Predictors of Contemporary under-5 Child Mortality in Low- and Middle-Income Countries: A Machine Learning Approach

Andrea Bizzego, Giulio Gabrieli, Marc H. Bornstein, Kirby Deater-Deckard, Jennifer E. Lansford, Robert H. Bradley, Megan Costa and Gianluca Esposito
Additional contact information
Andrea Bizzego: Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy
Giulio Gabrieli: School of Social Sciences, Nanyang Technological University, Singapore 639798, Singapore
Marc H. Bornstein: Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20892, USA
Kirby Deater-Deckard: University of Massachusetts Amherst, Amherst, MA 01003, USA
Jennifer E. Lansford: Sanford School of Public Policy, Duke University, Durham, NC 27708, USA
Robert H. Bradley: T. Denny Sanford School of Social and Family Dynamics, Arizona State University, Tempe, AZ 85287, USA
Megan Costa: T. Denny Sanford School of Social and Family Dynamics, Arizona State University, Tempe, AZ 85287, USA
Gianluca Esposito: Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy

IJERPH, 2021, vol. 18, issue 3, 1-13

Abstract: Child Mortality (CM) is a worldwide concern, annually affecting as many as 6.81% children in low- and middle-income countries (LMIC). We used data of the Multiple Indicators Cluster Survey (MICS) (N = 275,160) from 27 LMIC and a machine-learning approach to rank 37 distal causes of CM and identify the top 10 causes in terms of predictive potency. Based on the top 10 causes, we identified households with improved conditions. We retrospectively validated the results by investigating the association between variations of CM and variations of the percentage of households with improved conditions at country-level, between the 2005–2007 and the 2013–2017 administrations of the MICS. A unique contribution of our approach is to identify lesser-known distal causes which likely account for better-known proximal causes: notably, the identified distal causes and preventable and treatable through social, educational, and physical interventions. We demonstrate how machine learning can be used to obtain operational information from big dataset to guide interventions and policy makers.

Keywords: child development; child mortality; machine learning; education; big data (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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