UC Berkeley

Globally over 249 million children in low- and middle-income countries (LMICs) are at risk of not meeting developmental milestones on the motor, cognitive, language or socio-emotional domains. This compromised development may result in less educational attainment and lower economic earnings later in life. The nurturing care framework, developed by the World Health Organization, summarizes the inputs required to promote healthy child development. These inputs include good health, nutrition, responsive caregiving, access to early learning, and safety and security. In pursuit of improved child development, caregiver support interventions have been developed and implemented in numerous LMICs, and have succeeded at improving child language and cognitive development. Most of these interventions, however, were designed to be implemented on a small scale and focus primarily on access to early learning and responsive caregiving.

This dissertation aims to inform the design and evaluation of large-scale interventions to improve child development over the life-course in low-resource settings. In Chapter 1, I evaluate the effects of a multicomponent intervention, that covers early learning, responsive caregiving nutrition, water, sanitation, hygiene, caregiver mental health and lead exposure prevention on child development and risk factors for poor child development. I use data from a cluster randomized controlled trial of an intervention that was delivered primarily in groups in Kishoreganj, Bangladesh. This research contributes to existing literature on the effects on caregiver support interventions on early child development and expands the scope of these interventions to include additional components that may result in larger and more sustained impacts on child outcomes. In Chapter 2, I assess the concurrent correlation between two measures of child development: (i) the Bangladeshi-adapted Ages and Stages Questionnaire: Inventory and (ii) the Bayley Scales of Infant Development-III. The Ages and Stages Questionnaire: Inventory is primarily a caregiver report measure that is feasible to implement to evaluate intervention effects when financial, human, and time resources are constrained, as may be the case with large-scale evaluations. The Bayley Scales of Infant Development-III is a direct assessment measure for child development that has been adapted for use in Bangladesh, and is considered to be less biased than caregiver report in evaluating the effects of early child development interventions. I use endline data from the same cluster randomized trial used in Chapter 1 to examine correlations overall, by age, and by intervention status. This work illustrates the potential of the Bangladeshi-adapted Ages and Stages Questionnaire: Inventory for use in the evaluation of large-scale child development interventions. Finally, in Chapter 3, I describe how the distribution of risk factors for poor child development shifted in response to experiences during the COVID-19 pandemic in Chatmohar, Bangladesh. I analyze data from the evaluation of a large-scale multicomponent child development intervention in Chatmohar, Bangladesh. This analysis highlights the shift of intervention targets for improving population-level early child development in the context of the COVID-19 pandemic. Taken together, this dissertation contributes to the evidence for the design and measurement of effective and scalable interventions to improve child development in LMICs.