Channel Allocation and Equity in Preventive Campaigns for Older Adults: Agent-Based Modeling Study

Area of Science:

• Public Health
• Health Communication
• Computational Social Science

Background:

• Preventive campaigns for older adults face challenges in resource allocation across media channels.
• Current strategies often lack explicit criteria for distributional equity or structured planning, potentially widening health disparities.

Purpose of the Study:

• To develop and apply a data-driven agent-based model (ABM) for strategic planning of preventive campaigns targeting older adults.
• To compare channel allocation, personalization, and loss framing strategies under budget and equity constraints.

Main Methods:

• An agent-based model was calibrated using South Korean national survey data on influenza vaccination and health screening.
• Fifteen campaign scenarios were simulated, varying media channel allocation, budget, personalization strategies, and loss framing.
• Outcomes included adoption rates, time to adoption, and equity metrics (minimum class-level adoption, 90-10 gap).

Main Results:

• Personalization strategies significantly improved both adoption rates and equity compared to baseline.
• In vaccination models, adoption increased, minimum class adoption rose, and the equity gap narrowed.
• Screening models showed similar improvements in adoption and equity with personalization.

Conclusions:

• Agent-based modeling can enhance preventive campaign design by integrating explicit equity and budget criteria.
• Equity-focused personalization and tailored media portfolios improve outcomes and distributional equity for older adults.
• Prioritizing targeted personalization over undifferentiated channel diversification is recommended for efficiency and equity.