A PREDICTIVE MODEL FOR ASSESSING HYPERTENSION ASSOCIATED FACTORS IN HOTAT BANI TAMIM PROVINCE
Keywords:
a predictive model, hypertension, odds ratio, associated factors, logistic regression.DOI:
https://doi.org/10.17654/0972361722040Abstract
Hypertension is a main public health issue and a crucial area of research due to its high prevalence and being a considerable risk factor for cardiovascular defects and other difficulties. Furthermore, hypertension prevalence in the Middle East is not well-defined. The aim of the study is to assess the prevalence of hypertension and its related factors in Hotat Bani Tamim province. The study adopted the questionnaire in the data collection process. The sample size of the study was 210 participants. Descriptive statistics and logistic regression were adopted. The study found that there was a significant relationship between hypertension and the variables: education level primary (OR:0.939, 95% CI:0.095-12.769), secondary (OR:0.232, 95% CI:0.583-9.208), and university education (OR:0.700, 95% CI:0.264-2.445). Participants from urban areas (OR:0.368, 95% CI:0.142-0.959), having family history of hypertension (OR:2.773, 95% CI:1.101-6.989), and the body mass index normal weight (OR:0.0.149, 95% CI:0.230-0.965), overweight (OR:0.148, 95% CI:0.024-0.924), obesity class1 (OR:0.298, 95% CI:0.034-2.050), and obesity class2 (OR:0.042, 95% CI:0.004-0.462) were less likely to have hypertension as compared to the participants who had obesity class3. Whilst the model revealed an insignificant relationship between hypertension and the variables: age, gender, social status, and economic status. Saudi Arabia is facing a rising burden of hypertension diseases because of rapid changes in behaviors and lifestyle. Our findings prove the need for considerable intervention to lower this burden and to engage other sectors of the government and the community in these issues.
Received: March 25, 2022
Revised: April 28, 2022
Accepted: May 10, 2022
References
Matthew R. Alexander, M. S. Madhur, D. G. Harrison, Medscape, A. W. Dreisbach and Kamran Riaz, What is the global prevalence of hypertension (high blood pressure)? Medscape, 2019. Retrieved from: https://www.medscape.com/ answers/241381-7614/what-is-the-global-prevalence-of-hypertension-high-blood-pressure
WHO, https://www.who.int/health-opics/hypertension/#tab=tab_1 2020.
Series Etp, World Health Organization, Regional Office for Eastern Mediterranean, Clinical guideline for the management of hypertension, Cairo, 2005.
WHO, Hypertension, World Health Organization, World Health Organization, 2021.
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.
Y. E. Achhab, L. Nazek, M. Maalej, M. Alami and C. Nejjari, Prevalence, control and risk factors related to hypertension among Moroccan adults: a multicentre study, East Mediterr Health J. 25(7) (2019), 447-456.
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 in Nairobi, Kenya, BMC Public Health 15 (2015), 2-9.
A. S. R. Abdallah and M. O. M. Mohammed, Modeling factors affecting blood pressure among Sudanese reproductive women, JP Journal of Biostatistics 18(1) (2021), 135-147.
STATISTICS CBO, Sudan Multiple Indicator Cluster Survey 2014, 2014.
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), doi: 10.1155/2017/5491838
J. Wang, W. Sun, Z. L. Gaw, L. T. JW 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), DOI: 10.1371/journal.pone.0195340
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), 56-70.
Organization WHO, Sudan - Khartoum STEPS Noncommunicable Disease Risk Factors Survey, Global Health Data Exchange, 2005-2006.
WHO, Annual Health Statistical Report, 2008.
H. Awadalla, N. E. Elmak, E. F. El-Sayed, A. O. Almobarak, M. M. W. Osman, S. K. Noor and M. H. Ahamed, 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.
M. M. Asemul, A. W. Yalew, N. D. Kabeta and D. Mekonnen, Prevalence and risk factors of hypertension among adults: A community-based study in Addis Ababa, Ethiopia, PLoS One 16(4) (2021), DOI: 10.1371/journal.pone.0248934
R. Mahapatra, A. Kaliyappan, P. Chinnakali, N. Hanumanthappa, R. Govindarajalou and C. Bammigatti, Prevalence and risk factors for resistant hypertension: cross-sectional study from a tertiary care referral hospital in South India, Cureus 13(10) (2021), DOI: 10.7759/cureus.18779
M. Ezzati, Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants, Lancet 398 (2021), 957-980.
S. Opoku, E. A. Yobo, D. Trofimovitch, R. B. Opoku, L. Joseph, Y. Gan and Z. Lu, Increased prevalence of hypertension in Ghana: New 2017 American College of Cardiology/American Hypertension Association hypertension guidelines application, Journal of Global Health 10(2) (2020), DOI: 10.7189/jogh.10.020408
C. E. Bcheraoui, Z. A. Memish, M. Tuffaha, F. Daoud, M. Robinson, S. Jaber, S. Mikhitarian and A. A. Al Rabeeah, Hypertension and its Associated Risk Factors in the Kingdom of Saudi Arabia, 2013: A National Survey, International Journal of Hypertension, 2014.
