Consider Taking Zinc Every Time You Travel

Zinc lozenges are pretty well established to prevent or shorten the duration of colds. People are more likely to get colds while travelling, especially if doing so by plane and/or to a destination full of other people who also travelled by plane. I have a vague sense you shouldn’t take zinc 100% of the time, but given the risks it might make sense to take zinc prophylactically while travelling.

How much does zinc help? A meta-analysis I didn’t drill into further says it shortens colds by 33%, and that’s implied to be for people who waited until they were symptomatic to take it: taken preemptively I’m going to ballpark it at 50% shorter (including some colds never coming into existence at all). This is about 4 days, depending on which study you ask.

[Note: only a few forms of Zinc work for this. You want acetate if possible, gluconate if not, and it needs to be a lozenge, not something you swallow. Zinc works by physically coating your throat to prevent infection, it’s not a nutrient in this case. You need much more than you think to achieve the effect, the brand I use barely fits in my tiny mouth.]

Some risk factors for illness in general are “being around a lot of people”, “poor sleep” and “poor diet”. These factors compound: being around people who have been around a lot of people, or who have poor sleep or diet, is worse than being around a lot of well-rested, well-fed hermits. Travel often involves all of these things, especially by air and especially for large gatherings like conferences and weddings (people driving to camp in the wilderness: you are off the hook).

I struggled to find hard numbers for risk of infection during travel. It’s going to vary a lot by season, and of course covid has confused everything. Hocking and Foster gives a 20% chance of catching a cold after a flight during flu season, which seems high to me, but multiple friends reported a 50% chance of illness after travel, so fill in your own number here. Mine is probably 10%.

If my overall risk of a cold is 10%, and I lower the duration by 50%/4 days, I’ve in expectation saved myself 0.4 days of a cold, plus whatever damage I would have done spreading the cold to others, plus the remaining days are milder. Carrying around the lozenges, remembering to take them, and working eating and drinking around them is kind of inconvenient, so this isn’t a slam dunk for me but is worth best-effort (while writing this I ordered a second bottle of zinc to sit in my travel toiletry bag). It’s probably worth a lot for my friends with a 50% risk of illness, have unusually long colds, or live with small children who get cranky when sick. You know better than me where you fall.

Things that would change this cost-benefit estimate:

  • Seasonality
  • Personal reaction to zinc, or beliefs about its long term effects
  • Covid (all the numbers I used were pre-covid)
  • Different estimates for risk of illness during travel
  • Different estimates for the benefit of zinc
  • Personal susceptibility to illness

Caveats: anything that does anything real can cause damage. The side effects we know about for zinc lozenges are typically low, but pay attention to your own reaction in case you are unlucky. I remain an internet person with no medical credentials or accreditation. I attempt to follow my own advice and I’ve advised my parents to do this as well, but sometimes I’m rushed and forget.

ETA: I originally wrote this aimed at friends who already believed zinc was useful but hadn’t considered prophylactic use, and as such didn’t work very hard on it. I mistook some rando meta-analysis for a Cochrane review, and didn’t look further. There’s a pre-registered study that has come out since showing no effect from zinc. There could be other studies showing the opposite, I haven’t looked very closely. Plausibly that makes publishing this irresponsible- you definitely should judge me for mistaking a review that mentioned Cochrane for an actual Cochrane review. OTOH, writing too defensively inhibits learning, and I want to think my readers in particular are well calibrated on how much to trust off the cuff writing (but I hindered that by mislabeling the review as from Cochrane).

Review: The End Is Always Near

The date is November 10th, 2019. Covid has plausibly started, but I don’t know it yet. I am a huge fan of Dan Carlin’s Hardcore History podcast, and have been conducting my own lit review on civilizational collapse. I have been eagerly anticipating Carlin’s upcoming book, The End is Always Near, for months (affiliate link). I am in a coffee shop with a friend, very excited to have a dedicated time to read and Epistemic Spot Check it. 

I do not remember what I read. I remember that I lost all interest in Carlin’s podcast afterward, and was so sure I’d remember the problem that I didn’t write it down, which led to 2 years of awkwardly saying “yeah his book was so terrible I lost interest, no don’t remember why, yes I see how that’s less useful for you.” I never checked any claims it made; I’d have records of that, which means that whatever the problem was, it wasn’t just a factual error

I sat down today to read enough of the book to remind myself of why I so vehemently disliked it, and in the course doing so discovered that I had written down the problems in Goodreads, but had forgotten that along with everything else. (I also got the date wrong: I remember starting it in January, but that doesn’t fit because I know I was reading one of its sources in December). My review, in its entirety:

I went in wanting a meaty history book with many claims I could follow up on. In the first few chapters I could only extract a few claims, always what other historians thought (but without countervailing arguments), and it never coheres into models or cruxes.

Mystery solved, I guess. It’s not actually clear to me I should have given up on the podcast based on this, since I don’t remember it having the same problem. But since I already went through all this trouble, let me read a chapter or two and see if I agree with my pre-covid assessment.

Claim: “In many earlier eras of history writing, a large part of the historian’s or author’s goal was to impart or teach some sort of moral lesson, usually by historical example.” (footnote on page 1)

Ah yes, the before times, when people manipulated nominally factual data to their own ends. So glad we grew out of that in … *checks watch* … hmmm, must be broken.

Claim: Sparta super kicked ass (page 7)

Bret Devereaux spent a long time debunking this and I spent a somewhat shorter time checking his work (it passed). Carlin also repeatedly says “Spartan” when he means “Spartan ruling class”, which is a common mistake but I think a revealing one.

Okay, I have finished chapter 1, which is seven pages long. It is titled “Do Tough Times Make for Tougher People?”, a reference to this meme:

I do not know if Carlin thinks tough times create tougher people. If you put a gun to my head I would say “Probably, except for if literally anything else is involved, perhaps?”  I do not know how he defines toughness. This is dumb. Toughness is easy to define, he shouldn’t have to spell it out, and yet I’m rereading the pages trying to figure out a coherent definition that makes sense and is meaningful all the way through. I feel fuzzy and slippery and then angry that I feel that way.

Contrast that with Devereaux’s 6 part series, The Fremen Mirage, which addresses the same question. Devereaux takes a strong stance (“no they fucking don’t”) and spends only two paragraphs before defining exactly the argument he is making. Then he spends a while complaining about people who cite “…weak men create times…” without strict definitions. 

