Erasmus University Rotterdam

Nov 23, 2023

Journal of Medical Internet Research This paper is in the following e-collection/theme issue: March 07, 2023 Public Preferences for Digital Health Data Sharing: Discrete Choice Experiment Study in 12 European Countries Authors of this article: 2Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy 3Centre for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden 4Curtin School of Population Health, Curtin University, Bentley, Australia 5Department of Health Systems and Policy, University of Gondar, Gondar, Ethiopia 6Centre for Medical Ethics, Faculty of Medicine, University of Oslo, Oslo, Norway 7Norwegian Research Center for Computers and Law, Faculty of Law, University of Oslo, Oslo, Norway 8Centre for Health, Law and Emerging Technologies (HeLEX), Faculty of Law, University of Oxford, Oxford, United Kingdom 9Centre for Health, Law and Emerging Technologies, Melbourne Law School, University of Melbourne, Melbourne, Australia 10Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy 11Ethics and Biosciences Platform (Genotoul Societal), Genotoul, Centre for Epidemiology and Research in Population Health, UMR1295, Inserm, Toulouse, France 12Centre for Epidemiology and Research in Population Health, National Institute for Health and Medical Research (Inserm)/Toulouse University, Toulouse, France 13FIZ Karlsruhe – Leibniz-Institut für Informationsinfrastruktur, Eggenstein-Leopoldshafen, Germany 14Social Science Research Institute, University of Iceland, Reykjavik, Iceland 15Departamento de Filosofía I, Universidad de Granada, Granada, Spain 16FiloLab-UGR, Department of Philosophy 1, University of Granada, Granada, Spain 17Centre for Medical STS (MeST), Department of Public Health, University of Copenhagen, Copenhagen, Denmark 18School of Law, University of Kwazulunatal, Durban, South Africa 19Department of Cellular, Computational, and Integrative Biology, University of Trento, Trento, Italy 20Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, Netherlands 21Erasmus Choice Modeling Centre, Erasmus University Rotterdam, Rotterdam, Netherlands *these authors contributed equally Eurac Research Abstract 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. J Med Internet Res 2023;25:e47066 N/A aThe class share is as follows: Class 1, 22.04%; Class 2, 23.61%; Class 3, 15.7%; Class 4, 38.66%. bSignificant at the 1% level. cSignificant at the 5% level. dSignificant at the 10% level. eReference class membership: Northern Europe. fN/A: not applicable. ‎ Figure 2. Relative importance of attributes for the latent classes identified through the latent class model with data from all the countries. As a data collector, class 1 and 2 respondents preferred an academic research project over their health care provider (not significant in class 1). The opposite pattern was found in classes 3 and 4. In all the classes, respondents expressed disutility for a technological company compared to their health care provider. Compared to sharing data with a national authority, class 1, 3, and 4 respondents preferred to share data with an academic research project and showed disutility for a technological company. Class 3 and 4 respondents also showed disutility for a pharmaceutical company compared to a national authority, even though this was more acceptable than a technological company. In class 2, this attribute did not impact decision-making. Health data sharing for promotion, advertising, and marketing purposes provided the most disutility in all classes. For class 1 respondents, developing a new product or service was preferred over quality evaluation. Sharing digital health data for quality evaluation was most acceptable by class 2, 3, and 4 respondents. For class 2 respondents, this was followed by investigating a policy initiative, while developing a new product showed disutility. For class 3 and 4 respondents, developing a new product or service was less acceptable than quality evaluation. For class 4 respondents, investigating a policy initiative was also acceptable but less than quality evaluation. Furthermore, respondents preferred being informed and providing consent for data sharing over being informed and being offered to opt-out. Class 2 respondents also expressed disutility for being only informed. Sharing health data without being informed resulted in substantial disutility in all classes. Class 1 and 3 respondents preferred review of data transfer only compared to review of data transfer and use. The opposite was found for class 2 and 4 respondents. The absence of review provided disutility in all the classes. Compared to respondents from Northern Europe, respondents from Central Europe were more likely to belong to class 3 and less likely to belong to class 1 (compared to class 4). Respondents from Southern Europe were more likely to belong to class 2 and less likely to belong to class 3. Regional Preference and Regional Heterogeneity in Europe A 4-class latent model was fitted to the northern, central, and southern regions showing preference heterogeneity for sharing health data digitally within each region (see Tables S1-S3 in Multimedia Appendix 