Dr Ellie Murray, ScD Profile picture
Dec 5 9 tweets 2 min read Twitter logo Read on Twitter
When people talk about masks & mask mandates for preventing respiratory infections, they love to talk about improper mask wearing.

And it’s true not everyone wears good masks correctly. But it’s also true that MOST people wear good masks well.

How many is most? About 85%.
Data from *Kentucky* show that during their mask mandates, 95% of people in indoor public spaces were seen to be wearing any mask.

And 87% of people were wearing masks that fully covered mouth and nose.

journals.plos.org/plosone/articl…
We did a separate study in Boston right around the same time — without knowing the Kentucky study was even happening.

What did we find?

95% of people were wearing masks in indoor public spaces. And 85% had them correctly over mouth & nose.
So, yes, even when there are mask mandates, you will see people without masks on and you will see people wearing masks badly.

But those people are a small group.
Most people mask & most people mask pretty well, when they are told that they need to.

They would probably mask even better if anyone with any real reach had bothered to explain how.
The data on mask mandates and infection are also clear.

During the most widespread mandates, the rates of common respiratory infections plummeted worldwide!
Mask mandates reduced flu transmission. And reduced flu deaths. Especially among little kids.

We reduced the flu sl much that one flu strain was very likely entirely eradicated worldwide!

We did not know we could prevent flu so well. We do now. Will we choose to?
Mask mandates also work to reduce COVID.

We can see this most clearly in schools.

When mandates went away, COVID rates & sick days rapidly increased.

nejm.org/doi/full/10.10…
So maybe you don’t like masks. That’s ok. You can have your own opinion.

But the facts are clear.

Masks reduce respiratory infections. Mask mandates reduce respiratory infections.

Even if some people don’t mask. Even if some masks are worn badly. Even if some masks are cloth.

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More from @EpiEllie

Dec 6
Me, an epidemiologist: research shows widespread mask use reduces respiratory infections

Them, a rando: OMG WHY DO YOU WANT EVERYONE TO ALWAYS MASK EVERYWHERE FOREVER?!!!!
Me, an epidemiologist: research shows widespread mask use reduces respiratory infections

Them, another rando: oh yeah? Well one time I saw a mask and then I got sick, so how do you explain that, huh?!
Me, an epidemiologist: research shows widespread mask use reduces respiratory infections

Them, yet another rando: UUUGH, WHY ARE YOU STILL SO OBSESSED WITH DISEASES?!! WHAT ARE YOU EVEN?!!
Read 4 tweets
Nov 13
Out now in Osteoarthritis and Cartilage: our new paper presents a graphical causal model for treatment of pain in knee osteoarthritis.

Open access link: oarsijournal.com/article/S1063-…
Image
We outline a 6-step process for creating causal graphical models using scientific evidence and expert knowledge.

Identify core variables, create a list of potential covariates, conduct a semi-systematic literature review, discuss with experts, synthesize, and visualize. Image
What do we mean by "semi-systematic" literature review?

Like a systematic review, you begin with a carefully created search hedge: key words for your exposure, outcome, and/or other core variables, combined with your covariate list.
Read 22 tweets
Oct 3
Nate Silver’s blog👇🏼 is a nice example of using triangulation for causal attribution.

He lays out starting assumptions, then varies his assumptions to see what changes.

He’s not estimating a causal effect. Instead, he’s using descriptive stats to hypothesize about the cause.
Despite this type of triangulation being a key and crucial part of science (and just like, life!), we often don’t spend enough time teaching students how to do it in a reasoned and systematic way.
In epidemiology, triangulation is often pitted against causal effect estimation. But they have different purposes.

Causal effect estimation is critical when we want to know the *size* of an effect.

Triangulation helps us answer the question: is this likely a cause *at all*?
Read 16 tweets
Sep 19
THINK LIKE AN EPIDEMIOLOGIST:
Science is not magic. It always requires assumptions.

When done perfectly, some types of studies make fewer assumptions. But no one’s perfect.

We can’t decide quality from study type alone: a good observational study can be better than a bad RCT. A continuum of bias in study design.   At the top, a double-headed arrow is labeled “unbiased”  on the left with “ideal randomized controlled trial” as an example and “intractably biased” on the right with “relying on ‘gut feeling’” as an example.  Below the arrow are 4 overlapping bars with relative positions of 4 main study types. From left to right: explanatory randomized trials, pragmatic randomized trials, observational studies, simulation studies.   Each study type covers a range of quality. For each of the four types listed, under the very best version of the design possible that des...
@benedictecallan I like to call this the “mad scientist target trial framework”.

Not “what RCT would you do”, as a starting point for observational study design.

What experiment would the mad scientist version of you, as a start for designing any study, experiment, observation, or model.
@Kcummins760 This is, in fact, a fatal flaw in the GRADE criteria approach to evidence synthesis.

Which is the topic of a paper of mine currently languishing in the morass that is peer-review. Turns out people don’t like it when you point out major flaws in foundational research tools, 🤷🏼‍♀️
Read 5 tweets
Sep 5
Our @NEJM study showed school mask mandates *reduce* COVID illness & absences.

Others disagreed. But we take scientific critique seriously. So we checked everything suggested to us. And now we’ve published an updated Appendix with the details.

Our results hold: Mandates work.
Here’s the link to our original study:

nejm.org/doi/full/10.10…
And here’s the updated Appendix with all our new sensitivity analyses, plus some additional explanation about what types of causal questions we can and can’t answer with these data. 👇🏼

nejm.org/doi/suppl/10.1…
Read 7 tweets
Aug 27
SUNDAY PUBLIC HEALTH MINUTE:

Ever heard “it takes a village to raise a child”?

Well, it also takes a village to start an outbreak. And to stop one.

How do we teach our communities better public health? The answer: public health literacy.

A 🧵
What is “public health literacy”?

Despite the fact that ensuring good public health is one of the most important functions of society, it turns out no one* has ever really defined “public health literacy”!

___
*We’ve found <5 scientific articles ever on public health literacy!
To address this major gap, our team enlisted the help of MPH student Maggie Burkhardt (she graduated May 2023 🥳) to do a deep dive on anything and everything that was possibly related to this idea.
Read 12 tweets

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