Author Archives: Nuetzel

Harbingers of COVID Fade, But Not the Pretense for Hysteria

17 Thursday Dec 2020

Posted by in Coronavirus, Pandemic, Vaccinations

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My pre-Thanksgiving optimism about a crest in the fall wave of the coronavirus has been borne out for the Midwest and Mountain states in the U.S. These regions were the epicenter of the fall wave through October and most of November, but new cases in those states have continued to decline. Cases in a number of other states began to climb in November, however, contributing to a continuing rise in total new cases nationally. Some of these states are still in the throes of this wave, with the virus impacting subsets of the population that were relatively unscathed up till now.

My disclaimer: COVID is obviously a nasty virus. I don’t want to get it. However, on the whole, it is not a cataclysm on the order of many pandemics of the past. In fact, excess deaths this year will add just over 10% to projections of total deaths based on a five-year average. That level puts us in line with average annual deaths of about twenty years ago. And many of those excess deaths have been caused by our overreaction to the pandemic, not by the virus itself. As my endocrinologist has said, this is the greatest overreaction in all medical history. Unfortunately, a fading pandemic does not mean we can expect an end to the undue panic, or pretense for panic, on the part of interventionists.

This post will focus largely on trends in newly diagnosed COVID cases. I have been highly critical of our testing regime and COVID case counts because the most prominent diagnostic test (PCR) falsely identifies a large number of uninfected individuals as COVID-positive. However, case numbers are widely tracked and it’s fairly easy to find information across geographies for comparison. Deflate all the numbers by 30% if you want, or by any other factor, but please indulge me because I think the trends are meaningful, even if the absolute level of cases is inflated.

I’ll start with the good news and work my way down to states in which cases are still climbing (all of the following charts are from @Humble_Analysis (PLC)). The first chart is for the Great Plains, where cases peaked a little before Thanksgiving and have continued to fall since then. That peak came about six weeks after it began in earnest and cases have faded over the last four weeks.

Next we have the Mountain states, where again, cases peaked around Thanksgiving, though Idaho saw a rebound after the holiday. You’ll see below that a number of states had a distinct drop in new cases during the week of Thanksgiving. There was somewhat of a pause in testing during that week, so the subsequent rebounds are largely due to a “catch-up” at testing sites, rather than some kind of Thanksgiving-induced spike in infections.

Back to the Mountain region, the peak came an average of about six or seven weeks into the wave, but the duration of the wave appears to have been longer in Montana and Wyoming.

Here are the Southern Plain states, where cases plateaued around Thanksgiving (though cases in Missouri have clearly declined from their peak). In this region, case counts accelerated in October after a slow climb over the summer.

The situation is somewhat similar in the Midwest. where cases have generally plateaued. There were some post-Thanksgiving rebounds in several states, especially Tennessee. The wave began a little later in this region, in mid- to late October, and it is now seven to eight weeks into the wave, on average.

Here are the Mid-Atlantic states, which may be showing signs of a peak, though North Carolina has had the greatest caseload and is still climbing. These states are about seven weeks into the wave, on average.

The Northeast also shows signs of a possible peak and is about seven weeks into the wave, except for Rhode Island, which saw an earlier onset and the most severe wave among these states.

And finally we have the South, which is defined quite broadly in PLC’s construction. It’s a mixed bag, with a few states showing signs of a peak after about seven weeks. However, cases are still climbing in several states, notably California and Florida, among a few others.

Oregon and Washington were skipped, but they appear as the Pacific NW in the following chart, along with aggregations for all the other regions. Maine is Part of the “Rural NE”, which was also skipped. The fall wave can be grouped roughly into two sets of regions: those in which waves began in late September or early October, and those where waves began in early to mid-November. The first group has moved beyond a peak or at least has plateaued. The latter group may be reaching peaks now or one hopes very soon. It seems to take about seven weeks to reach the peak of these regional waves, so a late December peak for the latter group would be consistent.

Justin Hart has a take on the duration of these waves, but he does so in terms of the share of emergency room (ER) visits in which symptoms of COVID-like illness (CLI) are presented. CLI tends to precede case counts slightly. Hart puts the duration of these waves at eight to ten weeks, but that’s a judgement call, and I might put it a bit longer using caseloads as a guide. Still, this color-coded chart from Hart is interesting.

If this sort of cyclical duration holds up, it’s consistent with the view that cases in many of the still “hot” states should be peaking this month.

Aggregate cases for the U.S. appear below. The growth rate of new cases has slowed, and the peak is likely to occur soon. However, because it combines all of the regional waves, the duration of the wave nationwide will appear to be greater than for the individual regions. COVID-attributed deaths are also plotted, but they are reported deaths, not by date of death (DOD) or actual deaths, as I sometimes call them. Deaths by DOD are available only with a lag. As always, some of the reported deaths shown below occurred weeks before their reported date. Actual deaths were still rising as of late November, and are likely still rising. However, another indicator suggests they should be close to a peak.

A leading indicator of actual deaths I’ve discussed in the past now shows a more definitive improvement than it did just after Thanksgiving, as the next chart shows. This is the CLI share discussed above. An even better predictor of COVID deaths by actual DOD is the sum of CLI and the share of ER patients presenting symptoms of influenza-like illness (ILI), but ILI has been fairly low and stable, so it isn’t contributing much to changes in trend at the moment. There has been about a three-week lead between movements in CLI+ILI and COVID deaths by DOD.

(The reason the sum, CLI+ILI, has been a better predictor than CLI alone is because for some individuals, there are similarities in the symptoms of COVID and the flu.)

The chart shows that CLI peaked right around the Thanksgiving holiday (and so did CLI+ILI), but it remained on something of a plateau through the first week of December before declining. Some of the last few days on this chart are subject to revision, but the recent trend is encouraging. Allowing for a three-week lead, this indicates that peak deaths by DOD should occur around mid-December, but we won’t know exactly until early to mid-January. To be conservative, we might say the latter half of December will mark the peak in actual deaths.

The regional COVID waves this summer and fall seem to have run their course within 10 – 12 weeks. Several former hot spots have seen cases drop since Thanksgiving after surges of six to seven weeks. However, there are several other regions with populous states where the fall wave is still close to “mid-cycle”, as it were, showing signs of possible peaks after roughly seven weeks of rising cases. The national CLI share peaked around Thanksgiving, but it did not give up much ground until early December. That suggests that actual deaths (as opposed to reported deaths), at least in some regions, will peak around the time of the winter solstice. Let’s hope it’s sooner.

Successive waves within a region seem to reach particular subsets of the population with relatively few reinfections. The 10 – 12 week cycle discussed above is sufficient to achieve an effective herd immunity within these subsets. But once again, a large share of the vulnerable, and a large share of COVID deaths, are still concentrated in the elderly, high-risk population and in care homes. The vaccine(s) currently being administered to residents of those homes are likely to hasten the decline in COVID deaths beginning sometime in January, perhaps as early as mid-month. By then, however, we should already see a decline underway as this wave of the virus finally burns itself out. As vaccines reach a larger share of the population through the winter and spring, the likelihood of additional severe waves of the virus will diminish.

Lest there be any misunderstanding, the reasons for the contagion’s fade to come have mostly to do with reaching the effective herd immunity threshold within afflicted subsets of the population (sub-herds). Social distancing certainly plays a role as well. Nearly all of that is voluntary, though it has been encouraged by panicked pronouncement by certain public officials and the media. Direct interventions or lockdown measures are in general counter-productive, however, and they create a death toll of their own. Unfortunately, the fading pandemic might not rein-in the curtailment of basic liberties we’ve witnessed this year.

