Predictions: Damage caused by government’s response to the coronavirus pandemic.

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How much harm was caused by government reaction to the pandemic? I will put into print my predictions of what we eventually learn of the damage willfully caused by the federal and state governments.

Introduction to this series is here.

Concluding comments are here.

Over the last two years reports have surfaced here and there hinting at the following predictions. In the next few months more reports will emerge.

It will take honest, serious researchers years before statistically valid research provides solid evidence for these predictions. Strong, verifiable, reproducible proof will emerge with time.

I predict that in a decade or two there will be a consensus throughout the country that the government reaction to the coronavirus pandemic was the worst, most destructive government policy in U.S. history.

Seven predictions follow:

1 – Government response caused more deaths as measured by years-of-life-lost than deaths prevented by the government response as measured by years-of-life-lost.

Rephrased, the economic shutdown and other actions carried out by the federal and state governments caused more deaths than they saved, as measured by years-of-life-lost.

To describe this concept let’s construct a hypothetical. Let’s say a teenager, aged 15 who would otherwise be expected to live to a ripe old 85, commits suicide at 15 because of the isolation required by government policy with the expected depression and despair. That would count as 70 years life lost. A 75-year-old who does not catch Covid and die because of government restrictions and therefore would be expected to live until age 85 counts as 10 years life saved.

In my hypothetical example, if less than seven 75-year-olds were saved by government action then the loss of that one teenager would constitute more years-life-lost due to government policies than were saved.

This concept carries over to people who did not get colonoscopies in their 50s, people who did not get cancer screening in their 20s or 30s even though there were odd symptoms, and people locked in nursing homes who were not allowed to see the doctors in order to adjust the medicines or get checked out for new symptoms. The deaths caused by government policy also includes people in their 70s, 80s, and 90s who were forcibly isolated from family, then deteriorated mentally and physically from the enforced solitary confinement, then died from the isolation and neglect.

2 – Government response caused more economic harm, more medical harm, more educational harm, and more psychological harm than the government response avoided or prevented. Government caused serious damage in those four areas.

Economic damage caused by government policy is significant and it will take several more years for full recovery. The disruption to medical health due to canceled or delayed screening and treatment will cause serious long-term damage and unnecessary deaths. The spike in overdose deaths and rising levels of drug and alcohol abuse are already documented.

The psychological harm to adults and teenagers is visible in numerous news reports which I’ve described in previous posts. Look around your friends and family and I am confident you have seen the damage.

The damage to elderly living in care facilities will be difficult to quantify, but after quantification we will learn it was one or two magnitudes beyond merely horrible.

The damage to children losing a year of education, losing the ability to read faces to help comprehension of spoken words, and disrupting their early development will cause decades of damage.

Anecdotal information I’m picking up suggest that a huge number of black and brown children checked out of education while they were on-line and even when they were eventually allowed to attend class in-person. Losing a year of math and reading in elementary school with our educational system not capable of getting those children caught up will leave large number of children seriously behind.

The deeper damage is that if you don’t learn any math in your third or fifth grade, you likely won’t understand anything taught in the next year, you will be completely clueless the following year, and lacking even the vaguest understanding of anything the math teacher says in high school will produce massive numbers of drop-outs early in high school.

A small but significant percentage of schoolchildren have essentially stopped learning. Our educational system has no capacity to get them caught up. What will be their economic prospects in life? That devastation will fall disproportionately into poor and minority communities.

Rephrased, this prediction is that the multi-spectrum devastation caused by government policies will be more severe than any harm which was prevented by the government policies.

3 – It is possible but less likely that the government response caused more economic, medical, educational, and psychological harm than was caused by coronavirus infections.

This is a lower likelihood but still quite possible. I think and predict there is a small possibility the harm caused by government policies in those four areas might even be greater than the harm caused by the coronavirus bug.

I am not predicting government policies caused more deaths than the coronavirus.  My expectation is government policies will not prove to be as deadly as the bug, but not for lack of effort by federal and state leaders.

