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I have been thinking and arguing about COVID terminology since the pandemic started. I am especially interested in how public health professionals (both officials and outside experts) talk about COVID. There are terms that public health professionals often use imprecisely. There are other terms they often use incorrectly. And there are still other terms they often use in a way that’s technically accurate but almost sure to be misunderstood by much of the public – and then when they’re predictably misunderstood, they claim they were clear and the public is “confused.”
Politicians and journalists fall into these COVID language traps more too. But for obvious reasons I think public health professionals should be held to a higher standard. And to a great extent they set the precedent. If they start to do a better job of communicating clearly about COVID, maybe there’s hope that others will improve as well.
Here in no particular order are some of my musings about COVID language traps. This isn’t really an article. It’s a list. Read the entries that interest you.
Misinformation 
The term “misinformation” used to mean and should mean verifiably false factual claims – claims that virtually all well-informed people consider to have been disproved.
Sometimes it still means that. But to deeply committed people in many fields, “misinformation” now often means “the other side’s” unproven hypotheses and speculations, but not “our side’s” unproven hypotheses and speculations. I’d rather we didn’t dub as “misinformation” overconfident, unproven claims that might or might not turn out true – though I concede that knowingly pretending an unproven claim is established truth is a kind of misinformation. But what’s especially upsetting is when we apply a double standard to these claims depending on which side is making them.
I totally get that that some unproven hypotheses are likelier than others. And unproven hypotheses differ in how much harm will result if people trust that they’re true and then they turn out mistaken. Even so, your unproven hypotheses are no more or less “misinformation” than mine.
Worse still, in political contexts “misinformation” has all too often come to mean merely statements – even verifiably true factual claims – that seem likely to lead people to conclusions or policies the speaker considers undesirable.
And sadly but unavoidably, COVID debates have become political. Any COVID-related statement that makes people less likely to get vaccinated or less likely to wear masks, for example, will be deemed “misinformation” by many public health professionals – and therefore sometimes by social media censors. That’s true not just of a verifiably false factual claim, but also of a debatable factual claim where the evidence is mixed; or of an opinion or recommendation that’s not a factual claim at all; or even of a verifiably true factual claim.
The criterion for “misinformation” about COVID is no longer simply whether what you said is actually, verifiably false. Quite often the criterion now is whether what you said (regardless of its truth or falsity) is likely to lead your audience astray, away from what the public health mainstream considers the right conclusion or the right course of action. Sometimes the public health mainstream gets things wrong, of course – but that’s not my point. My point is that even when the public health mainstream has pretty solid evidentiary reasons for urging a specific behavior, that doesn’t turn every statement likely to discourage the behavior into “misinformation.”
Consider a tweet that COVID is usually mild so we should just rely on natural immunity and go about our business without boosters or other precautions. Speaking as a 77-year-old, I think this is unwise advice. But its only factual claim, “usually mild,” is verifiably true. Calling this tweet “misinformation” misuses the term.
Here’s one of my favorite examples of a factually accurate COVID-related claim widely considered misinformation by public health professionals: COVID vaccines were granted Emergency Use Authorizations despite zero proof that they reduced the COVID death rate. This is flat-out true. Demonstrating a reduction in mortality would have required much bigger and longer-lasting phase three trials; enough people in the placebo groups would have had to die to yield a statistically significant benefit of vaccination. That would have delayed the vaccine rollouts unconscionably. So the Food and Drug Administration sensibly settled for proof that the vaccines reduced the incidence of symptomatic illness.
And an example of an arguable opinion that public health professionals often consider misinformation: the claim that requiring masks on schoolchildren did more harm than good.
Conversely, even false factual claims are generally not deemed “misinformation” by public health professionals if they seem likely to lead people to conclusions that public health considers desirable.
I could offer a long litany of what I consider officially sanctioned COVID misinformation that public health professionals have declined to call misinformation at various times – claims about the efficacy of cloth masks; the benefits of lockdowns and school closures; whether transmission is nearly always via droplets or often via aerosols; the risk of outdoor transmission; the likelihood of breakthrough infections of fully vaccinated and boosted people; and on and on. Some of these false factual claims seemed likely to turn out true when they were advanced, and are “misinformation” only in hindsight – but even in hindsight public health professionals rarely use that term about their own mistakes. And some of these claims were advanced (almost always for prosocial reasons) by public health professionals who knew they were unproven or even had grounds to suspect they were false.
Even intentional falsehoods aren’t considered misinformation when perpetrated by the good guys.
I rather like the convention of calling intentional misinformation “disinformation.” I’ll readily grant that assessing intentionality is complicated. We’re all vulnerable to making claims that if cross-examined we would reluctantly acknowledge we “sort-of knew” were false – but in the heat of the argument they felt true-ish enough when we claimed them. So distinguishing disinformation from mere misinformation is challenging. But the far bigger problem is the way too many public health professionals misuse the label “misinformation” in the first place – applying it all too liberally to positions they don’t like and all too rarely to their own positions.
Both sides misuse “misinformation,” of course. But it’s mostly public health’s side that insists it’s “following the science.”
Public health’s double standard predates COVID by many years. For example, doctors routinely tell parents that every dose on the CDC’s recommended childhood vaccination schedule is necessary. This is not true – to cite just one case in point – of the second rubella vaccine dose, which is given to millions of kids every year only because it is part of a measles-mumps-rubella combo shot. If a rubella-only shot were available in the U.S., one dose would do. Some doctors simply don’t know this. But public health experts who know better keep mum or fib, because they don’t want to tell anxious parents that their children’s second rubella dose isn’t necessary. The CDC, too, tells parents that “every dose is important.” Do public health professionals therefore accuse the CDC of perpetrating “misinformation”? Of course not.
Mask
The problem with the term “mask” is that it’s too broad. It is applied to face coverings that provide meaningful inbound and outbound protection against COVID – e.g. N95s. And it is applied to face coverings that provide little to no protection against COVID – e.g. loose-fitting cloth masks, sometimes even the flimsiest of bandanas and neck gaiters.
Ideally we would have different terms for masks of different protective value. We have one such term – “respirator” for the most effective face covering – but it’s probably a lost cause to get people to use it much.
These distinctions are not subtle. You’re probably safer wearing a well-fitting N95 respirator in a roomful of unmasked people than wearing a typical cloth mask in a roomful of people also wearing typical cloth masks. So hassling the unmasked for putting you at risk is arguably silly if your own mask isn’t an N95 or something like it.
Because most mask mandates deploy a broad lowest-common-denominator definition of “mask,” they end up mandating a precaution that is sometimes more burdensome than beneficial. Evidence on the effectiveness of such generalized mask mandates is mixed. (The relevant literature is huge. We’re getting to where we need a literature review of the literature reviews and a meta-analysis of the meta-analyses.) Many mask studies and most mask commentaries sound as if all masks were created equal. It wouldn’t surprise me if mask mandates were effective in direct proportion to which masks people voluntarily pick when mandated to wear whatever kind they want. A narrower definition of “mask” – specifying an N95 or equivalent – would add to the burden of mask mandates. But it would also be far less vulnerable to charges of precaution theatre.
