Application of Logistic Regression in Determining Factors Affecting Hypertension: Findings of the Tabari Cohort Study

Hypertension is a global problem due to its consequences. Recognizing warning signs and taking necessary measures are effective in preventing the disease and its complications. We used the logistic regression model to determine the factors affecting blood pressure based on the results of the Tabari Cohort Study.

This cross-sectional descriptive-analytical study was conducted in people older than 35 years old in Sari, whose information (demographic and anthropometric characteristics, and risk factors) was available at the Tabari Cohort Center in Mazandaran province. Logistic regression model was used to determine the factors affecting hypertension. We did statistical analyses using SPSS V26.

The participants included 6622 people (41.3% men) with an average age of 48.97±8.94 years old. There were 1481 people with high blood pressure (22.4%). According to multivariate logistic regression model, age (10-year period) (OR=2.04-8.11), body mass index (OR=1.72-2.35), total cholesterol (OR=1.34), triglyceride (OR=1.30), the ratio of waist to hip circumferences (OR=1.31), history of cardiovascular diseases (OR=2.09), and diabetes (OR=1.81) were among the factors associated with hypertension (P<0.05).

According to the results of the multivariable logistic regression model, obesity management as the main factor and screening of people for diagnosis, follow-up, and prevention of hypertension are suggested.