Current Rheumatology Reports

, Volume 13, Issue 3, pp 273–282 | Cite as

Potential and Pitfalls of Using Large Administrative Claims Data to Study the Safety of Osteoporosis Therapies

  • Jie Zhang
  • Huifeng Yun
  • Nicole C. Wright
  • Meredith Kilgore
  • Kenneth G. Saag
  • Elizabeth Delzell
Article

Abstract

Long-term bisphosphonate use may be associated with several rare adverse events. Such associations are not optimally evaluated in conventional randomized controlled trials due to the requirements of large numbers of patients and long-term follow-up. Alternatively, administrative claims data from various sources such as Medicare have been used. Because claims data are collected for billing and reimbursement purposes, they have limitations, including uncertain diagnostic validity and lack of detailed clinical information. Using such data for pharmacoepidemiologic research requires complex methodologies that may be less familiar to many researchers and clinicians. In this review, we discuss the strengths and limitations of using claims data for osteoporosis drug safety research, summarize recent advancements in methodologies that may be used to address the limitations, and present directions for future research using claims data.

Keywords

Osteoporosis medication Safety monitoring Claims data 

References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. 1.
    Becker DJ, Kilgore ML, Morrisey MA. The societal burden of osteoporosis. Curr Rheumatol Rep. 2010;12(3):186–91.PubMedCrossRefGoogle Scholar
  2. 2.
    Abrahamsen B, Eiken P, Eastell R. Subtrochanteric and diaphyseal femur fractures in patients treated with alendronate: a register-based national cohort study. J Bone Miner Res. 2009;24(6):1095–102.PubMedCrossRefGoogle Scholar
  3. 3.
    Nieves JW, Bilezikian JP, Lane JM, et al. Fragility fractures of the hip and femur: incidence and patient characteristics. Osteoporos Int. 2010;21(3):399–408.PubMedCrossRefGoogle Scholar
  4. 4.
    Cartsos VM, Zhu S, Zavras AI. Bisphosphonate use and the risk of adverse jaw outcomes: a medical claims study of 714, 217 people. J Am Dent Assoc. 2008;139(1):23–30.PubMedGoogle Scholar
  5. 5.
    Green J, Czanner G, Reeves G, et al. Oral bisphosphonates and risk of cancer of oesophagus, stomach, and colorectum: case-control analysis within a UK primary care cohort. BMJ. 2010;341:c4444.PubMedCrossRefGoogle Scholar
  6. 6.
    Cardwell CR, Abnet CC, Cantwell MM, Murray LJ. Exposure to oral bisphosphonates and risk of esophageal cancer. JAMA. 2010;304(6):657–663.PubMedCrossRefGoogle Scholar
  7. 7.
    Schneeweiss S. Developments in post-marketing comparative effectiveness research. Clin Pharmacol Ther. 2007;82(2):143–156.PubMedCrossRefGoogle Scholar
  8. 8.
    Sorensen HT, Lash TL, Rothman KJ. Beyond randomized controlled trials: a critical comparison of trials with nonrandomized studies. Hepatology. 2006;44(5):1075–1082.PubMedCrossRefGoogle Scholar
  9. 9.
    Black DM, Kelly MP, Genant HK, et al. Bisphosphonates and fractures of the subtrochanteric or diaphyseal femur. N Engl J Med. 2010;362(19):1761–1771.PubMedCrossRefGoogle Scholar
  10. 10.
    Psaty BM, Korn D. Congress responds to the IOM drug safety report–in full. JAMA. 2007;298(18):2185–2187.PubMedCrossRefGoogle Scholar
  11. 11.
    Schneeweiss S, Avorn J. A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol. 2005;58(4):323–337.PubMedCrossRefGoogle Scholar
  12. 12.
    Suissa S, Garbe E. Primer: administrative health databases in observational studies of drug effects–advantages and disadvantages. Nat Clin Pract Rheumatol. 2007;3(12):725–732.PubMedCrossRefGoogle Scholar
  13. 13.
    Schneeweiss S, Patrick AR, Sturmer T, et al. Increasing levels of restriction in pharmacoepidemiologic database studies of elderly and comparison with randomized trial results. Med Care. 2007;45(10 Supl 2):S131–142.PubMedCrossRefGoogle Scholar
  14. 14.
    Greenwald LM. Medicare part D data: major changes on the horizon. Med Care. 2007;45(10 Supl 2):S9–S12.PubMedCrossRefGoogle Scholar
  15. 15.
    Cheng H, Gary LC, Curtis JR, et al. Estimated prevalence and patterns of presumed osteoporosis among older Americans based on Medicare data. Osteoporos Int. 2009;20(9):1507–1515.PubMedCrossRefGoogle Scholar
  16. 16.
