UMMS Affiliation

Meyers Primary Care Institute

Date

8-1-2009

Document Type

Article

Medical Subject Headings

Comorbidity; Epidemiologic Measurements

Disciplines

Health Services Research | Primary Care

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.

Rights and Permissions

Citation: Clin Epidemiol. 2009 Aug 9;1:93-100. Link to article on publisher's website

Related Resources

Link to Article in PubMed

PubMed ID

20865090

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