Monthly Archives: September 2024

Contents

• COVID
  • Psychological Defense Mechanisms
  • Studies
    • Self-reported body function and daily life activities 18 months after Covid-19: A nationwide cohort study
    • Changes in memory and cognition during the SARS-CoV-2 human challenge study

COVID

Psychological Defense Mechanisms

I have been thinking about the psychology of the COVID response in the context of Elisabeth Kübler-Ross and her stages of grief, but it didn’t seem to fit well. I came across a Tweet thread that provided some good examples of what is going on with people pushing back against being cautious. I’m copying the text of the entire thread here, particularly for those who don’t use Twitter with the permission of the author, Mike Hoerger, PhD MSCR MBA (@michael_hoerger).

As a clinical health psychologist, I notice that many people are using psychological defense mechanisms to downplay the risk of COVID. These are my Top 7 examples:

#1 – Denial – Pretending a problem does not exist to provide artificial relief from anxiety.

• “During COVID” or “During the pandemic” (past tense)
• “The pandemic is over”
• “Covid is mild”
• “It’s gotten milder”
• “Covid is now like a cold or the flu”
• “Masks don’t work anyway”
• “Covid is NOT airborne”
• “Pandemic of the unvaccinated”
• “Schools are safe”
• “Children don’t transmit COVID”
• “Covid is mild in young people”
• “Summer flu”
• “I’m sick but it’s not Covid”
• Taking a rapid test only once
• Using self-reported case estimates (25x underestimate) rather than wastewater-derived case estimation
• Using hospitalization capacity estimates to enact public health precautions (lagging indicator)
• Citing mortality estimates rather than excess mortality estimates.
• Citing excess mortality without adjusting for survivorship bias.

#2 – Projection – When someone takes what they are feeling and attempts to put it on someone else to artificially reduce their own anxiety.

• “Stop living in fear.” (the attacker is living in fear)
• “You can take your mask off.” (they are insecure about being unmasked themselves)
• “When are you going to stop masking?”
• “You can’t live in fear forever.”

#3 – Displacement – When someone takes their pandemic anxiety and redirects their discomfort toward someone or something else.

• Angry, seemingly inexplicable outbursts by co-workers, strangers, or family
• White affluent people caring less about the pandemic after learning that it disproportionately affects lower-socioeconomic status people of color
• Scapegoating based on vaccination status, masking behavior, etc.
• “Pandemic of the unvaccinated”
• Vax and relax
• “How many of them were vaccinated?” (troll comment on Covid deaths or long Covid)
• Redirecting anxiety about mitigating a highly-contagious airborne virus by encouraging people to do simple ineffective mitigation like handwashing
• “You do you” (complainers are the problem, not Covid)
• Telling people to get vaccinated or take other precautions against the flu or RSV but not mentioning Covid
• Parents artificially reducing their own anxiety by placing children in poorly mitigated environments
• Clinicians artificially reducing their own anxiety by placing patients in poorly mitigated environments
• Housework to distract from stress
• Peer pressure not to mask

#4 – Compartmentalization – Holding two conflicting ideas or behaviors, such as caution and incaution, rather than dealing with the anxiety evoked by considering the incautious behaviors more deeply (hypocrisy)

• Hospitals and clinicians claim to value health/safety but then don’t require universal precautions
• Public health officials claim to value evidence but then give non-evidence based advice (handwashing over masking), obscure or use low-value data over high-quality data (self-reported case counts over wastewater), etc.
• Getting a flu vaccine but not a Covid vaccine
• Interviewing long Covid experts who recommend masking in indoor public spaces but then going to Applebee’s
• Masking in one potentially risky setting (grocery store) but not masking in another similar or more-risky setting (classroom)
• Infectious disease conference where people are unmasked
• Long Covid and other patient-advocacy meetings where only half the people mask In-person only
• EDI events
• Not testing because it’s just family
• Mask breaks

#5 – Reaction formation – expressing artificial positive feelings when actually experiencing anxiety

• “It’s good I got my infection out of the way before the holidays”
• “I had Covid but it was mild”
• Anything quoted in Dr. Jonathan Howard’s book, “We Want Them Infected: How the Failed Quest for Herd Immunity Led Doctors to Embrace Anti-Vaccine Movement”
• Herd immunity (infections help)
• Hybrid immunity (infections help)
• “It’s okay because I was recently vaccinated”
• “Omicron is milder”
• “Textbook virus”
• “Building immunity”

#6 – Rationalization – Artificially reducing Covid anxiety through a weak justification.

