There hasn’t been an update on the teen recently, which I suspect may mean no improvement.
The second alarming thing is the results of the sequencing of the virus from the teen. One thing about H5N1 so far has been that the virus has not been easily spread person to person. However, the virus from the teen had two key changes in the hemagglutinin gene. Hemagglutinin is a protein on the surface of certain viruses, including influenza, that binds to the sialic acid receptors on cells that it will infect. Think of it as the key that unlocks the door to gain entry into the cell. Those two substitutions are known to enhance binding to mammalian receptors, ie, it makes it much more easy to infect a person.
Why this Is Important
I tweeted this almost two years ago.
We are getting very close to human-to-human transmission. That risk will increase significantly as seasonal influenza comes into play. Influenza is a very sloppy replicator and will mix its genes as well as mop up genes from the environment.
People simply do not comprehend the scale of what could happen with H5N1.
Even if we took a more conservative mortality rate of 25%, that still means 600 million deaths worldwide. For another perspective on that number, it would be like everyone in the United States (except those in Massachusetts) dying…TWICE.
Those number also are assuming that everyone infected would get good healthcare. We don’t have that capacity, so the numbers would likely be much higher.
In addition, it also doesn’t reflect the mortality related to other causes as supply chains and services are disrupted.
There is another wild card today that didn’t exist in 1918 – immunocompromised people. If cytokine storms are the result of a healthy, overactive immune system, what happens at the other end of that spectrum among those with untreated HIV or are on immunosuppressants? Does that mean that they could amplify the virus and become superspreaders? I tweeted about this as well.
We live in a world of very complex systems. The more complex a system is, the more opportunities it has for failure. This problem was addressed very well in an article by Debora Mackenzie in the New Scientist in 2008. This is the one to read if you really want to have a grasp of this threat, but it is behind a paywall. I found the text of it here as well.
If you wonder how I sleep at night, lately, not very well.
Note: CDC had changed the structure of a data file this week, which made the percentage of ED visits break in my state files. I looked at trying to fix them all, but then realized that there will likely need to be a massive data overhaul in about a week since hospitals are required to report data again. This will require a complete rebuild of the file for each state, so I decided to just wait and see what kind of data is available next week.
Epidemic curves are simply a means to represent cases or deaths over time. For example, are the deaths from the Spanish Flu from different cities. Note at the peak in NYC, the mortality rate was running about 6%.
It’s also worth pointing out there there was a small wave in late June/July which can be more easily seen here.
That’s almost reminiscent of how smaller waves preceded both the delta and omicron waves from COVID, which also disproves the claim that viruses get milder over time. It’s also worth pointing out that once rapid tests came out, that cases really don’t paint an accurate picture of the burden of COVID in the US anymore, which is why I plot wastewater, positivity, and ED visits on the site.
Another way to analyze the impact of a disease is to view deaths by age group. Normally, influenza has a U-shaped curve, with most of the deaths occurring in the very young and very elderly, as represented by the dotted line on the graph below. During the Spanish Flu pandemic, there was a w-shaped curve (solid line), with a disproportionate amount of death in the young and healthy. In this case, the likely cause was a cytokine storm driven by the virus. Those with developed, healthy immune systems were at higher risk of this outcome as the immune system over-responded to the infection. In fact, the damage was so sever that the lung tissue from those victims looks like it had been exposed to chemical weapons.
A Brief Aside about COVID Mortality
Here’s a graph of COVID acute mortality in the US. COVID deaths are undercounted for a number of reasons, contrary to minimizers claims. Yes, a few get miscategorized, but that is the exception rather than the rule.
The red line on the right is what I want to emphasize and is my expectations for the future. COVID causes MANY chronic diseases as well as immune system disruption. The line represents the climb in chronic disease deaths from these sequelae. Acute COVID deaths will likely continue their normal wave patterns (unless we get a much better vaccine) built on top of these deaths. This of it as the x-axis curving up due to chronic disease deaths. Of course, these will likely be undercounted as COVID deaths as well. This is a VERY different pattern than what we see with seasonal influenza. It can cause other problems, but that generally happens within a few months of infection, such as a rise in acute myocardial infarction deaths, which are related to the inflammatory process of influenza. COVID is different in that it causes small clots in blood vessels, leading to focal tissue damage, death, and scar tissue from oxygen starvation, which will take a number of years to manifest.
