, compared to 13% among adult men for the same year (Instituto Nacional de Estadística e Informática 2016). While the economy slowed between 2014 and 2017, it is expected to have increased in 2018 (The World Bank 2018a).

Vietnam has experienced rapid economic growth, with rapidly expanding access to basic services and access to housing infrastructure over the past 20 years (The World Bank 2018b). The medium-term outlook is over 6% growth projected through 2020, and the new middle class is expected to continue to grow rapidly (The World Bank 2018b). While child malnutrition has declined, there is still concern for the nutritional trends among the most vulnerable populations by ethnicity and socioeconomic status (Kien et al. 2016).

2.2 Anthropometric Data

High quality data on height, weight, and age in months were collected in standardized ways at ages 1, 5, 8, 12, and 15 for the younger cohort and ages 8, 12, 15, 19, and 22 for the older cohort in Ethiopia, India, Peru, and Vietnam. Child supine length at age one year and height at older ages were measured to 1 mm with standardized stadiometers. To increase precision, two measurements were taken of height/length; the final heights used were the averages of the two measures. These data were used to calculate height-for-age z-scores (HAZ). HAZ at each round were calculated using World Health Organization reference standards and children’s ages in months up to age 20 years (De Onis et al. 2004). Stunting was defined as a HAZ of more than 2 standard deviations below the median (HAZ<-2).

We calculate overweight (defined to include either overweight or obese) using the international cutoffs for BMI between the ages of two and 18 years that were developed as part of the International Obesity Task Force, which used extensive data from six countries around the world (Cole et al. 2000). Since cutoff points for overweight are not defined at less than two years of age, all children are classified as not overweight/obese at age one year. For ages above 18 years, we use standard cutoff points for adults to classify overweight/obese.

2.2 Methods

We first plot aggregated nutritional trajectories for each cohort by country. In appendix A, we extend this analysis to plot trajectories by child sex, rural or urban residence, wealth index quartile in round one, and an indicator for whether the child’s mother had completed grades of schooling in the top 25th percentile specific to each cohort-country. Survey age is the average age at which the survey was conducted. Wealth is measured as an index that ranges

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from 0 to 1 and is constructed as the simple average of three indicators of standards of living in round one: consumer durables, sanitation and services, and housing quality. This measure is described in detail elsewhere (Briones 2017). Wealth index quartiles are constructed separately for each country-cohort pair. For descriptive graphs, we limit the sample to those children who are surveyed in round five, regardless of whether they are interviewed between rounds 1 and 5.

Next, we perform group-based trajectory modeling to uncover heterogeneity in growth trajectories within each country-cohort. Group-based trajectory modeling is a form of finite mixture modeling that identifies clusters of individual trajectories. Maximum likelihood estimation identifies model parameters, with maximization using a quasi-Newton procedure. To characterize trajectories in stunting and overweight, we use the traj command in Stata 13.1. We compared the Schwarz-Bayesian information criterion (Raftery 1995) for models with two to five distinct groups and noted that additional groups improved model fit. We specified three groups as a manageable number of groups that still reveals heterogeneity in trajectories. Though children’s ages were collected in months, we transform them into years (i.e., ages are not limited to integer values for years). We model the probability densities of stunting and overweight over ages allowing for a cubic relationship with age as

P(Yi|agei,j;Bj) = ∏t=i

T

p ( y¿∨age¿ , j ; B j)

where Yi is a vector of individual i’s longitudinal sequence of either overweight or stunting, agei is the age in non-integer continuous years at which that measurement is taken, j is the number of latent groups (j=3), Bj is an unknown parameter vector, T is equal to five measures of Yi over 14 years, and p(.) is a binary logit distribution (Jones and Nagin 2012).

Once trajectory group membership is determined, we conduct multinomial logit regressions of group membership on baseline wealth quartile and urban residence, maternal schooling in the top quartile, and an indicator for females, separately for each cohort-country. The traj command in Stata assigns the reference group as the one with the largest percentage of the children classified into that group. We present relative risk ratios (RRR), the risk associated with each covariate of membership in a given group relative to that of the reference group.

3. Results

3.1 Descriptive findings

Table 1 and Figure 1 show that the prevalence of stunting declines among both the older and younger cohorts throughout childhood into young adulthood in Ethiopia and, to some extent, in Vietnam. While the prevalence of stunting is relatively high and persistent in India and Peru for the older cohort, it is declining for the younger cohort. Prevalence of overweight appears relatively steady in Ethiopia, begins to increase by young adulthood (age 19 years) in India, and rises substantially in Peru, where prevalence reaches almost 40% by age 22. In Vietnam, the percentage of children who are overweight rises only slightly over childhood in

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the aggregate. Descriptive means by sex, wealth index, urban residence, and maternal schooling for each country-cohort appear in Appendix A along with a discussion of results.

Stunting and overweight can occur simultaneously. We find that children who are stunted at the round one measurement (age one for the younger cohort; age eight for the older cohort) are no more likely to be overweight at any later age (Table 2). Although some children in all four countries who are stunted at round one eventually become overweight, more commonly children who are not stunted initially are more likely to become overweight at a later age.

3.2 Group-Based Trajectory Models

Group-based trajectory modeling identifies three risk trajectories for each country-cohort pair (parameter estimates for the two outcomes and the eight country-cohorts may be found in Appendix B). For simplicity, we label these three groups as “low,” “medium,” and “high” for both stunting and overweight risk trajectories. In some cases, two or more of the groups start with similar probabilities of stunting or overweight, and then diverge, while in other cases, the three groups start from differing levels of overweight and stunting probabilities. Figures 2 and 3 plot the observed group means and estimated trajectories for each of the trajectory groups identified from the statistical models.

In all four countries, there is a trajectory with persistently high probabilities of stunting and another with persistently low probabilities. In the younger cohort in Ethiopia, about half (50.8%) of children are on a trajectory with low stunting probability over early childhood, while almost a fifth (17.2%) are on a trajectory with high stunting probability, though decreasing after age eight or so. A similar pattern holds in India, Peru, and Vietnam, where 24.0%, 19.5%, and 16.3% of children, respectively, are on a trajectory with high stunting probability, and 41.5%, 42.3%, and 60.0% are on a trajectory with low stunting probability over early childhood. The older cohort shows a similar pattern of high and low stunting probability trajectories for many children; however, the analysis reveals a group that starts off with high stunting probability which then declines (as children apparently “recover” from stunting) between the ages of 12 and 19 years in Ethiopia, India, Peru, and Vietnam, for 20.1%, 20.5%, 16.9%, and 14.0% of children, respectively. The paths of these trajectory groups are consistent with the conjecture that adolescence is a key period of growth opportunity during which early deficits may be corrected. In India and Peru, the high stunting probability trajectories are of particular concern, as the probability of stunting increases from age 12 to 22, perhaps revealing the corollary to such a conjecture – it is possible that this adolescent growth opportunity window could be missed, leaving some children at even higher risk of stunting.

Models of overweight reveal trajectories in all four countries that move from low overweight probability to high overweight probability, even in Ethiopia and Vietnam where the majority of children are not on such a path. In the younger cohort in Ethiopia, there is a group (representing only 2.5% of children) with slight increases in the probability of overweight by age 15, but in the older cohort the probability of overweight for the high overweight probability trajectory group increases substantially from age 15 to age 22, albeit only for 0.5% of children.

