Modified Bayesian survival analysis of Diabetes Mellitus in selected hospital facilities in Nasarawa, Nigeria

Main Article Content

Peter Enesi Omaku Ganaka Kubi Musa Titus Onyi

Abstract

Diabetes mellitus is a global chronic health problem affecting over 400 million people. The study focused on the commonest type of Diabetes-Type II diabetes. The disease is associated with morbidity and mortality. Bayesian survival model may be utilized to assess the risk factors associated with Diabetes. The study utilized secondary data from 532 diabetic patients from two General Hospital facilities in Nasarawa State, Nigeria. The aim of the paper was to apply a Bayesian survival model on diabetic dataset to assess some risk factors pertaining to the disease. This Bayesian model was modified to Diabetic Additive Models (DAMS) and further extended to the Diabetic Additive Constant Hazard Model (DACHM), the coded version C. DACHM (when all metrical covariates were coded) and Diabetic Additive Accelerated Failure Time Model (DAAFTM). The results show that C.DACHM outperforms the other model with least values of Watanabe Akaike Information Criterion (WAIC), Deviance Information Criterion (DIC), and a large predictive power measured by the Log Pseudo Maximum Likelihood (LPML). The C.DACH model suggests that; good management of type II diabetes patients aged 40 years and above in both hospitals reduced the risk of death. Considerably, low Body Mass Index (BMI) increased the risk of death of patients with the disease. Body Mass Index, BMI greater than 24.9 (overweight) are 5.41E-17 times at risk of death from diabetes than those of normal weight. High Systolic Blood Pressure, SBP, greater than 140 (high) increases the risk of dying from the diseases by 1.51 times than those of normal SBP. High Diastolic Blood Pressure, DBP, greater than or equal to 90 (high) increases the risk of dying from the diseases by 7.81 times than those of normal DBP. Male patients were 1.28 times at risk of death from diabetes than their female patients. Patients of General Hospital Keffi experience are 1.02 times at risk of death than those of the General Hospital Nasarawa. The research recommends patients’ drug compliance especially for patients above 40 years, maintenance of a healthy body mass index and maintenance of a healthy blood pressure.

Keywords: Diabetes, Bayesian Survival Model, Proportional Hazard, Accelerated Failure Time Model, Additive Model

Article Details

How to Cite
OMAKU, Peter Enesi; MUSA, Ganaka Kubi; ONYI, Titus. Modified Bayesian survival analysis of Diabetes Mellitus in selected hospital facilities in Nasarawa, Nigeria. Medical Research Archives, [S.l.], v. 11, n. 4, apr. 2023. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/3796>. Date accessed: 29 may 2023. doi: https://doi.org/10.18103/mra.v11i4.3796.
Section
Research Articles

References

1. WHO;. Definition, diagnosis and classification of diabetes mellitus and its complications, part 1. Geneva, 1999: https://www.who.int/health-topics/diabetes#tab=tab_1
2. Lucas A.O, Gilles, H.M. Short Textbook of Public Health Medicine for the Tropics, Revised Fourth Edition; (2003). 363-374.
3. Centre for Disease Control (CDC) Diabetes. Accessed online on 19th March 2023 through Cdc https://www.cdc.gov/diabetes/basics/diabetes.html
4. Hajhosseini, B.; Gurtner, G.C.; Sen, C.K. Abstract 48. Plast. Reconstr. Surg. Glob. Open 2019, 7, 34–35. [CrossRef]
5. Chang, M.; Nguyen, T.T. Strategy for Treatment of Infected Diabetic Foot Ulcers. Accounts Chem. Res. 2021 , 54, 1080–1093.
6. Uloko AE, Ofoegbu EN, Chinenye S, Fasanmade O.A, Fasanmade A.A, Ogbera AO, et al. Profile of Nigerians with diabetes mellitus—Diabcare Nigeria study group (2008): results of a multicenter study. Indian J Endocrinol Metab. 2012;16(4):558–564. (ERRATUM IN: Indian J Endocrinol Metab. 2012;16(6):981). [PMC free article] [PubMed]View at: Publisher Site | Google Scholar
7. International Diabetes Federation. Diabetes atlas. 8th ed. Brussels: International Diabetes Federation; 2017.
8. Nyanzi, R, Wamala, R. and Atuhaire, L. K, “Diabetes and quality of life: a Ugandan perspective,” Journal of Diabetes Research, vol. 2014, Article ID 402012, 9 pages, 2014.
9. Derdachew A.T, Fikre E., Cheru .A. 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 Ee-JRIF (2015) Vol. 7 no. 1 : pp (20-39)
10. Simeftiany I.L, Sugivarto S. & Endang .D. A Survival Analysis with Cox Regression Interaction Model of Type II Diabetes Mellitus in Indonesian June 2021 Journal Profesi Medika Jurnal Kedokteran dan Kesehatan 15(1) DOI:10.33533/jpm.v15i1.2942
11. Assaye B, Bizuwork D.A, Solomon, A.A .Survival Analysis on Time-To-Recovery of Diabetic Patients at Minlik Referral Hospital, Ethiopia: Retrospective Cohort Study Date: November 19th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-1015864/v1
12. Adedayo.F., A., Oluwaseun A.O, Hilary I.O, Opeyemi P.O. Analysis of Reported Cases of Diabetes Disease in Nigeria: A Survival Analysis Approach. 2021 Vol 17. No.2 https://doi.org/10.18280/ijsdp.170229
13. Gurprit A. Alka S. and Juhi M. A Bayesian Approach for Estimating Onset Time of Nephropathy for type 2 Diabetic Patients Under various Health Condition international Journal of Statistics and Probability; Vol. 2, No. 2; 2013 ISSN 1927-7032 E-ISSN 1927-7040 Published by Canadian Center of Science and Education
14. Kubi M.G, Lasisi K.E, Rasheed B.A. Parametric and Semi-Parametric Survival Models with Application to Diabetes Data. Sci J Biomed Eng Biomed Sci. 2022 Nov 30;3(1): 001-010.
15. Tigabu H.K , Bayesian survival analysis of diabetes mellitus patients: a case study intikur anbessa specialized hospital, addis ababa, Ethiopia 2018 vol. 11 issue 2 https://journals.riverpublishers.com/index.php/JRSS/index ISSN: 2229-5666 (Online Version)
16. Chen, Y., Hanson, T.,& Zhang, J. “Accelerated Hazards Model Based on Parametric Families Generalized With Bernstein Polynomials, 2014.” Biometrics, 70(1), 192–201.
17. Omaku, P.E and Oyejola B.A "A piece-wise additive model of survival data with linear rut". Australian Journal of Science & Technology. ISSN Number (2208-6404) Volume 4; Issue 4; December 2020.