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

Author

Listed:
  • 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
    Institute for Fiscal Studies, London WC1E 7AE, UK
    UNICEF, New York, NY 10038, 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
    School of Social Sciences, Nanyang Technological University, Singapore 639798, Singapore
    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.

Suggested Citation

  • Andrea Bizzego & Giulio Gabrieli & Marc H. Bornstein & Kirby Deater-Deckard & Jennifer E. Lansford & Robert H. Bradley & Megan Costa & Gianluca Esposito, 2021. "Predictors of Contemporary under-5 Child Mortality in Low- and Middle-Income Countries: A Machine Learning Approach," IJERPH, MDPI, vol. 18(3), pages 1-13, February.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:3:p:1315-:d:491292
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    Citations

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    Cited by:

    1. Israel C. Avelino & Joaquim Van-Dúnem & Luís Varandas, 2024. "Under-Five Mortality and Associated Risk Factors in Children Hospitalized at David Bernardino Pediatric Hospital (DBPH), Angola: A Hierarchical Approach," IJERPH, MDPI, vol. 21(8), pages 1-16, August.
    2. Wei Wei & Tanwne Sarker & Wioletta Żukiewicz-Sobczak & Rana Roy & G. M. Monirul Alam & Md. Ghulam Rabbany & Mohammad Shakhawat Hossain & Noshaba Aziz, 2021. "The Influence of Women’s Empowerment on Poverty Reduction in the Rural Areas of Bangladesh: Focus on Health, Education and Living Standard," IJERPH, MDPI, vol. 18(13), pages 1-18, June.

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