Binary Logistic Regression Modeling for Characterisation of Hypertension and Some Non-Hereditary Risk Factors
DOI:
https://doi.org/10.56919/usci.2541.038Keywords:
Hypertension, diabetes, logistic, Wald test, OddsAbstract
Study’s Excerpt:
• The study explores key health issues causing serious illness and high death rates.
• It aimed to find controllable risk factors linked to hypertension.
• A binary logistic regression model was fitted on factors like age, gender, and smoking.
• Age, gender, alcohol, height, BMI, and rest time were found to be positive predictive factors hypertension.
• The study helps improve awareness, care, and treatment of hypertension.
Full Abstract:
High blood pressure is a serious concern for public health, which is a crucial area of study as it is a key transmitting agent for coronary artery diseases and other complications. The population of hypertensive individuals is on the increase due to a number of factors, and the rate of prevalence of this morbidity and its terminal effect on humanity is alarming. This study aimed to identify some preventable and controllable risk factors of hypertension. Secondary data (n = 310) were collected from the departments of endocrinology and cardiology of the Teaching Hospital at the University of Ilorin (UITH), Kwara State. A binary logistic regression model was fitted on factors like age, gender, and smoking. Results showed that age, gender, alcohol consumption, height, BMI, and hours of daily rest are positive predictive factors for hypertension, where gender, working status, and BMI are statistically significant response variables (p-value < 0.05). Also, the odds of developing hypertension with respect to gender, working status, and BMI are 4.25, 0.55, and 7.09, respectively, when other predictor variables are held constant. The receiver operating characteristics, ROC, which measures the sensitivity and specificity of the model (AUC = 0.7141), indicated the probability that the model is more likely to assign a higher probability to a positive case (hypertension) compared to a negative case. In conclusion, the developed model, being one of the recent studies to examine the predictive power of a model, can be adopted for better precisions of the explanatory risk factor variables for hypertension and ultimately help to reduce its prevalence.
References
Bewick, V., Cheek, L., & Ball, J. (2005). Statistics review 14: Logistic regression. Critical Care, 9(1). https://doi.org/10.1186/cc3045
Boutayeb, A., & Boutayeb, S. (2005). The burden of non-communicable diseases in developing countries. International Journal for Equity in Health, 4(1), 2. https://doi.org/10.1186/1475-9276-4-2
Campbell, N. R., Lackland, D. T., & Niebylski, M. L. (2014). High blood pressure: Why prevention and control are urgent and important: A 2014 fact sheet from the World Hypertension League and the International Society of Hypertension. Journal of Clinical Hypertension, 16(8), 551–553. https://doi.org/10.1111/jch.12372
Chobanian, A. V., Bakris, G. L., & Black, H. R. (2003). The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: The JNC 7 report. JAMA, 289(19), 2560–2572. https://doi.org/10.1001/jama.289.19.2560
Contractor, A., Sarkar, B. K., Arora, M., et al. (2014). Addressing cardiovascular disease burden in low and middle income countries (LMICs). Current Cardiovascular Risk Reports, 8, Article 405. https://doi.org/10.1007/s12170-014-0405-6
Ezzati, M., Lopez, A. D., & Rodgers, A. (2002). Selected major risk factors and global and regional burden of disease. The Lancet, 360(9343), 1347–1360. https://doi.org/10.1016/S0140-6736(02)11403-6
Forouzanfar, M. H., Liu, P., Roth, G. A., et al. (2017). Global burden of hypertension and systolic blood pressure of at least 110 to 115 mm Hg, 1990–2015. JAMA, 317(2), 165–182. https://doi.org/10.1001/jama.2016.19043
Galav, A., Bhatanajar, R., Megharal, S. C., & Jain, M. (2015). Prevalence of hypertension among rural and urban population in southern Rajasthan. National Journal of Community Medicine, 6(2), 174–178. https://njcmindia.com/index.php/file/article/view/1159
Grandner, M., Mullington, J. M., Hashmi, S. D., Redeker, N. S., Watson, N. F., & Morgenthaler, T. I. (2018). Sleep duration and hypertension: Analysis of > 700,000 adults by age and sex. Journal of Clinical Sleep Medicine, 14(6). https://doi.org/10.5664/jcsm.7176
Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). John Wiley & Sons. https://doi.org/10.1002/0471722146
Jareebi, M. A. (2024). The association between smoking behavior and the risk of hypertension: Review of the observational and genetic evidence. Journal of Multidisciplinary Healthcare, 17, 3265–3281. https://doi.org/10.2147/JMDH.S470589
Manandhar, N., & Raman, T. P. (2016). Risk factors of hypertension: Logistic regression analysis. SCIREA Journal of Health, 1(1). http://www.scirea.org/journal/PMH
Mills, K. T., Stefanescu, A., & He, J. (2020). The global epidemiology of hypertension. Nature Reviews Nephrology, 16(4), 223–237. https://doi.org/10.1038/s41581-019-0244-2
Nwoga, H. O. (2023). Assessment of risk factors for hypertension amongst the staff of a tertiary institution in Nigeria. European Journal of Medical and Health Sciences, 5(5). https://doi.org/10.24018/ejmed.2023.5.5.1856
Omoleke, S. A. (2013). Chronic non-communicable disease as a new epidemic in Africa: Focus on The Gambia. Pan African Medical Journal, 14, 87. https://doi.org/10.11604/pamj.2013.14.87.1899
Salciccioli, J. D., Crutain, Y., Komorowski, M., & Marshall, D. C. (2016). Sensitivity analysis and model validation. In Secondary analysis of electronic health records (pp. 289–306). Springer. https://doi.org/10.1007/978-3-319-43742-2_17
Shikha, S., Ravi, S., & Gyan, P. S. (2017). Prevalence and associated risk factors of hypertension: A cross-sectional study in urban Varanasi. International Journal of Hypertension. https://doi.org/10.1155/2017/5491838
Song, L., Li, J., Yu, S., Cai, Y., He, H., Lun, J., Zheng, L., & Ye, J. (2023). Body Mass Index is associated with blood pressure and vital capacity in medical students. Lipids in Health and Disease. https://doi.org/10.1186/s12944-023-01920-1
Syer, R., Teoh, X. Y., Aiman, W., Aiful, & Har, C. S. (2010). The prevalence of hypertension and its associated risk factors in two rural communities in Penang, Malaysia. JSME Publication.
World Health Organization. (2002). Global report on diabetes and hypertension.
Yeo, W.-J., Abraham, R., L., A., Surapaneni, S., Schlosser, P., Ballew, S. H., Ozkan, B., Flaherty, C. M., et al. (2024). Sex differences in hypertension and its management throughout life. Hypertension, 81(11). https://doi.org/10.1161/hypertensionAHA.124.22980
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Abiodun D Obaromi, Oga Ode, Mutah Wadai, Joy Omoha
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