• “I didn’t mask but I used nasal spray”
• “I don’t need to mask because I was recently vaccinated”
• “It finally got me.”
• “You’re going to get Covid again and again and again over your life.”
• “It’s not Covid because I don’t have a sore throat.”
• “It’s not Covid because I took a rapid test 3 days ago.”
• “It’s not Covid because I’m vaccinated.”
• “Airplanes have excellent ventilation.”
• “I’ve had Covid three times. It’s mild.”
• “Verily was cheaper.”
• “Nobody else is masking.”
• “Nobody else is testing.”
• “My roommates don’t take any precautions, so there’s no point in me either.”
• “I have a large family, so there’s no point in taking precautions.”
• Surgical masks (they are actual “procedure masks,” by the way)
• Various pseudo-scientific treatments used by the left and right
• Handwashing as the primary Covid public health recommendation
• Droplet transmission as a thing
• Public health guidance that begins with “data shows” (sic)
• Risk maps that never turn deep red
• 5 expired rapid tests
• “Masks recommended” instead of universal precautions
• “Seasonal”

#7 – Intellectualization – using extensive cognitive arguments to artificially circumvent Covid anxiety

• Unending threads to justify indoor dining
• Data-rich public health dashboards that use low-quality metrics and/or don’t change public health recommendations as risk increases
• The entire justification for “off-ramps”
• Oster, Wen, Prasad Schools denying air cleaners because it “could make children anxious”
• Schools not rapid testing this surge because it “could make children anxious”
• The mental gymnastics underlying the rationales for who can get vaccinated, how frequently, or with what brand
• Service workers told not to mask because it could make clients uncomfortable
• “What comorbidities did they have?”
• “The vulnerable will fall by the wayside”
• Musicians and others holding large indoor events
• 5-day isolation periods

Here’s a link to the full book, a newer edition than what I own. The information on defense mechanisms begins on textbook page 100. Please let me know if there’s a more accessible alt-text solution that you would prefer so I can do better next time.

Studies

Self-reported body function and daily life activities 18 months after Covid-19: A nationwide cohort study

Studies out of Sweden are particularly interesting because of the way that the minimizers tried to push the laissez-faire approach taken by the country. It hasn’t worked out so well. About 1/3 of the 11,935 people who had to take sick leave responded to a survey that was given 18 months after their first day of sick leave. The distribution is telling of the damage that was caused by the disease. “The reported prevalence of problems with daily life activities was 46%; 9.5% reported a small problem, 26% reported some problem and 10.3% reported a big problem.” Maybe letting it rip as suggested by those who signed the Great Barrington Declaration wasn’t such a good idea.

Changes in memory and cognition during the SARS-CoV-2 human challenge study

First, I will state that I do not think that this is an ethical study. Intentionally infecting young adult (18–30-year-old) volunteers is madness. I truly doubt that the volunteers who participated in this study really gave “informed” consent. I don’t think any rational person who is fully informed about this disease would consent to be infected.

“The main cognitive endpoint was a baseline-corrected global cognitive composite score (bcGCCS), defined as the baseline-corrected, standardised mean across all 11 tasks…

1. Motor Control–Measures visuomotor accuracy and reaction time
2. Object Memory (Immediate)–Measures short term precision recognition memory
3. Simple Reaction Time–Measures reaction time
4. Choice Reaction Time–Measures complex reaction time
5. 2D Manipulations–Measures mental manipulation of 2D visuospatial information
6. Four Towers–Measures mental manipulation of 3D visuospatial information
7. Spatial Span–Measures spatial working memory capacity
8. Target Detection–Measures attention and distractibility
9. Tower of London–Measures spatial planning
10. Verbal Analogies–Measures semantic reasoning
11. Object Memory (Delayed)–Measures medium term precision recognition memory”

There was a very important statement made in the middle of the study. “Notably, none of the volunteers reported subjective cognitive deficits.” This is a bit alarming in that people are not recognizing that they are impaired. It seems similar to how someone who has “only had a few drinks” may not realize that they are a danger driving on the road.

“In conclusion, this study confirmed that prospectively controlled infection with Wildtype SARS-CoV-2 is followed by objectively measurable reductions in cognitive task performance that can persist for at least a year. Immediate and delayed memory, and executive function were the most sensitive cognitive domains.”

