Pakistan

Article

Pakistan is a recurring place in the Astral Codex Ten archive, appearing 12 times across 12 issues between August 17, 2021 and June 18, 2025. The archive places it in contexts such as ““a strain of literature focuses on big Third World disasters, like an earthquake in Pakistan””; “Looked at from this lens the Pakistan study looks much worse”; “Ghauri et al: Pakistan, 95 patients”. It most often appears alongside COVID, Egypt, Google.

Metadata

Category: Places
Mention count: 12
Issue count: 12
First seen: August 17, 2021
Last seen: June 18, 2025

Appears In

- COVID (6 shared issues)
- Egypt (5 shared issues)
- Google (5 shared issues)
- US (5 shared issues)
- Britain (4 shared issues)
- China (4 shared issues)
- India (4 shared issues)
- Mexico (4 shared issues)
- Nigeria (4 shared issues)
- Spain (4 shared issues)
- Twitter (4 shared issues)
- United States (4 shared issues)

External Links

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Kids Can Recover From Missing Even Quite A Lot Of School

August 17, 2021 · Original source

Another strain of literature focuses on big Third World disasters, like an earthquake in Pakistan. These disasters usually destroy schools and give students a few years’ gap in learning, and studies do find that the students do worse just after the disaster. But importantly, they are doing more worse than the schooling gap can account for - eg if they miss school for a year, they’re two or three years below grade level. Most studies I read attribute this to some combination of malnutrition and PTSD. The grade losses worsen even after the students are back in school (ie they might start two years behind, and after two more years of schooling they’re four years behind). The commentators attribute this to “being promoted too quickly”, but I interpret this as further evidence that something beyond just missing school is affecting this. Given that the Hurricane Katrina study didn’t find this, I suspect that in a First World environment where malnutrition is less of a risk, there’s less need to worry about this.

Inline links: an earthquake in Pakistan

Highlights From The Comments On Missing School

August 26, 2021 · Original source

Something that was missing from almost all of those studies is a measurement of resources required for catching the students back up. Almost all systems have some stabilizing mechanisms to help bring up students who e.g. got cancer back up to grade level (extra attention by classroom teachers, special ed, social workers, etc). Even disasters come with extra resources in the US to help support the students. Just because a system kept results within it's normal parameters during normal times doesn't imply it can do so during abnormal times. Looked at from this lens the Pakistan study looks much worse.

