Category: pandemic

A good article on Covid-19 testing, a way forward, and where we screwed up

October 2, 2020 • 1:45 pm

I commend to your attention this article in The Atlantic on Covid-19.  The authors, Robinson Meyer and Alexis Madrigal (staff writers on science and technology), discuss the best ways to stem the pandemic, the advantages and disadvantages of various tests for infection, and how the U.S. screwed up in its response. Click on the screenshot to read:

It’s very good and clear on the science, though I can’t judge the efficacy of their plan, which involves continual “spit testing”, a very quick but not completely accurate way of diagnosing the virus through its antigens, like the spike protein. PCR tests are much more accurate, but are expensive and take time, yet if we do continual antigen testing, the errors tend to go away, and we could get results in 15 minutes on a strip of paper. You could do this before flights, before entering restaurants, and so on.

The problems with PCR tests are numerous, the most serious being that it can’t distinguish between a new infection, which is contagious, and one that’s a month old, which isn’t contagious. And they’re much more expensive to distribute and more time-consuming to diagnose. The authors discuss “pooling”, a cute way to cut down on money and time by bundling together swab results (or spit) from a bunch of people. If there’s no positive in the mix, you needn’t go further. If there is, you subdivide, and so on.

The main reason we screwed up is, of course, Trump. In this case the authors indict him for failing to invoke the Defense Production Act, a wartime regulation, still on the books, that allows the government to force companies to mass-produce things in case of a national crisis, like this one. One excerpt:

. . . the Trump administration has addressed the lack of testing as if it is a nuisance, not a national-security threat. In March and April, the White House encouraged as many different PCR companies to sell COVID-19 tests as possible, declining to endorse any one option. While this idea allowed for competition in theory, it was a nightmare in practice. It effectively forced major labs to invest in several different types of PCR machines at the same time, and to be ready to switch among them as needed, lest a reagent run short. Today, the government cannot use the Defense Production Act to remedy the shortage of PCR machines or reagents—because the private labs running the tests are too invested in too many different machines.

Because of its trust in PCR, and its assumption that the pandemic would quickly abate, the administration also failed to encourage companies with alternative testing technologies to develop their products. Many companies that could have started work in April waited on the sidelines, because it wasn’t clear whether investing in COVID-19 testing would make sense, Sri Kosaraju, a member of the Testing for America governing council and a former director at JP Morgan, told us.

The Trump administration hoped that the free market would right this imbalance. But firms had no incentive to invest in testing, or assurance that their investments would pay off. Consider the high costs of building an automated testing factory, as Ginkgo is doing, said Stuelpnagel, the Illumina co-founder. A company would typically amortize the costs of that investment over three to five years. But that calculation breaks down in the pandemic. “There’s no way that we’re doing high-throughput COVID testing five years from now. And I hope there’s not COVID testing being done three years from now that would require this scale of lab,” he said. Companies aren’t built to deal with that level of uncertainty, or to serve a market that would dramatically shrink, or disappear altogether, if their product did its job. Even if the experimentation would benefit the public, it doesn’t make sense for individual businesses to take on those risks.

So nothing happened—for months. Only in the past few weeks has the federal government begun to address these concerns.

Even if you don’t see the use of mass antigen testing as a big step forward until (and if) we get an effective vaccine, this article will teach you a lot.

The efficacy of different face masks in reducing droplet emission

August 11, 2020 • 8:45 am

What kind of mask should you wear during the pandemic? (Yes, you should always wear one if you’re around people.) A new paper in Science Advances (click on screenshot below, free pdf here, reference at bottom) gives a tentative answer to that question, assuming that you’re wearing the mask to avoid infecting other people. And that presumes that you’re carrying the virus, symptomatic or asymptomatic. (If you’re symptomatic, you shouldn’t be going out anyway.)

Note that the question here is probably not the one many people have, which is “Which kind of mask should I wear if I don’t want to get infected by others?” This is not the same question, as some of the features of the masks that decrease their efficacy (i.e., breaking up expelled droplets into smaller droplets), might not work in reverse. In general, though, those masks that reduce the number of droplets expelled when you’re speaking should also reduce the number of droplets coming in when someone’s speaking to you. The short answer is that fitted N95 masks (which most of us can’t get) are the best at keeping your droplets in,, followed by surgical masks and then two-layer masks of polyproplylene and cotton (multiple layers, as you might guess, are important). N95 masks with valves to allow you to exhale aren’t that great, as you might also expect, and knitted or fleece masks, particularly with one layer, are pretty useless. Bandanas are dreadful—barely better than controls, yet I’ve seen many people wear them.

