Identifying Foremost Factors Relevant to Blood Pressure Level Using Logistic Regression Analysis: A Case Study (Desert Dwellers Data)
Article Sidebar
-
Logistic Regression Model, Blood Pressure , Hypertension , Hosmer and Lemeshow Test, Receiver Operating Characteristic Curve
Abstract
The current research investigates the use of logistic regression as a statistical technique for modelling real blood pressure (BP) data. This study uses a dataset collected from a desert community in southwestern Libya. Six factors that are widely believed to play an important role in the process of BP were considered. Statistical analyses of the available dataset revealed that the main cause of hypertension in such community is age. The proposed multiple logistic regression analysis also revealed that two factors, age and systolic BP, showed greater significance among the six examined variables. These two variables were identified as having a significant effect on blood pressure performance. Based on a determined criterion, each page as the main cause of hypertension in such community participants was classified as hypertensive or not, significant variables were selected based on the p-value associated with the model significance level, and these factors were selected based on the criteria to achieve the model significance level (p < 0.05). The statistical analysis was carried out using R language.
Full text article
References
Abed, Y. and Abu-Haddaf, S. (2013). Risk Factors of Hypertension at UNRWA Primary Health Care Centres in Gaza Governorates, International Scholarly Research Notices Epidemiology, Volume 2013. DOI: https://doi.org/10.5402/2013/720760
Ahmad, W. M. A. W., Aleng, N. A. and Ali, Z. (2010). Binary logistic regression analysis technique used in analyzing the categorical data in education science: a case study of Terengganu state, Malaysia. World Appl. Sci. J. 9(9): 1062- 1066.
Ahmad, W.M.A.W., Nawi M.A. B. A. Aleng N. A., Halim N.A., Mamat M, Hamzah M.P. and Ali Z. (2014). Association of hypertension with risk factors using logistic regression. Applied Mathematical Sciences, 8:2563 – 2572 DOI: https://doi.org/10.12988/ams.2014.42130
Brundtland, G. H. (2002). From the World Health Organization. Reducing risks to health, promoting healthy life. Jama. 288:1974. DOI: https://doi.org/10.1001/jama.288.16.1974
Dua, S., Bhuker, M., Sharma, P., Dhall, M., & Kapoor, S. (2014). Body mass index relates to blood pressure among adults. North American Journal of Medical Sciences, 6(2), 89–95. DOI: https://doi.org/10.4103/1947-2714.127751
Ezzati, M. Lopez, A .D. Rodgers A., Vander Hoorn, S., and Murray, C. J. (2002); Comparative Risk Assessment Collaborating Group. Selected Major Risk Factors and Global and Regional Burden of Disease. 360: 1347-60. DOI: https://doi.org/10.1016/S0140-6736(02)11403-6
Haberman, S. (1978). Analysis of qualitative data (Vol. 1). New York: Academic Press.
Hosmer D. W. and Lemeshow S., (2000). Applied logistic regression, (2nd ed.). New York, NY: A Wiley-Interscience Publication. DOI: https://doi.org/10.1002/0471722146
Institute for Health Metrics and Evaluation (2013). The Global Burden of Disease: Generating Evidence, Guiding Policy. Seattle, WA, IHME,
Jo I, Ahn Y, Lee J, Shin KR, Lee HK, Shin C. Prevalence, awareness, treatment, control and risk factors of hypertension in Korea: the Ansan study. J Hypertens. 2001 Sep;19(9):1523-32. doi: 10.1097/00004872-200109000-00002. PMID: 11564970. DOI: https://doi.org/10.1097/00004872-200109000-00002
Kotsis, V., Stabouli, S., Bouldin, M., Low, A., Toumanidis, S., Zakopoulos, N. (2005). Impact of Obesity on 24-Hour Ambulatory Blood Pressure and Hypertension. Hypertension. 45:602-607. DOI: https://doi.org/10.1161/01.HYP.0000158261.86674.8e
Lawes, C. M., Rodgers, A., Bennett DA, Parag, V., Suh, I., and Ueshima, H. (2004). Blood Pressure and Cardiovascular Disease in the Asia Pacific Region. Journal of Hypertension. 21(4):707-16. DOI: https://doi.org/10.1097/00004872-200304000-00013
Mendis, S., Puska, P. and Norrving, B. (2011). Global Atlas on Cardiovascular Disease Prevention and Control. Geneva, Switzerland: World Health Organization; 2011.
http://www.who.int/cardiovascular_diseases/publications/atlas_cvd/en/. 2011.
Muhammad Akbar Ali Shah, Shakila Khanum, and Huma Qasim (2014). Statistical Modelling for Risk Factors of High Blood Pressure in Women. American Journal of Health Research. 2(5): 298-302. doi: 10.11648/j.ajhr.20140205.22. DOI: https://doi.org/10.11648/j.ajhr.20140205.22
Nura N. Altajori1 and Yousef M. Elshrek (2017). Review Article: Risk Factors for Non-communicable Diseases in Libya. The Egyptian Journal of Hospital Medicine. 1(66): 202-214. DOI: https://doi.org/10.12816/0034654
O'Connell, A. (2005). Logistic regression models for ordinal response variables. Thousand Oaks, CA: Sage Publications. Quantitative Applications in the Social Sciences (No.146).