Ivermectin: Much More Than You Wanted To Know

November 17, 2021 · Original source

A defiant Flavio Cadegiani. Imagine a guy who looks like this telling you to take ultra-high-dose antiandrogens. Ahmed et al: And we’re back in Bangladesh. 72 hospital patients were randomized to one of three arms: ivermectin only, ivermectin + doxycycline, and placebo. Primary endpoint was time to negative PCR, which was 9.7 days for ivermectin only and 12.7 days for placebo (p = 0.03). Other endpoints including duration of hospitalization (9.6 days ivermectin vs. 9.7 days placebo, not significant). This looks pretty good for ivermectin and does not have any signs of fraud or methodological problems. If I wanted to pick at it anyway, I would point out that the ivermectin + doxycycline group didn’t really differ from placebo, and that if you average out both ivermectin groups (with and without doxycycline) it looks like the difference would not be significant. I had previously committed to considering only ivermectin alone in trials that had multiple ivermectin groups, so I’m not going to do this. I can’t find any evidence this trial was preregistered so I don’t know whether they waited to see what would come out positive and then made that their primary endpoint, but virological clearance is a pretty normal primary endpoint and this isn’t that suspicious. It’s impossible to find any useful commentary on this study because Elgazzar (the guy who ran the most famous fraudulent ivermectin study) had the first name Ahmed, everyone is talking about Elgazzar all the time, and this overwhelms Google whenever I try to search for Ahmed et al. For now I’ll just keep this as a mildly positive and mildly plausible virological clearance result, in the context of no effect on hospitalization length or most symptoms. Chaccour et al: 24 patients in Spain were randomized to receive either medium-dose ivermectin or placebo. The primary outcome was percent of patients with negative PCR at day 7; secondary outcomes were viral load and symptoms. The primary endpoint ended up being kind of a wash - everyone still PCR positive by day 7 so it was impossible to compare groups. Ivermectin trended toward lower viral load but never reached significance. Weirdly, ivermectin did seem to help symptoms, but only anosmia and cough towards the end (p = 0.03), which you would usually think of as lingering post-COVID problems. The paper says: Given these findings, consideration could be given to alternative mechanisms of action different from a direct antiviral effect. One alternative explanation might be a positive allosteric modulation of the nicotinic acetylcholine receptor caused by ivermectin and leading to a downregulation of the ACE-2 receptor and viral entry into the cells of the respiratory epithelium and olfactory bulb. Another mechanism through which ivermectin might influence the reversal of anosmia is by inhibiting the activation of pro-inflammatory pathways in the olfactory epithelium. Inflammation of the olfactory mucosa is thought to play a key role in the development of anosmia in SARS-CoV-2 infection This seems kind of hedge-y. If you’re wondering where things went from there, Dr. Chaccour is now a passionate anti-ivermectin activist: @Finneganporter in @BusinessInsider \n\nThe roots of #ivermectin mania: How South America incubated a fake-medicine craze that took the US by storm\n\n","username":"carlos_chaccour","name":"Dr. Carlos Chaccour ??????","profile_image_url":"","date":"Sun Nov 07 18:40:28 +0000 2021","photos":[],"quoted_tweet":{},"reply_count":0,"retweet_count":2,"like_count":9,"impression_count":0,"expanded_url":{"url":"https://www.businessinsider.in/international/news/the-roots-of-ivermectin-mania-how-south-america-incubated-a-fake-medicine-craze-that-took-the-us-by-storm/articleshow/87554081.cms","image":"https://bucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com/public/images/88d08e70-c9e2-46d4-a5df-96807b6c3a13_2000x1000.jpeg","title":"The roots of ivermectin mania: How South America incubated a fake-medicine craze that took the US by storm","description":"The popularity of unproven anti-parasitic drug ivermectin as a COVID-19 treatment is surging. Its use has roots in South America, where it was hyped by populist","domain":"businessinsider.in"},"video_url":null,"belowTheFold":true}" data-component-name="Twitter2ToDOM"> So I guess he must think of this trial as basically negative, although realistically it’s 24 people and we shouldn’t put too much weight on it either way. Ghauri et al: Pakistan, 95 patients. Nonrandom; the study compared patients who happened to be given ivermectin (along with hydroxychloroquine and azithromycin) vs. patients who were just given the latter two drugs. There’s some evidence this produced systematic differences between the two groups - for example, patients in the control group were 3x more likely to have had diarrhea (this makes sense; diarrhea is a potential ivermectin side effect, so you probably wouldn’t give it to people already struggling with this problem). Also, the control group was twice as likely to be getting corticosteroids, maybe a marker for illness severity. Primary outcome was what percent of both groups had a fever: on day 7 it was 21% of ivermectin patients vs. 65% of controls, p < 0.001. No other outcomes were reported. I don’t hate this study, but I think the nonrandom assignment (and observed systematic differences) is a pretty fatal flaw. I can’t find anyone else talking about this one. At least no one seems to be saying anything bad. Babaloba et al: Be warned: if I have to refer to this one in real-life conversation, I will expand out the “et al” and call it “Babalola & Alakoloko”, because that’s really fun to say. This was a Nigerian RCT comparing 21 patients on low-dose ivermectin, 21 patients on high-dose ivermectin, and 20 patients on a combination of lopinavir and ritonavir, a combination antiviral which later studies found not to work for COVID and which might as well be considered a placebo. Primary outcome, as usual, was days until a negative PCR test. High dose ivermectin was 4.65 days, low dose was 6 days, control was 9.15, p = 0.035. Figure 2 is apparently a photograph of the computer screen where they did this calculation. Gideon Meyerowitz-Katz, part of the team that detects fraud in ivermectin papers, is not a fan of this one: He doesn’t say there what means, but elsewhere he tweets this figure: It’s always a bad sign when your study features in an image with “NUMEROUS IMPOSSIBLE NUMBERS” in red at the top. I think his point is that if you have 21 people, it’s impossible to have 50% of them have headache, because that would be 10.5. If 10 people have a headache, it would be 47.6%; if 11, 52%. So something is clearly wrong here. Seems like a relatively minor mistake, and Meyerowitz-Katz stops short of calling fraud, but it’s not a good look. I’m going to be slightly uncomfortable with this study without rejecting it entirely, and move on. Ravakirti et al: Here we’re in Eastern India - not exactly Bangladesh again, but a stone’s throw away from it. In this RCT patients were randomized into an ivermectin group (57) and a placebo group (58). Primary outcome was negative PCR on day 6, because doing it on day 7 like everyone else would be too easy. As with several other groups, this was a bad move; too few people had it to make a good comparison; it was 13% of intervention vs. 18% of placebo, p = 0.3. Secondary outcomes were also pretty boring, except for the most important: 4 people in the placebo group died, compared to 0 in ivermectin (p = 0.045). On the one hand, this is one outcome of many, reaching the barest significance threshold. Another fluke? Still, there are no real problems with this study, and nobody has anything to say against it. Let’s add this one to the scale as another very small and noisy piece of real evidence in ivermectin’s favor. Bukhari et al: Now we’re in Pakistan. 50 patients were randomized to low-dose ivermectin, another 50 got standard of care including vitamin D. There was no placebo, but primary outcome was number of days to reach negative PCR, which it seems hard for placebo to affect much, so I don’t care. 5 controls and 9 ivermectin patients left the hospital against medical advice and could not be followed up, which is bad but not necessarily study-ruining. They never measured their supposed primary outcome of “days to reach negative PCR” directly, but they did measure how many people had negative PCR on various days, and ivermectin had a clear advantage - for example, on day 7, it was 37/50 for IVR and only 20/50 for control. Even if we assume all the lost-to-followup patients had maximally bad-for-the-hypothesis results, that’s still a positive finding. Nobody else has much to say about this one, certainly no accusations that they’ve found anything suspicious. Keep. Mohan et al: India. RCT. 40 patients got low-dose ivermectin, 40 high-dose ivermectin, and 45 placebo. Primary outcomes were time to negative PCR, and viral load on day 5. In the results, they seem to have reinterpreted “time to negative PCR” as the subtly different “percent with negative PCR on some specific day”. High-dose ivermectin did best (47.5% negative on day 5) and placebo worst (31% negative), but it was insignificant (p = 0.3). There was no difference in viral load. All groups took about the same amount of time for symptoms to resolve. More placebo patients had failed to recover by the end of the study (6) than ivermectin patients (2), but this didn’t reach statistical significance (p = 0.4). Overall a well-done, boring, negative study, although ivermectin proponents will correctly point out that, like basically every other study we have looked at, the trend was in favor of ivermectin and this could potentially end up looking impressive in a meta-analysis. Biber et al: This is an RCT from Israel. 47 patients got ivermectin and 42 placebo. Primary endpoint was viral load on day 6. I am having trouble finding out what happened with this; as far as I can tell it was a negative result and they buried it in favor of more interesting things. In a "multivariable logistic regression model, the adjusted odds ratio of negative SARS-CoV-2 RT-PCR negative test" favored ivermectin over placebo (p = 0.03 for day 6, p = 0.01 for day 8), but this seems like the kind of thing you do when your primary outcome is boring and you’re angry. Gideon Meyerowitz-Katz is not a fan: He notes that the study excluded people with high viral load, but the preregistration didn’t say they would do that. Looking more closely, he finds they did that because, if you included these people, the study got no positive results. So probably they did the study, found no positive results, re-ran it with various subsets of patients until they did get a positive result, and then claimed to have “excluded” patients who weren’t in the subset that worked. I’m going to toss this one. Elalfy et al: What even is this? Where am I? As best I can tell, this is some kind of Egyptian trial. It might or might not be an RCT; it says stuff like “Patients were self-allocated to the treatment groups; the first 3 days of the week for the intervention arm while the other 3 days for symptomatic treatment”. Were they self-allocated in the sense that they got to choose? Doesn’t that mean it’s not random? Aren’t there seven days in a week? These are among the many questions that Elalfy et al do not answer for us. The control group (which they seem to think can also be called “the white group”) took zinc, paracetamol, and maybe azithromycin. The intervention group took zinc, nitazoxanide, ribavirin, and ivermectin. There were very large demographic differences between the groups of the sort which make the study unusable, which they mention and then ignore. From there, they follow this normal and totally comprehensible flowchart: There is no primary outcome assigned, but viral clearance rates on day seven were 58% in the yellow group compared to 0% in the white group, which I guess is a strong positive result. This table… …looks very impressive, in terms of the experimental group doing better than the control, except that they don’t specify whether it was before the trial or after it, and at least one online commentator thinks it might have been before, in which case it’s only impressive how thoroughly they failed to randomize their groups. Overall I don’t feel bad throwing this study out. I hope it one day succeeds in returning to its home planet. Lopez-Medina et al: Colombian RCT. 200 patients took ivermectin, another 200 took placebo. They originally worried the placebo might taste different than real ivermectin, then solved this by replacing it with a different placebo, which is a pretty high level of conscientiousness. Primary outcome was originally percent of patients whose symptoms worsened by two points, as rated on a complicated symptom scale when a researcher asked them over the phone. Halfway through the study, they realized nobody was worsening that much, so they changed the primary outcome to time until symptoms got better, as measured by the scale. In the ivermectin group, symptoms improved that much after 10 days; in the placebo group, after 12, p = 0.53. By the end of the study, symptoms had improved in 82% of ivermectin users and 79% of controls, also insignificant. 4 patients in the ivermectin group needed to be hospitalized compared to 6 in the placebo group, again insignificant. This study is bigger than most of the other RCTs, and more polished in terms of how many spelling errors, photographs of computer screens, etc, it contains. It was published in JAMA, one of the most prestigious US medical journals, as opposed to the crappy nth-tier journals most of the others have been in. When people say things like “sure, a lot of small studies show good results for ivermectin, but the bigger and more professional trials don’t”, this is one of the two big professional trials they’re talking about. Ivermectin proponents make some good arguments against it. In order to get as big as it did, Lopez-Medina had to compromise on rigor. Its outcome is how people self-score their symptoms on a hokey scale in a phone interview, instead of viral load or PCR results or anything like that. Still, this is basically what we want, right? In the end, we want people to feel better and less sick, not to get good scores on PCR tests. Also, it changed its primary outcome halfway through; isn’t that bad? I think maybe not; the reason we want a preregistered primary outcome is so that you don’t change halfway through to whatever outcome shows the results you want. The researchers in this study did a good job explaining why they changed their outcome, the change makes sense, and their original outcome would also have shown ivermectin not working (albeit less accurately and effectively). I don’t know of any evidence that they knew (or suspected) final results when switching to this new outcome, and it seems like the most reasonable new outcome to switch to. Finally, their original placebo tasted different from ivermectin (though they switched halfway through). This is one of the few studies where I actually care about placebo, because people are self-rating their symptoms. But realistically most of these people don’t know what ivermectin is supposed to taste like. Also, they did a re-analysis and found there was no difference between the people who got the old placebo and the new one. I’m making a big deal of this because ivmmeta.com - the really impressive meta-analysis site I’ve been going off of - puts a special warning letter underneath their discussion of this study, urging us not to trust it. They don’t do this for any of the other ones we’ve addressed so far - not the one by the guy whose other studies were all frauds, not the one where 50% of 21 people had headaches, not the unrandomized one where the groups were completely different before the experiment started, not even the one by the guy accused of crimes against humanity. Only this one. This makes me a lot less charitable to ivmmeta than I would otherwise be; I think it’s hard to choose this particular warning letter strategy out of well-intentioned commitment to truth. They just really don’t like this big study that shows ivermectin doesn’t work. Also, the warning itself irritates me, and includes paragraphs like: RCTs have a fundamental bias against finding an effect for interventions that are widely available — patients that believe they need treatment are more likely to decline participation and take the intervention [Yeh], i.e., RCTs are more likely to enroll low-risk participants that do not need treatment to recover (this does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable). This trial was run in a community where ivermectin was available OTC and very widely known and used. Nobody else worries about this, and there are a million biases that non-randomized studies have that would be super-relevant when discussing those, but somehow when they’re pro-ivermectin the site forgets to be this thorough. I think a better pro-ivermectin response to this study is to point out that all the trends support ivermectin. Symptoms took 10 days to resolve in the ivermectin group vs. 12 in placebo; 4 ivermectin patients were hospitalized vs. 6 placebo patients, etc. Just say that this was an unusually noisy trial because of the self-report methodology, and you’re confident that these small differences will add up to significance when you put them into a meta-analysis. Roy et al: We’re back in East India, and back to non-randomized trials. 56 patients were retrospectively examined; some had been given ivermectin + doxycycline, others hydroxychloroquine, other azithromycin, and others symptomatic treatment only. We don’t get any meaningful information about how this worked, but we are told that they did not differ in “clinical well-being reporting onset timing”. Whatever. Chahla et al: The first of many Argentine trials. 110 patients received medium-dose ivermectin; 144 were kept as a control (no placebo). This was “cluster randomized”, which means they randomize different health centers to either give the experimental drug or not. This is worse than regular randomization, because there could be differences between these health centers (eg one might have better doctors who otherwise give better treatment, one might be in the poor part of town and have sicker patients, etc). They checked to see if there were any differences between the groups, and it sure looks like there were (the experimental group had twice as many obese people as the controls), but as per them, these differences were not statistically significant. Note that if this did make a difference, it would presumably make ivermectin look worse, not better. The primary outcome was given as “increase discharge from outpatient care with COVID-19 mild disease”. This favored the treatment; only 2/110 patients in the ivermectin group failed to be discharged, compared to 20 patients in the control group. But, uh, these were at different medical centers. Can’t different medical centers just have different discharge policies? One discharges you as soon as you seem to be getting better, the other waits to really make sure? This is an utterly crap endpoint to do a cluster randomized controlled trial on. If you’re going to do cRCT, which is never a great idea, you should be using some extremely objective endpoint that doctors and clinic administrators can’t possibly affect, like viral load according to some third-party laboratory, using the same third-party laboratory for both clinics. This is such a bad idea that I can’t help worrying I’m missing or misunderstanding something. If not, this is dumb and bad and should be ignored. Mourya et al: We’re back in India. This is a nonrandomized study comparing 50 patients given ivermectin to 50 patients given hydroxychloroquine. No primary outcome was named, but they focus on PCR negativity. Only 6% of patients in the hydroxychloroquine group were negative, compared to 90% of patients in the ivermectin group! On what day did they do the test? Uh, kind of random, and they admit that “in [the hydroxychloroquine group], mean time difference from the date of initiation of treatment and second test was significantly longer (7.24±2.75 days) as compared to 5.22±1.21 days in [the ivermectin group] (p=0.021).” Since they assessed these groups at different times, we shouldn’t draw any conclusions from them getting different results. Except that as far as I can tell this should handicap ivermectin, making it especially impressive that it did better. But also, the ivermectin group was made mostly of people who had been asymptomatic at the beginning (70%), and the hydroxychloroquine group had almost no asymptomatic cases (8%) . They were giving the ivermectin to healthy people and the hydroxychloroquine to sick people! They admit deep in the discussion that this “may be a confounding factor”. So basically they got totally different groups of people, tested them at totally different times, and the two sets of test results differed. So what? So this is why normal people do RCTs instead of whatever the heck this is, that’s what. Loue et al: …this one isn’t going to be an RCT either. Loue tells a story about a cluster of COVID cases at the French nursing home where he works. He asked people if they wanted to try ivermectin; 10 did and 15 didn’t. 1 ivermectin patient died, compared to 5 non-ivermectin patients. The non-ivermectin group looked a bit sicker than the ivermectin group in the inevitable Table 1, though it’s hard to tell. One interesting possible confounder (not mentioned, but I’m imagining it) is that demented patients probably couldn’t consent to ivermectin and ended up in the control group. This is another case of “I’m not going to trust anything that isn’t an RCT”. Merino et al: Another (sigh) non-RCT. Mexico City tried a public health program where if you called a hotline and said you had COVID, they sent you an emergency kit with various useful supplies. One of those supplies was ivermectin tablets. 18,074 people got the kit (and presumably some appreciable fraction took the ivermectin, though there’s no way to prove that). Their control group is people from before they started giving out the kits, people from after they stopped giving out the kits, and people who didn’t want the kits. There are differences in who got COVID early in the epidemic vs. later, and in people who did opt for medical kits vs. didn’t. To correct these, the researchers tried to adjust for confounders, something which - as I keep trying to hammer home again and again - never works. They found that using the kit led to a 75% or so reduction in hospitalization, though they were unable to separate out the ivermectin from the other things in the kit (paracetamol and aspirin), or from the placebo effect of having a kit and feeling like you had already gotten some treatment (if I understand right, the decision to go to the hospital was left entirely to the patient). I think this study is a moderate point in favor of giving people kits in order to prevent hospital overcrowding, but I’m not willing to accept that it tells us much about ivermectin in particular. Faisal et al: This one was published in The Professional Medical Journal (mispelled as “Profesional Medical Journal” in its URL), so you know it’s going to be good! It describes itself as “a cross-sectional study”, but later says it “randomized patients into two groups”, which would make it an RCT - I think they might just be using the term “cross-sectional” different from the standard American usage. A hospital in Pakistan got 50 patients on ivermectin + azithromycin, and another 50 on azithromycin alone. Primary outcome was not mentioned, and the data were presented confusingly, but a typical result is that only 4% of the ivermectin group had symptoms lasting more than 10 days, whereas 16% of the control group did, p < 0.01. They do a really weird thing where they compare how long it took symptoms to resolve between IVM and control groups within each bin. That is, if I’m understanding correctly, they ask “of the people who took between 3-5 days for symptoms to resolve, did they resolve faster for IVM or control?”