If you can see through your mask, it’s a clue that it’s not a good one.

Click to see the article, which I’ve summarized below:

The authors used an inexpensive setup (roughly $200) to measure expelled droplets when individuals spoke through a mask for 40 seconds, repeating the phrase, “Stay healthy, people” five times, with the protocol repeated ten times for each mask and the control (no mask) trial. Droplet size and number were measured in a dark chamber using a laser and a cellphone camera.

I’m not sure why the authors are so keen on the inexpensiveness of the apparatus, as individuals aren’t going to do this at home, and a more professional setup in a lab would surely reveal general results that wouldn’t need to be replicated on this inexpensive apparatus. At any rate, the apparatus is diagrammed below, with the diagram and caption from the paper:

(From paper): Fig. 1 Schematic of the experimental setup. A laser beam is expanded vertically by a cylindrical lens and shined through slits in the enclosure. The camera is located at the back of the box, a hole for the speaker in the front. The inset shows scattering for water particles from a spray bottle with the front of the box removed. Photo Credit: Martin Fischer, Duke University.

They used a single speaker for most of the tests to reduce variation, but also used four other speakers on 3 masks and the no-mask control (also ten replicates each) to test the replicability of the results. And they tested fourteen masks, shown below. I’ll describe them as they do in the paper, for the table they give is hard to read.

Masks marked with an asterisks were tested by four different speakers saying the same thing (one different speaker for each of three masks and the control), while the rest of the masks were tested with a single speaker. Numbers below correspond to the diagram above except for the control (no mask) and “swath” mask, which I assume is like a turtleneck pulled up.

  1. “Surgical” mask*.  3 layer
  2. Valved N95
  3. Knitted
  4. “Polyprop”: 2 layer polypropylene apron mask
  5. “Poly/cotton: 3 later cottong/polypropylene/cotton mask
  6. MaxAT mask: 1 layer Maxima AT mask
  7. “Cotton2” 2-layer cotton pleated mask
  8. “Cotton4”: 2-layer cotton, Olson-style mask
  9. “Cotton3”: 2-layer cotton pleated style mask
  10. “Cotton1” 1-layer cotton, pleated style mask
  11. Fleece: Gaiter type neck fleece
  12. “Bandana”*: Double layer bandana
  13. Cotton5*: 2-layer cotton, pleated-style mask
  14. Fitted n95 mask: no exhalation valve and fitted
  15. “Swath” mask: swath of polypropylene mask material (not shown)
  16. “None”: control 

And the results in short are below, showing the number of droplet counts, relative to the control (green dot to the right). 1 means as many droplets expelled as with no mask, while close to zero means almost no droplets expelled relative to no mask. All black dots represent the means of ten replicates with a single (and the same0 speaker (lines are standard deviations), while the four colored dots represent the means for four other speakers. These are close to the single speaker using all three tested, giving confidence that the results may be general. Have a look:

(From paper): Droplet transmission through face masks. (A) Relative droplet transmission through the corresponding mask. Each solid data point represents the mean and standard deviation over 10 trials for the same mask, normalized to the control trial (no mask), and tested by one speaker. The hollow data points are the mean and standard deviations of the relative counts over four speakers. A plot with a logarithmic scale is shown in Supplementary Fig. S1.

 

Swath masks, which did well (fifth best) but aren’t shown, involve wearing swaths of polypropylene mask material, as shown below (source of photo is here, which gives a simpler summary of the recommendations):
Even cotton masks are okay, with 1 layer being better than pleated, but when you get to the knitted masks, bandanas, and fleece masks (which produced more droplets, probably by breaking up the big ones into smaller ones), you’d best avoid them.

So what does this mean for you? Assuming that you can’t get a fitted N95 mask, your best bets are surgical masks, which are available (remember, these are designed to keep medical professionals from exhaling microbes into patients’ wounds and bodies), multilayered poly/cotton and poly/propylene (masks 4 and 5 above), or a “swath” of mask material (polypropylene), shown above. If you have a choice, get a surgical mask, or wear masks 4 or 5. But, except for fleece, some mask is better than no mask.

Caveats: Remember first that these masks are tested for EXHALED DROPLETS, not droplets inhaled, which would be harder to test. So sites that imply that these are the “best masks for you to wear” are leaving out that crucial information. I suspect there will be a correlation, but perhaps not a perfect one.

