OBJECTIVES: To evaluate how postmortem anthropometric malnutrition (PAM) measures align with expert panel attribution of malnutrition as a causal or significant condition in under-5 mortality (U5M). DESIGN: Cohort study using data from the Child Health and Mortality Prevention Surveillance network, incorporating clinical records, postmortem anthropometrics, minimally invasive tissue sampling, clinical abstraction and verbal autopsy to determine multiple causes of death. SETTING/PARTICIPANTS: 1405 deaths of children aged 1-59 months from six African countries between 2016 and 2023. PRIMARY AND SECONDARY OUTCOME MEASURES: PAM was determined using z-scores from the WHO Child Growth Standards: underweight (weight-for-age<
(-2)), wasting (arm circumference-for-age or weight-for-length<
(-2)) and stunting (length-for-age <
(-2)). Performance metrics (sensitivity (SE), specificity (SP) and positive predictive values (PPV)) were calculated to determine the alignment between PAM and expert panel attribution of malnutrition as a causal or significant condition to death. RESULTS: Nearly 75% of cases demonstrated moderate-to-severe malnutrition by PAM, while expert panels attributed malnutrition in 41% of cases. Performance metrics varied across anthropometric indices: underweight exhibited the highest SE (89.7%), while wasting based on arm circumference had the highest SP (81.9%) and PPV (76.8%). Discrepancies between PAM classification and expert panel attribution differed significantly by site, age, location of death and preventability of death (p<
0.05). Adjusted multivariate regression showed that expert panel attribution was more likely with increasing severity of PAM. CONCLUSIONS: The proportion of U5M attributable to malnutrition ranged between 41% (expert panel attribution) and 74% (PAM). Variability in classification underscores the need for monitoring and quality improvement measures to address discrepancies. Improved alignment between PAM and panel assessments is essential for accurately identifying malnutrition-related deaths and designing effective interventions to reduce U5M.