Oliveira, G. F., Oliveira, T. R., Ikejiri, A. T., Andraus, M. P., Galvao, T. F., Silva, M. T., & Pereira, M. G. (2014). Prevalence of hypertension and associated factors in an indigenous community of central Brazil: a population-based study. PlOS one, 9(1):1-6. DOI: https://doi.org/10.1371/journal.pone.0086278
Park, H. and Kim, K. (2013). Associations Between Oral Contraceptive Use and Risks of Hypertension and Prehypertension in A Cross-Sectional Study of Korean Women, BMC Women’s Health, Vol. 13, No. 39. DOI: https://doi.org/10.1186/1472-6874-13-39
Patricia K, Megan W, Kristi R, Paul K, Whelton P, Jiang H. (2004). Worldwide Prevalence of Hypertension: A Systematic Review. Journal of Hypertension. 22(1):11-19. DOI: https://doi.org/10.1097/00004872-200401000-00003
Primatesta, P., and Poulter, N. R. (2006). Improvement in Hypertension Management in England: Results from the Health Survey for England 2003. Journal of Hypertension. 24(6):1187-92. DOI: https://doi.org/10.1097/01.hjh.0000226210.95936.bc
Qasim, H. (2014). Socioeconomic Causes of Diseases: A Case Study of Southern Punjab, M. Phil Thesis, Department of Statistics, The Islamia University of Pakistan.
Rankinen T, Church TS, Rice T, Bouchard C, and Blair SN. (2007). Cardio respiratory Fitness, BMI, and Risk of Hypertension: The HYPGENE Study. Medicine & Science in Sports and Exercise. 39(10):1687-92. DOI: https://doi.org/10.1249/mss.0b013e31812e527f
Rashid K., K. A. Narayan, Azizah Hj Ab Manan. (2008). The Prevalence of Hypertension among the Elderly in Fourteen Villages in Kedah, Malaysia. Malaysian Journal of Medicine and Health Sciences. 4(2):33-39.
Schlesselman, J. J., and Stolley, P. D. (1982). Case control studies: Design, conduct, analysis, (6th ed.). New York: Oxford University Press.
Shah, M., Ansari, A. S., & Qasim, H. (2014). Statistical Analysis of Socio-Economic Causes of Cardiovascular Disease. International Journal of Research, 1(5), 223-230.
Singh, R. B., Suh, I. L., Singh, V. P., Chaithiraphan, S., Laothavorn, P., and Sy, R. G.(2010). Hypertension and Stroke in Asia: Prevalence, Control and Strategies in Developing Countries for Prevention. Journal of Human Hypertension. 14(10/11): 749-763. DOI: https://doi.org/10.1038/sj.jhh.1001057
Tanji J. L., Champlin J. J., Wong, G. Y., Lew, E. Y., Brown, T. C., Amsterdam EA. (1989). Blood pressure recovery curves after submaximal exercise: a predictor of hypertension at ten-year follow-up. Am. J. Hypertension. 2: 135–138. DOI: https://doi.org/10.1093/ajh/2.3.135
Thomas J W, and Ramachandran, S. V., (2005). Epidemiology of Uncontrolled Hypertension in the United States. Circulation 112: 1651- 1662. DOI: https://doi.org/10.1161/CIRCULATIONAHA.104.490599
Truett J, Cornfield J, Kannel W. (1967). A multivariate analysis of the risk of coronary heart disease in Framingham. J Chronic Dis. 20(7):511-24. doi: 10.1016/0021-9681(67)90082-3. PMID: 6028270. DOI: https://doi.org/10.1016/0021-9681(67)90082-3
Vasan RS, Beiser A, Seshadri S, Larson MG, Kannel WB, D'Agostino RB, Levy D. (2002). Residual lifetime risk for developing hypertension in middle-aged women and men: The Framingham Heart Study. JAMA. 287(8):1003-10. doi: 10.1001/jama.287.8.1003. PMID: 11866648. DOI: https://doi.org/10.1001/jama.287.8.1003
World Health Organization (2010). Global Status Report on Noncommunicable Diseases 2010. Geneva, Switzerland.
http://www.who.int/cardiovascular_diseases/publications/pen2010/en/.
World Health Organization, EMRO (2015): High blood pressure: a public health problem. Regional office for the Eastern Mediterranean. http://www.emro.who.int/media/world-healthday/public-health-problem-factsheet-2013.html.
Authors
Copyright (c) 2023 Journal of Pure & Applied Sciences
This work is licensed under a Creative Commons Attribution 4.0 International License.
In a brief statement, the rights relate to the publication and distribution of research published in the journal of the University of Sebha where authors who have published their articles in the journal of the university of Sebha should how they can use or distribute their articles. They reserve all their rights to the published works, such as (but not limited to) the following rights:
- Copyright and other property rights related to the article, such as patent rights.
- Research published in the journal of the University of Sebha and used in its future works, including lectures and books, the right to reproduce articles for their own purposes, and the right to self-archive their articles.
- The right to enter a separate article, or for a non-exclusive distribution of their article with an acknowledgment of its initial publication in the journal of Sebha University.
Privacy Statement The names and e-mail addresses entered on the Sabha University Journal site will be used for the aforementioned purposes only and for which they were used.
