Valuing quality of life and improvements in glycemic control in people with type 2 diabetes

Outcomes research is used increasingly for assessing the health economic be nefits of new therapeutic programs and interventions. The measurement prope rties of the outcomes assessment tools are important. If overlooked, they c an mislead health care administrators and caregivers regarding the importan ce and value of these programs and interventions. We reviewed the literatur e and conducted two analyses to determine the absolute, relative, and opera tive quality-of-life ranges for people with type 2 diabetes. Quality of lif e and fasting blood glucose and HMI, concentrations were measured at baseli ne and at 4, 8, and 12 weeks of treatment in 569 men and women randomized t o either glipizide gastrointestinal therapeutic system (GITS) or placebo in a double-blind, multicenter clinical trial. A subgroup of 290 patients com pleted a diabetes-specific health states questionnaire at endpoint (week 12 or early termination) rating 10 health-state descriptions on a health ther mometer scale ranging from 0 (death) to 100 (full health). Health losses at the higher end of the scale had a greater negative utility than did compar able losses at lower health states, indicating patients' strong preferences for maintaining asymptomatic or mildly symptomatic conditions. patients ra ted their current health state at 83.4 +/- 0.8% of full health and indicate d that a loss of 27 points below this value would prevent them from living and working as they currently do. The calibration analysis applied to the q uality-of-life scales suggested that the targeted range for clinical invest igation and quality-of-care evaluation must be more narrowly focused. Effec t sizes as seemingly small as 2% (0.25 responsiveness units) on the absolut e scare can correspond to quality-of-life losses of 15-20% on the personal operative scale. Differences in glycemic control clearly affected quality o f life. Those patients with the best HbA(1c) responses (decreasing 1.5% or more from baseline) versus those with the worst responses (increasing 1.5% or more from baseline) were separated by 0.6 responsiveness units for the o verall quality-of-life summary measure. The calibration analysis suggested that this degree of better glycemic control provides a nearly 50% gain in q uality of life according to personal expectations within the operative rang e. In conclusion, general measures of quality of life may be too crude and insensitive to capture the important gains in health outcomes due to new th erapeutic interventions and programs in diabetes. Quality-of-care evaluatio ns for diabetes are at risk of favoring inferior programs with lower costs simply because gains or losses in health outcomes go undetected.