Using logistic regression models to determine factors affecting diabetes in the red sea state

Abstract

Diabetes is a disease influenced by some factors related to individual's behavior and the surrounding environment. This study aimed to identify the factors influencing diabetes infection in the Red Sea State for the period (2015-2018). The study depended on data collected from a diabetes treatment center in Port Sudan city eastern of Sudan. The sample size was (202). Data were analyzed using chi-square test, simple Binary logistic regression, and multiple logistic regression. Chi-square test results showed that there was a relationship between diabetes infection and the predictor variables gender, sex, family history, and kinship. Application of the binary logistic regression revealed that there was a significant association between the predictor variables: sex, age, and diabetes infection. However, educational level, income, and blood sugar were found to be statistically insignificant. The study recommended increasing the number of diabetes screening and treatment centers in all cities in the Red Sea State and developing existing centers.