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In India, similar patterns appear for both cohorts – all three groups start with very low probabilities of overweight, but for one trajectory group, children experience nearly certain overweight status by the end of the 14-year period, both in the younger and older cohorts, describing the trajectories for 3.4% and 6.3% of children, respectively. The patterns in Ethiopia and India suggest that while overweight may not yet be affecting large proportions of children, some children are already embarking upon a path towards overweight at this stage and overweight and obesity may pose a public health challenge in the future as economic development continues.

In the younger cohort in Vietnam, two groups representing a small portion of children show increases in the probability of overweight initially between ages one and five, but while one trajectory group then reverts to a low probability of overweight, the other group experiences substantial increases in probability of overweight, before declining slightly by age 15. In the older cohort in Vietnam, a small minority (2.7%) has high probability of overweight from age eight to 22, with a second trajectory group (representing 9.4% of children) experiencing increases to similar levels by age 22.

In Peru, the majority (71.4%) of the younger cohort demonstrates a trajectory with low overweight probabilities from ages one to 15 years, but the other two trajectories represent either early (19.4%) or late (9.2%) increases in probabilities of overweight over that same age range. The older cohort, however, highlights the increasing burden of overweight, as less than half (44.6%) of children are classified in the low overweight probability trajectory group. A substantial portion (40.9%) of children in the older cohort have followed a high overweight probability path, which remains relatively steady from age 8 to 22. Another group (14.5%) shows a path of later increases in the probability of overweight, from ages 15 to 22 years of age.

3.2 Multinomial Logit Predicting Group Membership

Multinomial logit results in table 3 reveal the associations between baseline characteristics measured at round one and trajectories over childhood and adolescence. For stunting trajectories reported in Panel A, maternal schooling, wealth quartile, and urban residence predict trajectory membership. In India, Peru, and Vietnam, having a mother with completed schooling in the highest quartile is strongly associated with lower risk of being in a medium or high stunting probability trajectory: the risk of being in the high stunting probability trajectory are lower by 33% (India, younger cohort) - 64% (India, older cohort). Being in the top two highest wealth quartiles also predicts lower risk of being in high stunting probability trajectories, particularly for children in the younger cohort in all four countries, where the risks of being on these trajectories are lower by 77% (Peru, younger cohort) - 84% (Vietnam, younger cohort) for those from the highest baseline wealth quartile, and 40% (Ethiopia, younger cohort) - 62% (Peru, younger cohort) for those from the next highest baseline wealth quartile. Urban residence is associated with lower risks of being in either the high or medium stunting probability trajectories for the younger cohorts in all four countries (and lower risks of being in the medium and high stunting trajectories for both cohorts in Peru).

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Being on high overweight probability trajectories is predicted by urban residence, top wealth quartiles, and being female, as shown in Table 3, Panel B. Living in an urban area at initial measurement is associated with higher risks of membership in the high overweight probability trajectories in India, Peru, and Vietnam for both cohorts (except for the older cohort in Vietnam), with the relative risks of being in the high overweight probability trajectories ranging from two (Vietnam, younger cohort) to three (India, younger cohort and Peru, older cohort) times higher. Being in the top wealth quartile at baseline measure is associated with higher odds of being in the high overweight probability trajectory group in India’s older cohort, Vietnam’s younger cohort, and both cohorts in Peru (ranging from three times higher risks in Peru’s older cohort to over five times higher risks in India’s older cohort). The associations with maternal schooling are less clear for overweight trajectories. Girls are at greater risks of being in the medium overweight probability trajectories in Ethiopia’s older cohort and Peru’s older cohort compared with boys and they also have greater risks of being in the high overweight probability trajectory in India’s younger cohort. Girls are at lower risk of being in the high overweight probability trajectory in Vietnam’s younger cohort.

4 . Discussion

This multi-country, longitudinal comparison of the double burden of malnutrition for two cohorts of young people in four different LMICs over critical nutritional periods in the lifecycle (age one through childhood and adolescence into young adulthood) uncovers similarities as well as heterogeneities in trajectories in the probabilities of stunting and overweight within and between countries. Each of the four countries faces differing manifestations of the double burden, as economic development and social changes related to food environments, diets, and physical activities evolve differently. On one end of the nutritional transition, Ethiopia (the least economically developed among the four countries) is least burdened by the two contrasting sides of malnutrition. Having experienced substantial declines in stunting (reaching less than 7% by age 22), Ethiopia has not yet experienced high prevalence of overweight and obesity, except among a small minority of youth. On the other end of the nutritional transition spectrum, Peru (the most economically developed) faces the double burden most severely: stunting remains relatively high in both cohorts of children and overweight emerges in early childhood and escalates at a steep pace. Peru faces an acute strain on the health system from disease burdens at both ends of the nutritional spectrum.

Group-based trajectory analysis of stunting illuminates a shared trajectory across the four countries in the older cohort, where a substantial number of children (15%-20% in each country) with high stunting probabilities in mid-childhood catch up with their peers and reach low probabilities of stunting at age 19. This strikingly similar pattern across all four countries suggests that adolescence may indeed represent a window of opportunity in which early insults to nutritional growth may be overcome. Such a finding counters claims that stunting is irreversible after the first 1,000 days of life.

Trajectory analysis also reveals a common trajectory in overweight for all eight country-cohorts: a trajectory of rapidly increasing probabilities of overweight (over childhood in the

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younger cohort, and into young adulthood in the older cohort). While the percentages of children following this path vary from a very small minority in Ethiopia (less than 1% in the older cohort) to a large portion of children in Peru (over 40% in the older cohort), the same phenomenon exists in all four countries. We can expect that for Ethiopia and India, the share of children in this trajectory group will increase along with economic development, involving increasing numbers of children as changes with urbanization and development continue to take place. In the older cohorts in Peru and Vietnam, of additional concern is that there is both a trajectory group showing persistently high probabilities of overweight over all ages as well as a trajectory group of children who previously had low probabilities of overweight, but whose probability of overweight increases substantially between ages 15 and 22.

There are likely important socioeconomic factors underlying changes in nutritional trajectories. Data presented in Appendix A show that the richest wealth quartile is at lower risk of stunting in all four countries, with the extent of inequality differing by country. At the same time, overweight prevalence is highest in the highest wealth quartiles. In Peru, overweight prevalence in the lowest wealth quartile remains low over early childhood, and only increases in later ages. Multinomial logistic regressions of trajectory group membership show that higher wealth and urban residence predict lower risks of membership in the medium and high stunting probability trajectories, while they tend to predict higher risks of membership in the medium and high overweight probability trajectories. While maternal schooling is protective against medium and high stunting probability trajectories, it does not have a clear relationship with overweight trajectories. It is important to note that, while in some sense, the same socioeconomic drivers may be behind both decreases in stunting and increases in overweight, these relations occur within a complex set of additional influences and are not the only drivers behind nutritional trends.