Devereaux’s Fremen Mirage is full of claims that are both load-bearing (as in, if they were wrong, the argument would collapse) and capable of being resolved one way or the other. It’s tractable to check his work and come to a conclusion. Meanwhile, I did write down some claims from chapter one of The End… but… none of them matter? Of the things that could be called cruxes, they’re all vague and would at best take a lot of work to develop an informed opinion on. But I think that’s optimistic, and most of them are not actually provable or disprovable in a meaningful way.

So there you go. The End is Always Near was not even tractable enough to be worth checking.

Thanks to Miranda Dixon-Luinenburg, Justis Mills, and Daniel Filan for copyediting. Patreon patrons you’re off the hook for this one since it was so short.

Review: Martyr Made Podcast

Introduction

Sometimes I consume media that makes factual claims. Sometimes I look up some of these claims to see how much trust I should place in said media, in a series I call epistemic spot checks. Over the years, I’ve gone back and forth on how useful this is. Focusing on evaluating particular works instead of developing a holistic opinion on an entire subject does feel perverse to me. OTOH, sometimes non-fiction is recreational, and I don’t think having some of my attention directed by people I find insightful and trustworthy is a bad thing, as long as I don’t swallow their views unquestioningly. Additionally, there’s a pleasant orderliness to doing ESCs, like the intellectual equivalent of cleaning my house. It’s not enough in and of itself, but it can free up RAM such that there’s room for deeper work.

I started listening to Darryl Cooper’s Martyr Made last year as part of a deep dive on cults, but kept going because I found him incredibly insightful. After listening to the 30+ hours of the God’s Socialist sequence, I Googled around and found a few accusations of racism against Cooper. I didn’t believe the accusations then, and I still don’t. People can go through the motions of saying what other people tell them to, but they can’t fake what Cooper does, which is to approach every human being as someone worthy of respect and compassion, whose actions are probably reasonable given their incentives. I value that a lot more than proper signaling.

Some time later I found an archive of Cooper’s deleted Twitter logs, and, uh, I get where people are coming from on the racism thing. I still absolutely believe in his respect and compassion for everyone except members of the USSR leadership (and even then, he’ll say very nice things about the intentions of early communists).  However, the thing about doing that genuinely instead of choosing a side and signaling allegiance is that it doesn’t compress well to 140 characters, and he said a bunch of things that were extremely easy to round to terrible beliefs. I might also have mistaken him for racist, if all I had was his Twitter. But given the podcasts, I am very sure that he respects-and-has-compassion-for every human being.

[Between when I started listening and when I published this Cooper returned to Twitter, which I have mixed feelings about. Namely “I think this is bad for him intellectually and emotionally” vs. “He’s talking to me! Hurray!”]

I’m not a big fan of emotion in my history podcasts. Martyr Made is an exception. Cooper goes hours out of his way to make sure you understand how something felt, without ever coming across as dishonest or manipulative. Some of that is that he often uses himself as an example and is very upfront about his flaws. Some of that is the aforementioned respect and compassion seeping into everything he does. Some is good writing. 

For example, God’s Socialist is nominally about Jim Jones and the Jonestown massacre, but Cooper doesn’t believe Jonestown makes any sense unless you understand the 60s, hippies, the Civil Rights movement, and the Black Power movement. The prologue consists of a description of various race riots/race wars, the contemporary and just-pre-Civil-Rights-movements, and easily 15 minutes on his interactions with some homeless people in his neighborhood. For the last of these, he observes that though he’s occasionally kind, he mostly just ignores the individuals in question, and that sometimes he thinks that on Judgement Day the only thing that’s going to matter is how he failed to really help those men- whatever he did, it was for the wrong motives and much too little. I wrote a bunch of angry notes about how virtue ethics was bullshit while listening to this part, but by the end it became clear that he wasn’t making a call to any particular action, it was just an honest accounting of suffering in the world. He was walking me through it because he felt it was necessary to understand Jim Jones, whose first acts as an adult were taking care of people most of society was stepping over. 

All of this is to say: Martyr Made is one of my favorite pieces of nonfiction in the world. I’ve learned so much from it both factually and emotionally, but I felt vulnerable talking about that until I was absolutely rock solid on the author’s epistemics. I finally had time to do an epistemic spot check on the start of God’s Socialist (still my favorite sequence in the series), and I’m extremely relieved to announce that he nailed it, although just like my ESC of Acoup, it is not so amazingly perfect that the follow up wasn’t worth doing (and I assume Cooper would agree with that, just like Bret Devereaux did).

A word on ESCs: there’s a range of things it can mean to check someone’s epistemics. Sometimes it means checking their simple concrete facts. You would be amazed how many problems this catches. Another is to check leaps of logic: they can have their facts right but draw wildly incorrect inferences from them. Finding these requires more cognition, but is also fairly easy. Cooper did great on both of these, which was not surprising. My concern was always that his facts were literally true but unrepresentative. Accurate-in-spirit representation is one of the hardest things to judge, especially about really contentious issues like racial violence where second opinions are just another thing to fact check. What I can say is that everything I checked I was either able to concretely verify, or was extremely consistent with what I was able to find but was open to other interpretations, because it’s a contentious area with motivated record keeping.

The God’s Socialist sequence of Martyr Made is 30 hours long. I have ESCed the prologue, which is 90 minutes long, and some especially load-bearing claims I remembered from later in the podcast. I also happen to have already read one of Cooper’s most quoted sources, The Warmth of Other Suns (affiliate link), back in 2014. 2014 is a long time ago and I didn’t ESC Warmth at the time, but what Cooper quoted was generally in accordance with my memory of it, on both a factual and model level.

