Journal of Clinical Epidemiology
Volume 59, Issue 9 , Pages 940-946 , September 2006

Length of comorbidity lookback period affected regression model performance of administrative health data

,Accepted 5 December 2005.

References 

  1. Ballared-Barbash R, Potosky AL, Haralin LC. Factors associated with surgical radiation therapy for early stage breast cancer in older women. J Natl Cancer Inst. 1996;88(11):716–726
  2. Bergman L, Dekker G, Van Kerkoff EHM, Peterse HI, Van Dongan JA, Van Leeuwen FE. Influence of age and comorbidity on treatment choice and survival in elderly patients with breast cancer. Breast Cancer Res Treat. 1991;18:189–194
  3. Newschaffer CJ, Penberthy L, Desch CE, Retchin SM, Whittemore M. The effect of age and comorbidity in the treatment of elderly women with metastic breast cancer. Arch Intern Med. 1996;156(1):85–90
  4. Satariano WA, Ragland DR. The effect of comorbidity on 3-year survival of women with primary breast cancer. Ann Intern Med. 1994;120:104
  5. Silliman RA, Lash TL. Comparison of interview-based and medical-record based indices of comorbidity among breast cancer patients. Med Care. 1999;37(4):339–349
  6. Zhang JX, Iwashyna TJ, Christakis NA. The performance of different lookback periods and sources of information for Charlson comorbidity adjustment in Medicare claims. Med Care. 1999;37(11):1128–1139
  7. Donaghy M, Chang CL, Poulter N. Duration, frequency, recency and type of migraine and the risk of ischaemic stroke in women of childbearing age. J Neurol Neurosurg Psychiatry. 2002;73:747–750
  8. McBean AM, Warren JL, Babish JD. Measuring the incidence of cancer in elderly Americans using Medicare claims data. Cancer. 1994;73:2417–2425
  9. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–383
  10. Australian Bureau of Statistics . Population by age and sex. Australian states and territories. Western Australia. Canberra: Australian Bureau of Statistics; 2004;(Report No. 3201.0)
  11. Holman CDJ, Bass J, Rouse IL, Hobbs MST. Population-based linkage of health records in Western Australia: development of a health services research linked database. Aust NZ J Public Health. 1999;23(5):453–459
  12. Holman CDJ, Preen DB, Baynham NJ, Finn JC, Semmens JB. Development of a Multipurpose Australian Comorbidity Scoring System. J Clin Epidemiol. 2005;58:1006–1014
  13. Australian National Diagnosis Related Group . AN-DRGs. Definitions manual version 3.1. Wallingford, CT: 3M Health Information System; 1996;
  14. Breslow NE. Covariance analysis of censored survival data. Biometrics. 1974;30:89–99
  15. Akaike H. A new look at the statistical model identification. IEEE Trans Auto Cont. 1974;19:716–723
  16. Dayton C. Model comparisons using information measures. J Mod Appl Stat Methods. 2003;2(2):281–292
  17. Cleary PD, Greenfield S, Mulley AG, Pauker SG, Schroeder SA, Wexler L. Variations in length of stay and outcomes for six medical and surgical conditions in Massachusetts and California. JAMA. 1991;266(1):73–79
  18. Libero J, Peiro S, Ordinana R. Chronic comorbidity and outcomes of hospital care: length of stay, mortality and readmission at 30 and 365 days. J Clin Epidemiol. 1999;52:171–179
  19. Waite K, Oddone E, Weinberger M, Samsa G, Foy M, Henderson W, et al. Lack of association between patient's measured burden of disease and risk for hospital readmission. J Clin Epidemiol. 1994;47(11):1229–1236
  20. Kieszak SM, Flanders DF, Kosinski AS, Shipp CC, Karp H. A comparison of the Charlson Comorbidity Index derived from medical record data and administrative billing data. J Clin Epidemiol. 1999;52(2):137–142
  21. O'Connell RL, Lim LL-Y. Utility of Charlson comorbidity index computed from routinely collected hospital discharge diagnosis codes. Method Inform Med. 2000;39:7–11
  22. Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol. 1999;46(10):1075–1079
  23. Iezzoni LI, Foley SM, Daley J, Hughes J, Fisher ES, Heeren T. Comorbidities, complications and coding bias. Does the number of diagnostic codes matter in predicting in-hospital mortality?. JAMA. 1992;267(11):2197–2203
  24. Green J, Wintfeld N. How accurate are hospital discharge data for evaluating effectiveness of care?. Med Care. 1993;31:719–731
  25. Preen DB, Holman CDJ, Lawrence DM, Bayhnam H, Semmens JB. Hospital chart review provided more accurate comorbidity information than data from a general practitioner survey or an administrative database. J Clin Epidemiol. 2004;57:1295–1304

PII: S0895-4356(06)00058-8

doi: 10.1016/j.jclinepi.2005.12.013

Journal of Clinical Epidemiology
Volume 59, Issue 9 , Pages 940-946 , September 2006