There are numerous strengths to this paper. First, we present longitudinal data for two cohorts in four diverse countries over a 14-year period using all five available rounds of data. Second, compared to a recent cross-sectional study on the double burden among adolescents through school survey data (Caleyachetty et al., 2018), our sample does not have potential selection biases due to “out of school” children more likely belonging to the most disadvantaged population groups, as all children are assessed at home. Third, although our data are not nationally representative, the distribution of socioeconomic status in the sample is largely comparable to the distribution of socio-economic status in nationally-representative household surveys (Barnett et al. 2012), increasing the external validity of our findings. Fourth, we conduct group-based trajectory modeling for the two outcomes at opposite ends of the nutritional transition, stunting and overweight, which allows us to reveal both heterogeneities within and between countries, as well as identify commonalities. Fifth, multinomial logits of trajectory-group membership on baseline characteristics allow us to identify associations between initial socioeconomic conditions and the later evolution of nutritional indicators.

One possible limitation of the study is that we use only baseline socioeconomic indicators in predicting trajectories. While it is the intent of this study to examine whether such characteristics at baseline can predict the evolution of nutritional indicators over time (and we find they can), it is important to note that socioeconomic indicators also evolve and change, for individual children over time, from one cohort to the next, and across countries. We may be

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missing some relationships that would exist were such changes taken into account. However, we would likely be underestimating true relationships if this were the case. Second, our focus is on descriptive associations between the round one socioeconomic and child characteristics and subsequent growth trajectories that can be given causal interpretations only under what may be strong assumptions, such as the absence of intergenerationally correlated unobserved endowments.

The findings presented here suggest: (1) adolescence may represent a second window of opportunity (after the first 1,000 days) for growth recovery, during which stunting can decline substantially for a subset of children; (2) there are two trajectories towards increasing overweight: one beginning in early childhood and a second during adolescence, suggesting two key intervention life-cycle stages to counteract this public health challenge; and (3) stunting and overweight trajectories are predicted by many of the same basic initial conditions for children growing up in these LMICs, including urban residence and wealth. Implications are that interventions must target the key life-cycle stages identified as responsive to change. While the first 1,000 days are undoubtedly a key life-cycle stage at which to intervene in malnourished populations, evidence here suggests that adolescence is also a window of opportunity to both reverse stunting as well as to avert youth from a path of increasing risk of overweight. Multisectoral and broad double-duty actions by policymakers, multilateral agencies, and nongovernmental organizations can take advantage of these key intervention periods to optimize nutrition and well-being in LMICs.

Many LMICs are at crucial crossroads where there may be opportunities to curb escalating rates of overweight through the implementation of policies that can address nutrition at both ends of the spectrum. Policies should be tailored to the specific nutritional challenges and food systems and environments in each country. For example, interventions for India, where overweight is concentrated among more advantaged populations, would probably be different from those in Peru, which would require a broader approach, given the escalating prevalence of overweight, particularly among girls (figure A1). As policies are considered to address stunting and overweight, it is important that programs do not inadvertently raise the risk of overweight (Hawkes et al., 2017). For example, encouraging families to participate more in markets might inadvertently result in greater reliance on mass-produced, energy-dense, nutrient-poor foods (Lobstein et al. 2015). Similarly, interventions intended to reduce poverty could inadvertently be associated with higher risk of overweight and obesity (Fernald, Gertler and Hou 2008). Overall, policies and programs should promote healthier food opportunities at accessible cost as well as improve access to and opportunities for increased fitness and physical activity.

Examples of such interventions include (i) legislation on marketing, labeling, and availability of nutrition-poor, high-caloric foods, (ii) working with the food industry to gain cooperation in reducing added sugar and fat content, (iii) promotion of healthy, fresh foods to boost dietary diversity, for instance, through information campaigns and fiscal policies (taxes and subsidies), (iv) preschool and school food programs that ensure healthy, fresh and diverse foods are provided to children, (v) broad public health programs targeting pregnant women to improve nutrition and ensure appropriate weight gain, (vi) ensuring breastfeeding is protected and promoted for new mothers, while also supporting nutrition in young children and healthy

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eating habits from birth, and (vii) nutrition-sensitive social protection programs that provide income support to encourage healthy food purchases, particularly among families with children. Successful interventions in these areas have already been implemented in several countries (for example, regulations on food provided in kiosks located at schools and on labeling in Chile (Arrúa et al. 2017, Corvalán et al. 2013), school-based physical education programs in Brazil and Chile (Bonhauser et al. 2005, Cunha 2002), and sugary beverage taxes (Colchero et al. 2016)), and can serve as models for additional and/or improvements in nutritional policies and programs.

Authorship StatementWS conducted the data analysis; WS and EA wrote the initial draft. JB, MP, EA, and WS contributed to the conceptualization and made substantial contributions to revisions of the draft. All authors approved the final article.

Acknowledgments

This work was supported by the Sackler Institute for Nutrition Sciences, New York, NY (“Informing the Delivery of Nutrition Interventions for Adolescent Girls and Women”). We thank the editor and the reviewers for very useful commentary on earlier versions of this study. The authors also thank the Young Lives teams in Oxford and the local country offices for facilitating and publicizing the data. We also wish to thank the Young Lives study children and their families for sharing their time and insights, without which this study would not be possible.

Declarations of interest: None

Role of funding source: The Sackler Institute for Nutrition Sciences supported this study but played no role in any stage of the study.

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Tables

Table 1. Descriptive Statistics

Table 2. Percent Overweight by Stunting in the Round One

Table 3. Relative Risk Ratios from Multinomial Logit Regressions of Trajectory Group Membership, by Cohort and Country

Figures

Figure 1. Percentages Stunted and Overweight, by Country and Cohort

Figure 2. Group-Based Trajectory Modeling with Logit of Stunting: Estimated Trajectories (Lines), Observed Group Means (Dots), and Estimated Group Percentages

Figure 3. Group-Based Trajectory Modeling with Logit of Overweight: Estimated Trajectories (Lines), Observed Group Means (Dots), and Estimated Group Percentages

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Table 1. Descriptive StatisticsEthiopia India Peru Vietnam

 Percent/

Mean (SD) ObsPercent/

Mean (SD) ObsPercent/

Mean (SD) ObsPercent/

Mean (SD) ObsYounger Cohort                Female (%) 46.9 1827 46.1 1909 49.6 1860 48.7 1941Mother grades completed 3.0 (3.9) 1810 3.7 (4.5) 1903 7.6 (4.6) 1733 6.9 (4.0) 1913Wealth index 0.209 (0.174) 1805 0.404 (0.202) 1904 0.426 (0.237) 1856 0.434 (0.215) 1941Urban Residence (%) 33.9 1827 23.9 1909 68.2 1860 19.3 1941Age (years)

Survey age 1 0.97 (0.3) 1827 0.99 (0.29) 1909 0.96 (0.29) 1860 0.97 (0.26) 1941Survey age 5 5.2 (0.3) 1826 5.4 (0.3) 1907 5.3 (0.4) 1837 5.3 (0.3) 1928Survey age 8 8.1 (0.3) 1818 8.0 (0.3) 1904 8.0 (0.3) 1841 8.0 (0.3) 1914Survey age 12 12.1 (0.3) 1819 12 (0.3) 1899 11.9 (0.3) 1818 12.2 (0.3) 1879Survey age 15 15.1 (0.3) 1805 15 (0.3) 1897 14.9 (0.3) 1858 15.2 (0.3) 1939

