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Grellety E, Golden MH. Change in quality of malnutrition surveys between 1986 and 2015. Emerg Themes Epidemiol. 2018;15:8doi: 10.1186/s12982-018-0075-9.
Grellety, E., & Golden, M. H. (2018). Change in quality of malnutrition surveys between 1986 and 2015. Emerging themes in epidemiology, 158. https://doi.org/10.1186/s12982-018-0075-9
Grellety, Emmanuel, and Golden, Michael H. "Change in quality of malnutrition surveys between 1986 and 2015." Emerging themes in epidemiology vol. 15 (2018): 8. doi: https://doi.org/10.1186/s12982-018-0075-9
Grellety E, Golden MH. Change in quality of malnutrition surveys between 1986 and 2015. Emerg Themes Epidemiol. 2018 May 28;15:8. doi: 10.1186/s12982-018-0075-9. eCollection 2018. PMID: 29872451; PMCID: PMC5972441.
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Emerg Themes Epidemiol. 2018 May 28;15:8. doi: 10.1186/s12982-018-0075-9. eCollection 2018.

Change in quality of malnutrition surveys between 1986 and 2015.

Emerging themes in epidemiology

Emmanuel Grellety, Michael H Golden

Affiliations

  1. 1Research Centre Health Policy and Systems - International Health, School of Public Health, Université Libre de Bruxelles, Brussels, Belgium.
  2. 2Department of Medicine and Therapeutics, University of Aberdeen, Aberdeen, Scotland, UK.

PMID: 29872451 PMCID: PMC5972441 DOI: 10.1186/s12982-018-0075-9

Abstract

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.

Keywords: Anthropometry; Child; Data quality; Height-for-age; Kurtosis; MUAC; Mid-upper arm circumference; Nutrition; Standard deviation; Survey; Weight-for-age; Weight-for-height

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