A H5N1 Curve
People will notice a very obvious difference with a H5N1 pandemic compared to COVID if it starts and maintains the mortality (25-50%) we have seen in the past. In addition, it is spread more readily than COVID because it is also spread by contact and fomites, which suggests it will be much more transmissible.
That would result in a much higher and narrower wave of death. To illustrate that in comparison to COVID, something like this would not be surprising. That will cripple healthcare instantly and will make the supply chain problems we had since the start of the pandemic look like child’s play.
A recently published study on new onset diabetes in children within 6 months of COVID infection left me a bit stunned. At the six-month mark, the authors found children who had been infected had a 58% increased risk. It seemed worth explaining why this is so alarming.
There are 72.5 million children in the US. The baseline incidence of pediatric diabetes is 13.8 per 100,000 per year, or 72,500,000 x (13.8/100,000) = 10,005 new cases/year.
COVID seroprevalence studies suggest that 96.3% of children have been infected with COVID at least once, which equals 72,500,000 x 0.963 = 69,817,500 are at increased risk.
How do we calculate excess diabetes as a result of COVID in children? First, we need to calculate the rate due to COVID, which is only going to occur in the children infected with COVID. That rate is 0.58 x 13.8 per 100,000, or 8.004 per 100,000. That provides us with 69,817,500 x (8.004/100,000), or 5,588 new cases of diabetes among children per year, but that is a gross underestimate for many reasons.
First, the original study was only looking at risk within a few months of a COVID infection. That means that this risk figure is more akin to a point estimate than looking at lifetime risk. This is in part due to COVID being a vascular disease that causes microthrombi and focal tissue necrosis. I still suspect that most of the chronic disease burden from COVID infections will take a decade to become manifest.
Second, we also know that repeated infection increases the diabetes risk in adults by 70%, and we can use that number to estimate what happens in kids.
Let’s assume that half of the pediatric population in the US has been infected twice, which would be 34,908,750 facing this increased risk. The rate from repeat COVID infection would add 8.004 x 0.7 x 34,908,750, or an additional 8.004 x 0.7 x 34,908,750 / 100,000, or another 1,956 new cases of diabetes per year among those who were infected twice. The annual burden of diabetes from RECENT COVID infection then becomes 7,544 cases/year. It’s reasonable to assume that each subsequent infection increases that risk even further.
This further supports my argument than most of the disease burden of COVID is really many years off in the future. We have become so focused on the acute phase of the disease and are ignoring these other serious sequelae.
Similar calculations can be made with other diseases, but again, it would only be a small fraction of what is to come. This is but one example of why I have such a mix of emotions about COVID, ranging from anger, futility, and to depression. All of the numbers I just calculated are just the tip of the iceberg of what we are doing to future generations. We do not have the capacity to handle this scale of disease. We are handing future generations a dystopia of our own making between this, H5N1, and climate change. Those who have the power to make decisions to protect the public and fail to do so will not be remembered kindly by history.
A few people asked if I could share the letter I wrote to help someone get approved for disability from the Social Security Administration. I wrote to them and asked if it would be ok for me to share. Since it doesn’t contain any personally identifiable information, they approved.
Since I’m not a clinician, which likely would be important for dealing with the SSA, I wrote my thoughts on their medical history and tied it to their symptoms. It was simply a means to provide their primary care provider with some ammunition to help with their claim. Sadly, I would be surprised if more than about 5% of physicians understood the scale of COVID sequelae. The letter is below the line.
I finally had a chance to review your records and pull together some of the research I have in my files. It sounds like it’s been a really rough time for you. I wish I could simply snap my fingers and make it go away.
With two known COVID infections, we know that your risks of multiple adverse outcomes increases as described by Bowe et al (2022). If you had asymptomatic infection(s) that went undiagnosed, these risks increase as well.
