keywords: Diabetes, Explanatory Variables, Risk factors, p-values
In this study, we applied Spatial Binary Logistic Regression Model (SBLRM) to predict the spread of diabetes epidemic infection, identify territories of high risk and determine important demographic and environmental factors that increase the risk of diabetes infection in Taraba State Nigeria. The monthly recorded cases of diabetes between January 2011 and December 2020 in the record department of Federal Medical Centre (FMC) Jalingo was used to measure the extent to which the odds in favour of being diabetic are raised when the levels of each explanatory variable is raised from the reference level (low) to the highest level (high), Thus, it shows that risk factors is the strongest determinant of being diabetic having of 70.981 and the least is Sex which account for of 09.504. Also the covariate (sex, marital status and height) are not statistically significant because they represent the odd ratio which measure the extent to which odds in favour of positive response are raised. While the p-values (age, risk factors, weight, blood pressure and blood sugar level) are statistically significant because their p-values are less than 0.05 (<0.05). The SBLRM could be applied to effectively predict the spread of diabetes disease in Taraba State Nigeria and provide support for the development of interventions for diabetes disease control and prevention.