Estimating lifetime healthcare costs with morbidity data

Area of Science:

• Health Economics
• Epidemiology
• Biostatistics

Background:

• Economic contractions impact healthcare resource allocation.
• Identifying high-cost individuals and resource allocation timing is crucial.
• Understanding life-course morbidity evolution is essential for chronic disease management.

Purpose of the Study:

• To introduce a longitudinal perspective into chronic disease management.
• To predict future morbidity and lifetime healthcare costs.
• To analyze gender differences in health status and costs.

Main Methods:

• Utilized population data from Baix Empordà, Catalonia (2004-2007).
• Employed the Clinical Risk Groups (CRG) model for population classification.
• Simulated future morbidity and lifetime costs using a stationary Markov chain and stochastic microsimulation.

Main Results:

• Obtained probabilities of future health status and lifetime cost distributions.
• Mean lifetime cost for women (€111,936) was significantly higher than for men (€81,566).
• Females had lower healthy life expectancy (46.99 years) and longer chronic disease duration (28.41 years) compared to males.

Conclusions:

• Stochastic microsimulation combined with morbidity classification can predict future population costs.
• Integrating a time perspective into chronic disease management offers potential efficiencies.
• Findings highlight significant gender disparities in health outcomes and lifetime healthcare expenditure.