The hazard ratio (HR) in this graph shows the risk of those who have been reinfected compared to those who have not been infected. A 95% CI (the range in parentheses) is what is known as the confidence interval, which can be thought of as the range where 95% of the variation is due to the condition itself and not just due to statistical anomalies.
I’ll focus on a few of your complaints. Your risk of fatigue is 2.33 (2.14-2.54) times higher, mental health issues 2.14 (2.04-2.24) times higher, and neurological problems 1.60 (1.51-1.69) times higher compared to those infected just once.
It’s also clear that the risk of just one problem as a result of a COVID infection is about twice as high for those with two infections compared to those who have not been infected. You can see how each reinfection increases the risk compared to never being infected.
One of the mechanisms behind the neurological damage caused by COVID has been described. It has to do with the endothelial damage in capillaries during an infection. These capillaries cease to function and become what are known as string vessels, which are just the remnants of the capillaries that are no longer bringing blood to the local tissue, depriving brain cells of oxygen. The difference in the numbers of string vessels between controls and infected study animals is quite apparent with microscopy. The yellow arrows point to the string vessels.
This can easily explain some of the problems you are having. With the loss of capillaries, brain tissue can be deprived of oxygen and some cell death may occur.
One editorial in the NEJM specifically describes some of what you have been going through. “The cardinal features of long Covid include fatigue, dysautonomia (or postural orthostatic tachycardia syndrome), postexertional malaise, and cognitive difficulties that are colloquially referred to as ‘brain fog.'” That makes a pretty clear case for a disability claim.
The authors continue, stating “A recent analysis of the U.S. Current Population Survey showed that after the start of the Covid-19 pandemic, an additional one million U.S. residents of working age reported having “‘serious difficulty’ remembering, concentrating, or making decisions” than at any time in the preceding 15 years.” That would make it extremely hard for someone who has to memorize lines for their work.
In a very large (n=112,964) study on cognition and memory, the authors point out how different variants could have different impacts on cognition. Given that you were infected at least twice and based on the earlier study I provided, this would explain why a couple of different factors may have made memory and cognition harder for you.
The authors concluded “In this observational study, we found objectively measurable cognitive deficits that may persist for a year or more after Covid-19. We also found that participants with resolved persistent symptoms had small deficits in cognitive scores, as compared with the no–Covid-19 group, that were similar to those in participants with shorter-duration illness. Early periods of the pandemic, longer illness duration, and hospitalization had the strongest associations with global cognitive deficits. The implications of longer-term persistence of cognitive deficits and their clinical relevance remain unclear and warrant ongoing surveillance.”
“Our findings revealed significant attention deficits in post-COVID patients across both neuropsychological measurements and experimental cognitive tasks, evidencing reduced performance in tasks involving interference resolution and selective and sustained attention.”
“Furthermore, our patient group exhibited significantly higher levels of state and trait anxiety, as well as depression scores, than the control group. Anxiety and depression are among the most common COVID-19 sequelae, reported both in hospitalized and non-hospitalized patients.”
This suggests that both cognitive function and emotions are adversely affected by COVID. Again, I would argue that multiple infections likely increases the chances of having these problems and may increase their severity.
I think this supports the letters that were written by the professionals supporting your case.
My particular area of expertise is in healthcare infection prevention. Given two known prior infections, it really behooves you to avoid getting a third. The insistence of the SSA that you be seen again really goes against your best health interest. You have provided them with plenty of clinical support for your claim and as I have shown, there is plenty of research support for it as well.
If they are not taking simple precautions of increased ventilation, air filtration, and respirator use in their offices, this puts you at further unnecessary risk.
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
“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 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.
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…
Motor Control–Measures visuomotor accuracy and reaction time
Object Memory (Immediate)–Measures short term precision recognition memory
Simple Reaction Time–Measures reaction time
Choice Reaction Time–Measures complex reaction time
2D Manipulations–Measures mental manipulation of 2D visuospatial information
Four Towers–Measures mental manipulation of 3D visuospatial information
Spatial Span–Measures spatial working memory capacity
Target Detection–Measures attention and distractibility
Tower of London–Measures spatial planning
Verbal Analogies–Measures semantic reasoning
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.”
