Forecasting the Incidence and the Prevalence Rate of Type 2 Diabetes in Iran Using Autoregressive Integrated Moving Average Model

Background: Type 2 diabetes (T2D) is an escalating global health concern, significantly contributing to mortality rates across both developed and developing countries. This study focused on analyzing and predicting the future incidence and prevalence of T2D using time series analysis methods. Methods: A cross-sectional survey covering previous data (1990–2019) was conducted to predict the future occurrence and rates of T2D in Iran from 2020 to 2040, categorized by gender. Time series analysis was employed by taking into account an autoregressive integrated moving average (ARIMA) model. The Box-Jenkins method was applied to evaluate trends and forecast the incidence of T2D for future years. Eventually, the R statistical software was used to define and select the most appropriate models for the analysis. Results: The ARIMA model (0, 1, 1) was utilized for predicting T2D occurrence in men, while ARIMA (1, 1, 0) was applied for this purpose for women. To predict its prevalence, ARIMA (0, 2, 2) was used for both genders. In 2020, the incidence of T2D in Iranian men and women was 476.31 per 100000 and 528.14 per 100000, respectively. Projections for 2040 suggest that these rates will rise to 649.80 per 100000 for men and 740.09 per 100000 for women. As for the prevalence, in 2020, it stood at 8468.40 per 100000 for men and 9,351.01 per 100000 for women. By 2040, these figures are expected to increase to 14,473.78 per 100000 for men and 16,984.73 per 100000 for women. Conclusion: The projected rise in T2D cases in Iran poses a significant public health challenge. Therefore, it is essential to prioritize and allocate resources effectively to improve diabetes prevention and management strategies moving forward.