Guesstimate Algorithm for Medical Research

This document is aimed at subcontractors doing medical research for me. I am sharing it in the hope it is more broadly useful, but have made no attempts to make it more widely accessible. 

Intro

Guesstimate is a tool I have found quite useful in my work, especially in making medical estimates in environments of high uncertainty. It’s not just that it makes it easy to do calculations incorporating many sources of data; guesstimate renders your thinking much more legible to readers, who can then more productively argue with you about your conclusions. 

The basis of guesstimate is breaking down a question you want an answer to (such as “what is the chance of long covid?”) into subquestions that can be tackled independently. Questions can have numerical answers in the form of a single number, a range, or a formula that references other questions. This allows you to highlight areas of relative certainty and relative uncertainty, to experiment with the importance of different assumptions, and for readers to play with your model and identify differences of opinion while incorporating the parts of your work they agree with.

Basics

If you’re not already familiar with guesstimate, please watch this video, which references this model. The video goes over two toy questions to help you familiarize yourself with the interface.

The Algorithm

The following is my basic algorithm for medical questions:

  1. Formalize the question you want an answer to. e.g. what is the risk to me of long covid?
  2. Break that question down into subquestions. The appropriate subquestion varies based on what data is available, and your idea of the correct subquestions is likely to change as you work.
    1. When I was studying long covid last year, I broke it into the following subquestions
      1. What is the risk with baseline covid?
      2. What is the vaccine risk modifier?
      3. What is the strain risk modifier?
      4. What’s the risk modifier for a given individual?
  3. In guesstimate, wire the questions together. For example, if you wanted to know your risk of hospitalization when newly vaccinated in May 2021, you might multiply the former hospitalization rate times a vaccine modifier. If you don’t know how to do that in guesstimate, watch the video above, it demonstrates it in a lot of detail.
  4. Use literature to fill in answers to subquestions as best you can. Unless the data is very good, these probably include giving ranges and making your best guess as to the shape of the distribution of values.
    1. Provide citations for where you got those numbers. This can be done in the guesstimate commenting interface, but that’s quite clunky. Sometimes it’s better to have a separate document where you lay out your reasoning. 
    2. The reader should be able to go from a particular node in the guesstimate to your reasoning for that node with as little effort as possible.
    3. Guesstimate will use log-normal distribution by default, but you can change it to uniform or normal if you believe that represents reality better.
  5. Sometimes there are questions literature literally can’t answer, or aren’t worth your time to research rigorously. Make your best guess, and call it out as a separate variable so people can identify it and apply their own best guess.
    1. This includes value judgments, like the value of a day in lockdown relative to a normal day, or how much one hates being sick.
    2. Or the 5-year recovery rate from long covid- no one can literally measure it, and while you could guess from other diseases, the additional precision isn’t necessarily worth the effort.
  6. Final product is both the guesstimate model and a document writing up your sources and reasoning.

Example: Trading off air quality and covid.

The final model is available here.

Every year California gets forest fires big enough to damage air quality even if you are quite far away, which pushes people indoors. This was mostly okay until covid, which made being indoors costly in various ways too. So how do we trade those off? I was particularly interested in trading off outdoor exercise vs the gym (and if both had been too awful I might have rethought my stance on how boring and unpleasant working out in my tiny apartment is).

What I want to know is the QALY hit from 10 minutes outdoors vs 10 minutes indoors. This depends a lot on the exact air quality and covid details for that particular day, so we’ll need to have variables for that.

For air quality, I used the calculations from this website to turn AQI into cigarettes. I found a cigarette -> micromort converter faster than cigarette -> QALY so I’m just going to use that. This is fine as long as covid and air quality have the same QALY:micromort ratio (unlikely) or if the final answer is clear enough that even large changes in the ratio would not change our decision (judgment call). 

For both values that use outside data I leave a comment with the source, which gives them a comment icon in the upper right corner.

But some people are more susceptible than others due to things like asthma or cancer, so I’ll add a personal modifier.  I’m not attempting to define this well: people with lung issues can make their best guess. They can’t read my mind though, so I’ll make it clear that 1=average and which direction is bad.

Okay how about 10 minutes inside? That depends a lot on local conditions. I could embed those all in my guesstimate, or I could punt to microcovid. I’m not sure if microcovid is still being maintained but I’m very sure I don’t feel like creating new numbers right now, so we’ll just do that. I add a comment with basic instructions.

How about microcovids to micromorts? The first source I found said 10k per infection, which is a suspiciously round number but it will do for now. I device the micromorts by 1 million, since each microcovid is 1/1,000,000 chance of catching covid.

They could just guess their personal risk modifier like they do for covid, or they could use this (pre-vaccine, pre-variant) covid risk calculator from the Economist, so I’ll leave a note for that.

But wait- there are two calculations happening in the microcovids -> micromorts cell, which makes it hard to edit if you disagree with me about the risk of covid. I’m going to move the /1,000,000 to the top cell so it’s easy to edit.

But the risk of catching covid outside isn’t zero. Microcovid says outdoors has 1/20th the risk. I’m very sure that’s out of date but don’t know the new number so I’ll make something up and list it separately so it’s easy to edit

But wait- I’m not necessarily with the same people indoors and out. The general density of people is comparable if I’m deciding to throw a party inside or outside, but not if I’m deciding to exercise outdoors or at a gym. So I should make that toggleable.

Eh, I’m still uncomfortable with that completely made up outdoor risk modifier. Let’s make it a range so we can see the scope of possible risks. Note that this only matters if we’re meeting people outdoors, which seems correct.

But that used guesstimate’s default probability distribution (log normal). I don’t see a reason probability density would concentrate at the low end of the distribution, so I switch it to normal.

Turns out to make very little difference in practice.

There are still a few problems here. Some of the numbers are more or less made up, and others have sources but I’ve done no work to verify them, which is almost as bad.

But unless the numbers are very off, covid is a full order of magnitude riskier than air pollution for the scenarios I picked. This makes me disinclined to spend a bunch of time tracking down better numbers.

Full list of limitations:

  • Only looks at micromorts, not QALYs
  • Individual adjustment basically made up, especially for pollution
  • Several numbers completely made up
  • Didn’t check any of my sources

Example: Individual’s chance of long covid given infection

This will be based on my post last year, Long covid is not necessarily your biggest problem, with some modification for pedagogical purposes. And made up numbers instead of real ones because the specific numbers have long been eclipsed by new data and strains. The final model is available here

Step one is to break your questions into subquestions. When I made this model a year ago, we only had data for baseline covid in unvaccinated people. Everyone wanted to know how vaccinations and the new strain would affect things. 

