Mapping the Insomnia Severity Index Instrument to EQ-5D Health State Utilities: A United Kingdom Perspective

Abstract

Objective This study aimed to map the Insomnia Severity Index (ISI) to the EQ-5D-3L utility values from a UK perspective. Methods Source data were derived from the 2020 National Health and Wellness Survey (NHWS) for France, Germany, Italy, Spain, the UK and the US. Ordinary least squares regression, generalised linear model (GLM), censored least absolute deviation, and adjusted limited dependent variable mixture model (ALDVMM) were employed to explore the relationship between ISI total summary score and EQ-5D utility while accounting for adjustment covariates derived from the NHWS. Fitting performance was assessed using standard metrics, including mean-squared error (MSE) and coefficient of determination (R 2 ). Results A total of 17,955 respondent observations were included, with a mean ISI score of 12.12 ± 5.32 and a mean EQ-5D-3L utility (UK tariff) of 0.71 ± 0.23. GLM gamma-log and ALDVMM were the two best performing models. The ALDVMM had better fitting performance (R 2 = 0.320, MSE 0.0347) than the GLM gamma-log (R 2 = 0.303, MSE 0.0353); in train-test split-sample validation, ALDVMM also slightly outperformed the GLM gamma-log model, with an MSE of 0.0351 versus 0.0355. Based on fitting performance, ALDVMM and GLM gamma-log were the preferred models. Conclusions In the absence of preference-based measures, this study provides an updated mapping algorithm for estimating EQ-5D-3L utilities from the ISI summary total score. This new mapping not only draws its strengths from the use of a large international dataset but also the incorporation of adjustment variables (including sociodemographic and general health characteristics) to reduce the effects of confounders.

Health-related quality of life and willingness to pay measurement among patients on warfarin in Thailand

“…Then, multivariable linear regression was employed to investigate the associations between the significant sociodemographic factors identified from the univariate analysis as independent variables and the WHOQOL-BREF domain scores as the dependent variable. However, the associations between the EQ-5D index, EQ-VAS scores, and WTP as dependent variables and the significant sociodemographic factors as independent variables were investigated using a generalized linear model (GLM) with Gamma distribution and the log link model because it can accommodate the skewness and heteroscedasticity of those scores’ distributions [ 31 , 32 ]. All analyses were performed using STATA 17 (StataCorp LLC, College Station, TX, USA), with a p-value of < 0.05 being considered statistically significant.…”

Insomnia in Germany—massively inadequate care?

“…Data from the NHWS survey conducted in 2020 form the basis of the evaluation. The 2020 Europe NHWS protocol was reviewed by the Pearl Institutional Review Board (Indianapolis, IN, USA; 19-KANT-204) [6].…”

“…Respondents are identified via a web-based consumer panel, i.e., a pre-recruited sample of adults who agree to participate in a survey. The survey is conducted in the US, Europe, and Asia and includes 35 validated scales and disease-specific assessment measures [6].…”

“…In order to obtain a representative sample of the adult population, a stratified randomization method was used according to gender, ethnicity, and age [6].…”

“…The aim of this analysis is to present the effects of insomnia on everyday life and the current drug treatment landscape. It is based on the analyzed data from the German cohort of a representative crosssectional survey conducted in 2020 as part of the National Health and Wellness Survey (NHWS), based on self-reports from affected individuals who stated that they had been diagnosed with insomnia [6].…”