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Table. 
Weighted C Statistics in Predicting Hip and Major Osteoporotic Fracturesa
Weighted C Statistics in Predicting Hip and Major Osteoporotic Fracturesa
1.
Unnanuntana  AGladnick  BPDonnelly  ELane  JM The assessment of fracture risk. J Bone Joint Surg Am 2010;92 (3) 743- 753
PubMedArticle
2.
World Health Organization Collaborating Centre for Metabolic Bone Diseases, FRAX WHO Fracture Risk Assessment Tool. http://www.shef.ac.uk/FRAX/tool.jsp?locationValue=9. Accessed Jun 23, 2010
3.
Ensrud  KELui  LYTaylor  BC  et al. Study of Osteoporotic Fractures Research Group, A comparison of prediction models for fractures in older women: is more better? Arch Intern Med 2009;169 (22) 2087- 2094
PubMedArticle
4.
Curtis  JRMcClure  LADelzell  E  et al.  Population-based fracture risk assessment and osteoporosis treatment disparities by race and gender. J Gen Intern Med 2009;24 (8) 956- 962
PubMedArticle
5.
Dawson-Hughes  BLooker  ACTosteson  ANAJohansson  HKanis  JAMelton  LJ  III The potential impact of new National Osteoporosis Foundation guidance on treatment patterns. Osteoporos Int 2010;21 (1) 41- 52
PubMedArticle
6.
An  AB Performing logistic regression on survey data with the new SURVEYLOGISTIC procedure.  Paper presented at: Twenty-seventh Annual SAS User Group International Conference April 14-17, 2002 Orlando, FL.Paper 258-27
7.
Izrael  DBattaglia  AAHoaglin  DCBattaglia  MP SAS macros and tools for working with weighted logistic regression models that use survey data.  Paper presented at: Twenty-eighth Annual SAS User Group International Conference March 30–April 2, 2003 Seattle, WA.Paper 275-28
Research Letters
November 22, 2010

Predicting Hip and Major Osteoporotic Fractures Using Administrative Data

Author Affiliations

Author Affiliations: Departments of Epidemiology (Drs Yun and Delzell) and Health Care Organization and Policy (Drs Kilgore, Becker, and Morrisey) and Division of Clinical Immunology and Rheumatology (Dr Curtis), University of Alabama at Birmingham; and Department of Medicine, Minneapolis Veterans Affairs Medical Center, Section of General Internal Medicine, Minneapolis, Minnesota (Dr Ensrud).

Arch Intern Med. 2010;170(21):1940-1942. doi:10.1001/archinternmed.2010.410

The Fracture Risk Assessment tool (FRAX) was released in 2008 by the World Health Organization (WHO).1 The FRAX algorithm uses bone mineral density (BMD) and 11 additional clinical and physiological risk factors to estimate a person's 10-year probability of hip and other major osteoporotic fracture.2 The latter is defined by the WHO as a hip, clinical vertebral, distal forearm, or humerus fracture. Ensrud et al,3 using risk prediction models that included only age and BMD or age and fracture history, concluded that these few risk factors predicted 10-year risk of hip and other major osteoporotic fractures as well as FRAX-based models. We performed a similar evaluation using administrative claims data, which do not include information on BMD. We derived and examined several fracture risk prediction models to determine if demographics, history of fracture, and comorbidities—all identifiable within administrative claims data—could be used to predict hip fracture and major osteoporotic fractures, as well as models with additional clinical information or models derived from FRAX. This type of prediction model might be useful for large health plans to target higher-risk individuals for more aggressive screening efforts including BMD testing.

Methods

We performed a retrospective cohort study using the Medicare Current Beneficiary Survey (MCBS), a rotating panel in-home survey of approximately 12 000 community- or institutional-dwelling beneficiaries linked to Medicare claims data, for the years 1999 through 2005. The MCBS can provide national estimates for the US Medicare population owing to its unique multistage sampling design. Eligible subjects for this analysis were 65 years or older and had Medicare part A and B coverage, 1 year of baseline data, and 2 years of follow-up data. For analyses of each type of fracture, beneficiaries with any claims for the particular fracture during the baseline were excluded.