My first question was “can we predict long covid from acute covid?” I dug into the data and concluded “Yes”, the risk of long covid seemed to be very well correlated with acute severity. This informed the shape of the model but not any particular values. Some people disagreed with me, and they would make a very different model. 

Once I made that determination, the model was pretty easy to create: It looked like [risk of hospitalization with baseline covid] * [risk of long covid given hospitalization rate] * [vaccination risk modifier] * [strain hospitalization modifier] * [personal risk modifier]. Note that the model I’m creating here does not perfectly match the one created last year; I’ve modified it to be a better teaching example. 

The risk of hospitalization is easy to establish unless you start including undetected/asymptomatic cases. This has become a bigger deal as home tests became more available and mild cases became more common, since government statistics are missing more mild or asymptomatic cases. So in my calculation, I broke down the risk of hospitalization given covid to the known case hospitalization rate and then inserted a separate term based on my estimate of the number of uncaught cases. In the original post I chose some example people and used base estimates for them from the Economist data. In this model, I made something up.

Honestly, I don’t remember how I calculated the risk of long covid given the hospitalization rate. It was very complicated and a long time ago. This is why I write companion documents to explain my reasoning. 

Vaccination modifier was quite easy, every scientist was eager to tell us that. However, there are now questions about vaccines waning over time, and an individual’s protection level is likely to vary. Because of that, in this test model I have entered a range of vaccine efficacies, rather than a point estimate. An individual who knew how recently they were vaccinated might choose to collapse that down. 

Similarly, strain hospitalization modifiers take some time to assess, but are eventually straightforwardly available. Your estimate early in a new strain will probably have a much wider confidence interval than your estimate late in the same wave. 

By definition, I can’t set the personal risk modifier for every person looking at the model. I suggested people get a more accurate estimate of their personal risk using the Economist calculator, and then enter that in the model.

Lastly, there is a factor I called “are you feeling lucky?”. Some people don’t have anything diagnosable but know they get every cold twice; other people could get bitten by a plague rat with no ill effects. This is even more impossible to provide for an individual but is in fact pretty important for an individual’s risk assessment, so I included it as a term in the model. Individuals using the model can set it as they see fit, including to 1 if they don’t want to think about it.

When I put this together, I get this guesstimate. [#TODO screenshot]. Remember the numbers are completely made up. If you follow the link you can play around with it yourself, but your changes will not be saved. If anyone wants to update my model with modern strains and vaccine efficacy, I would be delighted.

Tips and Tricks

I’m undoubtedly missing many, so please comment with your own and I’ll update or create a new version later.

When working with modifiers, it’s easy to forget whether a large number is good or bad, and what the acceptable range is. It can be good to mark them with “0 to 1, higher is less risky”, or “between 0 and 1 = less risk, >1 = more risk”

If you enter a range, the default distribution is log-normal. If you want something different, change it. 

The formulas in the cells can get almost arbitrarily complicated, although it’s often not worth it. 

No, seriously, write out your sources and reasoning somewhere else because you will come back in six months and not remember what the hell you were thinking. Guesstimate is less a tool for holding your entire model and more a tool for forcing you to make your model explicit.

Separate judgment calls from empirical data, even if you’re really sure you are right. 

Acknowledgements

Thanks to Ozzie Gooen and his team for creating Guesstimate.

Thanks to the FTX Regrant program and a shy regrantor for funding this work.

Quick Look: Asymptomatic Herpes Shedding

Tl;dr: Individuals shed and thus probably spread oral HSV1 while completely asymptomatic.

Introduction

“Herpes virus” can refer to several viruses in the herpes family, including chickenpox and Epstein-Barr (which causes mono). All herpesviridae infections are for life: once infected, the virus will curl up in its cell of choice, possibly to leap out and begin reproduction again later. If the virus produces visible symptoms, it is called symptomatic. If the virus is producing viable virions that can infect other people, it’s called shedding. How correlated symptoms and shedding are is the topic of this post. 

When people say “herpes” without further specification, they typically mean herpes simplex 1 or 2. HSV1 and 2 are both permanent infections of nerve cells that can lay dormant forever, or intermittently cause painful blisters on mucous membranes (typically mouth or genitals, occasionally eyes, very occasionally elsewhere). There are also concerns about subtle long-term effects, which I do not go into here.

There are two conventional pieces of conventional wisdom on HSV: “you can shed infectious virus at any time, even without a sore. Most people who catch herpes catch it from an asymptomatic individual” and “99.9% of shedding occurs during or right before a blister and there are distinct signs you can recognize if you’re paying attention. If you can recognize an oncoming blister the chances of infecting another human are negligible.” At the request of a client I performed two hours of research to judge between these.

It is definitely true that doctors will only run tests looking for the virus directly (as opposed to antibodies) if you have an active sore. However when researchers proactively sampled asymptomatic individuals using either genetic material tests (PCR/NAAT, which look for viral DNA in a sample) or viral culture (which attempt to breed virus from your test sample in a petri dish), they reliably found some people are shedding virus. 

HSV1 prefers the mouth but is well known to infect genitals as well. HSV2 is almost exclusively genital. Due to a dearth of studies I’ve included some HSV2 and genital HSV1 studies. 

Studies

Tronstein et al: This paper stupidly lumped in “0% shedding” with “>0% shedding” and I hate them. Ignoring that, they found that 10% of all days recorded from individuals with asymptomatic genital HSV2 involved shedding, and these were distributed on a long tail, with the peak at 0-5%. I cannot tell if they lumped 0% and 0.1% together because 0% never happens, or because they hate science. 

your buckets are bad and you should feel bad

Bowman et al: 14% of previously symptomatic genital HSV2 patients shed isolate-able virus (sampled every 8 weeks over ~3 years) while on antivirals. This study reports “isolating” virus without further details; I expect this means viral culture. 

Sacks et al: citing another paper: shedding across 6% of days in oral HSV1 patients (using viral culture). It also found the following asymptomatic shedding rates for genital herpes

Spruance: oral HSV1 patients shed isolatable virus 7.4% of the time (including while symptomatic). 60% of this occurred while experiencing mild symptoms that could have indicated an upcoming sore, but never developed into a sore.

Tateish et al: tested 1000 samples from oral surgery patients (not filtered for HSV infection status). 4.7% had PCR-detectable herpes DNA, and 2.7% had culturable virus. This includes patients without herpes (about 50% of people in Japan, where the research was done), but oral surgery is stressful and often stems from issues that make it easier to shed herpes, so I consider those to ~cancel out. 

Conclusion

My conclusion: it is definitely possible to shed HSV while asymptomatic, including if you are never symptomatic. The daily shedding rate is something like 3-12%, although with lots of interpersonal variability. This doesn’t translate directly to an infectiousness rate: human mouths might be harder or easier to infect than petri dishes (my guess is harder, based on the continued existence of serodiscordant couples). It may be possible for people who are antibody positive for HSV to never shed virus but we don’t know because no one ran the right tests. 