Without further adieu…

The Claims

Claim: A 2007 report from the Southern Poverty Law Center on Latino-on-Black violence in Los Angeles (1:02)

He reads this report very nearly word for word. All the differences I caught were very minor wording issues that didn’t change the meaning. I also checked some of SPLC’s claims

SPLC: “Since 1990, the African-American population of Los Angeles has dropped by half as blacks relocated to suburbs”, “Now, about 75% of Highland Park residents are Latinos. Only 2% are black. The rest are white and Asian.”  (8:17)

This was shockingly annoying to verify because I could find stats by year for LA county but not LA the city, and the county includes the suburbs. I did verify that:

  • In 2000 (seven years before the SPLC report came out), Highland Park was 72.4% Latino and 2.4% black (source). 
    • Note that if you read the Wikipedia article it says 8.4% black, but it cites my source above. This is plausibly an issue of how to assign mixed-race people (since Wikipedia’s percentages add up to >100%), or the ongoing confusion about how Latino is an ethnicity, not a race.
    • However, that particular neighborhood was already 2.2% black in 1990, although it was a little whiter and less Latino (source).
  • An LA time article also describes South LA shifting from an approximately 1:1 ratio of Latino and Black residents to 2:1 (Highland Park is in northeast LA).

Claim: A number of specific incidents of Latino-on-Black violence in Los Angeles, and some nebulous statistics

I Googled several of these as they came up and they always checked out, although LA’s a big city and Cooper is looking over a long time period, so it would be easy to cherry pick.

Cooper also gave some statistics on hate crime. However, these were always either for a particular neighborhood (too small, data liable to be noisy), or not quite as damning as his tone suggested they were. I found some statistics that came out the same year this episode did that support the general concept that Latino-on-Black violence happens, but I don’t trust the LAPD’s truthseeking on hate crimes. 

Which is to say, Cooper’s claims are well sourced and completely consistent with the available data, but the data is poor and his opinions are more controversial than he acknowledges. I’m sure someone with different motivations could use the same data to make the opposite case, or a different one entirely. Here’s an article published the same year as the SPLC report, calling the claims ridiculous. My tentative take on this is that racial tensions were high and spilling over into violence, but the claims that “all black people in LA were greenlit” (meaning, gang members had the okay from leaders to shoot them) and “all black people in Latino neighborhoods in LA were greenlit” are clearly insane; the murder rate would be much higher if that were true. 

Claim: Quote from Warmth of Other Suns: “In 1950, city aldermen and housing officials proposed restricting 13,000 new public housing units to people who had lived in Chicago for two years. The rule would presumably affect colored migrants and foreign immigrants alike. But it was the colored people who were having the most trouble finding housing and most likely to seek out such an alternative.” (23:00)

This quote is accurate, but my memory of it wasn’t: I had in my notes that this proposal was enacted, and only rechecked the recording when I couldn’t find any such record and wanted to see if he cited a source. His source, Warmth of Other Suns, cites a 1950 newspaper article that I couldn’t find online (it probably exists in ProQuest’s Historical Newspaper archive, but I lack access despite trying ProQuest via multiple libraries).

Claim: Description of the Cicero Riots of 1951 (31:00)

Everything he says is in accordance with the Wikipedia article: it was a horrific multi-day riot and lynching episode triggered by a black family moving into a white neighborhood. 

Cooper doesn’t mention this, but fun fact: according to Wikipedia, the landlord allowed the family to move in not for any noble anti-racism or even free-market motivations, but to punish the neighborhood for fining her for something else. 

Claim: Southern white people did not want black people to leave during the Great Migration, because they needed them as labor (35:00)

Warmth of Other Suns says the same, although that’s not independent confirmation because it’s at least one of Cooper’s sources as well. Wikipedia agrees.

Claim: Northern union leaders were resistant to black migrants because they reduced labor’s power (43:00)

I could not find a smoking gun on this, which makes sense because labor is not going to want to admit it. However I found a number of articles, modern and contemporary, on companies bringing in black workers from the south as strikebreakers, and it would be extremely weird if that didn’t upset union leaders. 

Claim: Jim Jones began as a dynamic and promising civil rights movement leader, branched out into communism (1:05:20)

Yup.

Claim: Jonestown residents were mostly poor and black, and disproportionately children (1:17:00)

Yup and yup.

Note that this was not true of the leadership of Jonestown, which was overwhelmingly white. Cooper gets into this later in the sequence.

Claim: Jim Jones led successful efforts to integrate businesses in Indianapolis (memory)

This claim came later in the sequence. It and the similar claim below were very significant to me and a number of changes in my own models rest on them, so I expanded the scope of the project to include them.

There are many sources repeating this claim, including Wikipedia, some book, and r/HistoryAnecdotes, and none denying it. I am a little suspicious because everyone seems to agree on exactly how many restaurants he integrated, but no one names them. They do name a hospital, but it seems like maybe “integrated” means “he accidentally got assigned to a black ward (because his doctor was black) and refused to leave”. But it’s not surprising that restaurants he integrated either no longer exist or don’t want to be remembered as “the place that excluded minorities until forced to change by the guy who later led America’s largest simultaneous suicide”.

Claim: Jim Jones helped members of his racially-integrated church tremendously (memory)

I found many secondary or tertiary sources saying this and no arguments against, but the only primary sources I could find joined the church in California. I couldn’t find any reports from people who joined while the church was in Indiana. That doesn’t seem damning to me; it’s kinda hard to tell people your lights got turned back on by Jim Jones before he was famous. This interview with a woman who joined in California and narrowly escaped the mass suicide confirms everything it can: she was a true believer in a bunch of good things but also kind of a joiner who ping-ponged between organizations until she found peace with People’s Temple. Another CA joiner talks about joining because her sister needed a rehab program and was recommended to People’s Temple’s program. 

Claim: Jim Jones adopted multiple children of color (memory)

True. The Jones family adopted three Korean children, one part-Native American child, and one black child, who they named James Jones Jr (they also had one biological child and adopted a white child from a People’s Temple member. There are also some People’s Temple kids of unclear paternity).

I recognize that transracial adoption is contentious and actions that were considered progressive and inclusive 60 years ago are now viewed as bad for the children they were supposed to benefit. I also get that lots of adoptive white parents were unprepared to deal with the realities of racism, or harbor it themselves, and that harmed their kids. The whole mass suicide thing casts some doubt on Jim Jones as a parent too. Nonetheless, a white man naming his black son after himself in 1961 was an extraordinarily big deal for which he undoubtedly paid a very high price, and from all this I have to conclude that fighting racism was extremely important to early Jim Jones.

Summary

Overall all of the claims were at least extremely defensible. I wish Cooper acknowledged more of the controversy around his interpretations, but I also appreciate that he comes to actual conclusions with models instead of spewing a bunch of isolated facts. I also wish he provided show notes with citations, because he’s inconsistent about providing sources in the audio.

