Tag Archives: Co-Morbidities

Allocating Vaccine Supplies: Lives or “Justice”?

29 Tuesday Dec 2020

Posted by in Pandemic, Public Health, Uncategorized, Vaccinations

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There are currently two vaccines in limited distribution across the U.S. from Pfizer and Moderna, but the number and variety of different vaccines will grow as we move through the winter. For now, the vaccine is in short supply, but that’s even more a matter of administering doses in a timely way as it is the quantity on hand. There are competing theories about how best to allocate the available doses, which is the subject of this post. I won’t debate the merits of refusing to take a vaccine except to say that I support anyone’s right to refuse it without coercion by public authorities. I also note that certain forms of discrimination on that basis are not necessarily unreasonable.

The vaccines in play all seem to be highly effective (> 90%, which is incredible by existing standards). There have been a few reports of side effects — certainly not in large numbers — but it remains to be seen whether the vaccines will have any long-term side effects. I’m optimistic, but I won’t dismiss the possibility.

Despite competing doctrines about how the available supplies of vaccine should be allocated, there is widespread acceptance that health care workers should go first. I have some reservations about this because, like Emma Woodhouse, I believe staff and residents at long-term care facilities should have at least equal priority. Yet they do not in the City of Chicago and probably in other areas. I have to wonder whether unionized health care workers there are the beneficiaries of political favoritism.

Beyond that question, we have the following competing priorities: 1) the vulnerable in care homes and other elderly individuals (75+, while younger individuals with co-morbidities come later); 2) “essential” workers of all ages (from police to grocery store clerks — decidedly arbitrary); and 3) basically the same as #2 with priority given to groups who have suffered historical inequities.

#1 is clearly the way to save the most lives, at least in the short-run. Over 40% of the deaths in the U.S. have been in elder-care settings, and COVID infection fatality rates mount exponentially with age:

To derive the implications of #1 and #2, it’s more convenient to look at the share of deaths within each age cohort, since it incorporates the differences in infection rates and fatality rates across age groups (the number of “other” deaths is much larger than COVID deaths, of course, despite similar death shares):

The 75+ age group has accounted for about 58% of all COVID deaths in the U.S., and ages 25 – 64 accounted for about 20% (an approximate age range for essential workers). This implies that nearly three times as many lives can be saved by prioritizing the elderly, at least if deaths among so-called essential workers mimic deaths in the 25 – 64 age cohorts. However, the gap would be smaller and perhaps reversed in terms of life-years saved.

Furthermore, this is a short-run calculation. Over a longer time frame, if essential workers are responsible for more transmission across all ages than the elderly, then it might throw the advantage to prioritizing essential workers over the elderly, but it would take a number of transmission cycles for the differential to play out. Yes, essential workers are more likely to be “super-spreaders” than work-at-home, corporate employees, or even the unemployed, but identifying true super-spreaders would require considerable luck. Moreover, care homes generally house a substantial number of elderly individuals and staff in a confined environment, where spread is likely to be rampant. So the transmission argument for #2 over #1 is questionable.

The over-riding problem is that of available supply. Suppose enough vaccine is available for all elderly individuals within a particular time frame. That’s about 6.6% of the total U.S. population. The same supply would cover only about 13% of the younger age group identified above. Essential workers are a subset of that group, but the same supply would fall far short of vaccinating all of them; lives saved under #2 would then fall far short of the lives saved under #1. Quantities of the vaccine are likely to increase over the course of a few months, but limited supplies at the outset force us to focus the allocation decision on the short-term, making #1 the clear winner.

Now let’s talk about #3, minority populations, historical inequities, and the logic of allocating vaccine on that basis. Minority populations have suffered disproportionately from COVID, so this is really a matter of objective risk, not historical inequities… unless the idea is to treat vaccine allocations as a form of reparation. Don’t laugh — that might not be far from the intent, and it won’t count as a credit toward the next demand for “justice”.

For the sake of argument, let’s assume that minorities have 3x the fatality rate of whites from COVID (a little high). Roughly 40% of the U.S. population is non-white or Hispanic. That’s more than six times the size of the full 75+ population. If all of the available doses were delivered to essential workers in that group, it would cover less than half of them and save perhaps 30% of minority COVID deaths over a few months. In contrast, minorities might account for up to two-thirds of the deaths among the elderly. Therefore, vaccinating all of the elderly would save 58% of elderly COVID deaths and about 39% of minority deaths overall!

