Estimation of absolute fracture risk among middle-aged and older men and women: the EPIC-Norfolk population cohort study

  • Alireza Moayyeri
  • Stephen Kaptoge
  • Robert N. Luben
  • Nicholas J. Wareham
  • Sheila Bingham
  • Jonathan Reeve
  • Kay Tee Khaw
Locomotor Diseases


While estimates of relative risks associated with risk factors such as age and bone mineral density (BMD) may be of interest for etiologic and comparative purposes, clinical questions such as who might benefit most from preventive interventions or BMD monitoring depend on estimates of absolute fracture risk. The European prospective investigation into cancer (EPIC)-Norfolk study included 25,311 participants (11,476 men) aged 4,079 years in 1993–1997. All participants were followed for osteoporotic fractures to March 2007. Ten-year absolute risk of fracture in men and women were calculated using the baseline survivor function in multivariable Cox proportional-hazards models adjusting for age, sex, history of fractures, body mass index, smoking, and alcohol intake. In comparison of those without history of fracture versus those with history of fracture, the 10-year absolute risk of any fracture in men ranged from 1.0 vs. 1.2% at age 40 years to 3.0 vs. 4.4% at age 75 years. The respective estimates in women ranged from 0.7 vs. 1.0% at age 40 years to 9.3 vs. 17.2% at age 75 years. Statistically significant interaction between age and sex was found (P < 0.001), which contributed to the differences in predicted absolute fracture risks for men and women at different ages. Our study shows the need for population-specific data to develop efficient well calibrated algorithms for assessment of fracture risk. The interaction observed between sex and age points to the need for further prospective studies among men.


Absolute risk Bone fractures Epidemiology Osteoporosis Prospective study 



Bone mineral density


European prospective investigation into cancer


World Health Organization


International classification of diseases



EPIC-Norfolk is supported by program grants from the Medical Research Council and Cancer Research UK with additional support from the Stroke Association, Research into Ageing, the Academy of Medical Sciences, British Heart Foundation, Department of Health, and the Wellcome Trust. The sponsors had no role in the design and conduct of the study, collection, management, analysis and interpretation of the data, and preparation, review or approval of the manuscript.

Conflict of interest statement

All authors declare that they have no conflict of interest in conducting this study and publication of the results.


