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Methodology, Design, and Analytic Techniques to Address Measurement of Comorbid Disease

¹ Geriatrics Section, Boston University School of Medicine, Massachusetts.
² Department of Epidemiology, Boston University School of Public Health, Massachusetts.
³ Department of Community Health, Brown University School of Medicine, Providence, Rhode Island.
⁴ Department of Medicine, University of South Carolina School of Medicine, Columbia.
⁵ National Institute on Aging, Baltimore, Maryland.
⁶ Division of Epidemiology, University of California, Berkeley, School of Public Health.

Address correspondence to Timothy L. Lash, DSc, MPH, Boston University School of Public Health, 715 Albany Street TE3, Boston, MA 02118. E-mail: tlash{at}bu.edu

Background. Measurement of comorbidity affects all variable axes that are considered in health care research: confounding, modifying, independent, and dependent variable. Comorbidity measurement particularly affects research involving older adults because they bear the disproportionate share of the comorbidity burden.

Methods. We examine how well researchers can expect to segregate study participants into those who are healthier and those who are less healthy, given the variable axis for which they are measuring comorbidity, the comorbidity measure they select, and the analytic method they choose. We also examine the impact of poor measurement of comorbidity.

Results. Available comorbidity measures make use of medical records, self-report, physician assessments, and administrative databases. Analyses using these scales introduce uncertainties that can be framed as measurement error or misclassification problems, and can be addressed by extant analytic methods. Newer analytic methods make efficient use of multiple sources of comorbidity information.

Conclusions. Consideration of the comorbidity measure, its role in the analysis, and analogous measurement error problems will yield an analytic solution and an appreciation for the likely direction and magnitude of the biases introduced.

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