[Future type 2 diabetes mellitus scenario estimated with a predictive dynamic simulation model]

Area of Science:

• Epidemiology
• Public Health Modeling
• Biostatistics

Context:

• Type 2 Diabetes Mellitus (T2DM) poses a significant public health challenge globally.
• Accurate forecasting of T2DM incidence is crucial for resource allocation and intervention planning.
• Mexico, specifically San Luis Potosí, faces increasing T2DM prevalence, necessitating predictive modeling.

Purpose:

• To develop and validate a predictive dynamic model for estimating future Type 2 Diabetes Mellitus (T2DM) incidence rates.
• To identify and quantify the influence of key socio-demographic factors on T2DM incidence.
• To project T2DM scenarios up to 2030.

Summary:

• A retrospective ecological study analyzed data from 58 municipalities in San Luis Potosí, Mexico (2013-2015).
• Predictive dynamic submodels for T2DM, urban population, television access in dwellings, and the 45-49 age group were developed using linear correlation, multiple linear regression, and structural equations.
• The holistic model explained 27.2% of T2DM variance, with population aged 45-49 years (156.69 units), television access (4.46 units), and urban population (2.84 units) being significant predictors.

Impact:

• The model forecasts exponential T2DM growth, with incidence rates per 100,000 population projected to rise from 1,052.4 in 2015 to 2,351.1 in 2030.
• Identified risk factors provide targeted insights for public health interventions.
• This dynamic model offers a framework for understanding and predicting T2DM trends in similar demographic contexts.