The authors offer other caveats, like the inability to measure total droplets in the chamber, and their use of a cellphone camera, which reduces sensitivity to detecting laser-reflecting particles. Even so, the droplets that could be detected in this method are half a micron: 0.001 mm or 0.00004 inches, which are small. They also emphasize the small number of speakers (five, with most masks tested using a single—and the same—speaker), and that would warrant replication, since some people speak more or less forcefully than the speaker they tested.

These are the necessary reservations, but these are valuable data nonetheless. But again, remember that these data tell you how to protect other people from your exhaled droplets. Even so, I’d suggest getting yourself some 3-layer surgical masks if you can. I believe you can re-use them if you let them disinfect for a week or so before you wear them again, but I’m not an expert here, so take that with a grain of salt and do your own checking.

h/t: SImon

_____________

Fischer, E. P., M. C. Fischer, D. Grass, I. Henrion, W. S. Warren, and E. Westman. 2020. Low-cost measurement of facemask efficacy for filtering expelled droplets during speech. Science Advances:eabd3083.

What’s the risk of death from coronavirus? A summary from Nature

June 25, 2020 • 9:00 am

A new article in Nature has gathered statistics from several studies to come up with an estimate of the overall death rate from coronavirus (click on screenshot below to read it, pdf here). If you’re paywalled, a judicious request might work. I’ll put the latest estimates at the bottom, as you’ll need to read the preliminary information since these figures come with many caveats.

As the article notes, when you’re estimating fatality rates, the gold standard is called the “infection fatality rate” (IFR), which is the proportion of all infected people, including those who are asymptomatic or haven’t been tested, who will die from the disease at issue.  You can imagine the difficulty of estimating this. While we can get an accurate handle on the fatality rate among those known to have the disease, that’s only a part of the statistic, and may either under- or over-estimate the IFR.  Further, if you have antibodies against the virus, you may have recovered from an infection without knowing you’ve had it. Yet that data must also be incorporated into the IFR, and antibody testing is not the same thing as testing for the virus. (How many of you have been antibody tested?) One study from Germany showed that 15.5% of the people in a town that had an outbreak had coronavirus antibodies—five times the proportion of people known to have had coronavirus at the time. Not doing antibody testing would have drastically overestimated the IFR.

Another complication is that some countries don’t test postmortem, and, importantly, the fatality rate in different groups (age, ethnicity, class and wealth, comorbidities, access to healthcare) haven’t been compiled thoroughly. Of course, if you’re infected or in a group that doesn’t have the average IFR, you’ll won’t care that much about the overall rate—you’ll want to know your own chance of dying.

Why do we need these data? As Nature notes:

Getting the number right is important because it helps governments and individuals to determine appropriate responses. “Calculate too low an IFR, and a community could underreact, and be underprepared. Too high, and the overreaction could be at best expensive, and at worst [could] also add harms from the overuse of interventions like lockdowns,” says Hilda Bastian, who studies evidence-based medicine, and is a PhD candidate at Bond University in the Gold Coast, Australia.

The article outlines other complications, but there’s no need to go into them here. I’ll just add that Nature presents the rate of six studies from five countries, and there isn’t much variance among them, with the first study, using data taken from a cruise ship in which everyone was tested, gives the only estimate of the true IFR. But the sample (3,711 people) was small.

So here are the data at hand, and realize that there are problems with all of the studies. But it is interesting that they tend to converge on a value of 0.5% to 1%. (Of course, if you’re old like me, or have other medical issues, this will be an underestimate):

Some scientists impute the small scatter to “luck” (whatever they mean by that) or coincidence, and virtually none of the data have been published in peer-reviewed manuscripts.  Finally, of course, we need to know the death rate for different groups, which will help in figuring out individual treatment, though for epidemiological purposes the IFR is necessary—if it’s from a random sample of people. (Nature cites one study from Switzerland estimating an overall IFR of 0.6%, but a tenfold higher rate of 5.6% for people 65 or older.

The lesson: so far across several populations, one’s chance of dying should they contract the disease is about 0.5% to 1%. But your mileage may vary (I have a lot of mileage and my figure would be higher), and it’s early days for these statistics.

