Mapping Healthy Days Scores to EQ-5D-5L Utilities: Development and Validation of an Algorithm Using Population Survey Data.

OBJECTIVES

To develop and validate an algorithm for estimating EQ-5D-5L utility scores from the nonpreference-based Healthy Days (HD) instrument using direct and indirect mapping approaches.

METHODS

Data were drawn from the Queensland Preventive Health Survey for 2022 (N = 12 491) and 2023 (N = 12 679). Survey-weighted ordinary least squares (OLS), Tobit, beta, majorize-minimize, and ordinal logistic regression models were evaluated. Algorithms were developed using an 80% random training sample from the 2022 data, tested in the remaining 20%, and externally validated using 2023 data. Predictive performance was assessed using root mean square error, mean absolute error, differences between predicted and observed mean utilities, and subgroup analyses by age, sex, body mass index, smoking status, and utility quartile.

RESULTS

Moderate correlations were observed between unhealthy days measures and EQ-5D-5L utilities (Pearson's r: -0.53 to -0.56). Predictive accuracy was broadly similar for the OLS, beta, and ordinal logistic models. The OLS model including physically unhealthy days, mentally unhealthy days, activity limitation days, self-rated health, age, and sex was selected as the preferred algorithm. It demonstrated strong and stable predictive performance across samples (root mean square error ≤ 0.117 and mean absolute error ≤ 0.066 in validation samples). All models overestimated utilities in the lowest utility quartile.

CONCLUSIONS

The HD instrument can be mapped to the EQ-5D-5L with favorable predictive accuracy in population survey data. The proposed algorithm provides a pragmatic approach for estimating mean utilities for quality-adjusted life-year calculation when only HD data are available, although caution is warranted in populations with very low baseline health status.