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Algorithms can be used to identify fragility fracture cases in physician-claims databases

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Abstract

Summary

Physician-billing claims databases can be used to determine the incidence of fractures in the community. This study tested three algorithms designed to accurately and reliably identify fractures from a physician-billing claims database and concluded that they were useful for identifying all types of fractures, except vertebral, sacral, and coccyx fractures.

Introduction

To develop and validate algorithms that identify fracture events from a physician-billing claims database (PCDs).

Methods

Three algorithms were developed using physician’s specialty, diagnostic, and medical service codes used in a PCD from the province of Quebec. Algorithm validity was assessed via calculation of positive predictive values (PPV; via verification of a sample of algorithm-identified cases with hospitalization files) and sensitivities (via cross-referencing respective algorithm-identified fracture cases with a well-characterized fracture cohort).

Results

PPV and sensitivity varied across fracture sites. For most fracture sites, the PPV with algorithm 3 was higher than with algorithms 1 or 2. Except for knee fracture, the PPVs ranged from 0.81 to 0.96. Sensitivities were low at the vertebral, sacral, and coccyx sites (0.40–0.50), but high at all other fracture sites. For 95% of fractures, the fracture site identified by algorithm agreed with the fracture site from patients’ medical records. Fracture dates identified by algorithm were within 2 days of the actual fracture date in 88% of fracture cases. Among cases identified by algorithm 3 to have had an open reduction (N = 461), 95% underwent surgery according to their respective medical charts.

Conclusion

Algorithms using PCDs are accurate and reliable for identifying incident fractures associated with osteoporosis-related fracture sites. The identification of these fractures in the community is important for helping to estimate the burden associated with osteoporosis and the utility of programs designed to reduce the rates of fragility fracture.

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Acknowledgments

This work was funded by an unrestricted grant from Servier Canada Inc. It is also part of the study Recognizing Osteoporosis and its Consequences in Québec (ROCQ), which has been made possible through the support of founding partners Merck Canada, Sanofi-Aventis Canada Inc., and Warner Chilcott, as well as major partner Amgen Canada Inc. and minor partners Eli Lilly Canada Inc. and Novartis Pharma Canada Inc.

We would like to acknowledge the important contribution of medical archivist Karine Picard who conducted the review of cases in hospitals’ medical records and Dr. K. Shawn Davison for revision of the manuscript.

Conflicts of interest

None.

Author information

Authors and Affiliations

  1. National Institute of Public Health of Québec, 945, Wolfe Avenue, Quebec, QC, G1V 5B3, Canada

    S. Jean & B. Candas

  2. Laval University, CHUQ Research Centre, Quebec, QC, Canada

    É. Belzile, L. Bessette, S. Dodin & J. P. Brown

  3. McGill University Health Centre, McGill University, Montreal, QC, Canada

    S. Morin

Authors
  1. S. Jean
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  2. B. Candas
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  3. É. Belzile
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  4. S. Morin
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  5. L. Bessette
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  6. S. Dodin
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  7. J. P. Brown
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Corresponding author

Correspondence to S. Jean.

Appendix 1

Appendix 1

Table 7 Medical services billing and ICD-9 codes selected in the physician claims database
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Table 8 Predictive positive value by fracture site and type of medical service billing code
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Jean, S., Candas, B., Belzile, É. et al. Algorithms can be used to identify fragility fracture cases in physician-claims databases. Osteoporos Int 23, 483–501 (2012). https://doi.org/10.1007/s00198-011-1559-4

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  • DOI: https://doi.org/10.1007/s00198-011-1559-4

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