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Volume 7, Issue 1, January 2018, Page: 21-28
Gaussian Longitudinal Analysis of Progression of Diabetes Mellitus Patients Using Fasting Blood Sugar Level: A Case of Debre Berhan Referral Hospital, Ethiopia
Wudneh Ketema Moges, Department of Statistics, College of Natural and Computational Science, Debre Berhan University, Debre Berhan, Ethiopia
A. R. Muralidharan, Department of Statistics, College of Natural and Computational Science, Debre Berhan University, Debre Berhan, Ethiopia
Haymanot Zeleke Tadesse, Department of Statistics, College of Natural and Computational Science, Debre Berhan University, Debre Berhan, Ethiopia
Received: Nov. 21, 2017;       Accepted: Dec. 4, 2017;       Published: Jan. 9, 2018
DOI: 10.11648/j.ajtas.20180701.13      View  1885      Downloads  174
Abstract
Diabetes mellitus is a metabolic disorder where by glucose cannot effectively get transported out of the blood. It is a chronic disease with a high prevalence and growing concern in world wide. There are two Types of diabetes, which are Type I and Type II. A longitudinal data analysis retrospective based study was conducted between 1st September, 2012 to 30th August 2015 in Debre Berhan referral hospital. The main objective of the study was Gaussian longitudinal analysis of progression of Diabetes mellitus patients using fasting blood sugar level count following insulin, metformin and to identify factors predicting the progression of diabetic infection. A total of 248 Diabetes mellitus patients were included in the study whom 111 (44.8%) were females and the rest 137 (55.8%) were males. The generalized linear mixed model would be used to model the progression of diabetic infection. The appropriate variance covariance structure was Compound symmetry selected for this study. This study showed that age, sex, time, illiterate with time, primary with time, address with time, age with time and time with time were statistically significant factors for the progression of fasting blood sugar level at a logarithmic fasting sugar level over time in generalized linear mixed model. The mean fasting blood sugar level showed an increasing progress over time after patients were initiated on insulin and metformin. The statistical modelling approaches linear mixed model and generalized linear mixed model have been compared for the analysis of fasting data and we obtained generalized linear mixed model exhibited the best fit for this data with smaller disturbance than linear mixed model for their estimated standard error.
Keywords
Diabetes, Fasting Glucose, GLMM, LMM, Risk Factors
To cite this article
Wudneh Ketema Moges, A. R. Muralidharan, Haymanot Zeleke Tadesse, Gaussian Longitudinal Analysis of Progression of Diabetes Mellitus Patients Using Fasting Blood Sugar Level: A Case of Debre Berhan Referral Hospital, Ethiopia, American Journal of Theoretical and Applied Statistics. Vol. 7, No. 1, 2018, pp. 21-28. doi: 10.11648/j.ajtas.20180701.13
Copyright
Copyright © 2018 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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