1 ). In each regional sample considered as a whole, “information on data sharing and consent” was the most important attribute. This was followed by “availability of review process” or “reason for data use,” while “data user” or “data collector” was the least important attribute ( Figure 3 ). Within each region, “information on data sharing and consent” was the most important attribute for most classes in Europe. Within each region, there was a class for which the other attributes were relatively unimportant compared to the most important attribute (class 3 for Northern Europe, class 2 for Central Europe, and class 2 for Southern Europe). ‎ Figure 3. Relative importance of attributes for the latent classes of Northern, Central, and Southern Europe. Classes are ordered according to respondents’ a priori preference for sharing their health data, with class 1 as the most negative toward data sharing, class 4 as the most positive, and classes 2 and 3 as showing conditional support or indifference (see Multimedia Appendix 1 ). In Northern Europe, “information on data sharing and consent” was the most important attribute in all classes, except in class 2, where “reason for data use” was the most important, and “availability of review process” was relatively more important compared to the other classes. Within Central Europe, a similar pattern was shown; however, for class 3, “information on data sharing and consent” and “reason for data use” were relatively equally important attributes. In 2 classes of Central Europe, “availability of review process” had less importance compared to “data collector” (for class 3 respondents) or “data user” (for class 4 respondents). Finally, in Southern Europe, “information on data sharing and consent” was the most important attribute in classes 2 and 4, while “reason for data use” was the most important attribute in classes 1 and 3, where “availability of review process” was less important than “data user” or “data collector.” Discussion Principal Findings and Comparison With Prior Work The results showed that people in the European countries sampled shared, to a certain degree, general commonalities regarding what is important to them about digital health data governance. However, they also showed different priorities and preferences depending on their region of residence. Importance of Information on Digital Health Data Sharing In general, “information on data sharing and consent” was the most important attribute when considering the whole respondent population and the pooled regional subgroups. “Information on data sharing and consent” was the most important attribute for 61.35% of European respondents ( Table 3 ), 67.37% of Northern European respondents (Table S1 in Multimedia Appendix 1 ), all Central European respondents (Table S2 in Multimedia Appendix 1 ), and 39.62% of Southern European respondents (Table S3 in Multimedia Appendix 1 ). Furthermore, for a subgroup of respondents in each region, the other attributes were relatively unimportant, thus reinforcing the primary relevance of information and consent for data sharing in the preferences of European residents. When asked about their preferences for receiving information and the possible mechanisms of consent, respondents generally found it most acceptable to be informed and asked to consent to data sharing. These findings indicated that it is valuable for respondents to exert control over digital health data sharing by being made aware of their data use and providing active consent to such use. Previous literature supports this finding, showing the high importance for individuals to have control over data sharing [ 35 - 37 ]. This suggests that to reflect broadly shared European values, establishing processes that guarantee access to transparent information on data sharing and provide mechanisms for citizens to express consent is crucial for governance initiatives at the European level. Regional heterogeneity emerged in the information and consent preferences. For Southern European respondents, being informed was essential. However, a variety of preferences related to information and consent was expressed (Table S3 in Multimedia Appendix 1 ): active consent (39.62%), opt-out (35.12%), and only informed (25.27%). All Central European respondents preferred being informed and asked to consent, thus generally showing a high interest in controlling their data being shared and used. More than half of Northern European respondents (56.66%; Table S1 in Multimedia Appendix 1 ) preferred being informed and having the possibility to opt-out, while the rest preferred being informed and providing active consent. By including data from Ireland and Denmark in this study, the pattern of preferences for information and the type of consent expressed by Northern European respondents slightly changed compared to what was found in the previous study [ 15 ] conducted in Sweden, Norway, Iceland, and the United Kingdom, whose respondents preferred to be informed and have the possibility to opt-out. In general, a review was also critical (second or third attribute as importance) for respondents. Throughout Europe and the