Forecasting the use of elderly care: a static micro-simulation model

Area of Science:

• Gerontology
• Health Economics
• Public Policy Analysis

Background:

• Accurate forecasting of publicly funded long-term elderly care use is essential for resource allocation.
• Existing models often do not fully integrate demographic shifts, health status changes, and socioeconomic factors.

Purpose of the Study:

• To develop and present a novel static micro-simulation model for forecasting elderly care utilization.
• To incorporate population aging, health status evolution, and socioeconomic influences into care use predictions.
• To enable simulation of policy measure impacts on elderly care demand.

Main Methods:

• A static micro-simulation model comprising an explanatory and a population model.
• The explanatory model statistically links individual characteristics to care use.
• The population model simulates future population composition, driving forecasts based on characteristic shifts.

Main Results:

• A projected 37% increase in Dutch elderly care use (from 7% to 9% of the 30+ population) by 2030, driven by population aging.
• Care use increase is moderated by decreasing disability levels and rising educational attainment.
• Simulated policy restricting residential care access to severely physically disabled elderly resulted in lower residential care growth (32% vs. 57%) but higher home care growth (35% vs. 32%).

Conclusions:

• The developed model provides a robust framework for forecasting elderly care demand under various demographic and policy scenarios.
• Population aging is a significant driver, but health and socioeconomic trends modulate its impact.
• Policy interventions can significantly alter the balance between residential and home care utilization.