TY - JOUR TI - A GIS-based analysis of the incidence and prevalence of type 2 diabetes in Saudi Arabia DO - https://doi.org/doi:10.7282/t3-mfe2-1t12 PY - 2019 AB - This research investigates type 2 diabet type 2 diabeteses (T2D) and its risk factors, analyzes its prevalence and progression and identifies which risk factors affect different regions of Saudi Arabia by utilizing the 2013 Saudi Health Interview Survey. Our findings showed a high prevalence of T2D in Saudi Arabia (11.5%), with the highest in Hail and Aseer at 16.9% and 15.3%, respectively. On the other hand, Eastern had the lowest at 7.9%. Overall, we found that age and BMI were the most effective predictors. Age was the most effective, impacting all thirteen regions. The group aged 65 years and older had the highest risk; the 45- to 64-year-old age group, however, had the highest number of diagnoses. Al-Madinah had the highest prevalence of T2D in this age group at 56.9%. The 15- to 44-year-old group had the highest risk in Aseer at 4.8%. In Northern Borders, the odds ratio was the highest for the 65 plus age group, at 59.15. BMI was an effective predictor of T2D in nine regions. In Eastern, the odds ratio for the obese group was 4.3 compared to people of normal weight. In Al-Jouf, obese women had the highest percentage of T2D at 44.44% of cases. Work status was an important risk factor in Tabuk, Eastern, Al-Jouf, and Aseer. The retired group had the highest prevalence in these regions, except for Aseer where the employed group had the highest risk; retired people in Aseer are still involved in farm work, which may contribute to fewer T2D cases. Smoking status was an effective predictor in Hail, Al-Qassim, Riyadh, and Makkah. Ex-smokers had the highest incidence of T2D. Marital status was an effective predictor for Hail, Al-Qassim, and Riyadh. The divorced group had the highest prevalence of T2D. Education was an effective predictor for Riyadh and Aseer; the illiterate group had the highest risk. Diet was a good predictor only in Aseer. Shisha and hookah use were effective predictors in Riyadh. Understanding this data can help lower the prevalence of diabetes, provide better diabetes care, and lower healthcare costs for the Ministry of Health. KW - Biomedical Informatics KW - Non-insulin-dependent diabetes LA - eng ER -