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.
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.
A study was published this past week that aligned well with the start of the school year, hence a little deeper dive into the impacts of COVID on children and how schools play a role in community spread.
In this multi-center study of 898 children, 147 of which were uninfected controls, the authors intentionally broke down the analysis into cohorts of school age-children and adolescents. The older group was composed of 4,469 adolescents (1,360 controls). The results were clear.
“In models adjusted for sex and race and ethnicity, 14 symptoms in both school-age children and adolescents were more common in those with SARS-CoV-2 infection history compared with those without infection history, with 4 additional symptoms in school-age children only and 3 in adolescents only. These symptoms affected almost every organ system. Combinations of symptoms most associated with infection history were identified to form a PASC research index for each age group; these indices correlated with poorer overall health and quality of life.”
44% of the children had problems with memory, focus, and sleep. In addition, daytime sleepiness was reported among 52% of the younger children and 89% of adolescents. This certainly suggests a contributing factor in the drop in ACT scores since the start of the pandemic.
I have many other studies on the impacts on children with links and quotes here. This sample of studies should make it quite obvious that we are doing a great deal of harm to children. In addition, those harms will also impact society in the future when they become adults who cannot function at their full potential. These societal impacts are also happening now while they are still in school.
It certainly didn’t help that people like Emily Oster wrote pieces like this that downplayed the role of schools in community spread. She has had many incorrect takes on the disease throughout that pandemic and is a poor source of pandemic information. I don’t understand why anyone would listen to an economist about anything related to the pandemic. They have overall been some of the worst minimizers. The truth is that about 70% of cases in households were caused by children bringing the disease home.
It’s not just a problem in the US. This person claims to be an infectious disease physician and PhD. I have my doubts based on some of the things that they have said which have no basis in science. Worse though is that they spread propaganda like this which harms and kills people. Don’t listen to anything that they say. It’s usually a tell when someone won’t identify themselves by name.
Another study found “This analysis shows that an increase in visits to both K–12 schools and colleges is associated with a subsequent increase in case and death growth rates. The estimates indicate that fully opening K–12 schools with in-person learning is associated with a 5 (SE = 2) percentage points increase in the growth rate of cases. We also find that the association of K–12 school visits or in-person school openings with case growth is stronger for counties that do not require staff to wear masks at schools.”
The data from that study is also a strong case for the benefit of requiring respiratory protection in schools.
The mean start date of schools in the US is between August 12th and 16th, yet some schools have already had to close due to COVID outbreaks. Of course, given the poor guidance coming from places like the CDC, one only planned to be closed for two days to do “thorough cleaning and sanitization before reopening.” The fact that schools think that this will do anything to help shows just how bad public health messaging has been through the pandemic.
In 2022, there were 4,228 candidates awaiting lung transplant in the US, compared to 4,208 in 2020. What appears to be a stable demand could simply be due to COVID killing a number of people who need lung transplant. In fact, the demand will be increasing due to COVID.
Currently, 10% of lung transplants are going to COVID patients. That certainly suggests what will be an increasing demand in the pulmonary fibrosis is a progressive disease, which means that it worsens over time.
In addition, some people are removing themselves from donor lists because they don’t like that programs require organ recipients to be vaccinated. That requirement is nothing new. It’s simply a political reaction to vaccine mandates. Recipients have been required to have certain vaccines to prevent themselves from being bumped to the bottom of the list. It’s the same idea as requiring those who were to get a lung transplant to have stopped smoking six months beforehand. There is no reason to give a scarce organ to someone who isn’t going to protect it.
Another study was recently published addressing some of the damage that occurs from a COVID infection at the cellular level. It is particularly important because it was looking only at those who were asymptomatic or only had a few symptoms.