Hydroxychloroquine and chloroquine are not only useless in treating Covid-19, but very harmful

May 22, 2020 • 10:30 am

UPDATES: The discussion of this paper has gone back and forth, and the cause is that neither Alex nor I read the paper carefully. I just skimmed it, and Alex read it quickly but paid most of his attention to the tables. That led to this first update in which he concluded (and I agreed) that the study wasn’t very useful at all:

UPDATE 1:

In two comments below, my own physician, Dr. Alex Lickerman, who carefully read the study I describe below  (see his first comment and his second), noted that the sickest patients were the ones more likely to be given the chloroquine drugs, and were also the patients with the highest comorbidities—factors like heart disease that would tend to make them sicker. In other words, as Alex notes,

So–was it the drugs that were responsible for their increased likelihood of death or the risk factors already known to increase the likelihood of death? We simply can’t tell from this study. This is the problem with the observational study design. We CAN almost certainly say that hydroxychloroquine and its ilk don’t improve survival in COVID-19, but whether or not they increase mortality in COVID-19 we don’t yet know.

This is only part of his analysis; read the whole thing in comment 1. He also notes in his second comment that there is no evidence that using hydroxychloroquine as a preventive has any benefit.

I am guilty of not having detected the flaws in the study, which are, I found, not even clearly pointed out by its authors in the traditional “here-are-some-weaknesses-in-this-study” part of the paper, and I thank Alex for the clarification. But even more culpable are the reviewers of that study, who did not insist on a clear outline of its limitations, as well as the medical/science journalists, who touted the study uncritically (like me!) Alex has helped me learn that many medical studies, even in journals as reputable as The Lancet, are pitifully weak or even fatally flawed.

UPDATE 2: In a very useful comment, reader BillyJoe noted that the paper does indeed say that the paper controlled to some extent for comorbidities, so its conclusions are stronger than we thought: we can have more confidence in its conclusions that hydroxychloroquine and chloroquine are positively dangerous when given to people sick with Covid-19.  Alex then said yes, he was wrong about the study not taking into account comorbidities, and has posted this comment in the thread:

Yikes! I’m guilty of the same criticism I made of others: not reading the trial carefully. You are absolutely correct that the authors made good-faith attempts to control for the inequalities/confounding variables between treatment groups. This is still a statistical adjustment, not a direct measurement as would be done in a randomized trial, so must be taken with a grain of salt, but to the authors’ credit, they address that.

The problem does remain that when you do the randomized trials, results are often different because of confounding variables the authors didn’t know about and therefore weren’t able to statistically adjust for—but also because sometimes their multivariate analysis (meant to adjust for known confounding variables)–also wasn’t adequate. So we still need a randomized trial to really know the answer definitively.

Nevertheless, I withdraw my criticism of the authors and the Lancet reviewers. I guess this is a good example of why science and statistics should always be done by more than one person! I’m quite embarrassed to have made this mistake. I apologize to readers and to our host, who must now fall on his sword with me.

___________________

 

Well, it’s official (I mean, of course, “provisional”): a new and large study published in the medical journal The Lancet (second link below; click screenshots to go to both articles) confirms that hydroxychloroquine and chloroquine not only don’t help patients seriously ill with Covid -19, but increases their mortality (in other words, kills them). Below is the CNN report, with a more layperson-y summary (my emphasis):

Researchers analyzed data from more than 96,000 patients with confirmed Covid-19 from 671 hospitals. All were hospitalized from late December to mid-April, and had died or been discharged by April 21.

Just below 15,000 patients were treated with the antimalarial drugs hydroxychloroquine or chloroquine, or one of those drugs combined with an antibiotic.

All four of those treatments were linked with a higher risk of dying in the hospital. About 1 in 11 patients in the control group died in the hospital. About 1 in 6 patients treated with chloroquine or hydroxychloroquine alone died in the hospital. About 1 in 5 treated with chloroquine and an antibiotic died and almost 1 in 4 treated with hydroxychloroquine and an antibiotic died. 

Researchers also found that serious cardiac arrhythmias were more common among patients receiving any of the four treatments. The largest increase was among the group treated with hydroxychloroquine and an antibiotic; 8% of those patients developed a heart arrhythmia, compared with 0.3% of patients in the control group.

Note that the mortality in the control group was about 9%, rising to about 16% with chloroquine or hydroxychloroquine alone, and 20-25% when either of the chloroquine drugs was supplemented with an antibiotic (remember, antibiotics kill bacteria, not viruses like Covid-19. Clearly, refraining from using these drugs is the wisest course of action.

Here’s The Lancet study that went online today, and the findings and summary, while more comprehensive, are the same (“macrolides”, as is meant here, refers to a class of antibiotics that includes erythromycin). If you can’t access the paper, a judicious inquiry will yield it.