Stunted (%)Survey age 1 41.6 1782 31.2 1892 27.8 1850 20.8 1934Survey age 5 30.8 1826 36.0 1900 33.1 1830 25.4 1919Survey age 8 20.9 1816 29.2 1902 20.1 1839 20.0 1899Survey age 12 28.9 1819 29.4 1899 18.8 1818 19.6 1878Survey age 15 25.5 1805 27.9 1897 16.4 1850 12.3 1939

Overweight (%)Survey age 1 0.0 1700 0.0 1891 0.0 1849 0.0 1936Survey age 5 3.5 1826 0.8 1900 21.2 1830 7.1 1919Survey age 8 0.6 1816 1.3 1902 18.5 1838 8.1 1890Survey age 12 0.4 1819 4.0 1899 24.4 1817 8.1 1878Survey age 15 1.1 1801 5.7 1895 22.5 1842 7.8 1939

Older CohortFemale (%) 47.4 814 50.7 929 47.4 608 50.9 910Mother grades completed 2.7 (3.5) 798 2.7 (4.0) 924 7.3 (4.5) 575 6.8 (3.9) 898Wealth index 0.208 (0.163) 813 0.404 (0.206) 929 0.481 (0.227) 603 0.445 (0.199) 909Urban Residence (%) 33.0 814 23.1 929 76.0 608 18.9 910Age (years)

Survey age 8 7.9 (0.3) 814 8.0 (0.3) 929 7.9 (0.3) 608 8.0 (0.3) 910Survey age 12 12.1 (0.3) 812 12.3 (0.4) 929 12.3 (0.5) 602 12.3 (0.3) 906Survey age 15 15.0 (0.3) 810 14.9 (0.3) 925 14.9 (0.3) 596 15.1 (0.3) 897Survey age 19 19.1 (0.3) 781 19.0 (0.3) 918 18.9 (0.4) 567 19.3 (0.4) 804Survey age 22 22.0 (0.3) 807 22.0 (0.4) 916 21.9 (0.4) 608 22.3 (0.3) 906

Stunted (%)Survey age 8 33.5 785 33.4 929 27.3 604 28.7 910Survey age 12 32.1 811 34.5 922 31.1 602 31.8 906Survey age 15 31.0 806 35.9 924 25.1 593 23.2 892Survey age 19 11.0 781 31.0 913 31.2 565 22.5 804Survey age 22 6.8 806 28.3 918 29.5 596 15.2 908

Overweight (%)Survey age 8 1.2 755 0.9 929 15.6 604 1.2 910Survey age 12 1.2 811 2.7 922 16.3 602 2.9 906Survey age 15 1.0 806 3.5 924 18.5 593 3.3 892Survey age 19 1.7 781 5.2 912 29.4 564 3.6 804Survey age 22 3.1 807 13.2 916 37.4 597 6.3 906

22

Table 2. Percentages Overweight by Stunting in the Round One

    Ethiopia India Peru Vietnam    All Female Male All Female Male All Female Male All Female MaleStunted at first round Age 5 3.6 4.0 3.1 0.2 0.0 0.4 16.3 15.0 18.1 3.3 2.1 5.1Not Stunted at first round 3.3 3.3 0.6 1.1 1.3 0.8 23.1 24.8 21.5 8.2 10.3 6.1p-value   0.702 0.519 0.813 0.042 0.030 0.562 0.002 0.001 0.284 0.001 0.001 0.631Stunted at first round Age 8 1.0 1.0 1.0 0.7 0.8 0.5 11.4 12.2 10.4 1.2 1.6 0.7Not Stunted at first round 0.6 0.1 1.1 1.4 1.3 1.5 20.2 21.5 19.0 7.3 9.2 5.5p-value   0.287 0.019 0.786 0.095 0.364 0.141 0.001 0.001 0.001 0.001 0.001 0.001Stunted at first round Age 12 0.2 0.3 0.0 2.4 1.6 3.3 11.3 11.1 11.7 2.9 2.6 3.3Not Stunted at first round 1.0 1.0 1.0 4.1 3.9 4.4 26.6 27.3 25.9 7.5 9.5 5.6p-value   0.018 0.163 0.043 0.017 0.001 0.360 0.001 0.001 0.000 0.001 0.001 0.112Stunted at first round Age 15 0.6 0.5 0.7 2.8 2.4 3.3 12.7 8.3 18.7 2.7 2.6 2.9Not Stunted at fist round 1.4 1.0 1.7 6.0 5.2 6.7 24.9 22.6 27.1 7.5 10.2 5.0p-value   0.069 0.331 0.152 0.001 0.010 0.015 0.001 0.001 0.004 0.001 0.001 0.131Stunted at first round Age 19 1.2 0.0 2.4 3.0 2.6 3.4 21.9 11.9 34.3 2.5 0.8 4.5Not Stunted at first round 1.9 0.7 3.3 6.2 4.3 8.0 32.2 28.1 36.5 4.1 5.6 2.9p-value   0.448 0.331 0.644 0.041 0.360 0.067 0.017 0.003 0.754 0.273 0.024 0.423Stunted at first round Age 22 2.3 0.7 4.0 7.8 8.4 7.2 31.7 27.6 36.5 5.0 3.6 2.9Not Stunted at first round 3.5 2.1 5.1 15.9 16.2 15.7 39.4 38.2 40.8 6.8 11.1 7.5p-value   0.359 0.310 0.637 0.001 0.021 0.011 0.082 0.078 0.520 0.302 0.009 0.066

Note: Both cohorts combined. Bold font indicates p<0.05.

23

Table 3. Relative Risk Ratios from Multinomial Logit Regressions of Trajectory Group Membership, by Cohort and Country

Country CohortTraj Group

% of Children Curve Shape Urban

Wealth Q2

Wealth Q3

Wealth Q4

Maternal Edu Female PseudoR2

Panel A. Stunting Ethiopia YC Low 50.8 Slightly decreasing (reference) 0.0438

(N=1,810) Medium 32.0 Slight U 0.685* 1.306 1.259 0.800 0.623† 0.657† 0.0438

High 17.2 Slight inverted U 1.043 0.918 0.604* 0.174† 0.694 0.675† 0.0438Ethiopia OC Low 67.1 Steady (reference) 0.0370

(N=798) Medium 20.1 Decreasing 0.665 0.891 0.749 0.365* 0.775 1.056 0.0370High 12.8 Slightly decreasing 1.042 0.952 0.687 0.477 0.735 0.478† 0.0370

India YC Low 41.5 Steady (reference) 0.0304(N=1,903

) Medium 34.5 Slight inverted U 0.984 0.705* 0.811 0.549† 0.742* 0.945 0.0304High 24.0 Slight U 0.640* 0.626† 0.535† 0.307† 0.670* 0.853 0.0304

India OC Low 57.3 Steady (reference) 0.0525(N=924) Medium 22.2 Increasing 0.763 1.119 0.840 1.071 0.536† 0.679* 0.0525