Contents

• Interpreting the US Graphs
• How I Got Here

Interpreting the US Graphs

Introduction

It’s probably time to do a little bit of an explanation of what is on some of the graphs I’ve created. Some should be obvious; others are a bit nuanced. I’ll use New York as an example. The headers are the names of the tabs, in order, L to R, at the time of this writing.

Early Indicators

One way to think about the first few tabs are graphs superimposed on the background of the percentages of different variants. I pull the data from CoVariants, and have used the same colors that are used there, although it looks like I may have miscopied the hex code for the color of one variant, which I will try to remember to update when I have to redo this entire file set again. The percentages for everything but the wastewater are on the left y-axis.

I placed this tab first because it has the earliest indicators of COVID cases climbing in each state. For some states, some of this data may be missing, such as how the positivity data for NY abruptly ends a few months ago. My thinking was that this is probably what most people want to know, especially if they need to travel somewhere.

I split the percentage of COVID ED visits into two separate lines. The solid red line is the real percentage. However, since that is a small proportion of all ED visits, it’s pretty dampened out and hard to really see the curve, which is much more important. I created the red dotted line by multiplying that number by ten. By doing so, one can see how both wastewater and positivity are also good indicators of COVID in the community.

Var-Sym

Same variant background, but this adds Google Search trends compiled by the Delphi Group at Carnegie Mellon. I made my own categories to make them fit in the legend better, but full details of the terms they used can be found on their site.

Var-Peds Admits

Same variant background but with pediatric COVID admissions in the foreground. The black line is the raw data, the red line is where I used a formula of reported number/percentage reporting, which is the same method for all of the following graphs that show adjustments. You can see where these two lines diverge when mandatory reporting ended in May.

Var-Adult Admits

Exactly the same as the prior, but using adult COVID admissions instead.

% ED Visits

Hopefully this is pretty self-explanatory showing the percentages of ED visits for COVID, influenza, and RSV. Note that COVID is obviously not seasonal. The colors have some transparency to show the curves instead of them being hidden behind a larger one in front.

% ED Visits (stacked)

This is the same data as the previous graph, but here each is stacked on the other to show the burden of respiratory disease in the EDs of a state.

Flu

The red line is influenza admissions and the scale is on the left y-axis. The yellowish areas are influenza ICU patients, and the blues are influenza hospitalized patients, both on the right y-axis. The darker areas of each are the numbers after doing the reporting adjustment calculation described earlier.

C19

This is the same methodology as the prior Flu tab, but with COVID instead and using different colors.

IP Bed Util

This may look complicated, but it’s very simple. The left y-axis is the number of hospital beds and inpatients. The lightest green are the available beds, the darker green are the total occupied hospital beds. Yellow and red are the the number of COVID and influenza patients.

The dotted lines are the percentages of hospitals reporting this data as given on the right y-axis. You can see the big impact when CDC stopped requiring reporting.

Adj IP Bed Util

This is the same as the previous graph, but uses the dotted lines to do the same adjustment previously described to adjust for reduced reporting. At the far right, you can see that the data doesn’t always align in each state. That has to do with exceptionally small or exceptionally large hospitals dropping out of reporting. When that happens, the adjusted numbers can get over-or under-inflated. At one time there was a way to adjust for this. CDC used to provide hospital level data for all hospitals reporting, but that’s gone now too.

ICU Bed Util

This is the same as IP Bed Util (two tabs back), but ICU beds instead.

Adj ICU Bed Util

This is the same as Adj IP Bed Util (two tabs back), but ICU beds instead.

How I Got Here

I thought some people might be curious as to how I wound up doing all of this work which until this year, had been completely on my own time, so I thought I would share that story.

This actually goes back to 2004. Besides doing hospital infection control and epidemiology, I had started speaking at conferences. Even though I’m an introvert, I found I enjoyed public speaking. This realization happened in 2001 when I took an elective course designed for dual PhD/DVM students while I was in grad school. The course was the Epidemiology of Zoonotic Disease. It was designed as a practicum, so instead of having the teachers teach it, the students had to teach it. I found I really enjoyed it and aced the course. That’s when I realized I wanted to do some public speaking. I had HATED speech in undergrad.

A professional colleague of mine asked me to speak at a regional conference on H5N1 and pandemics. One week before doing so, I was at the national conference for my professional association (APIC) and was at a breakfast. I told people at my table I needed to leave to finish preparing that presentation. A few asked if they could see my slides since H5N1 was a hot topic at the moment. That led to a number of speaking engagements around the country on pandemics, including keynote presentations, even at national conferences.