regions, respondents consistently found the review of use and transfer, or the review of transfer only, most acceptable. They found the absence of a review process least acceptable (with 1 exception in Southern Europe, see below). This is in line with the results of previous studies, which showed that the presence of reviewing mechanisms and oversight institutions regulating the data-sharing process was important for people [ 35 , 38 ]. Reluctance to Use Digital Health Data by Private Entities Overall, respondents found it the least acceptable for private enterprises to collect or use their digital health data. Specifically, European respondents generally found technological companies less acceptable as data collectors and users. A similar pattern was found in Northern Europe and Central Europe. Northern European respondents preferred the most when their health data were collected by their health care provider (75.47%) or an academic research project (24.53%) and used by a national authority (Table S1 in Multimedia Appendix 1 ). Central European respondents preferred the most their health care provider (81.49%) or an academic research project (18.51%) as a data collector and an academic research project as a data user (75.43%) (Table S2 in Multimedia Appendix 1 ). Most Southern European respondents (74.74%; Table S3 in Multimedia Appendix 1 ) preferred their data to be collected by their health care provider and found a technological company less acceptable as a data collector. This may result from a differential level of trust in the public or private character of the entity collecting or using data. Previous studies showed that respondents generally found public institutions trustable, accountable, and pursuing the common good, while private companies were perceived in the opposite way [ 35 , 36 ]. We may speculate that people’s preferences are not only influenced by a perception or belief about who is most trustable, but it also has to do with legitimacy: believing that entities such as health care providers, national authorities, and academic institutions could legitimately collect and use their data, while others (such as technological companies) could not. The overall dislike of technological companies as data collectors and users was accompanied by a general dislike of the use of data for commercial communication (marketing, promotion, and advertising). Marketing was previously found to be negatively perceived [ 37 ]. In general, quality evaluation or developing a new product or service were the most accepted purposes for data sharing. Furthermore, in Southern Europe, investigating a policy initiative was generally found to be less acceptable, while it was among the favorite purposes in Northern and Central Europe, perhaps indicating different levels of public trust in the perceived benefit of public health policy between Northern and Southern Europe. The preferences of Southern European respondents were relatively more fragmented. Specifically, 1 subgroup of Southern Europeans (25.27%; Table S3 in Multimedia Appendix 1 ) showed a pattern of preferences that contrasted with the preferences expressed by the other respondents. This subgroup preferred private entities as data collectors (technological companies) and data users (pharmaceutical companies). They found it less acceptable for their health care provider to be a data collector, and an academic research project and a national authority to be a data user. They found the use of their data for policy development to be less acceptable, and they preferred to be only informed and preferred the absence of review mechanisms. It would be important to characterize this subgroup further and investigate the reasons for their expressed pattern of preferences. Country-specific differences in preferences for health data governance have been previously reported [ 39 - 41 ]. The differences in preferences we found among respondents of different European regions may be related to general sociocultural and geopolitical factors (eg, trust in public institutions, solidarity in society, welfare, digitalization of the health sector, and eHealth literacy). Support for Digital Health Data Sharing Most respondents preferred not to share their health data (84.31%; Table 3 ). However, some of these respondents may be open to data sharing upon meeting certain conditions, thus showing conditional support. In each region, only a minority showed strong support for data sharing. This reflects the findings of previous empirical studies, which showed that support for health data sharing is not unconditional. Suitable control mechanisms, adequate transparency, and information on data use were common conditions for support identified in different studies [ 35 , 36 , 42 ]. From a European perspective, measures to create the conditions for trustworthy data-sharing contexts and to establish adequate governance mechanisms for digital health data sharing would be needed to promote citizens’ support for digital health data sharing. Recommendations for a Harmonized Process As a whole, the preferences expressed by the respondents in this study showed that people care