Thanks to anonymous client for funding the initial research and my Patreon patrons for supporting the public write-up.

New Water Quality x Obesity Dataset Available

Tl;dr: I created a dataset of US counties’ water contamination and obesity levels. So far I have failed to find anything really interesting with it, but maybe you will. If you are interested you can download the dataset here. Be warned every spreadsheet program will choke on it; you definitely need to be use statistical programming.

Photocredit: DALL-E and a lot of coaxing 

Many of you have read Slime Mold Time Mold’s series on the hypothesis that environmental contaminants are driving weight gain. I haven’t done a deep dive on their work, but their lit review is certainly suggestive. 

SMTM did some original analysis by looking at obesity levels by state, but this is pretty hopeless. They’re using average altitude by state as a proxy for water purity for the entire state, and then correlating that with the state’s % resident obesity. Water contamination does seem negatively correlated with its altitude, and its altitude is correlated with an end-user’s altitude, and that end user’s altitude is correlated with their average state altitude… but I think that’s too many steps removed with too much noise at each step. So the aggregation by state is basically meaningless, except for showing us Colorado is weird.

So I dug up a better data set, which had contamination levels for almost every water system in the country, accessible by zip code, and another one that had obesity prevalence by county. I combined these into a single spreadsheet and did some very basic statistical analysis on them to look for correlations.

Some caveats before we start:

  • The dataset looks reasonable to me, but I haven’t examined it exhaustively and don’t know where the holes are. 
  • Slime Mold Time Mold’s top contender for environmental contagion is lithium. While technically present in the database, litium had five entries so I ignored it. I haven’t investigated but my guess is no one tests for lithium.
  • It’s rare, but some zip codes have multiple water suppliers, and the spreadsheet treats them as two separate entities that coincidentally have the same obesity prevalence.
  • I’ve made no attempt to back out basic confounding variables like income or age.
  • “% obese” is a much worse metric than average BMI, which is itself a much worse metric than % body fat. 
  • None of those metrics would catch if a contaminant makes some people very fat while making others thin ( SMTM thinks paradoxical effects are a big deal, so this is a major gap for testing their model).
  • Correlation still does not equal causation.

The correlations (for contaminants with >10k entries):

ContaminantCorrelation# Samples
Nitrate-0.03921430
Total haloacetic acids (HAAs)0.05514666
Chloroform0.04615065
Barium (total)0.04017929
Total trihalomethanes (TTHMs)0.11721184
Copper-0.00217113
Dibromochloromethane0.08013856
Nitrate & nitrite0.03511902
Bromodichloromethane0.07914238
Lead (total)-0.00613031
Dichloroacetic acid-0.00310159

Of these, the only one that looks interesting is trihalomethanes, a chemical group that includes chloroform. Here’s the graph:

Visually this looks like the floor is rising much faster than the ceiling, but in a conversation on twitter SMTM suggested that’s an artifact of the biviariate distribution, it disappears if you look at log normal. 

Very casual googling suggests that TTHMs are definitely bad for pregnancy in sufficient quantities, and are maybe in a complicated relationship with Type 2 diabetes, but no slam dunks.

This is about as far as I’ve had time to get. My conclusions alas are not very actionable, but maybe someone else can do something interesting with the data.

Thanks to Austin Chen for zipping the two data sets together, Daniel Filan for doing additional data processing and statistical analysis, and my Patreon patrons for supporting this research.

Home Antigen Tests Aren’t Useful For Covid Screening

Epistemic status: I strongly believe this is the right conclusion given the available data. The best available data is not that good, and if better data comes out I reserve the right to change my opinion.

EDIT (4/27): In a development I consider deeply frustrating but probably ultimately good, the same office is now getting much more useful information from antigen tests. They aren’t tracking with same rigor so I can’t comapre results, but they are now beating the bar of “literally ever noticing covid”.

In an attempt to avoid covid without being miserable, many of my friends are hosting group events but requiring attendees to take a home covid test beforehand. Based on data from a medium-sized office, I believe testing for covid with the tests people are using, to be security theater and provide no decrease in riskAntigen tests don’t work for covid screening. There is a more expensive home test available that provides some value, and rapid PCR may still be viable.

It’s important to distinguish between test types here: antigen tests look for viral proteins, and genetic amplification tests amplify viral RNA until it reaches detectable levels. The latter are much more sensitive. Most home tests are antigen tests, with the exception of Cue, which uses NAAT (a type of genetic amplification). An office in the bay area used aggressive testing with both Cue and antigen tests to control covid in the office and kept meticulous notes, which they were kind enough to share with me. Here are the aggregated numbers: 

  • The office requested daily Cue tests from workers. I don’t know how many people this ultimately included, probably low hundreds? I expect compliance was >95% but not perfect.
    • The results are from January when the dominant strain was Omicron classic, but no one got strain tested.
  • 39 people had at least one positive Cue test, all of which were either asymptomatic or ambiguously symptomatic (e.g. symptoms could be explained by allergies) at the time, and 27 of which had recent negative cue tests (often but not always the day before, sometimes the same day)
  • Of these, 10 definitely went on to develop symptoms, 7 definitely did not, and 18 were ambiguous (and a few were missing data).
  • 33 people with positives were retested with cue tests, of which 9 were positive. 
  • Of those 24 who tested positive and then negative, 4 tested positive on tests 3 or 4.
  • Of the 20 people with a single positive test followed by multiple negative retests, 6 went on to develop symptoms.
  • 0 people tested positive on antigen tests. There was not a single positive antigen test across this group. They not only didn’t catch covid as early as Cue did, they did not catch any cases at all, including at least 2 people who took the tests while experiencing definitive systems.
    • Antigen tests were a mix of Binax and QuickVue.
    • Early cases took multiple antigen tests over several days, later cases stopped bothering entirely.
    • The “negative test while symptomatic” count is artificially low because I excluded people with ambiguous symptoms, and because later infectees didn’t bother with antigen tests. 
    • I suppose I can’t rule out the possibility that they had an unrelated disease with similar symptoms and a false positive on the Cue test. But it seems unlikely that that happened 10-28 times out a few hundred people without leaving other evidence.

A common defense of antigen tests is that they detect whether you’re contagious at that moment, not whether you will eventually become contagious. Given the existence of people who tested antigen-negative while Cue-positive and symptomatic, I can’t take that seriously.

Unfortunately Cue tests are very expensive. You need a dedicated reader, which is $250, and tests are $65 each (some discount if you sign up for a subscription). A reader can only run 1 test at a time and each test takes 30 minutes, so you need a lot for large gatherings even if people stagger their entrances. 