A colleague who reviewed a draft of this section saw the mask terminology trap a little differently:
The phrase “masks don’t work” is used by the two opposite poles of the public health debate at the same time! Extreme Selfish Bastards use it to mean “don’t make me wear anything,” while experts in industrial hygiene use it to mean “please wear a real respirator, as masks don’t work”! What could be more confusing??
Even if mask mandates are passé – which only time will tell – mask terminology is just as problematic when you’re advising people to mask up voluntarily. What sort of mask I’m wearing probably affects my health more than whether others around me are wearing any mask at all. Convincing people of this would be a lot easier if all masks didn’t have the same name.
Airborne
COVID is transmitted mainly in two ways: via biggish droplets that are expelled into the air by an infected person and reach the face of another person before they can drop to the ground; and via smaller particles, called aerosols, that are also expelled into the air by an infected person but hang in the air longer and travel through the air farther because they’re lighter.
The distinction between droplets and aerosols isn’t dichotomous. Nonetheless, it’s a distinction that matters. Some precautions like social distancing protect us mostly from droplets, while others like ventilation are effective against aerosols that have traveled.
After 2-1/2 years of COVID, we still don’t know what percentage of transmission is via droplets and what percentage is via aerosols (with a small remaining percentage via surfaces). We can’t even intelligibly ask the question without stipulating an arbitrary dichotomous border between the two.
In normal parlance, “airborne” means moving through the air. So in normal parlance, both droplets and aerosols are airborne, though droplets fall to the ground more quickly. For people who talk normally, the COVID transmission question has been airborne (droplets or aerosols) versus surfaces: “Can I breathe it in?” versus “Dare I touch that doorknob?”; wearing masks versus washing packages.
In public health parlance, on the other hand, the term “airborne” is usually applied only to aerosols. So early in the pandemic, when the World Health Organization insisted for many months that COVID was “NOT airborne,” it meant not (not ever?) transmitted via aerosols. Even before the consensus shifted and public health professionals decided WHO was wrong (though they rarely accused WHO of “misinformation”), many people found the claim confusing. WHO’s “NOT airborne” seemed to suggest that COVID must be transmitted mostly via surfaces, not when infected people expel virus particles into the air by coughing, sneezing, talking, or breathing. Public health professionals had already overemphasized the importance of sanitizing surfaces to prevent COVID. Confusion about the meaning of “airborne” exacerbated the misimpression.
Immunity
Most people see “immunity” as a dichotomous concept: You’re either immune (to criticism, say, or to prosecution) or you’re not. But in medicine and especially in public health, the term is often used non-dichotomously. You can be a little bit immune, even if you can’t be a little bit pregnant.
Not that public health professionals stick to this non-dichotomous use of the immunity concept. Especially after the first COVID vaccines were introduced, they often overgeneralized about people who “had immunity” to COVID and others who were “not immune.” Then they turned around and criticized nonprofessionals who similarly overgeneralized for a purpose they didn’t like – such as people who insisted that they were already “immune” from a prior COVID infection so they didn’t need to get vaccinated; or people who complained that they were misled that getting vaccinated would make them “immune” and then they got infected anyhow.
(As I have already noted, inconsistency and double standards are widespread sins on all sides of all controversies. Is it unfair to ask public health professionals to set a better example vis-à-vis COVID? Maybe so. But I think COVID language traps contribute significantly to COVID mortality and morbidity – so it’s worth a try.)
COVID vaccination gives the vaccinee immunity. How much immunity depends on the vaccine, the vaccinee, and the virus variant. And then that immunity – partial from the get-go – starts to wane.
I’ll get to “natural immunity” soon; it too is partial and wanes.
“How much immunity” vaccination confers is a crucial COVID question. But if you see “immunity” as a dichotomous concept, “how much immunity” is a confusing question. Worse: To many people, “waning immunity” feels like it isn’t immunity at all – which means COVID vaccination feels like a fraud. Public health professionals exacerbated this problem in the early days of the pandemic by sounding like COVID vaccines were a silver bullet, leading people to expect that once vaccinated they’d be “immune” in the dichotomous sense. But it’s not entirely public health’s fault; just the word itself set people up to get that impression.
Another complexity: “immunity” against what? Antibodies provide quickly waning immunity against infection. Memory cells provide more long-lasting immunity against severe illness and death. Neither is dichotomous.
There’s a whole class of risk-related terms with this “dichotomous or not” language problem. Two others I could have written about are “protection” and “prevention.” If a COVID precaution – a booster, say – sizably reduces your chances of serious illness, but not down to zero, does it “protect” you and “prevent” you from ending up in the hospital? No, if you think these terms are dichotomous. Yes, if you think they’re not. Since people use these terms sometimes one way and sometimes the other, if you use them without specifying you are setting yourself up to be misunderstood.
Herd immunity
“Herd immunity” has the same problem. A lot of ink has been spilled over the question of what percentage of the U.S. population (or the local population) needs to be vaccinated for us to achieve herd immunity. The question seems to assume that “herd immunity” is dichotomous. When we get to herd immunity, we were led to believe, we won’t have any more cases, or we won’t have any more surges, or we won’t have any more surges until a new variant comes along – something identifiably good will happen when we get there. But where is “there”? If “herd immunity” isn’t dichotomous in the first place, there’s no there there.
Thanks to vaccination and prior infection, we – the herd – are more immune than we used to be. Thanks to waning and new variants, we’re also less immune than we used to be. Herd immunity is a moving target.
There are dichotomous definitions of herd immunity. One that’s fairly common in the technical literature: enough immunity that the average infected person infects fewer than one other person, so outbreaks are self-limiting. If you stipulate this or some other dichotomous meaning, then the question “When will we achieve herd immunity against COVID?” can be asked intelligibly, even if the answer depends on factors (like new variants) we can’t predict. But most experts most of the time talk about the herd getting more immune or less immune, not “immune.”
The term “herd immunity” has other problems as well.
Early in the pandemic, the term was deployed more often by public health outliers such as the Great Barrington Declaration than it was by the public health mainstream – so it came to be seen as part of the outliers’ program, part of their campaign of “misinformation” (as the mainstream saw it). One version of the outlier position held that instead of implementing lockdowns to suppress transmission, we should focus on protecting the vulnerable and let healthy people go about their business, so lots of people will get infected ASAP – the fast track to herd immunity. Especially before COVID vaccines became available, that argument struck most public health professionals as both impractical and immoral. It made “herd immunity” a suspect term that mainstream public health communicators tried to avoid using at all.
More broadly, the term has acquired a distinct connotation of “time to relax our guard.” In the raging political battles over COVID policy, public health professionals have rarely been on the side of relaxing our guard. So they have had little use for promising that we’ll achieve herd immunity if we do the following…. And they have had even less use for claiming that we’ve achieved herd immunity already so we’ve done enough.
