Helena Johansson1,2, Sapna S Dela3, Bilkish Cassim4, Farhanah Paruk5, Susan L Brown6, Magda Conradie7, Nicholas C Harvey8, Johannes D Jordaan9, Asgar A Kalla10, Enwu Liu1, Mattias Lorentzon1,11, Mkhululi Lukhele12, Eugene V McCloskey2,13, Ozayr Mohamed14, Pariva Chutterpaul5, Liesbeth Vandenput1,15, John A Kanis16,17. 1. Mary McKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia. 2. Centre for Metabolic Bone Diseases, University of Sheffield Medical School, Beech Hill Road, S10 2RX, Sheffield, UK. 3. Department of Internal Medicine, Edendale Hospital, School of Clinical Medicine (SCM), University of KwaZulu-Natal, Durban, South Africa. 4. Department of Geriatrics, School of Clinical Medicine (SCM), College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa. 5. Division of Internal Medicine, School of Clinical Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa. 6. Department of Medicine, Mahathma Gandhi Memorial Hospital, Durban, South Africa. 7. Division of Endocrinology, University of Stellenbosch, Stellenbosch, South Africa. 8. MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK. 9. Division of Orthopaedics, University of Stellenbosch, Stellenbosch, South Africa. 10. Division of Rheumatology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa. 11. Geriatric Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden. 12. Department of Orthopaedics, University of Witwatersrand, Witwatersrand, South Africa. 13. Mellanby Centre for bone research, Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK. 14. Discipline of Public Health Medicine, SCM, College of Health Sciences, UKZN, Durban, South Africa. 15. Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. 16. Mary McKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia. w.j.pontefract@shef.ac.uk. 17. Centre for Metabolic Bone Diseases, University of Sheffield Medical School, Beech Hill Road, S10 2RX, Sheffield, UK. w.j.pontefract@shef.ac.uk.
Abstract
The hip fracture rates in South Africa were used to create ethnic-specific FRAX® models to facilitate fracture risk assessment. INTRODUCTION: The aim of this study was to develop FRAX models to compute the 10-year probability of hip fracture and major osteoporotic fracture and assess their potential clinical application. METHODS: Age- and sex-specific incidence of hip fracture and national mortality rates were incorporated into a FRAX model for the White, Black African, Coloured and Indian population of South Africa. Age-specific 10-year probabilities of a major osteoporotic fracture were calculated in women to determine fracture probabilities at a femoral neck T score of -2.5 SD, or those equivalent to a woman with a prior fragility fracture. Fracture probabilities were compared with those from selected countries. RESULTS: Probabilities were consistently higher in Indian than in Coloured men and women, in turn, higher than in Black South Africans. For White South Africans, probabilities were lower than in Indians at young ages up to the age of about 80 years. When a BMD T score of -2.5 SD was used as an intervention threshold, FRAX probabilities in women age 50 years were approximately 2-fold higher than in women of the same age but with an average BMD and no risk factors. The increment in risk associated with the BMD threshold decreased progressively with age such that, at the age of 80 years or more, a T score of -2.5 SD was no longer a risk factor. Probabilities equivalent to women with a previous fracture rose with age and identified women at increased risk at all ages. CONCLUSIONS: These FRAX models should enhance accuracy of determining fracture probability amongst the South African population and help guide decisions about treatment.
The hip fracture rates in South Africa were used to create ethnic-specific FRAX® models to facilitate fracture risk assessment. INTRODUCTION: The aim of this study was to develop FRAX models to compute the 10-year probability of hip fracture and major osteoporotic fracture and assess their potential clinical application. METHODS: Age- and sex-specific incidence of hip fracture and national mortality rates were incorporated into a FRAX model for the White, Black African, Coloured and Indian population of South Africa. Age-specific 10-year probabilities of a major osteoporotic fracture were calculated in women to determine fracture probabilities at a femoral neck T score of -2.5 SD, or those equivalent to a woman with a prior fragility fracture. Fracture probabilities were compared with those from selected countries. RESULTS: Probabilities were consistently higher in Indian than in Coloured men and women, in turn, higher than in Black South Africans. For White South Africans, probabilities were lower than in Indians at young ages up to the age of about 80 years. When a BMD T score of -2.5 SD was used as an intervention threshold, FRAX probabilities in women age 50 years were approximately 2-fold higher than in women of the same age but with an average BMD and no risk factors. The increment in risk associated with the BMD threshold decreased progressively with age such that, at the age of 80 years or more, a T score of -2.5 SD was no longer a risk factor. Probabilities equivalent to women with a previous fracture rose with age and identified women at increased risk at all ages. CONCLUSIONS: These FRAX models should enhance accuracy of determining fracture probability amongst the South African population and help guide decisions about treatment.
Entities:
Keywords:
Epidemiology; FRAX; Fracture probability; Hip fracture; Osteoporosis; South Africa
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