My contact’s best guess is that the aggressive testing reduced but did not eliminate in-office spread, but it’s hard to quantify because any given case could have been caught outside the office, and because they were trying so many interventions at once. Multiple people tested positive, took a second test right away, and got a negative result, some of whom went on to develop symptoms; we should probably assume the same chance of someone testing negative when a second test would have come back positive, and some of those would have been true positives. So even extremely aggressive testing has gaps.

Meanwhile, have I mentioned lately how good open windows and air purifiers are for covid? And other illnesses, and pollution? And that taping a HEPA filter to a box fan is a reasonable substitute for an air purifier achievable for a very small number of dollars? Have you changed your filter recently? 

PS. Before you throw your antigen tests out, note that they are more useful than Cue tests for determining if you’re over covid. Like PCR, NAAT can continue to pick up dead RNA for days, maybe weeks, after you have cleared the infection. A negative antigen test after symptoms have abated and there has been at least one positive test is still useful evidence to me. 

PPS. I went through some notes and back in September I estimated that antigen testing would catch 25-70% of presymptomatic covid cases. Omicron moves faster, maybe faster enough that 25% was reasonable for delta, but 70% looks obviously too high now. 

PPPS. Talked to another person at the office, their take is the Cue tests are oversensitive. I think this fits the data worse but feel obliged to pass it on since they were there and I wasn’t.

PPPPS (5/02): multiple people responded across platforms that they had gotten positive antigen tests. One or two of these was even presymptomatic. I acknowledge the existence proof but will not be updating until the data has a denominator. If you’re doing a large event like a conference I encourage you to give everyone both cue, antigen, and rapid PCR tests and record their results, and who eventually gets sick. If you’d like help designing this experiment in more detail please reach out (elizabeth-at-acesounderglass.com)

I Caught Covid And All I Got Was This Lousy Ambiguous Data

Tl;dr I tried to run an n of 1 study on niacin and covid, and it failed to confirm or disprove anything at all.

You may remember that back in October I published a very long post investigating a niacin-based treatment protocol for long covid. My overall conclusion was “seems promising but not a slam dunk; I expect more rigorous investigation to show nothing but we should definitely check”. 

Well recently I got covid and had run out of more productive things I was capable of doing, so decided to test the niacin theory. I learned nothing but it was a lot of effort and I deserve a blog post out of it null results are still results so I’m sharing anyway.

Background On Niacin

Niacin is a B-vitamin used in a ton of metabolic processes. If you’re really curious, I describe it in excruciating detail in the original post.

All B vitamins are water-soluble, and it is generaly believed that unless you take unbelievably stupid doses you will pee out any excess intake without noticing. It’s much harder to build up stores of water-soluble vitamins than fat vitamins, so you need a more regular supply.  Niacin is a little weird among the water-solubles in that it gives very obvious signs of overdose: called flush, the symptoms consist of itchy skin and feeling overheated. Large doses can lead to uncontrolled shaking, but why would you ever take that much, when it’s so easy to avoid?

People regularly report response patterns that sure look like their body has a store of niacin that can be depleted and refilled over time. A dose someone has been taking for weeks or months will suddenly start giving them flush, and if they don’t lower it the flush symptoms will get worse and worse. 

Some forms of niacin don’t produce flush. Open question if those offer the same benefits with no side effects, offer fewer benefits, or are completely useless.

Niacin And Long Covid

There’s an elaborate hypothesis about how covid depletes niacin (and downstream products), and this is a contributor to long covid. My full analysis is here. As of last year I hadn’t had covid (this is antibody test confirmed, I definitely didn’t have an asymptomatic case) but I did have lingering symptoms from my vaccine and not a lot else to try, so I gave the protocol a shot.

My experience was pretty consistent with the niacin-storage theory. I spent a long time at quite a high dose of the form of niacin the protocol recommends, nictonic acid. My peak dose without flush was at  least 250mg (1563% RDA) and maybe even 375mg (2345% RDA). When I hit my limit I lowered my dose until I started getting flush at the new dose, and eventually went off nicotnic acid entirely (although I restarted a B-vitamin that included 313% RDA of a different form). That ended in September or early October 2021. It made no difference in my lingering vaccine symptoms.

In early 2022 I tried nicotinic acid again. Even ¼ tablet (62.5mg, 390% RDA) gave me flush.

I Get Covid

Once I developed symptoms and had done all the more obviously useful things like getting Paxlovid, I decided it would be fun to test myself with niacin (and the rest of the supplement stack discussed in my post) and see if covid had any effect. So during my two weeks of illness and week of recovery I occasionally took nicotinic acid and recorded my results. Here’s the overall timeline:

  1. Day -2: am exposed to covid.
  2. Day 0: test positive on a cue test (a home test that uses genetic amplification).
    1. Lung capacity test: 470 (over 400 is considered health).
    2. Start Fluvamoxine and the vitamin cocktail, although I’m inconsistent with both the new and existing vitamins during the worst of the illness. Vitamin cocktail includes 313% RDA of no-flush niacin, but not nicotinic acid. 
  3. Day 1: symptomatic AF. 102.3 degree fever, awake only long enough to pee, refill my water, and make sure my O2 saturation isn’t going to kill me. I eat nothing the entire day.
    1. I monitored my O2 throughout this adventure but it never went into a dangerous zone so I’m leaving it out of the rest of the story.
  4. Day 2: start with 99 degree fever, end day with no fever. Start Paxlovid.
    1. Every day after this I am awake a little bit longer, eat a little bit more, and have a little more cognitive energy, although it takes a while to get back to normal. 
    2. Try ¼ tab nicotinic acid (62.5 mg/ 375% RDA), no flush.
    3. Lung capacity troughs at 350 (considered orange zone).
  5. Day 4: ½ tablet nictonic acid, mild flush.
  6. Day 7: lung capacity up to 450, it will continue to vary from 430-450 for the next two weeks before occasionally going higher.
  7. Day 9: ½ tablet nictonic acid, mild flush
  8. Day 10-17: ⅓ tablet nictonic acid, no flush
    1. Where by “⅓” tablet I mean “I bit off an amount of pill that was definitely >¼ and <½ and probably averaged to ~⅓ over time”
  9. Day 12: I test positive on a home antigen test
  10. Day 15: I test negative on a home antigen test (no tests in between) 
  11. Day 17: ⅓ tablet produces flush (and a second negative antigen test)
    1. This was also the first day I left my house. I had thought of myself as still prone to fatigue but ended up having a lot of energy once I got out of my house and have been pretty okay since.

