“Those who don’t remember the past are condemned to repeat it.” – George Santayana
This graph related to Kaposi’s sarcoma (KS) by @1goodtern has been making the rounds. I thought I would go into a little bit more of why it both doesn’t surprise me and why it gravely concerns me.
Classic KS normally appears in males of Mediterranean or Eastern European descent at 50-70 years of age. This graph is of 19 – 44-year-olds. One major exception to this is endemic KS in sub-Saharan Africa, where it is common in children and young adults. The only other population that this is commonly seen in besides AIDS patients is in organ transplant recipients due to immunosuppressive therapy
This has been my big concern with COVID. I keep thinking how the progression of HIV to AIDS is about a decade, yet here we are seeing some of the same sequela in just a few years after the onset of COVID.
Models suggested that by 2023, 70% of the US population had been infected. I wouldn’t be surprised by 80+% now. There is also some evidence suggesting that people don’t clear the virus. That could account for why we see less severe cases, particularly since mutations have mostly been antigenic drift. That would account for why there are fewer cases over time…people are still harboring a similar virus and maintain some level of antibody response. However, when an antigenic shift happens, we will have a much more serious surge.
The big concern I have is viral persistence. Fragments and proteins have been detected in blood and tissue samples two years after infection. That seems to suggest active, but perhaps latent infection causing chronic immune activation, autoimmune reactions, and the possibility of reactivation of other latent infections as a result, TB for one.
This is why I continue to wear respiratory protection except in my office where I have two HEPAs running. More importantly, it’s why I keep pushing others to do the same. I fear we are on the same trajectory as AIDS. I keep thinking that if HIV were airborne, would we require people to use respiratory protection? COVID is a far worse disease in the long term.
My current role is as director of infection prevention for a hospital system. I do this kind of work as a traveler, so I see the best and worst practices in many places.
Today I was working on our healthcare associated infection (HAI) plan for the execs. Hospitals routinely monitor rates of HAI and work to reduce them. I wanted to include a section about how COVID is going to make HAI rates climb. This is what I wrote.
One important measure to keep in mind is the impact COVID is going to have on HAI rates due to the immune dysregulation and vascular damage it causes. I had expected HAI rates to climb as a result and pulled UK data on MRSA bacteremia in late 2024. I did not use 2020 data for pre-COVID and pandemic trends because of the high number of confounding variables that year. The trend lines painted a picture far worse than expected.
The blue columns is the incidence of MRSA bacteremia. The green dotted line is the trend pre pandemic. The red dotted line is the trend since the pandemic.
Eventually data was published that showed similar trends with other organisms.
Typically, each line would hover around the 0% line with some minor fluctuation up and down. The fact that all of these are now climbing, most alarmingly among a couple that had been declining, is extremely disturbing.
It’s not just adults. These problems are showing up in pediatric populations, such as this study from JAMA around Group A strep infections.
This is also reflected in pediatric influenza deaths by season. One doesn’t even need trend lines to see a big difference in the patterns before COVID and now.
As part of assessing cognitive ability of HCWs, I used Google Search Trends to extrapolate some answers to brain function pre-COVID versus current, showing similar trends. This suggests that procedural errors that could result in HAI will become more frequent. In this case, I used a simple cutoff of March of 2021 to account for the lagging effects of problems like this after acute infections.
The dotted lines are the trends pre-pandemic. The solid lines represent the trend lines starting since March 2021, which is about the time that people might have started seeing these kinds of issues in themselves or others.
In the current era, maintaining infection rates may become challenging. This is compounded by the recent gutting of the CDC, which would normally provide data at a national level, although it lags far more than the data that is provided by the UK. It may be worth thinking of HAI goals in the framework of increasing at slower rates than the national average.
The chronic disease sequelae from COVID are going to place a high burden on healthcare in the coming years, particularly in combination with the aging baby boomer population. Further, these sequelae will hit healthcare workers as well, which will reduce the size of the HCW labor force. That will likely lead to more HCWs leaving the field from burnout, causing a positive feedback loop.
The excess burden of this combined with multiple infections will hasten this outcome. The reinfection burden was recognized back in 2022.