about the fate of their data and want to have control of their data being shared. The heterogeneity of preferences for health data sharing among and within European regions may render harmonization initiatives challenging. To provide for differing preferences and to acknowledge the value given to continuous information and data control, interactive informed consent models that enable individual preferences on the use of data within strong and generalized governance may be valuable as a base for developing uniform processes for data reuse within Europe [ 43 , 44 ]. Such a putative dynamically interactive consent model for digital health data sharing may envision categories of items on which the data subject is called to express a choice, which can be changed over time, thus providing for variations in preferences. For example, data subjects may be offered options on the type of collector, user, and purpose. Instead, an adaptive governance process shared within Europe that allows tailoring to the individual countries’ legislative and regulatory frameworks may define the information that must be provided, the typology of consent (opt-in and opt-out), and the necessary overview mechanisms. This would allow to offer a granularity of choices that adequately address the contextuality of data sharing; to provide meaningful and transparent information that guarantees data subject awareness of the use of the collected data; to provide consent mechanisms that are adequate in relation to the original consent and the contextual use of data; to protect individual rights and autonomy; and to provide oversight mechanisms that guarantee trustworthiness and transparency of the data sharing processes. Among the informed consent models [ 45 , 46 ], dynamic consent may be an apt approach for consent to digital health data sharing in a dynamic context. Dynamic consent is implemented within digital platforms; therefore, it would be suitable for the ongoing and progressive general digitalization of health [ 43 , 47 , 48 ]. It has been used in various contexts, such as biomedical research, biobanking, and clinical settings [ 43 , 49 , 50 ]. Based on interactive and ongoing communication with research participants or patients, dynamic consent offers the possibility to revise and change choices over time [ 43 ]. It has been reported that the possibility of changing choices over time and regular communication favor trust [ 49 , 51 ] and that granular control over data is desirable [ 52 ]. Dynamic consent responds to instances that directly arise from the findings obtained in this study. It offers ongoing information about the use of data and, through an interactive approach, offers the public direct control of data use. Dynamic consent would provide transparency in the ongoing use of data, give effect to the right to information, and provide a process for the control and change of preferences in data use. Dynamic consent may serve the interests of the stakeholders involved in data reuse (data collectors, data users, public, policy makers, etc) because it will allow a combination of transparency and individual control (which is desired by the public) and enable preferences to change over time, and will allow the possibility of uses in a variety of contexts. The proposed amendments to the draft EHDS introduce an opt-out, thus providing an avenue for the expression of individual preferences. In fact, dynamic consent may be conceived as a possibility for providing information interactively with an opt-out option, thus following the same direction as the proposals in the draft opinion of Parliament on the EHDS. The dynamic consent model that we propose here relates to the ethical requirement of consent to research as distinct to consent as a legal basis for the processing of personal data under the General Data Protection Regulation (GDPR). However, the dynamic consent model, if modified to have opt-in options only, could also meet the GDPR requirements of consent as a legal basis. This again demonstrates the adaptability of the model. If the infrastructure is put in place, it can be adapted to suit current legal and ethical requirements as they evolve. While enhancing the legitimacy of data repurposing, there is a risk that dynamic consent measures will privilege the most resourceful citizens who are most likely to have the means for navigating an increasingly complex digital infrastructure. How to balance these concerns will remain a political and moral challenge. Nonetheless, increasing digital literacy and access to digital resources will be key to promote autonomy and fairness. Limitations By design, the sample for each country aimed to reflect the respective national age range and gender distribution of the adult population (as of the most recent official information available from the respective national institutes of statistics at the time of sample design). Our data showed a generally higher proportion of female respondents and slight differences in mean age and educational level among the regions and countries. Previous studies reported that people of different ages had different levels of trust, risk perception, privacy