Cost-effectiveness of hearing and vision support for residents with dementia in long-term care in Ireland: A Health Economic Analysis Plan (HEAP) for a simulation model for the SENSE-Cog Care feasibility trial

Abstract

Introduction Sensory challenges exacerbate the dementia symptoms of nursing home residents. The Sense-Cog Care trial is piloting a multi-faceted intervention to investigate whether optimising and supporting hearing, vision, and sensory-friendly environments through the “sensory champion” model in Irish nursing homes can improve quality of life and dementia-related outcomes and be cost-effective (Connelly et al., 2023). A Health Economic Analysis Plan (HEAP) provides a standardised and comprehensive framework by outlining the systematic approach, methodologies, and key considerations involved in assessing the cost-effectiveness of a healthcare intervention. The HEAP presented in this paper describes the alongside-trial decision modelling approach used for preliminary estimates of cost-effectiveness and to inform the design of the final intervention. Methods A Markov model will be used to simulate residents' health progression with and without the intervention. Costs will be estimated from the Irish health and social care perspective. A key output of the planned economic analysis will be the Incremental-Cost-Effectiveness Ratio (ICER) which describes the cost over one additional unit of Quality-Adjusted Life Year (QALY). A Probabilistic Sensitivity Analysis (PSA) will be conducted in the form of a Monte Carlo simulation to examine parameter uncertainty and the probability of a sensory champion being cost-effective. Results Tables for inputs and outputs are presented, and graphical representations for uncertainty, such as a tornado plot and a Cost-Effectiveness Analysis Curve (CEAC), will be produced. The results will be interpreted in the context of the Irish cost-effectiveness thresholds of €20,000 and €45,000 per QALY. Discussion To our knowledge, this is the first study to model the cost-effectiveness of a sensory intervention for nursing home residents with dementia. We aim to develop an early-stage Bayesian model that serves as a prior for further research.