There’s another reason many in public health have generally avoided talking about herd immunity. Early in the pandemic, Tony Fauci, the federal government’s infectious diseases superstar, talked about it misleadingly. Asked what percentage of the American public needed to be “immune” before we got to “herd immunity,” Fauci first went along with the then-current 60–70% estimate. In the months that followed he “nudged” it up as high as 85%. And then he told the New York Times he did so “partly based on new science, and partly on his gut feeling that the country is finally ready to hear what he really thinks.” (More recently, Fauci has opined that COVID herd immunity “almost certainly is an unattainable goal.”) I’ll leave it to the reader to decide if this sort of rhetorical flexibility, even in a good cause, ought to be called “misinformation.” Some commentators did call it that. The public health mainstream did not. But the controversy may have put some public health professionals off the term.
And of course the COVID pandemic has given us a succession of variants increasingly capable of causing reinfections and breakthrough infections. So as we kept getting closer to herd immunity, herd immunity kept getting further away. After the “herd immunity” horizon had receded a couple of times, a lot of public health professionals learned to stop saying they could see it.
Finally, the word “herd” raises its own problems. To some, it’s offensive because it analogizes humans to animals. To others, it’s intrinsically communitarian – which makes it automatically a suspect term in a fiercely individualistic society like ours. Relatively few Americans aspire to be part of a herd, any herd.
Natural immunity
From time to time a term that public health professionals have been using for decades gets picked up by the general public – or worse yet, by a movement whose views are anathema to public health professionals (antivaxxers, for example). Sometimes they use the word incorrectly. But even when they’re using it correctly, public health professionals are likely to say they’re not – simply because they’re using it to make a point that public health professionals disapprove of.
That’s the fate of “natural immunity.” In the public health literature, this term is routinely used and has long been used to mean exactly what antivaxxers use it to mean: immunity resulting from prior infection or perhaps from good overall health, but in any case not from vaccination. But when huge numbers of COVID survivors started claiming that they had “natural immunity,” many public health professionals suddenly objected that the term is misleading, even “misinformation” – despite its being public health’s own term, used the same way public health uses it.
The tipoff is how many articles and op-eds started putting “natural immunity” in quotes to imply that it’s somehow not quite real immunity. No one in public health ever puts “vaccine-induced immunity” in disparaging quotes.
Of course “natural immunity” has the same dichotomous-or-not problem as “immunity” and “herd immunity.” Whether you know it or not, natural immunity doesn’t mean you can’t catch COVID, just as vaccine-induced immunity doesn’t mean you can’t catch COVID. But natural COVID immunity is real immunity, partial like all COVID immunity. Although public health professionals rightly want previously infected people to get vaccinated anyway, their objections to the term “natural immunity” are baseless.
I have been amused by how many articles I see comparing natural immunity to hybrid immunity (immunity after both infection and vaccination, in either order) – and how few articles I see comparing vaccine-induced immunity to hybrid immunity. Public health professionals really, really want us to know that vaccination adds value irrespective of whether you’ve been infected. They don’t especially want us to know that infection adds value irrespective of whether you’ve been vaccinated. I don’t dispute that the risk-benefit calculation for getting infected is a lot worse than the risk-benefit calculation for getting vaccinated. The former is way riskier than the latter, and public health professionals are right to say so. Nonetheless, the benefits are roughly comparable – a fact most public health professionals are loath to confirm.
I’m not suggesting that it makes sense to get infected on purpose, only that it makes sense to think you’re safer after catching COVID than you were before – and the conventional public health term for that increased safety is “natural immunity.”
In August 2022, the CDC issued revised COVID recommendations that finally took natural immunity into account, albeit without using the term. CDC’s expert explained that “both prior infection and vaccination confer some protection against severe illness, and so it really makes the most sense to not differentiate with our guidance or our recommendations based on vaccination status at this time.”
Sick/ill/illness/disease
Terms like “sick,” “ill,” and “illness” all apply to bothersome symptoms of disease – maybe not bothersome enough to call a doctor, but at least bothersome enough to affect how you feel and what activities you undertake. Infections aren’t illnesses until infected people feel sick. Asymptomatic infected people aren’t sick. Even symptomatic infected people aren’t sick if their symptoms don’t bother them.
“Disease” is a little different. If you’re totally symptom-free, it’s both weird and technically incorrect to say you have a disease, even if you’re infected with the relevant pathogen. Diseases have formal case definitions, lists of what symptoms you have to have to qualify as having that particular disease. It is possible to qualify for some diseases even if your symptoms are mild enough that you keep claiming, truthfully, that you’re not sick. And it is possible to feel sick as a result of infection with a particular pathogen, even though your symptoms don’t quite meet the specifications for the disease that pathogen causes.
In the early days of the pandemic, public health professionals tried to sustain a distinction between the virus named SARS-CoV-2 (not the virus’s original name, but that’s another story) and the disease named COVID (or more formally, COVID-19). If you’re infected with SARS-CoV-2, they said, you might or might not come down with COVID. They failed. Even public health professionals now frequently call both the virus and the disease COVID – certainly in their writing for the general public and frequently even in their writing for each other.
Best example of this conflation: the universal use of the term “COVID test” for tests that determine the presence of the SARS-CoV-2 virus or sometimes just fragments of the virus. Similarly, experts now routinely point out that “COVID is often asymptomatic” – a sentence that is oxymoronic for those who still insist that COVID is a disease, not merely an infection. Diseases are symptomatic by definition.
(The word “polio” has the same double-meaning problem. The disease “poliomyelitis” or “paralytic polio” is often just called “polio.” The “poliovirus” is also often just called polio. Like “COVID,” “polio” is both a virus and a disease. The opportunities for confusion are legion.)
Bottom line: We don’t know what we mean when we say people “have COVID.” Sometimes we mean that they’re sick; sometimes we mean that they have some symptoms, even if they feel okay; sometimes we mean only that they’re infected.
The confusion between infection and illness can be very basic. I had dinner with a friend recently who had spent five days in isolation after testing positive for COVID (really for SARS-CoV-2). She resented the isolation because, she explained, “I wasn’t sick.” My friend retired from a career in nursing so I assume she has been taught the distinction between infection and illness – but at some fundamental emotional level she felt like people who feel fine shouldn’t have to stay home.
The terminological confusion infects us all. A June 2022 CDC announcement regarding international travel advised travelers to “get tested for current infection … and not travel if they are sick.” What do you think the CDC meant by “sick” in this sentence? Did it mean “sick” as in “feels crappy,” or was the agency using “sick” as a synonym for a positive test result? Should a person get on the plane if she tests positive but feels fine? If he tests negative but feels sick? What if a person tests positive and feels mildly symptomatic but not really sick? What CDC is advising here is anybody’s guess.
Case
When public health professionals are being careful, the word “case” is almost always followed by “of.” Or at least there’s some nearby specification of what sort of case they’re talking about – a case of infection, or of a positive test result (with a specified test, PCR or rapid antigen), or of symptomatic illness, or of medically attended and lab-diagnosed disease, or of hospitalization, or of death.
When public health professionals aren’t being careful, the word “case” is often ambiguous. And like most of us, public health professionals are often careless about language – with each other because they assume their fellow professionals will understand; and with the general public because they’re not working hard enough to be understood (and sometimes because they prefer to be misunderstood).