One of the outcomes studied was blood leukocyte DNA methylation Age (DNAmAge). DNAmAge refers to the biological age of a person as determined by DNA methylation levels. This concept is based on the idea that DNA methylation patterns change with age, and these changes can be used to estimate an individual’s biological age, which may differ from their chronological age.
“Increased leukocyte DNAmAge correlates with the duration of SARS-CoV-2 infection (average 17 days) because prolonged infections lead to sustained inflammatory responses and cellular stress, which induce significant epigenetic changes. This mechanism is similar to that observed in other viral infections like HIV.
Where this gets really interesting (and concerning) is looking at this result in context with a study from 2021 on telomere length and biological aging from COVID. In the introduction, the authors state “In humans, telomere shortening is associated in vivo with the aging process and, in vitro, with cellular replicative senescence.” Cellular replicative senescence is a phenomenon where cells permanently stop dividing after a certain number of divisions. This process is primarily driven by the shortening of telomeres, which are protective caps at the ends of chromosomes. In human cells, that limit is about 50 divisions, because the telomeres become shorter each time the cell divides.
This is why I suspect that COVID appears to be more of a problem in the elderly. In their case, many cells in various types of tissue have reached the end of their replicative life, which means that the tissue that they compose can no longer function as well.
In children, uninfected cells still have a lot of replicative potential, so these cells divide sooner than would normally occur. That may make the disease seem milder in the short term, but it also has diminished the ability of cells to divide in the future, because they have used up some of their replication potential. This is still not a well understood part of biology yet and also varies by tissue type.
This also means that this likely has a cumulative effect, meaning that the tissue of a child infected multiple times over the course of the pandemic will have some tissue of a 70-year-old person even if they are chronologically only 40-50 years old.
It’s also important to think of that in the context of chronic diseases. Most chronic diseases don’t manifest themselves while a person is young, and they have a lot to do with the ability of tissue to function properly, but this becomes more difficult as more cells in the tissue can no longer replicate, leaving higher demand on the surrounding cells.
Campisi et al continue, “Our findings confirm that chronic diseases are linked to elevated DNAmAge, consistent with previous research on frailty, cancer, diabetes, cardiovascular diseases (CVDs), dementia, and decreased lung function (FEV1) in COPD patients, a known consequence of aging.”
Not only does this impact the future health of people, but it also affects their ability to work. Campisi also looked at Work Ability Index (WAI) scores. The WAI is a tool used to assess a worker’s ability to perform their job based on their health and job demands and is used most often in occupational health in healthcare settings. “HCWs with greater DNAmAge showed lower WAI scores, marking this as the first study to link leukocyte DNAmAge with WAI, consistent with the decline in work capacity due to aging and chronic diseases…Chronic job-related stress and inflammation accelerate telomere shortening, impairing cellular repair and function.”
The next sentence is quite telling. “This relationship is biologically plausible as shorter TL indicates advanced cellular aging, which reduces physical and cognitive capacity, impacting work ability.” It correlates well with what we have seen for increasing MVAs and is why I’m reluctant to take a commercial flight.
Finally, they used COPD patients as a control group to compare to the HCWs who participated in the study. “COPD patients are considered a suitable positive control group because they exemplify accelerated biological aging due to chronic inflammation and oxidative stress.
Our results revealed that the blood leukocytes and IS cells of HCWs are biologically older than those of COPD patients, as determined by AgeAcc and predicted TL. This indicates that COVID-19 may induce more pronounced epigenetic changes and telomere attrition than COPD.”
This is what is so upsetting about those who think we should allow children to get repeatedly infected. The data is quite solid that we are saddling them with chronic diseases and a shorter lifespan, although that has not yet become evident. Those behind and supporting The Great Barrington Declaration have really signed the death warrant of millions, even though it’s not an immediate execution. It’s criminal.
I still have trouble wrapping my mind around this idea. My fear is that it is a way to offset Medicare and Social Security costs in the future. The question is if the US government can be that evil.
In grad school, I had started my thesis work on bioterrorism preparedness planning in 2000. When 9/11 happened, it suddenly became far too big of a project due to the volume of publications. As part of my research prior to that day though, I had submitted some FOIA requests related to the topic. What I found was pretty disturbing, so yes, our government can be that evil.