High 20.5 Decreasing 0.506 0.883 0.615* 0.861 0.367† 2.134† 0.0525Peru YC Low 42.3 Steady (reference) 0.103

(N=1,733) Medium 38.2 Decreasing 0.636† 1.017 0.495† 0.384† 0.643† 0.943 0.103

High 19.5 Slight inverted U 0.357† 0.961 0.387† 0.230† 0.381† 0.831 0.103Peru OC Low 64.7 Steady (reference) 0.0703

(N=575) Medium 14.0 Decreasing 0.363† 0.990 0.331* 0.283* 1.151 0.990 0.0703High 21.3 Steady 0.480† 0.681 0.577 0.396* 0.612* 0.730 0.0703

Vietnam YC Low 60.0 Steady (reference) 0.0640(N=1,913

) Medium 23.7 Slightly decreasing 0.608* 0.833 0.812 0.505† 0.550† 0.663† 0.0640High 16.3 Slight inverted U 0.607 0.544† 0.484† 0.167† 0.494† 0.692† 0.0640

Vietnam OC Low 64.7 Steady (reference) 0.0372(N=898) Medium 14.0 Decreasing 0.565 1.477 1.493 0.564 0.744 1.157 0.0372

High 21.3 Steady 1.192 0.907 0.682 0.474* 0.529† 0.714 0.0372

Panel B. Overweight Ethiopia YC Low 92.5 Steady (reference) 0.0548

(N=1,810) Medium 2.5 Slight increase 0.979 0.000 3.428 10.150† 1.185 1.445 0.0548

High 4.9 Slight inverted U 0.559 0.760 1.474 0.949 1.002 0.830 0.0548Ethiopia OC Low 78.0 Steady (reference) 0.0508

(N=798) Medium 21.5 Steady, late increase 1.207 0.796 0.489 0.978 1.324 1.841* 0.0508

High 0.5 Increasing 443,678.844 0.000 0.000 12.4701660193.82

5 5145442.165 0.0508

24

Country CohortTraj Group

% of Children Curve Shape Urban

Wealth Q2

Wealth Q3

Wealth Q4

Maternal Edu Female PseudoR2

India YC Low 93.4 Steady (reference) 0.0835(N=1,903

) Medium 3.3 Slight inverted U 0.719 0.332 0.153* 1.306 1.987 0.829 0.0835High 3.4 Increasing 3.050† 2.130 2.168 2.610 1.778* 1.823* 0.0835

India OC Low 91.9 Steady (reference) 0.111(N=924) Medium 1.8 Early inverted U 2.797 1.096 0.476 1.497 0.948 1.642 0.111

High 6.3 Increasing 2.918* 2.533 2.427 5.592* 1.118 1.563 0.111Peru YC Low 71.4 Steady (reference) 0.0850

(N=1,733) Medium 9.2 Later increase 2.522† 1.484 3.099† 2.141* 1.338 0.990 0.0850

High 19.4 Increasing 2.075† 1.532 2.390† 3.810† 1.693† 0.922 0.0850Peru OC Low 44.6 Steady (reference) 0.0297

(N=575) Medium 40.9 Later increase 1.095 1.062 1.503 1.540 0.799 1.643† 0.0297High 14.5 Increasing 3.016* 1.159 1.801 3.077* 0.497* 1.543 0.0297

Vietnam YC Low 85.9 Steady (reference) 0.125(N=1,913

) Medium 6.0 Early inverted U 4.671† 0.661 0.222† 1.220 1.508 1.175 0.125High 8.1 Increasing 2.160† 0.815 0.881 3.164† 1.365 0.580† 0.125

Vietnam OC Low 88.0 Steady (reference) 0.0696(N=898) Medium 9.4 Later increase 1.643 0.487 0.771 1.230 0.708 0.706 0.0696

High 2.7 Inverted U, inflection 2.468 0.795 0.747 3.790 1.636 0.460 0.0696Notes: * p<0.05; † p<0.01. Values in bold are significantly nonzero at the 0.05 level. YC=Younger cohort; OC=Older Cohort. Traj Group = Trajectory Group. "Maternal Edu" = indicator of mother's schooling is in top quartile for country-cohort. Wealth quartiles calculated from country-cohort wealth index.

25

Figure 1. Percentages Stunted and Overweight, by Country and Cohort

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Younger Cohort Older Cohort

26

Figure 2. Group-Based Trajectory Modeling with Logit of Stunting: Estimated Trajectories (Lines), Observed Group Means (Dots), And Estimated Group Percentages

Notes: ET=Ethiopia, IN=India, PE=Peru, VN=Vietnam, YC=Younger Cohort, OC=Older Cohort

27

Figure 3. Group-Based Trajectory Modeling with Logit of Overweight: Estimated Trajectories (Lines), Observed Group Means (Dots), And Estimated Group Percentages

Notes: ET=Ethiopia, IN=India, PE=Peru, VN=Vietnam, YC=Younger Cohort, OC=Older Cohort

28

Appendix A

In the main body of the paper, we document trajectories for stunting and overweight for two cohorts each in four countries in different stages of economic development and of the nutritional transition. Examining these changes by sex (Figure A1), we see that boys have higher prevalence of stunting than girls in Ethiopia, with marked increases at survey age 15 years. Compared to the reference population of well-nourished children from which z-scores are constructed, boys in Ethiopia are shorter at age 15, but then begin to catch up by age 19. In India, girls in the older cohort have higher levels of stunting than boys, particularly into young adulthood. However, stunting levels for girls in the younger cohort in India are lower than in the older cohort, and on par with their male counterparts. Stunting prevalence in Peru holds steady among girls in the older cohort at just under 30% as girls grow older, while boys in the older cohort have more fluctuations. Both boys and girls in the younger cohort have lower levels of stunting at the overlapping ages (ages 8, 12, and 15) compared to their older peers. In Vietnam, a declining trend over childhood is present in the older cohort, with higher prevalence of stunting among males than females. In the younger cohort, this decline continues, but stunting differences between males and females reduce by age 15.

Overweight is relatively steady across ages in Ethiopia, but slight differences by sex appear by age 19, with boys having slightly higher prevalence of overweight than girls. In India, prevalence of overweight begins to increase slowly and then ticks upward between ages 19 and 22, for both boys and girls in the older cohort. In Peru, increases in prevalence of overweight are apparent, with girls having higher levels of overweight than boys in both the younger and older cohorts, though prevalence of overweight in males comes close to catching up to females by age 22. In Vietnam, males have a higher prevalence of overweight than females in both cohorts.

Differences by wealth quartile (highest compared to lowest, Figure A2) reveal that stunting is more prevalent among children from the poorest wealth quartiles in all four countries, though substantial declines in stunting occur among the poorest quartile in Ethiopia by age 19. This trend is similar in Vietnam, where declines in the poorest quartile are apparent by the end of the period for both cohorts. This trend does not occur for India and Peru, where levels continue to be relatively stable over the life course among the poorest wealth quartile.