. This is an utterly bizarre analysis to perform, although it doesn’t affect the fact that their other results still seem to favor ivermectin. Maybe I’m confused about what’s going on here. I’ve mostly been letting people off easy on no placebo, but I as far as I can tell (not very far) this paper seems to be going off whether patients reported continuing to have symptoms to the hospital doing the study, and I think that is potentially susceptible to placebo effects. Additionally, there’s no preregistration, and even though they talk a lot about doing PCR tests they don’t present the results. This is by no means the worst study here but I still think it’s pretty low quality and I don’t trust it. Aref et al: This one is published in the International Journal Of Nanomedicine, even though I’m pretty sure that isn’t a real thing. In this case the “nanomedicine” is a new nasal spray version of ivermectin which is so confusing I cannot for the life of me figure out what dose they are giving these patients. This Egyptian study gives 57 patients intranasal ivermectin plus hydroxychloroquine, azithromycin, oseltamavir, and some vitamins; another 57 patients get all that stuff except the ivermectin. Primary outcome is not stated, but they look at various symptoms, all of which look better in the ivermectin group: 95% of ivermectin patients got negative PCRs at some time point, compared to 75% of controls, p = 0.004. I am pretty suspicious of this study, not least because it comes from Egypt which has an awful reputation for fake studies, and it returns extreme results that I wouldn’t expect even if ivermectin was actually a wonder drug. But I cannot find any particular thing wrong with it, nor did anyone else I looked at, so I will grudgingly let it stand. Krolewiecki et al: Another Argentine study. This one is a real RCT. 30 patients received ivermectin, 15 were the control group (no placebo, again). Primary outcome was difference in viral load on day 5. The trend favored ivermectin but it was not statistically significant, although they were able to make it statistically significant if they looked at a subset of higher-IVM-plasma-concentration patients. They did not find any difference in clinical outcomes. A pro-ivermectin person could point out that in the subgroup with the highest ivermectin concentrations, the drug seemed to work. A skeptic could point out that this is exactly the kind of subgroup slicing that you are not supposed to do without pre-registering it, which I don’t think this team did. I agree with the skeptic. Vallejos et al: Another Argentine study. It’s big (250 people in each arm). It’s an RCT. It tries to define a primary outcome (“Primary outcome: the trial ended when the last patient who was included achieved the end of study visit”), but that’s not what “primary outcome” means, and they don’t offer an alternative. Other outcomes: no difference in PCR on days 3 or 12. Hospitalization is nonsignificantly better in the ivermectin group (14 vs. 21, p = 0.2), but death is nonsigificantly better in the placebo group (3 vs. 4, p = 0.7). This isn’t even the kind of nonsignificant that might contribute to an exciting meta-analysis later. This is just a pure null result. I cannot find any problem with this study, and neither can anyone else I checked. This is the biggest RCT we’ve seen so far, so we should take it seriously. TOGETHER Trial: Speaking of big RCTs… This one hasn’t been published yet. There’s a video of a talk about it, but I am not going to watch it, because it is a video, so I am getting information secondhand from eg here. Apparently, it compares 677 people (!) randomized to ivermectin to 678 people randomized to placebo. 86 ivermectin patients ended up in the hospital compared to 95 placebo patients, p-value not significant. This was a really big professional trial done by bigshot researchers from a major Canadian university, and the medical establishment is taking it much more seriously than any of these others. When it comes out, it will probably get published in a top journal. When discussing Lopez-Medina, I wrote: When people say things like “sure, a lot of small studies show good results for ivermectin, but the bigger and more professional trials don’t”, this is one of the two big professional trials they’re talking about. This is the other one. Not coincidentally, it’s also the other trial that ivmmeta.com has a warning letter underneath telling you to disregard. Their main concern is that instead of truly randomizing patients to ivermectin vs. placebo, they did a time-dependent randomization that meant during some weeks more patients were getting one or the other. This is a problem because the trial takes place in Brazil, where different variants were more common at different times. Here’s their image: On the one hand, I have immense contempt for ivmmeta for letting all those other awful studies pass and then pulling out all the stops to try to nitpick this one. I have no idea if their proposed randomization failure really happened. And no doubt the reason they’re even able to investigate this is that this study is really careful and transparent - most of them don’t tell you anything about their randomization method. I would be shocked if other studies don’t have all these problems and worse. On the other hand, the point isn’t to be fair, it’s to be right. And this is a potential confounder. Not a huge one. But a potential one. I guess all we can do is try to bound the damage. Even if the confounding is 100% real and bad, there’s no way to make this study consistent with the crazy super-pro-ivermectin results of studies like Espitia-Hernandez and Aref. And even if we deny any confounding, we see the same slight pro-ivermectin trend - 86 hospitalizations vs. 95 - that we’ve seen in so many other studies. Nothing is going to make me believe that this isn’t in the top 33% of studies we’ve been looking at, so let’s add it as grist for the meta-analysis (though maybe not quite as much grist as its vast size indicates) and move on, angrily. Buonfrate et al: An Italian RCT. Patients were randomized into low-dose ivermectin (32), placebo (29), or high-dose ivermectin (32). Primary outcome was viral load on day 7. There was no significant difference (average of 2 in ivermectin groups, 2.2 in placebo group). They admit that they failed to reach the planned sample size, but did a calculation to show that even if they had, the trial could not have returned a positive result. Clinically, an average of 2 patients were hospitalized in each of the ivermectin arms, compared to 0 in the placebo arm - which bucks our previously-very-constant pro-ivermectin trend. Mayer et al: Not an RCT. Patients in an Argentine province were offered the opportunity to try ivermectin; 3266 said yes and become the experimental group, 17966 said no and became the control group. There were many obvious differences between the groups, but they all seemed to handicap ivermectin. There was a nonsignificant trend toward less hospitalization and significantly less mortality (1.5% vs. 2.1%, p = 0.03). While looking into this study, I learned the term “immortal time bias”. This means a period in between selection for the study and the beginning of study recording where patient outcomes are not counted. I think the problem here is that if you signed up for the system on Day X, and if you got sick before they could give you ivermectin, you were in the control group. See this Twitter thread, I have not confirmed everything he says. This only hardens my resolve to stay away from non-RCTs. Borody et al: Our last paper! …is it a paper? I can’t find it published anywhere. It mostly seems to be on news sites. Doesn’t look peer-reviewed. And it starts with “Note that views expressed in this opinion article are the writer’s personal views”. Whatever. 600 Australians were treated with ivermectin, doxycycline, and zinc. The article compares this to an “equivalent control group” made of “contemporary infected subjects in Australia obtained from published Covid Tracking Data”; this is not how you control group, @#!% you. Then it gets excited about the fact that most patients had better symptoms at the end of the ten-day study period than the beginning (untreated COVID resolves in about ten days). Why are these people wasting my time with this? Let’s move on. The Analysis If we remove all fraudulent and methodologically unsound studies from the table above, we end up with this: Gideon Meyerowitz-Katz, who investigated many of the studies above for fraud, tried a similar exercise. I learned about his halfway through, couldn’t help seeing it briefly, but tried to avoid remembering it or using it when generating mine (also, I did take the result of his fraud investigations into account), so they should be considered not quite independent efforts. His looks like this: He nixed Chowdhury, Babaloba, Ghauri, Faisal, and Aref, but kept Szenta Fonseca, Biber (?), and Mayer. There was correlation of 0.45, which I guess is okay. I asked him about his decision-making, and he listed a combination of serious statistical errors and small red flags adding up. I was pretty uncomfortable with most of these studies myself, so I will err on the side of severity, and remove all studies that either I or Meyerowitz-Katz disliked. We end up with the following short list: We’ve gone from 29 studies to 11, getting rid of 18 along the way. For the record, we eliminated 2/19 for fraud, 1/19 for severe preregistration violations, 10 for methodological problems, and 6 because Meyerowitz-Katz was suspicious of them. …but honestly this table still looks pretty good for ivermectin, doesn’t it? Still lots of big green boxes. Meyerowitz-Katz accuses ivmmeta of cherry-picking what statistic to use for their forest plot. That is, if a study measures ten outcomes, they sometimes take the most pro-ivermectin outcome. Ivmmeta.com counters that they used a consistent and reasonable (if complicated) process for choosing their outcome of focus, that being: If studies report multiple kinds of effects then the most serious outcome is used in calculations for that study. For example, if effects for mortality and cases are both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcome is considered more important than PCR testing status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available (after most or all patients have recovered there is no room for an effective treatment to do better). If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. I’m having trouble judging this, partly because Meyerowitz-Katz says ivmmeta has corrected some earlier mistakes, and partly because there really is some reasonable debate over how to judge studies with lots of complicated endpoints. By this point I had completely forgotten what ivmmeta did, so I independently coded all 11 remaining studies following something in between my best understanding of their procedure and what I considered common sense. The only exception was that when the most severe outcome was measured in something other than patients (ie average number of virus copies per patient), I defaulted to one that was measured in patients instead, to keep everything with the same denominator. My results mostly matched ivmmeta’s, with one or two exceptions that I think are within the scope of argument or related to my minor deviations from their protocol. Placebo vs. ivermectin groups sometimes differed in size, which I’ve adjusted for and rounded off. Probably I’m forgetting some reason I can’t just do simple summary statistics to this, but whatever. It is p = 0.15, not significant. This is maybe unfair, because there aren’t a lot of deaths in the sample, so by focusing on death rather than more common outcomes we’re pointlessly throwing away sample size. What happens if I unprincipledly pick whatever I think the most reasonable outcome to use from each study is? I’ve chosen “most reasonable” as a balance between “is the most severe” and “has a lot of data points”: Now it’s p = 0.04, seemingly significant, but I had to make some unprincipled decisions to get there. I don’t think I specifically replaced negative findings with positive ones, but I can’t prove that even to myself, let alone to you. [UPDATE 5/31/22: A reader writes in to tell me that the t-test I used above is overly simplistic. A Dersimonian-Laird test is more appropriate for meta-analysis, and would have given 0.03 and 0.005 on the first and second analysis, where I got 0.15 and 0.04. This significantly strengthens the apparent benefit of ivermectin from ‘debatable’ to ‘clear’. I discuss some reasons below why I am not convinced by this apparent benefit.] (how come I’m finding a bunch of things on the edge of significance, but the original ivmmeta site found a lot of extremely significant things? Because they combined ratios, such that “one death in placebo, zero in ivermectin” looked like a nigh-infinite benefit for ivermectin, whereas I’m combining raw numbers. Possibly my way is statistically illegitimate for some reason, but I’m just trying to get a rough estimate of how convinced to be) So we are stuck somewhere between “nonsignificant trend in favor” and “maybe-significant trend in favor, after throwing out some best practices”. This is normally where I would compare my results to those of other meta-analyses made by real professionals. But when I look at them, they all include studies later found to be fake, like Elgazzar, and unsurprisingly come up with wildly positive conclusions. There are about six in this category. One of them later revised their results to exclude Elgazzar and still found strong efficacy for ivermectin, but they still included Niaee and some other dubious studies. The only meta-analysis that doesn’t make these mistakes is Popp (a Cochrane review), which is from before Elgazzar was found to be fraudulent, but coincidentally excludes it for other reasons. It also excludes a lot of good studies like Mahmud and Ravakirti because they give patients other things like HCQ and azithromycin - I chose to include them, because I don’t think they either work or have especially bad side effects, so they’re basically placebo - but Cochrane is always harsh like this. They end up with a point estimate where ivermectin cuts mortality by 40% - but say the confidence intervals are too wide to draw any conclusion. I think this basically agrees with my analyses above - the trends really are in ivermectin’s favor, but once you eliminate all the questionable studies there are too few studies left to have enough statistical power to reach significance. Except that everyone is still focusing on deaths and hospitalizations just because they’re flashy. Mahmud et al, which everyone agrees is a great study, found that ivermectin decreased days until clinical recovery, p = 0.003? So what do you do? This is one of the toughest questions in medicine. It comes up again and again. You have some drug. You read some studies. Again and again, more people are surviving (or avoiding complications) when they get the drug. It’s a pattern strong enough to common-sensically notice. But there isn’t an undeniable, unbreachable fortress of evidence. The drug is really safe and doesn’t have a lot of side effects. So do you give it to your patients? Do you take it yourself? Here this question is especially tough, because, uh, if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout “horse dewormer!” at you and compare you to Josef Mengele. But good doctors aren’t supposed to care about such things. Your only goal is to save your patient. Nothing else matters. I am telling you that Mahmud et al is a good study and it got p = 0.003 in favor of ivermectin. You can take the blue pill, and stay a decent respectable member of society. Or you can take the horse dewormer pill, and see where you end up. In a second, I’ll tell you my answer. But you won’t always have me to answer questions like this, and it might be morally edifying to observe your thought process in situations like this. So take a second, and meet me on the other side of the next section heading. … … … … … The Synthesis Hopefully you learned something interesting about yourself there. But my answer is: worms! As several doctors and researchers have pointed out (h/t especially Avi Bitterman and David Boulware), the most impressive studies come from places that are teeming with worms. Mahmud from Bangladesh, Ravakirti from East India, Lopez-Medina from Colombia, etc. Here’s the prevalence of roundworm infections by country (source). But alongside roundworms, there are threadworms, hookworms, blood flukes, liver flukes, nematodes, trematodes, all sorts of worms. Add them all up and somewhere between half and a quarter of people in the developing world have at least one parasitic worm in their body. Being full of worms may impact your ability to fight coronavirus. Gluchowska et al write: Helminth [ie worm] infections are among the most common infectious diseases. Bradbury et al. highlight the possible negative interactions between helminth infection and COVID-19 severity in helminth-endemic regions and note that alterations in the gut microbiome associated with helminth infection appear to have systemic immunomodulatory effects. It has also been proposed that helminth co-infection may increase the morbidity and mortality of COVID-19, because the immune system cannot efficiently respond to the virus; in addition, vaccines will be less effective for these patients, but treatment and prevention of helminth infections might reduce the negative effect of COVID-19. During millennia of parasite-host coevolution helminths evolved mechanisms suppressing the host immune responses, which may mitigate vaccine efficacy and increase severity of other infectious diseases. Treatment of worm infections might reduce the negative effect of COVID-19! And ivermectin is a deworming drug! You can see where this is going… The most relevant species of worm here is the roundworm Strongyloides stercoralis. Among the commonest treatments for COVID-19 is corticosteroids, a type of immunosuppresant drug. The types of immune responses it suppresses do more harm than good in coronavirus, so turning them off limits collateral damage and makes patients better on net. But these are also the types of immune responses that control Strongyloides. If you turn them off even very briefly, the worms multiply out of control, you get what’s called “Strongyloides hyperinfection”, and pretty often you die. According to the WHO: The current COVID-19 pandemic serves to highlight the risk of using systemic corticosteroids and, to a lesser extent, other immunosuppressive therapy, in populations with significant risk of underlying strongyloidiasis. Cases of strongyloidiasis hyperinfection in the setting of corticosteroid use as COVID-19 therapy have been described and draw attention to the necessity of addressing the risk of iatrogenic strongyloidiasis hyperinfection syndrome in infected individuals prior to corticosteroid administration. Although this has gained importance in the midst of a pandemic where corticosteroids are one of few therapies shown to improve mortality, its relevance is much broader given that corticosteroids and other immunosuppressive therapies have become increasingly common in treatment of chronic diseases (e.g. asthma or certain rheumatologic conditions). So you need to “address the risk” of strongyloides infection during COVID treatment in roundworm-endemic areas. And how might you address this, WHO? Treatment of chronic strongyloidiasis with ivermectin 200 µg/kg per day orally x 1-2 days is considered safe with potential contraindications including possible Loa loa infection (endemic in West and Central Africa), pregnancy, and weight <15kg. Given ivermectin’s safety profile, the United States has utilized presumptive treatment with ivermectin for strongyloidiasis in refugees resettling from endemic areas, and both Canada and the European Centre for Disease Prevention and Control have issued guidance on presumptive treatment to avoid hyperinfection in at risk populations. Screening and treatment, or where not available, addition of ivermectin to mass drug administration programs should be studied and considered. This is serious and common enough that, if you’re not going to screen for it, it might be worth “add[ing] ivermectin to mass drug administration programs” in affected areas! Dr. Avi Bitterman carries the hypothesis to the finish line: First two images are with all relevant studies; second two are a sensitivity analysis that removes some of the most dubious. The good ivermectin trials in areas with low Strongyloides prevalence, like Vallejos in Argentina, are mostly negative. The good ivermectin trials in areas with high Strongyloides prevalence, like Mahmud in Bangladesh, are mostly positive. Worms can’t explain the viral positivity outcomes (ie PCR), but Dr. Bitterman suggests that once you remove low quality trials and worm-related results, the rest looks like simple publication bias: This is still just a possibility. Maybe I’m over-focusing too hard on a couple positive results and this will all turn out to be nothing. Or who knows, maybe ivermectin does work against COVID a little - although it would have to be very little, fading to not at all in temperate worm-free countries. But this theory feels right to me. It feels right to me because it’s the most troll-ish possible solution. Everybody was wrong! The people who called it a miracle drug against COVID were wrong. The people who dismissed all the studies because they F@#king Love Science were wrong. Ivmmeta.com was wrong. Gideon Meyerowitz-Katz was…well, he was right, actually, I got the worm-related meta-analysis graphic above from his Twitter timeline. Still, an excellent troll. Also, the best part is that I ignorantly asked, in my description of Mahmud et al above: And it was! It was a fluke! A literal, physical, fluke! For my whole life, God has been placing terrible puns in my path to irritate me, and this would be the worst one ever! So it has to be true! The Scientific Takeaway About ten years ago, when the replication crisis started, we learned a certain set of tools for examining studies. Check for selection bias. Distrust “adjusting for confounders”. Check for p-hacking and forking paths. Make teams preregister their analyses. Do forest plots to find publication bias. Stop accepting p-values of 0.049. Wait for replications. Trust reviews and meta-analyses, instead of individual small studies. These were good tools. Having them was infinitely better than not having them. But even in 2014, I was writing about how many bad studies seemed to slip through the cracks even when we pushed this toolbox to its limits. We needed new tools. I think the methods that Meyerowitz-Katz, Sheldrake, Heathers, Brown, Lawrence and others brought to the limelight this year are some of the new tools we were waiting for. Part of this new toolset is to check for fraud. About 10 - 15% of the seemingly-good studies on ivermectin ended up extremely suspicious for fraud. Elgazzar, Carvallo, Niaee, Cadegiani, Samaha. There are ways to check for this even when you don’t have the raw data. Like: The Carlisle-Stouffer-Fisher method: Check some large group of comparisons, usually the Table 1 of an RCT where they compare the demographic characteristics of the control and experimental groups, for reasonable p-values. Real data will have p-values all over the map; one in every ten comparisons will have a p-value of 0.1 or less. Fakers seem bad at this and usually give everything a nice safe p-value like 0.8 or 0.9.

