1/ As an epidemiologist, I’m immensely grateful for anyone systematically documenting those refusing #COVID19 vaccination in health care and #LTC...and asking & recording their reasons why...
2/ Vaccine hesitancy is a major barrier to US achieving enough immunization to avoid rolling waves of #COVID19 outbreaks for years. It is *so* important to take it seriously and craft effective responses & strategies (feds should have started months ago, but that’s past)...
3/ It’s critical to watch this closely in this first round of vaccinations in #LTC longterm care/nursing homes and healthcare bc we have the benefit of a denominator in these settings...
4/ the only reason anyone can put a number on the vaccine refusals (20-30%, etc) is because these are defined populations. There’s a roster of employees you know you’re targeting. This is a “denominator” in #epitwitter speak. It’s a core principle in epidemiology.
5/ it’s crucially important to take advantage of the data in this stage bc, the way US is doing vaccine rollout, there are *not* clear denominators in the next stages...
6/ when vaccination moves out to general public, there’s no roster, no master list, there’s no clear denominator (we can pull from census, housing, public health risk assessments, etc to approximate local denominators but that takes local staff time - in short supply rn)...
7/ (Other countries have denominators and are proactively contacting and tracking people bc they have national universal health care and social safety net systems to build from. Also also they probably spent 2020 planning for & funding their #COVID19 vaccination rollouts)
8/ In the next stage (we see a preview in states like FL), we’ll see the disastrous shortages of vaccine & vaccination infrastructure, but we won’t see who is missing. Without knowing who we are missing, the US will walk into yet another totally predictable #COVID19 disaster...
9/ We will get to June or July and see vaccination rates dropping off. But we will have all this extra supply and wonder why. We will be scratching our heads wondering what happened. We were so focused on getting it out faster, we didn’t think about who we were missing...
10/ and then we will realize the implications of having so much of the population non-vaccinated & we will get panicked and then more panicked when we realize that the non-vaccinated folks are not randomly distributed throughout the country...
11/ that they are in socially networked pockets throughout the country...so much so that we remain at risk of frequent outbreaks, also these folks are the people who work in the service industry, in schools, in health care, in your stores, on your airplanes...
12/ then another scramble to address a foreseeable problem that public health folks (especially equity-trained folks) had been warning about for months and months and months. The US’s repeated failures to consider longterm consequences are exhausting.
13/ I don’t like the admonishing tone that some leaders & news accounts take towards health care and #LTC workers refusing vaccine in this round

They are the canary in the coal mine

You don’t get mad at the canary

You’re grateful for its lesson, and you heed its warning

• • •

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More from @WhitneyEpi

25 Dec 20
Some observations from reactions to my thread from last wkend (thank you all. I really appreciate all of you): 1/
1/
1. Great question from @drjenndowd: Let’s say one is open to idea that some exposure & infection differentials in the population (left & right circles) are as great as age-susceptibility differentials (bottom circle):
How can I calculate this all out?

2/
Similar point addressed by the author of a great modeling paper on #COVID19 vaccination: we don’t have the data! 3/
Read 28 tweets
21 Dec 20
OK, bc of Stanford Med #COVID19 vaccine algorithm fiasco & N@te Silver’s recent tweet re: vaccine priority guidelines, I have to talk about one of my favorite topics! I even drew an illustrative Venn diagram! (I’m already imagining @rdpeng rolling his eyes. I’m sorry, Roger!) 1/
Key background 1: Because of limited supplies of #COVID19 vaccine, a federal advisory board making recommendations about the order in which people should get vaccinated (don’t get distracted: these recs are non-binding; real action is at state level) 2/
nytimes.com/2020/12/20/hea…
Key background 2: Infection fatality rates (IFRs) for #COVID19 increase markedly with age. Note the y-axis is on a log-10 scale. The IFR for COVID-19 for an 80 yo is literally ~1,000 times greater than for a 5 yo & ~100X greater than for a 40 yo
3/
Read 18 tweets
19 Nov 20
Been thinking a lot about this, especially the “for a paycheck” part. I have many relatives working in jobs where it’s impossible to distance, avoid closed spaces, etc. It is 100% a failure of govt (US senators esp) to provide money for businesses & workers to stay home...1/
But me getting on a soapbox about
the stimulus bill US should have passed this summer is neither here nor there for my relatives who need to make decisions today about work, even when they know perfectly well (thanks to yours truly!) that their work environments are not safe 2/
If I sat in my home office or in my empty campus office typing away about how “everyone should just stay home & this would all be over, The End,” I’d have no credibility with them. (They love me though, so they’d just 🙄 & quietly ignore my posts. 😂) 3/
Read 14 tweets
18 Nov 20
This is a nice 🧵 of 🧵s.

Recent paper published on impacts of #COVID19 #K-12 school closures on well-being of students over the very longterm. This topic of major interest to me, but paper not great. Thread critiques methods & interpretation informatively & constructively!
I particularly appreciated @ikashnitsky’s critique. I believe her use of “deterministic” corresponds to #epitwitter term, “causal”. @MariaGlymour What do you think of her assessment of this literature?
Ok, LOL. Looking back, @jburnmurdoch’s tone is more acerbic than I recalled. I only note this bc I posed this thread as a model of constructive critique in tweet 1 but some of the tone spicier than my personal ideal. 😂
Read 4 tweets
16 Nov 20
This is amazing, Amazing, AMAZING news!

Someone close to me is in this vaccine trial. I am so moved by every single volunteer for these trials. They took a risk to give the world a tremendous gift. I’m literally crying. The world owes them so much...
And it looks like all the severe cases were in placebo group, so vaccine may not just reduce risk of getting sick overall but also prevent severe illness. 🙏🏾
Also some promising news about placebo arm folks: “Dr. Zaks said Moderna’s study results were so strong that the company felt an ethical obligation to offer the vaccine to the placebo group as soon as possible.” From @nytimes. Stay tuned
Read 8 tweets
15 Nov 20
I know what I’m listening to on my Sunday morning walk!👇🏾

As you know from my earlier interview on this podcast, structurally defined Selection Bias is my fave #epitwitter bias. And @ER_Mayeda is a super expert. This will be a treat! podcasts.apple.com/us/podcast/9-w…
.@ER_Mayeda cautions, Methods of correcting for selection bias, eg, IPW, only as good as understanding of selection processes that gave rise to data. Reminds me of polling in 2016 & 2020 US elections. When system is dynamic, can’t rely on past trends; need other ways of knowing
Read 4 tweets

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