Skip to main content

Developing innovative analytics to estimate age and cause specific child mortality for low and middle income countries

3a

Associate Name: Li Liu

Funding Source/Period of the Grant: NICHD-PDB R21 4/1/2018 - 3/31/2020

Project Description

Project Summary: Globally, an estimated 5.9 million children died before reaching their fifth birthday in 2015. The majority died in low- and middle-income countries (LMICs), where quality information on age- and cause-specific child mortality (ACSCM) is rarely available. Recently, the US government and the international community have renewed their commitment to end preventable child deaths in a generation. We have been publishing modeled national COD distributions for LMICs since 2010, where we estimated COD distribution for 0-1 and 1-59-month olds. However, demographic and epidemiological evidence amounts to the conclusion that child COD is not uniform in the 1-59-month period. National empirical data at levels of specificity below 1-59 months are often not available in LMICs due to weak civil registration systems. Such data and estimates bear considerable scientific value to inform the development and impact evaluation of age-specific childhood interventions and their scale-up. Therefore, understanding the COD distribution among finer age groups in the 1-59-month period is warranted. Previous research has suffered from four main drawbacks: (i) using custom-collected data to understand age dynamics in a single cause; (ii) estimating ACSCM only in broad age groups; (iii) producing estimates in each age group separately and independently; and (iv) developing ACSCM in two separate estimation frameworks. The goals of this study are to systematically describe and make publicly available empirical age patterns of child COD in LMICs with accurate uncertainty intervals, and to develop the innovative theory-driven, parsimonious Bayesian hierarchical modeling framework to derive estimates of national COD distributions in LMICs with partial data among finer age groups than previous research. We will achieve the goals through three aims: 1) To extend and evaluate all-age demographic models to estimate age patterns in child deaths with VR data. 2) To conceptualize, develop and evaluate novel simultaneous ACSCM estimators using VR data in high-income countries; and 3) To extrapolate the unified ACSCM estimation framework to LMICs. The proposed study has two important innovations. First, it proposes the first unified framework for simultaneously estimating all-age, all-cause, age- and cause-specific child mortality. If successful, the study will offer systematically estimated ACSCM with valid uncertainty for selected LMICs, and lay the foundation for developing methods to systematically estimate ACSCM for all LMICs, including those low quality, limited, or even no data. Second, this framework produces estimates at an age granularity not yet seen in published research. This additional information is crucial to enable under-five child survival policy development and program evaluation at granular levels of ages and causes that would further contribute to the Sustainable Development Goals of equitably in reducing under-5 and neonatal mortality rates across countries. This study will lay the groundwork for future research, such as extending the Human Mortality Database to children under five to produce quality assessed, bias adjusted, and systematically organized ACSCM estimates.