In the absence of a clear specification – a case of what – a “case” in epidemiology is usually assumed to mean somebody who feels sick or at least symptomatic and has been diagnosed with a specific disease. Sometimes the diagnosis has to be laboratory-confirmed; other times a clinical diagnosis is sufficient. Sometimes the diagnosed symptoms have to satisfy the specifications in a formal “case definition”; other times close is close enough. Sometimes the symptoms have to be serious, maybe even serious enough to require hospitalization; other times mild symptoms are sufficient.
But somebody who feels perfectly fine – asymptomatic – is not usually considered a “case,” even if for some reason that perfectly healthy person got a test for a specific pathogen and tested positive. This is true even though that “perfectly healthy” person might still be able to transmit the pathogen and give other people the disease.
Unless otherwise specified, in other words, cases are cases of disease, not just cases of infection.
That’s not true for COVID. If there’s no specification, the number of COVID “cases” is probably one of three numbers:
- The biggest of the three is the number of people who are infected with SARS-CoV-2, whether they’re aware of it or not – a number nobody knows, but experts can try to estimate it with modeling or surveillance sampling.
- The middle and most commonly used number is the number of people who have tested positive for SARS-CoV-2 in some public venue where test results can be counted. This number goes up or down not just depending on how much of the virus is around, but also depending on how many publicly reported tests are being done.
- The smallest and technically most accurate number of “COVID cases” is the number of people who are known to have tested positive for SARS-CoV-2 and have also been diagnosed with COVID because they have symptoms that satisfy the case definition for COVID-19 disease.
One more complexity: People with COVID (however you define it) typically get better, at which point they no longer have COVID. But they may still test positive. (A PCR test can be positive for weeks just because it’s detecting harmless fragments of RNA.) Are they still a COVID “case”? Alternatively, they may no longer test positive but still have symptoms. At some point we say they have “long COVID” – but are they still a COVID “case”? These questions don’t affect cumulative totals of how many COVID cases there have been, but they greatly affect snapshots of how many cases there are currently. Every time experts tell us how many “COVID cases” there are right now in a city, state, or country, they are making assumptions (usually unspecified assumptions) about both what it takes to become a “case” and what it takes to no longer be a “case.”
The definition of a COVID “case” is crucial when we start talking about “case fatality rates.”
The case fatality rate is a fraction. The denominator is the total number of cases. The numerator is how many of those cases died.
If we’re being super-careful, the “case fatality rate” of COVID means the percentage of those who have the disease (meaning that they’re symptomatic, meet the case definition, and have been formally diagnosed) who then die of it. The percentage of infected people who die is the “infection fatality rate.” That’s a much lower number, both because many infected people don’t become diseased and because COVID-19 disease is a lot deadlier than SARS-CoV-2 infection.
Wikipedia has a crystal-clear explanation of what “case fatality rate” and “infection fatality rate” mean when public health professionals are being careful. The case fatality rate is “the proportion of people diagnosed with a certain disease, who end up dying of it.” The infection fatality rate is “the proportion of deaths among all infected individuals, including all asymptomatic and undiagnosed subjects.”
But since we’ve gotten sloppy about the meaning of a COVID “case,” we can’t be sure anymore what we mean by the COVID case fatality rate.
More often than not, references to the COVID “case fatality rate” are talking about the percentage of those who are known to have tested positive for SARS-CoV-2 (including the asymptomatic ones) who then die of COVID – or even die of something else while still testing positive.
This is a misleadingly low number in that it leaves in the denominator the legions of people whose positive test results were counted, even if they never got sick. Some of them got tested only because their employer or their travel plans required it, or because they were about to spend time with a vulnerable friend. If you think only sick people should be considered COVID “cases,” these healthy positives should be excluded from the denominator. And since almost none of them died, excluding them would yield a higher case fatality rate.
It’s a misleadingly high number in two ways. First, it leaves out of the denominator the huge number of infected people who never got a test that was counted. Maybe they never got a test, period. Maybe they tested positive privately at home, but never got sick enough to see a doctor and get an official test. If you think everybody who’s infected ought to be considered a “case,” these untested and home-tested positives should be included in the denominator – and since almost none of them died, including them would yield a lower case fatality rate. As fewer and fewer people – and only the sickest people – get COVID tests that are counted, the COVID case fatality rate goes up without COVID actually causing any more deaths.
The second way this conventional COVID case fatality rate calculation is misleadingly high: It leaves in the numerator people who happened to have COVID but died of something else. Maybe they tested positive for COVID on their way into the hospital with a fatal cancer or after a disastrous auto accident. Maybe they caught COVID while in the hospital dying of their cancer or crash injuries. The more mild COVID infections there are, the more dying people will coincidentally be infected with COVID. Trying to distinguish people who die of COVID from people who die with COVID is a challenge, since many comorbidities make COVID more deadly and COVID makes many comorbidities more deadly. But when somebody with a positive test result dies, even the most obviously coincidental COVID “case” is likely to get counted as a COVID death, inflating the COVID case fatality rate.
No wonder we see endless arguments over how deadly COVID is!
Booster
Many people think of a “booster shot” as any dose after the first one against a specific disease. But to experts, “booster” usually means a follow-up dose when protection used to be sufficient but isn’t sufficient any longer. If it takes more than one dose to get people sufficiently protected in the first place, then the second (or third, or nth) dose isn’t technically a booster; it’s part of what’s called the “primary series.” The Pfizer and Moderna COVID vaccines – the ones most commonly used in the U.S. – both have a two-dose primary series.
For some diseases (measles, for example), an additional vaccine dose is recommended because the first dose might not have taken at all. That’s not a booster either, technically; it’s an insurance dose that’s unnecessary for most vaccinees but essential for a few. Similarly, many immunocompromised people never had a decent immune response to their first two COVID shots. So a third COVID shot is a booster for those of us who did, but it’s arguably part of the primary series for those who didn’t.
For most Americans so far, any COVID vaccine dose after the first two Pfizer or Moderna doses is aimed at bumping up prior protection that was originally considered adequate. So it’s a booster, regardless of why the bump is “needed” (another word that deserves exegesis): because the virus has changed, or because immunity has waned, or merely because even more immunity seems worth the minimal downsides of yet another shot.
The much less popular Johnson & Johnson vaccine has a “one-and-done” dosing schedule. That was originally expected to be its big marketing advantage. But one J&J dose turned out less effective than two doses of Pfizer or Moderna, so J&J vaccinees really, really need a booster. Some experts have argued cogently, but so far unsuccessfully, that the first J&J dose isn’t really effective enough – not just because it wanes, but even in the short term. So they think the second J&J dose should be considered part of the primary series, not a booster. The same argument is sometimes raised about the first two Pfizer and Moderna doses; maybe they aren’t effective enough either, so maybe the third dose of those two vaccines should also be considered part of the primary series, not a booster.
COVID’s “booster” terminological tussle is grounded in the controversy over COVID vaccination. As soon as a COVID vaccine was available, it quickly became clear that convincing the reluctant half or so of the country to roll up their sleeves was going to be a challenge. Most public health professionals figured the challenge would get tougher still if people realized that boosters might be needed down the road. So anyone who received two Pfizer or Moderna doses or one J&J dose was deemed “fully vaccinated” against COVID.
