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; Gianluca Esposito

doi:10.3390/ijerph18031315

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

Int J Environ Res Public Health. 2021 Feb 1;18(3):1315. doi: 10.3390/ijerph18031315.

Authors

Andrea Bizzego¹, Giulio Gabrieli², Marc H Bornstein^{3

4

5}, Kirby Deater-Deckard⁶, Jennifer E Lansford⁷, Robert H Bradley⁸, Megan Costa⁸, Gianluca Esposito^{1

2

9}

Affiliations

¹ Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy.
² School of Social Sciences, Nanyang Technological University, Singapore 639798, Singapore.
³ Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20892, USA.
⁴ Institute for Fiscal Studies, London WC1E 7AE, UK.
⁵ UNICEF, New York, NY 10038, USA.
⁶ University of Massachusetts Amherst, Amherst, MA 01003, USA.
⁷ Sanford School of Public Policy, Duke University, Durham, NC 27708, USA.
⁸ T. Denny Sanford School of Social and Family Dynamics, Arizona State University, Tempe, AZ 85287, USA.
⁹ Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore.

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: big data; child development; child mortality; education; machine learning.

Publication types

Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't

MeSH terms

Child
Child Mortality*
Developing Countries*
Humans
Income
Machine Learning
Retrospective Studies