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Assessment of nutritional status using anthropometric variables by multivariate analysis

BACKGROUND: Undernutrition is a serious health problem and highly prevalent in developing countries. There is no as such confirmatory test to measure undernutrition. The objective of the present study is to determine a new Composite Score using anthropometric measurements. Composite Score was then c...

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Autores principales: Bhattacharya, Ankita, Pal, Baidyanath, Mukherjee, Shankarashis, Roy, Subrata Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6683359/
https://www.ncbi.nlm.nih.gov/pubmed/31382936
http://dx.doi.org/10.1186/s12889-019-7372-2
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author Bhattacharya, Ankita
Pal, Baidyanath
Mukherjee, Shankarashis
Roy, Subrata Kumar
author_facet Bhattacharya, Ankita
Pal, Baidyanath
Mukherjee, Shankarashis
Roy, Subrata Kumar
author_sort Bhattacharya, Ankita
collection PubMed
description BACKGROUND: Undernutrition is a serious health problem and highly prevalent in developing countries. There is no as such confirmatory test to measure undernutrition. The objective of the present study is to determine a new Composite Score using anthropometric measurements. Composite Score was then compared with other methods like body mass index (BMI) and mid-upper arm circumference (MUAC) classification, to test the significance of the method. METHODS: Anthropometric data were collected from 780 adult Oraon (Male = 387, Female = 393) labourers of Alipurduar district of West Bengal, India, following standard instruments, and protocols. Nutritional status of the study participants were assessed by conventional methods, BMI and MUAC. Confirmatory factor analysis was carried out to reduce 12 anthropometric variables into a single Composite Score (C) and classification of nutritional status was done on the basis of the score. Furthermore, all the methods (BMI, MUAC and C) were compared and discriminant function analysis was adopted to find out the percentage of correctly classified individuals by each of the three methods. RESULT: The frequency of undernutrition was 45.9% according to BMI category, 56.7% according to MUAC category and 51.8% according to newly computed Composite Score. Further analysis showed that Composite Score has a higher strength of correct classification (98.7%), compared to BMI (95.9%) and MUAC (96.2%). CONCLUSION: Therefore, anthropometric measurements can be used to identify nutritional status in the population more correctly by calculating Composite Score of the measurements and it is a non-invasive and relatively correct way of identification.
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spelling pubmed-66833592019-08-09 Assessment of nutritional status using anthropometric variables by multivariate analysis Bhattacharya, Ankita Pal, Baidyanath Mukherjee, Shankarashis Roy, Subrata Kumar BMC Public Health Research Article BACKGROUND: Undernutrition is a serious health problem and highly prevalent in developing countries. There is no as such confirmatory test to measure undernutrition. The objective of the present study is to determine a new Composite Score using anthropometric measurements. Composite Score was then compared with other methods like body mass index (BMI) and mid-upper arm circumference (MUAC) classification, to test the significance of the method. METHODS: Anthropometric data were collected from 780 adult Oraon (Male = 387, Female = 393) labourers of Alipurduar district of West Bengal, India, following standard instruments, and protocols. Nutritional status of the study participants were assessed by conventional methods, BMI and MUAC. Confirmatory factor analysis was carried out to reduce 12 anthropometric variables into a single Composite Score (C) and classification of nutritional status was done on the basis of the score. Furthermore, all the methods (BMI, MUAC and C) were compared and discriminant function analysis was adopted to find out the percentage of correctly classified individuals by each of the three methods. RESULT: The frequency of undernutrition was 45.9% according to BMI category, 56.7% according to MUAC category and 51.8% according to newly computed Composite Score. Further analysis showed that Composite Score has a higher strength of correct classification (98.7%), compared to BMI (95.9%) and MUAC (96.2%). CONCLUSION: Therefore, anthropometric measurements can be used to identify nutritional status in the population more correctly by calculating Composite Score of the measurements and it is a non-invasive and relatively correct way of identification. BioMed Central 2019-08-05 /pmc/articles/PMC6683359/ /pubmed/31382936 http://dx.doi.org/10.1186/s12889-019-7372-2 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Bhattacharya, Ankita
Pal, Baidyanath
Mukherjee, Shankarashis
Roy, Subrata Kumar
Assessment of nutritional status using anthropometric variables by multivariate analysis
title Assessment of nutritional status using anthropometric variables by multivariate analysis
title_full Assessment of nutritional status using anthropometric variables by multivariate analysis
title_fullStr Assessment of nutritional status using anthropometric variables by multivariate analysis
title_full_unstemmed Assessment of nutritional status using anthropometric variables by multivariate analysis
title_short Assessment of nutritional status using anthropometric variables by multivariate analysis
title_sort assessment of nutritional status using anthropometric variables by multivariate analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6683359/
https://www.ncbi.nlm.nih.gov/pubmed/31382936
http://dx.doi.org/10.1186/s12889-019-7372-2
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