Conclusions

My case of covid was about as bad as you get while still technically counting as mild. Assuming I went into it with niacin stores such that 62.5mg nicotinic acid would generate flush, it looks like covid immediately took a small bite out of them. Or it reduced my absorption of vitamins, such that the same oral dosage resulted in less niacin being taken in. There’s no way to know covid had a larger effect on niacin than other illnesses, because I don’t have any to compare it to. Or maybe the whole thing was an artifact of “not eating for two days, and then only barely, and being inconsistent with my vitamins for a week”.

Niacin as a treatment for covid? (Probably no, but I’m glad we’re checking)

Introduction

This article contains an interview with a doctor who believes NAD+ is the secret to covid’s heavy morbidity and mortality toll. The description was unusually well done for internet crackpottery. This is hard to convey rigorously, but it had a mechanistic-ness and the right level of complexity about it, and it made the right level of promises for a treatment. None of this is to say it’s definitely correct, but it had a bunch better chance of being correct than your average alt-covid-cure scribbled out in crayon. So I did some checks on it.

[Didn’t you say the risk of long covid was small? NO I SAID IT WAS TOO SMALL TO MEASURE AGAINST THE DELUGE OF CRAP THAT HAPPENS TO US EVERYDAY THAT IS NOT THE SAME]

*ahem*

This post is organized as follows:

  • Description of theory. 
  • Long section defining terms. These are all useful for understanding the claims I check later on, but depending on who you are they may not be helpful, and you may find the contextless infodump kind of a drag. Feel free to skip if it’s not useful to you personally, and know that it’s there if you need it.
  • Deep dive onto particular claims the article makes.
  • Does it work?
  • Is it safe?
  • My personal experience with the protocol 
  • Some meta

This is your reminder that my only credential is a BA in biology and I didn’t specialize in anything relevant. It is a sign of civilizational inadequacy that this post exists at all, and you should think really hard and do your own research before putting too much weight on it.

For those of you would like to skip to the take home message: science is very hard, I’m glad they’re running larger studies to follow up on all of these because that’s a reasonable thing for a rich society to do, but I’m not super hopeful about this protocol.

The Theory

As described by Dr. Ade Wentze:

There is an extremely widely used coenzyme in your body, NAD. The more active form of this compound, NAD+, is depleted by covid (converted to NADH). In people with a preexisting deficiency or difficulty rebounding after depletion, covid infection results in a persistent NAD+ deficit. This is bad in and of itself, but causes additional problems when your body tries to make up for it by requisitioning all your tryptophan to make more. Tryptophan is also a precursor for serotonin, so this leads to either low serotonin or activation of mast cells to release their serotonin stores, accompanied by histamines (which cause allergies and other issues). 

Background

There is a lot of vocabulary in that theory and in the supporting claims, which I go over here. If you’re reading for conclusions rather than deep understanding I would skip this.

NAD+

Nicotinamide adenine dinucleotide is a coenzyme that plays an essential role in hundreds of chemical reactions in your cells, including many relating to processing energy and genetic transcription.  This is a mixed blessing as a foundation for crackpot theories go: something involved in hundreds of processes across every kind of tissue in your body can cause almost any symptom, which is great because long covid has a lot of symptoms to cover. On the other hand, it can cause almost any symptom, which means it’s hard to disprove, and you should distrust things in proportion to the difficulty to disprove them. Alas, sometimes core processes are impaired and they do express that impairment in a range of unpredictable ways that vary across people, but it’s also an easy home for crackpots. 

NAD+ has two major components, one made from either tryptophan or aspartic acid (both amino acids), or by altering niacin.

Niacin

Like many vitamins, niacin aka vitamin B3 refers to a few different closely related compounds (most commonly nicotinic acid, nicotinamide, nicotinamide riboside, and inositol nicotinate, but there are others) that are almost but not quite interchangeable.

Chemical structures of niacin compounds: (a) nicotinamide; (b) nicotinic acid; (c) nicotinamide adenine dinucleotide (NAD þ ); (d) nicotinamide adenine dinucleotide phosphate (NADP þ ) (source)

Niacin is commonly prescribed for treating high cholesterol, although a metareview found it did not reduce overall mortality and may contribute to the development of type-2 diabetes. 

Severe niacin deficiency is called pellagra, and can be caused by either insufficient consumption or problems processing the vitamin. Pellagra is mostly defined as niacin deficiency but can also be caused by tryptophan deficiency, which you may remember is another path to manufacturing NAD+. Pellagra can cause diarrhea, dermatitis, dementia, and death, which are not a great match for acute or long covid. Niacin supplementation treats pellagra, often within a few days.

SIRT1

Sirtuin 1, also known as NAD-dependent deacetylase sirtuin-1, is a protein that regulates the expression of some genes in ways that haven’t yet been made clear to me but seem to be associated with aging (more SIRT1 is associated with better outcomes, although we haven’t broken down cause and effect). As indicated by its name, it’s dependent on NAD+ to operate, which means NAD+ is involved in the regulation of expression of some genes via some mechanism, which means niacin is involved in the regulation of expression of some genes via some mechanism.

SIRT1 is downregulated in cells that have high insulin resistance and inducing its expression increases insulin sensitivity, suggesting the molecule is associated with improving insulin sensitivity.

SIRT1 may be upregulated by selenium.

PARP

Another many-purposed enzyme whose activities include DNA repair, killing cells that are beyond repair. PARP requires NAD+ as a coenzyme.

Individual Claims

Groups with low NAD+ suffer more from covid

NAD+ declines with age

NAD+ does definitely decline with age but so does literally everything bad in your body, so I don’t find this very compelling.

Correlation between NAD+ levels and Age in (A) Males (B) Females (source)

Obese people have lower NAD+ levels, leading to worse outcomes

Yes, although obese people tend to do worse on a lot of metrics. However, that paper highlights that SIRT1 seems to be involved in this correlation somehow.

Diabetics have worse NAD+ levels

Yes, although diabetics also have more immune problems generally (definitely Type 2, some pop sites said the same for Type 1 and that’s believable but I didn’t quickly find a paper I liked that backed the claim).

Low selenium is associated with bad outcomes in covid

The post cites Zhang et al, which took advantage of high variations in selenium consumption in China to do a natural experiment. Variations in the population selenium levels do seem insanely correlated with the overall cure rate (defined as not dying). The study took place in February 2020 so neither data collection nor treatment was very good, but damn that is interesting.

Moreover, this study, which came out several months after the blog post was published, took advantage of the same variation and came to the same conclusion, with a much larger sample size and much more reasonable case fatality rate (1.17% in areas with no deficiency to 3.16% in severely deficient areas, P = 0.002). (Note: several authors on that paper are also named Zhang, but I assume that’s because it’s a common name in China).

Some pharma company thinks selenium is promising enough to launch a trial for it, although recruitment hasn’t started yet.

