The purpose of this study was to assess the spatial distribution of malaria prevalence rates among selected rural part of woredas in SNNPR, Ethiopia. This work is based on data available from the 2011 malaria indicator survey (MIS 2011) of Ethiopian Public Health Institution. ESDA, Spatial regression model and Bayesian Spatial analysis were employed for data analysis. From ESDA, we found positive spatial autocorrelation in malaria prevalence rate. Relying on specification diagnostics and measures of fit; Spatial lag model was found to be the best model for modeling malaria prevalence rate data. The relationship between malaria prevalence and its risk factors was assessed using spatial models. The spatial models also showed an increase of malaria prevalence with a number of factors. From results, increase in the proportion of households sprayed in 12 months and the average altitude in the woreda estimated to decrease the average malaria prevalence. The result also demonstrated that increase in the House hold size of the district, proportion of households having access to piped water, proportion of households having access to radio, proportion of households having access to radio and Main construction material of the room’s wall are estimated to raise the average malaria prevalence rate. Finally, the study concluded that malaria is spatially clustered in space and the risk factors exhibit effect on the malaria prevalence in the study area. Based on the results of the study, We recommend for policy makers on the way to reduce malaria prevalence in the rural part of woreda of SNNPR using spatial information.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 6, Issue 6) |
| DOI | 10.11648/j.ajtas.20170606.11 |
| Page(s) | 254-269 |
| 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 |
Malaria, Spatial Autocorrelation, Malaria Prevalence, Bayesian Spatial Analysis
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APA Style
Dereje Bekele Dessie. (2017). Spatial Modelling of Malaria Prevalence and Its Risk Factors in Rural SNNPR, Ethiopia: Classical and Bayesian Approaches. American Journal of Theoretical and Applied Statistics, 6(6), 254-269. https://doi.org/10.11648/j.ajtas.20170606.11
ACS Style
Dereje Bekele Dessie. Spatial Modelling of Malaria Prevalence and Its Risk Factors in Rural SNNPR, Ethiopia: Classical and Bayesian Approaches. Am. J. Theor. Appl. Stat. 2017, 6(6), 254-269. doi: 10.11648/j.ajtas.20170606.11
AMA Style
Dereje Bekele Dessie. Spatial Modelling of Malaria Prevalence and Its Risk Factors in Rural SNNPR, Ethiopia: Classical and Bayesian Approaches. Am J Theor Appl Stat. 2017;6(6):254-269. doi: 10.11648/j.ajtas.20170606.11
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Download@article{10.11648/j.ajtas.20170606.11,
author = {Dereje Bekele Dessie},
title = {Spatial Modelling of Malaria Prevalence and Its Risk Factors in Rural SNNPR, Ethiopia: Classical and Bayesian Approaches},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {6},
number = {6},
pages = {254-269},
doi = {10.11648/j.ajtas.20170606.11},
url = {https://doi.org/10.11648/j.ajtas.20170606.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20170606.11},
abstract = {The purpose of this study was to assess the spatial distribution of malaria prevalence rates among selected rural part of woredas in SNNPR, Ethiopia. This work is based on data available from the 2011 malaria indicator survey (MIS 2011) of Ethiopian Public Health Institution. ESDA, Spatial regression model and Bayesian Spatial analysis were employed for data analysis. From ESDA, we found positive spatial autocorrelation in malaria prevalence rate. Relying on specification diagnostics and measures of fit; Spatial lag model was found to be the best model for modeling malaria prevalence rate data. The relationship between malaria prevalence and its risk factors was assessed using spatial models. The spatial models also showed an increase of malaria prevalence with a number of factors. From results, increase in the proportion of households sprayed in 12 months and the average altitude in the woreda estimated to decrease the average malaria prevalence. The result also demonstrated that increase in the House hold size of the district, proportion of households having access to piped water, proportion of households having access to radio, proportion of households having access to radio and Main construction material of the room’s wall are estimated to raise the average malaria prevalence rate. Finally, the study concluded that malaria is spatially clustered in space and the risk factors exhibit effect on the malaria prevalence in the study area. Based on the results of the study, We recommend for policy makers on the way to reduce malaria prevalence in the rural part of woreda of SNNPR using spatial information.},
year = {2017}
}
TY - JOUR T1 - Spatial Modelling of Malaria Prevalence and Its Risk Factors in Rural SNNPR, Ethiopia: Classical and Bayesian Approaches AU - Dereje Bekele Dessie Y1 - 2017/11/03 PY - 2017 N1 - https://doi.org/10.11648/j.ajtas.20170606.11 DO - 10.11648/j.ajtas.20170606.11 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 - 254 EP - 269 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20170606.11 AB - The purpose of this study was to assess the spatial distribution of malaria prevalence rates among selected rural part of woredas in SNNPR, Ethiopia. This work is based on data available from the 2011 malaria indicator survey (MIS 2011) of Ethiopian Public Health Institution. ESDA, Spatial regression model and Bayesian Spatial analysis were employed for data analysis. From ESDA, we found positive spatial autocorrelation in malaria prevalence rate. Relying on specification diagnostics and measures of fit; Spatial lag model was found to be the best model for modeling malaria prevalence rate data. The relationship between malaria prevalence and its risk factors was assessed using spatial models. The spatial models also showed an increase of malaria prevalence with a number of factors. From results, increase in the proportion of households sprayed in 12 months and the average altitude in the woreda estimated to decrease the average malaria prevalence. The result also demonstrated that increase in the House hold size of the district, proportion of households having access to piped water, proportion of households having access to radio, proportion of households having access to radio and Main construction material of the room’s wall are estimated to raise the average malaria prevalence rate. Finally, the study concluded that malaria is spatially clustered in space and the risk factors exhibit effect on the malaria prevalence in the study area. Based on the results of the study, We recommend for policy makers on the way to reduce malaria prevalence in the rural part of woreda of SNNPR using spatial information. VL - 6 IS - 6 ER -
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