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Development and Application of a Model to Estimate the Impact of Type 1 Diabetes on Health-Related Quality of Life

  1. Shin-Yi Wu, MS,
  2. François Sainfort, PHD,
  3. Russell H Tomar, MD,
  4. James L Tollios, MA,
  5. Dennis G Fryback, PHD,
  6. Ronald Klein, MD, MPH and
  7. Barbara EK Klein, MD, MPH
  1. Department of Industrial Engineering, the University of Wisconsin-Madison Madison, Wisconsin
  2. Department of Preventive Medicine, the University of Wisconsin-Madison Madison, Wisconsin
  3. Department of Pathology and Laboratory Medicine, the University of Wisconsin-Madison Madison, Wisconsin
  4. Department of Ophthalmology, the University of Wisconsin-Madison Madison, Wisconsin
  5. University Health Care Madison, Wisconsin
  1. Address correspondence and reprint requests to Francois Sainfort, PhD, Department oflndustrial Engineering, University of Wisconsin-Madison, 1513 University Ave., Madison, WI 53706. E-mail: sainfort{at}engr.wisc.edu.

Abstract

OBJECTIVE To develop a simulation model to assess the impact of type 1 diabetes and its associated complications on health-related quality of life of a population.

RESEARCH DESIGN AND METHODS The methodology builds upon 1) an existing population model of type 1 diabetes progression, 2) an empirical study designed to measure state- and age-specific health statuses of type 1 diabetes, and 3) existing literature to quantify quality of life of the corresponding health status. Health statuses were measured in a group of type 1 diabetic patients using the Medical Outcomes Study short form 36 (SF-36). A published empirical regression equation was then used to predict corresponding Quality of Well-Being Index (QWB) scores from these assessments. The QWB scores were incorporated into a previously developed type 1 diabetes progressionand cost simulation model. Sensitivity analyses on key parameters were performed, and the model was found to be robust.

RESULTS The augmented model can estimate quality-adjusted life years (QALYs) as well as costs associated with type 1 diabetes on any population of interest over any period of time. The model is used to compare intensive versus conventional treatment strategies using a simplified set of assumptions regarding the relative effects of these alternative treatments. With these assumptions, intensive strategy produces more QALYs than does conventional strategy and is cost-beneficial after 5 years.

CONCLUSIONS The model enables health planners to perform cost-effectiveness analyses to compare alternative treatment strategies for type 1 diabetes and support subsequent decision making.

  • Received September 23, 1997.
  • Revision received January 22, 1998.
  • Accepted January 22, 1998.
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