ACT Scores
These are ACT scores since 2010. The green band is < 2 standard deviations (SD) below the mean (dotted black line). The yellow band is 2-3 SD below the mean.
Normally speaking, values within 2 SD is considered just normal variation in the data. We start thinking that some effect is happening when it gets higher than that. It’s pretty obvious that we could exceed -3 SD in 2024.
I admit that some of this might be due to virtual school at the start of the pandemic, but that effect should have washed out by now and I don’t think it would have been this pronounced.
I think what we are seeing is MUCH more influenced by the cognitive impact of an infection, or in kids, repeated infections, since so many believe it’s minor for them. Schools are a main source of community spread. We are going into the season with COVID cases at full throttle.
Ivermectin Grifters
I’m very happy to report that two big ivermectin grifters and misinformation spreaders who are a part of the Front Line COVID-19 Critical Care Alliance had their board certifications revoked by the American Board of Internal Medicine (ABIM). Pierre Kory, MD, is no longer certified in critical care medicine, pulmonary disease, and internal medicine and Paul Ellis Marik, MD, is no longer certified in critical care medicine or internal medicine.
All one has to do is look at who people like this associate with to figure out if one should avoid them as clinicians.
Yoda probably said it best. “Hmm…FAFO they did.”
Mpox
A new clade (1b) of mpox (formerly monkeypox) emerged in September, 2023 in the Democratic Republic of the Congo. This is different than the clade that began to spread globally a couple of years ago, which had its highest impact in the MSM community. There have been 548 deaths from mpox in the DRC this year, but equally as alarming, about “40% of cases are in children under 5 years old.” This suggests that this is much more infectious than what we had seen in the past and is likely being spread via contact, fomite, and airborne transmission routes.
On August 13th, the Africa Centres for Disease Control and Prevention declared a public health emergency for the first time in their history, and the WHO declared a global public health emergency the following day.
On August 16th, the first case was reported outside of Africa, in a person who returned to Sweden after a visit to the DRC.
The other big unanswered question with this disease is how much impact a COVID-damaged immune system will have on both the course of the disease in an individual and how that will affect spread to those around them.
Given how people flaunted public health recommendations, the likely lack of adequate vaccine volume as well as vaccine hesitancy by many, and how poorly we had done at the start of the COVID pandemic, I’m incredibly worried. This could be considerably harder to protect yourself from compared to COVID.
The US state graphs now have the new variants included in a data visualization. In addition, they have been slightly blurred, which will make the mind perceive them as being in the background, which makes the other data trends much more easy to follow.
COVID and Motor Vehicle Accidents (MVAs)
A very interesting study was published by the AAA Foundation for Traffic Safety this week. A single graph sums up the findings on traffic deaths related to COVID. The paper also demonstrated the accuracy of their model in predicting MVA fatalities.
We have known for some time that infectious diseases have impacts on brain function, and even have been correlated with motor vehicle accidents. One example is toxoplasmosis.
The danger of a MVA is also heightened after a COVID infection due to the immune system damage caused by the virus. In a study in Australia by Ingram et al., “a novel finding was that motor vehicle accidents (MVAs) accounted for 78% of all trauma-related cases, suggesting MVAs should receive greater recognition as a potential precipitant of cutaneous mucormycosis.”
This is a CT image of a skull from that study of someone who had cutaneous murcomycosis.
One of the factors that may drive increasing MVA rates after COVID is the impact on the brain’s ability to process and construct visual data, as measured by the Rey-Osterrieth Complex Figure Test (ROCF), which was used in a study by de Paula et al., as quoted below.
