We estimated polyserial correlations of the global items with the EQ-5D. In addition, we examined item-scale correlations and conducted confirmatory categorical factor analysis (based on polychoric correlations) to evaluate whether the 10 global health items could be combined into a single unidimensional scale. Next, we performed exploratory factor analysis on the matrix of polychoric correlations to identify the number of underlying dimensions. We evaluated the resulting two factors by estimating item-scale correlations and internal consistency reliability. We used Mplus 5.1 software [11 ] to estimate confirmatory categorical factor analysis models, specifying weighted least squares mean and variance estimation. Because of our large sample size we do not rely on the chi-square statistic to evaluate the acceptability of the models. We estimated practical fit of the models using the confirmatory fit index (CFI), Tucker–Lewis index (TLI), and the root mean square error of approximation (RMSEA). We averaged items to form physical and mental health composites and estimated associations of these composites with the EQ-5D and the nine PROMIS domain scores (physical functioning, pain behavior, pain impact, fatigue, anxiety, anger, depressive symptoms, satisfaction with discretionary social activities, satisfaction with social roles). Finally, we estimated item threshold and discrimination parameters for the final physical and mental health scales using the graded response model [12 (link), 13 ]. Based on the item parameters we calculated item information, the contribution of each item to overall test precision [12 (link)]. As an estimate of the contribution of each item to overall test precision, we weighted item-level information values, which are computed as the expected item information across the score distribution of our sample.