  1. 1.
    Kanis JA, Johnell O, Oden A, Dawson A, De Laet C, Jonsson B. Ten year probabilities of osteoporotic fractures according to BMD and diagnostic thresholds. Osteoporos Int. 2001;12:989–95. doi: 10.1007/s001980170006.PubMedCrossRefGoogle Scholar
  2. 2.
    Tucker G, Metcalfe A, Pearce C, Need AG, Dick IM, Prince RL, et al. The importance of calculating absolute rather than relative fracture risk. Bone. 2007;41:937–41. doi: 10.1016/j.bone.2007.07.015.PubMedCrossRefGoogle Scholar
  3. 3.
    Siminoski K, Leslie WD, Frame H, Hodsman A, Josse RG, Khan A, et al. Recommendations for bone mineral density reporting in Canada. Can Assoc Radiol J. 2005;56:178–88.PubMedGoogle Scholar
  4. 4.
    Kanis JA, Borgstrom F, De Laet C, Johansson H, Johnell O, Jonsson B, et al. Assessment of fracture risk. Osteoporos Int. 2005;16:581–9. doi: 10.1007/s00198-004-1780-5.PubMedCrossRefGoogle Scholar
  5. 5.
    Leslie WD, Metge C, Ward L. Contribution of clinical risk factors to bone density-based absolute fracture risk assessment in postmenopausal women. Osteoporos Int. 2003;14:334–8. doi: 10.1007/s00198-003-1375-6.PubMedCrossRefGoogle Scholar
  6. 6.
    Cummings SR, Bates D, Black DM. Clinical use of bone densitometry: scientific review. JAMA. 2002;288:1889–97. doi: 10.1001/jama.288.15.1889.PubMedCrossRefGoogle Scholar
  7. 7.
    De Laet C, Oden A, Johansson H, Johnell O, Jonsson B, Kanis JA. The impact of the use of multiple risk indicators for fracture on case-finding strategies: a mathematical approach. Osteoporos Int. 2005;16:313–8. doi: 10.1007/s00198-004-1689-z.PubMedCrossRefGoogle Scholar
  8. 8.
    Ettinger B, Hillier TA, Pressman A, Che M, Hanley DA. Simple computer model for calculating and reporting 5-year osteoporotic fracture risk in postmenopausal women. J Womens Health (Larchmt). 2005;14:159–71. doi: 10.1089/jwh.2005.14.159.CrossRefGoogle Scholar
  9. 9.
    Melton LJIII, Johnell O, Lau E, Mautalen CA, Seeman E. Osteoporosis and the global competition for health care resources. J Bone Miner Res. 2004;19:1055–8. doi: 10.1359/JBMR.040316.PubMedCrossRefGoogle Scholar
  10. 10.
    Kanis JA. World Health Organization Scientific Group. Assessment of osteoporosis at the primary health-care level. Technical Report. UK: World Health Organization Collaborating Center for Metabolic Bone Disease, University of Sheffield; 2007.Google Scholar
  11. 11.
    Kanis JA, Johnell O, Oden A, Johansson H, McCloskey E. FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos Int. 2008;19:385–97. doi: 10.1007/s00198-007-0543-5.PubMedCrossRefGoogle Scholar
  12. 12.
    Cooper C, Campion G, Melton LJIII. Hip fractures in the elderly: a world-wide projection. Osteoporos Int. 1992;2:285–9. doi: 10.1007/BF01623184.PubMedCrossRefGoogle Scholar
  13. 13.
    Schwartz AV, Kelsey JL, Maggi S, Tuttleman M, Ho SC, Jonsson PV, et al. International variation in the incidence of hip fractures: cross-national project on osteoporosis for the World Health Organization Program for research on aging. Osteoporos Int. 1999;9:242–53. doi: 10.1007/s001980050144.PubMedCrossRefGoogle Scholar
  14. 14.
    Kannus P, Parkkari J, Sievanen H, Heinonen A, Vuori I, Jarvinen M. Epidemiology of hip fractures. Bone. 1996;18:57S–63S. doi: 10.1016/8756-3282(95)00381-9.PubMedCrossRefGoogle Scholar
  15. 15.
    Cooper C. Epidemiology and public health impact of osteoporosis. Baillieres Clin Rheumatol. 1993;7:459–77. doi: 10.1016/S0950-3579(05)80073-1.PubMedCrossRefGoogle Scholar
  16. 16.
    Day N, Oakes S, Luben R, Khaw KT, Bingham S, Welch A, et al. EPIC-Norfolk: study design and characteristics of the cohort. European Prospective Investigation of Cancer. Br J Cancer. 1999;80(Suppl 1):95–103.PubMedGoogle Scholar
  17. 17.
    Cox DR, Oakes D. Analysis of survival data. London: Chapman and Hall; 1984.Google Scholar
  18. 18.
