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Change in quality of malnutrition surveys between 1986 and 2015

BACKGROUND: Representative surveys collecting weight, height and MUAC are used to estimate the prevalence of acute malnutrition. The results are then used to assess the scale of malnutrition in a population and type of nutritional intervention required. There have been changes in methodology over re...

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Autores principales: Grellety, Emmanuel, Golden, Michael H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5972441/
https://www.ncbi.nlm.nih.gov/pubmed/29872451
http://dx.doi.org/10.1186/s12982-018-0075-9
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author Grellety, Emmanuel
Golden, Michael H.
author_facet Grellety, Emmanuel
Golden, Michael H.
author_sort Grellety, Emmanuel
collection PubMed
description BACKGROUND: Representative surveys collecting weight, height and MUAC are used to estimate the prevalence of acute malnutrition. The results are then used to assess the scale of malnutrition in a population and type of nutritional intervention required. There have been changes in methodology over recent decades; the objective of this study was to determine if these have resulted in higher quality surveys. METHODS: In order to examine the change in reliability of such surveys we have analysed the statistical distributions of the derived anthropometric parameters from 1843 surveys conducted by 19 agencies between 1986 and 2015. RESULTS: With the introduction of standardised guidelines and software by 2003 and their more general application from 2007 the mean standard deviation, kurtosis and skewness of the parameters used to assess nutritional status have each moved to now approximate the distribution of the WHO standards when the exclusion of outliers from analysis is based upon SMART flagging procedure. Where WHO flags, that only exclude data incompatible with life, are used the quality of anthropometric surveys has improved and the results now approach those seen with SMART flags and the WHO standards distribution. Agencies vary in their uptake and adherence to standard guidelines. Those agencies that fully implement the guidelines achieve the most consistently reliable results. CONCLUSIONS: Standard methods should be universally used to produce reliable data and tests of data quality and SMART type flagging procedures should be applied and reported to ensure that the data are credible and therefore inform appropriate intervention. Use of SMART guidelines has coincided with reliable anthropometric data since 2007. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12982-018-0075-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-59724412018-06-05 Change in quality of malnutrition surveys between 1986 and 2015 Grellety, Emmanuel Golden, Michael H. Emerg Themes Epidemiol Research Article BACKGROUND: Representative surveys collecting weight, height and MUAC are used to estimate the prevalence of acute malnutrition. The results are then used to assess the scale of malnutrition in a population and type of nutritional intervention required. There have been changes in methodology over recent decades; the objective of this study was to determine if these have resulted in higher quality surveys. METHODS: In order to examine the change in reliability of such surveys we have analysed the statistical distributions of the derived anthropometric parameters from 1843 surveys conducted by 19 agencies between 1986 and 2015. RESULTS: With the introduction of standardised guidelines and software by 2003 and their more general application from 2007 the mean standard deviation, kurtosis and skewness of the parameters used to assess nutritional status have each moved to now approximate the distribution of the WHO standards when the exclusion of outliers from analysis is based upon SMART flagging procedure. Where WHO flags, that only exclude data incompatible with life, are used the quality of anthropometric surveys has improved and the results now approach those seen with SMART flags and the WHO standards distribution. Agencies vary in their uptake and adherence to standard guidelines. Those agencies that fully implement the guidelines achieve the most consistently reliable results. CONCLUSIONS: Standard methods should be universally used to produce reliable data and tests of data quality and SMART type flagging procedures should be applied and reported to ensure that the data are credible and therefore inform appropriate intervention. Use of SMART guidelines has coincided with reliable anthropometric data since 2007. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12982-018-0075-9) contains supplementary material, which is available to authorized users. BioMed Central 2018-05-28 /pmc/articles/PMC5972441/ /pubmed/29872451 http://dx.doi.org/10.1186/s12982-018-0075-9 Text en © The Author(s) 2018 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
Grellety, Emmanuel
Golden, Michael H.
Change in quality of malnutrition surveys between 1986 and 2015
title Change in quality of malnutrition surveys between 1986 and 2015
title_full Change in quality of malnutrition surveys between 1986 and 2015
title_fullStr Change in quality of malnutrition surveys between 1986 and 2015
title_full_unstemmed Change in quality of malnutrition surveys between 1986 and 2015
title_short Change in quality of malnutrition surveys between 1986 and 2015
title_sort change in quality of malnutrition surveys between 1986 and 2015
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5972441/
https://www.ncbi.nlm.nih.gov/pubmed/29872451
http://dx.doi.org/10.1186/s12982-018-0075-9
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