The study aimed to examine the factors affecting blood pressure among Sudanese reproductive women. This study uses the Sudan Multiple Indicator Cluster Survey (MICS) conducted in the year 2014, and the focus was on the reproductive women age (15-49) years. The sample size was 4586 participants selected from the total sample of the survey. The study adopted the binary logistic regression model. The application of the model showed that the overall prevalence of blood pressure was 20.4% among women in age group (15-49) years. The prevalence of blood pressure was 54.4% among rural and 27.1% among urban. The study showed that there was a significant association between the variables age, education level, wealth index quantile, and the area of sample members. However, the model showed an insignificant association between blood pressure and marital status.

factors, affecting, blood pressure, reproductive women.

Received: December 21, 2020; Accepted: January 27, 2021; Published: February 22, 2021

How to cite this article: Ahmed Saied Rahama Abdallah and Mohammed Omar Musa Mohammed, Modeling factors affecting blood pressure among sudanese reproductive women, JP Journal of Biostatistics 18(1) (2021), 135-147. DOI: 10.17654/BS018010135

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

Reference:

[1] W. LLC, Med Scape, 1-12-2020. [Online]. Available: https://www.medscape.com/answers/241381-7614/what-is-the-global-prevalence-of-hypertension-high-blood-pressure
[2] WHO, 1-12-2020. [Online]. Available: https://www.who.int/health-topics/hypertension/#tab=tab_1.
[3] E. t. p. series, World Health Organization, Regional Office for Eastern Mediterranean, Clinical guideline for management of hypertension, Cairo, 2005.
[4] M. Naresh, S. Viral, K. Sudham, C. Mahesh and G. Kalpesh, Assessment of risk factors of hypertension: a cross-sectional study, Journal of Evolution of Medical and Dental Sciences 1(4) (2012), 519-526.
[5] Y. E. Achhab, L. Nazek, M. Maalej, M. Alami and C. Nejjari, Prevalence, control and risk factors related to hypertension among Moroccan adults: a multicenter study, East. Mediterr. Health J. 25(7) (2019), 447-453.
[6] B. Olack, F. Wabwire-Mangen, L. Smeeth, J. M. Montgomery, N. Kiwanuka and R. F. Breiman, Risk factors of hypertension among adults aged 35-64 years living in an urban slum Nairobi, Kenya, BMC Public Health 15 (2015), 2-9.
[7] C. B. O. Statistics, Sudan multiple indicator cluster survey 2014, 2014.
[8] S. Singh, R. Shankar and G. P. Singh, Prevalence and associated risk factors of hypertension: a cross-sectional study in Urban Varanasi, International Journal of Hypertension 2017 (2017), Article ID 5491838.
[9] J. Wang, W. Sun, G. A. Wells, Z. Li, T. Li, J. Wu, Y. Zhang, Y. Liu, L. Li, Y. Yu, Y. Liu, C. Qi, Y. Lu, N. Liu, Y. Yan, L. Liu, G. Hui and B. Liu, Differences in prevalence of hypertension and associated risk factors in urban and rural residents of the northeastern region of the People’s Republic of China: a cross-sectional study, PLoS ONE 13(4) (2018), 1-14.
[10] B. A. Elfaki, H. E. Mustafa and M. K. Elnimeiri, Prevalence and awareness of hypertension among Sudanese rural population, Sinnar State, Sudan, 2016, Journal of Nursing Education and Practice 7(6) (2017), 1-5.
[11] W. H. Organization, Sudan - Khartoum STEPS Noncommunicable Disease Risk Factors Survey, 2005-2006.
[12] WHO, Annual Health Statistical Report, 2008.
[13] H. Awadalla, N. E. Elmak, E. F. El-Sayed, A. O. Almobarak, W. M. M. Osman, S. K. Noor and M. H. Ahmed, Hypertension in Sudanese individuals and associated risk factors: the critical intersection between salt and sugar intake, Cardiovascular Diagnosis and Therapy 8(4) (2018), 432-438.
[14] S. T. Shaf and T. Shaf, A survey of hypertension prevalence, awareness, treatment, and control in health screening camps of rural central Punjab, Pakistan, Journal of Epidemiology and Global Health 7 (2017), 135-140.
[15] A. A. Saeed, N. A. Al-Hamdan, A. A. Bahnassy, A. M. Abdalla, M. A. F. Abbas and L. Z. Abuzaid, Prevalence, awareness, treatment, and control of hypertension among Saudi adult population: A National Survey, International Journal of Hypertension 2011 (2011), Article ID 174135.
[16] J. Kishore, N. Gupta, C. Kohli and N. Kumar, Prevalence of hypertension and determination of its risk factors in rural Delhi, International Journal of Hypertension 34 (2016), 1-6.
[17] C. E. Bcheraoui, Z. A. Memish, M. Tuffaha, F. Daoud, M. Robinson, S. Jaber, S. Mikhitarian, M. A. Saeedi, M. A. AlMazroa, A. H. Mokdad and A. A. A. Rabeeah, Hypertension and its associated risk factors in the Kingdom of Saudi Arabia, 2013: A National Survey, International Journal of Hypertension (2014), 1 9.
[18] F. Tesfaye, P. Byass and S. Wall, Population based prevalence of high blood pressure among adults in Addis Ababa: uncovering a silent epidemic, BMC Cardiovascular Disorders 9 (2009), Article Number 39. https://doi.org/10.1186/1471-2261-9-39.
[19] D. Cox and E. Snell, Analysis of Binary Data, London: Chapman and Hall, 1989.
[20] C. Rao, Linear Statistical Inference and its Application, Second ed., New York: Wiley, 1973.
[21] D. Hosmer, T. Hosmer and S. Lemeshow, A goodness-of-fit tests for the multiple logistic regression model, Communication in Statistics 10 (1980), 1043-1069.
[22] S. Lemeshow and D. Hosmer, The use of goodness-of-fit statistics in the development of logistic regression models, American Journal of Epidemiology 115 (1982), 92-106.