# Mapping EQ-5D-5L Utilities in Dementia: Integrating Self and Proxy Reports

## TL;DR

This study improves health quality assessments in dementia by combining self and proxy reports, enhancing accuracy for mild-to-moderate cases.

## Contribution

The novel contribution is developing mapping models to predict combined utility values from self and proxy EQ-5D reports in dementia.

## Key findings

- Combined utility values better reflect health-related quality of life and detect subtle health changes in mild-to-moderate dementia.
- Proxy data are more effective for certain EQ-5D dimensions, suggesting their usefulness in specific reporting scenarios.
- Mapping models perform well for mild-to-moderate dementia but are less accurate in severe cases due to limited data.

## Abstract

EQ-5D is widely used in dementia research, but cognitive impairments often necessitate proxy assessments, resulting in differences between self- and proxy-reported data. Some EQ-5D dimensions are better reported by people with dementia (PwD), particularly in the mild to moderate stages, whereas others are more accurately captured by proxies. This study evaluates whether a combined utility value, integrating both PwD and proxy reports, can be predicted when data from only 1 respondent type are available.

Data from 2 dementia studies, ACTIFCARE and EPIC, were used to develop mapping models aimed at predicting combined utility values. These models integrate dimension-specific EQ-5D-5L responses from both respondent types to enhance health-related quality-of-life (HRQoL) assessments. Response mapping with ordered probit models was used to predict EQ-5D-5L responses when only 1 respondent type’s data were available. Model performance was evaluated by comparing observed and predicted data across dementia severity stages.

Combined utility values provided a more accurate reflection of HRQoL, showing greater sensitivity to health status changes. Proxy data proved to be more effective in predicting PwD responses for certain EQ-5D-5L dimensions, suggesting that proxy data collection may be particularly useful in specific situations. The mapping models performed well for mild-to-moderate dementia but were less accurate in severe dementia because of limited data.

Combined utility values improve HRQoL assessments, particularly in detecting subtle health changes in mild-to-moderate dementia. These models support their use in economic evaluations of dementia interventions, although challenges remain in severe dementia because of data limitations.

•This study addresses the evidence gap in dementia research by developing mapping models that integrate self- and proxy EQ-5D responses to predict combined utility values.•Key findings show that combining self- and proxy-reported data improves health-related quality-of-life assessments, particularly in detecting subtle health status changes in mild-to-moderate dementia.•These results have significant implications for healthcare decision making, enhancing the accuracy of economic evaluations for dementia interventions and informing better health outcomes assessments in health economics and outcome research practice.

This study addresses the evidence gap in dementia research by developing mapping models that integrate self- and proxy EQ-5D responses to predict combined utility values.

Key findings show that combining self- and proxy-reported data improves health-related quality-of-life assessments, particularly in detecting subtle health status changes in mild-to-moderate dementia.

These results have significant implications for healthcare decision making, enhancing the accuracy of economic evaluations for dementia interventions and informing better health outcomes assessments in health economics and outcome research practice.