    Patrick AR, Brookhart MA, Losina E, et al. The complex relation between bisphosphonate adherence and fracture reduction. J Clin Endocrinol Metab. 2010;95(7):3251–3259.PubMedCrossRefGoogle Scholar
  17. 17.
    Curtis JR, Westfall AO, Cheng H, et al. RisedronatE and ALendronate Intervention over Three Years (REALITY): minimal differences in fracture risk reduction. Osteoporos Int. 2009;20(6):973–978.PubMedCrossRefGoogle Scholar
  18. 18.
    National Osteoporosis Foundation. Clinician’s guide to prevention and treatment of osteoporosis (2010).Google Scholar
  19. 19.
    Choudhry NK, Shrank WH. Four-dollar generics—increased accessibility, impaired quality assurance. N Engl J Med. 2010;363(20):1885–1887.PubMedCrossRefGoogle Scholar
  20. 20.
    • Suissa S. Immeasurable time bias in observational studies of drug effects on mortality. Am J Epidemiol. 2008;168(3):329–335. This article empirically demonstrated how immeasurable time bias can distort the results of a pharmacoepidemiologic study and evaluated the application of various methods to correct for the bias.PubMedCrossRefGoogle Scholar
  21. 21.
    Cramer J, Roy A, Burrell A. Medication compliance and persistence: terminology and definitions. Value Health. 2008;11(44–47).Google Scholar
  22. 22.
    Nikitovic M, Solomon D, Cadarette S. Methods to examine the impact of compliance to osteoporosis pharmacotherapy on fracture risk: systematic review and recommendations. Ther Adv Chron Dis. 2010;1(4):149–163.CrossRefGoogle Scholar
  23. 23.
    Andrade SE, Kahler KH, Frech F, Chan KA. Methods for evaluation of medication adherence and persistence using automated databases. Pharmacoepidemiol Drug Saf. 2006;15(8):565–574. discussion 75-7.PubMedCrossRefGoogle Scholar
  24. 24.
    Grymonpre R, Cheang M, Fraser M, et al. Validity of a prescription claims database to estimate medication adherence in older persons. Med Care. 2006;44(5):471–477.PubMedCrossRefGoogle Scholar
  25. 25.
    Curtis JR, Westfall AO, Cheng H, et al. Benefit of adherence with bisphosphonates depends on age and fracture type: results from an analysis of 101, 038 new bisphosphonate users. J Bone Miner Res. 2008;23(9):1435–1441.PubMedCrossRefGoogle Scholar
  26. 26.
    • Virnig B, Durham SB, Folsom AR, Cerhan J. Linking the Iowa Women’s Health Study cohort to Medicare data: linkage results and application to hip fracture. Am J Epidemiol. 2010;172(3):327–333. This article presented the linkage of data from a conventional prospective cohort study to Medicare data and compared the data from the two sources with regard to the occurrence of hip fracture and postfracture mortality. The authors demonstrated that the incidences of both outcomes were underestimated using self-reported data.PubMedCrossRefGoogle Scholar
  27. 27.
    • St Peter WL, Liu J, Weinhandl ED, Fan Q. Linking Centers for Medicare & Medicaid Services data with prospective DCOR trial data: methods and data comparison results. Hemodial Int. 2008;12(4):480–491. This article presented the linkage of data from multicenter, randomized, open-label trials to Medicare end-stage renal disease data. Significantly, 287 more deaths were identified using Medicare data that were not captured in the original trial.PubMedCrossRefGoogle Scholar
  28. 28.
    Shane E, Burr D, Ebeling PR, et al. Atypical subtrochanteric and diaphyseal femoral fractures: report of a task force of the American Society for Bone and Mineral Research. J Bone Miner Res. 2010;25(11):2267–2294.PubMedCrossRefGoogle Scholar
  29. 29.
    Narongroeknawin P, Patkar NM, Shakoory B, et al. Validation of diagnostic codes for subtrochanteric, diaphyseal, and typical hip fractures using administrative claims data. Arthritis Rheum. 2010;62(10 (supplement)):S1573.Google Scholar
  30. 30.
    Wilkinson GS, Kuo YF, Freeman JL, Goodwin JS. Intravenous bisphosphonate therapy and inflammatory conditions or surgery of the jaw: a population-based analysis. J Natl Cancer Inst. 2007;99(13):1016–1024.PubMedCrossRefGoogle Scholar
  31. 31.
    Tennis P, Zavras A, Laskarides C, Tan H. Predictive value of ICD and CPT codes for identifying osteonecrosis of the jaw (ONJ). Pharmacoepidemiol Drug Saf. 2009;18(S1):S1–S273.CrossRefGoogle Scholar
  32. 32.
    Taylor AJ, Gary LC, Arora T, et al. Clinical and demographic factors associated with fractures among older Americans. Osteoporos Int. 2010.Google Scholar
  33. 33.