The pre-print servers are littered with natural experiments highlighting correlations that failed as interventions, but this is very strong for a correlation.

Niacin just generally seems to help lung damage

That is indeed what their citation says, however that paper’s only source looked at the effect of niacin on lung damage in hamsters deliberately induced with a chemotherapy drug, and it’s not obvious to me that that translates to damage from infection or immune reaction. There are some other scattered studies in rodents, combining niacin with other substances, none of which looked at damage from infectious disease.

The treatment for NAD+ deficiency is niacin

Their citation backs this up: niacin supplementation led patients (n=5) and controls (healthy people given the same supplementation, n=8) to increased NAD+ levels, and arguably increased strength, although with that much variation and such a small sample size I’m not convinced. Martens et al supports this with modest benefits seen in n=24 subjects.

A few minutes investigation found some other studies:

  • Dietary niacin deficiency led to NAD+ deficiency in baby rats. This paper works damn hard to hide its sample size but I think it was 10-15 per treatment group.
  • The same author exposed some rats (n=6 per treatment group) to excess oxygen and found that those with a niacin deficient diet had less NAD+ in the lungs and responded less to the damage caused by excess oxygen, but had the same wet/dry ratio as their well-fed friends (wet/dry ratio is a measure of lung health).
  • Ng et al found that in catfish liver NAD increased linearly with dietary niacin supplementation, but health returns like size and mortality dropped off between 6 and 9 mg/kg. They further found that tryptophan supplementation could not make up for a niacin deficiency (in catfish).

Plus niacin is so well established as a treatment for pellagra that no one bothers to cite anything for it, and that does seem to mediate through NAD+.

Nicotinic acid may act as a one of a kind bioenergetic “pump” of inflammatory molecules out of cells

They link to a preprint which has since been taken down, and I could not find it on my own. 

NAD+ problems have been indicated in chronic fatigue syndrome

Everything has been indicated in chronic fatigue syndrome; I’m not looking this up.

Low serotonin -> mast cell activation -> histamine release

Mast cells indeed produce serotonin, in mice. Note that that paper highlights fluoxetine as a way to reverse serotonin deficiency in mast-cell-deficient mice, and since the article was published fluoxetine has shown promise as a covid treatment. However this study says that while serotonin-producing mast cells are common, humans in particular don’t have them while healthy (although it still shows serotonin affecting mast cell movements). This appears to be an area of some controversy.

Mast cells releasing histamine in response to allergens is uncontroversial. Moreover, histamines and serotonin are stored in the same compartments (in mice). Second source (still in mice). 

Some Guy did an informal study based on this theory and it worked

Some guy (Birth name: Gez Mendinger) did indeed report this, and I have to say, for an uncredentialed dude on youtube recommending OTC supplements to treat a nebulously defined disease, this guy looks really credible, and his reasonably good analysis was quite promising. He shared his results with me, and it continued to look promising when I first dug into it with assistance from a statistician, but the deeper we drilled the less promising it looked (details). By the end, the most I could say is “yeah, worth a harder look”, but the history of things that look promising in small, poorly organized studies that wilt under large, well-organized ones is just too dismal to ignore. 

Mouse study shows low NAD+ hurts you via SIRT1

The interview also cites this mouse study featuring a direct NAD+ drip and a slightly different coronavirus. They show improved symptoms but not viral load. They don’t list the sample size anywhere I can find, judging from the low-resolution graph it looks like 7 mice in the control group and maybe 12 in the treatment group? Except for the embolism test which had many more mice.

(apologies for poor image quality, the PDF was crap)

(note: that article was up when I started this post but disappeared before I verified the SIRT1-specific part of the claim)

Quercetin increases NAD+ levels 

Yes, in rats and mice. Specifically, it speeds up the transition from NADH to NAD+

Male pattern balding and low vitamin D are both associated with poor covid outcomes and low NAD+.

The balding citation does indeed say that, but it only looked at hospitalized patients so it’s useless. Moreover, balding is associated with a testosterone derivative, and testosterone weakens the immune system. But when I went to find some cites for those, I found that within hospitalized patients, low testosterone was associated with worse outcomes. However these patients were already hospitalized, so the causality could easily go the other way.

Meanwhile I found several folk-wisdom level comments indicating a link between NAD+ and male pattern balding, but nothing rigorous.

Low vitamin D does seem to be associated with poor covid outcomes, maybe, but treatment doesn’t seem to help (at least not if you wait until patients are hospitalized). 

Chang and Kim assert that Vitamin D activates the NAD-SIRT1 pathway in fat cells in vitro, which if it held up elsewhere would be even stronger evidence for the overall theory than this claim attempts. Byers et al found that vitamin D did not protect guinea pigs against the NAD+ depleting effects of mustard gas. This is not a slam dunk.

Covid depletes NAD+ by activating PARP

Curtin et al lay out a theoretical case for using PARP-inhibitors to treat covid-caused ARDS.

Heer et al “we show that SARS-CoV-2 infection strikingly upregulates MARylating PARPs and induces the expression of genes encoding enzymes for salvage NAD synthesis from nicotinamide (NAM) and nicotinamide riboside (NR), while downregulating other NAD biosynthetic pathways” (notably, the forms not used in the protocol), “overexpression of PARP10 is sufficient to depress cellular NAD and that the activities of the transcriptionally induced enzymes PARP7, PARP10, PARP12 and PARP14 are limited by cellular NAD and can be enhanced by pharmacological activation of NAD synthesis”, “MHV induces a severe attack on host cell NAD+ and NADP+.” (MHV being used as a model)

Long covid and Pellagra share a lot of symptoms, including hyponosmia

Scattered claims pellagra causes hyponosmia but you have to look really hard, it doesn’t show up on any of the common descriptions. I checked in Spanish and didn’t find anything either.

Sen (published only last month) suggests that serotonin deficiency causes anosmia and other neuro symptoms in covid. They propose a different method for the depletion (ACE2 is a mechanism for moving serotonin into the cell), but it’s not mutually exclusive with Wentzel’s theory (that NAD+ depletion causes the body to use up tryptophan trying to produce more NAD+).

Your body hijacks tryptophan to make NAD+ at the expense of serotonin

Tryptophan can indeed be used to make NAD (albeit niacin is better) and serotonin. How your body prioritizes under a given set of circumstances is anyone’s guess.

NAD+ and the immune system

Probably at least some of long covid stems from autoimmune issues, as witnessed by the fact that it’s much more common in women and sometimes helped by steroids. The post and paper don’t make any claims on this beyond the effect of NAD+ on mast cells, which are implicated in autoimmune disorders, but out of curiosity I did some quick googling and found that NAD+ downregulate inflammation via CD4 cells (in mice) and activating SIRT1, the pathway mentioned previously (still in mice).

