Peer-reviewed article
Importance of postmortem anthropometric evaluation in defining the role of malnutrition as a cause of infant and child deaths in Sub-Saharan Africa and South Asia: a cohort study.
Priya Mehta-Gupta Das, Zachary J Madewell, Dianna M Blau, Cynthia G Whitney, Usha Ramakrishnan et al. ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study examined how postmortem measurements of malnutrition in deceased children align with expert assessments of whether malnutrition caused or contributed to death. The researchers analyzed 1,405 child deaths aged 1-59 months across six African countries using body measurements taken after death and clinical records. Postmortem measurements indicated severe malnutrition in 75% of cases, but expert panels attributed malnutrition as a cause in only 41% of cases. The findings highlight significant discrepancies in how malnutrition-related deaths are classified, suggesting a need for standardized methods to accurately identify malnutrition as a cause of child death.
Key points
- Postmortem anthropometric measurements showed moderate-to-severe malnutrition in nearly 75% of the 1,405 child deaths studied
- Expert panels attributed malnutrition as a causal or significant condition in only 41% of cases, showing major disagreement with postmortem measurements
- Underweight indicators had the highest sensitivity (89.7%), while arm circumference-based wasting had the highest specificity (81.9%) and predictive value (76.8%)
- Classification discrepancies varied significantly by site, age, location of death, and whether the death was preventable
- Expert panel attribution was more likely when postmortem malnutrition severity increased
Limitations
The study uses postmortem measurements which may be affected by decomposition and cannot capture malnutrition status at the time of death with certainty. The analysis relies on expert panel judgement for cause-of-death attribution, which itself may be subjective and variable across reviewers.
Abstract
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.