"The decision to buy a handgun for the first time is typically motivated by self-protection. But..."

"... it also raises the purchasers’ risk of deliberately shooting themselves by ninefold on average, with the danger most acute in the weeks after purchase, scientists reported on Wednesday. The risk remains elevated for years, they said," the NYT reports.

Thanks, scientists, but did you exclude the people who bought guns because they'd already formed an intention to shoot themselves? Or maybe it's just the NYT that wrote it that way, making it sound as though there are a lot of people who buy a handgun for self-defense and then somehow — once they've got that handgun — embark for the first time into suicidal ideation.

Of course, it's easy to see that people who have a gun are more likely to shoot themselves than people who don't have a gun, but they're talking about first-time handgun owners. So the comparison is first-time handgun owners and longterm handgun owners? NO!

The study tracked nearly 700,000 first-time handgun buyers, year by year, and compared them with similar non-owners, breaking out risk by gender. Men who bought a gun for the first time were eight times as likely to kill themselves by gunshot in the subsequent 12 years than non-owners; women were 35 times as likely to do so.

Well, the non-owners number would be extremely small, so 8 times that and even 35 times doesn't sound so big.

Toward the end of the article, there's a reference to "so-called reverse causation." That's the situation that I mentioned, above, that the handgun was bought for the purpose of suicide, but the researchers had no way to tell the difference between these people and those who bought the handgun for self-protection (and the protection of others).

I got the feeling the article was written to inspire readers not to arm themselves lest the gun would change them into a person who'd commit suicide. This is the message that if you don't want to die, don't arm yourself because you'll be arming your most-likely murderer: YOU!

Testing... your intelligence.

Not providing context on the increase in testing is such a basic error, and has been so widespread, that it's revealing about the media's goals. It's more interested in telling plausibly-true stories ("narratives") that sound smart to its audience than in accuracy/truth per se. https://t.co/abYbgkVFbj

— Nate Silver (@NateSilver538) May 7, 2020

"It’s a Bayesian thing. Part of Bayesian reasoning is to think like a Bayesian; another part is to assess other people’s conclusions as if they are Bayesians..."

"... and use this to deduce their priors. I’m not saying that other researchers are Bayesian—indeed I’m not always so Bayesian myself—rather, I’m arguing that looking at inferences from this implicit Bayesian perspective can be helpful, in the same way that economists can look at people’s decisions and deduce their implicit utilities. It’s a Neumann thing: again, you won’t learn people’s 'true priors' any more than you’ll learn their 'true utilities'—or, for that matter, any more than a test will reveal students’ 'true abilities'—but it’s a baseline."

From "Reverse-engineering priors in coronavirus discourse" by Andrew (at Statistical Modeling, Causal Inference, and Social Science,) via "What’s the Deal With Bayesian Statistics?" by Kevin Drum (at Mother Jones).

Both of these posts went up yesterday, that is, 2 days after I said, "Shouldn't we talk about Bayes theorem?" I'm not saying I caused that. I'm just saying maybe you should use Bayesian reasoning to figure out if I did. I will stand back and say, this is not my field. I'm only here to encourage it.

"[T]he mortality rate among patients over age 65 exceeded 26 percent, and almost all patients over 65 who needed mechanical ventilation during that period died."

According to a new JAMA article (which studied coronavirus patients in Northwell Health hospitals "in and around New York City"), reported in "Do You Want to Die in an I.C.U.? Pandemic Makes Question All Too Real/Sobering statistics for older patients sharpen the need to draw up advanced directives for treatment and share them with their families" (NYT).

A new study in JAMA Internal Medicine questioned 180 patients over age 60 with serious illnesses; most said they would trade a year of life if that meant they could avoid dying in an I.C.U. on life support.... “Many older patients we’ve encountered with Covid-19 have opted not to undergo ventilation and an I.C.U.,” Dr. White said. “No one should impose that on a patient, though if there’s true scarcity, that may arise. But patients might choose it for themselves.”...

