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The cost-benefit calculations in terms of excess deaths are irrelevant. It was clear even during the initial outbreak that policymakers primary constraints were the due to uncertainty about the virus and limits on availability of protective gear and ICU beds. Without protective gear there was no way to reduce transmission without isolation and once ICU beds are at capacity mortality rates double and people are dying in the streets and the medical system collapses.

There is no scenarios where the collapse of the medical system is a viable outcome for policymakers that is also compatible with a functioning economy. A collapse of the medical system means non-covid fatalities also increase. Heart attacks, car accidents and childbirth all become mortality contributors. The most likely outcome of a medical system collapse would be martial law, mass graves, economic collapse and potential collapse of essential systems like food distribution.

The idea that policymakers were primarily constrained by the thought of grandpa dying a few years early and weighing it against the grandkids social isolation needs to die.



Initial constraints like ICU bed shortages and gear scarcity definitely had a basis. But those measure lasted long after the bottlenecks got resolved and the risk assessment became clearer. For example, public schools stayed remote while virtually every private school switched to in-person (even California's governor opted for private in-person schooling for their kids).

The public's frustration is that these prolonged, seemingly arbitrary measures outlasted their initial justification.


ICU capacity was a bottleneck that never got resolved. In the US there was never a nationwide effort to mobilize ICU resources to target hot spots other than an initial aborted attempt to gather respirators for the surge in NYC and send a military floating hospital.

Regional programs were put into place to shift patients during a local surge and to mobilize hallways and other non-traditional capacity. In California once capacity dropped below 10% health orders went into effect.

At the beginning of 2023 and well after widespread vaccination the California statewide capacity was at 24% availability with 7% of beds being taken by Covid patients.

https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/Reg...


I am still struggling to find transfer facilities with available ICU beds.


This has been studied ALOT lately. Here is one such paper

https://ieeexplore.ieee.org/document/9383939


Didn't that huge hospital ship in New York sit unused until it eventually left


The hospital ship was never intended to take covid patients. The idea was the ship would take non-covid cases leaving ICU capacity in hospitals. It turns out that military hospital ships aren’t great for the general population given bulkheads and the general layout of ships.


The massive Javits Center was also made available. 2500 beds, only 141 used.

https://gothamist.com/news/fema-begin-strategic-drawdown-lar...


In Chicago we also spent millions of dollars building extra temporary capacity. It only got something like 38 patients and was quietly dismantled after one month.


Hospitals were closer to collapse in 2022 than they were in 2020 or 2021 in my experience.


Any way to substantiate that claim? I also assume you mean in the US.


Deaths peaked in 2021 but hospitalizations peaked in 2022. Hospitals were at higher capacity in 2022 but treatment protocols had improved reducing mortality rates. Deaths caught the headlines so people missed the surge.

https://ourworldindata.org/covid-hospitalizations


I wonder if suddenly going from masks and sanitizing constantly to immediately stopping (at least in my country) may have contributed to the sudden jump. Also we were encouraged not to visit the hospital unless it was necessary so many opted not to for almost 3 years.


The ICU bed issue was very long ongoing issue in Germany too.

And everyone working at those hospitals didn't see any light for month/years


It's not irrelevant, especially considering there were restrictions lasting far longer than "initial uncertainty." It's not like your only choice is to allow for a "collapse" -- as an extreme example, you could decide you're not going to treat covid at hospitals at all (by the way, we make such determinations for other conditions or diseases all the time).


ICU capacity consists of the room, the equipment and the staffing. Once oxygen saturation starts to drop the treatment options are oxygen and eventually intubation and a medically induced coma. There is no realistic scenario where untrained or semi-trained individuals are going to be able to provide that support and even if the staffing shortages could be worked around there wasn’t adequate equipment.

The at home treatment option was to watch the patient drown in their own fluids. There are few people in that scenario that would have the emotional capacity to calmly give support to a close family member or friend when there was no ICU capacity. Their more realistic scenario would be people loading the patient into the car and driving to the nearest hospital. Some percentage of that population will likely turn violent.