Post-Script: Let’s hope the side effects of the vaccines are not particularly severe in the elderly. That’s a little uncertain, because that sub-population was not tested in very high numbers.

COVID Interventions: Costly, Deadly, and Ineffective

14 Monday Dec 2020

Posted by in Coronavirus, Liberty, Lockdowns, Public Health

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What does it take to shake people out of their statist stupor? Evidently, the sweet “logic” of universal confinement is very appealing to the prescriptive mindset of busybodies everywhere, who anxiously wag their fingers at those whom they view as insufficiently frightened. As difficult as it is for these shrieking, authoritarian curs to fathom, measures like lockdowns, restrictions on business activity, school closures, and mandates on behavior have at best a limited impact on the spread of the coronavirus, and they are enormously costly in terms of economic well-being and many dimensions of public health. Yet the storm of propaganda to the contrary continues. Media outlets routinely run scare stories, dwelling on rising case numbers but ignoring them when they fall; they emphasize inflated measures of pandemic severity; certain researchers and so-called health experts can’t learn the lessons that are plain in the data; and too many public officials feel compelled to assert presumed but unconstitutional powers. At least the World Health Organization has managed to see things clearly, but many don’t want to listen.

I’ll be the first to say I thought the federalist approach to COVID policy was commendable: allow states and local governments to craft policies appropriate to local conditions and political preferences, rather than have the federal government dictate a one-size-fits-all policy. I haven’t wavered in that assessment, but let’s just say I expected more variety. What I failed to appreciate was the extent to which state and local leaders are captive to provincial busybodies, mavens of precautionary excess, and fraudulent claims to scientific wisdom.

Of course, it should be obvious that the “knowledge problem” articulated by Friedrich Hayek is just as dangerous at low-levels of government as it is in a central Leviathan. And it’s not just a knowledge problem, but a political problem: officials become panicked because they fear bad outcomes will spell doom for their careers. Politicians are particularly prone to the hazards of “do-somethingism”, especially if they have willing, status-seeking “experts” to back them up. But as Scott Sumner says:

“When issues strongly impact society, the science no longer ‘speaks for itself’.

Well, the science is not quite as clear as the “follow-the-science” crowd would have you believe. And unfortunately, public officials have little interest in sober assessments of the unintended effects of lockdown policy.

In my last post, I presented a simple framework for thinking about the benefits and costs of lockdown measures, or non-pharmaceutical interventions (NPIs). I also emphasized the knowledge problem: even if there is some point at which NPI stringencies are “optimized”, government does not possess the knowledge to find that point. It lacks detailed information on both the costs and benefits of NPIs, but individual actors know their own tolerance for risk, and they surely have some sense of the risks they pose to others in their normal course of affairs. While voluntary precautions might be imperfect, they accomplish much of what interventionists hope will be gained via coercion. But, in an effort to “sell” NPIs to constituents and assert their authority, officials vastly over-estimate benefits of NPIs and under-estimate the costs.

NPI Stringency and COVID Outcomes

Let’s take a look at a measure of the strength of NPIs by state — the University of Oxford Stringency Index — and compare those to CDC all-cause excess deaths in each state. If it’s hard to read, try clicking on the image or turn your phone sideways. This plot covers outcomes through mid-November:

The chart doesn’t suggest any benefit to the imposition of greater restrictions, or more stringent NPIs. In fact, the truth is that people will do most of the work on their own based on perceptions of risk. That’s partly because government restrictions add little risk mitigation to what can be accomplished by voluntary social distancing and other precautions.

Here’s a similar chart with cross-country comparisons, though the data here ended in early October (I apologize for the fuzzy image):

But what about reverse causality? Maybe the imposition of stringency was a response to more severe contagions. Now that the virus has swept most of the U.S and Europe in three distinct waves, and given the variety and timing of NPIs that have been tried, it’s harder to make that argument. States like South Dakota have done fairly well with low stringency, while states like New Jersey with high stringency have fared poorly. The charts above provide multiple pair-wise examples and counter-examples of states or countries having faced hard waves with different results.

But let’s look at a few specific situations.

The countries shown above have converged somewhat over the past month: Sweden’s daily deaths have risen while the others have declined to greater or lesser degrees, but the implications for mask usage are unaltered.

And of course we have this gem, predicated on the mental gymnastics lockdown enthusiasts are fond of performing:

But seriously, it’s been a typical pattern: cases rise to a point at which officials muster the political will to impose restrictions, often well after the “exponential” phase of the wave or even the peak has passed. For the sake of argument, if we were to stipulate that lockdowns save lives, it would take time for these measures to mitigate new infections, time for some of the infected individuals to become symptomatic, and more time for diagnosis. For the lockdown arguments to be persuasive, the implementation of NPIs would have to precede the point at which the growth of cases begins to decline by a few weeks. That’s something we’ve seldom observed, but officials always seem to take credit for the inevitable decline in cases.

More informed lockdown proponents have been hanging their hats on this paper in Nature by Seth Flaxman, et al, published in July. As Philippe LeMoine has shown, however, Flaxman and his coauthors essentially assumed their result. After a fairly exhaustive analysis, Lemoine, a man who understands sophisticated mathematics, offers these damning comments:

“Their paper is a prime example of propaganda masquerading as science that weaponizes complicated mathematics to promote questionable policies. Complicated mathematics always impresses people because they don’t understand it and it makes the analysis look scientific, but often it’s used to launder totally implausible assumptions, which anyone could recognize as such if they were stated in plain language. I think it’s exactly what happened with Flaxman et al.’s paper, which has been used as a cudgel to defend lockdowns, even though it has no practical relevance whatsoever.”

The Economic Costs of Stringency

So the benefits of stringent lockdowns in terms of averting sickness and death from COVID are speculative at best. What about the costs of lockdowns? We can start with their negative impact on economic activity:

That’s a pretty bad reflection on NPI stringency. In the U.S, a 10% decline in GDP in 2020 amounts to about $2.1 trillion in lost goods and services. That’s just for starters. The many destroyed businesses and livelihoods carry an ongoing cost that could take years to fade, as this graphic on permanent business closures shows:

If you’re wondering about the distributional effects of lockdowns, here’s more bad news:

It’s possible to do many high-paying jobs from home. Not so for blue-collar workers. And distributional effects by size of enterprise are also heavily-skewed in favor of big companies. Within the retail industry, big-box stores are often designated as “essential”, while small shops and restaurants are not. The restaurant industry has been destroyed in many areas, inflicting a huge blow to owners and workers. This despite evidence from contact tracing showing that restaurants and bars account for a very small share of transmission. To add insult to injury, many restaurants invested heavily in safety measures and equipment to facilitate new, safer ways of doing business, only to be double-crossed by officials like Andrew Cuomo and Eric Garcetti, who later shut them down.

Public Health Costs of Stringency

Lives are lost due to lockdowns, but here’s a little exercise for the sake of argument: The life value implied by individual willingness-to-pay for risk reduction comes in at less than $4 million. Even if the supposed 300,000 COVID deaths had all been saved by lockdowns, that would have amounted to a value of $1.2 trillion, about half of the GDP loss indicated above. Of course, it would be outrageously generous to concede that lives saved by NPI’s have approached 300,000, so lockdowns fall far short at the very outset of any cost-benefit comparison, even if we value individual lives at far more than $4 million.

