The Open Public Health Journal




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RESEARCH ARTICLE

Spatial Modelling of Under-five Mortality in Lesotho with Reference to 2014 Demographic and Health Surveillance Dataset



Mthobisi Mxolisi Zondi1, *, Henry G Mwambi1, Sileshi Fanta Melesse1
1 University of KwaZulu-Natal-School of Mathematics, Statistics and Computer Science, Pietermaritzburg, KwaZulu-Natal, South Africa

Abstract

Background:

Lesotho is the country located in the Sub-Saharan region of Africa countries where under-five mortality (U5M) is still a big issue due to some significant social and demographic risk factors. Hence, the investigation of some social and demographic factors that are associated with the U5M, is a critical problem that needs due consideration.

Methods:

This study used the 2014 Lesotho Demographic and Health Survey (LDHS) that had a sample of over 9000 representative households. Individually, data consisting of a nationally representative sample of 9,543 households in the 2014 Lesotho Demographic and Health Survey were analysed. The Random Walk second-order (RW2) model was adopted for analysis. Maps construction and modelling were done through the spatially structured and unstructured random effects using the Gaussian Markov Random Field and a zero-mean Gaussian process, respectively. The full Bayesian inference was adopted to produce the results using the Integrated Nested Laplace Approximation (INLA) function in R-software.

Results:

In this study, age at death of an under-five child was found to have a linear association with the U5M in Lesotho. The non-stationary models outperform the stationary models. The low-risk pattern was found in the north of Lesotho, and the highest risk occurs in the centre through the south, east, west, southeast, and northwest. Breastfeeding has contributed significantly to under-five mortality to most of Lesotho districts.

Conclusion:

This study adopted the newly developed statistical models to model and mapped the U5M in Lesotho. The full Bayesian inference was used to produce the results using R-INLA package. The findings from this study can help introduce new policies that will help reduce disparity in Lesotho.

Keywords: Under-five mortality, Conditional autoregressive model, Random walk, Fully bayesian, Spatial modelling, Integrated nested laplace approximation.


Article Information


Identifiers and Pagination:

Year: 2020
Volume: 13
First Page: 289
Last Page: 297
Publisher Id: TOPHJ-13-289
DOI: 10.2174/1874944502013010289

Article History:

Received Date: 23/01/2020
Revision Received Date: 26/04/2020
Acceptance Date: 29/04/2020
Electronic publication date: 21/06/2020
Collection year: 2020

© 2020 Zondi et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Correspondence: Address correspondence to this author at the University of KwaZulu-Natal-School of Mathematics, Statistics and Computer Science, Pietermaritzburg, KwaZulu-Natal, South Africa; Tel: 033 260 5111;
E-mail: melesse@ukzn.ac.za




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