Dermatology Life Quality Index (DLQI) scores are used in many countries as access and reimbursement criteria for costly dermatological treatments. In this study we examined how time trade-off (TTO) utility valuations made by individuals from the general population are related to combinations of DLQI severity levels characterizing dermatologically relevant health states, with the ultimate purpose of developing a value set for the DLQI.
We used data from an online cross-sectional survey conducted in Hungary in 2020 (n = 842 after sample exclusions). Respondents were assigned to one of 18 random blocks and were asked to provide 10-year TTO valuations for the corresponding five hypothetical health states. To analyze the relationship between DLQI severity levels and utility valuations, we estimated linear, censored, ordinal, and beta regression models, complemented by two-part scalable models accommodating heterogeneity effects in respondents’ valuation scale usage. Successive severity levels (0–3) of each DLQI item were represented by dummy variables. We used cross-validation methods to reduce the initial set of 30 dummy variables and improve model robustness.
Our final, censored linear regression model with 13 dummy variables had R2 = 0.136, thus accounting for 36.9% of the incremental explanatory power of a maximal (full-information) benchmark model (R2 = 0.148) over the uni-dimensional model (R2 = 0.129). Each DLQI item was found to have a negative effect on the valuation of health states, yet this effect was largely heterogeneous across DLQI items, and the relative contribution of distinctive severity levels also varied substantially. Overall, we found that the social/interpersonal consequences of skin conditions (in the areas of social and leisure activities, work and school, close personal relationships, and sexuality) had roughly twice as large disutility impact as the physical/practical aspects.
We have developed an experimental value set for the DLQI, which could prospectively be used for quantifying the quality-adjusted life years impact of dermatological treatments and serve as a basis for cost-effectiveness analyses. We suggest that, after validation of our main results through confirmatory studies, population-specific DLQI value sets could be developed and used for conducting cost-effectiveness analyses and developing financing guidelines in dermatological care.