As AJ Kay says, the social and human costs go far beyond economic losses:

I cited specific examples of losses in many of these categories in an earlier post. But for the moment, instead of focusing on causes of death, take a look at this table provided by Justin Hart showing a measure of non-COVID excess deaths by age group in the far right-hand column:

The numbers here are derived by averaging deaths by age group over the previous five years and subtracting COVID deaths in each group. I believe Hart’s numbers go through November. Of greatest interest here is the fact that younger age groups, having far less risk of death from COVID than older age groups, have suffered large numbers of excess deaths NOT attributed to COVID. As Hart notes later in his thread:

These deaths are a tragic consequence of lockdowns.

Educational Costs of Stringency

Many schools have been closed to in-person instruction during the pandemic, leading to severe disruptions to the education f children. This report from the Fairfax County, VA School District is indicative, and it is extremely disheartening. The report includes the following table:

Note the deterioration for disabled students, English learners, and the economically disadvantaged. The surfeit of failing grades is especially damaging to groups already struggling in school relative to their peers, such as blacks and Hispanics. Not only has the disruption to in-person instruction been disastrous to many students and their futures; it has also yielded little benefit in mitigating the contagion. A recent study in The Lancet confirms once again that transmission is low in educational settings. Also see here and here for more evidence on that point.

Conclusion

It’s clear that the “follow-the-science” mantra as a rationale for stringent NPIs was always a fraud, as was the knee-jerk response from those who conflated lockdowns with “leadership”. Such was the wrongheaded and ultimately deadly pressure to “do something”. We can be thankful that pressure was resisted at the federal level by President Trump. The extraordinary damage inflicted by ongoing NPIs was quite foreseeable, but there is one more very ominous implication. I’ll allow J.D. Tucille to sum that up with some of the pointed quotes he provides:

“‘The first global pandemic of the digital age has accelerated the international adoption of surveillance and public security technologies, normalising new forms of widespread, overt state surveillance,’ warned Kelsey Munro and Danielle Cave of the Australian Strategic Policy Institute’s Cyber Policy Centre last month.

‘Numerous governments have used the COVID-pandemic to repress expression in violation of their obligations under human rights law,’ United Nations Special Rapporteur on Freedom of Expression David Kaye noted in July.

‘For authoritarian-minded leaders, the coronavirus crisis is offering a convenient pretext to silence critics and consolidate power,’ Human Rights Watch warned back in April.

There’s widespread agreement, then, that government officials around the world are exploiting the pandemic to expand their power and to suppress opposition. That’s the case not only among the usual suspects where authorities don’t pretend to take elections and civil liberties seriously, but also in countries that are traditionally considered ‘free.’ … It’s wildly optimistic to expect that newly acquired surveillance tools and enforcement powers will simply evaporate once COVID-19 is sent on its way. The post-pandemic new normal is almost certain to be more authoritarian than what went before.”

COVID Externalities: the Costs and Benefits of Intervention

13 Sunday Dec 2020

Posted by in Coronavirus, Public Health, Social Costs

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This post offers a simple representation of the argument against public non-pharmaceutical interventions (NPIs) to subdue the COVID-19 pandemic. The chart below features two lines, one representing the presumed life-saving benefits of lockdown measures or NPI stringency, and another representing the costs inflicted by those measures. The values on the axes here are not critical, though measures of stringency exist (e.g., the University of Oxford Stringency Index) and take values from zero to 100.

The benefits of lives saved due to NPI stringency are assigned a value on the vertical axis, as are the costs of lives lost due to deferred health care, isolation, and other stressors caused by stringency. In addition, there are the more straightforward losses caused by suspending economic activity, which should be included in costs.

One can think of the benefits curve as representing gains from forcing individuals, via lockdown measures, to internalize the external costs of risk inflicted on others. However, this curve captures only benefits incremental to those achieved through voluntary action. Thus, NPI benefits include only extra gains from coercing individuals to internalize risks, while losses from NPI stringency are captured by the cost curve.

My contention is that the benefits of stringency diminish and may in fact turn down at some point, and that costs always increase in the level of stringency. In the chart, for what it’s worth, the “optimal” level of stringency would be at a value of 2, where the difference between total benefits and total costs is maximized (and where the benefits of incremental stringency are equal to the marginal costs or losses). However, I am not convinced that the benefits of lockdown measures ever exceed costs, as they do in the chart above. That is, voluntary action may be sufficient. But if the benefits of NPIs do exceed costs, it’s likely to be only at low levels of stringency.

To the extent that people are aware of the pandemic and recognize risk, the external costs of possible infectiousness are already internalized to some degree. Moreover, there is mutual risk in most interactions, and all individuals face risks that are proportional to those to which they expose others: if your contacts are more varied and your interactions are more frequent and intimate, you face correspondingly higher risks yourself. After all, in a pandemic, an individual’s failure to exercise caution may lead to a very hard internalization of costs if an infection strikes them. This mutuality is an element absent from most situations involving externalities. And to the extent that you take voluntary precautions, you and your contacts both benefit. Nevertheless, I concede that there are individuals who face less risk themselves (the young or healthy) but who might behave recklessly, and they might not internalize all risk for which they are responsible. Yes, stringency may have benefits, but that does not mean it has net benefits.

Even if there is some meaningful point at which NPIs are “optimized”, government does not possess the knowledge required to find that point. It lacks detailed knowledge of both costs and benefits of NPIs. This is a manifestation of the “knowledge problem” articulated by Friedrich Hayek, which hampers all efforts at central planning. In contrast, individual actors know their own tolerance for risk, and they surely have some sense of the risks they create in their normal course of affairs. And again, there is a strong degree of proportionality and voluntary internalization of mutual risks.

While relying on voluntary action is economically inefficient relative to an ideal, full-information and perfectly altruistic solution, it is at least based on information that individuals possess: their own risk profile and risk preferences. In contrast, government does not possess information necessary to impose rules in an optimal way, and those rules are rife with unintended consequences and costs inflicted on individuals.

My next post will present empirical evidence of the weakness of lockdown measures in curbing the coronavirus as well as the high costs of those measures. The coronavirus is a serious infection, but it is not terribly deadly or damaging to the longer-term health of the vast majority of people. This, in and of itself, should be sufficient to demonstrate that the array of non-pharmaceutical interventions imposed in the U.S. and abroad were and are not worthwhile. People are capable of assessing risks for themselves. The externality argument, that NPIs are necessary because people do not adequately assess the risk they pose to others, relies on an authority’s ability to assess that risk, and they invariably go overboard on interventions for which they underestimate costs. COVID is not serious enough to justify a surrender of our constitutional rights, and like every concession to government authority, those rights will be difficult to recover.

A Good Historical Backdrop for the Pandemic

07 Monday Dec 2020

Posted by in Pandemic, Public Health

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In this short post I’m trying to do my part to put our pandemic experience in perspective. Lord knows, I was on the low side in my U.S. case-load “guesstimate” last March, as well as the number of deaths induced by COVID. (A number of others, using highly sophisticated models, erred more severely in the opposite direction.) I also failed to anticipate the strength of the later seasonal waves we’ve experienced — I was excessively optimistic as the first wave ebbed. But now, as the fall wave is showing some signs of topping out, what can we say about the pandemic in historical perspective? I came across an interesting chart that sheds some light on the question.

In terms of all-cause mortality, we’ve clearly seen a bump upward this year. Take a look at the chart below. It shows deaths per million (DPM) of population (from all causes). Try clicking on it if it’s hard to read, or turn your phone sideways. See the little blip on the lower right? That’s our pandemic year through August. The blip made 2020, at least through August, look something like a normal year of the early 2000s.

