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Updated: Dec 6, 2025
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Does weight-for-height and mid upper-arm circumference diagnose the same children as wasted? An analysis using survey
Tomás Zaba1, Mara Nyawo2, Jose Luis Álvarez Morán3
1United Nations Children's Fund, 1440 Zimbabwe Avenue, Maputo, Mozambique.
Insights
Discrepancies in diagnosing acute malnutrition in Mozambique using weight-for-height (WHZ) and middle upper arm circumference (MUAC) alone underestimate children needing treatment. Combined prevalence estimates using WHZ, MUAC, and oedema are recommended for accurate nutrition program planning.
Area of Science:
- Pediatrics
- Public Health
- Nutrition Science
Background:
- Acute malnutrition in children (6-59 months) is diagnosed using weight-for-height (WHZ), middle upper arm circumference (MUAC), and bilateral pitting oedema.
- Prevalence data from surveys inform nutrition program planning, but discrepancies arise when not all diagnostic criteria are consistently applied.
- This study investigates such discrepancies in Mozambique, a country facing significant challenges in child nutrition.
Purpose of the Study:
- To assess the extent of agreement between WHZ and MUAC diagnostic criteria for acute malnutrition in Mozambican children.
- To identify factors influencing the classification of acute malnutrition using WHZ and MUAC.
- To evaluate the impact of using single versus combined diagnostic criteria on estimating malnutrition caseloads.
Main Methods:
- Analysis of population-based anthropometric survey data from 45 districts in Mozambique (2017-2019).
- Statistical methods included Cohen's kappa for agreement, Spearman's rank-order for correlation, and binary logistic regression.
- Comparison of caseload estimates derived from WHZ, MUAC, and oedema individually versus combined prevalence estimates.
Main Results:
- Low agreement was observed between WHZ and MUAC classifications (κ = 0.353), with results consistent across provinces.
- While positively correlated (rho = 0.593), WHZ and MUAC models explained limited variation (3.1% for WHZ, 12.3% for MUAC).
- Factors influencing MUAC included younger age (<24 months), stunting, and being female; WHZ was influenced by younger age and stunting.
- Estimates using WHZ or MUAC alone, with or without oedema, consistently underestimated caseloads compared to combined prevalence estimates for both global acute malnutrition and severe acute malnutrition (SAM).
Conclusions:
- Sole reliance on WHZ or MUAC for diagnosing acute malnutrition in Mozambique leads to underestimation of children requiring treatment, impacting program planning.
- The study recommends the official adoption of combined prevalence estimates using WHZ, MUAC, and oedema for accurate burden assessment.
- Further research is needed to understand the programmatic implications of implementing combined diagnostic criteria.
Background:
Three different diagnostic criteria are used to identify children aged 6 to 59 months with acute malnutrition: weight-for-height (WHZ), middle upper arm circumference (MUAC) and bilateral pitting oedema. Prevalence of malnutrition from surveys is among the most-used decision support data, however not all diagnostic criteria are used to calculate need, creating a mismatch between programme planning and implementation. With this paper, we investigate if such discrepancies are observed in Mozambique.
Methods:
Population-based nutritional anthropometric surveys from 45 districts in Mozambique conducted by the Technical Secretariat for Food Security and Nutrition (SETSAN) and UNICEF between 2017 and 2019 were analysed. We used Cohen's kappa coefficient to measure inter-rater agreement between WHZ and MUAC, Spearman's rank-order coefficient to assess the correlation, binary logistic regression to investigate factors influencing WHZ and MUAC diagnostic classification. We compared acute malnutrition caseload estimates by WHZ, MUAC and oedema to caseloads from combined prevalence estimates.
Results:
WHZ and MUAC rarely agree on their diagnostic classification (κ = 0.353, ρ < 0.001) and results did not vary by province. We found positive correlation between WHZ and MUAC (rho = 0.593, ρ < 0.0001). Binary logistic regression explained 3.1% of variation in WHZ and 12.3% in the MUAC model. Girls (AOR = 1.6, ρ < 0.0001), children < 24 months (AOR = 5.3, ρ < 0.0001) and stunted children (AOR = 3.5, ρ < 0.0001) influenced the MUAC classification. In the WHZ model, children < 24 months (AOR = 2.4, ρ < 0.0001) and stunted children (AOR = 1.7, ρ < 0.0001) influenced the classification, sex had no effect. Caseload calculations of global acute malnutrition by WHZ and/oedema-only and by MUAC and/oedema-only yielded less children than caseload calculations using the combined prevalence estimates. Similarly, caseload calculations for SAM by WHZ and/oedema-only and SAM by MUAC and/oedema-only yielded less children than the respective combined prevalence calculations.
Conclusions:
Given the discrepancy in diagnostic classification between WHZ and MUAC in Mozambique, using either one alone for calculating burden underestimates the real number of children in need of treatment and negatively affects nutrition programme planning. We recommend that use of the combined prevalence estimates, based on the three diagnostic criteria of WHZ, MUAC and oedema, be officially adopted. Further analysis is needed to detail the programmatic impact of this change.
