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A Model for Determining Predictors of the MUAC in Acute Malnutrition in Ghana
The issue of malnutrition is perhaps the most important public health determinant of global wellbeing. It is one of the main causes of improper mental and physical development as well as death of many children. The Mid Upper Arm Circumference (MUAC) rapid text setup is able to diagnose malnutrition...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038631/ https://www.ncbi.nlm.nih.gov/pubmed/33916468 http://dx.doi.org/10.3390/ijerph18073792 |
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author | Sarpong, Smart Asomaning Sarpong, Abena Kyeraa Lee, Youngjo |
author_facet | Sarpong, Smart Asomaning Sarpong, Abena Kyeraa Lee, Youngjo |
author_sort | Sarpong, Smart Asomaning |
collection | PubMed |
description | The issue of malnutrition is perhaps the most important public health determinant of global wellbeing. It is one of the main causes of improper mental and physical development as well as death of many children. The Mid Upper Arm Circumference (MUAC) rapid text setup is able to diagnose malnutrition due to the fact that the human arm contains subcutaneous fat and muscle mass. When proportional food intake increases or reduces, the corresponding increase or reduction in the subcutaneous fat and muscle mass leads to an increase or decrease in the MUAC. In this study, the researchers attempt to develop a model for determining the performance of MUAC in predicting Child malnutrition in Ghana. It focuses on the Joint Generalized Linear Model (Joint-GLM) instead of the traditional Generalized Linear Model (GLM). The analysis is based on primary data measured on children under six years, who were undergoing nutritional treatment at the Princess Marie Louise (PML) Children’s Hospital in the Ashiedu Keteke sub-metro area of Accra Metropolis. The study found that a precisely measured weight of a child, height, and albumen levels were positive determinants of the predicted MUAC value. The study also reveals that, of all the variables used in determining the MUAC outcome, the hemoglobin and total protein levels of a child would be the main causes of any variation between the exact nutritional status of a child and that suggested by the MUAC value. The final Joint-GLM suggests that, if there are occasions where the MUAC gave false results, it could be a result of an imbalance in the child’s hemoglobin and protein levels. If these two are within acceptable levels in a child, the MUAC is most likely to be consistent in predicting the child’s nutritional status accurately. This study therefore recommends the continued use of MUAC in diagnosis of child malnutrition but urges Ghana and countries in Sub-Saharan Africa to roll out an effective nutrition intervention plan targeting the poor and vulnerable suburbs so that the nutritional status of children under five years of age, who were the focus of the current study, may be improved. |
format | Online Article Text |
id | pubmed-8038631 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80386312021-04-12 A Model for Determining Predictors of the MUAC in Acute Malnutrition in Ghana Sarpong, Smart Asomaning Sarpong, Abena Kyeraa Lee, Youngjo Int J Environ Res Public Health Article The issue of malnutrition is perhaps the most important public health determinant of global wellbeing. It is one of the main causes of improper mental and physical development as well as death of many children. The Mid Upper Arm Circumference (MUAC) rapid text setup is able to diagnose malnutrition due to the fact that the human arm contains subcutaneous fat and muscle mass. When proportional food intake increases or reduces, the corresponding increase or reduction in the subcutaneous fat and muscle mass leads to an increase or decrease in the MUAC. In this study, the researchers attempt to develop a model for determining the performance of MUAC in predicting Child malnutrition in Ghana. It focuses on the Joint Generalized Linear Model (Joint-GLM) instead of the traditional Generalized Linear Model (GLM). The analysis is based on primary data measured on children under six years, who were undergoing nutritional treatment at the Princess Marie Louise (PML) Children’s Hospital in the Ashiedu Keteke sub-metro area of Accra Metropolis. The study found that a precisely measured weight of a child, height, and albumen levels were positive determinants of the predicted MUAC value. The study also reveals that, of all the variables used in determining the MUAC outcome, the hemoglobin and total protein levels of a child would be the main causes of any variation between the exact nutritional status of a child and that suggested by the MUAC value. The final Joint-GLM suggests that, if there are occasions where the MUAC gave false results, it could be a result of an imbalance in the child’s hemoglobin and protein levels. If these two are within acceptable levels in a child, the MUAC is most likely to be consistent in predicting the child’s nutritional status accurately. This study therefore recommends the continued use of MUAC in diagnosis of child malnutrition but urges Ghana and countries in Sub-Saharan Africa to roll out an effective nutrition intervention plan targeting the poor and vulnerable suburbs so that the nutritional status of children under five years of age, who were the focus of the current study, may be improved. MDPI 2021-04-05 /pmc/articles/PMC8038631/ /pubmed/33916468 http://dx.doi.org/10.3390/ijerph18073792 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sarpong, Smart Asomaning Sarpong, Abena Kyeraa Lee, Youngjo A Model for Determining Predictors of the MUAC in Acute Malnutrition in Ghana |
title | A Model for Determining Predictors of the MUAC in Acute Malnutrition in Ghana |
title_full | A Model for Determining Predictors of the MUAC in Acute Malnutrition in Ghana |
title_fullStr | A Model for Determining Predictors of the MUAC in Acute Malnutrition in Ghana |
title_full_unstemmed | A Model for Determining Predictors of the MUAC in Acute Malnutrition in Ghana |
title_short | A Model for Determining Predictors of the MUAC in Acute Malnutrition in Ghana |
title_sort | model for determining predictors of the muac in acute malnutrition in ghana |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038631/ https://www.ncbi.nlm.nih.gov/pubmed/33916468 http://dx.doi.org/10.3390/ijerph18073792 |
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