Axel Svedbom1, Fredrik Borgström2, Emma Hernlund3, Oskar Ström2, Vidmantas Alekna4, Maria Luisa Bianchi5, Patricia Clark6, Manuel Díaz Curiel7,8, Hans Peter Dimai9, Mikk Jürisson10, Anneli Uusküla10, Margus Lember10, Riina Kallikorm10, Olga Lesnyak11,12, Eugene McCloskey13, Olga Ershova14, Kerrie M Sanders15, Stuart Silverman16, Marija Tamulaitiene4, Thierry Thomas17, Anna N A Tosteson18, Bengt Jönsson19, John A Kanis15,20. 1. Mapi, Stockholm, Sweden. axel.svedbom@mapigroup.se. 2. LIME/MMC, Karolinska Institutet, Stockholm, Sweden. 3. Mapi, Stockholm, Sweden. 4. Faculty of Medicine, Vilnius University, Vilnius, Lithuania. 5. Bone Metabolism Unit, Istituto Auxologico Italiano IRCCS, Milan, Italy. 6. Clinical Epidemiology Unit, Hospital Infantil Federico Gómez and Faculty of Medicine UNAM, Mexico City, Mexico. 7. Servicio de Medicina Interna/Enfermedades Metabolicas Oseas, Fundacion Jimenez Diaz, Madrid, Spain. 8. Catedra de Enfermedades Metabolicas Óseas, Universidad Autonoma, Madrid, Spain. 9. Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria. 10. Faculty of Medicine, University of Tartu, Tartu, Estonia. 11. Ural State Medical University, Yekaterinburg, Russia. 12. North West Mechnikov State Medical University, St. Petersburg, Russia. 13. Academic Unit of Bone Metabolism, Centre for Integrated research in Musculoskeletal Ageing, Mellanby Centre for Bone research, University of Sheffield, University of Sheffield, Sheffield, UK. 14. Yaroslavl State Medical University, Yaroslavl, Russia. 15. Institute for Health and Ageing, Australian Catholic University, Melbourne, 3000, Australia. 16. Cedars-Sinai Medical Center and David Geffen School of Medicine, University of California, Los Angeles, CA, USA. 17. INSERM U1059, Lab Biologie Intégrée du Tissu Osseux, Service de Rhumatologie, CHU de Saint-Etienne, Université de Lyon, Saint Etienne, France. 18. The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, USA. 19. Stockholm School of Economics, Stockholm, Sweden. 20. Centre for Metabolic Bone Diseases, University of Sheffield, Sheffield, UK.
Abstract
INTRODUCTION: The International Costs and Utilities Related to Osteoporotic fractures Study is a multinational observational study set up to describe the costs and quality of life (QoL) consequences of fragility fracture. This paper aims to estimate and compare QoL after hip, vertebral, and distal forearm fracture using time-trade-off (TTO), the EuroQol (EQ) Visual Analogue Scale (EQ-VAS), and the EQ-5D-3L valued using the hypothetical UK value set. METHODS: Data were collected at four time-points for five QoL point estimates: within 2 weeks after fracture (including pre-fracture recall), and at 4, 12, and 18 months after fracture. Health state utility values (HSUVs) were derived for each fracture type and time-point using the three approaches (TTO, EQ-VAS, EQ-5D-3L). HSUV were used to estimate accumulated QoL loss and QoL multipliers. RESULTS: In total, 1410 patients (505 with hip, 316 with vertebral, and 589 with distal forearm fracture) were eligible for analysis. Across all time-points for the three fracture types, TTO provided the highest HSUVs, whereas EQ-5D-3L consistently provided the lowest HSUVs directly after fracture. Except for 13-18 months after distal forearm fracture, EQ-5D-3L generated lower QoL multipliers than the other two methods, whereas no equally clear pattern was observed between EQ-VAS and TTO. On average, the most marked differences between the three approaches were observed immediately after the fracture. CONCLUSIONS: The approach to derive QoL markedly influences the estimated QoL impact of fracture. Therefore the choice of approach may be important for the outcome and interpretation of cost-effectiveness analysis of fracture prevention.
INTRODUCTION: The International Costs and Utilities Related to Osteoporotic fractures Study is a multinational observational study set up to describe the costs and quality of life (QoL) consequences of fragility fracture. This paper aims to estimate and compare QoL after hip, vertebral, and distal forearm fracture using time-trade-off (TTO), the EuroQol (EQ) Visual Analogue Scale (EQ-VAS), and the EQ-5D-3L valued using the hypothetical UK value set. METHODS: Data were collected at four time-points for five QoL point estimates: within 2 weeks after fracture (including pre-fracture recall), and at 4, 12, and 18 months after fracture. Health state utility values (HSUVs) were derived for each fracture type and time-point using the three approaches (TTO, EQ-VAS, EQ-5D-3L). HSUV were used to estimate accumulated QoL loss and QoL multipliers. RESULTS: In total, 1410 patients (505 with hip, 316 with vertebral, and 589 with distal forearm fracture) were eligible for analysis. Across all time-points for the three fracture types, TTO provided the highest HSUVs, whereas EQ-5D-3L consistently provided the lowest HSUVs directly after fracture. Except for 13-18 months after distal forearm fracture, EQ-5D-3L generated lower QoL multipliers than the other two methods, whereas no equally clear pattern was observed between EQ-VAS and TTO. On average, the most marked differences between the three approaches were observed immediately after the fracture. CONCLUSIONS: The approach to derive QoL markedly influences the estimated QoL impact of fracture. Therefore the choice of approach may be important for the outcome and interpretation of cost-effectiveness analysis of fracture prevention.
Entities:
Keywords:
Fracture; Health utility; Health-related quality of life; Osteoporosis
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