Factors Associated With 5-Year Risk of Hip Fracture in Postmenopausal Women | Geriatrics | JAMA | JAMA Network
[Skip to Navigation]
Access to paid content on this site is currently suspended due to excessive activity being detected from your IP address 34.239.150.57. Please contact the publisher to request reinstatement.
1.
National Center for Health Statistics.  National Hospital Discharge and Ambulatory Surgery Data. http://www.cdc.gov/nchs/about/major/hdasd/nhdstab.htm. Accessed July 7, 2007
2.
Braithwaite RS, Col NF, Wong JB. Estimating hip fracture morbidity, mortality and costs.  J Am Geriatr Soc. 2003;51(3):364-37012588580Google ScholarCrossref
3.
Cummings SR, Nevitt MC, Browner WS.  et al.  Risk factors for hip fracture in white women: Study of Osteoporotic Fractures Research Group.  N Engl J Med. 1995;332(12):767-7737862179Google ScholarCrossref
4.
Black DM, Steinbuch M, Palermo L.  et al.  An assessment tool for predicting fracture risk in postmenopausal women.  Osteoporos Int. 2001;12(7):519-52811527048Google ScholarCrossref
5.
Buist DS, LaCroix AZ, Manfredonia D, Abbott T. Identifying postmenopausal women at high risk of fracture in populations: a comparison of three strategies.  J Am Geriatr Soc. 2002;50(6):1031-103812110062Google ScholarCrossref
6.
McGrother CW, Donaldson MM, Clayton D, Abrams KR, Clarke M. Evaluation of a hip fracture risk score for assessing elderly women: the Melton Osteoporotic Fracture (MOF) study.  Osteoporos Int. 2002;13(1):89-9611883411Google ScholarCrossref
7.
van Staa TP, Leufkens HG, Cooper C. Utility of medical and drug history in fracture risk prediction among men and women.  Bone. 2002;31(4):508-51412398948Google ScholarCrossref
8.
Hofman A, Grobbee DE, de Jong PT, van den Ouweland FA. Determinants of disease and disability in the elderly: the Rotterdam Elderly Study.  Eur J Epidemiol. 1991;7(4):403-4221833235Google ScholarCrossref
9.
Wainwright SA, Marshall LM, Ensrud KE.  et al.  Hip fracture in women without osteoporosis.  J Clin Endocrinol Metab. 2005;90(5):2787-279315728213Google ScholarCrossref
10.
Fang J, Freeman R, Jeganathan R, Alderman MH. Variations in hip fracture hospitalization rates among different race/ethnicity groups in New York City.  Ethn Dis. 2004;14(2):280-28415132215Google Scholar
11.
Ritenbaugh C, Patterson RE, Chlebowski RT.  et al.  The Women's Health Initiative Dietary Modification trial: overview and baseline characteristics of participants.  Ann Epidemiol. 2003;13(9):(suppl)  S87-S9714575941Google ScholarCrossref
12.
Stefanick ML, Cochrane BB, Hsia J, Barad DH, Liu JH, Johnson SR. The Women's Health Initiative postmenopausal hormone trials: overview and baseline characteristics of participants.  Ann Epidemiol. 2003;13(9):(suppl)  S78-S8614575940Google ScholarCrossref
13.
Jackson RD, LaCroix AZ, Cauley JA, McGowan J. The Women's Health Initiative calcium-vitamin D trial: overview and baseline characteristics of participants.  Ann Epidemiol. 2003;13(9):(suppl)  S98-S10614575942Google ScholarCrossref
14.
 Design of the Women's Health Initiative clinical trial and observational study: The Women's Health Initiative Study Group.  Control Clin Trials. 1998;19(1):61-1099492970Google ScholarCrossref
15.
Curb JD, McTiernan A, Heckbert SR.  et al.  Outcomes ascertainment and adjudication methods in the Women's Health Initiative.  Ann Epidemiol. 2003;13(9):(suppl)  S122-S12814575944Google ScholarCrossref
16.
Anderson GL, Manson J, Wallace R.  et al.  Implementation of the Women's Health Initiative study design.  Ann Epidemiol. 2003;13(9):(suppl)  S5-S1714575938Google ScholarCrossref
17.
Jackson RD, LaCroix AZ, Gass M.  et al.  Calcium plus vitamin D supplementation and the risk of fractures.  N Engl J Med. 2006;354(7):669-68316481635Google ScholarCrossref
18.
Anderson GL, Limacher M, Assaf AR.  et al.  Effects of conjugated equine estrogen in postmenopausal women with hysterectomy: the Women's Health Initiative randomized controlled trial.  JAMA. 2004;291(14):1701-171215082697Google ScholarCrossref
19.
Cauley JA, Robbins J, Chen Z.  et al.  Effects of estrogen plus progestin on risk of fracture and bone mineral density: the Women's Health Initiative randomized trial.  JAMA. 2003;290(13):1729-173814519707Google ScholarCrossref
20.
Langer RD, White E, Lewis CE, Kotchen JM, Hendrix SL, Trevisan M. The Women's Health Initiative Observational Study: baseline characteristics of participants and reliability of baseline measures.  Ann Epidemiol. 2003;13(9):(suppl)  S107-S12114575943Google ScholarCrossref
21.
Hays J, Hunt JR, Hubbell FA.  et al.  The Women's Health Initiative recruitment methods and results.  Ann Epidemiol. 2003;13(9):(suppl)  S18-S7714575939Google ScholarCrossref
22.
Chen Z, Kooperberg C, Pettinger MB.  et al.  Validity of self-report for fractures among a multiethnic cohort of postmenopausal women: results from the Women's Health Initiative observational study and clinical trials.  Menopause. 2004;11(3):264-27415167305Google ScholarCrossref
23.
Hsia J, Wu L, Allen C.  et al.  Physical activity and diabetes risk in postmenopausal women.  Am J Prev Med. 2005;28(1):19-2515626551Google ScholarCrossref
24.
Wolf AM, Hunter DJ, Colditz GA.  et al.  Reproducibility and validity of a self-administered physical activity questionnaire.  Int J Epidemiol. 1994;23(5):991-9997860180Google ScholarCrossref
25.
Ainsworth BE, Haskell WL, Leon AS.  et al.  Compendium of physical activities: classification of energy costs of human physical activities.  Med Sci Sports Exerc. 1993;25(1):71-808292105Google ScholarCrossref
26.
Andresen EM, Malmgren JA, Carter WB, Patrick DL. Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale).  Am J Prev Med. 1994;10(2):77-848037935Google Scholar
27.
Patterson RE, Kristal AR, Tinker LF, Carter RA, Bolton MP, Agurs-Collins T. Measurement characteristics of the Women's Health Initiative food frequency questionnaire.  Ann Epidemiol. 1999;9(3):178-18710192650Google ScholarCrossref
28.
Neuhouser ML, Patterson RE, King IB, Horner NK, Lampe JW. Selected nutritional biomarkers predict diet quality.  Public Health Nutr. 2003;6(7):703-70914552672Google ScholarCrossref
29.
Stone M. Cross-Validatory Choice and Assessment of Statistical Predictions.  J R Stat Soc [Ser B]. 1974;36(2):111-147Google Scholar
30.
Lloyd-Jones DM, Liu K, Tian L, Greenland P. Narrative review: assessment of C-reactive protein in risk prediction for cardiovascular disease.  Ann Intern Med. 2006;145(1):35-4216818927Google ScholarCrossref
31.
Kooperberg C. polspline: Polynomial spline routines [computer program]. R package version 1.0.14. 2007
32.
Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: visualizing classifier performance in R.  Bioinformatics. 2005;21(20):3940-394116096348Google ScholarCrossref
33.
 R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing;2007. http://www.R-project.org. Accessed November 6, 2007
Original Contribution
November 28, 2007

