Beyond Spatial Proximity: Optimizing Nursing Home Bed Allocation with a Proposed '4A' Model-Insights from Guangzhou,...

As Guangzhou's population ages rapidly, the supply-and-demand gap in elderly care facilities has become severe. We propose a '4A' model that draws on accessibility, affordability, acceptability, and availability from the classic '5A' access framework to optimize nursing home bed allocation from existing facilities.

We applied the '4A' model to 2630 communities/villages and 243 nursing homes in Guangzhou, using linear programming to maximize a matching score under capacity, demand, and occupancy constraints. Inequality was assessed using the Gini coefficient, Theil index, Moran's I, and hot spot analysis. Four policy scenarios were simulated under resource-scarce and resource-abundant conditions.

A total of 753 (28.6%) communities/villages had no access to nursing homes. The unmet demand rates followed a 'dual-center' clustering pattern, and hot spots were not only in the urban core, where 'scale disadvantage' and 'matching disadvantage' coexisted, but also in outer suburban districts to form a 'supply vacuum.' Inequity was moderate, and it originated from between-district disparities. Compared with a 'distance-only variant' model, our '4A' model allocated 3813 more beds. Policy simulations showed that minimum service (α=0.1) eliminated all unserved communities/villages; targeted bed expansion increased satisfaction rates to 74.8% and reduced the Gini coefficient to 0.280; and subsidies and quality upgrades became effective only when bed supply was abundant. The four simulations were phased into a three-phase policy roadmap.

The '4A' model transforms the qualitative '5A' framework into a computable allocation matrix, offering actionable recommendations for equitable access to elderly care resources.