Governance mechanisms for sharing of health data: An approach towards selecting attributes for complex discrete choice experiment studies

Abstract Background Discrete Choice Experiment (DCE) is a well-established technique to elicit individual preferences, but it has rarely been used to elicit governance preferences for health data sharing. Objectives The aim of this article was to describe the process of identifying attributes for a DCE study aiming to elicit preferences of citizens in Sweden, Iceland and the UK for governance mechanisms for digitally sharing different kinds of health data in different contexts. Methods A three-step approach was utilised to inform the attribute and level selection: 1) Attribute identification, 2) Attribute development and 3) Attribute refinement. First, we developed an initial set of potential attributes from a literature review and a workshop with experts. To further develop attributes, focus group discussions with citizens (n = 13), ranking exercises among focus group participants (n = 48) and expert interviews (n = 18) were performed. Thereafter, attributes were refined using group discussion (n = 3) with experts as well as cognitive interviews with citizens (n = 11). Results The results led to the selection of seven attributes for further development: 1) level of identification, 2) the purpose of data use, 3) type of information, 4) consent, 5) new data user, 6) collector and 7) the oversight of data sharing. Differences were found between countries regarding the order of top three attributes. The process outlined participants’ conceptualisation of the chosen attributes, and what we learned for our attribute development phase. Conclusions This study demonstrates a process for selection of attributes for a (multi-country) DCE involving three stages: Attribute identification, Attribute development and Attribute refinement. This study can contribute to improve the ethical aspects and good practice of this phase in DCE studies. Specifically, it can contribute to the development of governance mechanisms in the digital world, where people's health data are shared for multiple purposes.

[1]  Timea Mariann Helter,et al.  Developing attributes for discrete choice experiments in health: a systematic literature review and case study of alcohol misuse interventions , 2016, Journal of substance use.

[2]  E. McColl,et al.  Systematic review of participants’ attitudes towards data sharing: a thematic synthesis , 2018, Journal of health services research & policy.

[3]  M. Hansson,et al.  Research participants’ preferences for receiving genetic risk information: a discrete choice experiment , 2019, Genetics in Medicine.

[4]  M. Aitken,et al.  Public Preferences regarding Data Linkage for Health Research: A Discrete Choice Experiment , 2018, International journal of population data science.

[5]  Joanna Coast,et al.  Using qualitative methods for attribute development for discrete choice experiments: issues and recommendations. , 2012, Health economics.

[6]  Nila A. Sathe,et al.  A systematic literature review of individuals’ perspectives on privacy and genetic information in the United States , 2018, PLoS ONE.

[7]  S. Grandy,et al.  Discrete Choice Experiment Attribute Selection Using a Multinational Interview Study: Treatment Features Important to Patients with Type 2 Diabetes Mellitus , 2017, The Patient - Patient-Centered Outcomes Research.

[8]  Andrew Lloyd,et al.  Conjoint analysis applications in health--a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. , 2011, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[9]  H. D. de Melker,et al.  Consistency between stated and revealed preferences: a discrete choice experiment and a behavioural experiment on vaccination behaviour compared , 2015, BMC Medical Research Methodology.

[10]  Mhairi Aitken,et al.  Public responses to the sharing and linkage of health data for research purposes: a systematic review and thematic synthesis of qualitative studies , 2016, BMC medical ethics.

[11]  Richard A. Krueger,et al.  Focus groups : a practical guide for applied research / by Richard A. Krueger , 1989 .

[12]  J. Kaye,et al.  Sharing data for future research—engaging participants’ views about data governance beyond the original project: a DIRECT Study , 2018, Genetics in Medicine.

[13]  J. Kaye,et al.  Preferences of the Public for Sharing Health Data: Discrete Choice Experiment , 2021, JMIR medical informatics.

[14]  Jordan J. Louviere,et al.  Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data , 1983 .

[15]  John M. Rose,et al.  Applied Choice Analysis , 2015 .

