Implications of controlling for comorbid conditions in cost-of-illness estimates: a case study of osteoarthritis from a managed care system perspective

Value Health. Jul-Aug 2001;4(4):329-34. doi: 10.1046/j.1524-4733.2001.44012.x.


Objectives: Current methods for estimating the cost of illness inconsistently control for the effect of comorbid conditions. This analysis examines the implications of controlling for comorbid conditions on the estimated cost of illness. These implications are illustrated using the cost of osteoarthritis as an example.

Methods and data: Medical claims data from 1996 were obtained for inpatient, outpatient, and pharmacy services for members in four United HealthCare health plans. Total annual costs for osteoarthritis (OA) were compared to costs among an equal number of comparison members. Multivariate regression analysis was used to compare the natural log of costs between the OA and comparison groups under two alternative controls for comorbid conditions: no controls, and controls for all conditions.

Results: Controlling for no or all comorbid conditions resulted in estimates of the annual cost of members with OA that ranged between 261% and 151% of the cost of members without OA, respectively.

Conclusions: Existing cost-of-illness estimates may seriously underestimate the true cost by including statistical controls for all comorbid conditions, or seriously overestimate the true cost by failing to control for enough comorbid conditions. In the case of OA, the range of potential bias is substantial.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Bias
  • Causality
  • Child
  • Child, Preschool
  • Comorbidity*
  • Cost of Illness*
  • Female
  • Health Care Costs / statistics & numerical data*
  • Humans
  • Infant
  • Infant, Newborn
  • Male
  • Managed Care Programs / economics*
  • Middle Aged
  • Models, Econometric*
  • Organizational Case Studies
  • Osteoarthritis / complications*
  • Osteoarthritis / economics*
  • Reproducibility of Results
  • United States