It’s always a bad sign when your study features in an image with “NUMEROUS IMPOSSIBLE NUMBERS” in red at the top. I think his point is that if you have 21 people, it’s impossible to have 50% of them have headache, because that would be 10.5. If 10 people have a headache, it would be 47.6%; if 11, 52%. So something is clearly wrong here. Seems like a relatively minor mistake, and Meyerowitz-Katz stops short of calling fraud, but it’s not a good look. I’m going to be slightly uncomfortable with this study without rejecting it entirely, and move on. Ravakirti et al: Here we’re in Eastern India - not exactly Bangladesh again, but a stone’s throw away from it. In this RCT patients were randomized into an ivermectin group (57) and a placebo group (58). Primary outcome was negative PCR on day 6, because doing it on day 7 like everyone else would be too easy. As with several other groups, this was a bad move; too few people had it to make a good comparison; it was 13% of intervention vs. 18% of placebo, p = 0.3. Secondary outcomes were also pretty boring, except for the most important: 4 people in the placebo group died, compared to 0 in ivermectin (p = 0.045). On the one hand, this is one outcome of many, reaching the barest significance threshold. Another fluke? Still, there are no real problems with this study, and nobody has anything to say against it. Let’s add this one to the scale as another very small and noisy piece of real evidence in ivermectin’s favor. Bukhari et al: Now we’re in Pakistan. 50 patients were randomized to low-dose ivermectin, another 50 got standard of care including vitamin D. There was no placebo, but primary outcome was number of days to reach negative PCR, which it seems hard for placebo to affect much, so I don’t care. 5 controls and 9 ivermectin patients left the hospital against medical advice and could not be followed up, which is bad but not necessarily study-ruining. They never measured their supposed primary outcome of “days to reach negative PCR” directly, but they did measure how many people had negative PCR on various days, and ivermectin had a clear advantage - for example, on day 7, it was 37/50 for IVR and only 20/50 for control. Even if we assume all the lost-to-followup patients had maximally bad-for-the-hypothesis results, that’s still a positive finding. Nobody else has much to say about this one, certainly no accusations that they’ve found anything suspicious. Keep. Mohan et al: India. RCT. 40 patients got low-dose ivermectin, 40 high-dose ivermectin, and 45 placebo. Primary outcomes were time to negative PCR, and viral load on day 5. In the results, they seem to have reinterpreted “time to negative PCR” as the subtly different “percent with negative PCR on some specific day”. High-dose ivermectin did best (47.5% negative on day 5) and placebo worst (31% negative), but it was insignificant (p = 0.3). There was no difference in viral load. All groups took about the same amount of time for symptoms to resolve. More placebo patients had failed to recover by the end of the study (6) than ivermectin patients (2), but this didn’t reach statistical significance (p = 0.4). Overall a well-done, boring, negative study, although ivermectin proponents will correctly point out that, like basically every other study we have looked at, the trend was in favor of ivermectin and this could potentially end up looking impressive in a meta-analysis. Biber et al: This is an RCT from Israel. 47 patients got ivermectin and 42 placebo. Primary endpoint was viral load on day 6. I am having trouble finding out what happened with this; as far as I can tell it was a negative result and they buried it in favor of more interesting things. In a "multivariable logistic regression model, the adjusted odds ratio of negative SARS-CoV-2 RT-PCR negative test" favored ivermectin over placebo (p = 0.03 for day 6, p = 0.01 for day 8), but this seems like the kind of thing you do when your primary outcome is boring and you’re angry. Gideon Meyerowitz-Katz is not a fan: He notes that the study excluded people with high viral load, but the preregistration didn’t say they would do that. Looking more closely, he finds they did that because, if you included these people, the study got no positive results. So probably they did the study, found no positive results, re-ran it with various subsets of patients until they did get a positive result, and then claimed to have “excluded” patients who weren’t in the subset that worked. I’m going to toss this one. Elalfy et al: What even is this? Where am I? As best I can tell, this is some kind of Egyptian trial. It might or might not be an RCT; it says stuff like “Patients were self-allocated to the treatment groups; the first 3 days of the week for the intervention arm while the other 3 days for symptomatic treatment”. Were they self-allocated in the sense that they got to choose? Doesn’t that mean it’s not random? Aren’t there seven days in a week? These are among the many questions that Elalfy et al do not answer for us. The control group (which they seem to think can also be called “the white group”) took zinc, paracetamol, and maybe azithromycin. The intervention group took zinc, nitazoxanide, ribavirin, and ivermectin. There were very large demographic differences between the groups of the sort which make the study unusable, which they mention and then ignore. From there, they follow this normal and totally comprehensible flowchart: There is no primary outcome assigned, but viral clearance rates on day seven were 58% in the yellow group compared to 0% in the white group, which I guess is a strong positive result. This table… …looks very impressive, in terms of the experimental group doing better than the control, except that they don’t specify whether it was before the trial or after it, and at least one online commentator thinks it might have been before, in which case it’s only impressive how thoroughly they failed to randomize their groups. Overall I don’t feel bad throwing this study out. I hope it one day succeeds in returning to its home planet. Lopez-Medina et al: Colombian RCT. 200 patients took ivermectin, another 200 took placebo. They originally worried the placebo might taste different than real ivermectin, then solved this by replacing it with a different placebo, which is a pretty high level of conscientiousness. Primary outcome was originally percent of patients whose symptoms worsened by two points, as rated on a complicated symptom scale when a researcher asked them over the phone. Halfway through the study, they realized nobody was worsening that much, so they changed the primary outcome to time until symptoms got better, as measured by the scale. In the ivermectin group, symptoms improved that much after 10 days; in the placebo group, after 12, p = 0.53. By the end of the study, symptoms had improved in 82% of ivermectin users and 79% of controls, also insignificant. 4 patients in the ivermectin group needed to be hospitalized compared to 6 in the placebo group, again insignificant. This study is bigger than most of the other RCTs, and more polished in terms of how many spelling errors, photographs of computer screens, etc, it contains. It was published in JAMA, one of the most prestigious US medical journals, as opposed to the crappy nth-tier journals most of the others have been in. When people say things like “sure, a lot of small studies show good results for ivermectin, but the bigger and more professional trials don’t”, this is one of the two big professional trials they’re talking about. Ivermectin proponents make some good arguments against it. In order to get as big as it did, Lopez-Medina had to compromise on rigor. Its outcome is how people self-score their symptoms on a hokey scale in a phone interview, instead of viral load or PCR results or anything like that. Still, this is basically what we want, right? In the end, we want people to feel better and less sick, not to get good scores on PCR tests. Also, it changed its primary outcome halfway through; isn’t that bad? I think maybe not; the reason we want a preregistered primary outcome is so that you don’t change halfway through to whatever outcome shows the results you want. The researchers in this study did a good job explaining why they changed their outcome, the change makes sense, and their original outcome would also have shown ivermectin not working (albeit less accurately and effectively). I don’t know of any evidence that they knew (or suspected) final results when switching to this new outcome, and it seems like the most reasonable new outcome to switch to. Finally, their original placebo tasted different from ivermectin (though they switched halfway through). This is one of the few studies where I actually care about placebo, because people are self-rating their symptoms. But realistically most of these people don’t know what ivermectin is supposed to taste like. Also, they did a re-analysis and found there was no difference between the people who got the old placebo and the new one. I’m making a big deal of this because ivmmeta.com - the really impressive meta-analysis site I’ve been going off of - puts a special warning letter underneath their discussion of this study, urging us not to trust it. They don’t do this for any of the other ones we’ve addressed so far - not the one by the guy whose other studies were all frauds, not the one where 50% of 21 people had headaches, not the unrandomized one where the groups were completely different before the experiment started, not even the one by the guy accused of crimes against humanity. Only this one. This makes me a lot less charitable to ivmmeta than I would otherwise be; I think it’s hard to choose this particular warning letter strategy out of well-intentioned commitment to truth. They just really don’t like this big study that shows ivermectin doesn’t work. Also, the warning itself irritates me, and includes paragraphs like: RCTs have a fundamental bias against finding an effect for interventions that are widely available — patients that believe they need treatment are more likely to decline participation and take the intervention [Yeh], i.e., RCTs are more likely to enroll low-risk participants that do not need treatment to recover (this does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable). This trial was run in a community where ivermectin was available OTC and very widely known and used. Nobody else worries about this, and there are a million biases that non-randomized studies have that would be super-relevant when discussing those, but somehow when they’re pro-ivermectin the site forgets to be this thorough. I think a better pro-ivermectin response to this study is to point out that all the trends support ivermectin. Symptoms took 10 days to resolve in the ivermectin group vs. 12 in placebo; 4 ivermectin patients were hospitalized vs. 6 placebo patients, etc. Just say that this was an unusually noisy trial because of the self-report methodology, and you’re confident that these small differences will add up to significance when you put them into a meta-analysis. Roy et al: We’re back in East India, and back to non-randomized trials. 56 patients were retrospectively examined; some had been given ivermectin + doxycycline, others hydroxychloroquine, other azithromycin, and others symptomatic treatment only. We don’t get any meaningful information about how this worked, but we are told that they did not differ in “clinical well-being reporting onset timing”. Whatever. Chahla et al: The first of many Argentine trials. 110 patients received medium-dose ivermectin; 144 were kept as a control (no placebo). This was “cluster randomized”, which means they randomize different health centers to either give the experimental drug or not. This is worse than regular randomization, because there could be differences between these health centers (eg one might have better doctors who otherwise give better treatment, one might be in the poor part of town and have sicker patients, etc). They checked to see if there were any differences between the groups, and it sure looks like there were (the experimental group had twice as many obese people as the controls), but as per them, these differences were not statistically significant. Note that if this did make a difference, it would presumably make ivermectin look worse, not better. The primary outcome was given as “increase discharge from outpatient care with COVID-19 mild disease”. This favored the treatment; only 2/110 patients in the ivermectin group failed to be discharged, compared to 20 patients in the control group. But, uh, these were at different medical centers. Can’t different medical centers just have different discharge policies? One discharges you as soon as you seem to be getting better, the other waits to really make sure? This is an utterly crap endpoint to do a cluster randomized controlled trial on. If you’re going to do cRCT, which is never a great idea, you should be using some extremely objective endpoint that doctors and clinic administrators can’t possibly affect, like viral load according to some third-party laboratory, using the same third-party laboratory for both clinics. This is such a bad idea that I can’t help worrying I’m missing or misunderstanding something. If not, this is dumb and bad and should be ignored. Mourya et al: We’re back in India. This is a nonrandomized study comparing 50 patients given ivermectin to 50 patients given hydroxychloroquine. No primary outcome was named, but they focus on PCR negativity. Only 6% of patients in the hydroxychloroquine group were negative, compared to 90% of patients in the ivermectin group! On what day did they do the test? Uh, kind of random, and they admit that “in [the hydroxychloroquine group], mean time difference from the date of initiation of treatment and second test was significantly longer (7.24±2.75 days) as compared to 5.22±1.21 days in [the ivermectin group] (p=0.021).” Since they assessed these groups at different times, we shouldn’t draw any conclusions from them getting different results. Except that as far as I can tell this should handicap ivermectin, making it especially impressive that it did better. But also, the ivermectin group was made mostly of people who had been asymptomatic at the beginning (70%), and the hydroxychloroquine group had almost no asymptomatic cases (8%) . They were giving the ivermectin to healthy people and the hydroxychloroquine to sick people! They admit deep in the discussion that this “may be a confounding factor”. So basically they got totally different groups of people, tested them at totally different times, and the two sets of test results differed. So what? So this is why normal people do RCTs instead of whatever the heck this is, that’s what. Loue et al: …this one isn’t going to be an RCT either. Loue tells a story about a cluster of COVID cases at the French nursing home where he works. He asked people if they wanted to try ivermectin; 10 did and 15 didn’t. 1 ivermectin patient died, compared to 5 non-ivermectin patients. The non-ivermectin group looked a bit sicker than the ivermectin group in the inevitable Table 1, though it’s hard to tell. One interesting possible confounder (not mentioned, but I’m imagining it) is that demented patients probably couldn’t consent to ivermectin and ended up in the control group. This is another case of “I’m not going to trust anything that isn’t an RCT”. Merino et al: Another (sigh) non-RCT. Mexico City tried a public health program where if you called a hotline and said you had COVID, they sent you an emergency kit with various useful supplies. One of those supplies was ivermectin tablets. 18,074 people got the kit (and presumably some appreciable fraction took the ivermectin, though there’s no way to prove that). Their control group is people from before they started giving out the kits, people from after they stopped giving out the kits, and people who didn’t want the kits. There are differences in who got COVID early in the epidemic vs. later, and in people who did opt for medical kits vs. didn’t. To correct these, the researchers tried to adjust for confounders, something which - as I keep trying to hammer home again and again - never works. They found that using the kit led to a 75% or so reduction in hospitalization, though they were unable to separate out the ivermectin from the other things in the kit (paracetamol and aspirin), or from the placebo effect of having a kit and feeling like you had already gotten some treatment (if I understand right, the decision to go to the hospital was left entirely to the patient). I think this study is a moderate point in favor of giving people kits in order to prevent hospital overcrowding, but I’m not willing to accept that it tells us much about ivermectin in particular. Faisal et al: This one was published in The Professional Medical Journal (mispelled as “Profesional Medical Journal” in its URL), so you know it’s going to be good! It describes itself as “a cross-sectional study”, but later says it “randomized patients into two groups”, which would make it an RCT - I think they might just be using the term “cross-sectional” different from the standard American usage. A hospital in Pakistan got 50 patients on ivermectin + azithromycin, and another 50 on azithromycin alone. Primary outcome was not mentioned, and the data were presented confusingly, but a typical result is that only 4% of the ivermectin group had symptoms lasting more than 10 days, whereas 16% of the control group did, p < 0.01. They do a really weird thing where they compare how long it took symptoms to resolve between IVM and control groups within each bin. That is, if I’m understanding correctly, they ask “of the people who took between 3-5 days for symptoms to resolve, did they resolve faster for IVM or control?”. This is an utterly bizarre analysis to perform, although it doesn’t affect the fact that their other results still seem to favor ivermectin. Maybe I’m confused about what’s going on here. I’ve mostly been letting people off easy on no placebo, but I as far as I can tell (not very far) this paper seems to be going off whether patients reported continuing to have symptoms to the hospital doing the study, and I think that is potentially susceptible to placebo effects. Additionally, there’s no preregistration, and even though they talk a lot about doing PCR tests they don’t present the results. This is by no means the worst study here but I still think it’s pretty low quality and I don’t trust it. Aref et al: This one is published in the International Journal Of Nanomedicine, even though I’m pretty sure that isn’t a real thing. In this case the “nanomedicine” is a new nasal spray version of ivermectin which is so confusing I cannot for the life of me figure out what dose they are giving these patients. This Egyptian study gives 57 patients intranasal ivermectin plus hydroxychloroquine, azithromycin, oseltamavir, and some vitamins; another 57 patients get all that stuff except the ivermectin. Primary outcome is not stated, but they look at various symptoms, all of which look better in the ivermectin group: 95% of ivermectin patients got negative PCRs at some time point, compared to 75% of controls, p = 0.004. I am pretty suspicious of this study, not least because it comes from Egypt which has an awful reputation for fake studies, and it returns extreme results that I wouldn’t expect even if ivermectin was actually a wonder drug. But I cannot find any particular thing wrong with it, nor did anyone else I looked at, so I will grudgingly let it stand. Krolewiecki et al: Another Argentine study. This one is a real RCT. 30 patients received ivermectin, 15 were the control group (no placebo, again). Primary outcome was difference in viral load on day 5. The trend favored ivermectin but it was not statistically significant, although they were able to make it statistically significant if they looked at a subset of higher-IVM-plasma-concentration patients. They did not find any difference in clinical outcomes. A pro-ivermectin person could point out that in the subgroup with the highest ivermectin concentrations, the drug seemed to work. A skeptic could point out that this is exactly the kind of subgroup slicing that you are not supposed to do without pre-registering it, which I don’t think this team did. I agree with the skeptic. Vallejos et al: Another Argentine study. It’s big (250 people in each arm). It’s an RCT. It tries to define a primary outcome (“Primary outcome: the trial ended when the last patient who was included achieved the end of study visit”), but that’s not what “primary outcome” means, and they don’t offer an alternative. Other outcomes: no difference in PCR on days 3 or 12. Hospitalization is nonsignificantly better in the ivermectin group (14 vs. 21, p = 0.2), but death is nonsigificantly better in the placebo group (3 vs. 4, p = 0.7). This isn’t even the kind of nonsignificant that might contribute to an exciting meta-analysis later. This is just a pure null result. I cannot find any problem with this study, and neither can anyone else I checked. This is the biggest RCT we’ve seen so far, so we should take it seriously. TOGETHER Trial: Speaking of big RCTs… This one hasn’t been published yet. There’s a video of a talk about it, but I am not going to watch it, because it is a video, so I am getting information secondhand from eg here. Apparently, it compares 677 people (!) randomized to ivermectin to 678 people randomized to placebo. 