 

Findings:

96 032 patients (mean age 53·8 years, 46·3% women) with COVID-19 were hospitalised during the study period and met the inclusion criteria. Of these, 14 888 patients were in the treatment groups (1868 received chloroquine, 3783 received chloroquine with a macrolide, 3016 received hydroxychloroquine, and 6221 received hydroxychloroquine with a macrolide) and 81 144 patients were in the control group. 10 698 (11·1%) patients died in hospital. After controlling for multiple confounding factors (age, sex, race or ethnicity, body-mass index, underlying cardiovascular disease and its risk factors, diabetes, underlying lung disease, smoking, immunosuppressed condition, and baseline disease severity), when compared with mortality in the control group (9·3%), hydroxychloroquine (18·0%; hazard ratio 1·335, 95% CI 1·223–1·457), hydroxychloroquine with a macrolide (23·8%; 1·447, 1·368–1·531), chloroquine (16·4%; 1·365, 1·218–1·531), and chloroquine with a macrolide (22·2%; 1·368, 1·273–1·469) were each independently associated with an increased risk of in-hospital mortality. Compared with the control group (0·3%), hydroxychloroquine (6·1%; 2·369, 1·935–2·900), hydroxychloroquine with a macrolide (8·1%; 5·106, 4·106–5·983), chloroquine (4·3%; 3·561, 2·760–4·596), and chloroquine with a macrolide (6·5%; 4·011, 3·344–4·812) were independently associated with an increased risk of de-novo ventricular arrhythmia during hospitalisation.

Interpretation:

We were unable to confirm a benefit of hydroxychloroquine or chloroquine, when used alone or with a macrolide, on in-hospital outcomes for COVID-19. Each of these drug regimens was associated with decreased in-hospital survival and an increased frequency of ventricular arrhythmias when used for treatment of COVID-19.
You know the upshot: DO NOT TAKE HYDROXYCHLOROQUINE as a Covid-19 drug, as it causes heart problems and, overall, is much worse than standard treatment, doubling your chance of dying. This also means that since there’s yet no evidence that the drug staves off the virus, the side effects on those taking it as a preventive (like Trump) will also include heart issues. That’s been known from earlier but smaller studies.

What Trump is doing is not only injurious to himself (I suspect his heart is a ticking time bomb given his weight and penchant for McDonald’s food), but sets a terrible example to the public. It’s a President flaunting quackery, and of course his supporters are more likely to dose themselves or ask for the drug if they get the virus. The saving grace is that no decent doctor will give an infected patient hydroxychloroquine.

But remember, Trump’s osteopath official physician, Sean Conley, in consultation with Trump, decided that the potential benefits outweighed the risks when prescribing Trump the drug as a preventive. That’s doubly shameful: a faux President being treated by a quack physician with a useless drug, and the President bragging about it and lying about the drug’s “benefits.” No wonder other countries look upon the U.S. with pity! 

Trump’s doctor, an osteopath, approved his patient’s use of hydroxychloroquine. That’s quackery, regardless of the doctor’s credentials.

May 20, 2020 • 11:00 am

As you know, last week Donald Trump asserted that he was taking hydroxychloroquine as a preventive for coronavirus, said that thousands of front-line medical workers were also taking it for the same reason, and argued that the drug had proved efficacious against the virus.  Yesterday I thought that all three claims might be lies (the first two certainly are), but now the president’s physician, Sean Conley, has weighed in saying that the Prez is indeed dosing himself with the nostrum, and on Conley’s advice.

It turns out that Sean Conley is an osteopath, as outlined in this Guardian article (click to read the screenshot). Here are his credentials:

Conley received his Doctor of Osteopathic Medicine degree from the Philadelphia College of Osteopathic Medicine in 2006. He is a 2013 graduate of the Emergency Medicine Residency Program of Naval Medical Center Portsmouth in Portsmouth, Virginia. He received the Honor Graduate Award, Nurses’ Choice Award for Outstanding Senior Resident Award, and the Resident Research Award.

 

Here’s the letter testifying to Trump’s use of the drug on the advice of Conley:

Now I’m not going to say that Conley is a quack just because he’s a DO rather than an MD, though it is odd. Many osteopaths have training nearly identical to that of MDs, and some, I’ve heard, are fine physicians. But this one isn’t.

For Conley is violating the first part of the Hippocratic Oath (well, the revised version of that oath): “First do no harm.” And that’s the problem with hydroxychloroquine: it’s not only not efficacious against coronavirus, but it can be dangerous, causing heart problems, hallucinations, paranoia, and other “neuropsychiatric symptoms” (see article below), which gives one pause.

