Bayesian disease mapping is a field of statistics that is used to model the spatial distribution of disease outcomes especially in application to studies in spatial biostatistics and also as a tool to help develop the required intervention strategies. In this study, we perform a spatial modeling of ARI among children less than five years in Kenya using data from the 2014 Kenya Demographic and Health Survey (KDHS). Four models were used in this study namely the logistic regression model, the normal unstructured heterogeneity random effects model, ICAR (Integrated Conditional Autoregressive) spatial random effects model and the convolution model. A full Bayesian approach was used and the models were implemented using the Winbugs software version 1.4. Model selection was based on the DIC value where the model with the lowest DIC value was considered to be the best. The convolution model was the best model in this case and was used to map ARI across the different counties in Kenya. The national prevalence was 47.3%. The prevalence was found to be highest in the counties in the western part of Kenya. From the analysis, it’s clear that ARI is still a menace that need to be controlled. Proper planning and allocation of resources need to be put in place by the county governments in order to curb the rising cases of ARI.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 6, Issue 2) |
| DOI | 10.11648/j.ajtas.20170602.18 |
| Page(s) | 123-128 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Prevalence, Spatial, Bayesian
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APA Style
Kinyua Ann Muthoni, Oscar Owino Ngesa. (2017). Determinants and Spatial Modeling of Acute Respiratory Infections (ARI) Among Children Less Than Five Years in Kenya. American Journal of Theoretical and Applied Statistics, 6(2), 123-128. https://doi.org/10.11648/j.ajtas.20170602.18
ACS Style
Kinyua Ann Muthoni; Oscar Owino Ngesa. Determinants and Spatial Modeling of Acute Respiratory Infections (ARI) Among Children Less Than Five Years in Kenya. Am. J. Theor. Appl. Stat. 2017, 6(2), 123-128. doi: 10.11648/j.ajtas.20170602.18
AMA Style
Kinyua Ann Muthoni, Oscar Owino Ngesa. Determinants and Spatial Modeling of Acute Respiratory Infections (ARI) Among Children Less Than Five Years in Kenya. Am J Theor Appl Stat. 2017;6(2):123-128. doi: 10.11648/j.ajtas.20170602.18
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Download@article{10.11648/j.ajtas.20170602.18,
author = {Kinyua Ann Muthoni and Oscar Owino Ngesa},
title = {Determinants and Spatial Modeling of Acute Respiratory Infections (ARI) Among Children Less Than Five Years in Kenya},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {6},
number = {2},
pages = {123-128},
doi = {10.11648/j.ajtas.20170602.18},
url = {https://doi.org/10.11648/j.ajtas.20170602.18},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20170602.18},
abstract = {Bayesian disease mapping is a field of statistics that is used to model the spatial distribution of disease outcomes especially in application to studies in spatial biostatistics and also as a tool to help develop the required intervention strategies. In this study, we perform a spatial modeling of ARI among children less than five years in Kenya using data from the 2014 Kenya Demographic and Health Survey (KDHS). Four models were used in this study namely the logistic regression model, the normal unstructured heterogeneity random effects model, ICAR (Integrated Conditional Autoregressive) spatial random effects model and the convolution model. A full Bayesian approach was used and the models were implemented using the Winbugs software version 1.4. Model selection was based on the DIC value where the model with the lowest DIC value was considered to be the best. The convolution model was the best model in this case and was used to map ARI across the different counties in Kenya. The national prevalence was 47.3%. The prevalence was found to be highest in the counties in the western part of Kenya. From the analysis, it’s clear that ARI is still a menace that need to be controlled. Proper planning and allocation of resources need to be put in place by the county governments in order to curb the rising cases of ARI.},
year = {2017}
}
TY - JOUR T1 - Determinants and Spatial Modeling of Acute Respiratory Infections (ARI) Among Children Less Than Five Years in Kenya AU - Kinyua Ann Muthoni AU - Oscar Owino Ngesa Y1 - 2017/03/21 PY - 2017 N1 - https://doi.org/10.11648/j.ajtas.20170602.18 DO - 10.11648/j.ajtas.20170602.18 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 123 EP - 128 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20170602.18 AB - Bayesian disease mapping is a field of statistics that is used to model the spatial distribution of disease outcomes especially in application to studies in spatial biostatistics and also as a tool to help develop the required intervention strategies. In this study, we perform a spatial modeling of ARI among children less than five years in Kenya using data from the 2014 Kenya Demographic and Health Survey (KDHS). Four models were used in this study namely the logistic regression model, the normal unstructured heterogeneity random effects model, ICAR (Integrated Conditional Autoregressive) spatial random effects model and the convolution model. A full Bayesian approach was used and the models were implemented using the Winbugs software version 1.4. Model selection was based on the DIC value where the model with the lowest DIC value was considered to be the best. The convolution model was the best model in this case and was used to map ARI across the different counties in Kenya. The national prevalence was 47.3%. The prevalence was found to be highest in the counties in the western part of Kenya. From the analysis, it’s clear that ARI is still a menace that need to be controlled. Proper planning and allocation of resources need to be put in place by the county governments in order to curb the rising cases of ARI. VL - 6 IS - 2 ER -
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