[Ageing and Alzheimer disease - system dynamics model prediction]

Area of Science:

• Gerontology and Public Health
• Health Economics
• Computational Modeling

Background:

• Alzheimer's disease (AD) is the most prevalent form of dementia, strongly linked to aging populations.
• The European Union faces demographic shifts with increasing life expectancy and low birth rates, exacerbating the aging population.
• Predictive modeling is crucial for understanding the future burden of AD and its economic consequences.

Purpose of the Study:

• To develop and present a system dynamics model for predicting the number of Alzheimer's disease patients in the EU.
• To estimate the associated financial impacts and economic burden of Alzheimer's disease in the future EU.
• To integrate a financial submodel for cost estimation based on existing studies.

Main Methods:

• Application of a system dynamics modeling approach.
• Development of a dynamic model simulating the EU population and Alzheimer's disease patient prevalence.
• Integration of a financial submodel to estimate patient costs using data from three cost studies.

Main Results:

• The system dynamics model provides future projections for Alzheimer's disease patient numbers within the EU.
• The financial submodel estimates the economic costs associated with the predicted increase in Alzheimer's disease patients.
• The study highlights the significant economic implications of an aging population on healthcare systems due to Alzheimer's disease.

Conclusions:

• System dynamics modeling is an effective tool for predicting the future prevalence of Alzheimer's disease and its economic impact.
• The aging EU population presents a substantial challenge, necessitating proactive planning for Alzheimer's disease care and associated costs.
• The model provides valuable insights for policymakers and healthcare planners to address the growing burden of Alzheimer's disease.