The COVID mortality risk to the average white individual in the elderly population is far greater than that faced by the average minority individual in the working age population. Therefore, no part of #3 is sensible from a purely mathematical perspective. Race/ethnicity overlaps significantly with various co-morbidities and the number of co-morbidities with which individuals are afflicted. Further analysis might reveal whether there is more to be gained by prioritizing by co-morbidities rather than race/ethnicity.

Megan McArdle has an interesting column on the CDC’s vaccination guidelines issued in November, which emphasized equity, like #3 above. But the CDC walked back that decision in December. The initial November decision was merely the latest of the the agency’s fumbles on COVID policy. In her column, McArdle notes that the public has understood that the priority was to save lives since the very start of the pandemic. Ideally, if objective measures show that identifiable characteristics are associated with greater vulnerability, then those should be considered in prioritizing individuals who desire vaccinations. This includes age, co-morbidities, race/ethnicity, and elements of occupational risk. But lesser associations with risk should not take precedence over greater associations with risk unless an advantage can be demonstrated in terms of lives saved, historical inequities or otherwise.

The priorities for the early rounds of vaccinations may differ by state or jurisdiction, but they are all heavily influenced by the CDC’s guidelines. Some states pay lip service to equity considerations (if they simply said race/ethnicity, they’d be forced to operationalize it), while others might actually prioritize doses by race/ethnicity to some degree. Once the initial phase of vaccinations is complete, there are likely to be more granular prioritizations based on different co-morbidities, for example, as well as race/ethnicity. Thankfully, the most severe risk gradient, advanced age, will have been addressed by then.

One last point: the Pfizer and Moderna vaccines both require two doses. Alex Tabarrok points out that first doses appear to be highly effective on their own. In his opinion, while supplies are short, the second dose should be delayed until all groups at substantially elevated risk can be vaccinated…. doubling the supply of initial doses! The idea has merit, but it is unlikely to receive much consideration in the U.S. except to the extent that supply chain problems make it unavoidable, and they might.

The Vagaries of Excess Deaths

02 Saturday May 2020

Posted by in Liberty, Pandemic, Tyranny

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The New York Times ran a piece this week suggesting that excess mortality from Covid-19 in the U.S. is, or will be, quite high. The analysis was based on seven “hard hit” states, including three of the top four states in Covid death rate and five of the top ten. Two states in the analysis, New York and New Jersey, together account for over half of all U.S. active cases. This was thinly-veiled cherry picking by the Times, as Jacob Sullum notes in his discussion of what excess mortality does and doesn’t mean. Local and regional impacts of the virus have varied widely, depending on population density, international travel connections, cultural practices, the quality of medical care, and private and public reaction to news of the virus. To suggest that the experience in the rest of the country is likely to bear any similarity to these seven states is complete nonsense. Make no mistake: there have been excess deaths in the U.S. over the past few weeks of available data, but again, not of the magnitude the Times seems to intimate will be coming.

Beyond all that, the Times asserts that the CDC’s all-cause death count as of April 11 is a significant undercount, though the vast majority of deaths are counted within a three week time frame. In fact, CDC data at this link show that U.S. all-cause mortality was at a multi-year low during the first week of April. The author admits, however, that the most recent data is incomplete. The count will rise as reporting catches up, but even an allowance for the likely additions to come would leave the count for the U.S. well below the kinds of levels suggested by the Times‘s fear-mongering article, based as it was on the seven cherry-picked states.

The author of this Twitter thread, John Burn-Murdoch, seems to engage in the same practice with respect to Europe. He shows charts with excess deaths in 12 countries, almost all of which show significant, recent bumps in excess deaths (the sole exception being Denmark). Inexplicably, he excludes Germany and a number of other countries with low excess deaths or even “valleys” of negative excess deaths. His most recent update is a bit more inclusive, however. (It was the source of the chart at the top of this post.) Euromomo is a site that tracks excess mortality in 24 European countries or major regions (non-overlapping), and by my count, 13 of have no or very little excess mortality. And by the way, even this fails to account for a number of other Eastern European nations having low Covid deaths.

Excess mortality is a tricky metric: it cannot be measured with certainty, and almost any measure has conceptual shortcomings. In the case of Covid-19, excess mortality seeks to measure the number of deaths attributable to the virus net of deaths that would have occurred anyway in the absence of the virus. For example, abstracting from some of the details, suppose there are 360 deaths per hundred-thousand of population during the average month of a pandemic. If the “normal” mortality rate is 60 per hundred-thousand, then excess mortality is 300 per month. It can also be expressed as a percentage of the population (0.3% in the example). But that’s just one way to measure it.

