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

41 Pages Posted: 17 Jul 2020

See all articles by Andrea Bizzego

Andrea Bizzego

University of Trento - Department of Psychology and Cognitive Science

Giulio Gabrieli

Nanyang Technological University (NTU) - School of Humanities & Social Sciences

Marc H. Bornstein

NICHD; Government of the United States of America - National Institutes of Health (NIH)

Kirby Deater-Deckard

University of Massachusetts

Jennifer E. Lansford

Duke University

Robert H. Bradley

Arizona State University (ASU) - T. Denny Sanford School of Social and Family Dynamics

Megan Costa

Arizona State University (ASU) - T. Denny Sanford School of Social and Family Dynamics

Gianluca Esposito

University of Trento - Division of Psychology; Nanyang Technological University (NTU) - Psychology Program; Nanyang Technological University (NTU) - Lee Kong Chian School of Medicine

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Abstract

Using a novel machine learning approach, we identify and prioritize in terms of predictive potency the top 10 distal causes of under-5 child mortality from almost 40 potential distal causes in more than a quarter of a million households in more than 25 low- and middle-income countries. Notably, all 10 distal causes are preventable and treatable through social, educational, and physical intervention. Thus, a unique contribution of our machine learning approach is to identify lesser-known preventable and treatable distal causes of under-5 child mortality that likely account for better-known proximal causes.

Funding Statement: A.B. was supported by a Post-doctoral Fellowship within MIUR programme framework ”Dipartimenti di Eccellenza” (DiPSCO, University of Trento). G.E. was supported by NAP SUG 2015, Singapore Ministry of Education ACR Tier 1 (RG149/16 and RT10/19). M.H.B. was supported by the Intramural Research Program of the NIH/NICHD, USA, and an International Research Fellowship at the Institute for Fiscal Studies (IFS), London, UK, funded by the European Research Council (ERC) under the Horizon 2020 research and innovation programme (grant agreement No 695300- HKADeC-ERC-2015-AdG). Computational resources were provided by the National Super Computing Center of Singapore (Project ID: 12001609; Computational study of Child Development in Low Resource Contexts).

Declaration of Interests: The Authors declare no conflict of interest.

Ethics Approval Statement: Ethics approvals were handled in each site in which data were collected.

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

Suggested Citation

Bizzego, Andrea and Gabrieli, Giulio and Bornstein, Marc H. and Deater-Deckard, Kirby and Lansford, Jennifer E. and Bradley, Robert H. and Costa, Megan and Esposito, Gianluca, Predictors of Contemporary Under-5 Child Mortality in Low- and Middle-Income Countries: A Machine-Learning Approach (4/16/2020). Available at SSRN: https://ssrn.com/abstract=3578751 or http://dx.doi.org/10.2139/ssrn.3578751

Andrea Bizzego

University of Trento - Department of Psychology and Cognitive Science

Trento
Italy

Giulio Gabrieli

Nanyang Technological University (NTU) - School of Humanities & Social Sciences

Blk S3.2-B2
Nanyang Avenue
Singapore, 639798
Singapore

Marc H. Bornstein

NICHD

31 Center Drive
Building 31, Room 2A32
Bethesda, MD 20892-2425
United States

Government of the United States of America - National Institutes of Health (NIH) ( email )

9000 Rockville Pike
Bethesda, MD 20892
United States

Kirby Deater-Deckard

University of Massachusetts

MA
United States

Jennifer E. Lansford

Duke University ( email )

100 Fuqua Drive
Durham, NC 27708-0204
United States

Robert H. Bradley

Arizona State University (ASU) - T. Denny Sanford School of Social and Family Dynamics

P.O. BOX 873701
Tempe, AZ 85287-3701
United States

Megan Costa

Arizona State University (ASU) - T. Denny Sanford School of Social and Family Dynamics

P.O. BOX 873701
Tempe, AZ 85287-3701
United States

Gianluca Esposito (Contact Author)

University of Trento - Division of Psychology

Trento
Italy

Nanyang Technological University (NTU) - Psychology Program

S3 B2-A28 Nanyang Avenue
Singapore, 639798
Singapore

Nanyang Technological University (NTU) - Lee Kong Chian School of Medicine

Singapore

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