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Using weight-for-age for predicting wasted children in Cameroon

Georges Nguefack-Tsague1, Agatha Tanya Nguti Kien, Charles Ntungwen Fokunang

  • 1Department of Public Health, Faculty of Medicine and Biomedical Sciences, University of Yaoundé 1, Cameroon.

The Pan African Medical Journal
|May 30, 2013
PubMed

Insights

Weight-for-age can accurately predict child wasting in Cameroon, where height measurements are often unavailable. This finding aids in early identification and intervention for acute undernutrition, reducing mortality and healthcare costs.

Area of Science:

  • Pediatric Nutrition
  • Public Health
  • Biostatistics

Background:

  • Height measurement tools are scarce and often inaccurate in Cameroonian health centers.
  • Weight-for-age data is more readily available and frequently collected.
  • Accurate assessment of child wasting is crucial for timely nutritional interventions.

Purpose of the Study:

  • To develop statistical models for predicting child wasting using weight-for-age data.
  • To address the challenge of unavailable or inaccurate height measurements in Cameroon.
  • To provide a reliable method for identifying wasted children in resource-limited settings.

Main Methods:

  • Utilized data from the 2004 Cameroon Demographic and Health Surveys (DHS).
  • Included 3742 children aged 0-59 months in a nationwide cross-sectional survey.
  • Employed discriminant statistical methods to build predictive models.

Main Results:

  • A strong association was found between underweight and wasting.
  • Statistical models demonstrated high accuracy with test error rates below 5%.
  • The Area Under the Curve (AUC) for the Receiver Operating Characteristic (ROC) analysis was 0.86, indicating good predictive performance.

Conclusions:

  • Weight-for-age is a viable and accurate predictor of child wasting in Cameroon.
  • This method is particularly useful where height measurements are not feasible.
  • Findings support the development of nutrition interventions to combat acute undernutrition and reduce associated morbidity and mortality.
Abstract

Keywords:
Anthropometric measuresdiscriminant analysisnutritional statusunderweightwasting

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