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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number1315
Pages (from-to)1-12
Number of pages12
JournalInternational journal of environmental research and public health
Volume18
Issue number3
DOIs
StatePublished - Feb 1 2021

Keywords

  • Big data
  • Child development
  • Child mortality
  • Education
  • Machine learning

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis

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