Determinants of under-nutrition among children under five years of age in Ethiopia

Gashu Workneh Kassie1, Demeke Lakew Workie2

  • 1Department of Statistics, University of Gondar, P.O. Box: 96, Gondar, Ethiopia.

BMC Public Health
|March 30, 2020
PubMed

Insights

Child under-nutrition in Ethiopia affects nearly half of children under five. Key factors include maternal health, household wealth, and education, highlighting the need for targeted interventions to improve child health outcomes.

Area of Science:

  • Public Health
  • Pediatrics
  • Biostatistics

Background:

  • Child under-nutrition is a significant public health issue in Ethiopia.
  • Previous studies used multiple indicators; this study utilizes a single composite index.
  • Focuses on identifying determinants of under-nutrition in children under five.

Purpose of the Study:

  • To identify factors associated with child under-nutrition in Ethiopia using a novel composite index.
  • To compare the performance of a partial proportional odds model against other ordinal regression models.

Main Methods:

  • Utilized data from Ethiopia's Demographic and Health Survey 2016 (n=9494 children).
  • Created a composite under-nutrition index from height-for-age, weight-for-height, and weight-for-age z-scores using Principal Component Analysis.
  • Applied a partial proportional odds model to identify significant determinants.

Main Results:

  • Nearly half (49.0%) of children exhibited under-nutrition (19.8% moderate, 29.2% severe).
  • The partial proportional odds model demonstrated improved fit compared to other ordinal models.
  • Factors significantly associated with severe under-nutrition include low maternal BMI, poverty, and lack of husband's education.

Conclusions:

  • Child age and sex, maternal education and BMI, household wealth, and husband's education are significant predictors of under-nutrition.
  • Interventions should target household wealth, food security, maternal health and education, and healthcare access.
  • The partial proportional odds model is effective for analyzing complex child under-nutrition data.
Abstract

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