A Bayesian Geo-Additive Modeling of Childhood Anemia in India

Abstract Background The geographical differences that caused 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 aimed 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 children who are anaemic; 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. 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 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 these unmeasurable factors should be focused in the states of central and northern India which have shown significant positive spatial effects.

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

BMC Pediatrics ◽

10.1186/s12887-021-03008-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Holendro Singh Chungkham ◽

Strong P. Marbaniang ◽

Pralip Kumar Narzary

Keyword(s):

Logistic Regression ◽

Child Health ◽

Additive Model ◽

Spatial Effect ◽

Spatial Effects ◽

Geographical Differences ◽

Wealth Status ◽

Linear Effect ◽

Continuous Covariates ◽

Non Linear

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.

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Modelling chronic malnutrition in Zambia: A Bayesian distributional regression approach

PLoS ONE ◽

10.1371/journal.pone.0255073 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0255073

Author(s):

Given Moonga ◽

Stephan Böse-O’Reilly ◽

Ursula Berger ◽

Kenneth Harttgen ◽

Charles Michelo ◽

...

Keyword(s):

Maternal Education ◽

Credible Interval ◽

Sub Saharan Africa ◽

Spatial Effects ◽

Z Score ◽

Chronic Malnutrition ◽

Under Five ◽

Non Linear ◽

Distributional Regression ◽

Height For Age

Background The burden of child under-nutrition still remains a global challenge, with greater severity being faced by low- and middle-income countries, despite the strategies in the Sustainable Development Goals (SDGs). Globally, malnutrition is the one of the most important risk factors associated with illness and death, affecting hundreds of millions of pregnant women and young children. Sub-Saharan Africa is one of the regions in the world struggling with the burden of chronic malnutrition. The 2018 Zambia Demographic and Health Survey (ZDHS) report estimated that 35% of the children under five years of age are stunted. The objective of this study was to analyse the distribution, and associated factors of stunting in Zambia. Methods We analysed the relationships between socio-economic, and remote sensed characteristics and anthropometric outcomes in under five children, using Bayesian distributional regression. Georeferenced data was available for 25,852 children from two waves of the ZDHS, 31% observation were from the 2007 and 69% were from the 2013/14. We assessed the linear, non-linear and spatial effects of covariates on the height-for-age z-score. Results Stunting decreased between 2007 and 2013/14 from a mean z-score of 1.59 (credible interval (CI): -1.63; -1.55) to -1.47 (CI: -1.49; -1.44). We found a strong non-linear relationship for the education of the mother and the wealth of the household on the height-for-age z-score. Moreover, increasing levels of maternal education above the eighth grade were associated with a reduced variation of stunting. Our study finds that remote sensed covariates alone explain little of the variation of the height-for-age z-score, which highlights the importance to collect socio-economic characteristics, and to control for socio-economic characteristics of the individual and the household. Conclusions While stunting still remains unacceptably high in Zambia with remarkable regional inequalities, the decline is lagging behind goal two of the SDGs. This emphasises the need for policies that help to reduce the share of chronic malnourished children within Zambia.

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Using Hurdle Models to Appraise the Non-Linear Effect of age in Entrepreneurship

Proceedings of the 19th European Conference on Research Methods ◽

10.34190/erm.20.0127 ◽

2020 ◽

Keyword(s):

Linear Effect ◽

Hurdle Models ◽

Non Linear Effect ◽

Non Linear ◽

Effect Of Age

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The non-linear effect of technological diversification on regional productivity: implications for growth and Smart Specialisation Strategies

Regional Studies ◽

10.1080/00343404.2021.1939292 ◽

2021 ◽

pp. 1-16

Author(s):

Silvia Rocchetta ◽

Raquel Ortega-Argilés ◽

Dieter F. Kogler

Keyword(s):

Regional Productivity ◽

Linear Effect ◽

Non Linear Effect ◽

Technological Diversification ◽

Smart Specialisation ◽

Non Linear

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Variable Selection for the Spatial Autoregressive Model with Autoregressive Disturbances

Mathematics ◽

10.3390/math9121448 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1448

Author(s):

Xuan Liu ◽

Jianbao Chen

Keyword(s):

Variable Selection ◽

Autoregressive Model ◽

Rapid Development ◽

Heterogeneous Data ◽

Spatial Effect ◽

Spatial Effects ◽

Spatial Autoregressive Model ◽

Monte Carlo Studies ◽

Spatial Autoregressive ◽

Theoretical Results

Along with the rapid development of the geographic information system, high-dimensional spatial heterogeneous data has emerged bringing theoretical and computational challenges to statistical modeling and analysis. As a result, effective dimensionality reduction and spatial effect recognition has become very important. This paper focuses on variable selection in the spatial autoregressive model with autoregressive disturbances (SARAR) which contains a more comprehensive spatial effect. The variable selection procedure is presented by using the so-called penalized quasi-likelihood approach. Under suitable regular conditions, we obtain the rate of convergence and the asymptotic normality of the estimators. The theoretical results ensure that the proposed method can effectively identify spatial effects of dependent variables, find spatial heterogeneity in error terms, reduce the dimension, and estimate unknown parameters simultaneously. Based on step-by-step transformation, a feasible iterative algorithm is developed to realize spatial effect identification, variable selection, and parameter estimation. In the setting of finite samples, Monte Carlo studies and real data analysis demonstrate that the proposed penalized method performs well and is consistent with the theoretical results.

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