PURPOSE: The common practice of using administrative diagnosis codes as the sole source of data on potential confounders in pharmacoepidemiologic studies has been shown to leave substantial residual confounding. We explored reasons why adjustment for comorbid illness defined from International Classification of Diseases (ICD) codes fails to remove confounding. METHODS: We used data from a case-control study among immunocompetent seniors enrolled in Group Health to estimate bias in the estimated association between receipt of influenza vaccine and the risk of community-acquired pneumonia during non-influenza control periods and to estimate the effects of adjusting for comorbid illnesses defined from either ICD codes or the medical record. We also estimated the accuracy of ICD codes for identifying comorbid illnesses compared with the gold standard of medical record review. RESULTS: Sensitivity of ICD codes for illnesses recorded in the medical record ranged from 59 to 97% (median, 76%). Strong confounding was present in the vaccine/pneumonia association, as evidenced by the non-null odds ratio of 0.60 (95% confidence interval, 0.38-0.95) during this control period. Adjusting for the presence/absence of comorbid illnesses defined from either medical record review (odds ratio, 0.73) or from ICD codes (odds ratio, 0.68) left considerable residual confounding. CONCLUSIONS: ICD codes may fail to control for confounding because they often lack sensitivity for detecting comorbid illnesses and because measures of the presence/absence of comorbid illnesses may be insufficient to remove confounding. These findings call for caution in the use of ICD codes to control for confounding.
PURPOSE: The common practice of using administrative diagnosis codes as the sole source of data on potential confounders in pharmacoepidemiologic studies has been shown to leave substantial residual confounding. We explored reasons why adjustment for comorbid illness defined from International Classification of Diseases (ICD) codes fails to remove confounding. METHODS: We used data from a case-control study among immunocompetent seniors enrolled in Group Health to estimate bias in the estimated association between receipt of influenza vaccine and the risk of community-acquired pneumonia during non-influenza control periods and to estimate the effects of adjusting for comorbid illnesses defined from either ICD codes or the medical record. We also estimated the accuracy of ICD codes for identifying comorbid illnesses compared with the gold standard of medical record review. RESULTS: Sensitivity of ICD codes for illnesses recorded in the medical record ranged from 59 to 97% (median, 76%). Strong confounding was present in the vaccine/pneumonia association, as evidenced by the non-null odds ratio of 0.60 (95% confidence interval, 0.38-0.95) during this control period. Adjusting for the presence/absence of comorbid illnesses defined from either medical record review (odds ratio, 0.73) or from ICD codes (odds ratio, 0.68) left considerable residual confounding. CONCLUSIONS:ICD codes may fail to control for confounding because they often lack sensitivity for detecting comorbid illnesses and because measures of the presence/absence of comorbid illnesses may be insufficient to remove confounding. These findings call for caution in the use of ICD codes to control for confounding.
Authors: Hilal Maradit Kremers; Laura W Lewallen; Brian D Lahr; Tad M Mabry; James M Steckelberg; Daniel J Berry; Arlen D Hanssen; Elie F Berbari; Douglas R Osmon Journal: Clin Orthop Relat Res Date: 2014-12-06 Impact factor: 4.176
Authors: Leah J McGrath; Stephen R Cole; Abhijit V Kshirsagar; David J Weber; Til Stürmer; M Alan Brookhart Journal: Med Care Date: 2013-12 Impact factor: 2.983
Authors: Jennifer Clark Nelson; Tracey Marsh; Thomas Lumley; Eric B Larson; Lisa A Jackson; Michael L Jackson Journal: J Clin Epidemiol Date: 2013-08 Impact factor: 6.437
Authors: Michael L Jackson; Onchee Yu; Jennifer C Nelson; Allison Naleway; Edward A Belongia; Roger Baxter; Komal Narwaney; Steven J Jacobsen; David K Shay; Lisa A Jackson Journal: Am J Epidemiol Date: 2013-08-26 Impact factor: 4.897