And Trump isn’t exactly the picture of health. According to the Guardian, he weighs around 239 pounds, just at the threshold of being “obese” (not “morbidly obese”, though, as Nancy Pelosi stated), sleeps 4-5 hours per night, eats a lot of junk food (especially from McDonald’s), and gets no exercise save golf (he probably uses a cart).

Only a fool, I think, would prescribe a useless drug that could be dangerous for someone in Trump’s condition—indeed, for someone in any condition.  Conley is not practicing evidence-based medicine, and in behaving this way is endangering his only patient. But give Trump some “credit”, too, for he knows the stuff doesn’t work and is still taking it—perhaps to reassure Americans that there can be a preventive. As Trump argued, “What do you have to lose?” My answer, “Your life, fool!” Conley’s claim in the letter that “the potential benefit from treatment outweighed the relative risks” is pure cant—in fact, the statement cannot possibly be true based on the data we have.

The FDA itself has declared that hydroxychloroquine “has not been shown to be safe and effective”. What on earth is Trump’s doctor doing?

At any rate, the article below from Just Security (click on screenshot), ends with 9 questions for “Dr.” Conley.

Yes, it would be good if Conley answered those questions, but he won’t. Here are four of them:

4. What is the complete medical record of Donald Trump that might put him at risk of dangerous side effects in taking the drug? More specifically, does Trump have any history of heart trouble or disease, or any other medical condition that would make it dangerous for him to take this drug?

5. Conley’s memo states: “After numerous discussions…we concluded the potential benefit from the treatment outweighed the relative risks.” The use of “we” in this sentence is notable. Is Dr. Conley saying that he would not have recommended use of the drug? In other words, is Dr. Conley hiding that Trump’s views outweighed the physician’s sound medical advice of the risks? Why did it take numerous discussions?

6. If it is true that Trump is taking this drug, why has Dr. Conley knowingly prescribed a drug that the FDA and other authorities have determined is potentially fatal, and, moreover, whose beneficial effects on treating COVID-19 are unproven? Why has Dr. Conley prescribed a drug possibly risking the life of the president and in violation of FDA guidelines, medical standards, ethics, and professionalism?

9. Medical researchers have concluded that hydroxychloroquine may cause neuropsychiatric symptoms, “including agitation, insomnia, confusion, mania, hallucinations, paranoia, depression, catatonia, psychosis, and suicidal ideation.” Has Dr. Conley properly assessed his patient, President Trump, for his susceptibility to these symptoms? Since Trump has been taking the drug has Dr. Conley observed that it has produced or exacerbated any of these symptoms in President Trump?

I know that some people will be wishing for Trump’s demise from this drug (you can see it among the usual-suspect bloggers). I don’t wish for anyone’s demise, though I want Trump to be defeated in November. But by putting his imprimatur on the use of hydroxychloroquine to prevent Covid-19 infection, Conley is setting a terrible example not just for those worried about the virus, but for anyone who has confidence in modern medicine.

Sean Conley, quack

h/t: David

Two anniversaries today, both marking the end of wars, one against people, the other against a virus

May 8, 2020 • 9:45 am

I missed this because I left out today’s anniversaries in the Hili dialogue. There are two big ones today, both pointed out by Fiona Fox, director of the Science Media Centre in Britain. Dr. Fox quotes remembrances from two of her experts (h/t Steve Jones):

From Professor Geoffrey L Smith FRS, Head, Department of Pathology, University of Cambridge

Today is VE-Day. [JAC: the 75th anniversary.] It is also the 40th anniversary of the WHO declaration of the eradication of smallpox, which in the 20th century alone killed an estimated 400 million people, many more people than in both world wars. Whilst in the midst of another viral pandemic, we should remember the magnificent role that WHO played in ridding the world of smallpox and the power of vaccination. WHO should be encouraged, supported and funded in its efforts to control and eliminate COVID-19.

Quoting Macaulay, History of England 5, 2468-70, (1914)

“Smallpox, the most terrible of all the ministers of death: The smallpox was always present, filling the churchyard with corpses, tormenting with constant fears all whom it had not yet stricken, leaving on those whose lives it spared the hideous traces of its power, turning the babe into a changeling at which the mother shuddered, and making the eyes and cheeks of the betrothed maidens objects of horror to the lover”

Edward Jenner predicted the global eradication of smallpox in 1801 when he said

“it now becomes too manifest to admit of controversy that the annihilation of the smallpox, the most dreadful scourge of the human species, must be the final result of this practice”

Now let us do this to COVID-19.

JAC: Here’s what smallpox does even when it spares a life. This child will be irreparably scarred:

From Wikipedia: A child with smallpox in Bangladesh in 1973. The bumps filled with thick fluid and a depression or dimple in the center are characteristic.