While you're thinking about that, here's the ad the NYT served up for me in the middle of the article:



"More than 20% of Americans think vampires are real/More than 25% think climate change is not"... therefore you might want to donate $1,000 to the World Health Organization. We're supposed to worry that a fifth to a quarter of Americans are so science-ignorant that we should give money to an organization that may or may not represent good science. How would I know? Well, one thing is: I'm wondering if it is really true that 20% of Americans think vampires are real, because if they don't, then the organization is passing on fake statistics and that's evidence against its dedication to good science!

Here's a study from last year (at YouGov) that says 13% of Americans believe in vampires — 14% of Republicans and 8% of Democrats. And here's an IPSOS survey from last year that said "Almost half of Americans believe that ghosts are real (46%), and a third believe that aliens visit earth (32%), while only a small amount believe in vampires (7%) and zombies (6%)."

For $1,000, you need to do better with the statistics. And now I'm wondering about the value of the statistics about how likely you are to die if you're over 65 and end up on a ventilator. Just as the World Health Organization wants its donations, the health care system would benefit if you decline its services and accept home-based death.

"Density alone doesn’t seem to account for the scale of the differential between New York’s fatality rates and those of other cities."

"New York has twice the density of London but three times the deaths, and the differential is even higher [comparing NYC to] cities such as San Francisco and Los Angeles. Deaths have occurred disproportionately in poorer areas, where the incidence of long untreated morbidities such as heart disease and diabetes have contributed significantly. But the same is true in all other cities. The high dependence on mass transit also seems to be a factor. In other major cities, car commutes are much more common. As Joel Kotkin, a scholar of cities at Chapman University in California, says, it may be the lethal convergence of all three factors. 'If you put together density, levels of poverty and reliance on a mass-transit system, you have a hat trick,' he told me.… But even that may not explain the extent of New York’s unique catastrophe. Around the world, the highest death rates have occurred where hospital systems were overwhelmed in the early stages of the crisis. This is especially true in northern Italy. Anecdotally, at least, it seems that the same happened in New York: Large numbers of sick people never got to hospitals, arrived too late or, in the impossible circumstances that medical personnel were confronted with, were given ineffective treatment.… It will be a while before we get a proper understanding of what went so tragically wrong...."

From "The Covid-19 Catastrophe Unfolding in New York Is Unique" (Wall Street Journal), quoted at my son John's Facebook page.

John writes:

I'm not sure this is a logical argument:

"Density alone doesn’t seem to account for the scale of the differential between New York’s fatality rates and those of other cities. New York has twice the density of London but three times the deaths, and the differential is even higher for cities such as San Francisco and Los Angeles."

Doesn't that assume there's a linear relationship between density and infection rates, and isn't that not necessarily the case?

My question is about the comparison of New York to northern Italy, where hospitals were overrun. Were NY hospitals overrun? I thought they weren't.  I think the 3 factors named — density, reliance on mass-transit, and the bad health conditions represented by the term "poverty" — are enough to explain what happened. These things are interactive. Shouldn't we talk about Bayes theorem?

"De Blasio’s senior staff in near revolt over his coronavirus response."

The NY Post reports.

When Mayor de Blasio dragged aides and members of his NYPD security detail to his Brooklyn YMCA Monday morning amidst the coronavirus outbreak, fellow fitness enthusiasts were coughing and sneezing — and a mentally ill person was walking around touching the equipment, a gym source said.

“It’s crazy that he made his staff and detail come with him to the gym and expose them like that,” the source said.

That scene lends insight into this: "Majority of NYC’s coronavirus cases are men between 18 and 49 years old."

But wait a minute. That headline is not right. The majority of cases are men (59%), and the majority of cases are people between the ages of 18 and 49 (54%). You can't combine these 2 facts that way! That's some serious innumeracy!

"Numbers today represent reality as of 3 weeks ago."

I'm passing along, with permission, something Freeman Hunt posted on Facebook.

A friend thought I should post something my husband wrote in a message. Here 'tis.

"Today Italy had 4207 new cases and 475 new deaths in 24 hours (and they are even admitting they can't count all deaths now).

