Survival Analysis of Type II Diabetes Mellitus Patients: A Case Study at Menellik II Referral Hospital Addis Ababa, Ethiopia
American Journal of Theoretical and Applied Statistics
Volume 6, Issue 6, November 2017, Pages: 311-324
Received: Sep. 16, 2017; Accepted: Sep. 30, 2017; Published: Dec. 23, 2017
Views 200      Downloads 12
Authors
Getnet Bogale Begashaw, Department of Statistics, College of Natural and Computational Science, Debre Berhan University, Debre Berhan, Ethiopia
Yordanos Berihun Yohannes, Department of Statistics, College of Natural and Computational Science, Debre Berhan University, Debre Berhan, 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
Article Tools
Follow on us
Abstract
Diabetes Mellitus is characterized by abnormally high levels of glucose in the blood. It is a chronic disease with a high prevalence and a growing concern worldwide. The main objective of this study was to analyze Survival Analysis of Type II Diabetic patients in Menellik II referral hospital found in Addis Ababa. The study was based on the data which have 151 type II Diabetes Mellitus patients with 10 independent variables. The independent variables that considered in the study were Sex, Age, past medical history, Family History, Complication Status, Regimen type, Specific drug (treatment) type order by Physician, Systolic Blood Pressure of Patients, Diastolic Blood Pressure of Patients and Weight of Patients. The study more explored using survival models such as semi-parametric (Cox PH model) and parametric models (Accelerated Failure Time). The number of patients that recovers from type II Diabetes Mellitus were 116 (76.82%) while the remaining 36 (23.18%) did not recovered from type II Diabetes Mellitus. Finally, the result of this study showed that in Cox PH model past medical history and weight of type II Diabetes Mellitus patients were statistically significant with time to recover from type II Diabetes Mellitus. Likewise age, systolic blood pressure and diastolic blood pressure of patients were time dependent covariates and they were satisfies assumption of proportionality. Accordingly, we obtained gamma distribution was the best fit among other distribution in case of AFT model.
Keywords
Accelerated Failure Time, Cox Proportional Hazards Model, Diabetes Mellitus, Recover from Type II Diabetes Mellitus
To cite this article
Getnet Bogale Begashaw, Yordanos Berihun Yohannes, Wudneh Ketema Moges, A. R. Muralidharan, Survival Analysis of Type II Diabetes Mellitus Patients: A Case Study at Menellik II Referral Hospital Addis Ababa, Ethiopia, American Journal of Theoretical and Applied Statistics. Vol. 6, No. 6, 2017, pp. 311-324. doi: 10.11648/j.ajtas.20170606.18
Copyright
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.
References
[1]
Kahn SE, Cooper ME, Del Prato S. Pathophysiology and treatment of type 2 diabetes, 2008.
[2]
Yibru, E., Menon, M., Belayneh, Y. and Seyifu, D., 2015. The Effect of Coriandrum Sativum Seed Extract on Hyperglycemia, Lipid Profile and Renal Function in Streptozotocin Induced Type-2 Diabetic Swiss Albino Mice. International Journal of Health Sciences and Research (IJHSR), 5(7), pp. 166-177.
[3]
Tadesse, B., 2008. Antidiabetic activity and phytochemical screening of crude extracts of Stevia rebaudiana Bertoni and Ajuga remota Benth grown in Ethiopia on alloxan-induced diabetic mice (Doctoral dissertation, aau).
[4]
Gill GV, Mbanya JC, Tesfaye S, A Sub-Sahara African prospective of diabetes, DIabtologia, 2009; 52:8-16.
[5]
Whiting DR, Guariguata L, Weil C, Shaw J: IDF diabetes atlas: Global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract, 2011, 94(3):311–321.
[6]
Abebe et. al. Diabetes mellitus in Northwest Ethiopia: A community based study, BMC Public Health 2014, 14 (97):1-8.
[7]
Roupa Ζ., Κoulouri Α., Sotiropoulou P.,Makrinika E., Marneras X., Lahana Ι.,Gourni Μ. Anxiety and depression in patients with type II diabetes mellitus, depending on sex and body mass index. Health Science Journal. 2009; 3(1).
[8]
Munir S, Tarin A. Global “epidemic” of diabetes. Nishtar Med J. 2010; 2(2):56-60.
[9]
Temesgen, et al., (2014). Prevalence of Chronic Kidney Disease and Associated Risk Factors among Diabetic Patients in Southern Ethiopia. American Journal of Health Research. Vol. 2, No. 4, pp. 216-221. Doi: 10.11648/j.ajhr.20140204.28.
