Reducing rural health inequity through drone-based delivery: a spatial multi-criteria decision framework prioritizing epidemiological vulnerability in hub location

Objective To alleviate spatial health inequities in rural areas by optimizing unmanned aerial vehicle (UAV) medical hub location based on an epidemiological vulnerability-driven approach, correcting for the tendency of current logistics-centric models to overlook high-need rural populations. Methods A spatial multi-criteria decision-making (MCDM) framework was developed for a simulated rural–urban fringe experiencing severe demographic aging. We defined a novel Epidemiological Vulnerability Index (EVI) incorporating three core dimensions: demographic aging, chronic disease burden (e.g., cardiovascular risks), and baseline healthcare deprivation (ground EMS delay). The framework integrates Geographic Information Systems (GIS) with the enhanced two-step floating catchment area (E2SFCA) method, Analytic Hierarchy Process (AHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank and allocate highly-prioritized UAV hub sites across 400 grid cells. All results are derived from a calibrated simulation environment and should be interpreted as model-based projections rather than empirical observations. Results Simulation results indicated that the “Digital Inverse Care Law” emerged naturally under traditional logistics models, which allocated 85% of hubs to low-vulnerability urban centers. In contrast, the EVI-driven framework strategically anchored 65% of hubs in deep rural pockets. Under the baseline parameter assumptions, this reallocation of UAV hubs reduced the simulated average rural emergency response time by 74.8% (from 45.2 to 11.4 min) in the model. The 11.4-min figure represents UAV delivery time to the incident location, not complete emergency intervention time. This modeled improvement brought an estimated 88.4% of the highly vulnerable aging population into the “Golden 15-Minute” survival window within the simulation. Conclusion By embedding epidemiological metrics into spatial planning, this framework provides a decision-support tool that is traceable and reproducible for health policymakers, based on model outputs rather than empirical field validation. It illustrates how targeted digital health infrastructure deployment could, under the model assumptions, contribute to narrowing the rural–urban emergency health divide. These findings are contingent on the specific model assumptions and require field validation and formal health-economic evaluation before real-world implementation.