Public Preferences regarding Data Linkage for Health Research: A Discrete Choice Experiment

Mhairi Quiroz-Aitken, Gareth McAteer, Sara Davidson, Clive Frostick, Sarah Cunningham-Burley

Research output: Contribution to journalArticlepeer-review


The potential for data collected in the public and private sector to be linked and used in research has led to increasing interest in public acceptability of data sharing and data linkage. The literature has identified a range of factors that are important for shaping public responses and in particular has noted that public support for research conducted through data linkage or data sharing is contingent on a number of conditions being met. In order to examine the relative importance of these conditions a Discrete Choice Experiment (DCE) was conducted via an online questionnaire among members of Ipsos MORI’s online panel in Scotland. The survey was completed by 1,004 respondents. Overall the two most influential factors shaping respondents’ preferences are: the type of data being linked; and, how profits are managed and shared. The type of data being linked is roughly twice as important as who the researchers are. There were slight differences across age groups and between genders and slight differences when comparing respondents with and without long term health conditions. The most notable differences between respondents were found when comparing respondents according to employment and working sector. This study provides much needed evidence regarding the relative importance of various conditions which may be essential for securing and sustaining public support for data-linkage in health research. This may be useful for indicating which factors to focus on in future public engagement and has important implications for the design and delivery of research and public engagement activities. The continuously evolving nature of the field means it will be necessary to revisit the key conditions for public support on an ongoing basis and to examine the contexts and circumstances in which these might change.
Original languageEnglish
JournalInternational Journal of Population Data Science
Early online date28 Jun 2018
Publication statusE-pub ahead of print - 28 Jun 2018


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