We used inpatient and outpatient administrative claims data to obtain demographic, baseline comorbidity, and fracture history information and used MCBS survey data to obtain information on height, weight, activities of daily living, body mass index (BMI), current smoking status, osteoporosis drug use and glucocorticoid use. Alcohol status and fracture history were obtained from both claims and survey data. Because the MCBS does not contain information regarding family history of hip fractures, we used population-based data4 to simulate this risk factor according to previously published methods.5

We used multivariable logistic regression modeling to evaluate the predictive ability of models with varying degrees of complexity. The C statistic, a measure of area under the receiver operating characteristic curve, was reported and compared across models. To provide statistically valid inferences and account for sampling, we used survey logistic regression for the analysis.6 To obtain the weighted C statistic and its 95% confidence interval, we applied bootstrapping methods reported by Izrael et al.7

Results

Of the more than 12 000 beneficiaries eligible for evaluation of risk of hip fracture and other major osteoporotic fracture, 187 experienced a hip fracture and 430 had a major osteoporotic fracture (Table). In the analysis of hip fracture, the sex-specific, weighted C statistic was 0.74 for the model using only administrative claims data containing demographic characteristics, fracture history, and comorbidities, which minimally changed to 0.75 when we added the extra variables from MCBS. The C statistic for the model that used FRAX score only (using BMI) was 0.64. The analysis of major osteoporotic fractures found similar patterns with modestly lower C statistics. The C statistics were numerically higher in men than in women and higher in African American than in white beneficiaries, but confidence intervals were wide.

Comment

Our results indicate that simple models based on administrative claims data are useful for predicting hip and major osteoporotic fractures. Although BMD and BMI were not available in claims data, our models generated using only administrative data yielded comparable results compared with more complex models with clinical risk factors or FRAX without BMD. This result is consistent with those reported by Ensrud et al,3 and our C statistics are comparable with their results, including models with BMD. Because the follow-up time in MCBS was limited to 2 years, we could not assess the calibration of the risk prediction models, only their discrimination. However, our well-defined cohort is generalizable to the United States Medicare population. Our findings, which suggest that administrative data alone can risk stratify patients to identify those who should be considered higher priorities for further fracture risk assessment including BMD testing, have implications for screening at a population level by health plans with ready access to administrative data.

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Article Information

Correspondence: Dr Yun, Department of Epidemiology, University of Alabama at Birmingham, 1665 University Blvd, RPHB 517D, Birmingham, AL 35294 (yunn@uab.edu).

Author Contributions:Study concept and design: Yun, Delzell, Becker, and Curtis. Acquisition of data: Delzell, Kilgore, Morrisey, and Curtis. Analysis and interpretation of data: Yun, Delzell, Ensrud, Kilgore, Morrisey, and Curtis. Drafting of the manuscript: Yun and Curtis. Critical revision of the manuscript for important intellectual content: Yun, Delzell, Ensrud, Kilgore, Becker, Morrisey, and Curtis. Statistical analysis: Yun, Becker, and Curtis. Obtained funding: Delzell and Morrisey. Administrative, technical, and material support: Yun, Delzell, Kilgore, and Curtis. Study supervision: Delzell and Curtis.

Financial Disclosure: Dr Curtis was a consultant and performed research for and has received honoraria from Procter & Gamble, Merck, Novartis, and Eli Lilly.

Funding/Support: Dr Curtis receives support from NIH/NIAMS (grant AR053351). This research was supported by a contract between University of Alabama at Birmingham and Amgen Inc.

Role of the Sponsors: Only the authors from University of Alabama at Birmingham had access to the Medicare data used. The analysis, presentation, and interpretation of the results were solely the responsibility of the authors.

References
1.
Unnanuntana  AGladnick  BPDonnelly  ELane  JM The assessment of fracture risk. J Bone Joint Surg Am 2010;92 (3) 743- 753
PubMedArticle
2.
World Health Organization Collaborating Centre for Metabolic Bone Diseases, FRAX WHO Fracture Risk Assessment Tool. http://www.shef.ac.uk/FRAX/tool.jsp?locationValue=9. Accessed Jun 23, 2010
3.
Ensrud  KELui  LYTaylor  BC  et al. Study of Osteoporotic Fractures Research Group, A comparison of prediction models for fractures in older women: is more better? Arch Intern Med 2009;169 (22) 2087- 2094
PubMedArticle
4.
Curtis  JRMcClure  LADelzell  E  et al.  Population-based fracture risk assessment and osteoporosis treatment disparities by race and gender. J Gen Intern Med 2009;24 (8) 956- 962
PubMedArticle
5.
Dawson-Hughes  BLooker  ACTosteson  ANAJohansson  HKanis  JAMelton  LJ  III The potential impact of new National Osteoporosis Foundation guidance on treatment patterns. Osteoporos Int 2010;21 (1) 41- 52
PubMedArticle
6.
An  AB Performing logistic regression on survey data with the new SURVEYLOGISTIC procedure.  Paper presented at: Twenty-seventh Annual SAS User Group International Conference April 14-17, 2002 Orlando, FL.Paper 258-27
7.
Izrael  DBattaglia  AAHoaglin  DCBattaglia  MP SAS macros and tools for working with weighted logistic regression models that use survey data.  Paper presented at: Twenty-eighth Annual SAS User Group International Conference March 30–April 2, 2003 Seattle, WA.Paper 275-28
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