Logistic Regression Analysis on Cardiovascular Diseases in Jos Metropolis

Main Article Content

J. O. Delle
B. A. Garba
A. D. Anyah
A. Shepan

Abstract

Cardiovascular diseases (CVDs) remain a leading cause of morbidity and mortality worldwide, with a rising burden in low- and middle-income countries such as Nigeria, yet localized evidence on CVD risk determinants in Jos Metropolis is limited. This study aimed to develop and validate a multivariate logistic regression model to identify and quantify significant predictors of CVD among adults in Jos Metropolis using routinely collected data. A descriptive cross-sectional analysis was conducted among 489 adults (≥18 years) using retrospective electronic health records (2015–2023) and patient survey data from Jos University Teaching Hospital and the Plateau State Ministry of Health. Candidate predictors included hypertension, diabetes, obesity, smoking, physical inactivity, age, gender, and occupation. Logistic regression with backward elimination was employed for model development, and model performance was evaluated using split-sample validation and goodness-of-fit assessments. The findings revealed hypertension as the strongest predictor, with hypertensive individuals having 4.3-fold higher odds of CVD (95% CI: 2.74–6.88, p < 0.001). Smoking, diabetes, and obesity increased CVD odds by 2.7-, 2.8-, and 1.8-fold, respectively, while age showed a modest but significant effect, with each additional year associated with a 2.3% increase in CVD risk (p = 0.002). Gender approached statistical significance, suggesting potential male vulnerability (OR = 1.47, p = 0.053). Overall, the model demonstrated moderate explanatory power (Nagelkerke R² = 0.21) and acceptable discrimination (AUC = 0.73). The study concludes that hypertension and other modifiable lifestyle-related factors are critical drivers of CVD risk in Jos Metropolis and supports the prioritization of community-based hypertension screening, smoking cessation initiatives, and lifestyle-focused health education as key public health strategies.

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Article Details

How to Cite
Delle, J. O., Garba, B. A., Anyah, A. D., & Shepan, A. (2026). Logistic Regression Analysis on Cardiovascular Diseases in Jos Metropolis. Mikailalsys Journal of Mathematics and Statistics, 4(1), 183-198. https://doi.org/10.58578/mjms.v4i1.8067

References

Adebayo, R. A., Olunuga, T. O., & Durodola, A. O. (2021). Medication adherence and cardiovascular outcomes in hypertensive Nigerians: A prospective cohort study. Journal of Clinical Hypertension, 23(5), 1021–1029.

Bolijn, R., Kunst, A. E., Appelman, Y., Galenkamp, H., Moll van Charante, E. P., Stronks, K., Tan, H. L., & van Valkengoed, I. G. M. (2022). Prospective analysis of gender-related characteristics in relation to cardiovascular disease. Heart, 108(13), 1030–1038. https://doi.org/10.1136/heartjnl-2021-320414

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed.). Wiley.

Cheung, M. M. H., Culliford, S., Butler, T., Semmers, J., & Marwick, T. H. (2021). Guidelines for the use of echocardiography in the assessment of congenital heart disease in children: An update from the American Society of Echocardiography. Journal of the American Society of Echocardiography, 34(8), 819–837. https://doi.org/10.1016/j.echo.2021.04.017

Conroy, R. M., Pyörälä, K., Fitzgerald, A. P., Sans, S., Menotti, A., De Backer, G., De Bacquer, D., Ducimetière, P., Jousilahti, P., Keil, U., Njølstad, I., Oganov, R. G., Thomsen, T., Tunstall-Pedoe, H., Tverdal, A., Wedel, H., Whincup, P., Wilhelmsen, L., Graham, I. M., & SCORE project group. (2003). Estimation of ten-year risk of fatal cardiovascular disease in Europe: The SCORE project. European Heart Journal, 24(11), 987–1003. https://doi.org/10.1016/S0195-668X(03)00114-3

Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. https://doi.org/10.1111/j.2517-6161.1972.tb00899.x

D’Agostino, R. B., Sr., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., & Kannel, W. B. (2008). General cardiovascular risk profile for use in primary care: The Framingham Heart Study. Circulation, 117(6), 743–753. https://doi.org/10.1161/CIRCULATIONAHA.107.699579

Dickson, V. V., Howe, A., Deal, J. A., & McCarthy, M. M. (2012). The relationship of work, self-care, and quality of life in a sample of older working adults with cardiovascular disease. Heart & Lung: The Journal of Cardiopulmonary and Acute Care, 41(1), 5–14. https://doi.org/10.1016/j.hrtlng.2011.09.012

Ding, L., Liang, Y., Tan, E. C. K., Hu, Y., Zhang, C., Liu, Y., Li, Z., & Wang, Y. (2020). Smoking, heavy drinking, physical inactivity, and obesity among middle-aged and older adults in China: Cross-sectional findings from the baseline survey of CHARLS 2011–2012. BMC Public Health, 20(1), 1062. https://doi.org/10.1186/s12889-020-08625-5

Gutierrez, C., & Blanchard, D. G. (2016). Diagnosis and treatment of atrial fibrillation. American Family Physician, 94(6), 442–452.

