This section presents the participants selected for the study and their answers. The results are a compilation of responses from the mechanics of consent tables divided into two sections; the first quantitative results followed by qualitative findings emerged from the discussion amongst participants.
A total of 110 individuals responded to the advertisement. Out of the 110, 73 participants completed the questionnaire and registered; they were aged between 18 and 78 years old, with 46 women and 27 men from various socio-economic profiles. 63% of participants attained a higher education level; the sample included students, people active in the workforce, and retired. Table 3 shows participants’ characteristics.
Collective response rates: Attitudes to consent to donate their data
We listed a series of potential actors in the mechanism of consent tool, namely public hospitals, other public services, pharmaceutical companies, patient associations, other associations, the public and the individual participant. Overall participants tend to be positively inclined towards donating their data to a public hospital. Table 4. shows the results of the proposal ‘I will donate my data to do disease research’: an average of 92% of collective responses across participants groups would donate their data to a public hospital, all types of listed usages presented including improving information, their care, and research within and outside the health sector receive the same percentage of acceptance. The only category that participants were less likely to accept was donating their data to make money, this applies not only to the public hospital but also to all listed actors.
In addition, Table 4 shows that the next more positively perceived actors were oneself, most participants perceived positively this category with a small minority opposing it. This tendency remained for all data usage. Moreover, more than half of the participants would donate their data to patient associations, as long as the data were not used to generate profit. Overall, the least favorably perceived actors were private enterprises: none of the participants would donate them any data for any type of usage. Participants were also more negatively inclined towards open web-sharing with 90% of participants groups disagreeing with this type of sharing.
Donating data to the pharmaceutical industry for all type of usages tended to cause disagreement and if agreement was reached it was more likely to be against data donation. Participants’ views changed slightly if they could withdraw their data at any point. In Table 5, responses to the statement ‘I would donate my data to do disease research, if I can withdraw at any point’ show that having the possibility to withdraw made more than 10% of the participants change their mind about sharing with pharma, in comparison to Table 4, but donating to pharma still caused a large percentage of disagreement. The largest changes from Table 4 to Table 5 can be seen on self-profit, people would agree to it if more control were gained.
Regarding data usage categories, participants tended to converge to a positive consensus about donating their data to receive advice, adjust their own treatment, improve health care for others and perform clinical research. However, this was only true if the data recipient was the hospital or themselves. The rest of the actors, namely other public services, patient associations, pharmaceutical companies, private companies, and open web sharing, were more likely to be denied sharing instinctively. The least accepted purpose for data use was to generate profit. Table n. 6 ‘I will donate my data to making money’ shows that this type of usage was largely rejected across all data recipients listed in the exercise.
Even for the hospital, if profit was envisioned the large majority of participants were either against data sharing or lack of consensus. Table 6 shows profit making for patient associations and self-profit caused large disagreements among respondents. Furthermore, only 8% would agree to other public services and patient associations monetarizing their data, the same number of participants would allow self-profit. It is also important that an only slight minority of participants support banning people for making money with their own data, thus in general, participants were favorable to banning profit making with the data to any other actors listed. The most divisive category was banning the hospital for making money, the idea of the hospital profiting with people’s data was problematic and most participants were inclined to ban such usage too. See Table 7.
The upcoming section will elaborate on themes that emerged from arguments presented by participants to explain their choices.
Qualitative Results: Agency, protection and research for public good
This section presents the qualitative analysis on the discussion amongst participants while they were filling the tables. Four themes emerged: namely agency, risk and protections, adequate regulation and governance and the common good. The analysis draws into the relationship among them and their overlap.
The analysis exposes agency as a central argument to participants’ narratives: they would like to be able to have clear information and assess potential risk, foresee benefit and overall societal contribution of the research when deciding whether or not to donate their data. They also want assurance that their choice will be respected, and being able to withdraw their support according to their personal preferences and priorities. Their narratives illuminate various kinds of data that could be shared namely health records, biological samples, genomic sequence data, shopping habits, geographic location, and the like, while discussing the perceived risk that those might bear. Participants advocate that each person should be able to decide whom to share with, what type of data, for which purposes, and consequently what type of risk they would be willing to take. They believe that this opportunity is not always available to them. The following quote succinctly illustrates this point, hinting that is not the case right now and the sense of freedom that will accompany having such access and control
‘It will be liberating to have access and control to our own data, it will not only be on the hands of somebody else’
Participants’ narratives often use personal freedom to argue in favor of every individual ‘right’ to manage his/her health data, therefore, most participants were reluctant to deny people this possibility, including profiting from it.
It depends on what data you are sharing, why. That is freedom; we are talking about liberty... Otherwise were are not an authoritarian regime’
They were also in favor of data bartering to gain a personal benefit like an insurance discount for example, thus being mindful of uneven power structures and risk of abusive exchanges. Participants overall expressed that at time of ill health despair might make people vulnerable to sharing and usage that participants deemed to be detrimental to the donor should be controlled and regulated.
Misuse and protections
Participants see that all data they produce through their everyday life and use of mobile phones, shopping transactions, web search engines, social media and wearable technology provides information on who they are, revealing their behavior trends, health status and needs. The expressed concern that this information can be and has been misused in the past. They indicated that such information made people vulnerable among other to discrimination and exploitative market practices, and might trigger further mistreatment across vulnerable populations. They showed great mistrust towards profit driven data acquisitions to them amid exploitation. The upcoming quote expresses the concerns.
