Risk adjustment to improve fairness and efficiency of resource allocation: a case from the Emilia Romagna region in...

Resource allocation formulas in National Health Systems (NHS) are crucial for distributing central funds to Regions/Local Health Authorities. However, under prospective capitation funding, resource constraints may generate incentives for selective rationing, as providers seek to contain costs within fixed budgets. While NHS features such as geographically based enrolment and funding limit explicit risk selection seen in competitive insurance markets, resource scarcity can lead to rationing in socially suboptimal ways, which might disproportionately affect vulnerable populations. This paper aims to quantify selective rationing incentives using the healthcare system in the Emilia-Romagna Region in Italy as a case study. We evaluate the current funding formula used in Italy and its impact on the Region and demonstrate how alternative formulas can be developed to minimize incentives for selective rationing in the Region while remaining feasible to implement, given heterogeneous data quality and availability across Regions that hinder formula development at a national level. Our results show that the current formula undercompensates specific population groups defined by age and sex and has inadequate responsiveness to need. Alternative formulas that risk-adjust using socioeconomic variables have low statistical fit but reduce funding disparities among deprived areas/poorer individuals. Nevertheless, they are less responsive to high-need individuals. Moreover, morbidity data, even at coarse granularity, contribute to more adequately funding high-cost groups like cancer patients. To explore broader implications, we simulate how these alternative formulas would alter allocations if applied at the national level, showing that accounting for risk variation would channel more resources to the Regions in need. Overall, we provide a rationale for transitioning from the current resource allocation formula to risk adjustment to reduce potential selective rationing incentives.