We observed significant cognitive impairment only in the ROCF, a drawing task test used to assess visuospatial abilities, executive functions and memory. The deficits observed in the ROCF could not be explained by socio-demographic factors, ophthalmologic deficits or psychiatric symptoms, suggesting cognitive deficit secondary to SARS-CoV-2 infection. Other factors which may influence performance, such as motor coordination, spatial neglect, visual attention, semantic knowledge, intelligence and executive functions were not likely to explain the observed difficulties, since we did not find any significant differences in other non-verbal (Trail Making Test and Five Points Test) and verbal tests (verbal fluency, digit span) also related to these processes…
…Visuoconstructive deficits are usually defined as an atypical difficulty in using visual and spatial information to guide complex behaviors like drawing, assembling objects or organizing multiple pieces of a more sophisticated stimuli. In drawing a complex figure, as in the ROCFT, the patient must organize visual and spatial information in a planned manner to execute the drawing per se, a processes that demand several more specific cognitive abilities related to perceiving, processing, storing and recalling visuospatial information, both regarding shape and position, as well the planning and execution of the drawing per se.
In one study on cognitive abilities that had nothing to do with viruses, the researchers happened to find Acanthocystis turfacea chlorella virus 1 (ATCV-1) in the oral swabs of the test subjects. “This family of algae-infecting viruses is common in aqueous environments but not previously thought to infect humans or animals or to inhabit human mucosal surfaces.”
“A significant association occurred between the presence of oropharyngeal ATCV-1 DNA and a lower level of performance on the Trail Making Test Part A (Trails A), a test of visual motor speed (P < 0.002), as well as the total score of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) (P < 0.014). Within the RBANS test, there were statistically significant differences between those who had detectable oropharyngeal ATCV-1 DNA and those who did not in the domains of delayed memory (P < 0.039) and attention (P < 0.011). These differences were independent of the covariates of age, sex, race, socioeconomic status, educational level, place of birth, and current cigarette smoking. On the other hand, no differences were observed between the presence/absence of ATCV-1 DNA and scores on the Wechsler Adult Intelligence Scale (WAIS) Information subtest, a test of general knowledge.”
Just this year, Serafim et al. found “The data indicate higher percentages of cognitive difficulties in the severe group, followed by the moderate group, compared to the mild group. Notably, even within the mild group, 11% of participants exhibited difficulties in at least one assessed cognitive function 18 months after COVID-19 infection.”
Of course, we’ve known all of this for well over 30 years. “Profound changes in behaviour are observed following infection of the central nervous system by some viruses. Irritability, insomnia, hyperactivity and learning disability are some of the behavioural disturbances that have been described in both humans and animals with central nervous system infection.”
What I really would like to know is how the impairment compares to that who are driving under the influence. I also wonder if DMVs should be performing a cognitive test as part of driver’s license exams and renewals to make the roads safer.
The data from The Insurance Institute for Highway Safety (IIHS) also shows a marked increase in traffic deaths since the start of the pandemic. The red line indicates the 2020 data points. The sharp increase
This is data from FRED combined with the IIHS data. The purpose was to determine if changes in passenger air miles would account for the increase in MVA deaths.
The black line is road miles, the light blue is air miles, and the red is MVA deaths per 100,000 population per 100,000 miles. The orange line is simply to make it easy to look at the year 2000 on all three. The drop in air and road miles is expected, but the big jump in mortality is telling. Part may be due to higher speeds on empty roads, but that also might be related to increased risk-taking behavior as a result of COVID infection. Now the highways are congested again, but the mortality hasn’t dropped. It’s another argument that COVID is driving up MVAs.
In addition, we could have expected MVA deaths to drop on less congested roads because those driving at significantly excess speeds at the time could have been outliers, which makes it even more suspicious since fatalities should have dropped during 2020.
Social media may also play a role in risk-taking behaviors due to a concept known as relative deprivation. A simple example is when a child complains that all of their friends are doing something that they are not allowed and the child proclaims “It’s not fair!” This kind of thinking is promoted on social media due to the biased nature of posts of people only showing the good things in their lives, which leads to some people thinking that their lives are substandard, and inevitable depression if they dwell on it.