The visible spike early in 2020 was the spring wave, which was concentrated on the east coast. Of course, the fall wave will yield another spike, probably a bit higher than the first. Nevertheless, against the historical backdrop, this chart shows that the magnitude of our current public health crisis is relatively minor.

If you scan to the left from 2020, you can see that DPM this year would have appeared normal around 2005. Remember how bad it was back in aught-five…. all the death? Yeah, me neither. That’s perspective.

The chart also reflects several mild flu seasons over the past few years. Because the flu, like COVID-19, tends to hit the elderly and infirm the hardest, the “soft” DPM numbers over the past few years support the theory that the population included a fair amount of so-called “dry tinder” for COVID as we entered the year.

One other note on the chart: the DPMs are “age-adjusted”, meaning that age groups are weighted for comparisons across countries with differing age distributions (not what we’re doing here). In this case, the DPM values are weighted based on the population in the year 2000.

It’s amazing how so many have bought into the narrative that the current pandemic is historically bad. Yes, our DPMs are high relative to the last decade, but a significant number of those deaths were caused not by COVID, but by our own overreaction to the virus. That’s something else I failed to anticipate in the spring. It’s something we can put behind us now, however, if only we’re willing to put our experience in perspective. Unfortunately, many public officials, along with their public health advisors, continue to promote the deluded view that the virus can only be stopped by stopping our lives, our educations, our earnings, our health, and our sanity.

Most Hospitals Have Ample Capacity

05 Saturday Dec 2020

Posted by in Coronavirus, Health Care

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Let’s get one thing straight: when you read that “hospitalizations have hit record highs”, as the Wall Street Journal headline blared Friday morning, they aren’t talking about total hospitalizations. They reference a far more limited set of patients: those admitted either “for” or “with” COVID. And yes, COVID admissions have increased this fall nationwide, and especially in certain hot spots (though some of those are now coming down). Admissions for respiratory illness tend to be highest in the winter months. However, overall hospital capacity utilization has been stable this fall. The same contrast holds for ICU utilization: more COVID patients, but overall occupancy rates have been fairly stable. Several factors account for these differing trends.

Admissions and Utilization

First, take a look at total staffed beds, beds occupied, and beds occupied by COVID patients (admitted “for” or “with” COVID), courtesy of Don Wolt. Notice that COVID patients occupied about 14% of all staffed beds over the past week or so, and total beds occupied are at about 70% of all staffed beds.

Is this unusual? Utilization is a little high based on the following annual averages of staffed-bed occupancy from Statista (which end in 2017, unfortunately). I don’t have a comparable utilization average for the November 30 date in recent years. However, the medical director interviewed at this link believes there is a consensus that the “optimal” capacity utilization rate for hospitals is as high as 85%! On that basis, we’re fine in the aggregate!

The chart below shows that about 21% of staffed Intensive Care Unit (ICU) beds are occupied by patients having COVID infections, and 74% of all ICU beds are occupied.

Here’s some information on the regional variation in ICU occupancy rates by COVID patients, which pretty much mirror the intensity of total beds occupied by COVID patients. Fortunately, new cases have declined recently in most of the states with high ICU occupancies.

Resolving an Apparent Contradiction

There are several factors that account for the upward trend in COVID admissions with stable total occupancy. Several links below are courtesy of AJ Kay:

  • The flu season has been remarkably light, though outpatients with symptoms of influenza-like illness (ILI) have ticked-up a bit in the past couple of weeks. Still, thus far, the light flu season has freed up hospital resources for COVID patients. Take a look at the low CDC numbers through the first nine weeks of the current flu season (from Phil Kerpen):
  • There is always flexibility in the number of staffed beds both in ICUs and otherwise. Hospitals adjust staffing levels, and beds are sometimes reassigned to ICUs or from outpatient use to inpatient use. More extreme adjustments are possible as well, as when hallways or tents are deployed for temporary beds. This tends to stabilize total bed utilization.
  • The panic about the fall wave of the virus sowed by media and public officials has no doubt “spooked” individuals into deferring care and elective procedures that might require hospitalization. This has been an unfortunate hallmark of the pandemic with terrible medical implications, but it has almost surely freed-up capacity.
  • COVID beds occupied are inflated by a failure to distinguish between patients admitted “for” COVID-like illness (CLI) and patients admitted for other reasons but who happen to test positive for COVID — patients “with” COVID (and all admissions are tested).
  • Case inflation from other kinds of admissions is amplified by false positives, which are rife. This leads to a direct reallocation of patients from “beds occupied” to “COVID beds occupied”.
  • In early October, the CDC changed its guidelines for bed counts. Out-patients presenting CLI symptoms or a positive test, and who are assigned to a bed for observation for more than eight hours, were henceforth to be included in COVID-occupied beds.
  • Also in October, the FDA approve an Emergency Use Authorization for Remdesivir as a first line treatment for COVID. That requires hospitalization, so it probably inflated COVID admissions.
  • The CDC also announced severe penalties in October for facilities which fail to meet its rather inclusive COVID reporting requirements, creating another incentive to capture any suspected COVID case in its reports.

In addition to the above, let’s not forget: early on, hospitals were given an incentive to diagnose patients with COVID, whether tested or merely “suspected”. The CARES Act authorized $175 billion dollars for hospitals for the care of COVID patients. In the spring and even now, hospitals have lost revenue due to the cancellation of many elective procedures, so the law helped replace those losses (though the distribution was highly uneven). The point is that incentives were and still are in place to diagnose COVID to the extent possible under the law (with a major assist from false-positive PCR tests).

Improved Treatment and Treatment

While more COVID patients are using beds, they are surviving their infections at a much higher rate than in the spring, according to data from FAIR Health. Moreover, the average length of their hospital stay has fallen by more than half, from 10.5 to 4.6 days. That means beds turn over more quickly, so more patients can be admitted over a week or month while maintaining a given level of hospital occupancy.

The CDC just published a report on “under-reported” hospitalization, but as AJ Kay notes, it can only be described as terrible research. Okay, propaganda is probably a better word! Biased research would be okay as well. The basic idea is to say that all non-hospitalized, symptomatic COVID patients should be counted as “under-counted” hospitalizations. We’ve entered the theater of the absurd! It’s certainly true that maxed-out hospitals must prioritize admissions based on the severity of cases. Some patients might be diverted to other facilities or sent home. Those decisions depend on professional judgement and sometimes on the basis of patient preference. But let’s not confuse beds that are unoccupied with beds that “should be occupied” if only every symptomatic COVID patient were admitted.

Regional Differences

Finally, here’s a little more information on regional variation in bed utilization from the HealthData.gov web site. The table below lists the top 25 states by staffed bed utilization at the end of November. A few states are highlighted based on my loose awareness of their status as “COVID “hot spots” this fall (and I’m sure I have overlooked a couple. Only two states were above 80% occupancy, however.

The next table shows the 25 states with the largest increase in staffed bed utilization during November. Only a handful would appear to be at all alarming based on these increases, but Missouri, for example, at the top of the list, still had 27% of beds unoccupied on November 30. Also, 21 states had decreases in bed utilization during November. Importantly, it is not unusual for hospitals to operate with this much headroom or less, which many administrators would actually prefer.

Of course, certain local markets and individual hospitals face greater capacity pressures at this point. Often, the most crimped situations are in small hospitals in underserved communities. This is exacerbated by more limited availability of staff members with school-age children at home due to school closures. Nevertheless, overall needs for beds look quite manageable, especially in view of some of the factors inflating COVID occupancy.

