Sociodemographic risk factors of under-five stunting in Bangladesh: Assessing the role of interactions using a machine learning method.

This paper aims to demonstrate the importance of studying interactions among various sociodemographic risk factors of childhood stunting in Bangladesh with the help of an interpretable machine learning method. Data used for the analyses are extracted from the Bangladesh Demographic and Health Survey...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mohaimen Mansur, Awan Afiaz, Md Saddam Hossain
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/085573fe122a4940bb57ea73c21430e7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:085573fe122a4940bb57ea73c21430e7
record_format	dspace
spelling	oai:doaj.org-article:085573fe122a4940bb57ea73c21430e72021-12-02T20:19:19ZSociodemographic risk factors of under-five stunting in Bangladesh: Assessing the role of interactions using a machine learning method.1932-620310.1371/journal.pone.0256729https://doaj.org/article/085573fe122a4940bb57ea73c21430e72021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256729https://doaj.org/toc/1932-6203This paper aims to demonstrate the importance of studying interactions among various sociodemographic risk factors of childhood stunting in Bangladesh with the help of an interpretable machine learning method. Data used for the analyses are extracted from the Bangladesh Demographic and Health Survey (BDHS) 2014 and pertain to a sample of 6,170 under-5 children. Social and economic determinants such as wealth, mother's decision making on healthcare, parental education are considered in addition to geographic divisions and common demographic characteristics of children including age, sex and birth order. A classification tree was first constructed to identify important interaction-based rules that characterize children with different profiles of risk for stunting. Then binary logistic regression models were fitted to measure the importance of these interactions along with the individual risk factors. Results revealed that, as individual factors, living in Sylhet division (OR: 1.57; CI: 1.26-1.96), being an urban resident (OR: 1.28; CI: 1.03-1.96) and having working mothers (OR: 1.21; CI: 1.02-1.44) were associated with higher likelihoods of childhood stunting, whereas belonging to the richest households (OR: 0.56; CI: 0.35-0.90), higher BMI of mothers (OR: 0.68 CI: 0.56-0.84) and mothers' involvement in decision making about children's healthcare with father (OR: 0.83, CI: 0.71-0.97) were linked to lower likelihoods of stunting. Importantly however, risk classifications defined by the interplay of multiple sociodemographic factors showed more extreme odds ratios (OR) of stunting than single factor ORs. For example, children aged 14 months or above who belong to poor wealth class, have lowly educated fathers and reside in either Dhaka, Barisal, Chittagong or Sylhet division are the most vulnerable to stunting (OR: 2.52, CI: 1.85-3.44). The findings endorse the need for tailored-intervention programs for children based on their distinct risk profiles and sociodemographic characteristics.Mohaimen MansurAwan AfiazMd Saddam HossainPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0256729 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mohaimen Mansur Awan Afiaz Md Saddam Hossain Sociodemographic risk factors of under-five stunting in Bangladesh: Assessing the role of interactions using a machine learning method.
description	This paper aims to demonstrate the importance of studying interactions among various sociodemographic risk factors of childhood stunting in Bangladesh with the help of an interpretable machine learning method. Data used for the analyses are extracted from the Bangladesh Demographic and Health Survey (BDHS) 2014 and pertain to a sample of 6,170 under-5 children. Social and economic determinants such as wealth, mother's decision making on healthcare, parental education are considered in addition to geographic divisions and common demographic characteristics of children including age, sex and birth order. A classification tree was first constructed to identify important interaction-based rules that characterize children with different profiles of risk for stunting. Then binary logistic regression models were fitted to measure the importance of these interactions along with the individual risk factors. Results revealed that, as individual factors, living in Sylhet division (OR: 1.57; CI: 1.26-1.96), being an urban resident (OR: 1.28; CI: 1.03-1.96) and having working mothers (OR: 1.21; CI: 1.02-1.44) were associated with higher likelihoods of childhood stunting, whereas belonging to the richest households (OR: 0.56; CI: 0.35-0.90), higher BMI of mothers (OR: 0.68 CI: 0.56-0.84) and mothers' involvement in decision making about children's healthcare with father (OR: 0.83, CI: 0.71-0.97) were linked to lower likelihoods of stunting. Importantly however, risk classifications defined by the interplay of multiple sociodemographic factors showed more extreme odds ratios (OR) of stunting than single factor ORs. For example, children aged 14 months or above who belong to poor wealth class, have lowly educated fathers and reside in either Dhaka, Barisal, Chittagong or Sylhet division are the most vulnerable to stunting (OR: 2.52, CI: 1.85-3.44). The findings endorse the need for tailored-intervention programs for children based on their distinct risk profiles and sociodemographic characteristics.
format	article
author	Mohaimen Mansur Awan Afiaz Md Saddam Hossain
author_facet	Mohaimen Mansur Awan Afiaz Md Saddam Hossain
author_sort	Mohaimen Mansur
title	Sociodemographic risk factors of under-five stunting in Bangladesh: Assessing the role of interactions using a machine learning method.
title_short	Sociodemographic risk factors of under-five stunting in Bangladesh: Assessing the role of interactions using a machine learning method.
title_full	Sociodemographic risk factors of under-five stunting in Bangladesh: Assessing the role of interactions using a machine learning method.
title_fullStr	Sociodemographic risk factors of under-five stunting in Bangladesh: Assessing the role of interactions using a machine learning method.
title_full_unstemmed	Sociodemographic risk factors of under-five stunting in Bangladesh: Assessing the role of interactions using a machine learning method.
title_sort	sociodemographic risk factors of under-five stunting in bangladesh: assessing the role of interactions using a machine learning method.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/085573fe122a4940bb57ea73c21430e7
work_keys_str_mv	AT mohaimenmansur sociodemographicriskfactorsofunderfivestuntinginbangladeshassessingtheroleofinteractionsusingamachinelearningmethod AT awanafiaz sociodemographicriskfactorsofunderfivestuntinginbangladeshassessingtheroleofinteractionsusingamachinelearningmethod AT mdsaddamhossain sociodemographicriskfactorsofunderfivestuntinginbangladeshassessingtheroleofinteractionsusingamachinelearningmethod
_version_	1718374208185040896

Sociodemographic risk factors of under-five stunting in Bangladesh: Assessing the role of interactions using a machine learning method.

Ejemplares similares