High-resolution mapping of essential maternal and child health service coverage in Nigeria: a machine learning approach.

Journal: BMJ open
PMID: 38858137

Abstract

BACKGROUND: National-level coverage estimates of maternal and child health (MCH) services mask district-level and community-level geographical inequities. The purpose of this study is to estimate grid-level coverage of essential MCH services in Nigeria using machine learning techniques.

Authors

  • Yoshito Kawakatsu
    Department of Global Health, University of Washington, Seattle, Washington, USA y.kawakatsu.0829@gmail.com.
  • Jonathan F Mosser
    Health Metrics Sciences, University of Washington, Seattle, Washington, USA.
  • Christopher Adolph
    Department of Political Science, University of Washington, Seattle, Washington, USA.
  • Peter Baffoe
    UNICEF, New York, New York, USA.
  • Fatima Cheshi
    UNICEF, New York, New York, USA.
  • Hirotsugu Aiga
    School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan.
  • D A Watkins
    Department of Medicine, University of Washington, Seattle, Seattle, Washington, USA.
  • Kenneth H Sherr
    Department of Global Health, University of Washington, Seattle, Washington, USA.

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