The Paper

Not that good. Feels associational rather than mechanistic. However Bordoni et al (published after the cited paper) found covid-19 was associated with diminished SIRT1- but Pinto et al found covid-19 upregulated SIRT1 and cite another study claiming that under conditions of energetic stress (which would imply low NAD+), SIRT1 substitutes for ACE2 (the receptor covid uses to enter the cell. Smith suggests that downregulating SIRT1 is good for fighting covid. So SIRT1, NAD+, and covid are probably related, but the first two items are very common so this isn’t damning.

Notably, this paper doesn’t explain why covid would deplete NAD+ more than other infectious diseases, which is an enormous hole.

Does it work? 

The mechanism and empirical data are definitely enough to merit more rigorous follow-up studies (which are in progress) and definitely not slam dunks. But you may need to make a decision before that’s in, so the real question is “should I take this stack if I get sick? Should my parents?”

My tentative answer is: the prescribed stack probably won’t physically hurt you (but see the next section), and it’s fairly cheap, so the limiting factor is probably “what do you have the energy to try”. This is a better thing to try than the interventions whose proof was actively made up or have been investigated and discarded, but there undoubtedly are or will be equally probable things floating around, and choosing between them will be a matter of taste..  

If you do end up giving this a shot, for covid long or acute, I invite you to preregister your complaints and intention with me (a comment here or email elizabeth@acesounderglass.com), so I can create my own little study. If you don’t feel like doing that I still encourage you to announce the intention somewhere, as a general good practice (I did so here). 

So you’re saying it’s safe then?

Anything that does anything is dangerous to you in sufficient dosages. If you’re considering an unverified supplement stack, you should carefully investigate the potential side effects of each substance and consider it in light of what you know of your own health (especially other medications you’re taking). Consider talking to a doctor, if you have a good one.

If any of you are thinking “oh niacin’s a water-soluble vitamin it must be fine”: that’s a pretty good heuristic but it doesn’t hold for niacin in particular.

My experience

As mentioned previously, I acquired lingering progressive chest congestion/inflammation from (probably) my covid vaccine. It’s always possible there was another reason but the timing and symptoms really do not match anything else. 

Since I never had covid (probably), my reaction can’t come from the infection itself, only my immune response to it. Since the theory doesn’t specify a mechanism that’s not disqualifying, but they do make it sound like it starts as a covid problem not an immune problem.

I started this supplement stack before doing any deep verification. The original blog post pattern matched to the kind of thing that was worth trying, everything on the list I either knew was generally safe or confirmed with a quick check (my doctor later confirmed my opinion on safety without endorsing the stack for any particular use), and I had a lot of client work to do. Shoemaker’s children go barefoot, and all that.  So by the time I was writing this I had been on the recommended supplement stack (and some other things besides) for 3 weeks, and was beginning to wean down. 

Overall: my chest pain got better but the timing fits better with attribution to a different intervention. The rash I got on matches very well with the supplement stack. I nonetheless was craving it after I weaned off, so probably there’s at least one thing in it I need, which hopefully isn’t the same as the thing causing the rash. 

[Alert twitter readers may have questions, since I previously was more positive on the stack. I had a major regression when I got a non-covid cold, and had to go back on the other treatment]

Interestingly, my tolerance for niacin increased and then plummeted. Originally I could take 250mg (the smallest size I could find in the right form) with only very mild flush, and that got better over time, to the point I tried 500 mg once (a mistake). But around week 3 my flush was getting worse. Lowering the dose helped, but it’s getting worse again, so I’m continuing to titrate down. This is extremely consistent with filling up NAD+ reserves over time, although very far from conclusive.

Meta

I was originally much more positive on this treatment/theory. I gave it more credit on Twitter, but that’s nothing compared to the excited messages I sent a few friends after an initial lit review. I wrote several much more positive versions of this post (and the forthcoming study analysis), but there kept being one more thing to check, until I talked my way down to what you see here. Some of my downgrade stemmed from asking better statistical questions, but some of it was just the emotional process of talking myself down from something that initially looked so promising, but ultimately had a similar amount of holes to many other things that looked equally promising and failed to pay off. This represents dozens of hours of work from me and my statistician, for the very disappointing result of “fringe treatment probably doesn’t do very much but can’t rule it out”. Reality is infinitely disappointing

Thanks to Alex Ray and my Patreon Patrons for partially funding this investigation, and Miranda Dixon-Luinenburg⁩ for copyediting.

“Eating Dirt Benefits Kids” is Basically Made Up

Sometimes people imply that epistemic spot checks are a waste of time, that it’s too easy to create false beliefs with statements that are literally true but fundamentally misleading. And sometimes they’re right.

On the other hand, sometimes you spend 4 hours and discover a tenet of modern parenting is based on absolutely nothing.

[EDIT: this definitely was a tenet among my friends, but apparently is less widespread than I thought.]

Sorry, did I say 4 hours? It was more like 90 minutes, but I spent another 2.5 hours checking my work just in case. It was unnecessary.

Intro

You are probably familiar with the notion that eating dirt is good for children’s immune systems, and you probably call that Hygiene Hypothesis, although that’s technically incorrect. 

Hygiene Hypothesis can refer to a few different things:

  1. A very specific hypothesis about the balance between specific kinds of immune cells.
  2. A broader hypothesis that exposure to nominally harmful germs provides the immune system training and challenge that ultimately reduces allergies.
    1. One particular form of this involves exposure to macroparasites, but that seems to have fallen out of favor.
  3. The hypothesis that exposure to things usually considered dirty helps populate a helpful microbiome (most often gut, but plausibly also skin, and occasionally eyeball), and that reduces allergies. This is more properly known as the Old Friends hypothesis, but everyone I know combines them.
  4. Pushback on the idea that everything children touch should be super sanitized
  5. The idea that eating dirt in particular is beneficial for children for vague allergy-related reasons.

I went into this research project very sold on the Hygiene Hypothesis (broad sense), and figured this would be a quick due diligence to demonstrate it and get some numbers. And it’s true, the backing for Hygiene and Old Friends Hypothesis seems reasonably good, although I didn’t dig into it because even if they’re true, the whole eating dirt thing doesn’t follow automatically. When I dug into that, what I found was spurious at best, and what gains there were had better explanations than dirt consumption.

This post is not exhaustive. Proving a negative is very tiring, and I felt like I did my due diligence checking the major books and articles making the claim, none of which had a leg to stand on. Counterevidence is welcome. 

Evidence

Being born via c-section instead of vaginally impoverishes a newborn’s microbiome, and applying vaginal fluid post-birth mitigates that

This has reasonable pilot studies supporting it, to the point I mentioned it to a pregnant friend.

