Predicting healthcare expenditure by multimorbidity groups

Area of Science:

• Health Economics
• Public Health
• Healthcare Management

Background:

• Healthcare expenditure modeling is crucial for efficient resource allocation.
• Understanding the impact of multimorbidity on healthcare costs is essential for accurate budgeting.
• Current budget allocation methods may not adequately account for population health needs.

Purpose of the Study:

• To develop a model for integrated healthcare expenditure based on multimorbidity using Clinical Risk Groups (CRG).
• To demonstrate the application of this predictive model for health budget allocation.
• To compare morbidity-based budgeting with population-based allocation.

Main Methods:

• Utilized a dataset of 156,811 inhabitants from a Spanish health district in 2013.
• Employed two-part models, including logit and log-linear OLS regression, to predict healthcare expenditure.
• Calculated relative weights for Clinical Risk Groups (CRG) to establish a case-mix for budget allocation.

Main Results:

• Multimorbidity-related variables significantly improved the explanation of integrated healthcare expenditure.
• Achieved an adjusted R-squared of 46-49% for predicting healthcare expenditure.
• CRG-derived weights proved valuable for identifying case-mix differences and informing budgetary decisions.

Conclusions:

• Models incorporating multimorbidity offer a more accurate approach to healthcare expenditure prediction.
• The developed model facilitates improved budget allocation between health districts.
• Budgeting based on morbidity, rather than solely population size, leads to more equitable resource distribution.