In India, overweight begins to emerge among the richest quartile by age 12, with the poorest quartile having low prevalence of overweight, up until age 22. At that point, however, there is a relatively large increase in prevalence of overweight even among the poorest wealth quartile. In Peru, differences between the richest and poorest wealth quartiles emerge at age 5, and the richest wealth quartile has much higher prevalence of overweight throughout childhood, though prevalence declines slightly for the younger cohort at age 15 among the richest quartile. In the older cohort, increases in prevalence of overweight occur between ages 15 and 22, even among the poorest wealth quartile, with levels similar between the quartiles at these older ages. In Vietnam, a large increase in the prevalence of overweight occurs by age five among the richest quartile of the younger cohort only, though prevalence declines slightly thereafter. The richest quartile has higher prevalence of overweight throughout much of the observed segments of the life course in the older cohort. Differences in the three outcomes by maternal schooling and by urban residence reveal similar patterns (Figures A3 and A4).

29

Examining these differences by sex and wealth quartile in additional graphs not shown, males in the poorest wealth quartile have the highest levels of stunting in Ethiopia, Vietnam and Peru, but girls in the poorest quartile have higher prevalence of stunting in India. For overweight, females in the richest wealth quartile have the highest prevalence of overweight in Ethiopia and India throughout childhood. In Peru, females in the lowest wealth quartile cross over to the highest prevalence at age 19. In Vietnam, males in the highest wealth quartile have the highest prevalence of overweight.

Gender differences in these graphical patterns show that girls are at greater risks of overweight in all countries except Vietnam.c In additional analysis not shown, among girls, early puberty is associated with subsequent weight gain, as is the delivery of a child. These findings indicate that key intervention periods for girls could be prior to pubertal development, at pregnancy, and during the post-partum period.

c Differing patterns in Vietnam could reflect intra-household allocation of resources related to the documented preference for boys in Vietnam (Anh et al. 1998, Guilmoto 2012, Haughton and Haughton 1995).

30

Appendix A Figures

Figure A1. Percentages Stunted and Overweight, by Sex, Country, and Cohort

Figure A2. Percentage Stunted and Overweight, by Highest and Lowest Wealth Quartiles, Country, and Cohort

Figure A3. Percentage Stunted and Overweight, by Maternal Schooling, Country, and Cohort

Figure A4. Percentage Stunted and Overweight, by Urban Residence, Country, and Cohort

31

Figure A1. Percentages Stunted and Overweight, by Sex, Country, and Cohort

010

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Ethiopia

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YC Females YC Males

OC Females OC Males

32

Figure A2. Percentages Stunted and Overweight, by Wealth Quartile, Country, and Cohort

010

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0 5 10 15 20Survey age

Ethiopia

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YC 1st (Poorest) Quartile YC 4th (Richest) Quartile

OC 1st (Poorest) Quartile OC 4th (Richest) Quartile

33

Figure A3. Percentage Stunted and Overweight, by Maternal Schooling, Country and Cohort

010

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YC Maternal Schooling Top Quartile YC Maternal Schooling Not Top Quartile

OC Maternal Schooling Top Quartile OC Maternal Schooling Not Top Quartile

34

Figure A4. Percentage Stunted and Overweight, by Urban Residence, Country, and Cohort

010

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Ethiopia

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YC Urban YC Rural

OC Urban OC Rural

35

Appendix B.

Table B1. Estimated Model Parameters, Group-Based Trajectory Analysis of Stunting, by Cohort and Country

Ethiopia, Younger Cohort, StuntingObservations: 1,827

T for H0:

Parameter Estimate Std. Error Parameter=0Prob > |T|

1 Intercept 0.57346 0.18406 3.116 0.0018Linear -0.6653 0.11631 -5.72 0Quadratic 0.07303 0.01804 4.049 0.0001Cubic -0.00219 0.00074 -2.953 0.0032

2 Intercept -2.37413 0.34499 -6.882 0Linear 1.57092 0.37742 4.162 0Quadratic -0.38147 0.08023 -4.755 0Cubic 0.01749 0.00364 4.799 0

3 Intercept 0.75156 0.3488 2.155 0.0312Linear 0.52323 0.3655 1.432 0.1523Quadratic -0.02441 0.0476 -0.513 0.6082Cubic -0.00059 0.00173 -0.343 0.7317Group Membership

1 (%) 31.9566 2.63499 12.128 02 (%) 50.83752 2.94073 17.287 03 (%) 17.20589 1.83189 9.392 0 BIC= -4508.90 (N=9048) BIC= -4497.70 (N=1827) AIC= -4459.12 L= -4445.12

Ethiopia, Older Cohort, StuntingObservations: 814

T for H0:

Parameter Estimate Std. Error Parameter=0Prob > |T|

1 Intercept -16.4597 23.28152 -0.707 0.4796Linear 4.3204 5.57827 0.775 0.4387Quadratic -0.36887 0.47567 -0.775 0.4381Cubic 0.01005 0.01269 0.792 0.4281

2 Intercept -4.00129 5.53297 -0.723 0.4696Linear 0.34079 1.25254 0.272 0.7856Quadratic -0.03534 0.08619 -0.41 0.6818Cubic 0.00109 0.0019 0.575 0.5652

3 Intercept -4.69061 7243.662 -0.001 0.9995Linear -7.15762 2066.685 -0.003 0.9972Quadratic 1.30165 196.1262 0.007 0.9947Cubic -0.06192 6.25055 -0.01 0.9921Group Membership

1 (%) 0.47998 0.54137 0.887 0.37532 (%) 21.46461 45.98492 0.467 0.64073 (%) 78.05541 46.54946 1.677 0.0937 BIC= -361.00 (N=3960) BIC= -349.93 (N=814) AIC= -317.01 L= -303.01

36

India, Younger Cohort, StuntingObservations: 1,909

T for H0:

Parameter Estimate Std. Error Parameter=0Prob > |T|

1 Intercept -0.90912 0.18116 -5.018 0Linear 0.289 0.11112 2.601 0.0093Quadratic -0.06399 0.01788 -3.578 0.0003Cubic 0.00301 0.00077 3.902 0.0001

2 Intercept -4.45405 1.44148 -3.09 0.002Linear 2.84111 1.5975 1.778 0.0754Quadratic -0.70145 0.3715 -1.888 0.059Cubic 0.03357 0.01731 1.94 0.0524

3 Intercept -0.45588 0.23083 -1.975 0.0483Linear 1.28311 0.24126 5.318 0Quadratic -0.12993 0.03495 -3.717 0.0002Cubic 0.00339 0.00136 2.494 0.0126Group Membership

1 (%) 34.47446 3.32256 10.376 02 (%) 41.54044 3.90744 10.631 03 (%) 23.9851 1.49303 16.065 0 BIC= -4549.18 (N=9488) BIC= -4537.96 (N=1909) AIC= -4499.07 L= -4485.07

India, Older Cohort, StuntingObservations: 929

T for H0:

Parameter Estimate Std. Error Parameter=0Prob > |T|

1 Intercept -21.4942 78.67542 -0.273 0.7847Linear 2.17451 14.55272 0.149 0.8812Quadratic -0.12851 0.88027 -0.146 0.8839Cubic 0.00313 0.01713 0.183 0.8549

2 Intercept -3.33126 41.66411 -0.08 0.9363Linear -0.40656 11.10316 -0.037 0.9708Quadratic 0.11033 0.93454 0.118 0.906Cubic -0.005 0.02535 -0.197 0.8438