86 ivermectin patients ended up in the hospital compared to 95 placebo patients, p-value not significant. This was a really big professional trial done by bigshot researchers from a major Canadian university, and the medical establishment is taking it much more seriously than any of these others. When it comes out, it will probably get published in a top journal. When discussing Lopez-Medina, I wrote: When people say things like “sure, a lot of small studies show good results for ivermectin, but the bigger and more professional trials don’t”, this is one of the two big professional trials they’re talking about. This is the other one. Not coincidentally, it’s also the other trial that ivmmeta.com has a warning letter underneath telling you to disregard. Their main concern is that instead of truly randomizing patients to ivermectin vs. placebo, they did a time-dependent randomization that meant during some weeks more patients were getting one or the other. This is a problem because the trial takes place in Brazil, where different variants were more common at different times. Here’s their image: On the one hand, I have immense contempt for ivmmeta for letting all those other awful studies pass and then pulling out all the stops to try to nitpick this one. I have no idea if their proposed randomization failure really happened. And no doubt the reason they’re even able to investigate this is that this study is really careful and transparent - most of them don’t tell you anything about their randomization method. I would be shocked if other studies don’t have all these problems and worse. On the other hand, the point isn’t to be fair, it’s to be right. And this is a potential confounder. Not a huge one. But a potential one. I guess all we can do is try to bound the damage. Even if the confounding is 100% real and bad, there’s no way to make this study consistent with the crazy super-pro-ivermectin results of studies like Espitia-Hernandez and Aref. And even if we deny any confounding, we see the same slight pro-ivermectin trend - 86 hospitalizations vs. 95 - that we’ve seen in so many other studies. Nothing is going to make me believe that this isn’t in the top 33% of studies we’ve been looking at, so let’s add it as grist for the meta-analysis (though maybe not quite as much grist as its vast size indicates) and move on, angrily. Buonfrate et al: An Italian RCT. Patients were randomized into low-dose ivermectin (32), placebo (29), or high-dose ivermectin (32). Primary outcome was viral load on day 7. There was no significant difference (average of 2 in ivermectin groups, 2.2 in placebo group). They admit that they failed to reach the planned sample size, but did a calculation to show that even if they had, the trial could not have returned a positive result. Clinically, an average of 2 patients were hospitalized in each of the ivermectin arms, compared to 0 in the placebo arm - which bucks our previously-very-constant pro-ivermectin trend. Mayer et al: Not an RCT. Patients in an Argentine province were offered the opportunity to try ivermectin; 3266 said yes and become the experimental group, 17966 said no and became the control group. There were many obvious differences between the groups, but they all seemed to handicap ivermectin. There was a nonsignificant trend toward less hospitalization and significantly less mortality (1.5% vs. 2.1%, p = 0.03). While looking into this study, I learned the term “immortal time bias”. This means a period in between selection for the study and the beginning of study recording where patient outcomes are not counted. I think the problem here is that if you signed up for the system on Day X, and if you got sick before they could give you ivermectin, you were in the control group. See this Twitter thread, I have not confirmed everything he says. This only hardens my resolve to stay away from non-RCTs. Borody et al: Our last paper! …is it a paper? I can’t find it published anywhere. It mostly seems to be on news sites. Doesn’t look peer-reviewed. And it starts with “Note that views expressed in this opinion article are the writer’s personal views”. Whatever. 600 Australians were treated with ivermectin, doxycycline, and zinc. The article compares this to an “equivalent control group” made of “contemporary infected subjects in Australia obtained from published Covid Tracking Data”; this is not how you control group, @#!% you. Then it gets excited about the fact that most patients had better symptoms at the end of the ten-day study period than the beginning (untreated COVID resolves in about ten days). Why are these people wasting my time with this? Let’s move on. The Analysis If we remove all fraudulent and methodologically unsound studies from the table above, we end up with this: Gideon Meyerowitz-Katz, who investigated many of the studies above for fraud, tried a similar exercise. I learned about his halfway through, couldn’t help seeing it briefly, but tried to avoid remembering it or using it when generating mine (also, I did take the result of his fraud investigations into account), so they should be considered not quite independent efforts. His looks like this: He nixed Chowdhury, Babaloba, Ghauri, Faisal, and Aref, but kept Szenta Fonseca, Biber (?), and Mayer. There was correlation of 0.45, which I guess is okay. I asked him about his decision-making, and he listed a combination of serious statistical errors and small red flags adding up. I was pretty uncomfortable with most of these studies myself, so I will err on the side of severity, and remove all studies that either I or Meyerowitz-Katz disliked. We end up with the following short list: We’ve gone from 29 studies to 11, getting rid of 18 along the way. For the record, we eliminated 2/19 for fraud, 1/19 for severe preregistration violations, 10 for methodological problems, and 6 because Meyerowitz-Katz was suspicious of them. …but honestly this table still looks pretty good for ivermectin, doesn’t it? Still lots of big green boxes. Meyerowitz-Katz accuses ivmmeta of cherry-picking what statistic to use for their forest plot. That is, if a study measures ten outcomes, they sometimes take the most pro-ivermectin outcome. Ivmmeta.com counters that they used a consistent and reasonable (if complicated) process for choosing their outcome of focus, that being: If studies report multiple kinds of effects then the most serious outcome is used in calculations for that study. For example, if effects for mortality and cases are both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcome is considered more important than PCR testing status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available (after most or all patients have recovered there is no room for an effective treatment to do better). If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. I’m having trouble judging this, partly because Meyerowitz-Katz says ivmmeta has corrected some earlier mistakes, and partly because there really is some reasonable debate over how to judge studies with lots of complicated endpoints. By this point I had completely forgotten what ivmmeta did, so I independently coded all 11 remaining studies following something in between my best understanding of their procedure and what I considered common sense. The only exception was that when the most severe outcome was measured in something other than patients (ie average number of virus copies per patient), I defaulted to one that was measured in patients instead, to keep everything with the same denominator. My results mostly matched ivmmeta’s, with one or two exceptions that I think are within the scope of argument or related to my minor deviations from their protocol. Placebo vs. ivermectin groups sometimes differed in size, which I’ve adjusted for and rounded off. Probably I’m forgetting some reason I can’t just do simple summary statistics to this, but whatever. It is p = 0.15, not significant. This is maybe unfair, because there aren’t a lot of deaths in the sample, so by focusing on death rather than more common outcomes we’re pointlessly throwing away sample size. What happens if I unprincipledly pick whatever I think the most reasonable outcome to use from each study is? I’ve chosen “most reasonable” as a balance between “is the most severe” and “has a lot of data points”: Now it’s p = 0.04, seemingly significant, but I had to make some unprincipled decisions to get there. I don’t think I specifically replaced negative findings with positive ones, but I can’t prove that even to myself, let alone to you. [UPDATE 5/31/22: A reader writes in to tell me that the t-test I used above is overly simplistic. A Dersimonian-Laird test is more appropriate for meta-analysis, and would have given 0.03 and 0.005 on the first and second analysis, where I got 0.15 and 0.04. This significantly strengthens the apparent benefit of ivermectin from ‘debatable’ to ‘clear’. I discuss some reasons below why I am not convinced by this apparent benefit.] (how come I’m finding a bunch of things on the edge of significance, but the original ivmmeta site found a lot of extremely significant things? Because they combined ratios, such that “one death in placebo, zero in ivermectin” looked like a nigh-infinite benefit for ivermectin, whereas I’m combining raw numbers. Possibly my way is statistically illegitimate for some reason, but I’m just trying to get a rough estimate of how convinced to be) So we are stuck somewhere between “nonsignificant trend in favor” and “maybe-significant trend in favor, after throwing out some best practices”. This is normally where I would compare my results to those of other meta-analyses made by real professionals. But when I look at them, they all include studies later found to be fake, like Elgazzar, and unsurprisingly come up with wildly positive conclusions. There are about six in this category. One of them later revised their results to exclude Elgazzar and still found strong efficacy for ivermectin, but they still included Niaee and some other dubious studies. The only meta-analysis that doesn’t make these mistakes is Popp (a Cochrane review), which is from before Elgazzar was found to be fraudulent, but coincidentally excludes it for other reasons. It also excludes a lot of good studies like Mahmud and Ravakirti because they give patients other things like HCQ and azithromycin - I chose to include them, because I don’t think they either work or have especially bad side effects, so they’re basically placebo - but Cochrane is always harsh like this. They end up with a point estimate where ivermectin cuts mortality by 40% - but say the confidence intervals are too wide to draw any conclusion. I think this basically agrees with my analyses above - the trends really are in ivermectin’s favor, but once you eliminate all the questionable studies there are too few studies left to have enough statistical power to reach significance. Except that everyone is still focusing on deaths and hospitalizations just because they’re flashy. Mahmud et al, which everyone agrees is a great study, found that ivermectin decreased days until clinical recovery, p = 0.003? So what do you do? This is one of the toughest questions in medicine. It comes up again and again. You have some drug. You read some studies. Again and again, more people are surviving (or avoiding complications) when they get the drug. It’s a pattern strong enough to common-sensically notice. But there isn’t an undeniable, unbreachable fortress of evidence. The drug is really safe and doesn’t have a lot of side effects. So do you give it to your patients? Do you take it yourself? Here this question is especially tough, because, uh, if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout “horse dewormer!” at you and compare you to Josef Mengele. But good doctors aren’t supposed to care about such things. Your only goal is to save your patient. Nothing else matters. I am telling you that Mahmud et al is a good study and it got p = 0.003 in favor of ivermectin. You can take the blue pill, and stay a decent respectable member of society. Or you can take the horse dewormer pill, and see where you end up. In a second, I’ll tell you my answer. But you won’t always have me to answer questions like this, and it might be morally edifying to observe your thought process in situations like this. So take a second, and meet me on the other side of the next section heading. … … … … … The Synthesis Hopefully you learned something interesting about yourself there. But my answer is: worms! As several doctors and researchers have pointed out (h/t especially Avi Bitterman and David Boulware), the most impressive studies come from places that are teeming with worms. Mahmud from Bangladesh, Ravakirti from East India, Lopez-Medina from Colombia, etc. Here’s the prevalence of roundworm infections by country (source). But alongside roundworms, there are threadworms, hookworms, blood flukes, liver flukes, nematodes, trematodes, all sorts of worms. Add them all up and somewhere between half and a quarter of people in the developing world have at least one parasitic worm in their body. Being full of worms may impact your ability to fight coronavirus. Gluchowska et al write: Helminth [ie worm] infections are among the most common infectious diseases. Bradbury et al. highlight the possible negative interactions between helminth infection and COVID-19 severity in helminth-endemic regions and note that alterations in the gut microbiome associated with helminth infection appear to have systemic immunomodulatory effects. It has also been proposed that helminth co-infection may increase the morbidity and mortality of COVID-19, because the immune system cannot efficiently respond to the virus; in addition, vaccines will be less effective for these patients, but treatment and prevention of helminth infections might reduce the negative effect of COVID-19. During millennia of parasite-host coevolution helminths evolved mechanisms suppressing the host immune responses, which may mitigate vaccine efficacy and increase severity of other infectious diseases. Treatment of worm infections might reduce the negative effect of COVID-19! And ivermectin is a deworming drug! You can see where this is going… The most relevant species of worm here is the roundworm Strongyloides stercoralis. Among the commonest treatments for COVID-19 is corticosteroids, a type of immunosuppresant drug. The types of immune responses it suppresses do more harm than good in coronavirus, so turning them off limits collateral damage and makes patients better on net. But these are also the types of immune responses that control Strongyloides. If you turn them off even very briefly, the worms multiply out of control, you get what’s called “Strongyloides hyperinfection”, and pretty often you die. According to the WHO: The current COVID-19 pandemic serves to highlight the risk of using systemic corticosteroids and, to a lesser extent, other immunosuppressive therapy, in populations with significant risk of underlying strongyloidiasis. Cases of strongyloidiasis hyperinfection in the setting of corticosteroid use as COVID-19 therapy have been described and draw attention to the necessity of addressing the risk of iatrogenic strongyloidiasis hyperinfection syndrome in infected individuals prior to corticosteroid administration. Although this has gained importance in the midst of a pandemic where corticosteroids are one of few therapies shown to improve mortality, its relevance is much broader given that corticosteroids and other immunosuppressive therapies have become increasingly common in treatment of chronic diseases (e.g. asthma or certain rheumatologic conditions). So you need to “address the risk” of strongyloides infection during COVID treatment in roundworm-endemic areas. And how might you address this, WHO? Treatment of chronic strongyloidiasis with ivermectin 200 µg/kg per day orally x 1-2 days is considered safe with potential contraindications including possible Loa loa infection (endemic in West and Central Africa), pregnancy, and weight <15kg. Given ivermectin’s safety profile, the United States has utilized presumptive treatment with ivermectin for strongyloidiasis in refugees resettling from endemic areas, and both Canada and the European Centre for Disease Prevention and Control have issued guidance on presumptive treatment to avoid hyperinfection in at risk populations. Screening and treatment, or where not available, addition of ivermectin to mass drug administration programs should be studied and considered. This is serious and common enough that, if you’re not going to screen for it, it might be worth “add[ing] ivermectin to mass drug administration programs” in affected areas! Dr. Avi Bitterman carries the hypothesis to the finish line: First two images are with all relevant studies; second two are a sensitivity analysis that removes some of the most dubious. The good ivermectin trials in areas with low Strongyloides prevalence, like Vallejos in Argentina, are mostly negative. The good ivermectin trials in areas with high Strongyloides prevalence, like Mahmud in Bangladesh, are mostly positive. Worms can’t explain the viral positivity outcomes (ie PCR), but Dr. Bitterman suggests that once you remove low quality trials and worm-related results, the rest looks like simple publication bias: This is still just a possibility. Maybe I’m over-focusing too hard on a couple positive results and this will all turn out to be nothing. Or who knows, maybe ivermectin does work against COVID a little - although it would have to be very little, fading to not at all in temperate worm-free countries. But this theory feels right to me. It feels right to me because it’s the most troll-ish possible solution. Everybody was wrong! The people who called it a miracle drug against COVID were wrong. The people who dismissed all the studies because they F@#king Love Science were wrong. Ivmmeta.com was wrong. Gideon Meyerowitz-Katz was…well, he was right, actually, I got the worm-related meta-analysis graphic above from his Twitter timeline. Still, an excellent troll. Also, the best part is that I ignorantly asked, in my description of Mahmud et al above: And it was! It was a fluke! A literal, physical, fluke! For my whole life, God has been placing terrible puns in my path to irritate me, and this would be the worst one ever! So it has to be true! The Scientific Takeaway About ten years ago, when the replication crisis started, we learned a certain set of tools for examining studies. Check for selection bias. Distrust “adjusting for confounders”. Check for p-hacking and forking paths. Make teams preregister their analyses. Do forest plots to find publication bias. Stop accepting p-values of 0.049. Wait for replications. Trust reviews and meta-analyses, instead of individual small studies. These were good tools. Having them was infinitely better than not having them. But even in 2014, I was writing about how many bad studies seemed to slip through the cracks even when we pushed this toolbox to its limits. We needed new tools. I think the methods that Meyerowitz-Katz, Sheldrake, Heathers, Brown, Lawrence and others brought to the limelight this year are some of the new tools we were waiting for. Part of this new toolset is to check for fraud. About 10 - 15% of the seemingly-good studies on ivermectin ended up extremely suspicious for fraud. Elgazzar, Carvallo, Niaee, Cadegiani, Samaha. There are ways to check for this even when you don’t have the raw data. Like: The Carlisle-Stouffer-Fisher method: Check some large group of comparisons, usually the Table 1 of an RCT where they compare the demographic characteristics of the control and experimental groups, for reasonable p-values. Real data will have p-values all over the map; one in every ten comparisons will have a p-value of 0.1 or less. Fakers seem bad at this and usually give everything a nice safe p-value like 0.8 or 0.9.

