Predicting Health Utilities Using Health Administrative Data: Leveraging Survey-linked Health Administrative Data from Ontario, Canada.

Abstract

Background: The quality-adjusted life year (QALY) is widely used to measure health outcome that combines the length of life and health-related quality of life (HRQoL). To be a reliable QALY measure, HRQoL measurements with a preference-based scoring algorithm need to be converted into health utilities on a scale from zero (dead) to one (perfect health). However, preference-based health utility data are often not available. We address this gap by developing a predictive model for health utilities.

Objectives: To develop a predictive model for health utilities using available demographic and morbidity variables in a health administrative dataset for non-institutionalised populations in Ontario, Canada.

Methods: The data were obtained from the 2009 to 2010 Canadian Community Health Survey containing Health Utilities Index Mark3 (HUI3), a generic multi-attribute preference-based health utility instrument linked with Ontario health administrative (OHA) data that were collected for administrative or billing purposes for patient encounters with the health care system. We employed four regression models (linear, Tobit, single-part beta mixture, and two-part beta mixture) and a calibration technique to identify the best-fit regression model.

Results: Our findings indicate that the two-part beta mixture model is the best-fit for predicting health utilities in the OHA data. The proposed predictive model reflects the original distribution of HUI3 in the population.

Conclusion: Our proposed predictive model generates reasonably accurate health utility predictions from OHA data. Our model-based prediction approach is a useful strategy for real-world applications, particularly when preference-based utility data are unavailable.