    Royston P, Ambler G, Sauerbrei W. The use of fractional polynomials to model continuous risk variables in epidemiology. Int J Epidemiol. 1999;28:964–74. doi: 10.1093/ije/28.5.964.PubMedCrossRefGoogle Scholar
  19. 19.
    Harrell FE Jr, Lee KL, Califf RM, Pryor DB, Rosati RA. Regression modelling strategies for improved prognostic prediction. Stat Med. 1984;3:143–52. doi: 10.1002/sim.4780030207.PubMedCrossRefGoogle Scholar
  20. 20.
    Hosmer DW, Lemeshow S. Applied survival analysis. New York: Wiley; 1999.Google Scholar
  21. 21.
    Nguyen ND, Frost SA, Center JR, Eisman JA, Nguyen TV. Development of a nomogram for individualizing hip fracture risk in men and women. Osteoporos Int. 2007;18:1109–17. doi: 10.1007/s00198-007-0362-8.PubMedCrossRefGoogle Scholar
  22. 22.
    Nguyen ND, Ahlborg HG, Center JR, Eisman JA, Nguyen TV. Residual lifetime risk of fractures in women and men. J Bone Miner Res. 2007;22:781–8. doi: 10.1359/jbmr.070315.PubMedCrossRefGoogle Scholar
  23. 23.
    Abrahamsen B, Vestergaard P, Rud B, Barenholdt O, Jensen JE, Nielsen SP, et al. Ten-year absolute risk of osteoporotic fractures according to BMD T score at menopause: the Danish osteoporosis prevention study. J Bone Miner Res. 2006;21:796–800. doi: 10.1359/jbmr.020604.PubMedCrossRefGoogle Scholar
  24. 24.
    Kanis JA, Johnell O, De Laet C, Jonsson B, Oden A, Ogelsby AK. International variations in hip fracture probabilities: implications for risk assessment. J Bone Miner Res. 2002;17:1237–44. doi: 10.1359/jbmr.2002.17.7.1237.PubMedCrossRefGoogle Scholar
  25. 25.
    Marshall D, Johnell O, Wedel H. Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures. BMJ. 1996;312:1254–9.PubMedGoogle Scholar
  26. 26.
    Kanis JA, Oden A, Johnell O, Johansson H, De Laet C, Brown J, et al. The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women. Osteoporos Int. 2007;18:1033–46. doi: 10.1007/s00198-007-0343-y.PubMedCrossRefGoogle Scholar
  27. 27.
    Richards JB, Leslie WD, Joseph L, Siminoski K, Hanley DA, Adachi JD, et al. Changes to osteoporosis prevalence according to method of risk assessment. J Bone Miner Res. 2007;22:228–34. doi: 10.1359/jbmr.061109.PubMedCrossRefGoogle Scholar
  28. 28.
    Leslie WD, Siminoski K, Brown JP. Comparative effects of densitometric and absolute fracture risk classification systems on projected intervention rates in postmenopausal women. J Clin Densitom. 2007;10:124–31. doi: 10.1016/j.jocd.2007.01.003.PubMedCrossRefGoogle Scholar
  29. 29.
    Jackson SA, Tenenhouse A, Robertson L. Vertebral fracture definition from population-based data: preliminary results from the Canadian Multicenter Osteoporosis Study (CaMos). Osteoporos Int. 2000;11:680–7. doi: 10.1007/s001980070066.PubMedCrossRefGoogle Scholar
  30. 30.
    Leslie WD, Metge C. Establishing a regional bone density program: lessons from the Manitoba experience. J Clin Densitom. 2003;6:275–82. doi: 10.1385/JCD:6:3:275.PubMedCrossRefGoogle Scholar
  31. 31.
    Khaw KT, Reeve J, Luben R, Bingham S, Welch A, Wareham N, et al. Prediction of total and hip fracture risk in men and women by quantitative ultrasound of the calcaneus: EPIC-Norfolk prospective population study. Lancet. 2004;363:197–202. doi: 10.1016/S0140-6736(03)15325-1.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Alireza Moayyeri
    • 1
  • Stephen Kaptoge
    • 1
  • Robert N. Luben
    • 1
  • Nicholas J. Wareham
    • 2
  • Sheila Bingham
    • 3
  • Jonathan Reeve
    • 4
  • Kay Tee Khaw
    • 1
  1. 1.Department of Public Health and Primary Care, Institute of Public Health, Strangeways Research LaboratoryUniversity of CambridgeCambridgeUK
  2. 2.MRC Epidemiology UnitInstitute of Metabolic ScienceCambridgeUK
  3. 3.MRC Dunn Human Nutrition UnitUniversity of CambridgeCambridgeUK
  4. 4.Department of MedicineUniversity of CambridgeCambridgeUK

Personalised recommendations