Doing this check reinforced my belief that having one source for any of your beliefs is malpractice and processing multiple sources is a requirement, however I will very happily continue to have Cooper as a significant source of information, and if I’m totally honest I’m not even going to check all his work this extensively. 

Thanks to Eli Tyre for research assistance, my Patreon Patrons for financial support of this post, and Justis Mills for editing.

Long Covid Is Not Necessarily Your Biggest Problem

Introduction

At this point, people I know are not that worried about dying from covid. We’re all vaccinated, we’re mostly young and healthy(ish), and it turns out the odds were always low for us. We’re also not that worried about hospitalization: it’s much more likely than death, but maintaining covid precautions indefinitely is very costly so by and large we’re willing to risk it.

The big unknown here has been long covid. Losing a few weeks to being extremely sick might be worth the risk, but a lifetime of fatigue and reduced cognition is a very big deal. With that in mind, I set out to do some math on what risks we were running. Unfortunately baseline covid has barely been around long enough to have data on long covid, most of it is still terrible, and the vaccine and Delta variant have not been widespread long enough to have much data at all. 

In the end, the conclusion I came to was that for vaccinated people under 40 with <=1 comorbidiy, the cognitive risks of long covid are lost in the noise of other risks they commonly take. Coming to this conclusion involved reading a number of papers, but also a lot of emotional processing around risk and health. I’ve included that processing under a “personal stuff” section, which you can skip if you just want the info but I encourage you to read if you feel yourself starting to yell that I’m not taking small risks of great suffering seriously. I do encourage you to read the caveats section before deciding how much weight to put on my conclusions.

Personal Stuff

This post took a long time to write, much longer than I wanted, because this is not an abstract topic to me. I have chronic pain from nerve damage in my jaw caused by medical incompetence, and my attempts to seek treatment for this continually run into the brick wall of a medical system that doesn’t consider my pain important (tangent: if you have a pain specialist you trust, anywhere in the US, please e-mail me (elizabeth@acesounderglass.com)). I empathize very much with the long covid sufferers who are being told their suffering doesn’t exist because it’s too hard to measure and we can’t prove what caused it.

Additionally, I’m still suffering from side effects from my covid vaccine in April. It’s very minor, chest congestion that doesn’t seem to affect my lung capacity (but I don’t have a clear before picture, so hard to say for sure). But it’s getting worse and while my medical practitioners are taking it seriously, this + the experience with dental pain make me very sensitive to the possibility they might stop if it becomes too much work for them. As I type this, I am taking a supplement stack from a high end internet crackpot because first line treatment failed and there aren’t a lot of other options. And that’s just from the vaccine; I imagine if I actually had covid I would not be one of the people who shakes it off the way I describe later in this post. 

All this is to say that when I describe the long term cognitive impact of covid as being too small to measure with our current tools against our current noise levels, that is very much not the same as saying it’s zero. It’s much worse than that. What I’m saying is that you are taking risks of similar levels of suffering and impairment constantly, which our health system is very bad at measuring, and against that background long covid does not make much of a difference for people within certain age and health parameters. 

A common complaint when people say “X isn’t dangerous to the young and healthy” is that it implies the death and suffering of those who aren’t young and healthy don’t matter. I’m not saying that. It matters a lot, and it’s impossible for me to forget that because I’m very unlikely to be one of the people who gets to totally walk covid off if I catch it. But from looking at the data, there don’t seem to be very many of us in my age group.

Caveats

Medical research in general is really bad, research of a live issue in a pandemic is worse, you should assume these are low quality studies unless I indicate otherwise.

This research was compiled for LessWrong and Redwood Research, with the goal of assessing safety for their office spaces populated by mostly-but-not-entirely-healthy people 25-40, who were much more interested in the cognitive and fatigue sequelae than the physical. Much of this research is applicable outside that group or the sources can be used in that way, but you should know that’s what I focused on.

There isn’t any data on long covid in vaccinated people with breakthrough delta-variant infections. Neither vaccines nor delta have been around long enough for that to exist. Baseline covid has barely been around long enough to have long-term data. What I have here is:

  • Data showing that strength of acute infection correlates with long term impact, although not perfectly
  • Data on the long term impact of baseline covid, given the strength of an initial infection
  • Data on how the vaccine impacts the strength of acute infections
  • Data on how delta impacts the strength of acute infections

Data

Long term outcomes correlate with short term outcomes

By far the best study (best does not mean good) comes out of the UK, where the BBC coincidentally started an online intelligence test in January 2020 (giving them a pre-covid baseline) and in May began asking participants if they’d had covid and if so how bad a case. When I said “assume the studies are terrible unless I note otherwise”, this is the study I wanted to highlight as reasonably good. Because they can compare test-takers in a given time period with and without covid they can control for some of the effects of changing a study population over time, which would be the biggest concern. Additionally, my statistical consultant described the paper as “not having any errors that affect the conclusion”, which is extremely good for a medical paper. This study was not ideal for determining sequelae persistence, but they did check if size of effect was correlated with time since symptom onset, and it wasn’t (but their average was only 2 months).

This study showed a very direct correlation between the severity of the acute infection and cognitive decline. I don’t trust its absolute numbers, but the pattern that more severe disease -> more severe persistent effects is very clear

A second study in Wuhan, China (hat tip Connor Flexman) examined long term outcomes of hospitalized patients, based on the intensity of their care (hospitalization, supplemental oxygen, ventilation) found an increase in acute severity was correlated with an increase in sequelae, although it didn’t hold for every symptom (there are a lot of symptoms and the highest-intervention group is small), and they barely looked at cognitive symptoms.

Taquet et al used electronic health records to get a relatively unbiased six figure sample size, that also showed a strong correlation between acute and long term outcomes, which we’ll talk about more below.

From this I conclude that your overall risk of long covid is strongly correlated with the strength of the initial infection.

Odds of acute outcomes

Sah et al estimate that 35% of covid cases (implied to be baseline and pre-vaccination) are asymptomatic, with large variation by age. Children (<18) are 46% likely to be asymptomatic, adults 18-59 are 32% likely, adults >=60 are 20% likely. I’m going to round the non-elderly adult number to ⅓ to make the math easier.

