Assessing the empirical validity of alternative multi-attribute utility measures in the maternity context

Multi-attribute utility measures are preference-based health-related quality of life measures that have been developed to inform economic evaluations of health care interventions. The objective of this study was to compare the empirical validity of two multi-attribute utility measures (EQ-5D and SF-6D) based on hypothetical preferences in a large maternity population in England.

Women who participated in a randomised controlled trial of additional postnatal support provided by trained community support workers represented the study population for this investigation. The women were asked to complete the EQ-5D descriptive system (which defines health-related quality of life in terms of five dimensions: mobility, self care, usual activities, pain/discomfort and anxiety/depression) and the SF-36 (which defines health-related quality of life, using 36 items, across eight dimensions: physical functioning, role limitations (physical), social functioning, bodily pain, general health, mental health, vitality and role limitations (emotional)) at six months postpartum. Their responses were converted into utility scores using the York A1 tariff set and the SF-6D utility algorithm, respectively. One-way analysis of variance was used to test the hypothetically-constructed preference rule that each set of utility scores differs significantly by self-reported health status (categorised as excellent, very good, good, fair or poor). The degree to which EQ-5D and SF-6D utility scores reflected alternative dichotomous configurations of self-reported health status and the Edinburgh Postnatal Depression Scale score was tested using the relative efficiency statistic and receiver operating characteristic (ROC) curves.

The mean utility score for the EQ-5D was 0.861 (95% CI: 0.844, 0.877), whilst the mean utility score for the SF-6D was 0.809 (95% CI: 0.796, 0.822), representing a mean difference in utility score of 0.052 (95% CI: 0.040, 0.064; p < 0.001). Both measures demonstrated statistically significant differences between subjects who described their health status as excellent, very good, good, fair or poor (p < 0.001), as well as monotonically decreasing utility scores (test for linear trend: p < 0.001). The SF-6D was between 29.1% and 423.6% more efficient than the EQ-5D at detecting differences in self-reported health status, and between 129.8% and 161.7% more efficient at detecting differences in the Edinburgh Postnatal Depression Scale score. In addition, the SF-6D generated higher area under the curve (AUC) scores generated by the ROC curves than the EQ-5D, indicating greater discriminatory power, although in all but one analysis the differences in AUC scores between the measures were not statistically significant.

This study provides evidence that the SF-6D is an empirically valid and efficient alternative multi-attribute utility measure to the EQ-5D, and is capable of discriminating between external indicators of maternal health.