Conclusion

Marc Boom, President and CEO of Houston Methodist Hospital, had some enlightening comments in this article:

“Hospital capacity is incredibly fluid, as Boom explained on the call, with shifting beds and staffing adjustments an ongoing affair. He also noted that as a rule, hospitals actually try to operate as near to capacity as possible in order to maximize resources and minimize cost burdens. Boom said numbers from one year ago, June 25, 2019, show that capacity was at 95%.”

So there are ample beds available at most hospitals. A few are pinched, but resources can and should be devoted to diverting serious COVID cases to other facilities. But on the whole, the panic over hospital capacity for COVID patients is unwarranted.

The Pernicious COVID PCR Test: Ditch It or Fix It

02 Wednesday Dec 2020

Posted by in Coronavirus, Public Health

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We have a false-positive problem and even the New York Times noticed! The number of active COVID cases has been vastly exaggerated and still is, but there is more than one fix.

COVID PCR tests, which are designed to detect coronavirus RNA from a nasal swab, have a “specificity” of about 97%, and perhaps much less in the field. That means at least 3% of tests on uninfected subjects are falsely positive. But the total number of false positive tests can be as large or larger than the total number of true positives identified. Let’s say 3% of the tested population is truly infected. Then out of every 100 individuals tested, three individuals are actively infected and 97 are not. Yet about 3 of those 97 will test positive anyway! So in this example, for every true infection identified, the test also falsely flags an uninfected individual. The number of active infections is exaggerated by 100%.

But again, it’s suspected to be much worse than that. The specificity of PCR tests depends on the number of DNA replications, or amplification cycles, to which a test sample is subjected. That process is illustrated through three cycles in the graphic above. It’s generally thought that 20 – 30 cycles is sufficient to pick-up DNA from a live virus infection. If a sample is subjected to more than 30 cycles, the likelihood that the test will detect insignificant dead fragments of the virus is increased. More than 35 cycles prompts real concern about the test’s reliability. But in the U.S., PCR tests are regularly subjected to upwards of 35 and even 40-plus cycles of amplification. This means the number of active cases is exaggerated, perhaps by several times. If you don’t believe me, just ask the great Dr. Anthony Fauci:

“It’s very frustrating for the patients as well as for the physicians … somebody comes in, and they repeat their PCR, and it’s like [a] 37 cycle threshold, but you almost never can culture virus from a 37 threshold cycle. So, I think if somebody does come in with 37, 38, even 36, you got to say, you know, it’s just dead nucleotides, period.“

Remember, the purpose of the test is to find active infections, but the window during which most COVID infections are active is fairly narrow, only for 10 – 15 days after the onset of symptoms, and often less; those individuals are infectious to others only up to about 10 days, and most tests lag behind the onset of symptoms. In fact, infected but asymptomatic individuals — a third or more of all those truly infected at any given time — are minimally infectious, if at all. So the window over which the test should be sensitive is fairly narrow, and many active infections are not infectious at all.

PCR tests are subject to a variety of other criticisms. Many of those are discussed in this external peer-review report on an early 2020 publication favorable to the tests. In addition to the many practical shortfalls of the test, the authors of the original paper are cited for conflicts of interest. And the original paper was accepted within 24 hours of submission to the journal Eurosurveillance (what a name!), which should raise eyebrows to anyone familiar with a typical journal review process.

The most obvious implication of all the false positives is that the COVID case numbers are exaggerated. The media and even public health officials have been very slow to catch onto this fact. As a result, their reaction has sown a panic among the public that active case numbers are spiraling out of control. In addition, false positives lead directly to mis-attribution of death: the CDC changed it’s guidelines in early April for attributing death to COVID (and only for COVID, not other causes of death). This, along with the vast increase in testing, means that false positives have led to an exaggeration of COVID as a cause of death. Even worse, false positives absorb scarce medical resources, as patients diagnosed with COVID require a high level of staffing and precaution, and the staff often requires isolation themselves.

Many have heard that Elon Musk tested positive twice in one day, and tested negative twice in the same day! The uncomfortable reality of a faulty test was recently recognized by an Appeals Court in Portugal, and we may see more litigation of this kind. The Court ruled in favor of four German tourists who were quarantined all summer after one of them tested positive. The Court said:

“In view of current scientific evidence, this test shows itself to be unable to determine beyond reasonable doubt that such positivity corresponds, in fact, to the infection of a person by the SARS-CoV-2 virus.” 

I don’t believe testing is a bad thing. The existence of diagnostic tests cannot be a bad thing. In fact, I have advocated for fast, cheap tests, even at the sacrifice of accuracy, so that individuals can test themselves at home repeatedly, if necessary. And fast, cheap tests exist, if only they would be approved by the FDA. Positive tests should always be followed-up immediately by additional testing, whether those are additional PCR tests, other molecular tests, or antigen tests. And as Brown University epidemiologist Andrew Bostom says, you should always ask for the cycle threshold used when you receive a positive result on a PCR test. If it’s above 30 and you feel okay, the test is probably not meaningful.

PCR tests are not ideal because repeat testing is time consuming and expensive, but PCR tests could be much better if the number of replication cycles was reduced to somewhere between 20 and 30. Like most flu and SARS viruses, COVID-19 is very dangerous to the aged and sick, so our resources should be focused on their safety. However, exaggerated case counts are a cause of unnecessary hysteria and cost, especially for a virus that is rather benign to most people.

Auspicious COVID News for Thanksgiving

25 Wednesday Nov 2020

Posted by in Coronavirus, Herd Immunity

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There are some hints of good news on the spread of the coronavirus in a few of the “hot spots“ that developed this fall. This could be very good news, but it’s a bit too early to draw definitive conclusions.

The number of new cases plateaued in Europe a few weeks ago. Of course, Europe’s average latitude is higher than in most of the U.S., and the seasonal spread began there a little earlier. It makes sense that it might ebb there a bit sooner than in the U.S. as well.

In the U.S., cases shot up in the upper Midwest four to six weeks ago, depending on the state. Now, however, new cases have turned down in Iowa, Nebraska, North Dakota, South Dakota, and Wisconsin (first chart below), and they appear to have plateaued in Illinois, Kansas, Minnesota, and Missouri (second chart below, but ending a few days earlier). These are the hottest of the recent hot states.

These plateaus and declines were preceded by a decline in the growth rates of new cases around 10 days ago, shown below.

The timing of these patterns roughly correspond to the timing of the spread in other regions earlier in the year. It’s been suggested that after seroprevalence reaches levels of around 15% – 25% that individuals with new antibodies, together with individuals having an existing pre-immunity from other coronaviruses, is enough to bring the virus reproduction rate (R) to a value of one or less. That means a breach of the effective herd immunity threshold. It’s possible that many of these states are reaching those levels. Of course, this is very uncertain, but the patterns are certainly encouraging.

Deaths lag behind new infections, and it generally takes several weeks before actual deaths by date-of-death are known with any precision. However, we might expect deaths to turn down within two to three weeks.

Deaths by date-of-death are strongly associated with emergency room patients from three weeks prior who presented symptoms of COVID-like illness (CLI) or influenza-like illness ((ILI). The following chart shows CLI and ILI separately for the entire U.S. (ILI is the lowest dashed line), but the last few observations of both series, after a peak on November 15th, suggest a downturn in CLI + ILI. If the relationship holds up, actual U.S. deaths by date-of-death should peak around December 7th, though we won’t know precisely until early in the new year.