> The most likely outcome of a medical system collapse would be martial law, mass graves, economic collapse and potential collapse of essential systems like food distribution.

No it’s not. You are dramatically overestimating how many people need life-saving medical services. If we had no emergency healthcare system at all and everyone who needed to go to the hospital just died instead, yes it would suck and yes we would have lower life expectancy but civilization would largely continue.


Yes, civilization would have largely survived a partial and temporary collapse of the US healthcare system that was a potential outcome from covid.

There were multiple countries where the medical system came close to collapse. India in 2021 is a case study of a medical system pushed beyond its limits (https://www.nytimes.com/2021/06/28/world/asia/india-coronavi...) and civilization largely continued.

But there is a lot of latitude between normal and survived.

Let’s assume that US policymakers considered taking no-action and let the ICUs collapse. Could policymakers in that scenario reasonably expect that teachers would continue to teach? Would the school system function if 20% of school teachers decided to not risk death and opted out?

That answer was easy to get and the answer was that schools would be shutting down.

How many essential workers would opt-out? How many need to drop-out to care for children? How many because they are sick? For how long? How does that impact fuel and food distribution?

Can we expect crowds would gather at hospitals? How many in that case become violent? How do we control crowds? Do we call in the national guard?

I’m not sure I can see any scenario that a policymaker could consider where reducing spread wasn’t the only viable option.


It’s still not as clear cut as patient in and gets bed. There was some tweaking that had to be done to triage patients better then determine who most likely needed a bed. At UF Hospitals, they used discrete event simulation to figure out how to streamline this process. Not sure how many others went through the same thing. Point is, hospitals share some of the blame too


Discrete event simulations is used to get better utilization of a constrained resource. The fact that hospitals were using it just reinforces that ICU capacity was considered a serious constraint by policymakers. Discrete event simulation will give policy makers more confidence in capacity models but doesn’t improve patient outcomes or reduce treatment time.

Hospital protocols also improved over time to reduce the need for respirators and the disease moderated but there was no magic protocol that really reduced disease progression or duration. Out of my social circle there were anti-vaxers that contracted covid late in the pandemic and spent weeks in the hospital (four total, two deaths) even with late stage protocols.


It is not for constrained resources. You might be thinking of linear programming. It deals in constraints to achieve an objective. Discrete event simulation deals in queues.


In this case the queue was wait time for an ICU bed and the simulation allowed them to better model how events (infection, admission, resolution) would impact ICU wait times. This allowed policymakers to better understand the tipping point where ICU wait times went non-linear.


[flagged]


CONCLUSIONS Treatment with ivermectin did not result in a lower incidence of medical admission to a hospital due to progression of Covid-19 or of prolonged emergency department observation among outpatients with an early diagnosis of Covid-19. (Funded by FastGrants and the Rainwater Charitable Foundation; TOGETHER ClinicalTrials.gov number, NCT04727424. opens in new tab.)

https://www.nejm.org/doi/full/10.1056/nejmoa2115869


This study has flaws.

Namely, the ivermectin was only administered with patients after 7 days of symptomatic COVID infection.

Meanwhile Paxlovid, the patented ACE2 inhibitor is received as a proven treatment when administered early in infection.

7 days after symptoms appear is not early. The game is rigged my friend


He doesn't care. Someone else got to him first.


Your argument is incoherent. You’re saying deaths is an irrelevant metric because actually the risk is the medical system collapses and you get … more deaths.


No, they're saying that excess deaths is a rearward looking metric that nobody had the luxury of actually having at the present time when decisions were being made.

It's very easy to make decisions in retrospect when you have all the information that could be useful. It's very hard to make decisions in the present when that information doesn't exist yet.


>>> What about ten lives? One thousand? Ten thousand?

Are 200,000 lives meaningful enough?

Silver's regression model shows that for each 1% increase in the vote share for the blue candidate, the covid death rate in that state (statistically) would fall by 15.5 per million population.

In areas where the blue candidate received 20% of the vote, and the red candidate received 80%, the model predicts 1,793 deaths per million.