"They went into lockdown 9 days ago when they had 1797 new cases and 97 deaths in a day.

"Even my friends who have been following this asked if this means the lockdown is not working. No, it is working, but the lag time on this disease is long. It can be 14 day incubation and then is about 10 days after symptoms start before people typically take a turn for the worse if they are going to. Death typically in the third week after symptom start.

"So it can be 5 weeks or even more between the time someone is infected and when they die. Can be over 3 weeks between infection and when they have to go to the hospital.

"Numbers today represent reality as of 3 weeks ago.

"If you wait till it gets as bad as Italy to lockdown it is too late. Your health system is overwhelmed. Some hospitals in America are already getting that way now. One in New Jersey has already converted an entire floor to COVID. Multiple hospitals already running out of equipment. Many health workers starting to get sick.

"This is what is going on in some places in Amercia NOW, and will be everywhere soon unless we get very serious very quickly.

"Even if we lockdown now the disease will get about 8 times worse before it turns the corner after 3 weeks or so of quarantine. Every day we wait is a 40% increase in the eventual numbers."

"Which Country Has Flattened the Curve for the Coronavirus?"

Check out the collection of graphs at the NYT (no subscription needed).

The answer to the question is China (according to the data China reports). Also, South Korea.

Not us. Not yet.

"New C.D.C. data showed that nearly 40 percent of patients sick enough to be hospitalized were aged 20 to 54. But the risk of dying was significantly higher in older people."

A subheadline at the NYT (which should be accessible whether you have a subscription or not).

I'm questioning "sick enough." Perhaps the degree of sickness warranting hospitalization is lower for younger people. Should the care be used on the sickest people or on the ones most likely to benefit from care?

Anyway, it's important for people who are faced with the effort of social distancing to see that youth is not immunity. Though "20 to 54" is a big category. What about those under 40? Under 30?

Here's a closer look from the underlying CDC report (also on this table):

As of March 16, a total of 4,226 COVID-19 cases had been reported in the United States, with reports increasing to 500 or more cases per day beginning March 14 (Figure 1). Among 2,449 patients with known age, 6% were aged ≥85, 25% were aged 65–84 years, 18% each were aged 55–64 years and 45–54 years, and 29% were aged 20–44 years (Figure 2). Only 5% of cases occurred in persons aged 0–19 years.

Among 508 (12%) patients known to have been hospitalized, 9% were aged ≥85 years, 26% were aged 65–84 years, 17% were aged 55–64 years, 18% were 45–54 years, and 20% were aged 20–44 years. Less than 1% of hospitalizations were among persons aged ≤19 years (Figure 2). The percentage of persons hospitalized increased with age, from 2%–3% among persons aged ≤9 years, to ≥31% among adults aged ≥85 years. (Table).

Among 121 patients known to have been admitted to an ICU, 7% of cases were reported among adults ≥85 years, 46% among adults aged 65–84 years, 36% among adults aged 45–64 years, and 12% among adults aged 20–44 years (Figure 2). No ICU admissions were reported among persons aged ≤19 years. Percentages of ICU admissions were lowest among adults aged 20–44 years (2%–4%) and highest among adults aged 75–84 years (11%–31%) (Table).

Among 44 cases with known outcome, 15 (34%) deaths were reported among adults aged ≥85 years, 20 (46%) among adults aged 65–84 years, and nine (20%) among adults aged 20–64 years. Case-fatality percentages increased with increasing age, from no deaths reported among persons aged ≤19 years to highest percentages (10%–27%) among adults aged ≥85 years (Table) (Figure 2).

It's easy to know how old each person is, but the differences may have more to do with each person's health and strength, which correlates roughly to age. If so and if anyone is thinking that those who vulnerable to this disease should accept their fate and not expect so much sacrifice from the rest of us, they ought to realize that it's not just about old and young. It's about weak and strong.

From the NYT article:

The report included no information about whether patients of any age had underlying risk factors, such as a chronic illness or a compromised immune system. So, it is impossible to determine whether the younger patients who were hospitalized were more susceptible to serious infection than most others in their age group....