Modeling the demand for long-term care services under uncertain information.

Area of Science:

• Health Services Research
• Health Economics
• Public Health Policy

Background:

• Developing long-term care (LTC) networks is a global health policy priority, particularly in National Health Service (NHS) systems.
• Accurate demand and utilization data are crucial for effective LTC network planning, but often unavailable.
• Predictive methods are essential for addressing future LTC service needs.

Purpose of the Study:

• To propose and validate a simulation model for predicting annual demand for LTC services at a small-area level.
• To inform the planning and financing of LTC services by estimating future needs, required resources, and associated costs.

Main Methods:

• A simulation model based on a Markov cycle tree structure was developed.
• The model is multiservice, predicting demand for formal/informal home-based, ambulatory, and institutional care.
• Uncertainty was addressed using scenario analysis and Monte Carlo simulation for probabilistic sensitivity analysis.

Main Results:

• The model successfully predicted annual demand for various LTC services, required resources (caregivers, visits, consultations, beds), and costs.
• Validation using historical data and international figures demonstrated the model's reliability.
• Application to Portugal (Lisbon) provided critical insights for LTC network planning.

Conclusions:

• The developed simulation model provides essential information for the strategic planning and financial management of LTC networks.
• The integrated approach to uncertainty modeling enhances the robustness of future demand predictions.
• This tool supports evidence-based decision-making for policymakers and healthcare providers in the LTC sector.