…looks very impressive, in terms of the experimental group doing better than the control, except that they don’t specify whether it was before the trial or after it, and at least one online commentator thinks it might have been before, in which case it’s only impressive how thoroughly they failed to randomize their groups. Overall I don’t feel bad throwing this study out. I hope it one day succeeds in returning to its home planet. Lopez-Medina et al: Colombian RCT. 200 patients took ivermectin, another 200 took placebo. They originally worried the placebo might taste different than real ivermectin, then solved this by replacing it with a different placebo, which is a pretty high level of conscientiousness. Primary outcome was originally percent of patients whose symptoms worsened by two points, as rated on a complicated symptom scale when a researcher asked them over the phone. Halfway through the study, they realized nobody was worsening that much, so they changed the primary outcome to time until symptoms got better, as measured by the scale. In the ivermectin group, symptoms improved that much after 10 days; in the placebo group, after 12, p = 0.53. By the end of the study, symptoms had improved in 82% of ivermectin users and 79% of controls, also insignificant. 4 patients in the ivermectin group needed to be hospitalized compared to 6 in the placebo group, again insignificant. This study is bigger than most of the other RCTs, and more polished in terms of how many spelling errors, photographs of computer screens, etc, it contains. It was published in JAMA, one of the most prestigious US medical journals, as opposed to the crappy nth-tier journals most of the others have been in. When people say things like “sure, a lot of small studies show good results for ivermectin, but the bigger and more professional trials don’t”, this is one of the two big professional trials they’re talking about. Ivermectin proponents make some good arguments against it. In order to get as big as it did, Lopez-Medina had to compromise on rigor. Its outcome is how people self-score their symptoms on a hokey scale in a phone interview, instead of viral load or PCR results or anything like that. Still, this is basically what we want, right? In the end, we want people to feel better and less sick, not to get good scores on PCR tests. Also, it changed its primary outcome halfway through; isn’t that bad? I think maybe not; the reason we want a preregistered primary outcome is so that you don’t change halfway through to whatever outcome shows the results you want. The researchers in this study did a good job explaining why they changed their outcome, the change makes sense, and their original outcome would also have shown ivermectin not working (albeit less accurately and effectively). I don’t know of any evidence that they knew (or suspected) final results when switching to this new outcome, and it seems like the most reasonable new outcome to switch to. Finally, their original placebo tasted different from ivermectin (though they switched halfway through). This is one of the few studies where I actually care about placebo, because people are self-rating their symptoms. But realistically most of these people don’t know what ivermectin is supposed to taste like. Also, they did a re-analysis and found there was no difference between the people who got the old placebo and the new one. I’m making a big deal of this because ivmmeta.com - the really impressive meta-analysis site I’ve been going off of - puts a special warning letter underneath their discussion of this study, urging us not to trust it. They don’t do this for any of the other ones we’ve addressed so far - not the one by the guy whose other studies were all frauds, not the one where 50% of 21 people had headaches, not the unrandomized one where the groups were completely different before the experiment started, not even the one by the guy accused of crimes against humanity. Only this one. This makes me a lot less charitable to ivmmeta than I would otherwise be; I think it’s hard to choose this particular warning letter strategy out of well-intentioned commitment to truth. They just really don’t like this big study that shows ivermectin doesn’t work. Also, the warning itself irritates me, and includes paragraphs like: RCTs have a fundamental bias against finding an effect for interventions that are widely available — patients that believe they need treatment are more likely to decline participation and take the intervention [Yeh], i.e., RCTs are more likely to enroll low-risk participants that do not need treatment to recover (this does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable). This trial was run in a community where ivermectin was available OTC and very widely known and used. Nobody else worries about this, and there are a million biases that non-randomized studies have that would be super-relevant when discussing those, but somehow when they’re pro-ivermectin the site forgets to be this thorough. I think a better pro-ivermectin response to this study is to point out that all the trends support ivermectin. Symptoms took 10 days to resolve in the ivermectin group vs. 12 in placebo; 4 ivermectin patients were hospitalized vs. 6 placebo patients, etc. Just say that this was an unusually noisy trial because of the self-report methodology, and you’re confident that these small differences will add up to significance when you put them into a meta-analysis. Roy et al: We’re back in East India, and back to non-randomized trials. 56 patients were retrospectively examined; some had been given ivermectin + doxycycline, others hydroxychloroquine, other azithromycin, and others symptomatic treatment only. We don’t get any meaningful information about how this worked, but we are told that they did not differ in “clinical well-being reporting onset timing”. Whatever. Chahla et al: The first of many Argentine trials. 110 patients received medium-dose ivermectin; 144 were kept as a control (no placebo). This was “cluster randomized”, which means they randomize different health centers to either give the experimental drug or not. This is worse than regular randomization, because there could be differences between these health centers (eg one might have better doctors who otherwise give better treatment, one might be in the poor part of town and have sicker patients, etc). They checked to see if there were any differences between the groups, and it sure looks like there were (the experimental group had twice as many obese people as the controls), but as per them, these differences were not statistically significant. Note that if this did make a difference, it would presumably make ivermectin look worse, not better. The primary outcome was given as “increase discharge from outpatient care with COVID-19 mild disease”. This favored the treatment; only 2/110 patients in the ivermectin group failed to be discharged, compared to 20 patients in the control group. But, uh, these were at different medical centers. Can’t different medical centers just have different discharge policies? One discharges you as soon as you seem to be getting better, the other waits to really make sure? This is an utterly crap endpoint to do a cluster randomized controlled trial on. If you’re going to do cRCT, which is never a great idea, you should be using some extremely objective endpoint that doctors and clinic administrators can’t possibly affect, like viral load according to some third-party laboratory, using the same third-party laboratory for both clinics. This is such a bad idea that I can’t help worrying I’m missing or misunderstanding something. If not, this is dumb and bad and should be ignored. Mourya et al: We’re back in India. This is a nonrandomized study comparing 50 patients given ivermectin to 50 patients given hydroxychloroquine. No primary outcome was named, but they focus on PCR negativity. Only 6% of patients in the hydroxychloroquine group were negative, compared to 90% of patients in the ivermectin group! On what day did they do the test? Uh, kind of random, and they admit that “in [the hydroxychloroquine group], mean time difference from the date of initiation of treatment and second test was significantly longer (7.24±2.75 days) as compared to 5.22±1.21 days in [the ivermectin group] (p=0.021).” Since they assessed these groups at different times, we shouldn’t draw any conclusions from them getting different results. Except that as far as I can tell this should handicap ivermectin, making it especially impressive that it did better. But also, the ivermectin group was made mostly of people who had been asymptomatic at the beginning (70%), and the hydroxychloroquine group had almost no asymptomatic cases (8%) . They were giving the ivermectin to healthy people and the hydroxychloroquine to sick people! They admit deep in the discussion that this “may be a confounding factor”. So basically they got totally different groups of people, tested them at totally different times, and the two sets of test results differed. So what? So this is why normal people do RCTs instead of whatever the heck this is, that’s what. Loue et al: …this one isn’t going to be an RCT either. Loue tells a story about a cluster of COVID cases at the French nursing home where he works. He asked people if they wanted to try ivermectin; 10 did and 15 didn’t. 1 ivermectin patient died, compared to 5 non-ivermectin patients. The non-ivermectin group looked a bit sicker than the ivermectin group in the inevitable Table 1, though it’s hard to tell. One interesting possible confounder (not mentioned, but I’m imagining it) is that demented patients probably couldn’t consent to ivermectin and ended up in the control group. This is another case of “I’m not going to trust anything that isn’t an RCT”. Merino et al: Another (sigh) non-RCT. Mexico City tried a public health program where if you called a hotline and said you had COVID, they sent you an emergency kit with various useful supplies. One of those supplies was ivermectin tablets. 18,074 people got the kit (and presumably some appreciable fraction took the ivermectin, though there’s no way to prove that). Their control group is people from before they started giving out the kits, people from after they stopped giving out the kits, and people who didn’t want the kits. There are differences in who got COVID early in the epidemic vs. later, and in people who did opt for medical kits vs. didn’t. To correct these, the researchers tried to adjust for confounders, something which - as I keep trying to hammer home again and again - never works. They found that using the kit led to a 75% or so reduction in hospitalization, though they were unable to separate out the ivermectin from the other things in the kit (paracetamol and aspirin), or from the placebo effect of having a kit and feeling like you had already gotten some treatment (if I understand right, the decision to go to the hospital was left entirely to the patient). I think this study is a moderate point in favor of giving people kits in order to prevent hospital overcrowding, but I’m not willing to accept that it tells us much about ivermectin in particular. Faisal et al: This one was published in The Professional Medical Journal (mispelled as “Profesional Medical Journal” in its URL), so you know it’s going to be good! It describes itself as “a cross-sectional study”, but later says it “randomized patients into two groups”, which would make it an RCT - I think they might just be using the term “cross-sectional” different from the standard American usage. A hospital in Pakistan got 50 patients on ivermectin + azithromycin, and another 50 on azithromycin alone. Primary outcome was not mentioned, and the data were presented confusingly, but a typical result is that only 4% of the ivermectin group had symptoms lasting more than 10 days, whereas 16% of the control group did, p < 0.01. They do a really weird thing where they compare how long it took symptoms to resolve between IVM and control groups within each bin. That is, if I’m understanding correctly, they ask “of the people who took between 3-5 days for symptoms to resolve, did they resolve faster for IVM or control?”. This is an utterly bizarre analysis to perform, although it doesn’t affect the fact that their other results still seem to favor ivermectin. Maybe I’m confused about what’s going on here. I’ve mostly been letting people off easy on no placebo, but I as far as I can tell (not very far) this paper seems to be going off whether patients reported continuing to have symptoms to the hospital doing the study, and I think that is potentially susceptible to placebo effects. Additionally, there’s no preregistration, and even though they talk a lot about doing PCR tests they don’t present the results. This is by no means the worst study here but I still think it’s pretty low quality and I don’t trust it. Aref et al: This one is published in the International Journal Of Nanomedicine, even though I’m pretty sure that isn’t a real thing. In this case the “nanomedicine” is a new nasal spray version of ivermectin which is so confusing I cannot for the life of me figure out what dose they are giving these patients. This Egyptian study gives 57 patients intranasal ivermectin plus hydroxychloroquine, azithromycin, oseltamavir, and some vitamins; another 57 patients get all that stuff except the ivermectin. Primary outcome is not stated, but they look at various symptoms, all of which look better in the ivermectin group: 95% of ivermectin patients got negative PCRs at some time point, compared to 75% of controls, p = 0.004. I am pretty suspicious of this study, not least because it comes from Egypt which has an awful reputation for fake studies, and it returns extreme results that I wouldn’t expect even if ivermectin was actually a wonder drug. But I cannot find any particular thing wrong with it, nor did anyone else I looked at, so I will grudgingly let it stand. Krolewiecki et al: Another Argentine study. This one is a real RCT. 30 patients received ivermectin, 15 were the control group (no placebo, again). Primary outcome was difference in viral load on day 5. The trend favored ivermectin but it was not statistically significant, although they were able to make it statistically significant if they looked at a subset of higher-IVM-plasma-concentration patients. They did not find any difference in clinical outcomes. A pro-ivermectin person could point out that in the subgroup with the highest ivermectin concentrations, the drug seemed to work. A skeptic could point out that this is exactly the kind of subgroup slicing that you are not supposed to do without pre-registering it, which I don’t think this team did. I agree with the skeptic. Vallejos et al: Another Argentine study. It’s big (250 people in each arm). It’s an RCT. It tries to define a primary outcome (“Primary outcome: the trial ended when the last patient who was included achieved the end of study visit”), but that’s not what “primary outcome” means, and they don’t offer an alternative. Other outcomes: no difference in PCR on days 3 or 12. Hospitalization is nonsignificantly better in the ivermectin group (14 vs. 21, p = 0.2), but death is nonsigificantly better in the placebo group (3 vs. 4, p = 0.7). This isn’t even the kind of nonsignificant that might contribute to an exciting meta-analysis later. This is just a pure null result. I cannot find any problem with this study, and neither can anyone else I checked. This is the biggest RCT we’ve seen so far, so we should take it seriously. TOGETHER Trial: Speaking of big RCTs… This one hasn’t been published yet. There’s a video of a talk about it, but I am not going to watch it, because it is a video, so I am getting information secondhand from eg here. Apparently, it compares 677 people (!) randomized to ivermectin to 678 people randomized to placebo. 86 ivermectin patients ended up in the hospital compared to 95 placebo patients, p-value not significant. This was a really big professional trial done by bigshot researchers from a major Canadian university, and the medical establishment is taking it much more seriously than any of these others. When it comes out, it will probably get published in a top journal. When discussing Lopez-Medina, I wrote: When people say things like “sure, a lot of small studies show good results for ivermectin, but the bigger and more professional trials don’t”, this is one of the two big professional trials they’re talking about. This is the other one. Not coincidentally, it’s also the other trial that ivmmeta.com has a warning letter underneath telling you to disregard. Their main concern is that instead of truly randomizing patients to ivermectin vs. placebo, they did a time-dependent randomization that meant during some weeks more patients were getting one or the other. This is a problem because the trial takes place in Brazil, where different variants were more common at different times. Here’s their image: On the one hand, I have immense contempt for ivmmeta for letting all those other awful studies pass and then pulling out all the stops to try to nitpick this one. I have no idea if their proposed randomization failure really happened. And no doubt the reason they’re even able to investigate this is that this study is really careful and transparent - most of them don’t tell you anything about their randomization method. I would be shocked if other studies don’t have all these problems and worse. On the other hand, the point isn’t to be fair, it’s to be right. And this is a potential confounder. Not a huge one. But a potential one. I guess all we can do is try to bound the damage. Even if the confounding is 100% real and bad, there’s no way to make this study consistent with the crazy super-pro-ivermectin results of studies like Espitia-Hernandez and Aref. And even if we deny any confounding, we see the same slight pro-ivermectin trend - 86 hospitalizations vs. 95 - that we’ve seen in so many other studies. Nothing is going to make me believe that this isn’t in the top 33% of studies we’ve been looking at, so let’s add it as grist for the meta-analysis (though maybe not quite as much grist as its vast size indicates) and move on, angrily. Buonfrate et al: An Italian RCT. Patients were randomized into low-dose ivermectin (32), placebo (29), or high-dose ivermectin (32). Primary outcome was viral load on day 7. There was no significant difference (average of 2 in ivermectin groups, 2.2 in placebo group). They admit that they failed to reach the planned sample size, but did a calculation to show that even if they had, the trial could not have returned a positive result. Clinically, an average of 2 patients were hospitalized in each of the ivermectin arms, compared to 0 in the placebo arm - which bucks our previously-very-constant pro-ivermectin trend. Mayer et al: Not an RCT. Patients in an Argentine province were offered the opportunity to try ivermectin; 3266 said yes and become the experimental group, 17966 said no and became the control group. There were many obvious differences between the groups, but they all seemed to handicap ivermectin. There was a nonsignificant trend toward less hospitalization and significantly less mortality (1.5% vs. 2.1%, p = 0.03). While looking into this study, I learned the term “immortal time bias”. This means a period in between selection for the study and the beginning of study recording where patient outcomes are not counted. I think the problem here is that if you signed up for the system on Day X, and if you got sick before they could give you ivermectin, you were in the control group. See this Twitter thread, I have not confirmed everything he says. This only hardens my resolve to stay away from non-RCTs. Borody et al: Our last paper! …is it a paper? I can’t find it published anywhere. It mostly seems to be on news sites. Doesn’t look peer-reviewed. And it starts with “Note that views expressed in this opinion article are the writer’s personal views”. Whatever. 600 Australians were treated with ivermectin, doxycycline, and zinc. The article compares this to an “equivalent control group” made of “contemporary infected subjects in Australia obtained from published Covid Tracking Data”; this is not how you control group, @#!% you. Then it gets excited about the fact that most patients had better symptoms at the end of the ten-day study period than the beginning (untreated COVID resolves in about ten days). Why are these people wasting my time with this? Let’s move on. The Analysis If we remove all fraudulent and methodologically unsound studies from the table above, we end up with this: Gideon Meyerowitz-Katz, who investigated many of the studies above for fraud, tried a similar exercise. I learned about his halfway through, couldn’t help seeing it briefly, but tried to avoid remembering it or using it when generating mine (also, I did take the result of his fraud investigations into account), so they should be considered not quite independent efforts. His looks like this: He nixed Chowdhury, Babaloba, Ghauri, Faisal, and Aref, but kept Szenta Fonseca, Biber (?), and Mayer. There was correlation of 0.45, which I guess is okay. I asked him about his decision-making, and he listed a combination of serious statistical errors and small red flags adding up. I was pretty uncomfortable with most of these studies myself, so I will err on the side of severity, and remove all studies that either I or Meyerowitz-Katz disliked. We end up with the following short list: We’ve gone from 29 studies to 11, getting rid of 18 along the way. For the record, we eliminated 2/19 for fraud, 1/19 for severe preregistration violations, 10 for methodological problems, and 6 because Meyerowitz-Katz was suspicious of them. …but honestly this table still looks pretty good for ivermectin, doesn’t it? Still lots of big green boxes. Meyerowitz-Katz accuses ivmmeta of cherry-picking what statistic to use for their forest plot. That is, if a study measures ten outcomes, they sometimes take the most pro-ivermectin outcome. Ivmmeta.com counters that they used a consistent and reasonable (if complicated) process for choosing their outcome of focus, that being: If studies report multiple kinds of effects then the most serious outcome is used in calculations for that study. For example, if effects for mortality and cases are both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcome is considered more important than PCR testing status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available (after most or all patients have recovered there is no room for an effective treatment to do better). If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. I’m having trouble judging this, partly because Meyerowitz-Katz says ivmmeta has corrected some earlier mistakes, and partly because there really is some reasonable debate over how to judge studies with lots of complicated endpoints. By this point I had completely forgotten what ivmmeta did, so I independently coded all 11 remaining studies following something in between my best understanding of their procedure and what I considered common sense. The only exception was that when the most severe outcome was measured in something other than patients (ie average number of virus copies per patient), I defaulted to one that was measured in patients instead, to keep everything with the same denominator. My results mostly matched ivmmeta’s, with one or two exceptions that I think are within the scope of argument or related to my minor deviations from their protocol. Placebo vs. ivermectin groups sometimes differed in size, which I’ve adjusted for and rounded off. Probably I’m forgetting some reason I can’t just do simple summary statistics to this, but whatever. It is p = 0.15, not significant. This is maybe unfair, because there aren’t a lot of deaths in the sample, so by focusing on death rather than more common outcomes we’re pointlessly throwing away sample size. What happens if I unprincipledly pick whatever I think the most reasonable outcome to use from each study is? I’ve chosen “most reasonable” as a balance between “is the most severe” and “has a lot of data points”: Now it’s p = 0.04, seemingly significant, but I had to make some unprincipled decisions to get there. I don’t think I specifically replaced negative findings with positive ones, but I can’t prove that even to myself, let alone to you. [UPDATE 5/31/22: A reader writes in to tell me that the t-test I used above is overly simplistic. A Dersimonian-Laird test is more appropriate for meta-analysis, and would have given 0.03 and 0.005 on the first and second analysis, where I got 0.15 and 0.04. This significantly strengthens the apparent benefit of ivermectin from ‘debatable’ to ‘clear’. I discuss some reasons below why I am not convinced by this apparent benefit.] (how come I’m finding a bunch of things on the edge of significance, but the original ivmmeta site found a lot of extremely significant things? Because they combined ratios, such that “one death in placebo, zero in ivermectin” looked like a nigh-infinite benefit for ivermectin, whereas I’m combining raw numbers. Possibly my way is statistically illegitimate for some reason, but I’m just trying to get a rough estimate of how convinced to be) So we are stuck somewhere between “nonsignificant trend in favor” and “maybe-significant trend in favor, after throwing out some best practices”. This is normally where I would compare my results to those of other meta-analyses made by real professionals. But when I look at them, they all include studies later found to be fake, like Elgazzar, and unsurprisingly come up with wildly positive conclusions. There are about six in this category. One of them later revised their results to exclude Elgazzar and still found strong efficacy for ivermectin, but they still included Niaee and some other dubious studies. The only meta-analysis that doesn’t make these mistakes is Popp (a Cochrane review), which is from before Elgazzar was found to be fraudulent, but coincidentally excludes it for other reasons. It also excludes a lot of good studies like Mahmud and Ravakirti because they give patients other things like HCQ and azithromycin - I chose to include them, because I don’t think they either work or have especially bad side effects, so they’re basically placebo - but Cochrane is always harsh like this. They end up with a point estimate where ivermectin cuts mortality by 40% - but say the confidence intervals are too wide to draw any conclusion. I think this basically agrees with my analyses above - the trends really are in ivermectin’s favor, but once you eliminate all the questionable studies there are too few studies left to have enough statistical power to reach significance. Except that everyone is still focusing on deaths and hospitalizations just because they’re flashy. Mahmud et al, which everyone agrees is a great study, found that ivermectin decreased days until clinical recovery, p = 0.003? So what do you do? This is one of the toughest questions in medicine. It comes up again and again. You have some drug. You read some studies. Again and again, more people are surviving (or avoiding complications) when they get the drug. It’s a pattern strong enough to common-sensically notice. But there isn’t an undeniable, unbreachable fortress of evidence. The drug is really safe and doesn’t have a lot of side effects. So do you give it to your patients? Do you take it yourself? Here this question is especially tough, because, uh, if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout “horse dewormer!” at you and compare you to Josef Mengele. But good doctors aren’t supposed to care about such things. Your only goal is to save your patient. Nothing else matters. I am telling you that Mahmud et al is a good study and it got p = 0.003 in favor of ivermectin. You can take the blue pill, and stay a decent respectable member of society. Or you can take the horse dewormer pill, and see where you end up. In a second, I’ll tell you my answer. But you won’t always have me to answer questions like this, and it might be morally edifying to observe your thought process in situations like this. So take a second, and meet me on the other side of the next section heading. … … … … … The Synthesis Hopefully you learned something interesting about yourself there. But my answer is: worms! As several doctors and researchers have pointed out (h/t especially Avi Bitterman and David Boulware), the most impressive studies come from places that are teeming with worms. Mahmud from Bangladesh, Ravakirti from East India, Lopez-Medina from Colombia, etc. Here’s the prevalence of roundworm infections by country (source). But alongside roundworms, there are threadworms, hookworms, blood flukes, liver flukes, nematodes, trematodes, all sorts of worms. Add them all up and somewhere between half and a quarter of people in the developing world have at least one parasitic worm in their body. Being full of worms may impact your ability to fight coronavirus. Gluchowska et al write: Helminth [ie worm] infections are among the most common infectious diseases. Bradbury et al. highlight the possible negative interactions between helminth infection and COVID-19 severity in helminth-endemic regions and note that alterations in the gut microbiome associated with helminth infection appear to have systemic immunomodulatory effects. It has also been proposed that helminth co-infection may increase the morbidity and mortality of COVID-19, because the immune system cannot efficiently respond to the virus; in addition, vaccines will be less effective for these patients, but treatment and prevention of helminth infections might reduce the negative effect of COVID-19. During millennia of parasite-host coevolution helminths evolved mechanisms suppressing the host immune responses, which may mitigate vaccine efficacy and increase severity of other infectious diseases. Treatment of worm infections might reduce the negative effect of COVID-19! And ivermectin is a deworming drug! You can see where this is going… The most relevant species of worm here is the roundworm Strongyloides stercoralis. Among the commonest treatments for COVID-19 is corticosteroids, a type of immunosuppresant drug. The types of immune responses it suppresses do more harm than good in coronavirus, so turning them off limits collateral damage and makes patients better on net. But these are also the types of immune responses that control Strongyloides. If you turn them off even very briefly, the worms multiply out of control, you get what’s called “Strongyloides hyperinfection”, and pretty often you die. According to the WHO: The current COVID-19 pandemic serves to highlight the risk of using systemic corticosteroids and, to a lesser extent, other immunosuppressive therapy, in populations with significant risk of underlying strongyloidiasis. Cases of strongyloidiasis hyperinfection in the setting of corticosteroid use as COVID-19 therapy have been described and draw attention to the necessity of addressing the risk of iatrogenic strongyloidiasis hyperinfection syndrome in infected individuals prior to corticosteroid administration. Although this has gained importance in the midst of a pandemic where corticosteroids are one of few therapies shown to improve mortality, its relevance is much broader given that corticosteroids and other immunosuppressive therapies have become increasingly common in treatment of chronic diseases (e.g. asthma or certain rheumatologic conditions). So you need to “address the risk” of strongyloides infection during COVID treatment in roundworm-endemic areas. And how might you address this, WHO? Treatment of chronic strongyloidiasis with ivermectin 200 µg/kg per day orally x 1-2 days is considered safe with potential contraindications including possible Loa loa infection (endemic in West and Central Africa), pregnancy, and weight <15kg. Given ivermectin’s safety profile, the United States has utilized presumptive treatment with ivermectin for strongyloidiasis in refugees resettling from endemic areas, and both Canada and the European Centre for Disease Prevention and Control have issued guidance on presumptive treatment to avoid hyperinfection in at risk populations. Screening and treatment, or where not available, addition of ivermectin to mass drug administration programs should be studied and considered. This is serious and common enough that, if you’re not going to screen for it, it might be worth “add[ing] ivermectin to mass drug administration programs” in affected areas! Dr. Avi Bitterman carries the hypothesis to the finish line: First two images are with all relevant studies; second two are a sensitivity analysis that removes some of the most dubious. The good ivermectin trials in areas with low Strongyloides prevalence, like Vallejos in Argentina, are mostly negative. The good ivermectin trials in areas with high Strongyloides prevalence, like Mahmud in Bangladesh, are mostly positive. Worms can’t explain the viral positivity outcomes (ie PCR), but Dr. Bitterman suggests that once you remove low quality trials and worm-related results, the rest looks like simple publication bias: This is still just a possibility. Maybe I’m over-focusing too hard on a couple positive results and this will all turn out to be nothing. Or who knows, maybe ivermectin does work against COVID a little - although it would have to be very little, fading to not at all in temperate worm-free countries. But this theory feels right to me. It feels right to me because it’s the most troll-ish possible solution. Everybody was wrong! The people who called it a miracle drug against COVID were wrong. The people who dismissed all the studies because they F@#king Love Science were wrong. Ivmmeta.com was wrong. Gideon Meyerowitz-Katz was…well, he was right, actually, I got the worm-related meta-analysis graphic above from his Twitter timeline. Still, an excellent troll. Also, the best part is that I ignorantly asked, in my description of Mahmud et al above: And it was! It was a fluke! A literal, physical, fluke! For my whole life, God has been placing terrible puns in my path to irritate me, and this would be the worst one ever! So it has to be true! The Scientific Takeaway About ten years ago, when the replication crisis started, we learned a certain set of tools for examining studies. Check for selection bias. Distrust “adjusting for confounders”. Check for p-hacking and forking paths. Make teams preregister their analyses. Do forest plots to find publication bias. Stop accepting p-values of 0.049. Wait for replications. Trust reviews and meta-analyses, instead of individual small studies. These were good tools. Having them was infinitely better than not having them. But even in 2014, I was writing about how many bad studies seemed to slip through the cracks even when we pushed this toolbox to its limits. We needed new tools. I think the methods that Meyerowitz-Katz, Sheldrake, Heathers, Brown, Lawrence and others brought to the limelight this year are some of the new tools we were waiting for. Part of this new toolset is to check for fraud. About 10 - 15% of the seemingly-good studies on ivermectin ended up extremely suspicious for fraud. Elgazzar, Carvallo, Niaee, Cadegiani, Samaha. There are ways to check for this even when you don’t have the raw data. Like: The Carlisle-Stouffer-Fisher method: Check some large group of comparisons, usually the Table 1 of an RCT where they compare the demographic characteristics of the control and experimental groups, for reasonable p-values. Real data will have p-values all over the map; one in every ten comparisons will have a p-value of 0.1 or less. Fakers seem bad at this and usually give everything a nice safe p-value like 0.8 or 0.9.