The Economist has a great calculator showing your pre-vaccine, pre-Delta risk of hospitalization and death, given your age, sex, and comorbidities. Note that this calculator only includes diagnosed cases, so it excludes both asymptomatic cases and those that did have symptoms but didn’t drive people to seek medical care. Here’s a few sample people:

  • A healthy 30 year old man has a 2.7% chance of hospitalization, and <0.1% risk of death
  • A healthy 30 year old woman has a 1.7% chance of hospitalization, and <0.1% risk of death
  • A 25 year old man with asthma has a 4.2% risk of hospitalization, and <0.1% risk of death
  • A 40 year old woman with obesity has a 6.5% risk of hospitalization, and 0.1% risk of death.
  • Risk of hospitalization rises steadily with age but the risk of death doesn’t really take off until 50, at which point our healthy man has a death risk of 0.4% and our health woman has a risk of 0.2%

If you’d like, you can use your own numbers in this guesstimate sheet.

And again, that’s only for officially diagnosed and registered cases. If you assume ⅓ of infections in that age group are asymptomatic, the risk drops by ⅓.

If you are hospitalized, your risk of being ventilated is currently very, very low even if you’re in a high risk category. The overall average percent of hospitalized patients who were ventilated was 2.0% in the last week for which data was available (2021-03-24), after dropping steadily for most of the plague. We can assume that’s disproportionately among the elderly and people with severe comorbidities, so if that’s not you your odds are better still. I’m going to count the risk of intubation for our cohort as 0.5%, although that’s likely still an overestimate.

How do vaccines change these odds? According to CDC data from a time period ending 2021-05-01 (so before delta took off), 27% of breakthrough infections that reached the attention of the CDC were asymptomatic, and only 7% were hospitalized due to covid (another 3% were hospitalized for non-covid reasons). It’s very likely that the CDC is undercounting asymptomatic cases, so we’ll continue using our ⅓ number for now. The minimum age of reported breakthrough infection deaths was 71, so we’ll continue to treat the risk of death as 0% for our sample subjects. Additionally, given the timing most vaccinated participants would be elderly or front line workers, raising their risk considerably. A CDC press release goes much farther, saying vaccinated people > 65 had 7% of the hospitalizations of age-matched controls. 

How does delta change these odds? A Scottish study estimated delta had 2x the risk of hospitalization as alpha, which a Danish study estimated as having 1.42x the risk of hospitalization as baseline covid. So very roughly, we’re looking at 3x the risk of hospitalization from delta, relative to baseline.

So for our sample cases above, we have the following odds (note I updated these on the night it was posted, due to a math error. Thanks to Rob Bensinger for catching it):

Risk given vaccine, deltaHospitalizedIntubated
Healthy 30yo man0.38% = 2.7*.07*3*2/3.002% = 0.38*.005
Healthy 30yo woman0.24% = 1.7*.07*3*2/3.002% = 0.24*.005
Asthmatic 25yo man0.58% = 4.2*.07*3*2/3.003% = 0.58*.005
Obese 40yo woman0.92% = 6.5*.07*3*2/3.005% = 0.92*.005

That’s not so far from the rate of hospitalization in that age range for the flu (0.6%), with some caveats (the CDC sample includes unvaccinated people and the bucket is 18-49 years old, with the higher end presumably carrying more of the disease burden).

There is concern that vaccine effectiveness wanes over time, which I haven’t incorporated here.

Odds of long term outcomes

In general I ignored studies that merely tracked number of persistent sequelae but not their severity or type, which made it impossible to distinguish between “sense of smell still iffy” from “permanent intellectual crippling”, and studies that didn’t track how long the sequelae persisted. This was, unfortunately, most of them.

We talked about the Great British Intelligence Test above. I initially found this study quite scary. The study used its own tests rather than IQ, but if you assume a standard deviation in their tests is equivalent to a standard deviation in an IQ test, the worst category (ventilation) is equivalent to a 7 point IQ loss. That’s twice as bad as a stroke in this study (although I suspect sampling bias). I suspect the truth is worse still, because the worse your recently acquired cognitive and health issues are, the less likely you are to take a fun internet test advertised as measuring your intellectual strengths. However as I noted above, you are extremely unlikely to be put on a ventilator. 

For people with “symptoms, but not respiratory symptoms”, the cognitive damage is ~equivalent to 0.6 IQ points. For “medical assistance at home”, it’s 1.8 points. These are both likely to be overestimates given that the study only included known (although not necessarily formally diagnosed) cases. Additionally, while the paper claims to control for education, income, etc, bad things are more likely to happen to people in worse environments, and it’s impossible to entirely back that out.

Taquet et al used electronic health records to get a relatively unbiased six figure sample size, and found unhospitalized diagnosed covid patients (pre-Delta, pre-vaccine) had a 11% likelihood of a new neuro or psych diagnosis after their covid diagnosis, hospitalized patients had a 15% likelihood, and ICU patients had 26% likelihood. The majority of these were mood disorders (3.86%/4.49%/5.82% for home/hospitalized/ICU) and anxiety (6.81%/6.91%/9.79%). This seems quite bad, until you compare it to the overall numbers for depression in the time period, a naive reading of which suggests that covid had a protective effect

These numbers aren’t directly comparable. The second study is much lower quality and includes rediagnoses (although the total depression diagnosis numbers for the covid patients are 13.10%/14.69%/15.43%- still under the total increase in depression in the general population study). 

Overall this seems well within what you’d expect from getting a scary disease at a scary time, and not evidence of widespread neuro or psych impact of covid. Even if you take the numbers at face value, they exclude most people who were asymptomatic or treated at home without a formal diagnosis.

A UK metareview found the prevalence at 12 weeks of symptoms affecting daily life ranged from 1.2% (average age: 20, minimum 18) to 4.8% (average age: 63). The cohort with average age 31 had a mean prevalence of 2.8%., which is is well within the Lizardman Constant. This is based on self-reports on survey data, which will again exclude asymptomatic cases, so even if you treat it as real, you need to discount it down to 2.8%.

On the other hand, medicine is notoriously bad at measuring persistent, low-level, amorphous-yet-real effects. The Lizardman Constant doesn’t mean prevalences below 4% don’t exist, it means they’re impossible to measure using naive tools.