In the spirit of Sullum’s article, it’s important to ask what we’re trying to learn from statistics on excess mortality. It’s easy to draw general conclusions if the number of Covid-19 deaths is far in excess of the normal death rate, but that depends on the quality of the data, and any conclusion is subject to limits on its applicability. Covid deaths are not that high in many places. By the same token, if the number of Covid deaths (defined narrowly) is below the normal death rate (measured by an average of prior years), it really conveys little information about whether excess mortality is positive of negative: that depends on the nature of the question. For each of the following I offer admittedly preliminary answers:

  • Are people dying from Covid-19? Of course, virtually everywhere. There is no “normal” death rate here. And while this is the most direct question, it might not be the “best” question.
  • Is Covid-19 causing an increase in respiratory deaths? Yes, in many places, but perhaps not everywhere. Here and below, the answer might depend on the time frame as well.
  • Is Covid-19 increasing deaths from infectious diseases (biological and viral)? Yes, but perhaps not everywhere.
  • Is Covid-19 increasing total deaths from natural causes? Yes, but not everywhere.
  • Is all-cause mortality increasing due to Covid-19? In some places, not others. Accurate global and national numbers are still a long way off.

All-cause mortality is the most “rough and ready” comparison we have, but it includes deaths that have no direct relationship to the disease. For example, traffic fatalities might be down significantly due to social distancing or regulation during a pandemic. Thus, if our purpose is purely epidemiological, traffic fatalities might bias excess mortality downward. On the other hand, delayed medical treatments or personal malaise during a pandemic might lead to higher deaths, creating an upward bias in excess deaths via comparisons based on all-cause mortality.

Do narrow comparisons give a more accurate picture? If we focus only on respiratory deaths then we exclude deaths from other causes and co-morbidities that would have occurred in the absence of the virus. That may create a bias in excess mortality. So narrow comparisons have their drawbacks, depending on our purpose.

That also goes for the length of time over which excess mortality is measured. It can make a big difference. Again, much has been made of the fact that so many victims of Covid-19 have been elderly or already ailing severely before the pandemic. There is no question that some of these deaths would have occurred anyway, which goes to the very point of calculating excess mortality. If the pandemic accelerates death by a matter of weeks or months for a certain percentage of victims, it is reasonable to measure excess mortality over a lengthier period of time, despite the (perhaps) highly valuable time lost by those victims (that being dependent on the decedent’s likely quality of life during the interval).

Conversely, too narrow a window in time can lead to biases that might run in either direction. Yet a cottage industry is busy calculating excess mortality even as we speak with the pandemic still underway. There are many fatalities to come that are excluded by premature calculations of excess mortality. On the other hand, if the peak in deaths is behind us, a narrow window and premature calculation may sharply exaggerate excess mortality.

Narrow measures of excess mortality are affected by the accuracy of cause-of-death statistics. There are always inaccuracies in this data because so many deaths involve multiple co-morbidities, so there is often an arbitrary element in these decisions. For Covid-19, cause-of-death attribution has been extremely problematic. Some cases are easy: those testing positive for the virus, or even its presence immediately after death, and having no other respiratory infections, can fairly be counted as Covid-19 deaths. But apparently just over half of Covid-19 deaths counted by the CDC are “Covid-Only” deaths. A significant share of deaths involve both Covid and the flu, pneumonia, or all three. There are also “probable” Covid-19 deaths now counted without testing. In fact, hospitals and nursing homes are being encouraged to code deaths that way, and there are often strong financial incentives to do so. Many deaths at home, sans autopsy, are now routinely classified as Covid-19 deaths. While I have no doubt there are many Covid deaths of untested individuals both inside or outside of hospitals, there is no question this practice will overcount Covid deaths. Whether you believe that or not, doubts about cause-of-death accuracy is another reason why narrow comparisons can be problematic.

More trustworthy estimates of the coronavirus’ excess mortality will be possible with the passage of time. It’s natural, in the heat of the pandemic, to ask about excess mortality, but such early estimates are subject to tremendous uncertainty. Unfortunately, those calculations are being leveraged and often mis-applied for political purposes. Don’t trust anyone who would use these statistics as a cudgel to deny your Constitutional rights, or otherwise to shame or threaten you.

New York’s Covid experience is not applicable to the country as a whole. Urban mortality statistics are not applicable to areas with lower population densities. Excess mortality for the elderly cannot be used to make broad generalizations about excess mortality for other age groups. And excess mortality at the peak of a pandemic cannot be used to make generalizations about the full course of the pandemic. In the end, I expect Covid-19 excess mortality to be positive, whether calculated by all-cause mortality or more narrow measures. However, it will not be uniform in its impact. Nor will it be of the magnitude we were warned to expect by the early epidemiological models.

