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Open Access 01-12-2024 | Research

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

Authors: Jorge-Eduardo Martínez-Pérez, Juan-Antonio Quesada-Torres, Eduardo Martínez-Gabaldón

Published in: Health Economics Review | Issue 1/2024

Abstract

Background

Due to population aging, healthcare expenditure is projected to increase substantially in developed countries like Spain. However, prior research indicates that health status, not merely age, is a key driver of healthcare costs. This study analyzed data from over 1.25 million residents of Spain's Murcia region to develop a capitation-based healthcare financing model incorporating health status via Adjusted Morbidity Groups (AMGs). The goal was to simulate an equitable area-based healthcare budget allocation reflecting population needs.

Methods

Using 2017 data on residents' age, sex, AMG designation, and individual healthcare costs, generalized linear models were built to predict healthcare expenditure based on health status indicators. Multiple link functions and distribution families were tested, with model selection guided by information criteria, residual analysis, and goodness-of-fit statistics. The selected model was used to estimate adjusted populations and simulate capitated budgets for the 9 healthcare districts in Murcia.

Results

The gamma distribution with logarithmic link function provided the best model fit. Comparisons of predicted and actual average costs revealed underfunded and overfunded areas within Murcia. If implemented, the capitation model would decrease funding for most districts (up to 15.5%) while increasing it for two high-need areas, emphasizing allocation based on health status and standardized utilization rather than historical spending alone.

Conclusions

AMG-based capitated budgeting could improve equity in healthcare financing across regions in Spain. By explicitly incorporating multimorbidity burden into allocation formulas, resources can be reallocated towards areas with poorer overall population health. Further policy analysis and adjustment is needed before full-scale implementation of such need-based global budgets.

Available only for authorised users

Regarding the error of the predictions, approximated through the MAE, it should be noted that the error increases with age and also escalates with the complexity of the AMG.

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Title: Predicting healthcare expenditure based on Adjusted Morbidity Groups to implement a needs-based capitation financing system
Authors: Jorge-Eduardo Martínez-Pérez
Juan-Antonio Quesada-Torres
Eduardo Martínez-Gabaldón
Publication date: 01-12-2024
Publisher: Springer Berlin Heidelberg
Published in: Health Economics Review / Issue 1/2024
Electronic ISSN: 2191-1991
DOI: https://doi.org/10.1186/s13561-024-00508-4

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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