As a side note, it continues to look like the flu season will be exceptionally mild this year. See the next chart. That’s tremendous because it should take some of the normal seasonable pressure off health care resources.

So Happy Thanksgiving!

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Note: I saved all those charts over the last few days but lost track of the individual sources on Twitter. I’m too lazy and busy to go back and search through Twitter posts, so instead I’ll just list a few of my frequent sources here with links to recent posts, which are not necessarily apropos of the above: Don Wolt, Justin Hart, AlexL, The Ethical Skeptic, Aaron Ginn, and HOLD2.

On COVID, NPIs, and “Human” Data Points

24 Tuesday Nov 2020

Posted by in Lockdowns, Pandemic, Public Health

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The other day a friend told me “your data points always seem to miss the people points.” He imagines a failure on my part to appreciate the human cost of the coronavirus. Evidently, he feels that I treat data on cases, hospitalizations, and deaths as mere accounting issues, all while emphasizing the negative aspects of government interventions.

This fellow reads my posts very selectively, hampered in part by his own mood affiliation. Indeed, he seems to lack an appreciation for the nuance and zeitgeist of my body of blogging on the topic… my oeuvre! This despite his past comments on the very things he claims I haven’t mentioned. His responses usually rely on anecdotes relayed to him by nurses or doctors he knows. Anecdotes can be important, of course. But I know nurses and doctors too, and they are not of the same mind as his nurses and doctors. Anecdotes! We’re talking about the determination of optimal policy here, and you know what Dr. Fauci says about relying on anecdotes!

Incremental Costs and Benefits

My friend must first understand that my views are based on an economic argument, one emphasizing the benefits and costs of particular actions, including human costs. COVID is dangerous, but primarily to the elderly, and no approach to managing the virus is free. Here are two rather disparate choices:

  1. Mandated minimization of economic and social interactions throughout society over some time interval in the hope of reducing the spread of the virus;
  2. Laissez faire for the general population while minimizing dangers to high-risk individuals, subject to free choice for mentally competent, high-risk individuals.

To be clear, #2 entails all voluntary actions taken by individuals to mitigate risks. Therefore, #1 implies a set of incremental binding restrictions on behavior beyond those voluntary actions. However, I also include in #1 the behavioral effects of scare mongering by public officials, who regularly issue pronouncements having no empirical basis.

The first option above entails so-called non-pharmaceutical interventions (NPIs) by government. These are the elements of so-called lockdowns, such as quarantines and other restrictions on mobility, business and consumer activity, social activities, health care activities, school closures, and mask mandates. NPIs carry costs that are increasing in the severity of constraints they impose on society.

And before I proceed, remember this: tallying all fatal COVID cases is really irrelevant to the policy exercise. Nothing we do, or could have done, would save all those lives. We should compare what lives can be saved from COVID via lockdowns, if any, with the cost of those lockdowns in terms of human life and human misery, including economic costs.

Economic Losses

NPIs involve a loss of economic output that can never be recovered… it is gone forever, and a loss is likely to continue for some time to come. That sounds so very anodyne, despite the tremendous magnitude of the loss involved. But let’s stay with it for just a second. The loss of U.S. output in 2020 due to COVID has been estimated at $2.5 trillion. As Don Boudreaux and Tyler Cowen have noted, what we normally spend on safety and precautionary measures (willingness-to-pay), together with the probabilities of losses, implies that we value our lives at less than $4 million on average. Let’s say the COVID death toll reaches 300,000 by year-end (that’s incremental in this case— but it might be a bit high). That equates to a total loss of $1.2 trillion in life-value if we ignore distinctions in life-years lost. Now ask this: if our $2.5 trillion output loss could have saved every one of those 300,000 lives, would it have been worth it? Not even close, and the truth is that the sacrifice will not have saved even a small fraction of those lives. I grant, however, that the economic losses are partly attributable to voluntary decisions, but goaded to a great extent by the alarmist commentary of public health officials.

The full depth of losses is far worse than the dollars and cents comparison above might sound. Output losses are always matched by (and, in value, are exactly the same as) income losses. That involves lost jobs, lost hours, failed businesses, and destroyed careers. Ah, now we’re getting a bit more “human”, aren’t we! It’s nothing short of callous to discount these costs. Unfortunately, the burden falls disproportionately on low-income workers. Our elites can mostly stay home and do their jobs remotely, and earn handsome incomes. The working poor spend their time in line at food banks.

Yes, government checks can help those with a loss of income compete with elites for the available supply of goods, but of course that doesn’t replace the lost supply of goods! Government aid of this kind is a palliative measure; it doesn’t offset the real losses during a suspension of economic activity.

Decimated Public Health

The strain of the losses has been massive in the U.S. and nearly everywhere in the world. People are struggling financially, making do with less on the table, depleting their savings, and seeking forbearance on debts. The emotional strains are no less real. Anxiety is rampant, drug overdoses have increased, calls to suicide hotlines have exploded, and the permanence of the economic losses may add to suicide rates for some time to come. Dr. Robert Redfield of the CDC says more teenagers will commit suicide this year than will die from COVID (also see here). There’s also been a terrifying escalation in domestic abuse during the pandemic, including domestic homicide. The despair caused by economic losses is all too real and should be viewed as a multiplier on the total cost of severe NPIs.

More on human costs: a health care disaster has befallen locked-down populations, including avoidance of care on account of panic fomented by so-called public health experts, the media, and government. Some of the consequences are listed here. But to name just a few, we have huge numbers of delayed cancer diagnoses, which sharply decrease survival time; mass avoidance of emergency room visits, including undiagnosed heart attacks and strokes; and unacceptable delays in cardiac treatments. Moreover, lockdowns worldwide have severely damaged efforts to deal with scourges like HIV, tuberculosis, and malaria.

The CDC reports that excess mortality among 25-44 year-olds this year was up more than 26%, and the vast bulk of these were non-COVID deaths. A Lancet study indicates that a measles outbreak is likely in 2021 due to skipped vaccinations caused by lockdowns. The WHO estimates that 130,000,000 people are starving worldwide due to lockdowns. That is roughly the population of the U.S. east coast. Again, the callousness with which people willfully ignore these repercussions is stunning, selfish and inhumane, or just stupid.

Excess Deaths

Can we quantify all this? Yes we can, as a matter of fact. I’ve offered estimates in the past, and I already mentioned that excess deaths, COVID and non-COVID, are reported on the CDC’s web site. The Ethical Skeptic (TES) does a good job of summarizing these statistics, though the last full set of estimates was from October 31. Here is the graphic from the TES Twitter feed:

Note particularly the huge number of excess deaths attributable to SAAAD (Suicide, Addiction Abandonment, Abuse and Despair): over 50,000! The estimate of life-years lost due to non-COVID excess deaths is almost double that of COVID deaths because of the difference in the age distributions of those deaths.

Here are a few supporting charts on selected categories of excess deaths, though they are a week behind the counts from above. The first is all non-COVID, natural-cause excess deaths (the vertical gap between the two lines), followed by excess deaths from Alzheimer’s and dementia, other respiratory diseases, and malignant neoplasms (cancer):

The clearest visual gap in these charts is the excess Alzheimer’s and dementia deaths. Note the increase corresponding to the start of the pandemic, when these patients were suddenly shut off from loved ones and the company of other patients. I also believe some of these deaths were (and are) due to overwhelmed staff at care homes struck by COVID, but even discounting this category of excess deaths leaves us with a huge number of non-COVD deaths that could have been avoided without lockdowns. This represents a human cost over and above those tied to the economic losses discussed earlier.