“A total of 11,270,763 adults were identified. Overall, 9,425,473 (84%) had received a COVID vaccine and 1,845,290 (16%) had not received a COVID vaccine at study baseline (July 31, 2021). The 2 groups spanned a diverse range of demographics, with comparable general health care utilization. The largest relative differences were that those who had not received a COVID vaccine were more likely to be younger, living in a rural area, and below the middle socioeconomic quintile. Those who had not received a vaccine also were more likely to have a diagnosis of alcohol misuse or depression and less likely to have a diagnosis of sleep apnea, diabetes, cancer, or dementia. About 4% had a past COVID diagnosis, with no major imbalance between the 2 groups.”
In that analysis, “living in a rural area” really stood out to me, particularly because I had picked up on how COVID was impacting Republicans and Democrats very differently, which is very fascinating if it weren’t so sad. Most people realize that Republicans dominate rural areas, as this voting map from 2020 shows.
Another important piece to tie this story together is education level. The data is very clear that Democrats have achieved a much higher level of education than Republicans.
Circling back to the original article, we can see the impact that education has on the severity of MVAs, and particularly during the pandemic.
Sadly, it’s not just those in vehicles who are impacted, but cyclists and pedestrians have been harmed in higher numbers as well.
When someone says “What do you care if I wear a mask or get vaccinated or not?” I get irritated. I care because it impacts others, whether through traffic injuries or fatalities, the loss of healthcare, and higher auto insurance and health insurance rates.
H5N1
Vectors
One study showed that blowflies are a potential vector of H5N1 in Japan. Guess what blowflies like. “Blowflies are well-known for their necrophagous habits, being attracted to deceased animals and birds to feed on decaying flesh. They are also attracted to feces, making them commonly observed insects around livestock facilities.”
“Farm B first noted dairy cattle illnesses on March 20th with the illnesses increasing over the next 13 days, eventually affecting an estimated 14% of the milking herd. On March 22, illnesses were first noted in the Farm B’s feral cats with cats showing lethargy, paralysis, and increased respiratory rate. Farm B estimated that 15-20 of their ∼40 feral cats died during the next 14 days.” That’s pretty suggestive of nearly a 50% mortality rate in felines.
“We identified several mutations that alter host cell specificity, target drug binding sites and known to cause antigenic shifts or cause mild drug resistance.” This in itself isn’t surprising since mutations readily happen in influenza viruses, but it does point to just how dangerous mutations are for the human population.
This is what is most concerning. “The second worker had a MN of 1:80. She worked in the Farm A’s cafeteria. She reported experiencing fever, cough or sore throat during that last 12 months as well as being around others at work with similar respiratory signs and symptoms. She had just recovered from a respiratory illness when we enrolled her.”
Given that she was a cafeteria worker, that means that she likely had little or no exposure to the cattle. This implies one of two possibilities. First, that somehow an airborne spread occurred from the location of the cattle into the cafeteria, which seems pretty unlikely. Much more alarming is that this case may represent human to human transmission. This is very problematic in that we are not doing much testing, just like early in the COVID pandemic, so we are unable to characterize what is really happening in the field.
COVID-19 policies were confirmed at all 67 patient-serving NCI-designated cancer centers. 28 cancer centers (41.8%) required universal masking in at least some clinical areas, with 12 (17.9%) requiring universal masking in all areas. Only 14 (20.9%) had accurate up-to-date policies flagged on the home page of their websites. In 8 cancer centers (12.0%), policies posted on websites differed from those noted by telephone. Cancer centers were more likely to require universal masking in at least some areas if they were located in the Northeast (11 [78.6%]), had longer NCI designation duration (first quintile: 10 [83.3%]), had more program funding (first quintile: 11 [84.6%]), or had a higher care ranking (first quintile: 11 [84.6%])
The data indicate higher percentages of cognitive difficulties in the severe group, followed by the moderate group, compared to the mild group. Notably, even within the mild group, 11% of participants exhibited difficulties in at least one assessed cognitive function 18 months after COVID-19 infection.
We showed that cognitive symptoms persist in mild cases and are even more prevalent in individuals with severe manifestations. Furthermore, we confirmed our central hypothesis: people with severe forms of COVID-19 show diminished cognitive performance 18 months after infection compared to those with mild to moderate forms.
