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Assessment of Child Anthropometry in a Large Epidemiologic Study
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Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

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Analysis of Correlated Outcomes of Anthropometric Measurements for Under-Five Children in Tanzania

Edgar E Pallangyo1,2, Amina S Msengwa1

  • 1Department of Statistics, University of Dar es Salaam, Dar es Salaam, Tanzania.

Health Services Research and Managerial Epidemiology
|February 12, 2024
PubMed

Insights

This study analyzed anthropometric measurements in Tanzanian children under five, identifying key factors like child’s age and mother’s education that influence malnutrition. Joint modeling revealed significant correlations between weight-for-age, height-for-age, and weight-for-height indicators.

Area of Science:

  • * Public Health & Nutrition
  • * Biostatistics & Statistical Modeling

Background:

  • * Childhood malnutrition remains a significant public health concern in low-income countries.
  • * Anthropometric measurements (WAZ, HAZ, WHZ) are crucial indicators of child growth and nutritional status.
  • * Understanding factors influencing these correlated outcomes is vital for effective interventions.

Purpose of the Study:

  • * To jointly model three key anthropometric measurements (WAZ, HAZ, WHZ) in Tanzanian children under five.
  • * To identify common factors associated with childhood malnutrition using Multivariate Generalized Linear Mixed Models.
  • * To compare the model fit of Multivariate Ordered Logit and Probit models.

Main Methods:

  • * Application of Multivariate Generalized Linear Mixed Models for joint analysis of anthropometric data.
  • * Analysis of a dataset comprising 9052 children under five in Tanzania with valid height and weight measurements.
  • * Model comparison using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC).

Main Results:

  • * Significant correlations were found between weight-for-age (WAZ) and height-for-age (HAZ) (P < 2e-16), and WAZ and weight-for-height (WHZ) (P < 2e-16).
  • * The Multivariate Ordered Logit Model demonstrated a better fit (AIC=53213.92, BIC=52727.95) compared to the Probit model.
  • * Key determinants of malnutrition identified include child's age, birth order, mother's education, child's gender, mother's working status, wealth index, marital status, and mother's BMI.

Conclusions:

  • * Child's age, birth order, mother's education, child's sex, mother's working status, wealth index, marital status, and mother's BMI are significant determinants of malnutrition.
  • * Analyzing correlated health outcomes, such as anthropometric measurements, is essential for a comprehensive understanding of malnutrition factors.
  • * Public health strategies should address these identified socio-demographic and maternal factors to combat childhood malnutrition in Tanzania.

Keywords:
correlated outcomesheight-for-agemultivariate ordinal regression modelweight-for-ageweight-for-height

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