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Modeling the Risk factors of hypertension in 35-65 years old individuals using logistic regression
Razieh Yousefi Ms1 , Azadeh Saki Dr * 2, Habibollah Esmaily Dr3 , Majid Ghayour Mobarhan Dr4 , Maryam Tayefi Dr4
1- Department of Epidemiology & Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
2- Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran , SakiA@mums.ac.ir
3- Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
4- Metabolic syndrome research center, Mashhad University of Medical Sciences, Mashhad, Iran
Abstract:   (351 Views)
Introduction and Aims: Hypertension is a common cause of cardiovascular disease in the world. Therefore identification of risk factors for hypertension is essential to carry out preventive masseurs. So this study was done with the aim of using logistic regression model to determine and assess the risk factors of hypertension, in Mashhad.
Materials and Methods: This Cross sectional study was carried out using the records of    individuals between 35- 65 years old from cross sectional phase of MASHHAD study. Age, gender, BMI, Smoking status, Family history of hypertension, Triglycerides (TG), HDL entered the model.
Results: The participants were 40%men and 60% female. The multivariate logistic regression model showed age(OR=1.080 , p-value= 0.000  ), Gender(OR=1.146   ,p-value= 0.000  ), Smoking(OR= 1.536  ,p-value=  0.000 ),  Fat (OR=  1.933 ,p-value=  0.000 ), TG(OR=  1.004 ,p-value= 0.000  ), Family history(OR=  1.296 ,p-value= 0.000  ) to be significantly associated with increase in severity of hypertension in 0.01 significance level.
Conclusion: According to the logistic regression method used in this study, smoking, obesity, high triglycerides and family history of hypertension, are factors associated with hypertension. Therefore, change in lifestyle plays an important role in preventing hypertension and thus cardiovascular disease.
 
Keywords: Hypertension, Cardiovascular Diseases, Regression Analysis, Logistic Models
     
Type of Study: Orginal Article | Subject: Biostatistics
Received: 2018/05/28 | Revised: 2019/05/21 | Accepted: 2018/07/9 | ePublished ahead of print: 2019/05/21
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