concerns, data sharing attitudes, and willingness to share data [ 39 , 40 , 53 , 54 ]. Associations between education level and data sharing attitude, education, and preference for a review of data access were found in previous studies [ 35 , 40 ]. Further analysis aiming to investigate the relationship between the above-described factors and expressed preferences for governance mechanisms would be relevant in understanding and characterizing the variety of preferences in the European population. This study did not include any Eastern European countries. Due to time and project budget constraints, we could not include additional countries in the study. This is a limitation of the generalizability of the results to Europe. It would be very important to extend the study further and ensure that all the European regions are covered to inform policy accurately and minimize possible biases in the results. As an expansion of this project, the survey may be distributed in other countries worldwide. This would allow obtaining further insights, which would be valuable to grasp differences and similarities in people’s preferences for the governance of digital health data taking into account geographical regions and contexts of data sharing. In this paper, we decided to group the countries according to the UN classification of European areas. The use of the UN geoscheme would facilitate comparison and generalization according to a shared and globally known scheme in case the study is expanded worldwide. From a conceptual perspective, within the qualitative phase of the project, England, Iceland, and Sweden were grouped because those countries shared “similarities in breaches of trust among the public regarding secondary uses of health data” [ 42 ]. Additionally, in the first round of quantitative analysis [ 15 ], including the United Kingdom, Iceland, Norway, and Sweden, we implicitly followed the UN scheme; therefore, we decided to repropose the same grouping in this study. All the data were collected through online surveys during the COVID-19 pandemic. It was reported that the pandemic impacted patient preferences for data sharing, resulting in increased comfort in personal health data sharing compared to the prepandemic time [ 55 ]. We may speculate that during the pandemic, the growth of digitalization in every aspect of life [ 56 ], the role of the internet and media in providing health information, the emergence of digital health technologies (contact tracing apps and approaches for digital medicine) [ 57 - 60 ], and the efforts in data sharing for research and public health purposes [ 61 , 62 ] may have impacted the respondents’ attitudes and views on digital health data sharing and the expressed preferences. As the study was designed since its conception as an online survey, we believe that the findings were not affected by the method of data collection. Conclusion This study, which explored public preferences in 12 European countries, showed the co-existence of overarching priorities (such as the importance of information and consent) and heterogeneous preferences for contexts of data sharing among and within European regions. This study has confirmed the previous findings of a study in Northern European countries [ 15 ], provided further nuances to the preferences of Northern European countries, and added the preferences of residents in Western and Southern Europe. It allowed us to understand the pattern of preferences for digital health data sharing in a much broader context and according to geographical regions. With these results, we were able to discuss the challenges of data-sharing harmonization initiatives within Europe. Based on these results, we believe that there is no “one size fits all” governance solution. Instead, an interactive and dynamic model of informed consent offering individual granular control over data sharing accompanied by oversight mechanisms may be a valuable compromise to provide people with the ability to control the secondary use of their health data and to address their preferences for data flow within different contexts. These preferences are contrary to some of the proposals contained within the EHDS. Although the EHDS proposed an independent review of the secondary use of data by a new public entity, a health data access body, the draft regulation on the EHDS removes any role for consent or individual control in the secondary use of data, and it is proposed that individuals will not have a right to be informed about the purpose and the entity that has accessed and used their data. Harmonization initiatives seeking to provide a common ground for cross-border digital health data sharing should be developed upon empirical evidence. Understanding public preferences for digital health data sharing is important for developing adequate answers in policy-making and ensuring that new initiatives are perceived to be trustworthy and operating in accordance with people’s expectations. Acknowledgments The study was funded by the Department of Innovation, Research and Universities of the Province of South Tyrol; the Innovative Medicines Initiative (IMI) – FACILITATE