We are facing a very harsh reality that much of the population is either ignorant about or is living in denial about. Neither changes the outcome. All we can do is try to mitigate the worst of this, but I have my doubts that we have the willpower (or current leadership) to do so.
I saw pertussis (whooping cough) data this week that reflects both antivaccine sentiment as well as the possibility of COVID damaged immune systems leading to spread. All of the data used in these graphs is from the UK.
I wanted to find data on diseases that weren’t vaccine preventable to look more closely at the immune damage component. I hit the jackpot with some data for organisms I’m very familiar within my particular field of healthcare infection prevention.
I was concerned though that many people may not be used to a data visualization like this, so I decided to take the raw data and place it into a form people would be more familiar with, but more importantly, adding pre-pandemic and mid-pandemic trend lines to compare to each other. I omitted the data from 2020 since there were so many other variables coming into play, particularly social distancing and much more focus on hand hygiene, which both would skew data for that year more than others. I also attempted to balance the dumber of quarters on each side of 2020 and used the most current data available.
This data is all bloodstream infections (except for C. difficile) with these organisms, ie, invasive disease, not just a topical infection on the skin.
Staphylococcus aureus
This organism is commonly found on the skin and is responsible for about 25% of serious surgical site infections.
MSSA is methicillin sensitive S. Aureus and is distinguished from an antibiotic-resistant strain known as methicillin resistant S. Aureus.
What is particularly interesting about these is that while the rate of MSSA in the population didn’t increase much, MRSA had been trending downward until COVID. That is a puzzle I’m very interested in solving.
Klebsiella spp.
Klebsiella infections also did not appear to have an increasing rate of infection due to COVID. However, an upward trend isn’t good regardless given how this organism is commonly associated with respiratory tract, urinary tract, and wound infections.
Pseudomonas aeruginosa
Pseudomonas aeruginosa is an environmental pathogen found in soil and water. It can cause a number of different types of infections in humans. One concern is that the rate of infections with these organisms was trending downward but is now trending upward. Another emerging concern is a report by Howard et al. about a strain of this organism that has acquired a gene to encode an enzyme that will dissolve a type of plastic that is commonly used in healthcare settings. The organism can obtain ALL of its carbon needs from this plastic. It seems like a story straight from The Andromeda Strain by Michael Crighton.
Escherichia coli
E. coli is a common organism in the gastrointestinal tract. It is also associated with a number of different infections.
Clostridioides difficile (C. diff)
C. diff is an organism that resides in the gastrointestinal tract of about 2-5% of healthy adults. It forms spores, which allow it to survive in harsh environments and make it important to control in healthcare settings. We use the abbreviation CDI for C. diff infection in healthcare. This is another organism where infections had been decreasing before COVID, but now are increasing.
Obviously, none of this proves that COVID immune damage is the cause, but, we do know from multiple studies that COVID causes damage to the immune system, so it is a reasonable assumption that immune damage is playing a role. I have a number of studies quoted and linked here.
California will be used to illustrate unless otherwise stated.
Early on, it became very apparent that COVID surges were linked to variants. This is pretty obvious when looking at proportions of variants in relation to hospitalizations.
Many sources of data have faded in and out, particularly good case data. That has necessitated other metrics to understand COVID in a particular geography. Positivity, wastewater, and percentage of emergency department visits have become other good early indicators and have strong concurrence, such as seen across the entire US in this view.
The coefficient of determination, or R-squared values, also support their tight relationships.
0.89 Wastewater and ED visits
0.86 Wastewater and Positvity
0.70 ED visits and positivity
This is a good explanation of r-squared if you want to dive a little deeper into statistics.
Here are the percentages of different variants with the plots of wastewater, positivity, and percentage ED visits on top of them. I also have the ED visits multiplied by 10 as a means to better see the curves. I’ll use the numbers and letters at the top to explain.
The vertical black lines represent the start of different surges, primarily using the wastewater data, but they correlate with the others as well.
Line 1 – This is the start of what is commonly known as the delta wave. Here are the three variants isolated.
The three delta variants are closely related as shown below. 21A is the parent to 21I and 21J. That is part of the reason that they comprised a single surge. One can also conclude that 21J had a much higher r-naught, or reproduction value, and quickly dominated the other two.