Unfortunately, vaccine efficacy waned more precipitously than the experts expected or hoped, especially against the various Omicron subvariants. So boosters became really important. For a while, pre-Omicron, mainstream public health resisted, fearful that a booster campaign would undermine the holdouts’ willingness to get vaccinated at all. But with Omicron the case for boosting became too strong to oppose.
That’s when a lot of public health professionals decided it was a mistake to call COVID boosters “boosters.” They feared that booster language would make a third shot sound optional rather than essential. Better to claim that the third shot was the completion of a three-shot primary series. That way, people with only two shots would feel some pressure to finish the job. Essentially, they wanted to give up on encouraging people to get their first shots by implying that two were enough, and focus instead on encouraging people to get their boosters by implying that three was the key.
Changing terminology in midstream this way has reputational costs, but it might have been worth trying if there were grounds for confidence that three would be enough. But vulnerable people are already being advised to get a second booster. Are public health professionals now going to suggest, “Well, actually, it’s a four-dose primary series”? And what if they end up recommending periodic boosters for years to come? How many times can they say a newly recommended shot isn’t a booster but part of an endlessly lengthening primary series? And isn’t “booster” the standard term in public health for an additional shot to boost immunity that has waned?
So CDC devised a compromise. It still says anybody with two Pfizer or Moderna doses or one J&J dose is “fully vaccinated.” And it still calls all additional shots “boosters.” But it introduced a new term, “up to date,” for people who have gotten all the boosters currently recommended for their cohort. If you want to be up to date, the recommended boosters aren’t optional.
Complicating things further, pathogens mutate, some more than others – and sometimes a new mutation requires a new vaccine. That’s why the flu vaccine changes nearly every year, as manufacturers try to match the flu strains thought likeliest to be circulating in the coming season. Public health professionals rarely call the annual flu shot a “booster.” It’s true that flu immunity wanes, so an annual flu shot is a good idea even if the circulating flu strains haven’t changed since last year. But most years the circulating strains have changed. Most years the flu vaccine has been newly reformulated to try to keep up with flu virus mutations. A “booster” is normally another shot of the same vaccine. So most years the annual flu shot isn’t a booster because this year’s flu vaccine is usually not the same as last year’s.
We don’t know yet how often the SARS-CoV-2 virus will mutate enough that experts decide a reformulated COVID vaccine is needed. Public health professionals don’t normally call a reformulated vaccine a “booster.” But in the case of COVID, it looks like that’s what they’re planning to do. 
In recent weeks the Food and Drug Administration has urged vaccine manufacturers to come up with a “bivalent” COVID booster, combining the original COVID vaccine dose with a new Omicron dose in the same shot. That new shot is expected to roll out in the fall. It will be called a “booster,” notwithstanding the fact that it will contain a new vaccine targeting the most recent COVID variant. At least so far, the FDA isn’t planning to replace the original COVID vaccine with the reformulated one for people’s primary series. As of right now, it looks like the recommended COVID vaccination schedule for most American adults will be two doses of the original vaccine followed by at least one “booster” of a reformulated vaccine.
A COVID “booster” is coming to mean any shot beyond the primary series that public health professionals want you to get – even if they now wish the primary series had included more than two shots, and even if the vaccine has been reformulated. That’s a different meaning for “booster” than the usual one for other diseases.
Whatever “booster” means or comes to mean, we can’t talk intelligibly about who ought to get another COVID shot without distinguishing the various reasons for getting one: your antibodies have waned; the virus has changed; you never had enough protection in the first place; you simply want more protection; etc.
Rare/common
Terms like “rare” and “common” – and terms in the middle like “uncommon,” “scarce,” and “unusual” – are intrinsically imprecise unless somebody stipulates precise definitions. So which term gets used in a specific situation may tell you more about the speaker than the situation.
A scary side effect of a vaccine you want people to take might get called “rare,” while a scary symptom of the disease itself is likelier to be called “common.” If you’re arguing against the vaccine, on the other hand, it’s the vaccine side effect that’s “common” while the disease symptom is “rare.”
Precise definitions do exist for these terms. But they vary from agency to agency, and from purpose to purpose. And even experts often use the terms without reference to the definitions.
The U.S. government officially defines a rare disease as one that impacts fewer than 200,000 Americans, a definition specified by Congress in the Orphan Drug Act of 1983. Given the current U.S. population of 335 million, that comes to one American in 1,675. The European Union defines a disease as rare when it affects fewer than one person in 2,000 – pretty close. A study of how various organizations define “rare diseases” found a range from 5 cases per 100,000 to 76 cases per 100,000, with “an average prevalence threshold between 40 and 50 cases/100,000 people.” That would be just a little rarer: 40 in 100,000 is one in 2,500; 50 in 100,000 is one in 2,000.
A rare side effect is a somewhat different story. Here for example is what the New Zealand Ministry of Health says about adverse reactions to medicines (grounded in a widely used international set of standards):
- very common – this means that 1 in every 10 people taking the medicine are likely to have the adverse reaction
- common – this means that between 1 in 10 and 1 in 100 people may be affected
- uncommon – this means that between 1 in 100 and 1 in 1,000 people may be affected
- rare – means that between 1 in 1,000 and 1 in 10,000 people may be affected
- very rare – means that fewer than 1 in 10,000 people may be affected
So to count as rare, a disease has to afflict fewer than one in 2,000 in the average organization. But New Zealand thinks a medicine’s side effect is rare when as many as one in 1,000 experience it. I’m fine with “rare” side effects being twice as common as “rare” diseases. After all, the denominators are different. For side effects, we’re talking about one in 1,000 people taking the medicine. For diseases, we’re talking about one in 2,000 people in the whole population.
It’s not necessary for “rare” to have the same meaning for side effects and diseases … and floods and phobias and everything else. It makes sense for doctors to be more vigilant for side effects of the meds they’re prescribing than for diseases they’ll probably never see.
I’m less fine with public health professionals deploying words like “rare” and “common” as ammunition more than description. Sometimes the word is chosen consciously in an effort to achieve the desired rhetorical effect. More often, I think, the authors’ intentions and values unconsciously bias their word choice.
A recent article in the New England Journal of Medicine, for example, tried to assess the frequency of chronic traumatic encephalopathy (CTE, a serious brain injury) in soldiers exposed to explosions. It found evidence of CTE in ten out of 225 veterans’ brains – 4.4%. The article said CTE was found “infrequently”; one of the authors called it “rather rare.” Environmental health professor Adam Finkel responded: “To experts in risk assessment and regulation of occupational/environmental disease, a 4.4% risk of grave harm is the opposite of ‘rare.’”
Here’s a COVID example. MIS-C (a pediatric side effect of COVID) is estimated to affect approximately one in 3,000–4,000 children and adolescents infected with SARS-CoV-2. That’s at least “rare” by U.S. government standards. And not every kid gets infected in the first place, so the odds of a child both catching COVID and ending up with MIS-C may be down around one in 10,000 – approaching the “very rare” threshold. The Minnesota Department of Health calls MIS-C “very rare.” Maybe because that implies that MIS-C shouldn’t be a big worry for parents, the CDC is a bit more alarmist; it says MIS-C is “rare,” as do most other public health sources. Johns Hopkins is more alarmist still, describing MIS-C as merely “uncommon” – which sounds way more common and thus way more scary than “very rare.”