project (grant agreement number 101034366); NordForsk (grant 81105); Economic and Social Research Council (part of United Kingdom Research and Innovation) as part of the Governance of Health Data in Cyberspace project; and European Union Grant agreements (101006430, 101006012, and 101071203). The authors thank the Department of Innovation, Research and University of the Autonomous Province of Bozen/Bolzano for covering the open access publication costs. The authors thank Klaus Hoeyer, Centre for Medical STS (MeST), Department of Public Health, University of Copenhagen, Denmark, for helping with the Danish translations and facilitating the Danish part of the study; and Sarah Coy, HeLEX Centre for Health, Law and Emerging Technologies, University of Oxford, United Kingdom, and Lee Andrew Bygrave, Norwegian Research Center for Computers and Law, University of Oslo, Norway, for contributing to the original project conducted in the countries in the Northern region. Conflicts of Interest References Kickbusch I, Piselli D, Agrawal A, Balicer R, Banner O, Adelhardt M, et al. Secretariat of the LancetFinancial Times Commission. The Lancet and Financial Times Commission on governing health futures 2030: growing up in a digital world. Lancet 2021 Nov 06;398(10312):1727-1776 [ CrossRef ] [ Medline ] Vayena E. Value from health data: European opportunity to catalyse progress in digital health. Lancet 2021 Mar 20;397(10275):652-653 [ CrossRef ] [ Medline ] European Data Governance Act. European Commission. URL: https://digital-strategy.ec.europa.eu/en/policies/data-governance-act [accessed 2023-05-26] Data Act. European Commission. URL: https://digital-strategy.ec.europa.eu/en/policies/data-act [accessed 2023-05-26] European Health Data Space. European Commission. URL: https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space_en [accessed 2023-05-26] Aitken M, de St Jorre J, Pagliari C, Jepson R, Cunningham-Burley S. Public responses to the sharing and linkage of health data for research purposes: a systematic review and thematic synthesis of qualitative studies. BMC Med Ethics 2016 Nov 10;17(1):73 [ https://bmcmedethics.biomedcentral.com/articles/10.1186/s12910-016-0153-x ] [ CrossRef ] [ Medline ] Platt JE, Jacobson PD, Kardia SLR. Public Trust in Health Information Sharing: A Measure of System Trust. Health Serv Res 2018 Apr 18;53(2):824-845 [ http://hdl.handle.net/2027.42/142931 ] [ CrossRef ] [ Medline ] Kalkman S, van Delden J, Banerjee A, Tyl B, Mostert M, van Thiel G. Patients' and public views and attitudes towards the sharing of health data for research: a narrative review of the empirical evidence. J Med Ethics 2022 Jan;48(1):3-13 [ http://jme.bmj.com/lookup/pmidlookup?view=long&pmid=31719155 ] [ CrossRef ] [ Medline ] Shabani M, Bezuidenhout L, Borry P. Attitudes of research participants and the general public towards genomic data sharing: a systematic literature review. Expert Rev Mol Diagn 2014 Nov 26;14(8):1053-1065 [ CrossRef ] [ Medline ] Skovgaard LL, Wadmann S, Hoeyer K. A review of attitudes towards the reuse of health data among people in the European Union: The primacy of purpose and the common good. Health Policy 2019 Jun;123(6):564-571 [ https://linkinghub.elsevier.com/retrieve/pii/S0168-8510(19)30081-8 ] [ CrossRef ] [ Medline ] Stockdale J, Cassell J, Ford E. "Giving something back": A systematic review and ethical enquiry into public views on the use of patient data for research in the United Kingdom and the Republic of Ireland. Wellcome Open Res 2018 Jan 17;3:6 [ https://europepmc.org/abstract/MED/30854470 ] [ CrossRef ] [ Medline ] Aitken M, McAteer G, Davidson S, Frostick C, Cunningham-Burley S. Public Preferences regarding Data Linkage for Health Research: A Discrete Choice Experiment. Int J Popul Data Sci 2018 Jun 26;3(1):429 [ https://europepmc.org/abstract/MED/32935004 ] [ CrossRef ] [ Medline ] Tully MP, Bernsten C, Aitken M, Vass C. Public preferences regarding data linkage for research: a discrete choice experiment comparing Scotland and Sweden. BMC Med Inform Decis Mak 2020 Jun 16;20(1):109 [ https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-01139-5 ] [ CrossRef ] [ Medline ] Patil S, Lu H, Saunders CL, Potoglou D, Robinson N. Public preferences for electronic health data storage, access, and sharing - evidence from a pan-European survey. J Am Med Inform Assoc 2016 Nov 23;23(6):1096-1106 [ https://europepmc.org/abstract/MED/27107445 ] [ CrossRef ] [ Medline ] Viberg Johansson J, Bentzen HB, Shah N, Haraldsdóttir E, Jónsdóttir G, Kaye J, et al. Preferences of the Public for Sharing Health Data: Discrete Choice Experiment. JMIR Med Inform 2021 Jul 05;9(7):e29614 [ https://medinform.jmir.org/2021/7/e29614/ ] [ CrossRef ] [ Medline ] Soekhai V, de Bekker-Grob EW, Ellis AR, Vass CM. Discrete Choice Experiments in Health Economics: Past, Present and Future. Pharmacoeconomics 2019 Mar 3;37(2):201-226 [ https://europepmc.org/abstract/MED/30392040 ] [ CrossRef ] [ Medline ] Soekhai V, Whichello C, Levitan B, Veldwijk J, Pinto CA, Donkers B, et al. Methods for exploring and eliciting patient preferences in the medical product lifecycle: a literature