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.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 7, Issue 1) |
| DOI | 10.11648/j.ajtas.20180701.13 |
| Page(s) | 21-28 |
| 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), 2018. Published by Science Publishing Group |
Diabetes, Fasting Glucose, GLMM, LMM, Risk Factors
| [1] | DIABETES ATLAS, sixth edition. (2013). International Diabetes Federation. DIGGLE, et al., (2009). Analysis of Longitudinal Data. 2nd ed. Oxford: Oxford University Press. |
| [2] | D. HEDEKER and R. D., (2006). Gibbons, Longitudinal Data Analysis, Wiley-Inter science, New Jersey. |
| [3] | DONALD R. HEDEKER and ROBERT D. GIBBONS., (2006). Longitudinal Data Analysis. J. Wiley and Sons. |
| [4] | D. R. WHITING, L. GUARIGUATA, C. WEIL, and J. SHAW., (2011 and 2030). IDF Diabetes Atlas, Global Estimates of the Prevalence of diabetes, Diabetes Research and Clinical Practice, vol. 94, no. 3, pp. 311–321. |
| [5] | G. M. FIZMAURICE, et al., (2004). Applied Longitudinal Analysis, Wiley Inter-science, New Jersey. |
| [6] | T. A. HARRISON, et al., (2003). Family History of Diabetes as a Potential Public health tool, American Journal of Preventive Medicine, vol. 152{15924, no. 2, pp.}. |
| [7] | CDC., (2010). Healthy People; National Center for Health Statistics Healthy People Stat Notes U. S. Dept. of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, (2001-2011). Retrieved from. |
| [8] | HAMIL M. et al., (1995). Diabetes in America. 2nd Edition. National Diabetes Data Group, 179-203. |
| [9] | MCKINLY J. et al., (2010). The Social Construction of Race/Ethnic Disparities in Diabetes-A Case of Misplaced Concreteness New England Journal of Medicine. |
| [10] | ZEIN, ZA. et al., the Ecology of Health and Disease in Ethiopia. |
| [11] | VERBEKE, G. and MOLENBERGHS, G., (2000). Linear Mixed Models for Longitudinal Data. New York, Springer Verlag. No. 2, pp. 31-40. |
APA Style
Wudneh Ketema Moges, A. R. Muralidharan, Haymanot Zeleke Tadesse. (2018). 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, 7(1), 21-28. https://doi.org/10.11648/j.ajtas.20180701.13
ACS Style
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. Am. J. Theor. Appl. Stat. 2018, 7(1), 21-28. doi: 10.11648/j.ajtas.20180701.13
AMA Style
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. Am J Theor Appl Stat. 2018;7(1):21-28. doi: 10.11648/j.ajtas.20180701.13
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ajtas.20180701.13,
author = {Wudneh Ketema Moges and A. R. Muralidharan and Haymanot Zeleke Tadesse},
title = {Gaussian Longitudinal Analysis of Progression of Diabetes Mellitus Patients Using Fasting Blood Sugar Level: A Case of Debre Berhan Referral Hospital, Ethiopia},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {7},
number = {1},
pages = {21-28},
doi = {10.11648/j.ajtas.20180701.13},
url = {https://doi.org/10.11648/j.ajtas.20180701.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20180701.13},
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.},
year = {2018}
}
TY - JOUR T1 - Gaussian Longitudinal Analysis of Progression of Diabetes Mellitus Patients Using Fasting Blood Sugar Level: A Case of Debre Berhan Referral Hospital, Ethiopia AU - Wudneh Ketema Moges AU - A. R. Muralidharan AU - Haymanot Zeleke Tadesse Y1 - 2018/01/09 PY - 2018 N1 - https://doi.org/10.11648/j.ajtas.20180701.13 DO - 10.11648/j.ajtas.20180701.13 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 - 21 EP - 28 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20180701.13 AB - 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. VL - 7 IS - 1 ER -
Information
Email: