Introduction
Cardiovascular disease (CVD) is a significant contributor to rising death rates in the U.S., particularly in areas prone to natural disasters. When natural phenomena happen, access to essential treatments becomes limited, leading to higher mortality rates among individuals with CVD.
This research presents an agent-based model to explore the impact of CVD patients lacking access to essential treatment during natural disasters, specifically hurricanes. It aims to forecast long-term health outcomes and plan public health interventions for disaster relief.
Simulation model
Researchers developed an agent-based simulation (ABS) model using AnyLogic, predictive modeling software, to investigate the impact of natural disasters on individuals' health outcomes.
The model simulates hurricane events and the consequent behavioral responses, including medication management and relocation. Each agent represents an individual characterized by specific attributes, states, and transition probabilities from one state to another. The model extends other research on the topic, incorporating the effects of natural disasters on CVD outcomes.
Read also how AnyLogic enables efficient healthcare management through resource utilization simulation modeling.
The model includes state charts for hurricane events, health behaviors and factors, and CVD outcomes. The hurricane state chart simulates the occurrence of a hurricane annually based on historical data. Agents transition between states such as "No Hurricane," "Hurricane," "Relocation," and "No Medication," with probabilities derived from literature. The model benefits from a user-friendly interface in AnyLogic, allowing customized simulation parameters and visualization of health outcomes and mortality rates over time.
Results
This study considered a population of 10,000 individuals over 25 years. The model was validated using mortality data from Hurricane Ike in Texas. The data showed a 14% average increase in CVD mortality after hurricane occurrences, aligning with published findings. With predictive modeling software, it became possible to estimate significant increases in CVD deaths with higher hurricane probabilities, peaking at a 41% probability level.
Intervention scenarios were also tested, showing that increasing medication adherence and supply in shelters could significantly reduce CVD mortality. A 15% increase in medication adherence and supply led to a marked decline in CVD deaths over 25 years.
The model demonstrated the potential of targeted health interventions to reduce CVD mortality influenced by individual health profiles and the effectiveness of intervention strategies over time.