Degraded Education and Health

Lockdowns have also been destructive to the education of children. The United Nations has estimated that 24 million children may drop out of school permanently as a result of lockdowns and school closures. This a burden that falls disproportionately on impoverished children. This article in the Journal of the American Medical Association Network notes the destructive impact of primary school closures on educational attainment. Its conclusions should make advocates of school closures reconsider their position, but it won’t:

“… missed instruction during 2020 could be associated with an estimated 5.53 million years of life lost. This loss in life expectancy was likely to be greater than would have been observed if leaving primary schools open had led to an expansion of the first wave of the pandemic.“

Lockdown Inefficacy

Lockdowns just don’t work. There was never any scientific evidence that they did. For one thing, they are difficult to enforce and compliance is not a given. Of course, Sweden offers a prime example that draconian lockdowns are unnecessary, and deaths remain low there. This Lancet study, published in July, found no association between lockdowns and country mortality, though early border closures were associated with lower COVID caseloads. A French research paper concludes that public decisions had no impact on COVID mortality across 188 countries, U.S. states, and Chinese states. A paper by a group of Irish physicians and scientists stated the following:

“Lockdown has not previously been employed as a strategy in pandemic management, in fact it was ruled out in 2019 WHO and Irish pandemic guidelines, and as expected, it has proven a poor mitigator of morbidity and mortality.”

One of the chief arguments in favor of lockdowns is the fear that asymptomatic individuals circulating in the community (and there are many) would spread the virus. However, there is no evidence that they do. In part, that’s because the window during which an individual with the virus is infectious is narrow, but tests may detect tiny fragments of the virus over a much longer span of time. And there is even some evidence that lockdown measures may increase the spread of the virus!

Lockdown decisions are invariably arbitrary in their impact as well. The crackdown on gyms is one noteworthy example, but gyms are safe. Restaurants don’t turn up in many contact traces either, and yet restaurants have been repeatedly implicated as danger zones. And think of the many small retailers shut down by government, while giant competitors like Wal-Mart continue to operate with little restriction. This is manifest corporatism!

Then there is the matter of mask mandates. As readers of this blog know, I think masks probably help reduce transmission from droplets issued by a carrier, that is, at close range. However, this recent Danish study in the Annals of Internal Medicine found that cloth masks are ineffective in protecting the wearer. They do not stop aerosols, which seem to be the primary source of transmission. They might reduce viral loads, at least if worn properly and either cleaned often or replaced. Those are big “ifs”.

To the extent that masks offer any protection, I’m happy to wear them within indoor public accommodations, at least for the time being. To the extent that people are “scared”, I’m happy to observe the courtesy of wearing a mask, but not outside in uncrowded conditions. To the extent that masks are required under private “house rules”, of course I comply. Public mask mandates outside of government buildings are over the line, however. The evidence that those mandates work is too tenuous and our liberties are too precious too allow that kind of coercion. And private facilities should be subject to private rules only.

QED

So my poor friend is quite correct that COVID is especially deadly to certain cohorts and challenging for the health care community. But he must come to grips with a few realities:

  • The virus won’t be defeated with NPIs; they don’t work!
  • NPIs inflict massive harm to human well-being.
  • Lockdowns or NPIs are little or no gain, high-pain propositions.

The rejection of NPI’s, or lockdowns, is based on compelling “human” data points. As Don Boudreaux says:

“The lockdowns and other restrictions on economic and social activities are astronomically costly – in a direct economic sense, in an emotional and spiritual sense, and in a ‘what-the-hell-do-these-arbitrary-diktats-portend-for-our-freedom?’ sense.” 

This doctor has a message for the those denizens of social media with an honest wish to dispense helpful public health advice:

“Americans have admitted that they will meet for Thanksgiving. Scolding and shaming them for wanting this is unlikely to slow the spread of SARS-CoV-2, though it may earn you likes and retweets. Starting with compassion, and thinking of ways they can meet, but as safely as possible, is the task of real public health. Now is the time to save public health from social media.”

And take some Vitamin D!

November Pandemic Perspective

18 Wednesday Nov 2020

Posted by in Coronavirus, Pandemic, Uncategorized

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I hope readers share my compulsion to see updated COVID numbers. It’s become a regular feature on this blog and will probably remain one until infections subside, vaccine or otherwise. Or maybe when people get used to the idea of living normally again in the presence of an endemic pathogen, as they have with many other pathogens and myriad risks of greater proportions, and as they should. That might require more court challenges, political changes, and plain old civil disobedience.

So here, then, is an update on the U.S. COVID numbers released over the past few days. The charts below are attributable to Don Wolt (@tlowdon on Twitter).

First, reported deaths began to creep up again in the latter half of October and have escalated in November. They’ve now reached the highs of the mid-summer wave in the south, but this time the outbreak is concentrated in the midwest and especially the upper midwest.

Reported deaths are the basis of claims that we are seeing 1,500 people dying every day, which is an obvious exaggeration. There have been recent days when reported deaths exceeded that level, but the weekly average of reported deaths is now between 1,100 and 1,200 a day.

It’s important to understand that deaths reported in a given week actually occurred earlier, sometimes eight or more weeks before the week in which they are reported. Most occur within three weeks of reporting, but sometimes the numbers added from four-plus weeks earlier are significant.

The following chart reproduces weekly reported deaths from above using blue bars, ending with the week of November 14th. Deaths by actual date-of-death (DOD) are shown by the orange bars. The most recent three-plus weeks always show less than complete counts of deaths by DOD. But going back to mid-October, actual weekly deaths were running below reported deaths. If the pattern were to follow the upswings of the first two waves of infections, then actual weekly deaths would exceed reported deaths by perhaps the end of October. However, it’s doubtful that will occur, in part because we’ve made substantial progress since the spring and summer in treating the disease.

To reinforce the last point, it’s helpful to view deaths relative to COVID case counts. Deaths by DOD are plotted below by the orange line using the scale on the right-hand vertical axis. New positive tests are represented by the solid blue line, using the left-hand axis, along with COVID hospitalizations. There is no question that the relationship between cases, hospitalizations, and deaths has weakened over time. My suspicions were aroused somewhat by the noticeable compression of the right axis for deaths relative to the two charts above, but on reviewing the actual patterns (peak relative to troughs) in those charts, I’m satisfied that the relationships have indeed “decoupled”, as Wolt puts it.

Cases are going through the roof, but there is strong evidence that a large share of these cases are false positives. COVID hospitalizations are up as well, but their apparent co-movement with new cases appears to be dampening with successive waves of the virus. That’s at least partly a consequence of the low number of tests early in the pandemic.

So where is this going? The next chart again shows COVID deaths by DOD using orange bars. Wolt has concluded, and I have reported here, that the single-best short-term predictor of COVID deaths by DOD is the percentage of emergency room visits at which patients presented symptoms of either COVID-like illness (CLI) or influenza-like illness (ILI). The sum of these percentages, CLI + ILI, is shown below by the dark blue line, but the values are shifted forward by three weeks to better align with deaths. This suggests that actual COVID deaths by DOD will be somewhere around 7,000 a week by the end of November, or about 1,000 a day. Beyond that time, the path will depend on a number of factors, including the weather, prevalence and immunity levels, and changes in mobility.

