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Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
A new way to measure mid-upper-arm circumference in african villages
Gregor Pollach1, Eleanore Bradley2, Abigail Cole2
1Department of Anesthesia, Queen Elizabeth Central Hospital , Blantyre, Malawi ; Department of Anesthesia and Intensive Care, University of Malawi , Blantyre, Malawi.
Insights
This study shows that measuring mid-upper-arm-circumference by hand can effectively detect childhood malnutrition. This simple method helps identify severe and moderate cases, guiding treatment decisions in villages.
Area of Science:
- Pediatrics
- Public Health
- Nutrition Science
Background:
- Childhood malnutrition remains a critical global health issue, particularly in resource-limited settings.
- Accurate and accessible screening tools are essential for early detection and intervention.
- Previous methods often require technical equipment, limiting their widespread use in remote areas.
Purpose of the Study:
- To validate a simple, tool-free method for detecting malnutrition in children using mid-upper-arm-circumference (MUAC).
- To assess the effectiveness of this method in differentiating between severe and moderate malnutrition.
- To establish a basis for decisions regarding admission to nutritional rehabilitation programs.
Main Methods:
- Training 63 participants across four groups on measuring artificial arms (9-13 cm circumference) using only hands and fingers.
- Implementing a brief training protocol including practice, testing, and discussion.
- Evaluating participant accuracy in identifying different levels of malnutrition based on MUAC measurements.
Main Results:
- High accuracy rates were achieved: 95.3% (general group) and 97.9% (intensely trained group) correctly identified severe malnutrition.
- Moderate malnutrition was identified with 87.3% (general) and 91.9% (intensely trained) accuracy.
- Well-nourished children were correctly excluded from feeding programs, optimizing resource allocation.
Conclusions:
- Hand-based MUAC measurement is a reliable and effective method for detecting childhood malnutrition.
- The technique is practical for widespread training and implementation in village settings.
- This approach offers a low-cost, sustainable solution for early malnutrition detection and management.
Abstract:
In 2011 we published a study on how to detect the threshold for malnutrition in children, simply using their own hands and without any technical tool. The fight against malnutrition can only be reached when its measurements involve every single child, almost continuously, in the affected villages. In this paper we try to show that, thanks to our method, it is possible to use mid-upper-arm-circumference as a measurement for malnutrition in children, discriminating between severe and moderate malnutrition and providing the basis for the decision on whether to admit a child to a nutritional rehabilitation unit or not. We trained 63 participants in four groups (Group 1: doctors and clinical officers; Group 2: nurses and students; as Group 3 we defined the 20 best participants and Group 4 consisted of 10 more intensely trained participants) to measure the circumference of 9 different artificial arms (between 9 and 13 cm) using their own fingers and hands. The training was short and consisted of an introduction of 5 min, a first training phase of 10-15 min, a test, the critical discussion of the results, a second training phase of 5 min and a final test. We found that 95.3% of participants in the general group and 97.9% in the intensely trained group have identified the severely malnourished child; 87.3% in the general group and 91.9% in the intensely trained group have additionally identified the moderately malnourished child. Both groups haven't admitted the well nourished child to a therapeutic feeding program retaining their resources. The third group reached without any additional training the results in the above categories. A subsequent discussion with the participants on the influence of procurement, maintenance and pricing of our tool, found our method much less vulnerable than others. We conclude that this method should be considered as a future training in the villages to detect the trend towards malnutrition early enough.
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