One conference was put together by the University of North Texas Health Science Center. It was primarily focused on pandemics and had some big names in the field, so I was a bit apprehensive. The university compiled all of the comments they received on each speaker and sent them to us. This is the top part of one of eight similar pages of comments I received. This is when I realized that I had developed street cred on pandemics.

Eventually I wound up leading pandemic tabletop exercises around the world. I was mostly working with either clinical or pharmaceutical leadership executives. Even before COVID hit, I wound up on a state pandemic ethics committee as well as developing the pandemic plans for a state’s primary care association.

In the fall of 2019, I had taken Al Gore’s Climate Reality Leadership Course. My intention that winter was to develop a conference presentation on the health impacts of climate change.

In February, everything changed. I was deployed as part of my federal disaster medicine role to be part of the mass quarantine efforts at Travis Air Force base in California. There are plenty of stories in the media on this. This is the link to the first one that came up. The people we were to serve were primarily State Department evacuees and their families from China.

A couple of days into this, I became symptomatic. Obviously, that made me very concerned. I was taken to a local ER and likely had one of the very first COVID tests given in the US, back when they probed to what felt like the back of the occiput. Fortunately (?), my influenza B test came back positive, so I was put in isolation and my entire team given Tamiflu. It was a scare though until that diagnosis.

I started trying to find data to get an idea of what was happening around the country. One of the first places that aggregate data was easily available in a usable form was at Johns Hopkins. I started pulling together case and death data for each state from there and plotting them together and posting them on Facebook.

After my deployment, I tried to gather as much information as possible on the disease since I had a very keen awareness of the much larger social impacts of a pandemic.

It was also about this time that I used two published sources and US Census data to determine the disease burden from COVID. A friend of mine is the CFO at a Level 1 Trauma center in my area and mentioned she wished she knew how it would affect them specifically. At the time, there was really good county level data and I told her I could probably figure out how to do that if I knew the draw area for the hospital She gave me the counties and I did just that.

From that, I realized that a number of hospitals and public health departments might benefit from a similar tool, so I built that out to where they could put in the parameter of an infection rate and the counties that they wanted the aggregated data and it would provide severity by age group for the selection. It was used widely across the US.

In March, I came across a Facebook group called Dr. Frank Models. He claimed to be a PhD chemist (true) with expertise in modeling outbreaks and pandemics (false). I realized within a week he was really mispresenting the data. When I commented on this, I got booted out of the group. I got back in, gave more evidence, and got booted again.

To counter his misinformation, I started another group called NOT Dr. Frank Models, which I knew would come up in searches at that time. I hoped to get people real information instead of his lies. He grew that group to around 50,000 members, but it eventually got banned after repeated posts of misinformation.

Now that his COVID grift is up, he moved on to election denialism. He’s Mike Lindell’s “scientist” with the “8th degree polynomial” that he claims proves election fraud. I won’t go further down that rabbit trail of his grift, but there is plenty of evidence countering his claims on both here.

I continued posting graphs on Facebook, but then realized that perhaps I should put them somewhere where someone didn’t have to be using a service. That’s what led to further development of this website, which really had just started as a blog which I used to answer questions about the Ebola case in Texas.

Since I had been able to pull together all of this county level data, I was also able to provide graphs of COVID in metropolitan areas. This came to the attention of the Chief Medical Officer of a Fortune 5 company who asked me to meet with him a couple of times a week (virtually, of course) to discuss what was going on with the pandemic.

What I never expected was the hate, vitriol, physical threats, and death threats that would come my way from trying to keep people from harm. People accused me of making this political. I believe I was the first person to identify the impacts of the politicization of the virus. If I truly were doing so, I would have just let the virus take its course without trying to intervene with Republicans, who died at a much higher rate from COVID than Democrats, other than during the original wave. That is because it hit dense population centers like NYC first.

It’s worth noting that 45 called coronavirus a big hoax at the very outset. He is a large part of the reason that it has remained politicized until this day and why Republicans are far less likely to get vaccinated and use respiratory protection. That hasn’t played out well for them.

Trump had also commented about how this was only a problem for large cities at the outset. I am now convinced that his initial plan was to let it go unchecked in large, dense population areas in order to harm more democrats for political gain. That is evil to its core.

When I started putting all of this data visualization together, I thought for sure that CDC, a university, or a foundation of some sort would start doing this type of work within a year or so. I hate the fact that a few years into this it’s just a few individuals for the most part who are trying to get good information to the public. I would much rather spend my time doing other things, but I know that there is a need and I will continue to try to fill it as long as I can.