Your Book Review: The Internationalists

July 01, 2022 · Original source

The US keeps starting or engaging in wars, like in Libya, Afghanistan, and Iraq. I will briefly summarize the 3 major sections of the book and how they tackle the first five claims. Section 1: The Old World Order This section refutes the claim that outlawry of war wasn't actually a significant change for anyone at the time. To do so, it covers the history of the international laws of war as described by Hugo Grotius in a set of books titled The Law of War and Peace, including how he came to write it, what the laws were, and how they were used and understood. In this section, H&S work to fully immerse us in the laws of war before the Peace Pact, and the ways that people understood war as a result. I’ve already included a number of things about this up above, so I’ll just put in a few interesting notes here, and if you want more persuasion that people viewed war differently, I’d suggest you pick up the book. There is lots of historical evidence that attitudes toward war before the Peace Pact were not like attitudes toward war today, that people - lawyers, diplomats, sovereigns, and citizens - believed it to be normal and legal, and frequently justified. Conquest in response to debts or offenses was one of the primary motivators of war in the period ruled by the Old World Order (generally, from some time before 1625 when Grotius wrote the rules down to 1928, when the Peace Pact was signed), though H&S also document some of the weirder ones, like a King who declared that they had the right to wage war against another because the other King stole his wife. But because Grotius had declared that no one outside the belligerents could determine whose side was just without violating neutrality, the reasons for war were largely whatever Monarchs could get away, which ran the gamut. Perhaps because it was fashionable, perhaps to convince their citizenry of their rightness, Monarchs paid handsomely for famous thinkers to write manifestos explaining why they were going to war, and other Monarchs and the citizenry generally accepted these reasons. It would be like if Putin had called up Google co-founder Sergey Brin and asked him to write out why Russia had the right to conquer Ukraine, and then everyone else shrugged and decided, sure, that sounds reasonable. Heads of state enlisted esteemed writers and scholars as well as experienced lawyers to draft [war manifestos]. The English military and political leader Oliver Cromwell commissioned John Milton, the great epic poet, to write A Manifesto of the Lord Protector of the Commonwealth in 1655 when he ordered the invasion of the Spanish possessions in the Caribbean. In 1703, the Holy Roman Emperor Leopold I employed Gottfried Leibniz, the rationalist philosopher, co-inventor of calculus, and a trained lawyer, to compose the Manifesto for the Defense of the Rights of Charles III, which defended the empire’s involvement in the War of the Spanish Succession. Commodore Perry arrived in Japan in 1853 and returned for real the next year. Because they were so confused about how the laws of war were supposed to work, Japan proceeded to send Nishi Amane to the Netherlands to study the Law of War and Peace, and twenty years later, in 1875, Japan conquered Korea. Their logic for doing so was that they were afraid Europe or China would get there first. The world recognized their conquest at the time, though after WWII they were made to give it up. Korea was alluring prey for aggressive Western nations. As Nishi Amane [the scholar who brought the Grotian rules to Japan] would later explain, defending one’s borders “is like riding in a third-class train; at first there is adequate space but as more passengers enter there is no place for them to sit. The logic of necessity requires the people to plant both feet firmly and expand their elbows into any opening that may occur for, unless this is done, others will close the opening. (Chapter 6) Section 2: The Transformation Period Recall our list of counterclaims, #s 2 and 3. 2. Outlawry wasn't taken seriously at the time by the signatories - that it was just feel-good propaganda. 3. World War II proves that it failed, so it wasn't important. This section tells the story of how the Peace Pact came into existence, including how influential it was on the thinkers of the time. Throughout the 1930s and 40s, thinkers and diplomats attempted to turn the Peace Pact into practice, and then, when World War II demonstrated that they needed significantly more teeth to make the Peace Pact real, created the United Nations and other international institutions dedicated to supporting the Pact’s goals. At the time, they viewed World War II as a sign that they hadn’t gotten the right combination of institutions to make the Peace Pact succeed, not that it wasn’t important. This was a classic situation of needing More Dakka and they did, indeed, keep adding more until it worked. In an account composed more than a decade later, Jackson recounted that this view of the Pact was shared by the president and his inner circle. The Peace Pact, he reported, “left no vestige of legal right for [a state] to resort to a war of aggression. From the beginning, Roosevelt, Hull, Welles, Stimson and I had been in agreement that Hitler’s war . . . was an illegal one, and that other powers were under no obligation to remain indifferent. (Chapter 11) There is some counter-evidence in support of #2, from the side of the Japanese at least. Japan, for example, did not think that it had renounced the rules of the Old World Order on August 27, 1928. Its signing of the “No-War Pact,” as the Paris Peace Pact was known in Japan, was regarded as a diplomatic gesture, a noble proclamation affirming the aspiration of all civilized nations to seek peace. Indeed, Japanese officials considered it a sign of how far their nation had come that it was included among the fifteen countries at the grand ceremony in Paris. (Chapter 7) But at least on the Allies side, they had intended it seriously, and as World War II went on, that intention redoubled. Sumner Welles, Undersecretary of State during World War II, was assigned by Roosevelt to create a plan for peace after the war. What he and James Shotwell authored was effectively an outline of the United Nations, and they put the Peace Pact at the very center of it. Shotwell was far from subtle about his effort to treat the Pact as a starting point. He placed the Pact at the start of his preliminary draft. Article 1 repeated the Pact verbatim. Article 2 provided that “[t]he United Nations, in order to strengthen and safeguard the peace of nations as set forth in the General Pact for the Renunciation of war, agree to cooperate in the establishment of the necessary instrumentalities for its effective maintenance.” What followed was an outline of nearly every essential institutional component of the modern-day United Nations. Ten days later he circulated a more detailed draft, now entitled “Provisional Outline of International Organization.” (Chapter 8) It wasn't just the United Nations. NATO was built off of the Atlantic Charter, and it was also designed to reinforce the Peace Pact. This is why it's reasonably accurate to describe it as a defensive alliance. The [first draft of the Atlantic Charter] was a remarkable document. It began by restating the principles of the Stimson Doctrine—there would be no conquest; the two countries would “seek no aggrandizement, territorial or other.” Moreover, there would be “no territorial changes that do not accord with the freely expressed wishes of the peoples concerned.” The Charter looked ahead to a time “after the final destruction of the Nazi tyranny”—a remarkable statement for a neutral in the war—and declared the two states’ “hope to see established a peace which will afford to all nations the means of dwelling in safety within their own boundaries. (Chapter 8) This section brings to bear quotes from leaders at the time showing how important they considered the outlawry of war, how they viewed it as changing the world, but also how unprepared they were for how to react to countries choosing to ignore the Pact. Most importantly, they show how the Allies were strongly motivated to fight World War II specifically to preserve and expand the Pact, to make the world safe for peace. Unfortunately, then, as now, Russia/the Soviet Union did not quite live up to the ideals that the Allies generally advocated for. The Soviet Union took territory after World War II, the only one of the Allies to do so. The only ally to gain any significant territory after the war was the Soviet Union. More than twenty million of the nation’s citizens had died in the course of the war, and Stalin insisted on several territorial gains as the price of peace—many, but not all, of them in areas previously contested. … These concessions to Stalin were seen by the other Allied powers as regrettable deviations from accepted law, not precedents to be followed in the future. (Chapter 13) To be fair, we are talking about Josef Stalin, here. Who’s surprised? Section 3: The New World Order Recall our list of counterclaims, #s 4 and 5. 4. The world isn't more peaceful post outlawry. 5. Any increase in peace since World War II is due to democracies, nuclear weapons, or other reasons, and not the Peace Pact. H&S walk through the best academic evidence we have of whether the world is more peaceful today than it was in the period from 1816 (when our data collection starts being decent) to the Peace Pact. They then spend some time discussing why the evidence better supports the Peace Pact than other causes. In particular, H&S highlight that only since the Peace Pact have countries been denied territorial gains from their conquests. There's a lot of detail in there. Here's just a taste of it. A loose team of political scientists has assembled comprehensive data to help them study war. The resulting project, with the intentionally clinical name “Correlates of War,” hosts datasets on everything from “militarized interstate disputes” to “world religion data” to “bilateral trade.” Most relevant here, it includes extensive data on “territorial change”—a record of every single territorial exchange between states from 1816 to 2014, totaling over eight hundred entries. What do our 254 cases of territorial change tell us? They tell us something that is at once striking and surprising: Conquest, once common, has nearly disappeared. Even more unexpected, the switch point is that now familiar year when the world came together to outlaw war, 1928. From the time the data start in 1816 until the Peace Pact opened for signature in 1928, there was, on average, approximately one conquest every ten months (1.21 conquests per year). Put another way, the average state during this period had a 1.33 percent chance of being the victim of conquest in any given year. Those may seem like pretty good odds. They are not: A state with a 1.33 percent annual chance of conquest can expect to lose territory in a conquest once in an ordinary human lifetime. After 1948, the chance an average state would suffer a conquest fell from once in a lifetime to once or twice a millennium. (Chapter 13) The US wars in Afghanistan, Iraq, and Libya One disappointment I have is that H&S do not spend much time discussing the US wars of the last two decades. The book was published in 2017, so there’s really no excuse for this. Even counting them, their claim that wars since the Peace Pact have been fewer and less world-changing than before the Peace Pact still holds up, but since they don’t directly discuss the most notable wars of the last two decades, they leave a significant hole in their argument. I can imagine defenses that they would make, but they should have made them. They mostly refer to these conflicts either as not a conquest (since the US isn’t officially running those places now) or as a side effect of the Peace Pact in allowing failed states (See Addendum 1 for more on that) More recently, the United States invaded Iraq in 2003, toppled Sadaam Hussein, and installed the Coalition Provisional Authority to govern the country. But what’s most notable about these “nonconquests” is how ineffective and unstable they usually are. Exerting influence indirectly is inefficient and expensive. (Chapter 13) And in 2015 alone, high-fatality civil wars continued in Nigeria, South Sudan, Yemen, Syria, Iraq, Afghanistan, Pakistan, Somalia, and Ukraine. Why, if war has been outlawed, is there still so much conflict? The answer is that these conflicts are not prohibited by the Pact. Indeed, they are the predictable consequences of it … the prohibition on the use of force by one state against the territory of another has allowed two sources of conflict to simmer… within [states]. (Chapter 15) The broader intellectual history of war Reading The Internationalists led me to want to read a broader intellectual history of war. H&S include some comments that hint at it, for example describing the Principle of Distinction and other agreements made about how to behave during war. Fortunately for the civilians of Europe, the biblical model of war was finally repudiated. By the middle of the eighteenth century, European armies had come to recognize a “Principle of Distinction,” the doctrine central to modern humanitarian law, which distinguishes between soldiers and civilians and protects the latter from the former. The Principle of Distinction was the first curtailment of Grotius’s blanket immunity for those waging war. In the next century, it was followed by a flood of new legal regulations placing stricter controls on a soldier’s license to kill. International treaties protected the wounded and medical personnel (First Geneva Convention, 1864) prohibited the use of fragmenting, explosive, and incendiary small arms ammunition (St. Petersburg Declaration, 1874) banned explosives from balloons, asphyxiating gas, and dum-dum bullets (First Hague Convention, 1899) and proscribed pillage, the execution of surrendering soldiers and prisoners of war, and forcing civilians to swear an allegiance to a foreign power (Second Hague Convention, 1907). (Chapter 3) But the history of this and other pre-Peace Pact intellectual history of war is thin within the text, as the point H&S are chasing is specific to the Peace Pact's relevance in history, not the broader history of war. Some of my favorite books are books that tie together aspects of history across wide gulfs, which The Internationalists succeeds at. It’s rare and delightful to see how a piratical ship capture by the Dutch in the 16th century ties together with the opening of Japan, the US battles with Mexico, and finally, the creation of the United Nations. H&S’s perspective is that the Peace Pact marks a turning point, and one that should not be forgotten. It’s also clear that it marks a capstone on a long history of small changes that are also, themselves, interesting battles in the long-running war to make the world less intolerable. In the end, they identify four key changes in the intellectual landscape, with Lauterpacht’s fingers in nearly all of them. Neutrality no longer requires impartiality. States can help those they view as victims.

Inline links: More Dakka

Response To Alexandros Contra Me On Ivermectin

February 01, 2023 · Original source

Carvallo said that zero people in the treatment group of his study got COVID, compared to 58% of people in the control group. This is a pretty implausibly big effect, even by the standards of other pro-ivermectin studies, although I don’t know if anyone else tried the exact same preventative protocol as Carvallo. I think this is a more nuanced story than Alexandros’ version where Buzzfeed just doesn’t know that sometimes studies happen at more than one hospital. Is fraud the best explanation? I think Alexandros thinks of Carvallo as just not keeping very good records, so he doesn’t have raw data, and probably mixed up his numbers a few times or gave false numbers, and didn’t have anything to send his collaborators when they asked. I think this is maybe possible, although it seems suspicious that he falsely said Dr. Lombardo was involved, falsely claimed the hospital involved was doing a different trial, and got very implausible results. I can imagine weird chains of events that would cause all of these things through honest misunderstandings. But they don’t seem like the best explanation. After discussing this with Alexandros, he objects to my use of the term “known fraudster”. Perhaps I should have said “highly credibly suspected fraudster” instead, although in a Bayesian sense nothing can ever be 100% and at some point plausibility shades imperceptibly into knowledge. Still, I feel like my description here was more accurate than Alexandros’, which just mentions the hospital approval issue and says nothing about any of the rest of this in a thousand word subsection about this study in particular. I did err in saying the Carvallo paper was retracted. According to the article: After BuzzFeed News raised questions about how the study’s data was collected and analyzed, a representative from the Journal of Biomedical Research and Clinical Investigation, which published the results, said late Monday, “We will remove the paper temporarily.” A link was removed from the table of contents — but was reinstated by Thursday. The journal’s explanation, provided after this story was published, was that the author “informed us that he has already provided the evidence of his study to the media.” I apologize for the error. Elalfy et al (still disagree with Alexandros) I described this as: As best I can tell, this is some kind of Egyptian trial. It might or might not be an RCT; it says stuff like “Patients were self-allocated to the treatment groups; the first 3 days of the week for the intervention arm while the other 3 days for symptomatic treatment”. Were they self-allocated in the sense that they got to choose? Doesn’t that mean it’s not random? Aren’t there seven days in a week? These are among the many questions that Elalfy et al do not answer for us. The control group (which they seem to think can also be called “the white group”) took zinc, paracetamol, and maybe azithromycin. The intervention group took zinc, nitazoxanide, ribavirin, and ivermectin. There were very large demographic differences between the groups of the sort which make the study unusable […] There is no primary outcome assigned, but viral clearance rates on day seven were 58% in the yellow group compared to 0% in the white group, which I guess is a strong positive result. This table looks very impressive, in terms of the experimental group doing better than the control, except that they don’t specify whether it was before the trial or after it, and at least one online commentator thinks it might have been before, in which case it’s only impressive how thoroughly they failed to randomize their groups. Overall I don’t feel bad throwing this study out. I hope it one day succeeds in returning to its home planet. In the summary post, Alexandros’ entire criticism of my coverage of this trial, one of the seven trials he focuses on as most unfairly covered and uses as the lynchpin of his argument that I am morally culpable for disastrously bad reporting, is: [Elalfy et al] are accused of incompetence for failing to randomize their groups multiple times in Scott’s piece. The paper writes in six separate places that it is not reporting on a randomized trial, amongst them on a diagram that Scott included in his own essay. Hard to imagine how else they could have made it clear. In his full post on this, he goes line by line to point out all the places they say they are non-randomized, pausing to snark about how dumb I am for not noticing each time4. But he never addresses the actual source of my confusion, which is the part of the paper where it says that: Patients were self-allocated to the treatment groups; the first 3 days of the week for the intervention arm while the other 3 days for symptomatic treatment. If this was done as described, it should be an (almost) random trial; patients who come in on Wednesdays shouldn’t systematically differ from patients who come in on Thursdays5. But in fact, it looks (assuming I am understanding a very ambiguous table correctly) like there are very large pre-existing differences between the groups, sufficient to explain the entire result. If they in fact followed their days-of-the-week protocol, and it was random as expected, then I’m misunderstanding the table seeming to show very large differences, and they have indeed found evidence for ivermectin’s efficacy. If they didn’t follow their day-of-the-week protocol and it’s non-random, then maybe I’m understanding the table correctly and their groups had large differences to begin with and the fact that they had large differences at the end of the trial doesn’t demonstrate anything about ivermectin. This is all I was trying to say in the post, and instead of having any opinion on it Alexandros just makes fun of me for saying it. I think our actual crux is that Alexandros thinks a table of big differences between the groups has to be post-treatment (based on how big the differences are), whereas I’m not sure (because it’s unclear in the study, and also because the authors describe what could be a randomization method but also go on and on about how nonrandom they are). This is why I thought it mattered how random it was! Maybe instead of mocking me for this, you can admit it’s an important and relevant question! Ghauri et al (still disagree with Alexandros) I describe this as: Pakistan, 95 patients. Nonrandom; the study compared patients who happened to be given ivermectin (along with hydroxychloroquine and azithromycin) vs. patients who were just given the latter two drugs. There’s some evidence this produced systematic differences between the two groups - for example, patients in the control group were 3x more likely to have had diarrhea (this makes sense; diarrhea is a potential ivermectin side effect, so you probably wouldn’t give it to people already struggling with this problem). Also, the control group was twice as likely to be getting corticosteroids, maybe a marker for illness severity. Primary outcome was what percent of both groups had a fever: on day 7 it was 21% of ivermectin patients vs. 65% of controls, p < 0.001. No other outcomes were reported. I don’t hate this study, but I think the nonrandom assignment (and observed systematic differences) is a pretty fatal flaw. Alexandros notes that these are three differences between experimental/control groups, out of 33 listed characteristics that could have been different. There is approximately a 23% chance (he calculates) that you could get these differences by chance. He accuses me of failing to do a formal Carlisle test - the usual test you would use to determine whether weird differences between randomized groups are because of fraud - instead eyeballing it and getting it wrong. Here I do want to defend myself: I am not accusing Ghauri et al of fraud. In fact, this would be nonsensical: they admit they are assigning patients nonrandomly. Carlisle tests are usually done to show that something about group assignment is impossible (and therefore fraudulent) in a fair random assignment. But these people aren’t claiming to have done a fair random assignment, so I’m not sure what a Carlisle test would prove. My argument is more like: this is nonrandom, therefore we should expect it to be unfair. It is unnecessary, but helpful, to note an actual apparent unfairness - there’s some evidence they gave the ivermectin to less severe patients (as measured by corticosteroid use). Therefore, we can’t necessarily trust this to be a fair trial (which it was never really claiming to be). In the end I kept Ghauri as an okay study, although GMK didn’t so it ended out trashed in the final analysis anyway. I think my thinking was that I never claimed to be only looking at RCTs, so this non-RCT whose between-group-differences confirmed that it was indeed a non-RCT with all the risk of bias that entails, didn’t necessarily need to be ruled out. Still, I don’t think I was wrong to mention this possibility, and I think Alexandros was wrong to suggest that I needed to do extra tests for this to be fair. Borody et al (still disagree with Alexandros) I described this as: Our last paper! …is it a paper? I can’t find it published anywhere. It mostly seems to be on news sites. Doesn’t look peer-reviewed. And it starts with “Note that views expressed in this opinion article are the writer’s personal views”. Whatever. 600 Australians were treated with ivermectin, doxycycline, and zinc. The article compares this to an “equivalent control group” made of “contemporary infected subjects in Australia obtained from published Covid Tracking Data”; this is not how you control group, @#!% you. Then it gets excited about the fact that most patients had better symptoms at the end of the ten-day study period than the beginning (untreated COVID resolves in about ten days). Why are these people wasting my time with this? Let’s move on. Alexandros lists his full concerns here. My summary: Scott is being incredibly disrespectful to the authors, who are in fact a legendary gastroenterologist who invented life-saving h. pylori therapy and a brilliant immunologist who invented a well-regarded bronchitis vaccine (in particular, in describing their control group, I said “this is not how you control group, @#!% you”.