How often did the authors of these three descriptions choose their adjectives based on the evidence of MIS-C incidence and some stipulated set of definitions for “rare,” “very rare,” and “uncommon”? Not often, I suspect – which leaves them free, consciously or unconsciously, to vary their word choice based on their communication goal. It’s got to be hard to persuade parents to take steps to protect their kids against a “very rare” medical condition. One that’s merely “uncommon” sounds like a much better pro-vaccination argument.
Another much-discussed COVID incidence controversy is how “rare” or “common” it is for vaccinated but not boosted people to get breakthrough infections leading to severe COVID cases. This was an especially hot question in fall 2021, when public health experts were debating among themselves whether boosters were “needed.” (As I noted earlier, “needed” is another term that deserves exegesis; I’ve discussed that one here.) Both sides were cherry-picking the scanty available data for evidence supporting their position. And I’ll wager that they tended to pick their adjectives to match.
One more example: The most effective COVID treatment to date is an antiviral called Paxlovid. But some COVID patients (including President Biden) briefly test negative after taking Paxlovid, then “rebound” and test positive again. How rare or common is Paxlovid rebound? The headline on a July 30, 2022 NBC article reads: “Biden’s repeat Covid is due to Paxlovid rebound. Experts insist recurrences are rare.” The article itself doesn’t say “rare.” It refers to “a small minority” and says rebound “does not occur very often.” It also cites some actual numbers: “Around 1% to 2%” in the manufacturer’s clinical trial; “around 5%” according to White House COVID response coordinator Ashish Jha; “less than 1%” and “6%” in two different studies. None of these numbers is anywhere near “rare.” Creating the misimpression that rebound is “rare” is desirable if you want to encourage people with COVID to take Paxlovid, which most U.S. experts consider underutilized.
The same issues arise with other evaluative terms. A July 12, 2022 article published by the University of Minnesota Center for Infectious Disease Research and Policy says in its lede that “mRNA COVID vaccines are associated with a slightly increased risk of myocarditis and pericarditis.” A less dismissive word than “slightly” might have been chosen if the topic were a COVID symptom rather than a vaccine side effect. In fact, the article’s last paragraph quotes the study authors that the vaccines “markedly” increased the risk for young males. Quite a different adverb!
I haven’t made a systematic study of how COVID vaccine side effects like myocarditis have been described. But it’s a safe bet that antivaxxers use different language than vaccine proponents to describe the same incidence statistics. And I think it would have been natural, almost inevitable, for public health professionals to gravitate toward a scarier term when they were explaining why they were suspending the vaccine rollout than when they were explaining its resumption a few weeks later.
Significant
I first inveighed against the word “significant” in a 2005 column on “Risk Words You Can’t Use.” I wrote:
To statisticians, a significant finding is unlikely to have occurred by chance; it means something. To public health experts, a significant outbreak affects lots of people; it is widespread (or likely to spread) as well as serious. Neither of these meanings sits well with ordinary (that is, normal) people, for whom personal, emotional significance is the significance that matters.
The distinction between public health significance and individual, personal significance has long been a bugaboo of mine. I see public health as intrinsically a wholesale operation. Clinical doctors rightly worry about individual patients, but public health professionals, in my view, should focus on more widespread health problems affecting communities. Rare diseases aren’t a significant public health issue unless there are grounds for concern that they won’t stay rare.
Not everyone agrees. In a 1980 case the U.S. Supreme Court ruled that the Occupational Safety and Health Administration can’t regulate a risk unless it’s “significant” – and defined a “significant” risk as anything over one-in-a-thousand per person exposed. The decision said nothing about how many people need to be exposed before OSHA can take action. So it’s arguable that a big risk to a small number of people is a “significant” public health issue.
But public health budgets aren’t infinite. Most public health agencies prioritize threats to lots of people over threats to just a few – rightly, in my view. I don’t think a threat to just a few people should be considered a significant public health issue.
Even so, public health professionals have to be careful how they talk about rare diseases. Victims of rare diseases don’t want to hear that their suffering is “insignificant.” The same is true of rare symptoms of COVID or rare side effects of COVID vaccines. (Of course as we’ve seen the term “rare” raises its own problems; what’s rare in your judgment may not be rare in mine.) Every illness is significant to the person who’s afflicted.
The more complicated problem with “significant” is about statistical significance. The following paragraphs may be tough sledding if you’re new to statistics, and may strike you as naïve and oversimplified if you’re a statistical sophisticate. Feel free to skip to the next section.
A research finding is statistically significant when it is unlikely to have happened by chance. By convention, if the odds are less than one in twenty that a finding happened by chance, we conclude that we’ve (probably) got something real, not just a coincidence. We say “p <.05” – which means the probability (p) is less than five in a hundred (one in twenty) that our result is just a coincidence. So we dismiss the “null hypothesis” of coincidence and conclude that we have (probably) found something real.
Whether a research finding is statistically significant depends on many factors. The three main ones are the commonness of the phenomenon we’re examining, the actual magnitude of the relationship we’re trying to assess, and the size of our sample. If we’re looking for a big relationship in a common phenomenon, a small sample can answer our question with statistical significance. If the phenomenon we’re looking at is pretty rare and the relationship we’re looking for is pretty small, we need a big sample to get a statistically significant answer.
A research finding can be statistically significant and still be thoroughly insignificant as a practical matter. Statistical significance means the relationship you think you found is probably real. It doesn’t necessarily mean the relationship is important. With a common enough phenomenon and a big enough sample, a tiny relationship of no practical importance can be statistically significant.
Here’s a controversial example: Does requiring masks in elementary schools reduce the number of COVID illnesses in the community? There are of course many arguments for and against school mask mandates, but stick to this one narrow question, whether making kids wear masks in class leads to fewer people sick with COVID in town.
Even though the overwhelming majority of the children are wearing porous cloth masks, not respirators, I think elementary school mask mandates almost certainly do reduce community COVID incidence. I also think the effect is almost certainly small enough to raise doubts over whether that’s a good reason to make kids wear masks. (There may be other, better reasons.) If you conduct a big enough and methodologically careful enough study, in other words, you can genuinely prove that school mask mandates “significantly” reduce community illness in the statistical sense, without necessarily proving anything significant in the policy sense.
To avoid making a big deal out of a statistically significant finding with tiny real-world significance, policymakers need to consider the size of an effect, not just whether it’s coincidental or (probably) real. “How many COVID illnesses does masking elementary schoolkids prevent?” is a question with real policy significance. “How sure are we that masking elementary schoolkids prevents more than zero COVID illnesses?” is the question that statistical significance answers.
