BACKGROUND: The English Department of Health has introduced routine collection of patient-reported outcome data for selected surgical procedures to facilitate patient choice and increase hospital accountability. However, using aggregate health outcome scores, such as EQ-5D utilities, for performance assessment purposes causes information loss and raises statistical and normative concerns. OBJECTIVES: . For hip replacement surgery, we explore a) the change in patient-reported outcomes between baseline and follow-up on 5 health dimensions (EQ-5D), b) the extent to which treatment impact varies across hospitals, and c) the extent to which hospital performance on EQ-5D dimensions is correlated with performance on the EQ-5D utility index. METHODS: . We combine information on pre- and postoperative EQ-5D outcomes with routine inpatient data for the financial year 2009-2010. The sample consists of 21,000 patients in 153 hospitals. We employ hierarchical ordered probit risk-adjustment models that recognize the multilevel nature of the data and the response distributions. The treatment impact is modeled as a random coefficient that varies at the hospital level. We obtain hospital-specific empirical Bayes (EB) estimates of this coefficient. We estimate separate models for each EQ-5D dimension and the EQ-5D utility index and analyze correlations of EB estimates across these. RESULTS: . Hospital treatment is associated with improvements in all EQ-5D dimensions. Variability in treatment impact is most pronounced on the mobility and usual activities dimensions. Conversely, only pain/discomfort and anxiety/depression correlate well with performance measures based on utilities. This leads to different assessments of hospital performance across metrics. CONCLUSIONS: . Our results indicate which hospitals are better than others in improving health across particular EQ-5D dimensions. We demonstrate the importance of evaluating dimensions of the EQ-5D separately for the purposes of hospital performance assessment.
BACKGROUND: The English Department of Health has introduced routine collection of patient-reported outcome data for selected surgical procedures to facilitate patient choice and increase hospital accountability. However, using aggregate health outcome scores, such as EQ-5D utilities, for performance assessment purposes causes information loss and raises statistical and normative concerns. OBJECTIVES: . For hip replacement surgery, we explore a) the change in patient-reported outcomes between baseline and follow-up on 5 health dimensions (EQ-5D), b) the extent to which treatment impact varies across hospitals, and c) the extent to which hospital performance on EQ-5D dimensions is correlated with performance on the EQ-5D utility index. METHODS: . We combine information on pre- and postoperative EQ-5D outcomes with routine inpatient data for the financial year 2009-2010. The sample consists of 21,000 patients in 153 hospitals. We employ hierarchical ordered probit risk-adjustment models that recognize the multilevel nature of the data and the response distributions. The treatment impact is modeled as a random coefficient that varies at the hospital level. We obtain hospital-specific empirical Bayes (EB) estimates of this coefficient. We estimate separate models for each EQ-5D dimension and the EQ-5D utility index and analyze correlations of EB estimates across these. RESULTS: . Hospital treatment is associated with improvements in all EQ-5D dimensions. Variability in treatment impact is most pronounced on the mobility and usual activities dimensions. Conversely, only pain/discomfort and anxiety/depression correlate well with performance measures based on utilities. This leads to different assessments of hospital performance across metrics. CONCLUSIONS: . Our results indicate which hospitals are better than others in improving health across particular EQ-5D dimensions. We demonstrate the importance of evaluating dimensions of the EQ-5D separately for the purposes of hospital performance assessment.
Authors: Matthias Vogl; Rainer Wilkesmann; Christian Lausmann; Matthias Hunger; Werner Plötz Journal: Health Qual Life Outcomes Date: 2014-08-07 Impact factor: 3.186