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Performance of comorbidity measures for predicting outcomes in population-based osteoporosis cohorts

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Abstract

Summary

The performance of five comorbidity measures, including the Charlson and Elixhauser indices, was investigated for predicting mortality, hospitalization, and fracture outcomes in two osteoporosis cohorts defined from administrative databases. The optimal comorbidity measure depended on the outcome of interest, although overall the Elixhauser index performed well.

Introduction

Studies that use administrative data to investigate population-based health outcomes often adopt risk-adjustment models that include comorbidities, conditions that coexist with the index disease. There has been limited research about the measurement of comorbidity in osteoporotic populations. The study purpose was to compare the performance of comorbidity measures for predicting mortality, fracture, and health service utilization outcomes in two cohorts with diagnosed or treated osteoporosis.

Methods

Administrative data were from the province of Saskatchewan, Canada. Osteoporosis cohorts were identified from diagnoses in hospital and physician data and prescriptions for osteo-protective medications using case definitions with high sensitivity or high specificity. Five diagnosis- and medication-based comorbidity measures and five 1-year outcomes, including mortality, hospitalization (two measures), osteoporotic-related fracture, and hip fracture, were defined. Performance of the comorbidity measures was assessed using the c-statistic (discrimination) and Brier score (prediction error) for multiple logistic regression models.

Results

In the specific cohort (n = 9,849) for the mortality outcome, the Elixhauser index resulted in the largest improvement (8.96%) in the c-statistic and lowest Brier score compared to a model that contained demographic and socioeconomic variables, followed by the Charlson index (6.06%). For hospitalization, the number of different diagnoses resulted in the largest improvement (14.01%) in the c-statistic. The Elixhauser index resulted in significant improvements in the c-statistic for osteoporosis-related and hip fractures. Similar results were observed for the sensitive cohort (n = 28,068).

Conclusions

Recommendations about the optimal comorbidity measure will vary with the outcome under investigation. Overall, the Elixhauser index performed well.

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Notes

  1. The complete list of service codes used to define osteoporotic fractures is available from the authors upon request.

References

  1. Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M (2009) Defining comorbidity: implications for understanding health and health services. Ann Fam Med 7:357–363

    Article  PubMed  Google Scholar 

  2. Charlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40:373–383

    Article  PubMed  CAS  Google Scholar 

  3. Elixhauser A, Steiner C, Harris DR, Coffey RM (1998) Comorbidity measures for use with administrative data. Med Care 36:8–27

    Article  PubMed  CAS  Google Scholar 

  4. Von Korff M, Wagner EH, Saunders K (1992) A chronic disease score from automated pharmacy data. J Clin Epidemiol 45:197–203

    Article  Google Scholar 

  5. Schneeweiss S (2006) Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf 15:291–303

    Article  PubMed  Google Scholar 

  6. Tenenhouse A, Joseph L, Kreiger N, Poliquin S, Murray TM, Blondeau L, Berger C, Hanley DA, Prior JC (2000) Estimation of the prevalence of low bone density in Canadian women and men using a population-specific DXA reference standard: the Canadian Multicentre Osteoporosis Study (CaMos). Osteoporos Int 11:897–904

    Article  PubMed  CAS  Google Scholar 

  7. Adachi JD, Ioannidis G, Olszynski WP, Brown JP, Hanley DA, Sebaldt RJ, Petrie A, Tenenhouse A, Stephenson GF, Papaioannou A, Guyatt GH, Goldsmith CH (2002) The impact of incident vertebral and non-vertebral fractures on health related quality of life in postmenopausal women. BMC Musculoskelet Disord 3:11

    Article  PubMed  Google Scholar 

  8. Lips P, van Schoor NM (2005) Quality of life in patients with osteoporosis. Osteoporos Int 16:447–455

    Article  PubMed  Google Scholar 

  9. Morin S, Lix LM, Azimaee M, Metge C, Caetano P, Leslie WD (2010) Mortality rates after incident non-traumatic fractures in older men and women. Osteoporos Int. doi:10.1007/s00198-010-1480-2

  10. Kanis JA, Oden A, Johnell O, De LC, Jonsson B, Oglesby AK (2003) The components of excess mortality after hip fracture. Bone 32:468–473

    Article  PubMed  CAS  Google Scholar 

  11. Bouza C, Lopez T, Palma M, Amate JM (2007) Hospitalised osteoporotic vertebral fractures in Spain: analysis of the national hospital discharge registry. Osteoporos Int 18:649–657

    Article  PubMed  CAS  Google Scholar 

  12. Orsini LS, Rousculp MD, Long SR, Wang S (2005) Health care utilization and expenditures in the United States: a study of osteoporosis-related fractures. Osteoporos Int 16:359–371