Line 2 marks the start of 21K, the original omicron variant. Notice how quickly that became dominant and caused a rapid surge like 21J
Line 3 and 4 are where things get interesting because of some competition between 21L and one of its descendants, 22C, both of which are quickly overtaken by another subvariant of 21L known as 22B, so once again, a closely related family for that surge.
Lines 5 and 6 follow the traditional single variant surge pattern, with 22E and 23A respectively, which have very different lineages.
Lines 7 and 8 get messy again with 23B, 23D, and 23 F competing with each other, 23F being a subvariant of 23D, but all of the same lineage. The lower surge in wastewater values suggests short term protection among these different variants by infection with one of the others since they are similar. It’s also worth noting that none of these hit the 50% of sample threshold, which I will come back to later.
Line 9 starts another big surge based on wastewater due to 24A.
Line A begins the current surge which started with 24E in the late summer. You can see another surge superimposed on it at Line B, due to 24F. These are very distinct lineages, hence there is very little protection from one when infected by the other.
Hopefully this helps clarify what drives surges. It’s not seasons or elections. It strictly has to do with variant lineages and variant reproduction rates. It also supports my argument that aside from very different lineages, surges are related to about a 50% dominance of a variant.
The aircraft made one go around after the initial attempt at landing. On the second attempt, it contacted the runway at about the halfway point, which left no space for the aircraft to skid to a stop before the end of the runway. That raises another question as to why it didn’t land at the beginning of the runway.
This all could be bad luck that couldn’t be avoided. However, there is plenty of evidence of that COVID may interfere with transportation safety.
Aviation incidents are relatively rare, although it does seem like there have been many more the past few years. However, motor vehicle accidents (MVAs) provide us with a much larger data set that provides clues and make a good surrogate measure.
This is MVA fatality rates. As you can see in every metric, they have been overall falling for some time. (Data source). That trend has a lot to do with both vehicle and roadway safety devices. Those have made the highways safer, so much of this is related to driver behavior.
The rate of decline as a population rate slowed in the 1990s, paralleling widespread adoption of cell phones and the subsequent distracted driving. A clear upward jump occurred in 2020.
It’s hard to distinguish what happened though related to the rates among the number of motor vehicles and miles driven due to the very high rates in the past. Zooming into that data shows the same increases in 2020, and the lowest points for both are higher than the peaks for the prior 10 years.
Earlier this year, I had used this data to look at rates in another way.
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 2020 on all three. The drop in air and road miles is expected, but the big jump in mortality is telling.
Some may try to argue that the lockdowns resulted in more speeding on empty roads, but the data does not support this as a cause of MVA deaths since the easing of restrictions.
Now the highways are congested again, but the mortality hasn’t dropped. It’s another argument that COVID is driving up MVAs. It’s not just the mortality rate going up in 2020, but it should have stayed steady or more likely have gone down with less vehicles on the road.
“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…
…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…
…the PT must organize visual and spatial information in a planned manner…a processes that demand several more specific cognitive abilities related to PERCEIVING, PROCESSING, STORING, AND RECALLING VISUOSPATIAL INFORMATION, both regarding shape and position.”
It’s also a problem in other countries as well. However, look how much better the data looks AFTER the US is extracted from it. That’s not surprising given how poorly the US handled the pandemic.
Air travel is significantly safer than road travel, which makes it much harder to tease out data. This should make a pretty good argument that some of what we have been seeing with aircraft incidents stemming from pilot, mechanic, or air traffic controller error could easily be due to the impact of COVID on the brain. I have a number of studies on the neurological impacts of COVID here.
Addendum
Someone sent me a link to a brief study that says it all.
“Findings indicate an association between acute COVID-19 rates and increased car crashes with an OR of 1.5 (1.23-1.26 95%CI)…The OR of car crashes associated with COVID-19 was comparable to driving under the influence of alcohol at legal limits or driving with a seizure disorder…The study suggests that acute COVID-19, regardless of Long COVID status, is linked to an increased risk of car crashes presumably due to neurologic changes caused by SARS-CoV-2.”
The data has become quite clear that the safety of our food supply has been endangered because of COVID. This is the number of FDA food recalls each month.
