PMS62 Nominal Group Technique to Select Attributes for Discrete Choice Experiments

Hiligsmann, M.; Durme, Caroline van; Geusens, P.; Dellaert, Benedict G. C.; Dirksen, C.D.; Weijden, Trudy van der; Reginster, Jacques; Boonen, Annelies

doi:10.1016/j.jval.2012.08.1417

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A 4-step approach was used for this process: (1) literature review; (2) 14 focus group discussions (including a nominal group technique [29] with members of the general population in the United Kingdom, Sweden, and Iceland); (3) expert interviews; and (4) confirmation ranking surveys. Steps 1 to 3 were part of a previous study described in detail elsewhere [15,30].…”

Section: Selection Of Attributes and Levelsmentioning

confidence: 99%

Public Preferences for Digital Health Data Sharing: Discrete Choice Experiment Study in 12 European Countries

Biasiotto,

Viberg Johansson,

Alemu

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

Background With new technologies, health data can be collected in a variety of different clinical, research, and public health contexts, and then can be used for a range of new purposes. Establishing the public’s views about digital health data sharing is essential for policy makers to develop effective harmonization initiatives for digital health data governance at the European level. Objective This study investigated public preferences for digital health data sharing. Methods A discrete choice experiment survey was administered to a sample of European residents in 12 European countries (Austria, Denmark, France, Germany, Iceland, Ireland, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom) from August 2020 to August 2021. Respondents answered whether hypothetical situations of data sharing were acceptable for them. Each hypothetical scenario was defined by 5 attributes (“data collector,” “data user,” “reason for data use,” “information on data sharing and consent,” and “availability of review process”), which had 3 to 4 attribute levels each. A latent class model was run across the whole data set and separately for different European regions (Northern, Central, and Southern Europe). Attribute relative importance was calculated for each latent class’s pooled and regional data sets. Results A total of 5015 completed surveys were analyzed. In general, the most important attribute for respondents was the availability of information and consent during health data sharing. In the latent class model, 4 classes of preference patterns were identified. While respondents in 2 classes strongly expressed their preferences for data sharing with opposing positions, respondents in the other 2 classes preferred not to share their data, but attribute levels of the situation could have had an impact on their preferences. Respondents generally found the following to be the most acceptable: a national authority or academic research project as the data user; being informed and asked to consent; and a review process for data transfer and use, or transfer only. On the other hand, collection of their data by a technological company and data use for commercial communication were the least acceptable. There was preference heterogeneity across Europe and within European regions. Conclusions This study showed the importance of transparency in data use and oversight of health-related data sharing for European respondents. Regional and intraregional preference heterogeneity for “data collector,” “data user,” “reason,” “type of consent,” and “review” calls for governance solutions that would grant data subjects the ability to control their digital health data being shared within different contexts. These results suggest that the use of data without consent will demand weighty and exceptional reasons. An interactive and dynamic informed consent model combined with oversight mechanisms may be a solution for policy initiatives aiming to harmonize health data use across Europe.

show abstract

Section: Selection Of Attributes and Levelsmentioning

confidence: 99%