CDC Sows Covid Case-Fatality Confusion

15 Wednesday Apr 2020

Posted by in Data Integrity, Pandemic

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The Centers for Disease Control has formally decided to inflate statistics on coronavirus deaths by adding so-called “probable” cases to the toll. This news follows the announcement yesterday that New York decided to add, in one day, about 4,000 deaths from over the past month to its now “probable” Covid-19 death toll. So much for clean accounting! We have a confirmed death toll up to April 14th. We have a probable death toll after. The error in timing alone introduced by this abrupt adjustment impairs efforts to track patterns of change. Case fatality rates are rendered meaningless. Data integrity, which was already weak, has been thrown out the window by our public heath authorities.

It’s no longer necessary for a deceased patient to have tested positive for Covid-19:

“A probable case or death is defined as one that meets clinical criteria such as symptoms and evidence of the disease with no lab test confirming Covid-19. It can also be classified as a probable case if there are death or other vital records listing coronavirus as a cause. A third way to classify it is through presumptive laboratory evidence and either clinical criteria or evidence of the disease.”

Consider the following:

  • to date, more than 80% of patients presenting symptoms sufficient to meet testing guidelines have tested negative for Covid-19;
  • the most severe cases of Covid-19 and other respiratory diseases are coincident with significant co-morbidities;
  • “probable” cases appear to be concentrated among the elderly and infirm, whose regular mortality rate is high.

Deaths involving mere symptoms, or mere symptoms and co-morbidities, and even deaths of undetermined cause, are now more likely to be over-counted as Covid-19 deaths. This is certain to distort, and I believe overcount, Covid-19 deaths. Of course, this was already happening in some states, as I mentioned last week in “Coronavirus Controversies“.

One of the charts I’ve presented in my Continue%20reading Coronavirus “Framing” posts tracks Covid-19 deaths. The change in these cause-of-death guidelines will make continued tracking into something of a farce. I’d be tempted to deduct the one-day distortion caused by the New York decision, but then the count will still be distorted going forward by the broader definition of Covid-19 death.

The only possible rationale for these decisions by New York and the CDC is that testing is still subject to severe rationing. I have my doubts, as the number of daily tests has stabilized. On the other hand, I have heard anecdotes about hospitals with large numbers of respiratory patients who have not been tested! And they are intermingling all of these patients?? I’m not sure I can reconcile these reports. Surely the patients meet the guidelines for testing. Perhaps the CDC’s decision is associated with an effort to spread testing capacity by allowing only new patients to be tested, counting those already hospitalized as presumptively Covid-infected. And if they aren’t already, they will be! A decision to count deaths within that group as “probable” Covid deaths  would fit conveniently into that approach, but that would be wildly misguided and perverse.

I’m obviously cynical about the motives here. I don’t trust government accounting when it bears on the credit or blame for crisis management. Who stands to gain from a higher Covid death toll? The CDC? State health authorities? “Hot spots” vying for federal resources?

A consistent approach to attributing cause of death would have been more useful for gauging the direction of the pandemic, but as I’ve said, there will always be uncertainty about the true Covid-19 death toll. Ultimately, the best estimates will have to rely on calculations of “excess deaths” in 2020 compared to a “normal” level from a larger set of causes. In fact, even that comparison will be suspect because the flu season leading up to the Covid outbreak was harsh. Was it really the flu later in the season?

Coronavirus Controversies

11 Saturday Apr 2020

Posted by in Health Care, Leftism, Pandemic

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The coronavirus and the tragedy it has wrought has prompted so many provocative discussions that it’s hard to pick just one of those topics for scarce blogging time. So I’ll try to cover two here: first, the question of whether coronavirus deaths are being miscounted; second, the politically-motivated controversy over the use of hydroxychloraquin to treat severe cases of Covid-19.

Counting Deaths

I’ve been suspicious that Covid deaths are being over-counted, but I’m no longer as sure of that. Of course, there are reasons to doubt the accuracy of the death counts. For example, there is a strong possibility that some Covid deaths are simply not being counted due to lack of diagnoses. But there are widespread suspicions that too many deaths with positive diagnoses are being counted as Covid deaths when decedents have severe co-morbidities. Members of that cohort die on an ongoing basis, but now many or all of those deaths are being attributed to Covid-19. A more perverse counting problem might occur when public health authorities instruct physicians to attribute various respiratory deaths to Covid even without a positive diagnosis! That is happening in some parts of the country.