And in areas where blue got 80% and red got 20%, the model predicts 864 deaths per million.

A 300% greater death rate [1] is a startlingly high penalty to pay for an ideology. This is at least 200,000 deaths [2] attributed to political ideology. Not to mention the tremendous excess suffering that did not result in death.

And, no, it's not because red areas are older than blue areas. It's because red ideologues got the vaccine in far lower numbers.

[1] Coefficients show that blue at 0% would have a death rate of 2,103 per million, and blue at 100% would have a death rate of 554 per million.

[2] 2,103 - 554 = 1,549 excess deaths per million for red, multiplied by 45% of the US population is roughly 230,000.


It's because red ideologues got the vaccine in far lower numbers.

Vaccine refusal played a part, certainly, in connection with the fact that conservatives are older. In many instances, a lot older. In other instances, not really that much older. The trend is unidirectional but the degree varies by state, as you'll find when you start comparing West Virginia and Florida to Maine and Vermont.

But the truth is inescapable: the older someone was, the more they needed to get vaccinated and boosted... and the less likely they did.


From TFA:

   *The differences in state death rates are very likely because of differences in vaccine uptake*

    Just to be clear, I don’t mean to imply that COVID is intrinsically more likely to target Republicans or anything like that. Rather, my claim is that COVID is considerably more deadly in people who haven’t been vaccinated, and since Republicans are less likely to be vaccinated than Democrats, state partisanship serves as a proxy for this.

    Indeed, we can look at vaccination directly.

    ...  age and vaccination rates alone explain more than half of the variation in COVID death rates between states since Feb. 2021.
However Silver didn't mention age being strongly correlated (either way) with being vaccinated, which is interesting, given the rest of the article it'd be expected to be stated if it was.


He actually spent a lot of time working around the obvious: that COVID absolutely is intrinsically more likely to target Republicans. Not because of any merits or demerits associated with being a Republican, but because "Republican" is strongly correlated with "old as hell" in many if not most US states, as well as "more likely to reject vaccines."

Right-wing media figures consistently fed their audience a diet of vaccine skepticism, conspiracy theories, and promotion of useless alternative treatments; e.g., https://www.nytimes.com/2021/07/17/us/politics/coronavirus-v... . Because their audience skews older, this messaging had unusually-deadly consequences.

Why there's anything controversial about any of this, I have no clue. How does it make sense for him to write "I don't mean to say that COVID targets Republicans," followed by a paragraph that states exactly why it does?


The whole point of the article is to address that criticism. Thats why he controlled for age. Also, red and blue states had similar death rates before vaccines, despite the virus still being more deadly to older people then.

The point of that statement is, the virus doesn't target someone because they're a Republican, but Republicans have been more likely to die from it because they're more likely to be unvaccinated.


> It's because red ideologues got the vaccine in far lower numbers.

Pretty sure it was something like 60% vs 70%. That doesn't seems like anywhere near different enough to explain those numbers.


The same article also does the regression on vaccination rates. The data is as clear as it gets.


Arguably, ideology is and has always been the largest contributor to death (and birth) rates no?


Obviously rearward looking metrics were irrelevant and no one, including me, is suggesting otherwise. You can make policies with the goal of reducing deaths in advance of measurement, and policymakers did.

The argument I am criticizing never mentions rearward metrics as a problem.


My impression was that a segment of the population felt that the lockdowns and other constraints imposed by policymaker were primarily fear based. This may have been due a perceived difference between religious versus secular acceptance of death (a continuation versus end). My point isn’t that policymakers can’t make nuanced decisions about the value of a human life (since it is done on a regular basis) but instead that from a policymakers perspective it was irrelevant. The bigger concern was the collapse of the health care system which is a non-negotiable.

The assumption that friends and family will calmly watch a family member’s untended death from covid when ICUs are over capacity and that essential workers will continue to show up for work knowing there is no hope for care if they fall ill is unrealistic in my opinion.


Toure seriously doing yeoman's work with these comments.




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