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

Abstract

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

Disclosure

This research was supported by the following grants from the National Cancer Institute: R01 CA 093772, R01 CA 118708, and K05 CA 092395. Mr Ahern’s effort was supported by a CDMRP pre-doctoral training award (BC073012). The authors are indebted to the following individuals for their contributions to the design, data collection, and data management aspects of the BOW cohort: Diana S.M. Buist, Group Health Center for Health Studies, Seattle, WA; Virginia P. Quinn, Kaiser Permanente Southern California, Pasenda, CA; Hans Petersen, Lovelace Respiratory Research Institute, Albuquerque, NM; Soe Soe Thwin, Department of Medicine, Boston University School of Medicine, Boston, MA; and Ann Geiger, Wake Forest University School of Medicine, Division of Public Health Sciences, Winston-Salem, NC.