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What is needed for a evidence-based vaccination strategy?

Next week, COVID-19 vaccinations are set to begin in the UK after the BioNTech vaccine was approved there. EU approval is also not far off. There is great euphoria, and at the same time, it is clear: not everyone can be vaccinated at once. Very likely, not everyone wants to be vaccinated either.

Acceptance and Voluntariness Require Credibility and Trust

On November 9, a joint working group consisting of members from the Standing Committee on Vaccination, the German Ethics Council, and the National Academy of Sciences Leopoldina issued a position paper: How should access to a COVID-19 vaccine be regulated? Those who read the paper will find a number of recommendations on WHAT should happen. However, the HOW is somewhat elusive. Neither vaccination goals nor priority population groups are precisely named. Why is this?

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The National Academy of Sciences Leopoldina provides independent advice to politics and society on key future topics.

We discussed these questions a few days ago in a workshop conversation at the Roman Herzog Institute. There are numerous challenges in implementing a distribution plan for the COVID-19 vaccine: prioritization, voluntariness, and fairness are mentioned as principles and are hardly criticizable as such. But how can we ensure that these principles are adhered to—and how can credible communication be achieved?

Vanessa Vu pinpointed the problem on the "Anne Will" talk show last Sunday:

"What I really miss is a clear political strategy where I, as a citizen, can see: This is what we are doing and this is where we are heading."

For both the vaccination itself and the prioritization of certain groups to find acceptance, a fundamental trust is required that three key statements are true:

1. There is a clear strategy.

2. This strategy is evidence-based and considers as many societal impacts as possible.

3. The strategy is continuously evaluated and corrected if essential assumptions prove false or conditions change.

This requires transparency, from the perspective of the citizens—who obviously understand something different than how political measures have been communicated in recent months. As a result, much trust in politics has been lost, and I fear, in science as well. It is thus a central challenge to now restore this trust, to lend credibility to the actors involved.

I do not believe it is necessary to impart to everyone a detailed medical or epidemiological understanding of the effectiveness of a vaccination. Nor do I think it is feasible. However, much would be achieved if trust is established that people are not being misled about the effectiveness and potential side effects.

"Therefore, we must repeatedly make clear that there are no absolute truths in this pandemic. We make decisions and find compromises, based on current knowledge and weighing different interests."

Jens Spahn articulated this in a FOCUS interview on September 11, 2020—unfortunately, it seems to have been only limitedly successful so far.

Transparency Requires Evidence Based on Systematic Planning

The upcoming vaccination strategy is a new opportunity to create transparency about political actions. From the beginning, it must be understandable which data and assumptions form the basis of the strategy and exactly how interests are weighed.

Concerning expected vaccination successes and side effects, this means clear statements are needed about:

- What percentage of vaccinated individuals are expected to experience side effects and how many people that will be, possibly also with regional differences;

- What will be done if the data allow the statistical conclusion that side effects are more serious or more frequent than expected, as is mandatory in every clinical study;

- How the effect of the vaccination shows and with what delay, and by what metrics it is measured;

- What will be done if the data allow the statistical conclusion that the vaccination is not working as expected; here, too, the standards of clinical studies should be followed;

- And last but not least, but most importantly: How confident one is in these statements and what possible scenarios could occur under what conditions.

The last point addresses the necessary handling of uncertainty. The first step is not about already having a perfect solution that suggests a certainty that does not exist—the WHAT. Rather, it is about the HOW: It's about defining a process that ensures that relevant data of high quality are collected for the evaluation of the strategy. Data that are valid and reliable, representative, unbiased, high-frequency, fine-grained. Only such data can create evidence.

Data experts must be intensively involved in this. It is unacceptable that, for example, the Federal Statistical Office has so far played no significant role in strategic planning—there, over 2,000 people work day by day on how to obtain reliable data and extract decision-relevant information from it. Unfortunately, this problem extends throughout Europe—the official statistics and national statistical societies are heard far too little.

The Vaccination: A Gigantic Phase-IV Study

Clinical studies therefore require a study protocol before they are conducted. If we transfer this approach to the COVID-19 vaccination, which is essentially nothing less than a gigantic Phase-IV study, considerations on the following points should be published

in advance:

- What is the exact design of the planned vaccination strategy?

- What are inclusion and exclusion criteria, who is prioritized?

- What effect model is assumed, i.e., what is the relationship between vaccination and the spread of infection?

- To what extent and at what intervals is an effect expected and how certain is one of this?

- What side effects are expected with what frequency?

- What are termination criteria or criteria that would lead to a modification of the strategy and what would that modification be?

- What data will be collected, what collection problems are expected, and how will these be dealt with?

- How will the data be continuously evaluated?

- What statistical result proves that the strategy has worked?

This ex ante disclosure is indispensable in my eyes. It is not enough to publish daily case numbers, occupied ICU beds, and deaths ex post. It is not enough to calculate metrics such as incidence or the reproduction number without discussing how strongly measurement problems (such as lack of representativeness, quality of the Corona tests, expansion of testing) affect them and how they are dealt with.

One of the major communication mistakes in recent months has been that these problems were obviously known: political decision-makers would hardly have waited for a 7-day incidence of 200 and more if they had not been aware that the incidence is evaluated differently today than in April. But no one has disclosed how to deal with this in detail, and therefore many people get the impression that the political handling of Corona is arbitrary and inconsistent. This is especially true if trust in politics and science is not unreserved.

"We will probably have to forgive each other a lot in a few months." (Jens Spahn)

It is okay to make mistakes, but please not the same mistake twice. So, if we talk about an evidence-based strategy—and nothing else should be up for discussion—then we must not only have this strategy but also credibly demonstrate it. Even if not everyone may agree with this strategy from the outset, it would at least be understandable in retrospect why it was decided and what to do if it proves to be wrong.


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