Factors Associated With 5-Year Risk of Hip Fracture in Postmenopausal Women

Author Affiliations
 

Author Affiliations: Department of Internal Medicine, University of California at Davis School of Medicine, Sacramento (Dr Robbins); Fred Hutchinson Cancer Research Center, Seattle, Washington (Mr Aragaki and Dr Kooperberg); University of Cincinnati College of Medicine, Cincinnati, Ohio (Dr Watts); Division of Endocrinology, Diabetes and Metabolism, University at Buffalo, Buffalo, New York (Dr Wactawski-Wende); Department of Internal Medicine and Physical Medicine, Ohio State University, Columbus (Dr Jackson); Department of Medicine, Harvard Medical School, Boston, Massachusetts (Dr LeBoff); Division of Preventive Medicine, University of Alabama at Birmingham, (Dr Lewis); Division of Epidemiology and Biostatistics, University of Arizona, Tucson (Dr Chen); Stanford Prevention Research Center, Stanford, California (Dr Stefanick); Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania (Dr Cauley).

JAMA. 2007;298(20):2389-2398. doi:10.1001/jama.298.20.2389
Abstract

Context The 329 000 hip fractures that annually occur in the United States are associated with high morbidity, mortality, and cost. Identification of those at high risk is a step toward prevention.

Objective To develop an algorithm to predict the 5-year risk of hip fracture in postmenopausal women.

Design, Setting, and Participants A total of 93 676 women who participated in the observational component of the Women's Health Initiative (WHI), a multiethnic longitudinal study, were used to develop a predictive algorithm based on commonly available clinical features. Selected factors that predicted hip fracture were then validated by 68 132 women who participated in the clinical trial. The model was tested in a subset of 10 750 women who had undergone dual-energy x-ray absorptiometry (DXA) scans for bone mass density assessment.

Main Outcome Measure The prediction of centrally adjudicated hip fracture, measured by the area under the receiver operator characteristic (ROC) curves.

Results During a mean (SD) follow-up of 7.6 (1.7) years, 1132 hip fractures were identified among women participating in the observational study (annualized rate, 0.16%), whereas during a mean follow-up of 8.0 (1.7) years, 791 hip fractures occurred among women participating in the clinical trial (annualized rate, 0.14%). Eleven factors predicted hip fracture within 5 years: age, self-reported health, weight, height, race/ethnicity, self-reported physical activity, history of fracture after age 54 years, parental hip fracture, current smoking, current corticosteroid use, and treated diabetes. Receiver operating characteristic curves showed that the algorithm had an area under the curve of 80% (95% confidence interval [CI], 0.77%-0.82%) when tested in the cohort of different women who were in the clinical trial. A simplified point score was developed for the probability of hip fracture. Receiver operating characteristic curves comparing DXA-scan prediction based on a 10% subset of the cohort and the algorithm among those who participated the clinical trial were similar, with an area under the curve of 79% (95% CI, 73%-85%) vs 71% (95% CI, 66%-76%).

Conclusion This algorithm, based on 11 clinical factors, may be useful to predict the 5-year risk of hip fracture among postmenopausal women of various ethnic backgrounds. Further studies are needed to assess the clinical implication of the algorithm in general and specifically to identify treatment benefits.

×