Back to Journals » Clinical Epidemiology » Volume 1

Potential misinterpretations caused by collapsing upper categories of comorbidity indices: An illustration from a cohort of older breast cancer survivors

Authors Ahern T, Bosco, Silliman R, Ulcickas Yood, Field T, Wei, Lash T

Published 23 June 2009 Volume 2009:1 Pages 93—100

DOI https://doi.org/10.2147/CLEP.S5757

Review by Single-blind

Peer reviewer comments 3

Thomas P Ahern1, Jaclyn LF Bosco2, Rebecca A Silliman2, Marianne Ulcickas Yood3, Terry S Field4, Feifei Wei5, Timothy L Lash1, On behalf of the BOW Investigators

1Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA; 2Department of Medicine, Section of Geriatrics, Boston University School of Medicine, Boston, MA, USA; 3Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT, USA; 4Meyers Primary Care Institute, University of Massachusetts Medical School, Worcester, MA, USA; 5HealthPartners Research Foundation, Minneapolis, MN, USA

Background: Comorbidity indices summarize complex medical histories into concise ordinal scales, facilitating stratification and regression in epidemiologic analyses. Low subject prevalence in the highest strata of a comorbidity index often prompts combination of upper categories into a single stratum (‘collapsing’).

Objective: We use data from a breast cancer cohort to illustrate potential inferential errors resulting from collapsing a comorbidity index.

Methods: Starting from a full index (0, 1, 2, 3, and ≥4 comorbidities), we sequentially collapsed upper categories to yield three collapsed categorizations. The full and collapsed categorizations were applied to analyses of (1) the association between comorbidity and all-cause mortality, wherein comorbidity was the exposure; (2) the association between older age and all-cause mortality, wherein comorbidity was a candidate confounder or effect modifier.

Results: Collapsing the index attenuated the association between comorbidity and mortality (risk ratio, full versus dichotomized categorization: 4.6 vs 2.1), reduced the apparent magnitude of confounding by comorbidity of the age/mortality association (relative risk due to confounding, full versus dichotomized categorization: 1.14 vs 1.09), and obscured modification of the association between age and mortality on both the absolute and relative scales.

Conclusions: Collapsing categories of a comorbidity index can alter inferences concerning comorbidity as an exposure, confounder and effect modifier.

Keywords: epidemiology, breast neoplasms, comorbidity, confounding factors (epidemiologic), bias (epidemiologic), statistical models

Creative Commons License This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.

Download Article [PDF]