BAYESIAN SURVIVAL ANALYSIS OF DIABETES MELLITUS IN LAGOS STATE NIGERIA
Keywords:
Diabetes Mellitus, Bayesian method, Survival Analysis, Chronic disease, Cox Proportional Model
Abstract
Diabetes Mellitus is an escalating public health issue in Nigeria, significantly affecting the population. This study analyzes the survival patterns of diabetes patients in Lagos State, Nigeria, using Bayesian survival analysis techniques, including the Cox Proportional Hazard model and Kaplan-Meier estimator, to identify key factors influencing survival rates and predict future outcomes. Drawing from a comprehensive, multi-year dataset that includes critical predictors and confounders, the study reveals a significant decline in survival probability over time. Posterior predictive checks confirm the models' adequacy, showing strong alignment between observed data and simulations. The Cox Proportional Hazard model identifies age, gender, and insulin use as key contributors to the hazard rate, while other factors are found to have limited impact. These findings underscore the importance of early intervention strategies targeting high-risk factors, such as age and insulin dependency, to improve outcomes and reduce the diabetes burden in Lagos State, Nigeria.
References
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[28] World Health Organisation Mortality and cardiovascular disease in type 1 and type 2 diabetes. Retrieved from https://www.who.int/health-topics/diabetes (2021)
[2] American Diabetes Association. Standards of medical care in diabetes Diabetes Care, 46(Suppl.1) (2023), S1-S212.
[3] B. Assaye, D. A Bizuwork, A. A. Solomon. Survival Analysis on Time-To-Recovery of Diabetic Patients at Minlik Referral Hospital, Ethiopia:, Retrospective Cohort Study.(2021).
[4] D. Collett. Modelling Survival Data in Medical Research. CRC Press,(2015).
[5] T. Dahiru, A. Aliyu, and AU. Shehu, A review of population-based studies on diabetes mellitus in Nigeria, Sub-Saharan African Journal of Medicine,3.2 (2016) 59-64
[6] A. T. Derdachew, E. Fikre and A. Cheru. Survival analysis of diabetes mellitus patients using parametric, non-parametric and semi-parametric approaches: Addis Ababa, Ethiopia. Ethiopian E-Journal For Research And Innovation Foresight.(2015) Vol. 7.no. 1: pp (20-39)
[7] A. Gurprit, S. Alka and M. Juhi A Bayesian Approach for Estimating Onset Time of Nephropathy for type 2 Diabetic Patients Under various Health Condition. International Journal of Statistics and Probability;(2013) Vol. 2, No. 2.
[8] F. B. Hu, J. E. Manson, M. J. Stampfer, G. Colditz, S. Liu, C. G. Solomon, & W. C. Willett Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. New England Journal of Medicine. (2001) 345(11), 790-797.
[9] J. G. Ibrahim, M. H. Chen, M. & D. Sinha, Bayesian survival analysis. Springer Series in Statistics. (2001)
[10] International Diabetes Federation. IDF Diabetes Atlas, 10th edition. Retrieved from [IDF Diabetes Atlas](https:// www.diabetesatlas.org);(2021)
[11] E. L. Kaplan, & P. Meier, Nonparametric estimation from incomplete observations. Journal of the American Statistical Association,(1958) 53(282), 457-481.
[12] J. P. Klein & M. L. Moeschberger Survival analysis: techniques for censored and truncated data Springer. New York;(2003)(vol. 1230)
[13] M. G. Kubi, K. E. Lasisi, & B. A. Rasheed Parametric and Semi-Parametric Survival Models with Application to Diabetes Data Sc]. J Biomed Eng Biomed Sci. (2022) 3(1):001-010.
[14] N. Lee, S. J. Park, D. Kang, J. Y. Jeon, H. J. Kim, D. J. Kim, & S. J. Han Characteristics and Clinical Course of Diabetes of the Exocrine Pancreas: A Nationwide Population-Based Cohort Studybrief Title: Diabetes of the Exocrine Pancreas. Available at SSRN (2021) 3895637.
[15] V. S. Malik, B. M. Popkin, G. A. Bray, J. P. Desprs, & F.B. Hu Sugar-sweetened beverages, obesity, type 2 diabetes mellitus, and cardiovascular disease risk. Circulation,(2010) 121(11), 1356-1364.
[16] A. Mahajan, D. Taliun, M. Thurner, N. R. Robertson, N. J. M. Torres, N. W. Rayner,... & M. I. McCarthy Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nature genetics, (2018) 50(11), 1505-1513.
[17] B. M. Mayosi, A. J. Flisher, U. G. Lalloo, F. Sitas, S. M. Tollman, & D. Bradshaw The burden of non-communicable diseases in South Africa. The lancet, (2009) 374(9693), 934-947.
[18] J. c. Morris, A. R. Shuldiner,M. Roden, I. Barroso, T. Wilsgaard, J. Beilby, and others it Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes, Nature genetics 44.9 (2012) 981-990,
[19] D. Mozaflarinn Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: a comprehensive review. Circulation, (2016) 133(2), 187-225.
[20] R. Nyanzi, R. Wamala,& L. K. Atuhaire Diabetes and quality of life: A Ugandan perspective. (2014)
[21] A. O. Ogbera, J. P. Klein,& M. L. Moeschberger Survival analysis: Techniques for censored and truncated data. Springer Science & Business Media.(2003)
[22] A. Rawshani, A. Rawshani, S. Franzn, B. Eliasson, A. M. Svensson, M. Miftaraj, ... & S. Gudbjrnsdottir Mortality and cardiovascular disease in type 1 and type 2 diabetes. New England journal of medicine, (2017) 376(15), 1407-1418.
[23] A. R. Shuldiner, M. Roden, ... & M. I. McCarthy Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes Nature genetics, (2012) 44(9), 981-990.
[24] L. L. Simeftiany, S. Sugivarto, & D. A. Endang Survival Analysis with Cox Regression Interaction Model of Type II Diabetes Mellitus In Indonesian. Journal Profesi Medika Jurnal Kedokteran dan Kesehatan (2021) 15(1) DOI:10.33533/jpm.v1511.2942
[25] A. J. Sutton, & K. R. Abrams Bayesian methods in meta-analysis and evidence synthesis Statistical methods in medical research, (2001) 10(4), 277-303.
[26] H. K. Tigabu Bayesian survival analysis of Diabetes mellitus patients: a case study intikuranbessa specialized hospital, Addis Ababa, Ethiopia (2018) vol.11 issue
[27] A. E. Uloko, B. M. Musa, M. A. Ramalan, I. D. Gezawa, F. H. Puepet, A. T. Uloko, ... & K. B. Sada Diabetes Therapy,(2018) 9, 1307-1316.
[28] World Health Organisation Mortality and cardiovascular disease in type 1 and type 2 diabetes. Retrieved from https://www.who.int/health-topics/diabetes (2021)
Published
2024-12-11
How to Cite
OGUNDEJI, R. K., Akinyemi, J. A., & Salako, O. R. (2024). BAYESIAN SURVIVAL ANALYSIS OF DIABETES MELLITUS IN LAGOS STATE NIGERIA. Unilag Journal of Mathematics and Applications, 4(1), 97-110. Retrieved from http://lagjma.unilag.edu.ng/article/view/2291
Section
Articles
Copyright (c) 2024 Rotimi K. OGUNDEJI, Joseph A. Akinyemi, Oluwaseyi R. Salako
This work is licensed under a Creative Commons Attribution 4.0 International License.