Conversely, a finding can fail to achieve statistical significance and still be significant in the real world. Suppose you’re in a big rush to answer a super-important question whether to do X or Y – for example, which monoclonal antibody cocktail to recommend for people infected with the newest Omicron subvariant. Your hurried study shows that X is a much better option than Y. But maybe because your sample was too small, your finding is statistically significant at p < .10 but not at p < .05. You’re more than 90% sure but less than 95% sure that what you found is real, not a coincidence. And you have to act now, choosing either X or Y. You should obviously choose X! Your study is a highly “significant” guide to your decision, even though its finding isn’t statistically “significant.”
Pfizer’s COVID antiviral Paxlovid has been shown to reduce hospitalization and death in high-risk people – a finding that looks “significant” in both the statistical and the policy sense. But what about less vulnerable people? Pfizer reported a 51% reduction in hospitalization and death for patients with merely “standard risk.” Sounds huge. But is it a real risk reduction or a coincidence? In this Omicron era, COVID only occasionally leads to hospitalization or death in people who aren’t classified as high-risk. Pfizer’s numbers: 10 out of 569 people (1.76%) were hospitalized or died in the placebo arm of the study, versus 5 out of 576 (0.87%) in the group that got Paxlovid. Statistical analysis showed this difference was “non-significant.” Because COVID hospitalization and death are fairly uncommon in standard-risk people and Pfizer’s sample was fairly small, the odds that the study finding was just a coincidence came out higher than one-in-twenty.
But of course it probably wasn’t a coincidence. Let’s assume Paxlovid actually cuts the risk of hospitalization and death by 51% for standard-risk COVID patients. Fifty-one percent is a huge reduction in relative risk. Even the nearly one percent reduction in absolute risk – from 1.76% to 0.87% – isn’t trivial. I suspect most patients would want the treatment. (Whether most insurance companies would want to pay for it might depend on which costs more, a lot of courses of Paxlovid or a few COVID hospitalizations.)
Even if most standard-risk patients would consider Paxlovid a “significant” benefit in terms of their health, at least so far it’s not a statistically “significant” benefit. Pfizer deserves credit for not trumpeting its value. But I would nonetheless advise my standard-risk friends with COVID to consider Paxlovid.
For an example of the opposite strategy, consider this disreputable CDC graphic showing one key result from a California Department of Public Health mask study, published by the CDC in February 2022.
link to graphic Will put into column when we post it.
The graphic appears to show that people who said they always wore a mask indoors were less likely to test positive for COVID than people who didn’t. In fact, that’s exactly what the headline says the graphic shows. The graphic explicitly claims a 56% risk reduction for wearers of cloth masks, and still greater risk reductions for wearers of better masks.
But there’s an asterisk, sending extremely careful readers to a tiny-type footnote pointing out that the results ballyhooed in the headline and graphic are “Not statistically significant.” (The study itself, as opposed to this graphic, is clear that the results for N95 respirators and surgical masks were statistically significant, while the result for cloth masks was not.)
A raft of other methodological criticisms have been voiced against this study. Setting those aside, I’m pretty impressed by a finding that people who said they habitually wore cloth masks indoors were 56% less likely to test positive for SARS-CoV-2 than people who said they usually went unmasked. If the truth is anywhere near 56%, that fact should obviously be “significant” to policymakers and to individuals deciding whether or not to mask up – regardless of the finding’s failure to be statistically “significant.”
Bottom line: “Significant” has one meaning to public health professionals, a second meaning to statisticians, and yet a third meaning to the general public. Every time we use the word, we need to consider which meaning we intend and which meaning our audience is likely to hear.
Emergency
To qualify as an emergency, an event normally should be important, bad, sudden, and short-term. We sometimes deviate from these specifications. We may talk about a “minor emergency,” for example, or even a “slow-motion emergency.” But the essence of an emergency is the need to put aside your normal concerns and focus on this big new problem. In general, a situation isn’t an emergency if it doesn’t matter much, or if it comes on slowly with lots of time to prepare, or if it keeps going and you have little choice but to integrate it into how you live.
COVID was clearly an emergency in early 2020. It’s debatable whether it is still an emergency in mid-2022 – though even if it isn't, a virulent new variant might make it an emergency again.
I’m almost certain that most Americans think COVID is no longer an emergency. Many public health professionals, on the other hand, say it is. Some may actually think COVID is still an emergency; others may want to keep calling it an emergency to justify some of the COVID precautions implemented under emergency conditions. COVID is still a very significant public health problem. We need to debate which precautions to keep in place, though I doubt that continuing to call COVID an “emergency” advances the debate.
It’s no surprise that much of the public is highly motivated to “get over” COVID and return to normal life, while many public health professionals want the public to stay focused on the biggest public health emergency of their careers. This disconnect between public health and the public is probably inevitable, but it is one main reason for COVID polarization and declining public trust in public health officials.
The meaning of the term “emergency” isn’t usually debated explicitly, but public health’s implicit definition and the public’s implicit definition keep getting further apart. And when a public health professional breaks ranks and suggests publicly that maybe the COVID “emergency” is over at least for now, too many other public health professionals excoriate that individual on Twitter.
Continuing the emergency designation has a legal element too, especially with regard to Emergency Use Authorizations (EUAs). In 2004, when Congress created the Emergency Use Authorization, it was thinking about bioterrorism, not pandemics. It wanted a way to let the Food and Drug Administration okay drugs, vaccines, and other emergency countermeasures without making the manufacturer jump through all the hoops required for formal licensure. Under the 2004 law, once the Secretary of Health and Human Services has declared an emergency that threatens public health and safety, the FDA can issue EUAs for that emergency, authorizing temporary use of products that aren’t licensed at all, or aren’t licensed for a novel use. For each EUA it grants, the FDA has to determine that it thinks the product’s “known and potential benefits” outweigh its “known and potential risks,” and that there are no suitable licensed alternatives available.
On February 4, 2020, the HHS Secretary formally determined that COVID-19 constituted a public health emergency. He followed that up with four formal declarations that the emergency justified the issuance of EUAs for drugs, tests, ventilators, and other equipment. The one for drugs and biological products, including vaccines, is dated April 1, 2020.
Individual EUAs can be revoked if the product involved turns out ineffective or dangerous. (That’s what happened to the EUA for hydroxychloroquine.) Otherwise, they’re all good until the HHS Secretary formally declares the end of the emergency, or until licensure. I haven’t found any specifications for when the Secretary has to do that. As far as I can see, then, for purposes of EUAs the legal COVID “emergency” will last until some HHS Secretary decides to declare that it’s over.
That doesn’t mean the manufacturer of a vaccine or medicine can’t go ahead and apply for a license. But there’s not always much incentive to do so. According to the FDA, there are current EUAs in effect for anthrax, Ebola, MERS, Zika, and H7N9 influenza, among others. As far as the U.S. government is concerned, they’re all still emergencies.
COVID-related EUAs keep getting issued and amended. They are routinely covered in the media with virtually no attention to whether there’s actually still a COVID “emergency” that justifies approving unlicensed products to be used by millions of people.