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.
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.
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 (2016), doi: 10.1155/2016/7962595
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(39) (2009), 1-10.
H. A. Thabit and M. A. Abdullah, Prevalence of hypertension among doctors and risk factors in Al-Thawra hospital, Sanaa in 2019, Biomedical Journal of Scientific and Technical Research 36(2) (2021), DOI: BJSTR.MS.ID.005838
P. Ghimire, A. Khadka, A. Anuwatnonthakate and S. Trongsakul, Prevalence and factors associated with hypertension among health workers of central hospitals in Nepal, The Indonesian Journal of Public Health 15(3) (2020), 325-338.
A. Agresti, An Introduction to Categorical Data Analysis, New Jersey: John Wiley & Sons, 2007.
D. R. Cox and E. J. Snell, Analysis of Binary Data: Chapman and Hall/CRC, 1989.
D. W. Hosmer, S. Lemeshow and R. X. Sturdivant, Applied Logistic Regression, New Jersey: John Wiley & Sons, 2013.
C. Mood, Logistic regression: Why we cannot do what we think we can do, and what we can do about it, European Sociological Review 26(1) (2010), 67-82.
D. Cook, P. Dixon, W. M. Duckworth, M. S. Kaiser, K. Koehler, Q. Meeker and D. W. R. Stephenson, Binary response and logistic regression analysis, Beyond Traditional Statistical Methods, 2001.
M. R. Abonazel and M. G. Ibrahim, On estimation methods for binary logistic regression model with missing values, International Journal of Mathematics and Computational Science 4(3) (2018), 79-85.
J. M. Hilbe, A practical guide to logistic regression, New York, Taylor & Francis Group, 2015.
C. R. Rao, Linear Statistical Inference and its Applications, New York, John Wiley & Sons, Inc., 1973.
P. J. Green, Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant, Journal of the Royal Statistical Society 46(2) (1984), 149-192.
D. Hosmer and S. Lemeshow, A goodness-of-fit test for the multiple logistic, Communication in Statistics (1980), 1043-1069.
S. Lemeshow and D. J. Hosmer, A review of goodness of fit statistics for use in the development of logistic regression models, American Journal of Epidemiology 115 (1982), 92-106.
V. Bewick, L. Cheek and J. Ball, Statistics Review 14: Logistic regression, Critical Care 9(1) (2005), 112-118.
I. Paek, Three Statistical Testing Procedures in Logistic Regression: Their Performance in Differential Item Functioning (DIF) Investigation, New Jersey, EST, 2009.
M. M. Al-Nozha, M. Abdullah, M. R. Arafah, M. Z. Khalil, N. B. Khan, Y. Y. Al-Mazrou and A. Al-Mobeireek, Hypertension in Saudi Arabia, Saudi Med. J. 28(1) (2007), 77-84.
N. Al-Hamdan, A. Saeed, A. Kutbi, A. J. Choudhry and R. Nooh, Characteristics, risk factors, and treatment practices of known adult hypertensive patients in Saudi Arabia, International Journal of Hypertension 7 (2010),
DOI: 10.4061/2010/168739
Z. A. Memish, S. Jaber, A. H. Mokdad, M. A. AlMazroa, J. C. L. Murray and A. A. A. Rabeeah, The burden of Disease, Injuries, and Risk Factors in the Kingdom of Saudi Arabia, 1990-2010, Preventing Chronic Disease 11 (2014), DOI: http://dx.doi.org/10.5888/pcd11.140176
A. Aldiab, M. M. Shubair, J. M. Al-Zahran, K. K. Aldossari, S. Al-Ghamdi, M. Househ and H. Jradi, Prevalence of hypertension and prehypertension and its associated cardioembolic risk factors; a population-based cross-sectional study in Alkharj, Saudi Arabia, BMC Public Health, 18, 2018.
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