    Article  PubMed  Google Scholar 

  13. Kannegaard PN, van der Mark S, Eiken P, Abrahamsen B (2010) Excess mortality in men compared with women following a hip fracture. National analysis of comedications, comorbidity and survival. Age Ageing 39:203–209

    Article  PubMed  Google Scholar 

  14. Souza RC, Pinheiro RS, Coeli CM, Camargo KR Jr (2008) The Charlson comorbidity index (CCI) for adjustment of hip fracture mortality in the elderly: analysis of the importance of recording secondary diagnoses. Cad Saúde Pública 24:315–322

    PubMed  Google Scholar 

  15. Southern DA, Quan H, Ghali WA (2004) Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data. Med Care 42:355–360

    Article  PubMed  Google Scholar 

  16. Edouard L, Rawson NS (1996) Reliability of the recording of hysterectomy in the Saskatchewan health care system. Br J Obstet Gynaecol 103:891–897

    Article  PubMed  CAS  Google Scholar 

  17. Liu L, Reeder B, Shuaib A, Mazagri R (1999) Validity of stroke diagnosis on hospital discharge records in Saskatchewan, Canada: implications for stroke surveillance. Cerebrovasc Dis 9:224–230

    Article  PubMed  CAS  Google Scholar 

  18. Rawson NS, D'Arcy C (1998) Assessing the validity of diagnostic information in administrative health care utilization data: experience in Saskatchewan. Pharmacoepidemiol Drug Saf 7:389–398

    Article  PubMed  CAS  Google Scholar 

  19. Alqaisi F, Williams LK, Peterson EL, Lanfear DE (2009) Comparing methods for identifying patients with heart failure using electronic data sources. BMC Health Serv Res 9:237

    Article  PubMed  Google Scholar 

  20. Fowles JB, Fowler EJ, Craft C (1998) Validation of claims diagnoses and self-reported conditions compared with medical records for selected chronic diseases. J Ambul Care Manage 21:24–34

    PubMed  CAS  Google Scholar 

  21. Leslie WD, Lix LM, Yogendran MS (2010) Validation of a case definition for osteoporosis disease surveillance. Osteoporos Int. doi:10.1007/s00198-010-1225-2

  22. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Saunders LD, Beck CA, Feasby TE, Ghali WA (2005) Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 43:1130–1139

    Article  PubMed  Google Scholar 

  23. Klabunde CN, Potosky AL, Legler JM, Warren JL (2000) Development of a comorbidity index using physician claims data. J Clin Epidemiol 53:1258–1267

    Article  PubMed  CAS  Google Scholar 

  24. Schneeweiss S, Maclure M (2000) Use of comorbidity scores for control of confounding in studies using administrative databases. Int J Epidemiol 29:891–898

    Article  PubMed  CAS  Google Scholar 

  25. Schneeweiss S, Seeger J, Maclure M, Wang P, Avorn J, Glynn RJ (2001) Performance of comirbidity scores to control for confounding in epidemiologic studies using claims data. Am J Epidemiol 154:854–865

    Article  PubMed  CAS  Google Scholar 

  26. Bolton JM, Metge C, Lix L, Prior H, Sareen J, Leslie WD (2008) Fracture risk from psychotropic medications: a population-based analysis. J Clin Psychopharmacol 28:384–391

    Article  PubMed  Google Scholar 

  27. Leslie WD, Tsang JF, Caetano PA, Lix LM (2007) Effectiveness of bone density measurement for predicting osteoporotic fractures in clinical practice. J Clin Endocrinol Metab 92:77–81

    Article  PubMed  CAS  Google Scholar 

  28. Cooke AL, Metge C, Lix L, Prior HJ, Leslie WD (2008) Tamoxifen use and osteoporotic fracture risk: a population-based analysis. J Clin Oncol 26:5227–5232

    Article  PubMed  Google Scholar 

  29. Leslie WD, Morin S, Lix LM (2010) A before-and-after study of fracture risk reporting and osteoporosis treatment initiation. Ann Intern Med 153:580–586

    PubMed  Google Scholar 

  30. Caro JJ, Ishak KJ, Huybrechts KF, Raggio G, Naujoks C (2003) The impact of poor compliance to osteoporosis treatment on risk of fractures in actual practice. Osteoporos Int 14:S78

    Google Scholar 

  31. Roos NP, Mustard CA (1997) Variation in health and health care use by socio-economic status in Winnipeg, Canada: the system works well? yes and no. Milbank Q 75:89–111

    Article  PubMed  CAS  Google Scholar 

  32. Reiter JP, Raghunathan TE (2007) The multiple adaptations of multiple imputation. J Am Stat Assoc 102:1462–1471

    Article  CAS  Google Scholar 

  33. Kanis JA, Borgstrom F, De Laet C, Johansson H, Johnell O, Jonsson B, Oden A, Zethraeus N, Pfleger B, Khaltaev N (2005) Assessment of fracture risk. Osteoporos Int 16:581–589