There are reports that a mother’s previous c-sections lower a newborn’s risks even further, but I suspect that’s caused by the fact below

Having older siblings reduces allergies

Study. The explanation given is a more germ-rich environment, although that’s not proven.

Daycare reduces later allergies, with a stronger effect the earlier you enter, unless you have older siblings in which case it doesn’t matter

Study. Again, there are other explanations, but contagious diseases sure look promising.

Living with animals when very young reduces allergies

This one is a little more contentious and I didn’t focus on it.  When the animal appears seems to matter a lot.

One very popular study used to bolster Dirt Eating is a comparison of Amish and Hutterite children. Amish children get ~⅙ of the allergies Hutterite children do, which pop articles are quick to attribute to dirt “because Amish children work on farms and Hutterite children don’t.” But there are a lot of differences between the populations: dust in Amish homes have 6x the bacterial toxins of Hutterite homes, the children have much more exposure to animals, and drink unpasteurized milk. 

Limitations of Farm Studies

Even if Amish children did eat more dirt and that was why they were healthier, there’s no transfer from that to urban parks treated with pesticides and highway exhaust. They might be net positive, the contaminants might not matter that much, your park in particular might be fine, no one has proven this dirt is harmful, etc. But you should not rest your decision on the belief that that dirt has been proven beneficial, because no one has looked.

Mouse Studies

There are several very small mouse studies showing mice had fewer allergies when exposed to Amish dirt, but:

  1. They are very small.
  2. They are in mice.
  3. The studies I found never involve feeding the mice dirt. Instead, they place it in bedding, or directly their nasal passages, or gently waft it into the cage with a fan. 

So eating dirt is bad then?

I don’t know! It could easily be fine or even beneficial, depending on the dirt (but I suspect the source of dirt matters a lot). It could be good on the margin for some children and bad for others. Also, avoiding a constant battle to keep your toddler from doing something they extraordinarily want to do is its own reward. What I am asserting is merely that anyone who confidently tells you eating arbitrary dirt is definitely good is wrong, because we haven’t done the experiments to check.

I think any of [communicable diseases, animals, unpasteurized milk] have more support as anti-allergy interventions than dirt, but I hesitate to recommend them given that a high childhood disease load is already known to have significant downsides and the other two are not without risks either.

Epilogue

The frightening thing about this for me is how this became common knowledge even, perhaps especially, among my highly intelligent, relatively authority-skeptical friends, despite falling apart the moment anyone applied any scrutiny. I already thought the state of medical knowledge and the popular translation of that knowledge was poor, but somehow it still found a way to disappoint me.

My full notes are available in Roam.

This post was commissioned by Sid Sijbrandij. It was preregistered on Twitter. I am releasing it under the Creative Commons Attribution 4.0 license. Our initial agreement was that I would be paid before starting work to avoid the appearance of influence; in practice I had the time free and the paperwork was taking forever so I did the research right away and sat on the results for a week.

Thanks to Miranda Dixon-Luinenburg⁩ for copyediting.

Alternate Views On Long Covid

Scott Alexander has published a post on long covid, which he rates as much more frequent and dangerous than I do. Scott and I spent a while hashing this out in private, and our cruxes seem to come down to:

  1. I think his studies are too small and sample-biased to be meaningful.
  2. He thinks my studies (especially Taquet) didn’t look at the right sequelae.
  3. I was only looking at cognition (including mood disorders), whereas he looked at everything.

Scott also didn’t do age-specific estimates, although that’s not a crux because I expect other post-infection syndromes to worsen with age as well.

I intended to include fatigue in my analysis of cognitive symptoms but in practice the studies I weighted most highly didn’t include them. Scott’s studies, which he admits are less rigorous although we differ on how much, did include them. Why the hell aren’t the large, EHR-based studies with control groups looking at fatigue?

Also, this isn’t relevant to the covid disagreement, but I baffled by the medical systems’ decision to declare chronic Lyme in particular as the definitely psychosomatic syndrome, given that Lyme is closely related to syphilis, which we know damn well has a long dormant period and a stunning array of possible long term consequences.

Although I didn’t update much on this particular disagreement, I have a lot of respect for Scott and encourage anyone making decisions based on bloggers’ estimates of the risk of long covid to check out his post as well.

Exercise Trade Offs: Followup

Last week I did some math on the risk/reward profile of exercising indoors (risking covid exposure) vs. outdoors (risking exposure to smoke from the CA fires), and found the numbers for the day I did the math (low-for-fire-season pollution outside, sparsely populated indoor gym) overwhelmingly favored exercise of any kind over not exercising, and any other factor was overwhelmed by how likely it was to create friction to exercising. 

Over on Facebook, a friend pointed out that I’d left out the biggest cost of exercise: the time in which it took place. I then realized a full accounting would also include the time to get to the gym and the risk of getting hit by a car en route. And was I sure the micromort estimate for exercise incorporated the risk of injury? (no, because the data is hidden in an appendix BJM paywalled and sci-hub doesn’t have. If you have BJM access and would like to help me out by emailing me (elizabeth@acesounderglass.com) the appendix for this article it would be much appreciated. EDIT: received and responded to. Thanks Steve!). But exercise has benefits beyond dying later, and I wasn’t fully accounting for any of those either. And time spent at or traveling to work out isn’t exactly lost: zipping around on my scooter is fun, and leaving my house regularly on some sort of schedule has been good for me. This gets unwieldy really quickly.

Nonetheless, time spent traveling and the accompanying risk of car accidents seemed really significant, so I updated the spreadsheet to incorporate it. Ignoring any positive effects beyond the microlives, this was enough to make going to my gym for cardio net costly (note: because the spreadsheet measures in micromorts a positive number is bad), although going to the gym for weights and my nearby friend’s backyard for cardio still come out ahead.

I still think gym cardio is net beneficial for me because I think my exercise is much more impactful than average. But I don’t think it’s so much more beneficial than my friend’s backyard treadmill, so I’m going to emphasize the latter except on very bad smoke days.

Water Pick Experimental Results

Since my last dental appointment (3 months ago), I’ve cleaned one half of my mouth with a water pick (in addition to brushing on both sides), with the goal of determining if it actually did anything useful. I was inspired by my dentist’s insistence that I Do Something despite not noticing when I consistently used the pick. I pre-registered on Facebook that if the hygienist spontaneously commented that one side looked better, or some objective measure like # of cavities was different, I would consider it evidence in favor of the water pick. Today was the appointment.

Final results:

  • Hygienist didn’t comment either way.
  • No new cavities on either side
  • Gum pocket measurements were worse on the water picked side.

Obviously one trial isn’t conclusive, but I’m giving up on the water pick. Next step: test flossing.