Establishing disability weights from pairwise comparisons for a US burden of disease study

Abstract

To determine valid and reliable disability weights for a U.S. burden of disease study, a convenience sample of 68 clinical experts was recruited, including representatives from over 20 NIH institutes and Centers for Disease Control and Prevention. Experts were given various health state valuation tasks including pairwise comparison, ranking, and Person Trade Off. Materials consisted of standardized descriptions of 11 attributes per health state (Classification and Measurement System of Functional Health, CLAMES). Attributes comprised up to 5 ordinal levels of disability. All states were displayed either with or without health state labels. Health state descriptions were taken from an existing comprehensive Canadian system. Conditional Logistic (CLR) and Probit Regression (PR) were used to derive disability weights. CLR and PR converged in yielding stable regression weights to construct disability weights, with a correlation of 0.816. The overall test-retest reliability amounted to 92.5% identical decisions. No significant difference was found for the presentation of health states with or without labels. A comparison of the expert valuations from our study with a standard gamble based valuation in the general population resulted in agreement of r = 0.61. The chosen methodology yielded valid and reliable and disability weights. As it is based on a modularized set of attributes, this methodology will allow derivation of disability weights on the basis of existing descriptions using the CLAMES.