    Article  PubMed  Google Scholar 

  34. Ikeda M, Ishigaki T, Yamauchi K (2002) Relationship between Brier score and area under the binormal ROC curve. Computer Meth Prog Biomedicine 67:187–194

    Article  Google Scholar 

  35. Harrell FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15:361–387

    Article  PubMed  Google Scholar 

  36. Delong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845

    Article  PubMed  CAS  Google Scholar 

  37. Redelmeier DA, Bloch DA, Hickam DH (1991) Assessing predictive accuracy: how to compare Brier scores. J Clin Epidemiol 44:1141–1146

    Article  PubMed  CAS  Google Scholar 

  38. SAS Institute Inc (2004) SAS/STAT user's guide. SAS Institute Inc, Cary

    Google Scholar 

  39. Maciejewski ML, Liu CF, Derleth A, McDonell M, Anderson S, Fihn SD (2005) The performance of administrative and self-reported measures for risk adjustment of veterans affairs expenditures. Health Serv Res 40:887–904

    Article  PubMed  Google Scholar 

  40. Perkins AJ, Kroenke K, Unutzer J, Katon W, Williams JW, Hope C, Callahan CM (2004) Common comorbidity scales were similar in their ability to predict health care costs and mortality. J Clin Epidemiol 57:1040–1048

    Article  PubMed  Google Scholar 

  41. Farley JF, Harley CR, Devine JW (2006) A comparison of comorbidity measurements to predict healthcare expenditures. Am J Manag Care 12:110–119

    PubMed  Google Scholar 

  42. Siminoski K, Leslie WD, Frame H, Hodsman A, Josse RG, Khan A, Lentle BC, Levesque J, Lyons D, Brown J (2005) Canadian recommendations for bone mineral density reporting. Can Assoc Radiol J 10:120–123

    Google Scholar 

  43. Leslie WD, Derksen S, Prior HS, Metge C, Lix LM, O'Neil J (2006) Assessment of the interaction between ethnicity and chronic disease risk factors for fracture in Canadian Aboriginals. Osteoporos Int 17:1358–1368

    Article  PubMed  CAS  Google Scholar 

  44. Vestergaard P, Mosekilde L (2002) Fracture risk in patients with celiac Disease, Crohn's disease, and ulcerative colitis: a nationwide follow-up study of 16,416 patients in Denmark. Am J Epidemiol 156:1–10

    Article  PubMed  Google Scholar 

  45. Schneeweiss S, Wang PS, Avorn J, Maclure M, Levin R, Glynn RJ (2004) Consistency of performance ranking of comorbidity adjustment scores in Canadian and U.S. utilization data. J Gen Intern Med 19:444–450

    Article  PubMed  Google Scholar 

  46. Lash TL, Mor V, Wieland D, Ferrucci L, Satariano W, Silliman RA (2007) Methodology, design, and analytic techniques to address measurement of comorbid disease. J Gerontol A Biol Sci Med Sci 62:281–285

    PubMed  Google Scholar 

  47. Radley DC, Gottlieb DJ, Fisher ES, Tosteson ANA (2008) Comorbidity risk-adjustment strategies are comparable among persons with hip fracture. J Clin Epidemiol 61:580–587

    Article  PubMed  Google Scholar 

  48. Clark DO, Von Korff M, Saunders K, Baluch WM, Simon GE (1995) A chronic disease score with empirically derived weights. Med Care 33:783–795

    Article  PubMed  CAS  Google Scholar 

  49. van Walraven C, Austin PC, Jennings A, Quan H, Forster AJ (2009) A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care 47:626–633

    Article  PubMed  Google Scholar 

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Acknowledgements

This research was supported in part by a Canadian Institutes of Health Research New Investigator Award and funding from the Centennial Chair Program, University of Saskatchewan to the first author. The authors are indebted to Nedeene Hudema of the Health Quality Council for assistance with data extraction and analysis. This study is based in part on de-identified data provided by the Saskatchewan Ministry of Health. The interpretation and conclusions contained herein do not necessarily represent those of the Government of Saskatchewan or the Ministry of Health.

Conflicts of interest

Lisa Lix received an unrestricted research grant from Amgen Canada.

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Authors and Affiliations

  1. School of Public Health, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK, Canada, S7N 5E5

    L. M. Lix

  2. Health Quality Council, Saskatoon, Canada

    L. M. Lix, J. Quail, G. Teare & B. Acan

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  1. L. M. Lix
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  2. J. Quail
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Correspondence to L. M. Lix.

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Lix, L.M., Quail, J., Teare, G. et al. Performance of comorbidity measures for predicting outcomes in population-based osteoporosis cohorts. Osteoporos Int 22, 2633–2643 (2011). https://doi.org/10.1007/s00198-010-1516-7

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