Risk Factor Identification Using Survival
Analysis

Akindutire O. R; Ogunlade T. O

doi:https://doi.org/10.14445/22315373/IJMTT-V66I3P516

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 66 | Issue 3 | Year 2020 | Article Id. IJMTT-V66I3P516 | DOI : https://doi.org/10.14445/22315373/IJMTT-V66I3P516

Risk Factor Identification Using Survival Analysis

Akindutire O. R, Ogunlade T. O

Citation :

Akindutire O. R, Ogunlade T. O, "Risk Factor Identification Using Survival Analysis," International Journal of Mathematics Trends and Technology (IJMTT), vol. 66, no. 3, pp. 104-109, 2020. Crossref, https://doi.org/10.14445/22315373/IJMTT-V66I3P516

Abstract

In the 1960s, Diabetes was considered insignificant in Nigeria. However, its increase prevalence has been contributed to by major socio-economic changes, with 12% cases recorded in some areas. The study assesses the contribution of some risk factors to the survival diabetes patients. Cox proportional hazards regression model, Long-rank and Kaplan- Meier methods were employed in analyzing data collected from a tertiary hospital. The Kaplan Meier curves indicate consistently better survival prognosis in Type 1 diabetes patients than type 2 and gestational diabetes. The long-rank test compared survival function of various levels of the covariates. It was observed that survival experience of different gender is not significantly different while that of various years of admission and diabetes categories did at 0.05 level of significance. Hazard ratio also revealed short survival time in patients diagnosed with type 1 and 2 diabetes relative to baseline hazard (gestational diabetes). However, diagnosis does not contribute significantly to the model. Based on the data, it has been established to a usable extent that better survival prognosis in type 1 diabetes patients than those with type 2 or gestational diabetes, Shorter survival time in female patients than male and no gender difference in survival of diabetes. The only significant factor amongst gender, year of admission and diagnosis was year of admission and earlier admission may be free of event.

Keywords

Survival analysis, diabetes, hazard function, regression coefficients.

References

[1] World Health Organization. Diabetes. 2019 [cited 2020 02/01/2020]; Health-topics Diabetes]. Available from: https://www.who.int/health-topics/diabetes.
[2] Sarwar, N., Gao, P., Seshasai, S. R., Gobin, R., Kaptoge, S., Di Angelantonio, E., ... & Stehouwer, C. D. (2010). Emerging Risk Factors Collaboration Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet, 375(9733), 2215-2222.
[3] International Diabetes Federation, IDF Diabetes Atlas Ninth edition 2019, in IDF Diabetes Atlas. 2019, International Diabetes Federation: Brussels, Belgium. p. 176.
[4] World Health Organization. Diabetes Fact Sheet. 2018 30/10/2018 [cited 2020; Available from: https://www.who.int/news-room/fact-sheets/detail/diabetes.
[5] Jeon, C.Y. and M.B. Murray, Diabetes mellitus increases the risk of active tuberculosis: a systematic review of 13 observational studies. PLoS medicine, 2008. 5(7): p. e152.
[6] Emerging Risk Factors Collaboration. (2011). Diabetes mellitus, fasting glucose, and risk of cause-specific death. New England Journal of Medicine, 364(9), 829-841.
[7] Bourne RR, Stevens GA, White RA, Smith JL, Flaxman SR, Price H, Jonas JB, Keeffe J, Leasher J, Naidoo K, Pesudovs K, Resnikoff S, Taylor HR; Vision Loss Expert Group., Causes of vision loss worldwide, 1990–2010: a systematic analysis. The lancet global health, 2013. 1(6): p. e339-e349.
[8] Hall V, Thomsen R.W, Henriksen O & Lohse N ., Diabetes in Sub Saharan Africa 1999-2011: epidemiology and public health implications. A systematic review. BMC public health, 2011. 11(1): p. 564.
[9] Pastakia, S. D., Pekny, C. R., Manyara, S. M., & Fischer, L. (2017). Diabetes in sub-Saharan Africa–from policy to practice to progress: targeting the existing gaps for future care for diabetes. Diabetes, metabolic syndrome and obesity: targets and therapy, 10, 247
[10] Mbanya JC, Motala AA, Sobngwi E, Assah FK, Enoru ST.., Diabetes in sub-saharan africa. The lancet, 2010. 375(9733): p. 2254-2266.
[11] Shaw, J.E., R.A. Sicree, and P.Z. Zimmet, Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes research and clinical practice, 2010. 87(1): p. 4-14.
[12] Akinkugbe, O. and O. Akinyanju, Non-communicable diseases in Nigeria: final report of a national survey. Lagos: Federal Ministry of Health—National Expert Committee on Non-Communicable Diseases, 1997: p. 1-12.
[13] Uloko AE, Musa BM, Ramalan MA, Gezawa ID, Puepet FH, Uloko AT, Borodo MM, Sada KB.., Prevalence and risk factors for diabetes mellitus in Nigeria: a systematic review and meta-analysis. Diabetes Therapy, 2018. 9(3): p. 1307-1316.
[14] Dahiru, T., A.A. Aliyu, and A. Shehu, A review of population-based studies on diabetes mellitus in Nigeria. Sub-Saharan African Journal of Medicine, 2016. 3(2): p. 59.
[15] Gezawa ID, Puepet FH, Mub BM , Uloko AE, Bakki B, Talle MA, Haliru I ., Socio-demographic and anthropometric risk factors for type 2 diabetes in Maiduguri, North-Eastern Nigeria. Sahel Medical Journal, 2015. 18(5): p. 1.
[16] Nyenwe EA, Odia OJ, Ihekwaba AE, Ojule A, Babatunde S. Type 2 diabetes in adult Nigerians: a study of its prevalence and risk factors in Port Harcourt, Nigeria. Diabetes research and clinical practice, 2003. 62(3): p. 177-185.
[17] Olatunbosun S. T, Ojo P. O, Fineberg N. S. and Bella A. F, Prevalence of diabetes mellitus and impaired glucose tolerance in a group of urban adults in Nigeria. Journal of the National Medical Association, 1998. 90(5): p. 293.