review. Drug Discov Today 2019 Jul;24(7):1324-1331 [ https://linkinghub.elsevier.com/retrieve/pii/S1359-6446(18)30537-3 ] [ CrossRef ] [ Medline ] Lancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: a user's guide. Pharmacoeconomics 2008;26(8):661-677 [ CrossRef ] [ Medline ] Ryan M, Gerard K, Amaya-Amaya M. Using Discrete Choice Experiments to Value Health and Health Care. Dordrecht. Springer; 2008. de Bekker-Grob E, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health Econ 2012 Mar;21(2):145-172 [ CrossRef ] [ Medline ] McFadden D. The Choice Theory Approach to Market Research. Marketing Science 1986 Nov;5(4):275-297 [ CrossRef ] Louviere J, Hensher D, Swait J. Stated Choice Methods: Analysis and Applications. Cambridge, United Kingdom. Cambridge University Press; 2000. Viney R, Lancsar E, Louviere J. Discrete choice experiments to measure consumer preferences for health and healthcare. Expert Rev Pharmacoecon Outcomes Res 2002 Aug 09;2(4):319-326 [ CrossRef ] [ Medline ] Hensher D, Rose J, Greene W. Applied Choice Analysis: A Primer. Cambridge, United Kingdom. Cambridge University Press; 2005. Gonzalez JM. A Guide to Measuring and Interpreting Attribute Importance. Patient 2019 Jun 25;12(3):287-295 [ CrossRef ] [ Medline ] Hauber AB, González J, Groothuis-Oudshoorn CG, Prior T, Marshall DA, Cunningham C, et al. Statistical Methods for the Analysis of Discrete Choice Experiments: A Report of the ISPOR Conjoint Analysis Good Research Practices Task Force. Value Health 2016 Jun;19(4):300-315 [ https://linkinghub.elsevier.com/retrieve/pii/S1098-3015(16)30291-1 ] [ CrossRef ] [ Medline ] Bridges JF, Hauber AB, Marshall D, Lloyd A, Prosser LA, Regier DA, et al. Conjoint analysis applications in health--a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value Health 2011 Jun;14(4):403-413 [ https://linkinghub.elsevier.com/retrieve/pii/S1098-3015(10)00083-5 ] [ CrossRef ] [ Medline ] SurveyEngine. URL: https://surveyengine.com/ [accessed 2023-05-26] Hiligsmann M, van Durme; Geusens P, Dellaert; Dirksen; van der Weijden T, et al. Boonen. Nominal group technique to select attributes for discrete choice experiments: an example for drug treatment choice in osteoporosis. PPA 2013 Feb:133 [ CrossRef ] Viberg Johansson J, Shah N, Haraldsdóttir E, Bentzen HB, Coy S, Kaye J, et al. Governance mechanisms for sharing of health data: An approach towards selecting attributes for complex discrete choice experiment studies. Technology in Society 2021 Aug;66:101625 [ CrossRef ] Greene WH, Hensher DA. A latent class model for discrete choice analysis: contrasts with mixed logit. Transportation Research Part B: Methodological 2003 Sep;37(8):681-698 [ CrossRef ] Fiebig DG, Keane MP, Louviere J, Wasi N. The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity. Marketing Science 2010 May;29(3):393-421 [ CrossRef ] Bech M, Gyrd-Hansen D. Effects coding in discrete choice experiments. Health Econ 2005 Oct;14(10):1079-1083 [ CrossRef ] [ Medline ] Hensher D, Rose J, Greene W. Applied Choice Analysis 2nd Edition. Cambridge, United Kingdom. Cambridge University Press; 2015. Muller SHA, van Thiel GJMW, Vrana M, Mostert M, van Delden JJM. Patients' and Publics' Preferences for Data-Intensive Health Research Governance: Survey Study. JMIR Hum Factors 2022 Sep 07;9(3):e36797 [ https://humanfactors.jmir.org/2022/3/e36797/ ] [ CrossRef ] [ Medline ] Rivas Velarde MC, Tsantoulis P, Burton-Jeangros C, Aceti M, Chappuis P, Hurst-Majno S. Citizens' views on sharing their health data: the role of competence, reliability and pursuing the common good. BMC Med Ethics 2021 May 18;22(1):62 [ https://bmcmedethics.biomedcentral.com/articles/10.1186/s12910-021-00633-3 ] [ CrossRef ] [ Medline ] Taylor MJ, Taylor N. Health research access to personal confidential data in England and Wales: assessing any gap in public attitude between preferable and acceptable models of consent. Life Sci Soc Policy 2014 Dec;10:15 [ https://europepmc.org/abstract/MED/26085451 ] [ CrossRef ] [ Medline ] McCormick J, Hopkins M. Exploring public concerns for sharing and governance of personal health information: a focus group study. JAMIA Open 2021 Oct;4(4):ooab098 [ https://europepmc.org/abstract/MED/34926998 ] [ CrossRef ] [ Medline ] Miltgen CL, Peyrat-Guillard D. Cultural and generational influences on privacy concerns: a qualitative study in seven European countries. European Journal of Information Systems 2019 Jan 28;23(2):103-125 [ CrossRef ] Karampela M, Ouhbi S, Isomursu M. Connected Health User Willingness to Share Personal Health Data: Questionnaire Study. J Med Internet Res 2019 Nov 27;21(11):e14537 [ https://www.jmir.org/2019/11/e14537/ ] [ CrossRef ] [ Medline ] Shah N, Coathup V, Teare H, Forgie I, Giordano GN, Hansen TH, et al. Motivations for data sharing-views of research participants from four European countries: A DIRECT study. Eur J Hum Genet 2019 May 30;27(5):721-729 [ https://europepmc.org/abstract/MED/30700834 ] [ CrossRef ] [ Medline ] Shah N, Viberg Johansson J, Haraldsdóttir E, Bentzen H, Coy S, Mascalzoni D, et al. Governing health data across changing contexts: A focus group study of citizen's views in England, Iceland, and