Comparison to other diseases

The Taquet study did compare covid patients to those with other respiratory diseases (including the flu, not controlling for disease severity or patient age), and found covid to be modestly worse except for myoneural junction and other muscular diseases, where covid 5xed the risk (although it’s still quite low in absolute terms). Dementia risk is also doubled, presumably mostly among the elderly.

Additionally, cognitive impairment following critical illness, and especially following intubation, is a well known phenomenon. This puts the Great British Intelligence Test numbers in perspective- being/needed to be ventilated is quite bad, but it’s always been that bad, there doesn’t appear to be any unique-to-covid badness.

Conclusion

My tentative conclusion is that the risks to me of cognitive, mood, or fatigue side effects lasting >12 weeks from long covid are small relative to risks I was already taking, including the risk of similar long term issues from other common infectious diseases. Being hospitalized would create a risk of noticeable side effects, but is very unlikely post-vaccine (although immunity persistence is a major unresolved concern).

I want to emphasize again that “small relative to risks you were already taking” doesn’t necessarily mean “too small to worry about”. For comparison, Josh Jacobson did a quick survey of the risks of driving and came to roughly the same conclusion: the risks are very small compared to the overall riskiness of life for people in their 30s. Josh isn’t stupid, so he obviously doesn’t mean “car accidents don’t happen” or “car accidents aren’t dangerous when they happen”. What he means is that if you’re 35 with 15 years driving experience and not currently impaired, the marginal returns to improvements are minor. 

And yet. I have a close friend who somehow got in three or four moderate car accidents in < 7 years, giving her maybe-permanent soft tissue damage (to answer the obvious question: no, the accidents weren’t her fault. Sometimes she wasn’t even driving). Statistically, that friend doesn’t exist. No one gets in that many car accidents that quickly without it being their fault. And yet the law of large numbers has to catch up with someone. Too small to measure can be very large.


What this means is not that covid is safe, but that you should think about covid in the context of your overall risk portfolio. Depending on who you are that could include other contagious diseases, driving, drugs-n-alcohol, skydiving, camping, poor diet, insufficient exercise, too much exercise, and breathing outside. If you decide your current risk level is too high, or are suddenly realizing you were too risk-tolerant in the past, reducing covid risk in particular might not be the best bang for your buck. Paying for a personal trainer, higher quality food, or a HEPA filter should be on your radar as much as reducing social contact, although for all I know that will end up being the best choice for you personally. 

Change my mind

My own behavior and plans have changed a lot based on this research, so I’m extremely interested in counterarguments. To make that easy, here’s a non-exhaustive list of things that would change my mind:

  1. Evidence that long covid gets worse over time, rather than slowly improving (note that I did look at data from SARS 1 and failed to find this).
  2. New variants increase the risk to what it was or was feared to be in April 2020
  3. Evidence of more severe vaccine attenuation than we’re currently seeing.
  4. Credible paths through which the risk could drop sharply in the next six months.

Thanks to LessWrong and Redwood Research for funding this research, Connor Flexman and Ray Arnold for comments on drafts, and Rob Bensinger and Lanrian for catching errors post-publication that did not affect my overall conclusion.

Exercise Trade Offs

Woman Exercise Bike - Free vector graphic on Pixabay

Update 9/2: A friend pointed out that I was ignoring the time costs of exercise, which ended up being pretty significant. See new numbers here. I then double checked the math on the microlife numbers and the news is not good.

Tl;dr: under my current conditions, outdoor exercise is slightly safer than indoor for me, but the risks of both are dwarfed by the benefits of exercise.

Recently I’ve been weighing trade offs around exercise. At the gym I’m risking covid exposure. I can reduce that by wearing a mask, at the cost of making the exercise less effective or enjoyable. I could use my friend’s outdoor gym, but it’s fire season here in California so there are prolonged periods where I don’t want to be sucking in all that unfiltered air. This is also addressable with a mask, but at the same cost. I could exercise indoors in my own home, but I do not have that much space and it gets miserable really fast. I could not exercise until conditions improve, but that has its own health costs. So I did some math.

Wikipedia says 10 minutes of exercise = 1 micromort lost (as in, you live longer). That’s obviously going to depend a lot on the type of exercise but we’ll use it.

This calculator translates time * AQI into cigarette equivalents. At 50 AQI, it takes 12 minutes to generate .01 cigarettes. I’m going to treat that as 10 minutes because exercising is slightly worse than merely existing out doors and it makes the math much easier.

Wikipedia lists an equivalent of 1.4 cigarettes = 1 micromort.

N95 masks block 95% of PM2.5 particles (which is what the AQI is based on). I couldn’t immediately find a translation of that to micromorts so let’s assume it’s linear discounting. EDIT: On Twitter Divia Eden points out that 95% assumes a perfect seal, which you probably don’t have. This isn’t material at my current air quality; I did this whole thing without including masks at all and then added them in afterwords, but when you do your own math you should include that.

That means that 10 minutes unmasked outdoor cardio, at 50 AQI = .01/1.4 = .007 micromorts, which is clearly dwarfed by the 1 micromort lost from exercise (even if you assume it’s 10x worse for me due to the existing chest congestion, and don’t give the exercise a corresponding impact bump). If I wear a mask the risk is probably below the significant figures I’m allowed. It’s so negligible compared to the benefits that if allowing myself to go outside increases total exercise by any amount at all, it’s obviously worth it.

How about covid risk?

My gym is personal training focused with a single cardio machine, which you must schedule in advance. If I’m doing cardio there will be at most two clients doing weight training and two trainers in the room, plus me, all > 10 feet away, in a large room with filtration they claim is good. If I’m doing weight training there’s me, my trainer (fairly nearby), and potentially a farther away client and trainer pair. In theory there could be an additional person on the cardio machine but I’ve yet to see it happen.

Under an excessively conservative set of assumptions (City-average vaccination, no mask, constant talking), my cardio scenario is 7 microcovids. If I give everyone masks it’s 0.5. My weight training scenario is <=10 microcovids (7 for the other pair, which may or may not exist, and 3 for my trainer. Note that weight training is 2.5x as long as cardio). But microcovids are not micromorts. The Economist calculator (pre-delta, pre-vaccine) has the risk of dying of acute covid at my age and sex as immeasurably low, despite it being prone to overestimate because its denominator is only diagnosed cases. Long covid is a concern (although I’ve tentatively concluded its overblown: more on that soon hopefully), but lack of exercise is bad for long covid in particular. If we generously use my age/sex hospitalization rate as the discount factor (2.6%), the micromorts from my indoor cardio are <=0.16, and my weight training is <=0.23. These are not quite as negligible as the pollution, but still very safely under the benefits of exercising.

