A Multivariate Prediction Model for Hypertension Incidence
https://doi.org/10.33860/jik.v19i1.4039
Keywords:
Hypertension, Body Mass Index (BMI), family history of hypertension, prediction modelAbstract
Hypertension is a major global non-communicable disease and a leading cause of premature death. This study aimed to develop a multivariate prediction model for hypertension incidence in the Banggai Community Health Center working area, Central Sulawesi, Indonesia. An analytical observational study with a case-control design was conducted involving 140 people, equally divided into case and control groups. Data were collected on age, family history of hypertension, obesity status, smoking status, coffee consumption habits, use of hormonal birth control, and place of residence. Univariate, bivariate, and multivariate analyses were performed. The final regression model included sex, age, family history of hypertension, body mass index (BMI), and smoking status, which together could predict hypertension incidence by 59.3%. Family history of hypertension was the most dominant variable, with those having a history being 25.6 times more likely to develop hypertension than those without a history (p < 0.001). Age ≥ 36 years, obesity, and smoking were also significant risk factors. The prediction model is useful for assessing individual hypertension risk and guiding early diagnosis and treatment. Family-based health education and screening for non-communicable diseases based on the prediction variables are recommended to reduce hypertension prevalence. Future research should consider prospective designs, involve more samples, and include additional variables such as diet, physical activity, and stress level to enhance the model's predictive accuracy.
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