Multiple Logistic Regression Modeling On Risk Factors of Diabetes. Case Study of Gitwe Hospital (2011-2013)

Abstract

The number of people with diabetes is increasing all over the world. A misconception that diabetes is a disease for urban areas while rural areas are also concerned, this is the motivation of the study. In this paper, a multiple logistic model is used to fit the risk factors of diabetes.

A three year period (2011 to 2013) data from Gitwe Hospital are used. The software package that is used to process the data is SPSS 15.0.

The test of independence between the dependent variable (diabetes) and the independent variables is performed. It is found that older age, alcohol consumption, cholesterol level, occupation status and hypertension were associated with the outcome of having diabetes. The predictors like gender; smoking, family history of diabetes had negligible association with having diabetes.

A multiple logistic regression model containing all the predictor variables is fitted and a test of significance on coefficients is performed. The Wald test reveals that on one hand, the significant predictors are: older age, Occupation status, Alcohol consumption, Cholesterol level and Hypertension. On the other hand, the predictors which are not statistically significant are: Gender, smoking and family history of diabetes.

From the odds ratio results, older age persons, patients who consume alcohol, patients with high cholesterol level and hypertensive persons are highly susceptible for diabetes occurrence.

Finally, a multiple logistic regression with only significant parameters was fitted. Based on their respective Receiver Operator Characteristic (ROC) curve and their overall explanatory strength the conclusion is that the reduced model fits better the data than the model with all predictor variables.

Keywords: Logistic regression, Diabetes.