Some caveats: I didn’t examine any of these numbers that closely because the verdict was so overwhelmingly clear; the values would need to be off by orders of magnitude to change my conclusion. But that is always an option, and when I tried to follow up on the 0.1 micromort/minute of exercise number, I hit a dead end.

I’ve made a very crude spreadsheet with sources linked in comments so you can make a copy and play around with your own numbers, based on your local air quality, covid prevalence, etc.

Follow Up: Predictions as a Substitute For Reviews

In August I wrote about using PredictionBook as a substitute for weekly reviews. Several months later, I am rather predictably not using it, for the same reason weekly reviews stop working: it reminded me of something I endorsed doing but didn’t finish, and that put an ugh field around the whole thing.

Weirdly, I re-picked-up that thing a week ago. This was completely independent: I haven’t looked at PredictionBook since September. I don’t think this has any bearing on the viability of predictions substituting for reviews, it’s just a cute coincidence.

Breaking Questions Down

Previously I talked about discovering that my basic unit of inquiry should be questions, not books. But what I didn’t talk about was how to generate those questions, and how to separate good questions from bad. That’s because I don’t know yet; my own process is mysterious and implicit to me. But I can give a few examples.

For any given question, your goal is to disambiguate it into smaller questions that, if an oracle gave you the answers to all of them, would allow you to answer the original question. Best case scenario, you repeat this process and hit bedrock, an empirical question for which you can find accurate data. You feed that answer into the parent question, and eventually it bubbles up to answering your original question.

That does not always happen. Sometimes the question is one of values, not facts. Sometimes sufficient accurate information is not available, and you’re forced to use a range- an uncertainty that will bubble up through parent answers. But just having the questions will clarify your thoughts and allow you to move more of your attention to the most important things.

Here are a few examples.  First, a reconstructed mind map of my process that led to several covid+economics posts. In the interests of being as informative as possible, this one is kind of stylized and uses developments I didn’t have at the time I actually did the research.

Vague covid panic@2x.png

If you’re curious about the results of this, the regular recession post is here and the oil crisis post is here.

Second, a map I created but have not yet researched, on the cost/benefit profile of a dental cleaning while covid is present.

Risk model of dental cleanings in particular@2x.png

Aside: Do people prefer the horizontal or vertical displays? Vertical would be my preference, but Whimsical does weird things with spacing so the tree ends up with a huge width either way.

Honestly this post isn’t really done; I have a lot more to figure out when it comes to how to create good questions. But I wanted to have something out before I published v0.1 of my Grand List of Steps, so here we are.

Many thanks to Rosie Campbell for inspiration and discussion on this idea.

How to Find Sources in an Unreliable World

I spent a long time stalling on this post because I was framing the problem as “how to choose a book (or paper. Whatever)?”. The point of my project is to be able to get to correct models even from bad starting places, and part of the reason for that goal is that assessing a work often requires the same skills/knowledge you were hoping to get from said work. You can’t identify a good book in a field until you’ve read several. But improving your starting place does save time, so I should talk about how to choose a starting place.

One difficulty is that this process is heavily adversarial. A lot of people want you to believe a particular thing, and a larger set don’t care what you believe as long as you find your truth via their amazon affiliate link (full disclosure: I use amazon affiliate links on this blog). The latter group fills me with anger and sadness; at least the people trying to convert you believe in something (maybe even the thing they’re trying to convince you of). The link farmers are just polluting the commons.

With those difficulties in mind, here are some heuristics for finding good starting places.

  • Search “best book TOPIC” on google
    • Most of what you find will be useless listicles. If you want to save time, ignore everything on a dedicated recommendation site that isn’t five books.
    • If you want to evaluate a list, look for a list author with deep models on both the problem they are trying to address, and why each book in particular helps educate on that problem.  Examples:
    • A bad list will typically have a topic rather than a question they are trying to answer, and will talk about why books they recommend are generically good, rather than how they address a particular issue. Quoting consumer reviews is an extremely bad sign and I’ve never seen it done without being content farming.
  • Search for your topic on Google Scholar
    • Look at highly cited papers. Even if they’re wrong, they’re probably important for understanding what else you read.
    • Look at what they cite or are cited by
    • Especially keep an eye out for review articles
  • Search for web forums on your topic (easy mode: just check reddit). Sometimes these will have intro guides with recommendations, sometimes they will have where-to-start posts, and sometimes you can ask them directly for recommendations. Examples:
  • Search Amazon for books on your topic. Check related books as well.
  • Ask your followers on social media. Better, announce what you are going to read and wait for people to tell you why you are wrong (appreciate it, Ian). Admittedly there’s a lot of prep work that goes into having friends/a following that makes this work, but it has a lot of other benefits so if it sounds fun to you I do recommend it. Example:
  • Ask an expert. If you already know an expert, great. If you don’t, this won’t necessarily save you any time, because you have to search for and assess the quality of the expert.
  • Follow interesting people on social media and squirrel away their recommendations as they make them, whether they’re relevant to your current projects or not.

Types of Knowledge

This is a system for sorting types of knowledge. There are many like it, but this one is mine.

First, there is knowledge you could regurgitate on a test. In any sane world this wouldn’t be called knowledge, but the school system sure looks enthusiastic about it, so I had to mention it. Examples:

  • Reciting the symptoms of childbed fever on command 
  • Reciting Newton’s first law of motion
  • Reciting a list of medications’ scientific and brand names
  • Reciting historical growth rate of the stock market
  • Reciting that acceleration due to gravity on Earth is 9.807 m/s²

 

Second, there is engineering knowledge- something you can repeat and get reasonably consistent results. It also lets you hill climb to local improvements. Examples:

  • Knowing how to wash your hands to prevent childbed fever and doing so
  • Driving without crashing
  • Making bread from a memorized recipe.
  • What are the average benefits and side effects from this antidepressant?
  • Knowing how much a mask will limit covid’s spread
  • Investing in index funds
  • Knowing that if you shoot a cannon ball of a certain weight at a certain speed, it will go X far.
  • Knowing people are nicer to me when I say “please” and “thank you”

 

Third, there is scientific knowledge. This is knowledge that lets you generate predictions for how a new thing will work, or how an old thing will work in a new environment, without any empirical knowledge.