Sweden. Int J Med Inform 2021 Dec;156:104623 [ CrossRef ] [ Medline ] Teare HJA, Prictor M, Kaye J. Reflections on dynamic consent in biomedical research: the story so far. Eur J Hum Genet 2021 Apr 28;29(4):649-656 [ https://europepmc.org/abstract/MED/33249421 ] [ CrossRef ] [ Medline ] Vayena E, Blasimme A. Health Research with Big Data: Time for Systemic Oversight. J Law Med Ethics 2018 Mar 27;46(1):119-129 [ https://europepmc.org/abstract/MED/30034208 ] [ CrossRef ] [ Medline ] Vayena E, Mastroianni A, Kahn J. Caught in the web: informed consent for online health research. Sci Transl Med 2013 Mar 20;5(173):173fs6 [ CrossRef ] [ Medline ] Wiertz S, Boldt J. Evaluating models of consent in changing health research environments. Med Health Care Philos 2022 Jun 14;25(2):269-280 [ https://europepmc.org/abstract/MED/35286521 ] [ CrossRef ] [ Medline ] Teare HJA, Hogg J, Kaye J, Luqmani R, Rush E, Turner A, et al. The RUDY study: using digital technologies to enable a research partnership. Eur J Hum Genet 2017 Jun 26;25(7):816-822 [ https://europepmc.org/abstract/MED/28443622 ] [ CrossRef ] [ Medline ] Kaye J, Curren L, Anderson N, Edwards K, Fullerton SM, Kanellopoulou N, et al. From patients to partners: participant-centric initiatives in biomedical research. Nat Rev Genet 2012 Apr 03;13(5):371-376 [ https://europepmc.org/abstract/MED/22473380 ] [ CrossRef ] [ Medline ] Mascalzoni D, Melotti R, Pattaro C, Pramstaller PP, Gögele M, De Grandi A, et al. Ten years of dynamic consent in the CHRIS study: informed consent as a dynamic process. Eur J Hum Genet 2022 Dec 05;30(12):1391-1397 [ https://europepmc.org/abstract/MED/36064788 ] [ CrossRef ] [ Medline ] Haas MA, Teare H, Prictor M, Ceregra G, Vidgen ME, Bunker D, et al. 'CTRL': an online, Dynamic Consent and participant engagement platform working towards solving the complexities of consent in genomic research. Eur J Hum Genet 2021 Apr 06;29(4):687-698 [ https://europepmc.org/abstract/MED/33408362 ] [ CrossRef ] [ Medline ] Spencer K, Sanders C, Whitley EA, Lund D, Kaye J, Dixon WG. Patient Perspectives on Sharing Anonymized Personal Health Data Using a Digital System for Dynamic Consent and Research Feedback: A Qualitative Study. J Med Internet Res 2016 Apr 15;18(4):e66 [ https://www.jmir.org/2016/4/e66/ ] [ CrossRef ] [ Medline ] Caine K, Hanania R. Patients want granular privacy control over health information in electronic medical records. J Am Med Inform Assoc 2013 Jan 01;20(1):7-15 [ https://europepmc.org/abstract/MED/23184192 ] [ CrossRef ] [ Medline ] Kalkman S, Mostert M, Gerlinger C, van Delden JJM, van Thiel GJMW. Responsible data sharing in international health research: a systematic review of principles and norms. BMC Med Ethics 2019 Mar 28;20(1):21 [ https://bmcmedethics.biomedcentral.com/articles/10.1186/s12910-019-0359-9 ] [ CrossRef ] [ Medline ] Ghafur S, Van Dael J, Leis M, Darzi A, Sheikh A. Public perceptions on data sharing: key insights from the UK and the USA. The Lancet Digital Health 2020 Sep;2(9):e444-e446 [ CrossRef ] Tosoni S, Voruganti I, Lajkosz K, Mustafa S, Phillips A, Kim SJ, et al. Patient consent preferences on sharing personal health information during the COVID-19 pandemic: "the more informed we are, the more likely we are to help". BMC Med Ethics 2022 May 20;23(1):53 [ https://bmcmedethics.biomedcentral.com/articles/10.1186/s12910-022-00790-z ] [ CrossRef ] [ Medline ] Davis S, Matsoso P. COVID-19 as a Digital Pandemic. Think Global Health. URL: https://www.thinkglobalhealth.org/article/covid-19-digital-pandemic [accessed 2023-05-26] Bengio Y, Janda R, Yu YW, Ippolito D, Jarvie M, Pilat D, et al. The need for privacy with public digital contact tracing during the COVID-19 pandemic. The Lancet Digital Health 2020 Jul;2(7):e342-e344 [ CrossRef ] Peek N, Sujan M, Scott P. Digital health and care in pandemic times: impact of COVID-19. BMJ Health Care Inform 2020 Jun 21;27(1):e100166 [ https://informatics.bmj.com/lookup/pmidlookup?view=long&pmid=32565418 ] [ CrossRef ] [ Medline ] Romero RA, Young SD. Ethical Perspectives in Sharing Digital Data for Public Health Surveillance Before and Shortly After the Onset of the COVID-19 Pandemic. Ethics Behav 2022;32(1):22-31 [ https://europepmc.org/abstract/MED/35340741 ] [ CrossRef ] [ Medline ] Gasser U, Ienca M, Scheibner J, Sleigh J, Vayena E. Digital tools against COVID-19: taxonomy, ethical challenges, and navigation aid. Lancet Digit Health 2020 Aug;2(8):e425-e434 [ https://linkinghub.elsevier.com/retrieve/pii/S2589-7500(20)30137-0 ] [ CrossRef ] [ Medline ] Tacconelli E, Gorska A, Carrara E, Davis R, Bonten M, Friedrich A, et al. Challenges of data sharing in European Covid-19 projects: A learning opportunity for advancing pandemic preparedness and response. Lancet Reg Health Eur 2022 Oct;21:100467 [ https://linkinghub.elsevier.com/retrieve/pii/S2666-7762(22)00163-6 ] [ CrossRef ] [ Medline ] Whitelaw S, Mamas M, Topol E, Van Spall H. Applications of digital technology in COVID-19 pandemic planning and response. Lancet Digit Health 2020 Aug;2(8):e435-e440 [ https://linkinghub.elsevier.com/retrieve/pii/S2589-7500(20)30142-4 ] [ CrossRef ] [ Medline ] ‎