4 – Lockdowns and restrictions required by government policy did not reduce the number of people infected, the number of people hospitalized, or the number of people who died.

Various reports and statistical analyses after the first surge of infections indicate the virus spread and slowed independent of government policies. One particular analysis showed that the first round of lockdowns were imposed after the infection rate was already dropping. California was the exception to that rule, where the lockdowns were imposed at a time that preceded the peak. There may have been a correlation between lockdowns in California and the turn an infection rate, however with that being the only state where that happened, it is more likely that was lucky, coincidental timing.

General pattern for the states is the virus dropped before government policies could have any impact.

Stats from the omicron wave point towards the bug doing what it wanted. Amusingly in California, new mask requirements were imposed statewide with infection rate increasing by factor of, oh, something in the range of 30 fold a month after the mandate went into effect. That suggests the mask mandate in California was simply ineffective in the face of the omicron variant.

At best, there may have been a slight drop in the number of infections or hospitalizations. However I predict we will eventually learn that lockdowns and other restrictions only postponed infections and hospitalizations.

5 – After adjusting for factors such as health conditions before the pandemic, population age, and other major variables, states and localities with stronger restrictions had worse outcomes on all measures than states and localities with milder restriction.

This is an easy prediction since there is already a host of analyses in print showing this is the case. 

Phrased differently, we will eventually see solid proof that states with harsher restrictions either experienced no beneficial impact from those harsh restrictions or experienced worse outcomes that states with mild restrictions.

These worse outcomes will include but not be limited to measurements of total infections, total hospitalizations, deaths, immediate level of employment, long term level of employment, income level for middle income and working class people, educational achievement, early childhood development, adolescent development, short-term physical health, long-term physical health, short and long term psychological health, life-time income, and life-time educational achievement.

In terms of level of impact, for most outcome measurements, I think we will see drastically worse outcomes in states with harsher restrictions.  We may see results that are close to the same.  Anything less than a drastic benefit means that the harsh restrictions were bad policy.

6 – The adverse impacts in states with harsher restrictions will fall disproportionately on lower income people and people of color.

This is another easy prediction. There are already lots of reports showing this is already visible.

7 – Measured from many different directions, the cost/benefit analysis of government policies will show a host of government decisions were clearly wrong.

This overlaps with several of the above predictions, but is worth mentioning separately.

If the cost of a decision in terms of direct and indirect impacts is greater than the benefits from that decision, then it was a bad decision.

This applies at multiple points in time:

  • when a decision was made and it should have been obvious at the time that the benefits would not be worth the cost (closing schools is the leading candidate for bad decisions looking at this timeframe),
  • after harmful results appear and the decision could have been reversed (which was the case by late 2020 for most decisions), or
  • after poor cost/benefit tradeoff is visible to everyone and failure ought to have been admitted with decisions reversed.

Two sub-predictions in terms of cost/benefit tradeoffs:

First, there will be multiple directions from which a bad cost/benefit tradeoff were visible and those negative tradeoffs were visible to decision makers if they had wanted to see.

Second, the direction of those bad cost/benefit tradeoffs will be seriously unbalanced; it won’t even be close on most factors.

These tradeoff calculations will be difficult. How do we make comparisons?

For example, we had ten million people thrown into unemployment, with declining living standards, loss of health insurance for many, poorer health outcomes, and reduced life expectancy for some as a result of less health care. How do we calculate a tradeoff of that harm with the small number of people whose lives were saved by government policy?

Another tradeoff calculation: What benefit do we put on the scale to offset a generation of K-12 students having their education disrupted for 12 or 18 or 24 months with a yet-to-be-determined portion of those youth seeing their education derailed with no plans to ever get them back on track? I await researchers finding any benefit that outweighs those costs.

Those are my predictions as of March 1, 2022.

Next post discusses why I put these predictions in print now and an estimate of how long it will take to prove these predictions.

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