Predicting healthcare expenditure based on Adjusted Morbidity Groups to implement a needs-based capitation financing...

Area of Science:

• Health Economics
• Public Health Policy
• Healthcare Financing

Background:

• Population aging drives rising healthcare expenditure in Spain.
• Health status, not solely age, is a primary determinant of healthcare costs.
• Existing healthcare financing models may not accurately reflect regional population needs.

Purpose of the Study:

• To develop and simulate a capitation-based healthcare financing model for Spain's Murcia region.
• To incorporate health status using Adjusted Morbidity Groups (AMGs) for equitable budget allocation.
• To assess the impact of an AMG-based model on healthcare resource distribution.

Main Methods:

• Analysis of 2017 data for over 1.25 million residents, including age, sex, AMG, and healthcare costs.
• Development of generalized linear models to predict healthcare expenditure based on health status.
• Simulation of capitated budgets for nine healthcare districts using the selected model.

Main Results:

• A gamma distribution with a logarithmic link function yielded the best model fit.
• The proposed capitation model revealed inequities, with most districts facing funding decreases and two high-need areas receiving increases.
• The model emphasizes allocation based on health status and standardized utilization over historical spending.

Conclusions:

• Adjusted Morbidity Group (AMG)-based capitated budgeting can enhance healthcare financing equity in Spain.
• Incorporating multimorbidity burden allows resource reallocation to areas with greater population health needs.
• Further policy analysis is recommended before implementing need-based global budgets.