I am highly skeptical that lockdowns have any independent effect in knocking down the virus, though interventionists will try to take credit if the wave happens to subside soon for any other reason. They won’t take credit for the grim lockdown deaths reaped by their policies.

Despite the bleak prospect of 1,000 or more COVID-attributed deaths a day by the end of November, the way in which these deaths are counted is suspect. Early in the pandemic, the CDC significantly altered guidelines for the completion of death certificates for COVID such that deaths are often improperly attributed to the virus. Some COVID deaths stem from false-positive PCR tests, and again, almost since the beginning of the pandemic, hospitals were given a financial incentive to classify inpatients as COVID-infected.

It’s also important to remember that while any true COVID fatality is premature, they are generally not even close to the prematurity of lockdown deaths. That’s a simple consequence of the age profile of COVID deaths, which indicate relatively few life-years lost, and the preponderance of co-morbidities among COVID fatalities.

Again, COVId deaths are bad enough, but we are seeing an unacceptable and ongoing level of lockdown deaths. This is now to the point where they may account for almost all of the continuing excess deaths, even with the fall COVID wave. It’s probable that public health would be better served with reduced emphasis on COVID-mitigation for the general population and more intense focus on protecting the vulnerable, including the distribution of vaccines.

COVID and Hospital Capacity

15 Sunday Nov 2020

Posted by in Health Care, Pandemic

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The fall wave of the coronavirus has brought with it an increase in COVID hospitalizations. It’s a serious situation for the infected and for those who care for them. But while hospital utilization is rising and is reaching tight conditions in some areas, claims that it is already a widespread national problem are without merit.

National and State Hospital Utilization

The table below shows national and state statistics comparing beds used during November 1-9 to the three-year average from 2017 – 19, from Justin Hart. There are some real flaws in the comparison: one is that full-year averages are not readily comparable to particular times of the year, with or without COVID. Nevertheless, the comparison does serve to show that current overall bed usage is not “crazy high” in most states, as it were. The increase in utilization shown in the table is highest in IA, MT, NV, PA, VT, and WI, and there are a few other states with sizable increases.

Another limitation is that the utilization rates in the far right column do not appear to be calculated on the basis of “staffed” beds, but total beds. The U.S. bed utilization rate would be 74% in terms of staffed beds.

Average historical hospital occupancy rates from Statista look like this:

Again, these don’t seem to be calculated on the basis of staffed beds, but current occupancies are probably higher now based on either staffed beds or total beds.

As of November 11th, a table available at HealthData.gov indicates that staffed bed utilization in the U.S. is at nearly 74%, with ICU utilization also at 74%. As the table above shows, states vary tremendously in their hospital bed utilization, a point to which I’ll return below.

COVID patients were using just over 9% of of all staffed beds and just over 19% of ICU beds as of November 11th. One caveat on the reported COVID shares you’ll see for dates going forward: the CDC changed its guidelines on counting COVID hospitalizations as of November 12th. It is now a COVID patient’s entire hospital stay, rather than only when a patient is in isolation with COVID. That might be a better metric if we can trust the accuracy of COVID tests (and I don’t), but either way, the change will cause a jump in the COVID share of occupied beds.

Interpreting Hospital Utilization

Many issues impinge on the interpretation of hospital utilization rates:

First, cases and utilization rates are increasing, which is worrisome, but the question is whether they have already reached crisis levels or will very soon. The data doesn’t suggest that is the case in the aggregate, but there certainly there are hospitals bumping up against capacity constraints in some parts of the country.

Second, occupancies are increasing due to COVID patients as well as patients suffering from lockdown-related problems such as self-harm, psychiatric problems, drug abuse, and conditions worsened by earlier deferrals of care. We can expect more of that in coming weeks.

Third, lockdowns create other hospital capacity issues related to staffing. Health care workers with school-aged children face the daunting task of caring for their kids and maintaining hours on jobs for which they are critically needed.

Fourth, there are capacity issues related to PPE and medical equipment that are not addressed by the statistics above. Different uses must compete for these resources within any hospital, so the share of COVID admissions has a strong bearing on how the care of other kinds of patients must be managed.

Fifth, some of the alarm is purely case-driven: all admissions are tested for COVID, and non-COVID admissions often become COVID admissions after false-positive PCR tests, or simply due to the presence of mild COVID with a more serious condition or injury. However, severe COVID cases have an outsized impact on utilization of staff because their care is relatively labor-intensive.

Sixth, there are reports that the average length of COVID patient stays has decreased markedly since the spring (it is hard to find nationwide figures), but it is also increasingly difficult to find facilities for post-acute care required for some patients on discharge. Nevertheless, if improved treatment reduces average length of stay, it helps hospitals deal with the surge.

Finally, thus far, the influenza season has been remarkably light, as the following chart from the CDC shows. It is still early in the season, but the near-complete absence of flu patients is helping hospitals manage their resources.

St. Louis Hotspot

The St. Louis metro area has been proclaimed a COVID “hotspot” by the local media and government officials, which certainly doesn’t make St. Louis unique in terms of conditions or alarmism. I’m curious about the data there, however, since it’s my hometown. Here is hospital occupancy on the Missouri side of the St. Louis region:

It seems this chart is based on total beds, not staffed beds, However, one of the interesting aspects of this chart is the variation in capacity over time, with several significant jumps in the series. This has to do with data coverage and some variation in daily reporting. Almost all of these data dashboards are relatively new, so their coverage has been increasing, but generally in fits and starts. Reporting is spotty on a day-to-day basis, so there are jagged patterns. And of course, capacity can vary from day-to-day and week-to-week — there is some flexibility in the number of beds that can be made available.

The share of St. Louis area beds in use was 61% as of November 11th (preliminary). COVID patients accounted for 12% of hospital beds. ICU utilization in the St. Louis region was a preliminary 67% as of Nov. 11, with COVID patients using 29% of ICU capacity (which is quite high). Again, these figures probably aren’t calculated on the basis of “staffed” beds, so actual hospital-bed and ICU-bed utilization rates could be several percentage points higher. More importantly, it does not appear that utilization in the St. Louis area has trended up over the past month.

At the moment, the St. Louis region appears to have more spare hospital capacity than the nation, but COVID patients are using a larger share of all beds and ICU beds in St. Louis than nationwide. So this is a mixed bag. And again, capacity is not spread evenly across hospitals, and it’s clear that hospitals are under pressure to manage capacity more actively. In fact, hospitals only have so many options as the share of COVID admissions increases: divert or discharge COVID and non-COVID patients, defer elective procedures, discharge COVID and non-COVID patients earlier, allow beds to be more thinly staffed and/or add temporary beds wherever possible.

Closing Thoughts

Anyone with severe symptoms of COVID-19 probably should be hospitalized. The beds must be available, or else at-home care will become more commonplace, as it was for non-COVID maladies earlier in the pandemic. A continued escalation in severe COVID cases would require more drastic steps to make hospital resources available. That said, we do not yet have a widespread capacity crisis, although that’s small consolation to areas now under stress. And a few of the states with the highest utilization rates now have been rather stable in terms of hospitalizations — they already had high average utilization rates, which is potentially dangerous.

COVID is a seasonal disease, and it’s no surprise that it’s raging now in areas that did not experience large outbreaks in the spring and summer. And those areas that had earlier outbreaks have not had a serious surge this fall, at least not yet. My expectation and hope is that the midwestern and northern states now seeing high case counts will soon reach a level of prevalence at which new infections will begin to subside. And we’re likely to see a far lower infection fatality rate than experienced in the Northeast last spring.