1/
Vaccine safety: We compared excess adverse events after #COVID19 vaccination (Pfizer-BioNTech) and after documented #SARSCoV2 infection.

nejm.org/doi/full/10.10…

Take-home message: Low excess risk of adverse events after vaccination, higher after infection.

Some thoughts👇
2/
Preferring #SARSCoV2 infection over vaccination has become even harder. (Remember: infection also increases the risk of severe disease/death)

This is a good illustration of how #randomized trials and #observational studies complement each other for better #causalinference...
3/
The original #randomized trial estimated vaccine effectiveness to prevent symptomatic infection, but was too small to quantify vaccine safety.

That's what #observational studies do.

Now a different sort of question: Why could we do this study in the first place?

2 reasons.
4/ First, we had DATA from millions of people.

We worked with high-quality data from @ClalitHealth, a health services organization that covers >50% of the Israeli population.

In contrast, looks like many countries aren't taking investment in healthcare databases seriously.
5/
Maintaining population health databases, available for immediate use in a public health crisis, is a matter of national security. How else can you make timely policy decisions?

Israel, UK (@Opensafely), and a few others are making great progress. How is everybody else doing?
6/
Countries/states with fragmented, unlinked, or not readily available databases will keep relying on others.

What happens if your questions are different? e.g., what's the safety of a different vaccine in a different population?

But good DATA weren't enough for our study...
7/
Second, we had health data EXPERTS

This study was led by a team of epidemiologists @ClalitResearch, an institute specifically created to make Clalit's data useful.

Governments: This is a good model
(also used by non-governmental organizations like @KPDOR and @KPWaResearch).
8/
Governments: Together with your health data resource, create/support a research institute. Then encourage external collaborations.

For this study, Clalit's experts worked with external experts @CAUSALab, @CCDD_HSPH, @Bos_CHIP, @HarvardDBMI

Everybody wins with collaboration

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More from @_MiguelHernan

18 Mar
1/

Many countries are vaccinating their elderly. Can we relax control measures now?

No.

Even with 50% of elderly vaccinated, uncontrolled #SARSCOV2 transmission may overrun the healthcare system.

We explain @AMJPublicHealth today, led by @_gmales
ajph.aphapublications.org/doi/10.2105/AJ…
2/

Data from Madrid, Spring 2020:

Critical care requirements peaked at 5 times the usual capacity.

Hospitals managed to increase ICU capacity by 3-fold.

Heroic but, sadly, insufficient.

The healthcare system collapsed. Not everybody who needed critical care received it.
3/

If 50% of the elderly had been vaccinated, critical care requirements still would have peaked at almost 4 times the usual capacity.

Greater than the ICU capacity of any country in the world.

Only a prolonged lockdown could return ICU requirements to normal.

One last thing:
Read 4 tweets
12 Mar
@ProfMattFox 1/
The odds ratio from a case-control study is an unbiased estimator of the

a. odds ratio in the underlying cohort when we sample controls among non-cases

b. rate ratio in the underlying cohort when we use with incidence density sampling

No rare outcome assumption required.
@ProfMattFox 2/
Because the odds ratio is approximately equal to the risk ratio when the outcome is rare, the odds ratio from a case-control study approximates the risk ratio in the underlying cohort when we sample controls among non-cases and the outcome is rare.

But...
@ProfMattFox 3/
... for an unbiased estimator of the risk ratio (regardless of the outcome being rare), we need a case-base design, not a classical case-control design.

Of course, all of the above only applies to time-fixed treatments or exposures.

As for the causal interpretation...
Read 5 tweets
5 Mar
1/
We recently confirmed the effectiveness of the Pfizer-BioNTech vaccine outside of randomized trials @NEJM.
nejm.org/doi/full/10.10…

Studies like ours are being used to promote a vaccine passport to travel in the US, UK, and European Union.

A few clarifications are in order.
2/
Before we start, a disclaimer:

Vaccine passports involve complex ethical, economic, and political considerations.

Here I talk exclusively about scientific issues. The goal is that those making decisions have a better understanding of what we do and do not know as of today.
3/
Based on our study, we can say confidently that the vaccine is highly effective in preventing you from getting sick with #COVID19.

Based on our study, we can't say confidently that the vaccine is highly effective in preventing you from getting infected and infecting others.
Read 9 tweets
24 Feb
1/
We've just confirmed the effectiveness of the Pfizer-BioNTech vaccine outside of randomized trials.

Details @NEJM: nejm.org/doi/full/10.10…

Yes, great news, but let's talk about methodological issues that arise when using #observational data to estimate vaccine effectiveness.
2/
A critical concern in observational studies of vaccine effectiveness is #confounding:

Suppose that people who get vaccinated have, on average, a lower risk of infection/disease than those who don't get vaccinated.

Then, even if the vaccine were useless, it'd look beneficial.
3/
To adjust for confounding:

We start by identifying potential confounders.

For example: Age
(vaccination campaigns prioritize older people and older people are more likely to develop severe disease)

Then we choose a valid adjustment method. In our paper, we matched on age.
Read 12 tweets
27 Nov 20
1/ Estimating the infection fatality risk (IFR) of #SARSCoV2 is hard.

Our estimates from Spain's #ENECOVID (just published):
Men: 1.1% to 1.4%
Women: 0.58% to 0.77%

After age 80
Men: 12% to 16%
Women: 4.6% to 6.5%

Why is the #IFR hard to estimate?
bmj.com/content/371/bm…
2/ The IFR in a population is the ratio of

number of deaths from SARS-CoV-2 infection (numerator)

and

number of individuals infected by SARS-CoV-2 (denominator)

during a prespecified period.

Both numerator and denominator are hard to quantify.
3/ Why is the denominator hard to quantify?

The number of infected with #SARSCoV2 is not the number of confirmed #COVID19 cases.

Because many infected individuals never have symptoms or have minor symptoms and are never diagnosed.

(For details, see journals.plos.org/plosntds/artic…)
Read 6 tweets
13 Nov 20
1/ Madrid es la única gran ciudad europea con más del 100% de sus camas de UCI en hospitales públicos ocupadas por enfermos con #COVID19

DURANTE DOS MESES.

Mantener este nivel de ocupación de UCIs es jugar con fuego.

Image
2/ Porque:

Se reasignan a UCI camas que se deberían usar para otras enfermedades.

Se agota al personal de UCI que debe atender a más pacientes.

Si la epidemia resurge, no queda capacidad de respuesta.

Las UCIs son nuestra última línea defensa.
3/ ¿Cómo se llegó a esto?

El primer problema fue permitir una epidemia increíblemente descontrolada durante tanto tiempo.

Otros países han tomado medidas mucho más duras cuando su nivel de ocupación de UCIs llegaba al 40%, el punto al que llego Madrid a finales de agosto.
Read 6 tweets

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