Hendriks, M. E., Bolarinwa, O. A., & Wit, F. W. (2022). Cardiovascular disease risk prediction in sub-Saharan Africa: A systematic review of prognostic models. BMJ Global Health, 7(3), e008102. https://doi.org/10.1136/bmjgh-2021-008102

Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). Wiley.

Kee, O. T., Harun, H., Mustafa, N., Murad, N. A. A., Chin, S. F., Jaafar, R., & Ismail, N. H. (2023). Cardiovascular complications in a diabetes prediction model using machine learning: A systematic review. Cardiovascular Diabetology, 22(1), Article 13. https://doi.org/10.1186/s12933-023-01741-7

Kleinbaum, D. G., & Klein, M. (2010). Logistic regression: A self-learning text (3rd ed.). Springer.

Maksimov, S. A., Orlov, P. S., & Ivanov, S. I. (2017). Age-specific features of ischemic heart disease risk factors in the working-age population in Russia. Kardiologiia, 57(1), 20–26. https://doi.org/10.18565/cardio.2017.1.20-26

Menard, S. (2002). Applied logistic regression analysis (2nd ed.). Sage Publications.

Mukamal, K. J. (2006). The effects of smoking and drinking on cardiovascular disease and risk factors. Alcohol Research & Health, 29(3), 199–202.

Nii, M., Maeda, K., Wakabayashi, H., Nishioka, S., & Tanaka, A. (2016). Nutritional improvement and energy intake are associated with functional recovery in patients after cerebrovascular disorders. Journal of Stroke and Cerebrovascular Diseases, 25(1), 57–62. https://doi.org/10.1016/j.jstrokecerebrovasdis.2015.08.037

Nwankwo, T. E., Salami, T. A., & Ogedengbe, J. O. (2019). Pattern of cardiovascular risk factors among a cohort of adults in a rural community in Plateau State, Nigeria. The Nigerian Health Journal, 19(2), 65–73.

Ojji, D. B., Ajayi, S. O., Mamven, M. H., & Alabi, P. (2019). Pattern of cardiovascular disease and associated risk factors in a Nigerian population with hypertension: A cross-sectional study. Cardiovascular Journal of Africa, 30(2), 78–83.

Opadijo, O. G., Omotoso, A. B., & Akande, A. A. (2014). Coronary artery disease and its risk factors in a Nigerian population: A logistic regression analysis. Nigerian Journal of Clinical Practice, 17(5), 658–662. https://doi.org/10.4103/1119-3077.141437

Rethemiotaki, I. (2023). Global prevalence of cardiovascular diseases by gender and age during 2010–2019. Archives of Medical Sciences Atherosclerotic Diseases, 8, e196–e205. https://doi.org/10.5114/amsad/176654

World Health Organization. (2011). Global atlas on cardiovascular disease prevention and control. https://www.who.int/cardiovascular_diseases/publications/atlas_cvd/en/

World Health Organization. (2023). Cardiovascular diseases (CVDs). https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)

Yang, L., Wu, H., Jin, X., Zheng, P., Hu, S., Xu, X., Yu, W., & Yan, J. (2020). Study of cardiovascular disease prediction model based on random forest in Eastern China. Scientific Reports, 10(1), 5245. https://doi.org/10.1038/s41598-020-62133-5

Yusuf, S., Hawken, S., Ôunpuu, S., Dans, T., Avezum, A., Lanas, F., McQueen, M., Budaj, A., Pais, P., Varigos, J., & Lisheng, L. (2004). Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): Case-control study. The Lancet, 364(9438), 937–952. https://doi.org/10.1016/S0140-6736(04)17018-9

Yusuf, S., Joseph, P., Rangarajan, S., Islam, S., Mente, A., Hystad, P., Brauer, M., Kutty, V. R., Gupta, R., Wielgosz, A., AlHabib, K. F., Dans, A., Lopez-Jaramillo, P., Avezum, A., Lanas, F., Oguz, A., Kruger, I. M., Diaz, R., Yusoff, K., … Dagenais, G. (2020). Modifiable risk factors, cardiovascular disease, and mortality in 155,722 individuals from 21 high-income, middle-income, and low-income countries (PURE): A prospective cohort study. The Lancet, 395(10226), 795–808. https://doi.org/10.1016/S0140-6736(19)32008-2


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