And here’s the resolution: short, sweet, and succinct:

From Michael A. Skinner, PhD FRSB, Reader in Virology, Imperial College London

With attention focused on the 75th anniversary of VE Day, and distracted by the COVID-19 pandemic, an important anniversary may pass us by, with some relevance to our present situation.

On 8 May 1980, the 33rd  Assembly of the World Health Organisation officially endorsed the successful eradication of smallpox in October 1979.

Several points are noteworthy:

Smallpox (with a mortality rate of about 30%) killed 300 million people in the 20th century alone (three times the death toll of both World Wars), and 500 million in its final hundred years).

– Smallpox is believed (informed by virus genome sequence data)  to have “jumped” from an animal source millennia ago (there is good evidence it was present in ancient Egypt)

– No one can therefore seriously suggest that the variola virus that causes it arose from anything other than a natural source.

– Infections were controlled and reduced by vaccination; the disease was finally eradicated by a massive global campaign spearheaded by WHO.

– That campaign relied on rigorous and extensive field epidemiology, using a “track, trace and [ring] vaccinate” approach (with no rapid molecular diagnostics available at the time, diagnosis relied on recognition of the distinctive lesions and other symptoms)

– The final battlegrounds for the eradication were Bangladesh, Ethiopia, Kenya and Somalia, where the last natural case was identified.

Refs.

WHO commemoration of the 40th anniversary of smallpox. 

Message below about celebratory press conference (including link) described on this page

And here’s a quiz for you. Another deadly viral disease, but in animals, has also been completely eliminated by a combination of monitoring and vaccination. This one was declared eliminated in 2011. Do you know the disease? Look here for the answer.

 

 

Faith-soaked physician to conduct study of prayer in curing Covid-19

May 2, 2020 • 10:30 am

Does prayer work to cure diseases? Anecdotal evidence from Lourdes, where amputees and the eyeless aren’t cured, suggest not. And we all know the results of the Templeton-funded study of the effects of intercessory prayer on recovery of cardiac patients, the most thorough study of intercessory prayer yet, involving over 1800 patients (Benson et al. 2006). Those results: no effect of prayer; or, as the study notes:

Our study had 2 main findings. First, intercessory prayer itself had no effect on whether complications occurred after CABG. Second, patients who were certain that intercessors would pray for them had a higher rate of complications than patients who were uncertain but did receive intercessory prayer.

In other words, the only effect even close to being statistically significant was that patients who knew they were being prayed for had more complications than patients not prayed for. Prayer worked in the wrong direction! That must have disappointed Templeton!

Further, a 2006 meta-analysis of 14 studies of medical effects of intercessory prayer showed no significant effects overall. The results and conclusions are in a red box below; note that the authors advise “that further resources not be allocated to this line of research.” (Click on screenshot to go to the study.)

But someone disagrees about there being no more need for research: Dr. Dhanunjaya Lakkireddy, a cardiologist at the Kansas City Heart Rhythm Institute. Lakireddy is doing a double-blind study of the effect of intercessory prayer on the mortality rates (and other indices of “being cured”) from Covid-19. NPR, which always has a weakness for the numinous, highlights it in the article below (click on screenshot):

There’s no audio yet, but the site says there will be. UPDATE: The online version is here, and it’s short (2 minutes) and not the same as the transcript. But there’s little difference between them.

Lakkireddy plans a study of 1000 patients in intensive care with Covid-19. Lakireddy is a true believer, and it shows in his comments to NPR (below). The emphases are mine.

“We all believe in science, and we also believe in faith,” Lakkireddy says. “If there is a supernatural power, which a lot of us believe, would that power of prayer and divine intervention change the outcomes in a concerted fashion? That was our question.”

We believe in faith? What does that mean? Faith is belief—belief without strong or convincing evidence! Perhaps Lakkkireddy means he believes that faith can cure, which is what he’s testing. But saying that we “believe” in science is a bête noire of mine, and bothered me enough that I wrote an article in Slate arguing that “faith” in science really means “confidence in the reliability of the methods and its outcomes”, not “blind adherence to unevidenced claims,” which is what religious faith is.

But wait! There’s more! Lakkireddy, who has dipped his toes into several faiths, and clearly has a weakness for the numinous, goes on:

The investigators will assess how long the patients remain on ventilators, how many suffer from organ failure, how quickly they are released from intensive care and how many die.

Lakkireddy describes himself as “born into Hinduism,” but he says he attended a Catholic school and has spent time in synagogues, Buddhist monasteries, and mosques.