    Pazianas M, Blumentals WA, Miller PD. Lack of association between oral bisphosphonates and osteonecrosis using jaw surgery as a surrogate marker. Osteoporos Int. 2008;19(6):773–779.PubMedCrossRefGoogle Scholar
  34. 34.
    Vandenbroucke JP. When are observational studies as credible as randomised trials? Lancet. 2004;363(9422):1728–1731.PubMedCrossRefGoogle Scholar
  35. 35.
    Klabunde CN, Potosky AL, Legler JM, Warren JL. Development of a comorbidity index using physician claims data. J Clin Epidemiol. 2000;53(12):1258–1267.PubMedCrossRefGoogle Scholar
  36. 36.
    • Schneeweiss S, Rassen JA, Glynn RJ, et al. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology. 2009;20(4):512–522. This article presented a multistep algorithm that identifies, prioritizes, and selects covariates to be used to construct a high-dimensional propensity score to adjust for confounding using administrative databases. The authors further applied and tested the algorithm in two separation studies and demonstrated that the algorithm performed better compared with using only predefined covariates.PubMedCrossRefGoogle Scholar
  37. 37.
    Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619.PubMedCrossRefGoogle Scholar
  38. 38.
    Kim SY, Schneeweiss S, Katz JN, et al. Oral bisphosphonates and risk of subtrochanteric or diaphyseal femur fractures in a population-based cohort. J Bone Miner Res. 2010.Google Scholar
  39. 39.
    Beaumont JJ, Steenland K, Minton A, Meyer S. A computer program for incidence density sampling of controls in case-control studies nested within occupational cohort studies. Am J Epidemiol. 1989;129(1):212–219.PubMedGoogle Scholar
  40. 40.
    Schneeweiss S, Wang PS, Avorn J, Glynn RJ. Improved comorbidity adjustment for predicting mortality in Medicare populations. Health Serv Res. 2003;38(4):1103–1120.PubMedCrossRefGoogle Scholar
  41. 41.
    Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41–55.CrossRefGoogle Scholar
  42. 42.
    Arbogast PG, Ray WA. Use of disease risk scores in pharmacoepidemiologic studies. Stat Methods Med Res. 2009;18(1):67–80.PubMedCrossRefGoogle Scholar
  43. 43.
    Ray WA, Murray KT, Meredith S, et al. Oral erythromycin and the risk of sudden death from cardiac causes. N Engl J Med. 2004;351(11):1089–1096.PubMedCrossRefGoogle Scholar
  44. 44.
    Curtis JR, Cheng H, Delzell E, et al. Adaptation of Bayesian data mining algorithms to longitudinal claims data: coxib safety as an example. Med Care. 2008;46(9):969–975.PubMedCrossRefGoogle Scholar
  45. 45.
    Graham DJ, Ouellet-Hellstrom R, MaCurdy TE, et al. Risk of acute myocardial infarction, stroke, heart failure, and death in elderly Medicare patients treated with rosiglitazone or pioglitazone. JAMA. 2010;304(4):411–418.PubMedCrossRefGoogle Scholar
  46. 46.
    FDA Approves Amgen’s XGEVA(TM) (Denosumab) for the prevention of skeletal-related events in patients with bone metastases from solid tumors. Available at http://wwwext.amgen.com/media/media_pr_detail.jsp?year=2010&releaseID=1498709.
  47. 47.
    Strom BL, Kimmel SE, editors. Textbook of pharmacoepidemiology. Chichester: Wiley; 2006.Google Scholar
  48. 48.
    Jung TI, Hoffmann F, Glaeske G, Felsenberg D. Disease-specific risk for an osteonecrosis of the jaw under bisphosphonate therapy. J Cancer Res Clin Oncol. 2010;136(3):363–370.PubMedCrossRefGoogle Scholar
  49. 49.
    Nguyen DM, Schwartz J, Richardson P, El-Serag HB. Oral bisphosphonate prescriptions and the risk of esophageal adenocarcinoma in patients with Barrett’s esophagus. Dig Dis Sci. 2010;55(12):3404–3407.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Jie Zhang
    • 1
  • Huifeng Yun
    • 2
  • Nicole C. Wright
    • 3
  • Meredith Kilgore
    • 4
  • Kenneth G. Saag
    • 5
  • Elizabeth Delzell
    • 6
  1. 1.Department of Epidemiology and Health Services/Comparative Effectiveness Research Training ProgramUniversity of Alabama at BirminghamBirminghamUSA
  2. 2.Department of EpidemiologyUniversity of Alabama at BirminghamBirminghamUSA
  3. 3.Department of EpidemiologyUniversity of Alabama at BirminghamBirminghamUSA
  4. 4.Department of Health Care Organization and PolicyUniversity of Alabama at BirminghamBirminghamUSA
  5. 5.Division of Clinical Immunology and RheumatologyUniversity of Alabama at BirminghamBirminghamUSA
  6. 6.Department of EpidemiologyUniversity of Alabama at BirminghamBirminghamUSA

Personalised recommendations