Spanish preference weights dataset for the ASCOT-SCT4
-
1.
University of Vigo
-
2.
Universidade da Coruña
Description
This dataset comprises two files: a variable dictionary and a CSV file containing responses from the Spanish general population to questions about hypothetical situations of care needs, based on the dimensions of the ASCOT-SCT4 instrument, together with demographic and socioeconomic information. The data were used to develop Spanish preference weights for the Adult Social Care Outcomes Toolkit Four-Level Self-Completion Tool (ASCOT-SCT4) and to propose a mapping function for converting these weights into utilities for estimating quality-adjusted life years in social care. The dataset includes responses from at least 1,000 individuals, recruited through an online panel provided by Nexo Soc. Coop. The sample is representative of the Spanish adult population in terms of age, gender, and region.
Methods
Description of methods used for collection/generation of data: An online panel (provided by Nexo Soc. Coop. And) was used to recruit a sample of at least 1,000 people between October and November 2023. The sample was representative of the Spanish adult population in terms of age, gender and region. The questionnaire was divided into four sections. In the first section, the aim of the study and the domains of the ASCOT instrument were explained. In the second section, the respondent was asked to rate eight social care-related quality of life (SCRQoL) states using the Best-Worst Scaling method. In the third section, respondents were asked to rate four SCRQoL states using the composite Time Trade-Off method. Finally, information related to the participant’s socioeconomic characteristics was collected.
Methods for processing the data: For BWS data, we first used a multinomial logit (MNL) model to estimate the coefficients of the domain levels. After that, we applied a scale MNL (S-MNL) model to get more reliable and consistent preference estimates. As scale factors, four dichotomous variables (0/1) were used: gender (=1 if male), age (=1 if older than 65 years), dependency (=1 if the respondent was dependent or lived with a dependent person) and time to complete the BWS questionnaire (=1 if duration ≤ 640 seconds). We used Control-4 (I have no control over my daily life) as the reference level. The models were estimated using maximum likelihood with BIOGEME. For the cTTO data, the mean of the thirty-three scored states was obtained using the methodology described above. To convert the latent BWS scores into utility scores, a mapping function was obtained by estimating a linear function between latent BWS scores and cTTO utilities; i.e. cTTOi = f (BWSi) + εi, where cTTOi was the mean value obtained for state i when the cTTO method was applied, and BWSi was the estimated value of state i resulting from the application of the estimated BWS model (S-MNL).
Notes
Research funded by Fundación Caser
Files
Restricted
The record is publicly accessible, but files are restricted to users with access.