“I believe in the power of all religions,” he says. “I think if we believe in the wonders of God and the universal good of any religion, then we’ve got to combine hands and join the forces of each of these faiths together for the single cause of saving humanity from this pandemic.”

He already knows that religion will help with the pandemic! Is this the right guy to conduct a double-blind study on Covid-19? He has an interest in the outcome, of course, but one can only hope that he’s being supervised by other people to ensure rigorous, double-blind methodology. But wait! There’s still more!

Scientific studies of the power of prayer have been attempted before. Lakkireddy’s description of his study lists six previous clinical trials involving religious intervention. Some showed slight improvement for patients receiving prayer. Other studies have found no significant prayer effect.

Note that the “other studies” links to the meta-analysis above: a summary of ALL studies, and a summary that shows no effect of prayer overall. As far as I can see, previous studies cited by Lakkireddy were already incorporated into that meta-analysis. Shame on NPR for pretending that a meta-analysis of 14 studies is the same thing as a group of studies.

Lakkireddy says he can not explain how people praying remotely for someone they don’t know (or a group of people,) could actually make a difference in their health outcomes, and he acknowledges that some of his medical colleagues have had “a mixed reaction” to his study proposal.

“Even from my wife, who’s a physician herself,” he says. “She was skeptical. She was, like, ‘OK, what is it that you’re looking at?”

Lakkireddy says he has no idea what he will find. “But it’s not like we’re putting anyone at risk,” he says. “A miracle could happen. There’s always hope, right?”

Yes, there’s always hope of a miracle. But given the meta-analysis above, which recommends that “we should stop this nonsense”, there are no data to give us hope. There are data to give us no hope. And hope is really something that should not be entertained by a principal investigator, for that gives rise to confirmation bias. You could, for example, do p-hacking, hoping that at least one outcome will be in your favor, reaching statistical significance.

You can learn more about Lakkireddy’s study at the “clinical trials” section of the National Institutes of Medicine, which registers all proposed and ongoing trials. It also adds the interesting tidbit that Lakkireddy’s prayers will involve those of five different denominations. Is Lakireddy testing which religion is “right”, i.e., prayers to its god are the only ones that work?

I can find no information about funding on the site.

Brief Summary:

This is a multicenter; double blind randomized controlled study investigating the role of remote intercessory multi-denominational prayer on clinical outcomes in COVID-19 + patients in the intensive care unit. All patients enrolled will be randomized to use of prayer vs. no prayer in a 1:1 ratio. Each patient randomized to the prayer arm will receive a “universal” prayer offered by 5 religious denominations (Christianity, Hinduism, Islam, Judaism and Buddhism) in addition to standard of care. Whereas the patients randomized to the control arm will receive standard of care outlined by their medical teams. During ICU stay, patients will have serial assessment of multi-organ function and APACHE-II/SOFA scores serial evaluation performed on a daily basis until discharge. Data assessed include those listed below.

I’m torn between thinking this is a waste of time, as an overview of previous studies shows no effect of prayer—not surprising in view of the inefficacy of God in “faith based healing” as practiced by various Christian sects, of the failure of prayer to restore missing limbs and eyes, and of no evidence for the presence of any God)—and, on the other hand, wanting it to proceed because, if the study is done properly and with sufficient rigor, it’s not going to support evidence for a prayer-answering God. (I do think that, as a true believer, Lakkireddy should let others run the study and analyze its results).

Now it is possible that the study will “work”: either prayer will have a significant effect, or prayers for one religion will have a significant effect. (If only Jewish prayers work, for example, will Christians, Hindus, and Muslims immediately abandon their faith? I wouldn’t bank on it!).

In Faith Versus Fact I detail what kind of results would make me (tentatively) accept a deity. Consistent effects of one kind of prayer (or all kinds of prayers) on healing would make me sit up and take notice, that’s for sure. But we haven’t had that.

Two more points. First, if the study shows no effect of prayer, I expect NPR to do a followup reporting that result. (They surely would if they find a positive effect!). And I will badger them about this after the study ends in August.

Finally, the mere existence of this study gives the lie to religionists’ claim that “Science cannot study the supernatural, for that realm is off limits to naturalistic analysis.” But, as even Lakkireddy admits, this is a case in which science can indeed study supernatural claims! But we shall see if they’re supported. These studies usually have a one-way effect: if they show an effect, the faithful trumpet it to the skies. But if they show no effect, the faithful quietly shelve the results and speak no more of them.

h/t: Bob