The impact of improved data quality on the prevalence estimates of anthropometric measures using DHS datasets in India

Abstract The importance of data quality to correctly determine prevalence estimates of child anthropometric failures has been a contentious issue among policymakers and researchers. Our research objective was to ascertain the impact of improved DHS data quality on the prevalence estimates of stuntin...

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Autores principales:	Harsh Vivek Harkare, Daniel J. Corsi, Rockli Kim, Sebastian Vollmer, S. V. Subramanian
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7f63d46e2fff42f4b46c3e77809dd3a5
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spelling	oai:doaj.org-article:7f63d46e2fff42f4b46c3e77809dd3a52021-12-02T16:51:31ZThe impact of improved data quality on the prevalence estimates of anthropometric measures using DHS datasets in India10.1038/s41598-021-89319-92045-2322https://doaj.org/article/7f63d46e2fff42f4b46c3e77809dd3a52021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89319-9https://doaj.org/toc/2045-2322Abstract The importance of data quality to correctly determine prevalence estimates of child anthropometric failures has been a contentious issue among policymakers and researchers. Our research objective was to ascertain the impact of improved DHS data quality on the prevalence estimates of stunting, wasting, and underweight. The study also looks for the drivers of data quality. Using five data quality indicators based on age, sex, anthropometric measurements, and normality distribution, we arrive at two datasets of differential data quality and their estimates of anthropometric failures. For this purpose, we use the 2005–2006 and 2015–2016 NFHS data covering 311,182 observations from India. The prevalence estimates of stunting and underweight were virtually unchanged after the application of quality checks. The estimate of wasting had fallen 2 percentage points, indicating an overestimation of the true prevalence. However, this differential impact on the estimate of wasting was driven by the flagging procedure’s sensitivity and was in accordance with empirical evidence from existing literature. We found DHS data quality to be of sufficiently high quality for the prevalence estimates of stunting and underweight, to not change significantly after further improving the data quality. The differential estimate of wasting is attributable to the sensitivity of the flagging procedure.Harsh Vivek HarkareDaniel J. CorsiRockli KimSebastian VollmerS. V. SubramanianNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Harsh Vivek Harkare Daniel J. Corsi Rockli Kim Sebastian Vollmer S. V. Subramanian The impact of improved data quality on the prevalence estimates of anthropometric measures using DHS datasets in India
description	Abstract The importance of data quality to correctly determine prevalence estimates of child anthropometric failures has been a contentious issue among policymakers and researchers. Our research objective was to ascertain the impact of improved DHS data quality on the prevalence estimates of stunting, wasting, and underweight. The study also looks for the drivers of data quality. Using five data quality indicators based on age, sex, anthropometric measurements, and normality distribution, we arrive at two datasets of differential data quality and their estimates of anthropometric failures. For this purpose, we use the 2005–2006 and 2015–2016 NFHS data covering 311,182 observations from India. The prevalence estimates of stunting and underweight were virtually unchanged after the application of quality checks. The estimate of wasting had fallen 2 percentage points, indicating an overestimation of the true prevalence. However, this differential impact on the estimate of wasting was driven by the flagging procedure’s sensitivity and was in accordance with empirical evidence from existing literature. We found DHS data quality to be of sufficiently high quality for the prevalence estimates of stunting and underweight, to not change significantly after further improving the data quality. The differential estimate of wasting is attributable to the sensitivity of the flagging procedure.
format	article
author	Harsh Vivek Harkare Daniel J. Corsi Rockli Kim Sebastian Vollmer S. V. Subramanian
author_facet	Harsh Vivek Harkare Daniel J. Corsi Rockli Kim Sebastian Vollmer S. V. Subramanian
author_sort	Harsh Vivek Harkare
title	The impact of improved data quality on the prevalence estimates of anthropometric measures using DHS datasets in India
title_short	The impact of improved data quality on the prevalence estimates of anthropometric measures using DHS datasets in India
title_full	The impact of improved data quality on the prevalence estimates of anthropometric measures using DHS datasets in India
title_fullStr	The impact of improved data quality on the prevalence estimates of anthropometric measures using DHS datasets in India
title_full_unstemmed	The impact of improved data quality on the prevalence estimates of anthropometric measures using DHS datasets in India
title_sort	impact of improved data quality on the prevalence estimates of anthropometric measures using dhs datasets in india
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7f63d46e2fff42f4b46c3e77809dd3a5
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The impact of improved data quality on the prevalence estimates of anthropometric measures using DHS datasets in India

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