Examples: 

  • Understanding germ theory of disease so you can take procedures that prevent gangrene and apply them to childbed fever.
  • Knowing the science of baking so you can create novel edible creations on your first try.
  • Knowing enough about engines and batteries to invent hybrid cars.
  • Actually understanding why any of those antidepressants works, in a mechanistic way, such that you can predict who they will and won’t work for.
  • A model of how covid is spread through aerosols, and how that is affected by properties of covid and the environment.
  • Having a model of economic change that allows you to make money off the stock market in excess of its growth rate, or know when to pull out of stocks and into crypto.
  • A model of gravity that lets you shoot a rocket into orbit on the first try.
  • A deep understanding of why certain people’s “please”s and “thank you”s get better results than others.

 

Engineering knowledge is a lot cheaper to get and maintain than scientific knowledge, and most of the time it works out. Maybe I pay more than I needed to for a car repair; I’ll live (although for some people the difference is very significant). You need scientific knowledge to do new things, which either means you’re trying something genuinely new, or you’re trying to maintain an existing system in a new environment.

I don’t know if you’ve noticed, but our environment was changing pretty rapidly before a highly contagious, somewhat deadly virus was released on the entire world, and while that had made things simpler in certain ways (such as my daily wardrobe), it has ultimately made it harder to maintain existing systems. This requires scientific knowledge to fix; engineering won’t cut it.

And it requires a lot of scientific knowledge at that- far more than I have time to generate. I could trust other people’s answers, but credentials and authority have never looked more useless, and identifying people I trust on any given subject is almost as time consuming as generating the answers myself.  And I don’t know what to do about that.

 

Where to Start Research?

When I began what I called the knowledge bootstrapping project, my ultimate goal was “Learn how to learn a subject from scratch, without deference to credentialed authorities”. That was too large and unpredictable for a single grant, so when I applied to LTFF, my stated goal was “learn how to study a single book”, on the theory that books are the natural subcomponents of learning (discounting papers because they’re too small). This turned out to have a flawed assumption baked into it.

As will be described in a forthcoming post, the method I eventually landed upon involves starting with a question, not a book. If I start with a book and investigate the questions it brings up (you know, like I’ve been doing for the last 3-6 years), the book is controlling which questions get brought up. That’s a lot of power to give to something I have explicitly decided not to trust yet. 

Examples:

  • When reading The Unbound Prometheus, I took the book’s word that a lower European birth rate would prove Europeans were more rational than Asians and focused on determining whether Europe’s birth rates were in fact lower (answer: it’s complicated), when on reflection it’s not at all clear to me that lower birth rates are evidence of rationality.
  • “Do humans have exactly 4 hours of work per day in them?” is not actually a very useful question. What I really wanted to know is “when can I stop beating myself up for not working?“, and the answer to the former doesn’t really help me with the latter. Even if humans on average have 4 hours, that doesn’t mean I do, and of course it varies by circumstances and type of work… and even “when can I stop beating myself up?” has some pretty problematic assumptions built into it, such as “beating myself up will produce more work, which is good.” The real question is something like “how can I approach my day to get the most out of it?”, and the research I did on verifying a paper on average daily work capacity didn’t inform the real question one way or the other.

 

What would have been better is if I’d started with the actual question I wanted to answer, and then looked for books that had information bearing on that question (including indirectly, including very indirectly). This is what I’ve started doing.

This can look very different depending on what type of research I’m doing. When I started doing covid research, I generated a long list of  fairly shallow questions.  Most of these questions were designed to inform specific choices, like “when should I wear what kind of mask?” and “how paranoid should I be about people without current symptoms?”, but some of them were broader and designed to inform multiple more specific questions, such as “what is the basic science of coronavirus?”. These broader, more basic questions helped me judge the information I used to inform the more specific, actionable questions (e.g., I saw a claim that covid lasted forever in your body the same way HIV does, which I could immediately dismiss because I knew HIV inserted itself your DNA and coronaviruses never enter the nucleus).

 


 

I used to read a lot of nonfiction for leisure. Then I started doing epistemic spot checks– taking selected claims from a book and investigating them for truth value, to assess the book’s overall credibility- and stopped being able to read nonfiction without doing that, unless it was one of a very short list of authors who’d made it onto my trust list. I couldn’t take the risk that I was reading something false and would absorb it as if it were true (or true but unrepresentative, and absorb it as representative). My time spent reading nonfiction went way down.

About 9 months ago I started taking really rigorous notes when I read nonfiction. The gap in quality of learning between rigorous notes and my previous mediocre notes was about the same as the gap between doing an epistemic spot check and not. My time spent reading nonfiction went way up (in part because I was studying the process of doing so), but my volume of words read dropped precipitously.

And then three months ago I shifted from my unit of inquiry being “a book”, to being “a question”. I’m sure you can guess where this is going- I read fewer words, but gained more understanding per word, and especially more core (as opposed to shell or test) understanding. 

The first two shifts happened naturally, and while I missed reading nonfiction for fun and with less effort, I didn’t feel any pull towards the old way after I discovered the new way. Giving up book-centered reading has been hard. Especially after five weeks of frantic covid research, all I wanted to do was to be sat down and told what questions were important, and perhaps be walked through some plausible answers. I labeled this a desire to learn, but when I compared it to question-centered research, it became clear that’s not what it was. Or maybe it was a desire to go through the act of learning something, but it was not a desire to answer a question I had and was not prioritized by the importance of a question. It was best classified as leisure in the form of learning, not resolving a curiosity I had.  And if I wanted leisure, better to consume something easier and less likely to lead me astray, so I started reading more fiction, and the rare non-fiction of a type that did not risk polluting my pool of data. And honestly I’m not sure that’s so safe: humans are built to extract lessons from fiction too.

Put another way: I goal factored (figured out what I actually wanted from) reading a nonfiction book, and the goal was almost never best served by using a nonfiction book as a starting point. Investigating a question I cared about was almost always better for learning (even if it did eventually cash out in reading a book), and fiction was almost always better for leisure, in part because it was less tiring, and thus left more energy for question-centered learning when that was what I wanted.