To avoid any bias in the count, I’ve advocated tracking mortality from all co-morbidities and comparing the total to historical or “normal” levels to calculate “excess deaths”. One could also look at all-cause mortality and do the same, though I don’t think that would be quite on point. For example, traffic deaths are certainly way down, which would distort the excess deaths calculation.

Despite the vagaries in counting, there is no question that the coronavirus has been especially deadly in its brief assault on humans. New York has experienced a sharp increase in deaths, as the chart below illustrates (the chart is a corrected version of what appeared in the Reason article at the prior link). The spike is way out of line with normal seasonal patterns, and it obviously corresponds closely with deaths attributable to Covid-19. It is expected to be short-lived, but it might taper over the course of several weeks or months, Once it does, I suspect that the cumulative deaths under all those other curves in the chart will exceed Covid deaths substantially. Also note that the yellow line for the flu just stops when Covid deaths begin, suggesting that the red line probably incorporates at least some “normal” flu deaths.

Once the virus abates, we’ll be able to tell with a bit more certainty just how deadly the pandemic has been. It will be revealed through analyses of excess deaths. For now, we have the statistics we have, and they should be interpreted cautiously.

Hydrocholraquin

A more boneheaded debate centers on the use of the anti-malarial drug hydroxychloraquin (HCQ) to treat coronavirus patients. There have been many successes, particularly in combination with a Z-Pak, or zinc. Guidelines issued by the American Society of  Thoracic Surgeons last week call for HCQ’s use in advanced cases of coronavirus infection. These and other therapies are being tested formally, but many are prescribed outside any formal testing framework. Remdesivir has been prominent among these. Plasma therapy has been as well, and several other possible treatments are under study.

With respect to HCQ, it’s almost as if the Left, much of the media, and a subset of overly “prescriptive” medical experts were goaded into an irrational position via pure Trump Derangement. Just Google or Bing “Hydroxychloraquine Coronavirus” for a bizarre list of alarmist articles about Trump’s mention of HCQ. To take just two of the claims, the idea that Trump stands to earn substantial personal profits from HCQ because he holds a few equity shares in a manufacturer of generic drugs is patently absurd. And claims that shortages for arthritis, lupus, and malaria patients are imminent are unconvincing, given the massive stockpiles now accumulated and the efforts to ramp-up production.

So much lefty hair is on fire over a potential therapy that is both promising and safe that the media message lacks credulity. But more ominously, the Democrat governors of Michigan and Nevada were so petulant that they banned HCQ’s use in their states, though at least Nevada’a governor rescinded his order. It’s almost as if they don’t want it to work, and don’t want to give it a chance to work. Or do I go too far? No, I don’t think so.

Victoria Taft has a good summary of the media backlash against President Trump’s hopeful statements about HCQ. Not only was the FDA’s authority over the use of HCQ misrepresented, there was also a good bit of smearing of various researchers who’d found preliminary evidence of HCQ’s effectiveness. Let’s be honest: the quality of medical research is often inflated by the research establishment. And the media eat up any study with findings that are noteworthy in any way. Over the years, a great deal of medical research has been based on small samples from which statistical hypothesis tests are shaky at best. That’s one reason for the legendary replication problem in medical research. In the case of HCQ, there has been widespread misuse of the term “anecdotal” in the media, prompted by experts like Dr. Anthony Fauci, who should know better. The term was used to describe clinical tests on moderately large groups of patients, at least one of which was a randomized control trial.

Every day we hear stories from individual patients that they were saved by HCQ. These are properly called anecdotal accounts. But we also hear from various physicians around the country and world who claim to be astonished at HCQ’s therapeutic efficacy on groups of patients. This link gives another strong indication of how physicians feel about HCQ at this point. These are not from RCTs, but they constitute clinical evidence, not mere “anecdotes”.

By virtue of state and federal right-to-try laws, terminally ill patients can choose to take medications that are unapproved by regulators. Beyond that, FDA approval of HCQ specifically for treating coronavirus was unnecessary because the drug was already legal to prescribe to cover patients as an “off-label” use. That’s true of all drugs approved by the FDA: they can be prescribed legally for off-label uses. When regulators like Dr. Fauci, and even practicing physicians like Dr. Jeffrey Singer (linked below) claim that the FDA hasn’t approved HCQ specifically for treating Covid, it is a technicality: the FDA can certainly “approve” it for that specific use, but it’s already legal to prescribe!

While it won’t end the silly argument, which is obviously grounded in other motives, Dr. Singer brings us to the only reasonable position: treatment of Covid with HCQ is between the patient and their doctor.