[16]  D. Moher,et al.  A scoping review of rapid review methods , 2015, BMC Medicine.

[17]  Stavros Petrou,et al.  Discrete Choice Experiments in Health Economics: A Review of the Literature , 2014, PharmacoEconomics.

[18]  J. Veldwijk,et al.  The Predictive Value of Discrete Choice Experiments in Public Health: An Exploratory Application , 2015, The Patient - Patient-Centered Outcomes Research.

[19]  Trudy van der Weijden,et al.  Nominal group technique to select attributes for discrete choice experiments: an example for drug treatment choice in osteoporosis , 2013, Patient preference and adherence.

[20]  Sudeh Cheraghi-Sohi,et al.  Making sense of patient priorities: applying discrete choice methods in primary care using 'think aloud' technique. , 2007, Family practice.

[21]  J. Bridges,et al.  Art and Science of Instrument Development for Stated-Preference Methods , 2017, The Patient - Patient-Centered Outcomes Research.

[22]  S. Fullerton,et al.  Return of incidental findings in genomic medicine: measuring what patients value—development of an instrument to measure preferences for information from next-generation testing (IMPRINT) , 2013, Genetics in Medicine.

[23]  J. Coast,et al.  Reporting Formative Qualitative Research to Support the Development of Quantitative Preference Study Protocols and Corresponding Survey Instruments: Guidelines for Authors and Reviewers , 2019, The Patient - Patient-Centered Outcomes Research.

[24]  Edgar A. Whitley,et al.  Patient Perspectives on Sharing Anonymized Personal Health Data Using a Digital System for Dynamic Consent and Research Feedback: A Qualitative Study , 2016, Journal of medical Internet research.

[25]  B. Chadwick,et al.  Analysing and presenting qualitative data , 2008, BDJ.

[26]  C. Petersen,et al.  Special Section on Ethics in Health Informatics , 2020, Yearbook of Medical Informatics.

[27]  H. Nissenbaum.  Privacy as contextual integrity , 2004 .

[28]  F. Casellas,et al.  A Qualitative Research for Defining Meaningful Attributes for the Treatment of Inflammatory Bowel Disease from the Patient Perspective , 2020, The Patient - Patient-Centered Outcomes Research.

[29]  M. Bliemer,et al.  Are Healthcare Choices Predictable? The Impact of Discrete Choice Experiment Designs and Models. , 2019, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[30]  Mandy Ryan,et al.  Discrete choice experiments in health economics: a review of the literature. , 2012, Health economics.

[31]  R. Jagsi,et al.  Patient Perspectives on the Ethical Implementation of a Rapid Learning System for Oncology Care. , 2017, Journal of oncology practice.

[32]  Richard A Spinello,et al.  The end of privacy. , 1997, America.

[33]  D. Bowen,et al.  De-identified genomic data sharing: the research participant perspective , 2017, Journal of Community Genetics.

[34]  Vikki Entwistle,et al.  Rationalising the 'irrational': a think aloud study of discrete choice experiment responses. , 2009, Health economics.

[35]  J. Kitzinger,et al.  Qualitative Research: Introducing focus groups , 1995 .

[36]  Alan R. Ellis,et al.  Discrete Choice Experiments in Health Economics: Past, Present and Future , 2018, PharmacoEconomics.

[37]  D. McFadden.  Conditional logit analysis of qualitative choice behavior , 1972 .

[38]  L. Bezuidenhout,et al.  Attitudes of research participants and the general public towards genomic data sharing: a systematic literature review , 2014, Expert review of molecular diagnostics.

[39]  Kathy Hudson,et al.  Subjects matter: a survey of public opinions about a large genetic cohort study , 2008, Genetics in Medicine.

[40]  C. Haug,et al.  Whose Data Are They Anyway? Can a Patient Perspective Advance the Data-Sharing Debate? , 2017, The New England journal of medicine.