There was an average of 3.7 recalls per month prior to August 2021, which is when the problem became an obvious permanent feature of our food supply system. This could easily be due to the brain fog, bad decisions, and risk-taking behavior driven by COVID infections. Since that time, the numbers have increased to 24.6 per month.
What happens when the agricultural labor force is reduced? Shortcuts get taken and remaining workers become overburdened in an already difficult job, potentially coming to work when ill to maintain their employment. This can increase the risk to the food supply directly if a worker has an infection spread via the fecal-oral route of transmission, such as norovirus or cryptosporidium.
It seems a pretty safe bet that we can expect higher risk food at higher prices.
CDC Findings: The Centers for Disease Control and Prevention (CDC) has conducted multiple studies that found no association between vaccines and autism. These studies also found no link between thimerosal, a mercury-based preservative used in some vaccines, and autism.
It’s pretty clear that all of this is a revenue and political scheme for him. Without those motives, most people would think he needs some professional help. He’s definitely not worthy of this position. Trump talked today about wanting RFK Jr. to investigate autism further, in spite of how much it has been studied. Even when given the information that the increase has to do with better detection, he still deflected, and even went as far as saying maybe chlorine in the water is causing it. It wasn’t that long ago that Trump was talking about injecting bleach for COVID. I can’t believe he is going to be in office again.
Dr. Jay Bhattacharya for the National Institutes of Health (NIH)
Bhattacharya is a professor of health policy at Stanford University and a proponent of natural immunity through infection.
Great Barrington Declaration: Bhattacharya co-authored the Great Barrington Declaration (GBD), which opposed lockdowns and advocated for “focused protection” of vulnerable populations. This stance was criticized by many public health experts who argued that it underestimated the risks of COVID-19 and could lead to higher mortality rates. There are many problems with the GBD.
Unrealistic Assumptions: The declaration assumes that “focused protection” of vulnerable populations is feasible. In reality, it is extremely difficult to isolate vulnerable individuals completely from the rest of the population.
Herd Immunity Misconception: It promotes the idea of achieving herd immunity through natural infection, which is risky and could lead to a high number of deaths and long-term health issues. The idea of getting a vascular disease known to cause damage to every organ system to provide immunity to that disease is insanity, especially when it would require repeat infections.
Lack of Long-Term Immunity: The declaration does not account for the fact that immunity from natural infection may not be long-lasting, especially with the emergence of new variants. We knew this would not work very early in the pandemic due to the events that unfolded in Manaus, Brazil.
Ethical Concerns: Many experts argue that allowing the virus to spread unchecked among the young and healthy is unethical and could overwhelm healthcare systems. Ethicists in the future will be reviewing this era as a case study around the notion of harming children to protect society.
“The true measure of any society can be found in how it treats its most vulnerable members.” – Mahatma Gandhi
Public Health Impact: The declaration undermines public health measures such as mask-wearing, social distancing, and vaccination, which are proven to reduce the spread of the virus. Problems with the GBD are thoroughly covered by Dr. Jonathan Howard in an episode of his excellent podcast titled “The Great Barrington Declaration’s Doomed Herd Immunity Plan.”
Criticism of Lockdowns and Mask Mandates: Bhattacharya has been vocal against lockdowns and mask mandates, which put him at odds with many public health officials. His testimony in a Tennessee school mask mandate case was described as “troubling and problematic” by the judge. Imagine how bad things would have been during the delta and original omicron waves if these interventions would not have been in place.
Social Media Censorship: His views on COVID-19 policies led to his Twitter account being placed on a “Trends Blacklist” to prevent his tweets from appearing in trending topics.
Lack of Leadership Experience: Critics have pointed out that Bhattacharya lacks leadership experience in government or large organizations, which raises concerns about his ability to lead the National Institutes of Health (NIH).
He also supports letting 25% of the population die of disease, such as during the plague of Athens.
Dr. David Weldon for the Centers for Disease Control and Prevention (CDC)
Weldon is a former U.S. Representative and has a history of opposing vaccines, including claiming that thimerosal causes autism, which has been debunked long ago. He was in Congress from 1995-2009, so there’s a pretty good chance that he didn’t stay very current on medicine during that time, although he returned to private practice.
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.