Childhood Mortality Adjusting for Cluster Effect Study in Ghana Demographic Health Survey
International Journal of Statistical Distributions and Applications
Volume 3, Issue 4, December 2017, Pages: 95-102
Received: Mar. 7, 2017; Accepted: Apr. 5, 2017; Published: Nov. 28, 2017
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Authors
Kankam Stephen, Department of Mathematics, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
Nana Kena Frimpong, Department of Mathematics, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
Kofi Adagbodzo Samuel, Department of Mathematics, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
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Abstract
In Ghana Demographic Health Survey (GDHS), information is collected on the demographic characteristics and health status which is representative sample of the entire population. The backbone for the survey is enumeration areas (EA), clusters which was done using two-stage probabilistic approach. This paper illustrates analysis of childhood mortality by adjusting for cluster effect using Generalized Estimation Equations (GEE). Ghana Demographic Survey Data -2008 (GDHS-2008) was used for the analysis. GEE model with three working correlation matrices independence, unstructured and exchangeable were adjusted for the data set. Logistic regression models and statistical tools were used to find association and select significant variables on childhood mortality. Age of mother, Total birth in last five years and region of residence were significance determinants of incidence of childhood mortality. We recommend that there should be clear policy and programs for educating, campaigning and increasing and improving health facilities. Suggestions for further study of childhood mortality were also in this paper.
Keywords
GEE, Childhood Mortality, Cluster, Ghana Demographic Health Survey (GDHS)
To cite this article
Kankam Stephen, Nana Kena Frimpong, Kofi Adagbodzo Samuel, Childhood Mortality Adjusting for Cluster Effect Study in Ghana Demographic Health Survey, International Journal of Statistical Distributions and Applications. Vol. 3, No. 4, 2017, pp. 95-102. doi: 10.11648/j.ijsd.20170304.16
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Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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