‘Those in poverty might sell their organs … they might face further exploitation if data becomes profitable, they will donate their data for money because they need it ‘
A large majority of participants expressed their support for banning open-web sharing, citing high risk of re-identification, discrimination, misuse, and exploitation. A minority was skeptical about banning sharing in any circumstances. Those skeptical pointed out that consensual unrestricted personal data sharing already occurs outside a regulated context, for example sharing health data on social media via advocacy groups and the like. The same example was used by those who supported banning calling into question the lack of protection and potential for exploitation occurring on those outlets. They considered that allowing people to consent to give their data to profiting private companies or others not knowing if it had been misused has resulted in often exploitative issues and this cannot longer be the case, sharing restriction may be a way to combat this in their view. They stressed the lack of awareness and that little attention has been given to business model of such companies and risk for misusages of data in such circumstances.
"A: I will not share with private companies
B: …..such as Facebook, we already sharing a lot. And, I am not sure that is good for everybody”
Participants believed anonymization is often presented as a protective shell though which no harm could occur, but believed that is no longer true. They argued that big data can precisely model and reveal health trends, needs, behaviors and other very sensitive information about sub- groups or even individuals and therefore anonymization is not enough protection in the era of big data. Harm can still occur and better protection is needed.
Adequate regulation and governance
Legal and policy frameworks that ensure protection to individuals that might donate their data for research was key to the discussions. Perception on the efficiency of current frameworks and governance structure at times became a much contested issue, while most participants expressed a high degree of confidence and trust in current legislation and its enforcement at the public hospital in Geneva. This degree of trust was not transferable to other institutions or jurisdictions particularly outside the country. Yet, some did not trust current governance structure and recalled that sometimes legislation changes alongside political transitions in any given democratic country. Therefore, it was considered good to remain prudent and rather critical towards legislative frameworks and protection granted by them. The upcoming quote shows this skepticism
A: I do not trust in democracy or legislation
We can’t, we see it in the (COUNTRY) now, things can change very quickly
So you do not trust in the politicians or democracy
B: No, I cannot trust in power elites
C: Me neither
Participants expressed that some data are more sensitive than others are and an appropriate level of protection has to be granted accordingly. All participants perceived clinical data and particularly genomic data as highly sensitive, less agreement was found regarding other data that is relevant to health generated through everyday life, i.e. nutrition and shopping habits at the supermarket. Discussing different actors and data usage showed how participants perceived governance structures and the protections granted by them. As outlined above reluctance attached to profit and private enterprises including health related was often referred to as the lack of protection against exploitation. The section above shows how donating data to profit-seeking activities and entities was largely rejected, for, among other reasons, it was linked to uncertainty regarding legal protection, amid exploitation and immoral behavior. As discussion progressed, arguments unveiled that under efficient, just and transparent governance structures profit maybe positive. Profit was not seen as inherently negative and some recognized the potential to become a tool for public good as the following quote demonstrates.
If would donate (my data for profit making to the hospital) they make by reinvesting in more doctors and other personnel to take care of patients, if making money is immoral, I am not sure...maybe is more a solution as the hospital does not always have the resources
Participants perceived that the protections granted to them in the online world regarding data transactions were either insufficient, limited, or non-existent. This transaction allude to included websites that might re-use the data for example genealogy sites or research actors publishing their databases. Although freedom online and efficient data use was highly valued, further protection and efficient governance was required, as well as, better accountability on behalf of research actors asking for blank consent from data donors, this practice among other reasons, may contribute to uncertainty regarding donating data for research. Thus, doing and supporting research for the public good was a central point of discussion. The upcoming section outlines these findings.
Facilitators clarify that the decision to participate or not in research and should not have an influence in the quality of the clinical care. The possibility of personal immediate gain by getting advice about their own health concerns or potential improvement of their own health care was seen as favorable and tended to make people more inclined to consent to donate their data. However, in their narratives they pointed out that it was not a decision-making factor for most participants. Nor was the idea or prioritizing certain diseases or treatments, the ultimate decision-maker factor was whether or not the overall aim of the research was to maximize public good. Such ability to generate public good was systematically linked with public nonprofit institutions.
"A determinant factor is if we are talking about a public service, I’ll trust and share my data with a public hospital but not in a private one, that reassures me …we have frameworks, ethics protocols, conventions, legislation’
Participants’ narratives suggest a high degree of trust in public institutions, which are seen as generating public good. Trust was associated with accountability mechanisms and having checks and balances in place that were considered sufficient. However, trust did not always translate to perceived competence to best utilize data. Participants also judged professional competence and legitimacy to conduct high quality research separately. For example, patient associations tended to be perceived rather positively, however, they were not perceived as competent research actors, therefore participants would not donate their data to them to do research in any area.
‘Doing research is not the role of a patient associations’
Thus, competence to do research without trust was not sufficient either for person to accept to share their data. This was the case of the pharmaceutical industry, which tended to be negatively perceived. None of the participants would donate their data for research to the pharmaceutical industry, they acknowledged the crucial role that pharma has on health research and development, but such a recognition was not sufficient to be willing to donate their data for research. The pharmaceutical industry was largely distrusted, and perceived as exploitative, non-transparent, and pursuing wealth rather than the public good. The following quote illustrates this:
“A private company, is on its description, is an entity for profit, we have to see it like it, is there for a purpose to make money. We have mechanisms and frameworks to control that they do their job, they are not there to give us advice, but they are not there for the public good"
Contributing to generate public good is in this case personal data donation to competent and socially accountable research actors that will provide a benefit to the public.