The Impact of WHO-2023 Malnutrition Criteria on Caseload of Infants Aged Under Six Months: Secondary Data Analysis.

Ayenew Negesse ORCID logo ; Tsinuel Girma ; Beruk Berhanu Desalegn ORCID logo ; Melkamu Berhane ORCID logo ; Marko Kerac ORCID logo ; (2025) The Impact of WHO-2023 Malnutrition Criteria on Caseload of Infants Aged Under Six Months: Secondary Data Analysis. Children, 12 (2). p. 118. DOI: 10.3390/children12020118
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BACKGROUND/OBJECTIVES: The 2023 World Health Organization (WHO) guideline on infants aged under six months (u6m) wasting now includes mid-upper arm circumference (MUAC) and weight for age Z score (WAZ) as malnutrition treatment programme enrolment criteria. Evidence for the new criteria and optimal cut-offs is, however, sparse. We aimed to explore the potential caseload implications of the new criteria and explore how different cut-offs might affect them. METHODS: We conducted a secondary analysis of data from 1048 Ethiopian infants u6m using STATA software version 17. Frequency tables and percentages were used to present malnutrition across various characteristics. The chi-square test with 95% confidence intervals (CIs) at a p value of <0.05 was used to compare infant caseload identified by the WHO-2023 versus WHO-2013 criteria. RESULTS: We found substantial overlaps among anthropometric indicators of malnutrition in infants defined by the WHO-2023 programme enrolment criteria. New WHO criteria result in a larger potential caseload (19.2% for 6 weeks to 6 months and 15.1% for infants under 6 weeks) compared with WHO-2013 criteria (2.4%). Whilst there are marked overlaps between low WAZ, low WLZ, and low MUAC, they capture different infants. An MUAC of <110 mm alone would capture only a third of all cases identified by WAZ and/or WLZ < -2. CONCLUSIONS: In Ethiopia, the WHO-2023 criteria markedly increase malnutrition caseloads compared with WHO-2013 criteria. There might be a case for increasing MUAC thresholds in MUAC-focused programs where WLZ or WAZ measurements are difficult. Future longitudinal data are needed to know which criteria best identify infants at highest risk of mortality/morbidity/poor development.


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