[10]
Kleefstra, N., Houweling, S. T., Bakker, S. J., Verhoeven, S., Gans, R. O., Meyboom-de Jong, B. and Bilo, H. J., 2007. Chromium treatment has no effect in patients with type 2 diabetes in a western population randomized, double-blind, placebo-controlled trial. Diabetes Care, 30(5), pp. 1092-1096.
[11]
Assefa, F., 2013. Effect of Combined Aerobic and Resistance Exercises on Blood Glucose Level and Risk Factors in Male Type II Diabetic Patients Residing in Haramaya University Main Campus and its Surrounding (Doctoral dissertation, Haramaya University).
[12]
Murugesan N, Snehalatha C, Shobhana R, Roglic G, Ramachandran A. Awareness about diabetes and its complications in the general and diabetic population in a city in southern India. Diabetes Res Clipart 2007; 77: 433-7.
[13]
Mohan D, Raj D, Shanthirani CS, Datta M, Unwin NC, Kapur A, et al. Awareness and knowledge of diabetes in Chennai - the Chennai urban rural epidemiology study (CURES-9). J Assoc Physicians India 2005; 53: 283-7.
[14]
Centers for Disease Control and Prevention. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention.
[15]
Shelton N. Diabetes. In: Ali A, et al. Health survey for England 2006: Volume 1 Cardiovascular disease and risk factors in adults. United Kingdom: The Information Center, 2008.
[16]
Munir S, Tarin A. Global “epidemic” of diabetes. Nishtar Med J. 2010; 2(2):56-60.
[17]
Maassen A, Hart LM, van Essen E, Heine RJ, Nijpels G, Jahangir Tafrechi RS, Raap AK, Janssen GMC, Lemkes HHPJ. Mitochondrial Diabetes: Molecular Mechanisms and Clinical Presentation. Diabetes 2004; 53 (suppl 1): S103-S109.
[18]
Eckel N, Mühlenbruch K, Meidtner K, Boeing H, Stefan N, Schulze MB. Characterization of metabolically unhealthy normal-weight individuals: Risk factors and their associations with type 2 diabetes. Metabolism (2015).
[19]
Ueda, H., Matsumoto, N., Ishimura, E., Fukumoto, S., Shoji, T., Miki, T., et al. (2003) Factors Affecting Progression of Renal Failure in Patients with Type 2 Diabetes. Diabetes Care, 26, 1530-1534.
[20]
Pierce M, Keen H, and Bradley C. Risk of diabetes in offspring of parents with non-insulin-dependent diabetes. Diabetic Medicine, 1995, 12: 6–13.
[21]
Elbagir MN et al. A population-based study of the prevalence of diabetes and impaired glucose tolerance in adults in northern Sudan. Diabetes Care, 1996, 19: 1126–1128.
[22]
Peters WH. A Study on the Prevalence of Diabetes Mellitus in Northern Ethiopia (Gonder Survey). Dtsh Gesundheitswesen, 1992; 38:1283-1288.
[23]
Bayu T. Chronic Disease Prevalence in Ethiopian Bank Employees. EMJ 1982; 20: 49-53 28.
[24]
Cohen MP et al. High Prevalence of Diabetes in Young Ethiopian Immigrants to Israel. Diabetes 1988; 37:824-827.
[25]
Terry M. Therneau and Patricia M. Grambsch. Modeling Survival Data: Extending the Cox Model. Springer, 1st edition, 2000.
[26]
Thomas H. Scheike and Torben Martinussen. Dynamic Regression Models for Survival Data. Springer, 1st edition, 2006.
[27]
Collet, D. (2004), Modeling Survival Data in Medical Research, 2nd edition, Chapman and Hall.
[28]
Maassen A, Hart LM, van Essen E, Heine RJ, Nijpels G, Jahangir Tafrechi RS, Raap AK, Janssen GMC, Lemkes HHPJ. Mitochondrial Diabetes: Molecular Mechanisms and Clinical Presentation. Diabetes 2004; 53 (suppl 1): S103-S109.
[29]
G. Geoffrey Vining. Statistical Methods for Engineers. Brooks/Cole, 3th edition, 2011.
[30]
Andreas Wienke. Frailty Models in Survival Analysis. Chapman and Hall, 1st edition, 2010.
[31]
David W. Hosmer and Standley Lemeshow. Applied survival analysis - regression modeling of time to event. John Wiley and Sons, 1st edition, 1999.
ADDRESS
Science Publishing Group
548 FASHION AVENUE
NEW YORK, NY 10018
U.S.A.
Tel: (001)347-688-8931