3 Intercept -5.71172 6.5675 -0.87 0.3845Linear 0.53546 1.47115 0.364 0.7159Quadratic -0.01746 0.10451 -0.167 0.8674Cubic 0.00042 0.00236 0.176 0.86Group Membership

1 (%) 91.88428 2.58923 35.487 02 (%) 1.80014 2.27229 0.792 0.42833 (%) 6.31558 1.00476 6.286 0 BIC= -718.18 (N=4597) BIC= -706.99 (N=929) AIC= -673.15 L= -659.15

Peru, Younger Cohort, Stunting

37

Observations: 1,860T for H0:

Parameter Estimate Std. Error Parameter=0Prob > |T|

1 Intercept -16.4678 222.0493 -0.074 0.9409Linear -21.3082 31.10958 -0.685 0.4934Quadratic 3.77296 5.67938 0.664 0.5065Cubic -0.15899 0.33025 -0.481 0.6302

2 Intercept -1.61232 0.21159 -7.62 0Linear 1.27735 0.15934 8.016 0Quadratic -0.26231 0.03147 -8.335 0Cubic 0.01167 0.00144 8.119 0

3 Intercept 0.27495 0.21561 1.275 0.2023Linear 0.87096 0.17345 5.021 0Quadratic -0.09591 0.02594 -3.697 0.0002Cubic 0.00259 0.00105 2.476 0.0133Group Membership

1 (%) 42.26364 2.35179 17.971 02 (%) 38.23562 2.09401 18.26 03 (%) 19.50074 1.31381 14.843 0 BIC= -3705.65 (N=9179) BIC= -3694.48 (N=1860) AIC= -3655.78 L= -3641.78

Peru, Older Cohort, StuntingObservations: 608

T for H0:

Parameter Estimate Std. Error Parameter=0Prob > |T|

1 Intercept -7.64446 401.8123 -0.019 0.9848Linear -12.3907 149.4956 -0.083 0.9339Quadratic 3.30327 19.30575 0.171 0.8642Cubic -0.20999 0.85922 -0.244 0.8069

2 Intercept 8.29509 3.65206 2.271 0.0232Linear -2.3692 0.82404 -2.875 0.0041Quadratic 0.15916 0.05644 2.82 0.0048Cubic -0.00307 0.00121 -2.529 0.0115

3 Intercept -17.0718 6.9988 -2.439 0.0148Linear 3.92346 1.64486 2.385 0.0171Quadratic -0.25832 0.11587 -2.229 0.0259Cubic 0.00541 0.00255 2.121 0.034Group Membership

1 (%) 44.60751 2.8674 15.557 02 (%) 40.9337 3.08214 13.281 03 (%) 14.45879 2.11723 6.829 0 BIC= -1314.43 (N=2960) BIC= -1303.35 (N=608) AIC= -1272.48 L= -1258.48

Vietnam, Younger Cohort, StuntingObservations: 1,941

T for H0:

Parameter Estimate Std. Error Parameter=0Prob > |T|

38

1 Intercept -284.192 108.2309 -2.626 0.0087Linear 62.27841 27.29739 2.281 0.0225Quadratic -4.56784 2.23079 -2.048 0.0406Cubic 0.11067 0.05942 1.863 0.0625

2 Intercept -0.69724 0.19445 -3.586 0.0003Linear 0.35819 0.12202 2.935 0.0033Quadratic -0.07105 0.02087 -3.404 0.0007Cubic 0.00268 0.00091 2.944 0.0032

3 Intercept 0.05004 0.23769 0.211 0.8333Linear 0.88611 0.20089 4.411 0Quadratic -0.08734 0.02927 -2.984 0.0029Cubic 0.002 0.00115 1.743 0.0813Group Membership

1 (%) 59.97997 1.66391 36.048 02 (%) 23.70178 1.55164 15.275 03 (%) 16.31825 1.23392 13.225 0 BIC= -3517.84 (N=9568) BIC= -3506.67 (N=1941) AIC= -3467.68 L= -3453.68

Vietnam, Older Cohort, StuntingObservations: 910

T for H0:

Parameter Estimate Std. Error Parameter=0Prob > |T|

1 Intercept -179.08 165.9061 -1.079 0.2805Linear 28.88763 26.98821 1.07 0.2845Quadratic -1.55762 1.44467 -1.078 0.281Cubic 0.0279 0.02548 1.095 0.2735

2 Intercept 153.0728 147.0573 1.041 0.298Linear -52.0148 44.25729 -1.175 0.2399Quadratic 5.56297 4.37804 1.271 0.2039Cubic -0.19359 0.14288 -1.355 0.1755

3 Intercept -24.5065 9.72413 -2.52 0.0118Linear 5.28644 2.25928 2.34 0.0193Quadratic -0.33885 0.15879 -2.134 0.0329Cubic 0.00681 0.00348 1.956 0.0505Group Membership

1 (%) 9.35403 1.80624 5.179 02 (%) 87.96823 1.86728 47.11 03 (%) 2.67774 0.59992 4.463 0 BIC= -551.86 (N=4418) BIC= -540.80 (N=910) AIC= -507.11 L= -493.11

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Table B2. Estimated Model Parameters, Group-Based Trajectory Analysis of Overweight, by Cohort and Country

Ethiopia, Younger Cohort, OverweightObservations: 1,827

T for H0:

Parameter Estimate Std. Error Parameter=0Prob > |T|

1 Intercept -22.3919 69889.23 0 0.9997Linear -12.6988 35365.12 0 0.9997Quadratic 4.72926 5971.344 0.001 0.9994Cubic -0.39816 335.1966 -0.001 0.9991

2 Intercept -9.84206 5.14317 -1.914 0.0557Linear 2.91454 1.7028 1.712 0.087Quadratic -0.32199 0.17644 -1.825 0.068Cubic 0.01133 0.00577 1.962 0.0498

3 Intercept -7.72338 26.32529 -0.293 0.7692Linear -4.53137 15.69878 -0.289 0.7729Quadratic 2.24585 3.29082 0.682 0.495Cubic -0.20412 0.22196 -0.92 0.3578Group Membership

1 (%) 92.53424 2.63777 35.08 02 (%) 2.52913 0.68287 3.704 0.00023 (%) 4.93663 2.66799 1.85 0.0643

BIC= -531.54 (N=8962) BIC= -520.41 (N=1827) AIC= -481.84 L= -467.84

India, Younger Cohort, OverweightObservations: 1,909

T for H0:

Parameter Estimate Std. Error Parameter=0Prob > |T|

1 Intercept -15.0557 27.33908 -0.551 0.5819Linear 4.65938 11.6343 0.4 0.6888Quadratic -0.46613 1.60441 -0.291 0.7714Cubic 0.01118 0.07325 0.153 0.8787

2 Intercept -37.1734 115.7174 -0.321 0.748Linear 6.36906 26.0963 0.244 0.8072Quadratic -0.41725 1.9597 -0.213 0.8314Cubic 0.00946 0.04898 0.193 0.8468

3 Intercept -5.61779 2.32422 -2.417 0.0157Linear 0.3724 0.90855 0.41 0.6819Quadratic 0.04532 0.1183 0.383 0.7016Cubic -0.00211 0.00475 -0.443 0.6576Group Membership

