Citizens' Jury on QCovid: Report on the jury's conclusions and key findings

Ipsos Scotland was commissioned to conduct a Citizens' Jury exploring views on QCovid®. QCovid® is a risk model developed to identify people at the highest risk of death or a poor outcome should they catch Covid-19. This report documents the Citizens' Jury process and findings.

8. Conclusions

This Citizens' Jury set out to help understand how the public in Scotland view any ethical issues associated with the Scottish Government's proposed use of QCovid® or similar risk prediction models. Through an in-depth process of learning and deliberation, the Citizens' Jury provided clear messages on the ethical concerns around deploying a model like QCovid®.

The jury looked in detail at each of the four possible tools associated with QCovid®, and this report has set out their principles (i.e., what would make use of each tool acceptable) and "red lines" (i.e., what would render use of each tool unacceptable). Rather than restate those principles and "red lines", here we highlight the overall themes that emerged from this public engagement exercise and the implications for future policy in this area.

Findings underscore the importance of transparency around the use of any such tool. Participants were generally accepting of the reasons for applying a risk prediction model, feeling that they could help minimise some of the most serious outcomes of the pandemic. However, a theme throughout the jury was the need to keep the public informed about how the model was being used and what that meant for individuals who were identified as at risk. This level of transparency was considered important for the Scottish Government to build public trust in the tool.

Linked to the need for transparency was the importance of communication. If the Scottish Government was to decide to use QCovid® or a model like it, participants felt that this should be clearly communicated to the public. Communication of the outcome of the risk prediction was also seen as an important consideration, with care needed in relation to how a high-risk individual is informed of the outcome.

The need for support for individuals deemed at high risk was one of the strongest themes to emerge. Participants stressed the potential severity of being told you are at high risk of serious outcomes from Covid-19, and the negative emotional impacts of being asked to isolate or reduce contact with others. In this respect, the timing of this public engagement exercise seemed to have had an impact – having lived through almost two years of Covid-19, participants were able to draw on their own experiences, or that of their family members, of being asked to shield early in the pandemic. If adequate support cannot be provided, then some felt the model should not be used. Any future use of the model should therefore consider what means of support will be available to high risk individuals and how this will be communicated to those individuals. Support would include three elements:

  • Emotional – to offer reassurance to people receiving a score which is upsetting to them.
  • Interpretative - to help people understand their risk score and what it means.
  • Practical – to help people understand what steps they needed to take to protect themselves and others, and support to help make sure they could access what they needed (e.g. access to food and essential items if they were being asked to shield).

The Citizens' Jury also highlighted the importance of having data security and privacy systems in place. Concerns around data security have been covered in previous public engagement exercises on this topic, so it is not surprising that they formed a key part of the deliberations in this Citizens' Jury. Across all the tools that were discussed, the general point raised was that an individual's data should be kept safe and not used for purposes unrelated to managing the health risk of the virus. This was particularly important in the case of the population tool using non-anonymised data. For any future use of the tool, it will therefore be important that data security protocols are in place and that these are clearly described to the public.

Findings suggest that attitudes towards risk prediction models can vary depending on the status of the virus. In particular, if there is low prevalence of the virus and vaccines are effective, participants felt there would need to be very clear justification from the Scottish Government for a model like QCovid® to be used. This was particularly the case for tools that carried relatively higher risk, such as the population level use of non-anonymised data (which had higher risks associated with data privacy and need for support for individuals). In the case of a new variant resistant to vaccines, participants felt that a model like QCovid® could potentially become more important, as the need to manage the impacts of the virus would be more serious and urgent. However, the same principles and conditions around its acceptability would still apply under that scenario.

Finally, our findings also highlight the impact that the process of deliberation can have on attitudes towards use of public health data. Participants' views developed over the course of the Citizens' Jury as they learned more about the tools and deliberated with each other. The wider public, who will not have taken part in deliberation, may receive information about a risk prediction model differently. If the Scottish Government is to use a model like QCovid®, it will therefore be important that the public engagement messaging draws on, and responds to, the range of ethical considerations highlighted by the Citizens' Jury.



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