EUAs are just the tip of the iceberg. A wide range of federal, state, and local government agencies have hooked various laws, regulations, and policies to the pandemic “emergency.” All sorts of requirements have been imposed or suspended; everything from Medicare rules to immigration rules to election rules have been altered. Some of these pandemic provisions are worth keeping in place after the pandemic “emergency” is declared to be over. Virtually all of them have constituencies that want them kept in place. But many of them were promulgated under “emergency” provisions, without meeting the procedural requirements for normal legislation or rulemaking. When the pandemic “emergency” ends, these provisions may automatically revert to their pre-pandemic status unless/until they’re promulgated in a non-emergency way – reason enough for many public health professionals to want the “emergency” status to continue.
Whether or not you think the medical and societal COVID “emergency” is over for now, the legal “emergency” shows no signs of abating.
Pandemic/endemic
There are lots of competing definitions of pandemics. What they all have in common is a disease, usually an infectious disease, that affects people (that’s the “-demic” part) and is spread widely over many countries on several continents (that’s the “pan-” part).
But even experts don’t tend to use the term “pandemic” unless three other conditions are met.
- A pandemic affects a lot of people. Rare diseases aren’t usually called pandemic no matter how geographically widespread the very few cases are.
- A pandemic is nontrivially harmful. We don’t talk about pandemics of minor illnesses.
- Perhaps most important, something about a pandemic is new – and new in a threatening way. Sometimes it’s a pathogen or at least a variant we haven’t seen before. Sometimes it’s a familiar pathogen that is suddenly affecting far more people in far more places than ever before. Or it has changed for the worse in some other way: more deadly, maybe, or more transmissible. Something is new and bad.
A circulating disease behaving normally – nothing’s new – isn’t “pandemic” even if it meets all the other pandemic specs. It is “endemic” in places where it’s circulating (which may or may not be worldwide). It’s baseline; it’s what we’ve come to expect.
Predictability is what the term “endemic” signifies. Endemic diseases aren’t necessarily ever-present. They may wax and wane. They may even disappear for years in some locations, while continuing to circulate in other places or in other species before they reappear. Their reappearances may follow a pattern, as with seasonal influenza; or they may be irregular, as with chikungunya and dengue. If the number of cases of an endemic disease is suddenly a lot higher than we’d normally expect in a particular area, that’s an epidemic. And if the epidemic is spreading virtually everywhere, that’s a pandemic. So pandemic diseases aren’t always new diseases. Seasonal flu is endemic in most of the world, but from time to time new flu strains go pandemic. In exactly the same way, COVID might become endemic and then give way to a new COVID variant that launches another COVID pandemic. Endemic diseases can go pandemic.
And pandemic diseases can become endemic, continuing to circulate (in some countries or all countries) without continuing to produce huge new waves in much of the planet at once. That’s what experts think will almost certainly happen with COVID, sooner or later. And that’s what some people think has already happened.
In recent decades, “pandemic” has become a scary word. So declaring an emerging infectious disease a pandemic is a fraught thing for an official agency to do. In 2009, the World Health Organization delayed calling swine flu a pandemic for weeks after virtually all experts were certain it was one. Again in the early months of COVID-19’s spread, there was great hesitation to declare or even predict a COVID pandemic. On February 22, 2020, my wife and colleague Jody Lanard and I wrote “Past Time to Tell the Public: It Will Probably Go Pandemic, and We Should All Prepare Now.” We weren’t the first to use the word “pandemic” about COVID. But we beat the World Health Organization and the U.S. Centers for Disease Control and Prevention.
Perhaps because the word “pandemic” is now so loaded and scary, the word “endemic” has come to seem to the public like something to hope for. Once COVID is endemic, many people figure (and some commentators claim), life will be normal again: no masks, no social distancing, no tests. It’s true that “endemic COVID” will be in some sense the new normal, so we’ll have to get used to it and decide how to live with it. But we don’t know how much worse than the old normal it will be. It’s possible that a COVID variant that stabilizes and becomes endemic could be worse (more deadly, more transmissible, more resistant to vaccines and treatments) than the pandemic variants we’ve endured so far.
To public health professionals, “endemic” has no connotation of “mild” or “manageable” or “easy to live with.” Malaria is endemic in much of Africa, where it is far deadlier than COVID. And to public health professionals, “pandemic” doesn’t connote horrific or devastating. The swine flu pandemic of 2009 was a genuine pandemic because it was a new nontrivial flu strain spreading worldwide, even though it was less deadly than many endemic flu seasons.
To much of the public on the other hand, “pandemic” does mean horrific and “endemic” does mean mild.
Endemic diseases can wax and wane, as I’ve said. Pandemics can wax and wane too, as we’ve all experienced with COVID. The difference is subtle. Pandemics keep surprising us, whereas the essence of endemicity is (relative) stability and predictability. Pretty obviously, then, it takes a while to know that a pandemic disease has become endemic. It’s only in hindsight that you can recognize the pattern.
But the fact that the term “pandemic” sounds bad to most people and “endemic” sounds a lot better has colored the question of when COVID will become endemic – or whether it’s endemic already. Most public health professionals want the public and policymakers to continue to take COVID precautions, and they want us to stay alert for the next shoe to drop. So they tend to insist that COVID isn’t endemic yet, and probably won’t be endemic any time soon. The people who speculate that endemicity is here or just around the corner tend to be the people who want everyone to calm down and get back to normal. Getting back to normal is championed by a minority of public health professionals; some of their more pessimistic colleagues disdain this viewpoint as “hopium.”
The actual meanings of “pandemic” and “endemic” notwithstanding, their connotations are what they are. Claiming that COVID is (or will soon be) “endemic” will be heard as claiming that it’s not worth worrying about so much anymore. If you want people to keep worrying, and especially if you want them to dial their worry level back up, “pandemic” is the more convincing word for your purposes.
All the choices are bad. If you use “pandemic” and “endemic” correctly, you will be widely misunderstood. If you use these two terms the way they’re understood by most people, you’ll be using them incorrectly. Since both uses are in circulation, when others use these two terms, you will have to figure out (or guess) which meanings they have in mind. And when you use these terms, people who are aware of the definitional problem will have to figure out (or guess) which meanings you have in mind.
The only decent choice is awkward and burdensome: Point out the definitional confusion and specify which meanings you have in mind. “When I say ‘pandemic’ and ‘endemic,’ I mean this and not that.”
I think that’s your best choice for all the terms I have discussed. It’s fine to avoid these terms when you can – but more often than not, you can’t. In 2005 I wrote a website column entitled “Risk Words You Can’t Use.” I was kidding myself. It’s almost impossible to talk about risk without using the terms in my 2005 column. And it’s almost impossible to talk about COVID without using the terms I’ve been discussing here.
I suppose you could try introducing new terms that don’t entail the ambiguities the old terms have acquired. But coining jargon poses its own daunting challenges to readers, even readers of technical articles. I certainly can’t recommend it when you’re aiming for clear communication to the general public. You’ve pretty much got to use the terms your readers already know, even though those terms have become language traps.
So once you decide to use these terms, be explicit about what you mean. Just as important: Be explicit about what you don’t mean. Warn your audience that other people often use the terms differently than you are using them. Or expect to be widely misunderstood.
And if you decide to go ahead and use a term in a way you know will be widely misunderstood, don’t you dare pretend later that you were clear and “the public is confused.”
Copyright © 2022 by Peter M. Sandman