1 (%) 3.29093 3.23351 1.018 0.30882 (%) 93.35553 3.28771 28.395 03 (%) 3.35354 0.51322 6.534 0 BIC= -849.12 (N=9486) BIC= -837.90 (N=1909) AIC= -799.02 L= -785.02

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India, Older Cohort, OverweightObservations: 929

T for H0:

Parameter Estimate Std. Error Parameter=0Prob > |T|

1 Intercept -3.586 3.5104 -1.022 0.3071Linear 0.12971 0.79131 0.164 0.8698Quadratic -0.00429 0.0555 -0.077 0.9385Cubic 0.00001 0.00122 0.007 0.9945

2 Intercept -10.5767 3.7142 -2.848 0.0044Linear 2.87516 0.84761 3.392 0.0007Quadratic -0.20319 0.05977 -3.4 0.0007Cubic 0.00411 0.00133 3.098 0.002

3 Intercept -17.3537 18.79578 -0.923 0.3559Linear 5.41449 5.16951 1.047 0.295Quadratic -0.53968 0.45669 -1.182 0.2374Cubic 0.01756 0.01306 1.345 0.1787Group Membership

1 (%) 57.31841 2.02906 28.249 02 (%) 20.45154 1.77135 11.546 03 (%) 22.23005 1.4543 15.286 0 BIC= -2304.94 (N=4597) BIC= -2293.75 (N=929) AIC= -2259.91 L= -2245.91

Ethiopia, Older Cohort, OverweightObservations: 814

T for H0:

Parameter Estimate Std. Error Parameter=0Prob > |T|

1 Intercept 0.64348 3.71505 0.173 0.8625Linear -0.74075 0.88567 -0.836 0.403Quadratic 0.05971 0.06566 0.909 0.3632Cubic -0.00167 0.00153 -1.09 0.2758

2 Intercept 23.62938 29.47244 0.802 0.4227Linear -6.88656 8.09682 -0.851 0.3951Quadratic 0.68635 0.7139 0.961 0.3364Cubic -0.0219 0.02034 -1.076 0.2819

3 Intercept 2.41903 5.02472 0.481 0.6302Linear -0.19527 1.13061 -0.173 0.8629Quadratic 0.02516 0.07889 0.319 0.7498Cubic -0.00098 0.00173 -0.568 0.5702Group Membership

1 (%) 67.10992 2.20817 30.392 02 (%) 20.13367 2.13687 9.422 03 (%) 12.75641 1.45283 8.78 0 BIC= -1635.27 (N=3989) BIC= -1624.14 (N=814) AIC= -1591.23 L= -1577.23

Peru, Younger Cohort, OverweightObservations: 1,860

T for H0:

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Parameter Estimate Std. Error Parameter=0Prob > |T|

1 Intercept -9.84364 1.16104 -8.478 0Linear 3.68331 0.48129 7.653 0Quadratic -0.51967 0.06279 -8.276 0Cubic 0.02031 0.00239 8.492 0

2 Intercept -174.014 314.4611 -0.553 0.58Linear 41.33105 69.92275 0.591 0.5545Quadratic -3.21221 5.14119 -0.625 0.5321Cubic 0.08198 0.12512 0.655 0.5124

3 Intercept -7.21374 0.97982 -7.362 0Linear 2.13605 0.35346 6.043 0Quadratic -0.15227 0.04069 -3.742 0.0002Cubic 0.00311 0.00147 2.112 0.0347Group Membership

1 (%) 71.35773 1.79391 39.778 02 (%) 9.24259 1.1249 8.216 03 (%) 19.39969 1.08298 17.913 0 BIC= -3181.75 (N=9176) BIC= -3170.58 (N=1860) AIC= -3131.88 L= -3117.88

Peru, Older Cohort, OverweightObservations: 608

T for H0:

Parameter Estimate Std. Error Parameter=0Prob > |T|

1 Intercept -899.752 25.04142 -35.931 0Linear 129.3613 5.62569 22.995 0Quadratic -6.19804 0.42511 -14.58 0Cubic 0.09861 0.00863 11.428 0

2 Intercept -22.6475 7.47378 -3.03 0.0025Linear 5.25318 1.77388 2.961 0.0031Quadratic -0.37173 0.12965 -2.867 0.0042Cubic 0.00787 0.00292 2.692 0.0071

3 Intercept 5.93026 3.25938 1.819 0.0689Linear -1.19855 0.76981 -1.557 0.1196Quadratic 0.0814 0.05667 1.436 0.151Cubic -0.00156 0.00132 -1.183 0.2368Group Membership

1 (%) 52.41957 2.39362 21.9 02 (%) 16.92267 2.6175 6.465 03 (%) 30.65776 2.58773 11.847 0 BIC= -1335.99 (N=2960) BIC= -1324.91 (N=608) AIC= -1294.04 L= -1280.04

Vietnam, Younger Cohort, OverweightObservations: 1,941

T for H0:

Parameter Estimate Std. Error Parameter=0Prob > |T|

1 Intercept -10.6489 4.21488 -2.526 0.0115Linear 4.17192 1.76596 2.362 0.0182

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Quadratic -0.46909 0.24192 -1.939 0.0525Cubic 0.01359 0.01118 1.216 0.224

2 Intercept -108.566 65.93543 -1.647 0.0997Linear 25.25778 15.35068 1.645 0.0999Quadratic -2.0297 1.19124 -1.704 0.0884Cubic 0.05403 0.03071 1.759 0.0786

3 Intercept -6.18857 1.15345 -5.365 0Linear 1.56769 0.42622 3.678 0.0002Quadratic -0.09208 0.04975 -1.851 0.0642Cubic 0.00131 0.00179 0.734 0.4631Group Membership

1 (%) 5.95541 0.87728 6.788 02 (%) 85.92036 1.03783 82.789 03 (%) 8.12423 0.70924 11.455 0 BIC= -1628.10 (N=9561) BIC= -1616.94 (N=1941) AIC= -1577.95 L= -1563.95

Vietnam, Older Cohort, OverweightObservations: 910

T for H0:

Parameter Estimate Std. Error Parameter=0Prob > |T|

1 Intercept 4.25111 5.28915 0.804 0.4216Linear -1.83605 1.22484 -1.499 0.1339Quadratic 0.13901 0.08558 1.624 0.1044Cubic -0.00324 0.00186 -1.738 0.0822

2 Intercept -38.1124 12.02582 -3.169 0.0015Linear 9.18591 2.91751 3.149 0.0017Quadratic -0.65154 0.21639 -3.011 0.0026Cubic 0.01366 0.0051 2.68 0.0074

3 Intercept 7.32184 3.29547 2.222 0.0263Linear -1.45033 0.74907 -1.936 0.0529Quadratic 0.11051 0.05277 2.094 0.0363Cubic -0.00266 0.00116 -2.29 0.0221Group Membership

1 (%) 64.72006 2.94466 21.979 02 (%) 14.02415 2.63893 5.314 03 (%) 21.25579 1.71846 12.369 0 BIC= -1922.38 (N=4418) BIC= -1911.32 (N=910) AIC= -1877.62 L= -1863.62

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