Estimating Population Burden of Stroke with an Agent-Based Model
Elizabeth Hunter, John D. Kelleher
TL;DR
Stroke imposes a major global burden, and understanding population-level effects of high-risk interventions requires an agent-based approach. The authors develop an ABM that simulates a decade of stroke risk in a synthetic Irish population, estimating DALYs via $DALYs = YLL + YLD$ and allowing risk to be reduced through health conversations and family influence guided by age-specific risk predictions. Results show modest but statistically significant reductions in strokes and DALYs, with larger effects when risk-reducing behaviors spread to family members, demonstrating the potential of ABMs to inform public health planning for stroke prevention. The work highlights how combining population health with personalized risk assessment can help anticipate the population impact of prevention strategies and guide policy decisions.
Abstract
Stroke is one of the leading causes of death and disability worldwide but it is believed to be highly preventable. The majority of stroke prevention focuses on targeting high-risk individuals but its is important to understand how the targeting of high-risk individuals might impact the overall societal burden of stroke. We propose using an agent-based model that follows agents through their pre-stroke and stroke journey to assess the impacts of different interventions at the population level. We present a case study looking at the impacts of agents being informed of their stroke risk at certain ages and those agents taking measure to reduce their risk. The results of our study show that if agents are aware of their risk and act accordingly we see a significant reduction in strokes and population DALYs. The case study highlights the importance of individuals understanding their own stroke risk for stroke prevention and the usefulness of agent-based models in assessing the impact of stroke interventions.