UC Berkeley

Background: Every child deserves an equal chance at good health and cognitive development, but growth failure from undernutrition and disease rob many poor children of this opportunity in the first 2 years of their life. Child malnutrition kills roughly three million children every year, and many more suffer long-term health consequences from impaired physical growth and cognitive development, especially if malnutrition occurs during the first 1000 days of life. Wasting and stunting, two markers of child malnutrition, are strongly associated with increased mortality and decreased cognitive development, and are still highly prevalent in developing countries, even though their reduction are targets of the World Health Organization Millennium Development Goals. Trials on nutritional interventions have had null results or not clinically meaningful effect sizes. Most existing research on exposures associated with stunting and wasting rely on small cohorts or large cross-sectional surveys focused on stunting and wasting prevalence. Pooled analyses of data from many longitudinal studies offer unique opportunities to identify novel patterns in child undernutrition across combinations of time, space, and human characteristics, which is not possible in smaller datasets. The objective of this research was to answer the following questions by pooling information across multiple cohorts: when and where growth failure occurs, what characteristics best identify children at risk, and how early growth failure affects later growth and mortality. Answering these questions may help inform generalizable rules for child monitoring and develop preventative intervention to stop the initial onset of wasting and stunting.

Methods: In this dissertation, I conduct an individual-participant-data meta-analysis of 35 different longitudinal cohorts and trials from 15 different countries to identify patterns in wasting incidence and recovery across child ages, across regions, and across seasons. I use machine learning and targeted learning methods to identify key child, parental, and household characteristics associated with wasting and stunting, adjusting for potential confounders. In addition to estimating relative risks of wasting between levels of characteristics, I estimate more policy-relevant population attributable fractions and a novel variable importance metric. In the first chapter I introduce the motivation for the analyses and give an overview of individual participant data meta-analysis methods and advantages. In the second chapter I examine longitudinal patterns in wasting incidence, recovery, seasonality, and concurrence with stunting by using a subset of 18 monthly-measured cohorts, and I compare differences in the epidemiology of wasting across regions. In the third chapter I estimate the relative importance of the causes of early life growth failure using targeted learning methodologies. Using machine learning to flexibly adjust for measured confounders, I estimate and rank order associations between 36 child, parental, and household characteristics and measures of growth failure in 31 cohorts and trials. In the fourth chapter I estimate the associations between early growth failure and subsequent adverse outcomes, including mortality. In the fifth chapter I discuss key subject-area advancements in knowledge from these analyses and offer the public health implications.

Significance: This dissertation is currently the largest scale individual-participant data analysis of longitudinal patterns in and causes of child malnutrition in the first 1000 days of life. Using longitudinal, high quality anthropometry measurements, I examined patterns in wasting onset, recovery, seasonality, and concurrence with stunting not possible from national health surveys or accurate from small cohorts. The data were large in scope as well as scale, with data from 35 separate research sites (94,019 children and 645,869 total measurements), 18 with monthly measured cohorts (10,854 children and 187,215 anthropometry measurements). I was able to examine effect modification of wasting incidence by region and season. The results from chapter two found a high prevalence of wasting incidence at birth and the first three months of life, especially in South Asia. This is earlier than the paradigm in the nutrition field that most wasting episodes occur in children older than six months, after the cessation of exclusive breastfeeding and the start of crawling behavior. My findings suggest that interventions to prevent child acute malnutrition may have the largest impact if delivered earlier, including maternal interventions during pregnancy and early lactation and child interventions through the first months of life. Development of maternally focused, prenatal interventions represents an important opportunity to prevent child growth failure in lower-or-middle income countries (LMICs), especially as I found that early growth failure had a strong relationship with mortality. Future research into potential tradeoffs in prioritizing exclusive breast feeding versus the prevention or treatment of wasting in the first 6 months is needed. I also found high seasonality in wasting in synchrony with rainfall patterns, possibly due to seasonality in food availability and infectious diseases, indicating the timing of intervention delivery should target seasonal peaks in wasting. The strongest determinants of child growth failure were maternal anthropometry and child birth size. Future research should use the key determinants identified to find the optimal subgroups of children who would benefit most from postnatal interventions to start and reduce the substantial global health burden of child growth failure.