“Childhood Anemia in India: an application of a Bayesian geo-additive model”

Abstract Background The geographical differences that cause anaemia can be partially explained by the variability in environmental factors, particularly nutrition and infections. The studies failed to explain the non-linear effect of the continuous covariates on childhood anaemia. The present paper...

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Autores principales:	Holendro Singh Chungkham, Strong P. Marbaniang, Pralip Kumar Narzary
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Spatial effects Geo-additive logistic regression P-splines Childhood anaemia Pediatrics RJ1-570
Acceso en línea:	https://doaj.org/article/eff3177951b9492c848b2339e7bd16f9
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spelling	oai:doaj.org-article:eff3177951b9492c848b2339e7bd16f92021-12-05T12:21:07Z“Childhood Anemia in India: an application of a Bayesian geo-additive model”10.1186/s12887-021-03008-01471-2431https://doaj.org/article/eff3177951b9492c848b2339e7bd16f92021-11-01T00:00:00Zhttps://doi.org/10.1186/s12887-021-03008-0https://doaj.org/toc/1471-2431Abstract Background The geographical differences that cause anaemia can be partially explained by the variability in environmental factors, particularly nutrition and infections. The studies failed to explain the non-linear effect of the continuous covariates on childhood anaemia. The present paper aims to investigate the risk factors of childhood anaemia in India with focus on geographical spatial effect. Methods Geo-additive logistic regression models were fitted to the data to understand fixed as well as spatial effects of childhood anaemia. Logistic regression was fitted for the categorical variable with outcomes (anaemia (Hb < 11) and no anaemia (Hb ≥ 11)). Continuous covariates were modelled by the penalized spline and spatial effects were smoothed by the two-dimensional spline. Results At 95% posterior credible interval, the influence of unobserved factors on childhood anaemia is very strong in the Northern and Central part of India. However, most of the states in North Eastern part of India showed negative spatial effects. A U-shape non-linear relationship was observed between childhood anaemia and mother’s age. This indicates that mothers of young and old ages are more likely to have anaemic children; in particular mothers aged 15 years to about 25 years. Then the risk of childhood anaemia starts declining after the age of 25 years and it continues till the age of around 37 years, thereafter again starts increasing. Further, the non-linear effects of duration of breastfeeding on childhood anaemia show that the risk of childhood anaemia decreases till 29 months thereafter increases. Conclusion Strong evidence of residual spatial effect to childhood anaemia in India is observed. Government child health programme should gear up in treating childhood anaemia by focusing on known measurable factors such as mother’s education, mother’s anaemia status, family wealth status, child health (fever), stunting, underweight, and wasting which have been found to be significant in this study. Attention should also be given to effects of unknown or unmeasured factors to childhood anaemia at the community level. Special attention to unmeasurable factors should be focused in the states of central and northern India which have shown significant positive spatial effects.Holendro Singh ChungkhamStrong P. MarbaniangPralip Kumar NarzaryBMCarticleSpatial effectsGeo-additive logistic regressionP-splinesChildhood anaemiaPediatricsRJ1-570ENBMC Pediatrics, Vol 21, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Spatial effects Geo-additive logistic regression P-splines Childhood anaemia Pediatrics RJ1-570
spellingShingle	Spatial effects Geo-additive logistic regression P-splines Childhood anaemia Pediatrics RJ1-570 Holendro Singh Chungkham Strong P. Marbaniang Pralip Kumar Narzary “Childhood Anemia in India: an application of a Bayesian geo-additive model”
description	Abstract Background The geographical differences that cause anaemia can be partially explained by the variability in environmental factors, particularly nutrition and infections. The studies failed to explain the non-linear effect of the continuous covariates on childhood anaemia. The present paper aims to investigate the risk factors of childhood anaemia in India with focus on geographical spatial effect. Methods Geo-additive logistic regression models were fitted to the data to understand fixed as well as spatial effects of childhood anaemia. Logistic regression was fitted for the categorical variable with outcomes (anaemia (Hb < 11) and no anaemia (Hb ≥ 11)). Continuous covariates were modelled by the penalized spline and spatial effects were smoothed by the two-dimensional spline. Results At 95% posterior credible interval, the influence of unobserved factors on childhood anaemia is very strong in the Northern and Central part of India. However, most of the states in North Eastern part of India showed negative spatial effects. A U-shape non-linear relationship was observed between childhood anaemia and mother’s age. This indicates that mothers of young and old ages are more likely to have anaemic children; in particular mothers aged 15 years to about 25 years. Then the risk of childhood anaemia starts declining after the age of 25 years and it continues till the age of around 37 years, thereafter again starts increasing. Further, the non-linear effects of duration of breastfeeding on childhood anaemia show that the risk of childhood anaemia decreases till 29 months thereafter increases. Conclusion Strong evidence of residual spatial effect to childhood anaemia in India is observed. Government child health programme should gear up in treating childhood anaemia by focusing on known measurable factors such as mother’s education, mother’s anaemia status, family wealth status, child health (fever), stunting, underweight, and wasting which have been found to be significant in this study. Attention should also be given to effects of unknown or unmeasured factors to childhood anaemia at the community level. Special attention to unmeasurable factors should be focused in the states of central and northern India which have shown significant positive spatial effects.
format	article
author	Holendro Singh Chungkham Strong P. Marbaniang Pralip Kumar Narzary
author_facet	Holendro Singh Chungkham Strong P. Marbaniang Pralip Kumar Narzary
author_sort	Holendro Singh Chungkham
title	“Childhood Anemia in India: an application of a Bayesian geo-additive model”
title_short	“Childhood Anemia in India: an application of a Bayesian geo-additive model”
title_full	“Childhood Anemia in India: an application of a Bayesian geo-additive model”
title_fullStr	“Childhood Anemia in India: an application of a Bayesian geo-additive model”
title_full_unstemmed	“Childhood Anemia in India: an application of a Bayesian geo-additive model”
title_sort	“childhood anemia in india: an application of a bayesian geo-additive model”
publisher	BMC
publishDate	2021
url	https://doaj.org/article/eff3177951b9492c848b2339e7bd16f9
work_keys_str_mv	AT holendrosinghchungkham childhoodanemiainindiaanapplicationofabayesiangeoadditivemodel AT strongpmarbaniang childhoodanemiainindiaanapplicationofabayesiangeoadditivemodel AT pralipkumarnarzary childhoodanemiainindiaanapplicationofabayesiangeoadditivemodel
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“Childhood Anemia in India: an application of a Bayesian geo-additive model”

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