Inline links: This table, his full post on this, 4, 5, here

Book Review: The Geography Of Madness

February 22, 2023 · Original source

She reminded me that yesterday she was unusually grumpy, so much so that she had apologized to me for it and tried to come up with explanations - and then later yesterday she had her period. Meanwhile, Bures’ counterargument is - what? That it sounds kind of sexist to accuse female hormones of making women overly emotional? Hasn’t he ever heard of stereotype accuracy? That people asked their doctors to be treated for it more often after they knew it was considered a medical condition, and was treatable? That seems to have a much simpler explanation! That there are no biomarkers? There are inconsistent biomarkers that work sometimes but not other times, just like for schizophrenia, epilepsy, cancer, and half the other conditions in medicine. That these conditions don’t occur in most cultures? From here: A World Health Organization (WHO) study on menstruation (1981) surveyed 5,322 women from Egypt, India, Indonesia, Jamaica, Korea, Mexico, Pakistan, Philippines, United Kingdom and Yugoslavia. . . The majority of women in all cultures report some premenstrual physical discomfort in addition to negative mood changes, however fewer women report mood change than physical change. The main cross-cultural difference was in the prevalence of specific symptoms. Immigrants to the United States report more PMDD the longer they’re here? True (source), but it’s a matter of degree, and seems more true of the PMDD diagnosis than specific symptoms. The diagnosis requires impairment, which is subjective. I imagine an immigrant from a culture where mental disorders are unthinkable - something that only happens to a few psychos in asylums - and where you work 12-hour days in sweatshops. Someone asks her “hey, has this mental disorder ever prevented you from working?”, and she says no, because obviously you grit your teeth and work through the symptoms. And I imagine an American seeing the same question and saying “Yeah, I did decide I had to take a couple of sick days because of that.” I’m not saying this definitely happened, just that it’s a possibility. Meanwhile, this entire area of study is a mess. The “PMDD is culture-bound” hypothesis was originally invented by feminist scholars trying to argue that the diagnosis was a sexist attempt to pathologize women as overemotional and untrustworthy (this is also where Bures got his “it’s just hysteria by a different name” idea). See for example here and here, the second of which says that “the feminist argument is that if women are angry/distressed, it is for good reason, not due to pathology”. Bures somehow swallowed and repeated this, and then some feminists on Vox wrote an article attacking him as a “male writer” who was denying women’s lived experiences of PMS and stereotyping them as stupid and gullible. Neither side has an argument beyond “I can think of a reason it would be sexist for people to disagree with me” and neither side will acknowledge that the other side is also feminists basing their argument entirely on how it would be sexist to disagree with them. Everything in every area of social science has been like this for at least the past twenty years. But also, this highlights the difficulties with declaring something culture-bound. How do you know if something’s culture-bound, vs. people don’t notice it or mention it if they don’t have a name for it? How do you know if something’s culture-bound vs. some cultures consider it too embarrassing or taboo to think about? How do you know if something’s culture-bound, vs. people will go to doctors about it if they think doctors can treat it, and otherwise they won’t? I’ll discuss these questions more later, but I want to finish Bures’ argument. He gestures at a few other possible candidates for culture-bound mental disorders, including repetitive strain injury and chronic pain. But he quickly moves on to a long section that tries to establish the reality of “voodoo death”, ie the thing where if you believe you are going to die hard enough, you actually die. I think most arguments for voodoo death are pretty bad, and I didn’t find Bures’ convincing. But bonus points for referencing a study claiming that chronically stressed people only die at higher rates if they believe chronic stress is bad for them, and if not then they don’t (this is not really how I interpret the abstract, but I haven’t looked closely) Is it weird to stay on the crazy train long enough to agree that cultural effects are strong enough to make you think witches are stealing your penis, and then get off it once people start talking about voodoo death? I think no - these are very different situations. Believing in koro can make you hallucinate that your penis is shrunken or gone, but no belief, however strong, can (directly) remove your penis itself. Culture → beliefs is fine; culture → reality is a step I’m not willing to take. V. Since I rejected Bures’ PMDD example, I want to digress to what I think is a stronger argument: anorexia, which Ethan Watters discusses in his book Crazy Like Us. Anorexia was mostly unknown in the West, until becoming “trendy” in the mid-1800s. During that period, doctors reported high prevalence of anorexia among “hysterics”, but the fad ended after about ten or twenty years, and it went back to being basically unknown. In 1983, famous singer Karen Carpenter died of anorexia, thrusting it back into the national news, and suddenly lots of people (in the West) were anorexic again. Meanwhile, foreign doctors who trained in the West went back to their home countries, searched far and wide for it, and found almost nothing. The few cases they did see didn’t resemble the typical Western version at all - for example, one Hong Kong psychiatrist was able to find a woman who refused to eat out of grief when a boyfriend left her, but she didn’t think she was fat, or feel any cultural pressure to be thinner. The absence of anorexia abroad was especially surprising since anorexics tend to end up in the hospital with extremely noticeable malnutrition that doesn’t really mimic anything else. It’s not really possible to hide severe anorexia the way you can hide severe depression. In 1994, Hong Kong got its own Karen Carpenter - a young girl died of anorexia, setting off a national panic and many public awareness campaigns. Near-instantly, anorexia rates shot up to the same level as the West, with the appropriate number of people presenting to hospital ERs with severe malnutrition. This story raises a lot of questions. For example: where did the first anorexics (Karen Carpenter, the girl in Hong Kong) come from? Why anorexia and not something else? And how come knowing about anorexia makes it spread so quickly? VI. Past this point I’m using this review to discuss my own thoughts, not Bures’ or Watters’. “Culture-bound” is less all-or-nothing than you’d think. Look hard enough, and you’ll find people having “culture-bound syndromes” from cultures they’ve never heard of. Ntouros et al in Thessaloniki describe “koro-like symptoms in two Greek men”. One, a paranoid schizophrenic: . . . reported for the first time a sensation that his penis retracts into the abdomen and a fear that it will subsequently be lost. This would be accompanied by anxiety and sadness pertaining only to the loss itself. He would then proceed to search manually for his penis and masturbate. No pleasure was gained by masturbation, but the anxiety would be lifted. Romero et al describe a case of koro in "an intellectually disabled Caucasian patient" in Spain. They write that "although it is widely regarded as an epidemic in South-east Asia, there are some isolated cases in other cultures as well." Wilson and Agin describe a 29 year old white male from New York, "not exposed to the Chinese culture”, who went to the doctor with a five month history of worrying that his genitals were retracting into his body: Sometimes, he would manually reaffirm the presence of his genitals. Occasionally he would, in private, remove his garments and visually confirm the presence of his genitals. On one occasion, while taking the train home from work, he experienced an acute exacerbation of these symptoms. His pain increased from 3/10 to 10/10, and he felt as if his genitals had fully retracted within his belly. Upon reaching his hometown, he immediately went to the local hospital emergency room where examinations for inguinal hernia, urinary tract infection, proctitis, prostatitis, and testicular disorders proved negative. He improved significantly on the anti-anxiety medication desipramine. Chowdhury surveys the evidence on koro and divides the condition into two types: culture-bound and non-culture-bound. The culture-bound type usually goes in large epidemics, hundreds to thousands of people, in koro-believing parts of Africa and Asia; the victims were usually previously psychologically normal. The non-culture-bound type hits a few scattered individuals, is not contagious, and can happen anywhere - Greece, Spain, America. Some patients are psychologically normal, but there are a disproportionate number of schizophrenics, drug users, brain damage victims, and other previously-mentally-ill people. Other culture-bound illnesses seem to be like this too. Running amok has been big in Malaysia for 300 years. The Columbine shooters seem to have been autocthonous American cases, equivalent to that one New Yorker who got koro - before their fame inscribed amok onto the US collective consciousness the same way Karen Carpenter’s inscribed anorexia. Japan’s jikoshu-kyofu affects occasional victims in the US under the name olfactory reference syndrome. Watters admits there were a tiny handful of unusual anorexia cases in Hong Kong before Westernization. And even that Indian there’s-a-lizard-in-my-skin condition differs only in species from delusional parasitosis. Delusional parasitosis - the false belief that you are infested with parasites and can feel them crawling in your skin - is actually an especially interesting case. Two groups are disproportionately represented among patients: menopausal women and cocaine addicts. Relatedly, two biological conditions that can sometimes cause weird skin sensations that feel like crawling insects are . . . menopause and cocaine use. So there’s no mystery here. But, also represented among delusional parasitosis patients are the roommates and family members of these people. The index case hallucinates insects for a well-understood biological reason; their close contacts hallucinate insects through social contagion. So a unified theory of these conditions might be: Some people have the condition for a normal biological or psychiatric reason. For example, someone might believe a lizard is crawling under their skin because they use cocaine, which causes hallucinatory crawling sensations. Or someone might believe their penis is missing because they’re schizophrenic, which makes them naturally hallucination-prone.

Inline links: stereotype accuracy, inconsistent biomarkers that work sometimes but not other times, here, source, here, here, and then some feminists on Vox wrote an article, chronic pain, are pretty bad, a study, Ethan Watters discusses in his book, Ntouros et al, Romero et al, Wilson and Agin, surveys the evidence, olfactory reference syndrome, delusional parasitosis

Against Ice Age Civilizations

March 03, 2023 · Original source

The top 80m of the Great Pyramid would rise above the waterline, forming a little island. The part of the Pyramid above the water would still be taller than the entire Leaning Tower of Pisa. It would be pretty hard to miss! So a 120m sea level rise wouldn’t be enough to wipe out evidence of our crop of ancient civilizations, and shouldn’t be enough to wipe out evidence of a previous crop, unless they had a very different geographic distribution than ours. Argument 2: Where Are The Crops And Livestock? We can do genetic analysis of crops and livestock, compare them to wild plants and animals, and make good guesses about where and when they were domesticated. Wheat was domesticated somewhere around Karaca Dag, Turkey, around 9000 BC. Barley was domesticated somewhere around Jarmo, Iraq, around 9000 BC. Cows were domesticated somewhere around Cayonu Tepesi, Iraq, in 8500 BC (then a second time, in Pakistan, later on). Rice was domesticated in two places in China around 10,000 BC. All of these crops were invented exactly where the standard historical narrative says there were late pre-agricultural people of exactly the type who would domesticate crops. They spread at about the same rate as sedentary living in general, monuments, and other signs of complex civilization. The only known exception is Gobekli Tepe, a megalithic site in Turkey, which may very slightly predate known agriculture. But it also might very slightly post-date known agriculture, or exactly-date known agriculture (it’s just sixty miles from Karaca Dag, and it would make sense if they were the people who domesticated wheat). That one anomaly aside, there’s a very tight agriculture <—> things that seem to require agriculture coupling. So if there were Ice Age civilizations, what did they eat? It couldn’t have been any of our known crops, which post-date them. Could it have been their own crops, which were later lost? Seems unlikely. Throughout most of history, civilizations have risen and fallen, but they don’t lose agriculture! The empire divided longs to unite, the empire united longs to divide, but the Chinese never fragmented so hard that they forget how to cultivate rice and rice went extinct. Maize has survived nine millennia of rising and falling bloodthirsty Mexican empires. Almost everyone in the Amazon died in the 1500s when European diseases swept through, but they still left us manioc, squash, and chiles. Could Ice Age civilizations have thrived without domesticating any plants? We increasingly realize that agriculture isn’t all-or-nothing, there’s a spectrum from picking wild plants when you come across them to domestication, irrigation, and the full suite of agricultural technologies. It wouldn’t surprise me if some combination of early-non-domestication-involving agriculture and hunting-gathering off of very rich lands could create enough sophistication to build a Stonehenge or a Gobekli Tepe. But you’re not getting Egypt or Great Britain off of that, sorry. Argument 3: Lead Levels Thanks to commenter WTFwhatthehell for bringing this one up. Many ancient civilizations mined lead. Some of the lead made it into the atmosphere and settled down again in other places. You can measure the amount of lead in different places to see how much lead humans are mining. This isn’t perfect - the resolution is closer to continental than global - but you can check lots of different continents and get an okay reading. This paper finds lead levels started rising 1000 BC, which it links to the Phoenician expansion happening around that time. In theory, this could suggest that no ancient civilization reached a tech level where it started mining lead, ie the tech level the Phoenicians had in 1000 BC. This is in theory only, because I can’t find a clear record of anyone checking. I assume ice core scientists would have noticed if it happened, but there’s no publicly available dataset with lead levels 10,000 years before present, nor is there a paper titled “We Checked To See If There Were Anthropogenic Lead Emissions In 10,000 BC And There Definitely Weren’t”. Here is a paper that looks at lead level in human bones. They don’t do a great job explaining how lead makes it into human bones, but it seems like a mix of the kind of lead pollution that makes it to Greenland ice cores, plus personally wearing or consuming things that have touched lead. This study investigates skeletons from 12,000 BC onwards, and finds that lead levels start rising in 5,000 BC, when people developed “cupellation”, a technique for using lead to purify gold and silver (it then goes up much further between 1000 - 500 BC, probably the same spike the Greenland cores found). So this presents some very weak evidence against significantly elevated lead from 12,000 BC onward. But it doesn’t rule out small amounts of lead mining far away from the bones’ previous owners, and doesn’t rule out a civilization lasting from 15,000 - 13,000 BC. A Great Britain-level civilization would be expected to raise lead levels a lot, and this pretty strongly rules it out. I would expect an Egypt-level civilization to at least invent cupellation, but I don’t know if its lead would necessarily make it to wherever these bones came from. A Stonehenge or Gobekli Tepe level civilization isn’t ruled out at all. Conclusion I think there’s pretty strong evidence against lost Egypt- or Great Britain- level Ice Age civilizations. I don’t want to rule out a lost Stonehenge or Gobekli Tepe level civilization, but there’s not much positive evidence, and there’s some negative evidence. Stonehenge was built by Neolithic farmer-pastoralists, who had lots of domesticated crops and animals. Gobekli Tepe was built right next to the area where wheat was domesticated at around the same time. Existing early monuments mostly suggest a story where sedentary city- and temple- building civilizations either require domesticated agriculture, or invent it very quickly. None of this means Ice Age people didn’t have fascinating cultures of their own which were advanced in other ways - interesting laws, taboos, mythologies, customs, oral traditions. Tyler Cowen says that everything started earlier than you think, and this is what we’ve been finding about various forms of human culture too (cf. Against The Grain, The Dawn Of Everything). I just don’t expect lost Ice Age cities or giant monuments. I think Michael Shermer’s attempt to argue the same case is weak, relies on a still-controversial rejection of the Younger Dryas Impact Hypothesis, and generally leans too much on the absurdity heuristic without moving the needle one way or the other. All of the following predictions are about structures on Earth built by homo sapiens without time travel: 20% chance we ever find something demonstrating equal or greater architectural advancement to Gobekli Tepe, dating from before 11,000 BC.

Inline links: Karaca Dag, Jarmo, Iraq, Cayonu Tepesi, Iraq, in two places in China, WTFwhatthehell, This paper finds lead levels started rising 1000 BC, https://substackcdn.com/image/fetch/$s_!rPgI!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6265c4a7-a563-477c-afbc-7b3c9b8dd73e_1280x1247.jpeg, Here is a paper, https://substackcdn.com/image/fetch/$s_!SS8H!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0b5de41a-38d8-4145-821d-5e2f6d2de228_534x215.png, Against The Grain, The Dawn Of Everything, Michael Shermer’s attempt to argue the same case, Younger Dryas Impact Hypothesis

Half An Hour Before Dawn In San Francisco

March 20, 2023 · Original source

A Muslim woman walks by in traditional dress, followed by a dark black man in African garb. All clothing sends a message; theirs is “everything that ever happened anywhere in the world however far away has converged here for this moment; it was all for this.” A crazy person walks by, mumbling to himself. We nod at him and let him pass; he seems to know the score. Here we have all gathered, abandoning our green and pleasant homes in Pakistan or Nigeria or Michigan to see the doomed summoning-city at the end of time. Chicxulub or bust. It’s a miracle we only get one or two madmen per city block.

Links For September 2023

September 28, 2023 · Original source

23: India is discussing changing its name to “Bharat” (the Hindi word for India) on some level. Unconfirmed rumors about Pakistan being interested in claiming the name “India” for itself. No word yet on who would take “Pakistan”, but I hear Macedonia is looking for a new name.

Inline links: changing its name to “Bharat”, Pakistan being interested in claiming the name “India” for itself

February 20, 2024 · Original source

Will Israel launch airstrikes on Pakistan in the next year? Answer: 1%

Everyone's A Based Post-Christian Vitalist Until The Grooming Gangs Show Up

January 23, 2025 · Original source

So it was revealing to watch some of these people trip over themselves to say we should invade Britain because of its tolerance for Pakistani grooming gangs.

In case you’ve been under a rock recently, in the early 2010s, several organized child sexual assault rings got busted in Britain - but only after the police spent years deliberately ignoring them, because the perpetrators were mostly Pakistani and busting them might seem racist. A recent legal dispute got them back in the news, and since social media is less censored now, the topic went viral in a way it didn’t before. Now the entire Right is demanding investigations, heads on pikes, and (in some cases) the American invasion of Britain.

Inline links: organized child sexual assault rings

(if you’re going to get hung up on whether the suffering is because of a human bad actor or a natural cause, then can I interest you in donating to Bedari, a charity that prevents domestic abuse in the Third World? Most of their operations are in Pakistan, so you don’t even have to leave your comfort zone of being against Pakistani abusers in particular!)

Inline links: Bedari

ACX Grants 1-3 Year Updates

June 18, 2025 · Original source

Codebuff, an AI coding startup I probably can’t take full credit for all of this just from giving them $20K in seed funding, but I continue to appreciate everything they do for this community and the world. 35: Further S’s Political Career This person didn’t win their election, but has since pivoted to AI safety and works in a well-regarded AI policy think tank. 36: Seeds Of Science, A Journal Of Non-Traditional Research No update received, but this was a public journal and it is easy to follow their work, see their website and Substack. They published two dozen articles of widely varying quality through 2023 and 2024, then closed in 2025. A remnant of the original vision survives as a science blogging aggregator. This was about my median expectation for this grant, but it was very inexpensive and I decided to take a chance on it anyway. 37: Good Science Project, Working To Improve Federal Science Funding No update received, but they have a public Substack discussing their progress. Their proposals for NIH reform have influenced Congress and made government agencies pay more attention to scientific integrity. 38: Advising Developing Countries On How To Grow Their Economies With our initial ACX grant, we piloted the Growth Teams model in Rwanda, helping the government jumpstart the export-oriented call center (BPO) industry. Since 2022, that effort has contributed to the creation of 2,000 formal jobs and the emergence of some of the country’s largest private employers. We’ve since expanded to Tanzania, Malawi, and the Indian states of Goa and Meghalaya. To refocus the global development discourse on broad-based economic growth, we co-organized the Growth Summit with the Center for Global Development and the Charter Cities Institute, and have published articles in leading outlets including Stanford Social Innovation Review, ProMarket, and the Global Prosperity Institute. Our work has attracted support from Open Philanthropy, Schmidt Futures, and Mulago Foundation, and our advisors now include economists Lant Pritchett, Stefan Dercon, and Kunal Sen. 39: Help Luca De Leo Get Started In AI Safety Research No update received, but Luca now runs the AI safety group at the University of Buenos Aires, Argentina. 40: Typist For Saharon Shelah This was another ACXG+ Grant, funded by an anonymous outside funder and not listed in the original announcement. Saharon is a prolific and influential Israeli mathematician, but many of his discoveries are hand-written in an unpublishable format. This grant funded a typist to help make his results suitable for publication. According to this page, they have made over fifty new papers and preprints available. Second Cohort: One Year Updates 41: Lead-Acid Battery Recycling In Nigeria The Nigeria field research was a major success. We spent most of September doing field research in multiple major cities in Nigeria, and got a good sense of the used lead-acid battery supply chain. This field research served as the foundation for expanding our project, and has been very impactful in shaping our ongoing research. We published our findings from Nigeria, which were shared with Nigerian government regulators and global NGOs working on lead poisoning. The grant also gave us the on-the-ground experience we needed to both fully understand and credibly engage with groups, both in Nigeria and globally, on the ULAB issue. In the meantime, beyond continued research, we’ve also launched a dashboard (trade.leadbatteries.org) for analyzing global lead trade data. Right now, we’re: Launching two studies (one RCT, one environmental analysis) in Nigeria in collaboration with local universities to develop a more rigorous understanding of lead pollution due to low-standard ULAB recycling in Nigeria Collaborating with a non-profit incubator to launch an NGO focused on demand-side solutions Beginning a partnership with a West African environmental regulator to scale cheap air monitoring technology to quickly identify and reduce lead pollution from low-standard smelting If any of this sounds interesting to you, please sign up for our Substack (leadbatteries.substack.com) or send us an email at hugosmith@uchicago.edu! 42: Compensation For Kidney Donors The End Kidney Deaths Act (H.R. 2687 / EKDA) is a groundbreaking ten-year pilot program designed to save lives and reduce healthcare costs. It provides a refundable tax credit of $10,000 per year for five years, a total of $50,000, to living kidney donors who donate to a stranger, helping those who’ve waited the longest on the transplant list. Between 2010 and 2021, 100,000 Americans died while qualified and waiting for a kidney. The EKDA aims to change that trajectory. Within ten years of its passage, up to 100,000 Americans could receive a life-saving living donor kidney which typically lasts twice as long as a deceased donor kidney. This would not only save lives but also save taxpayers up to $37 billion. The legislation has been reintroduced in the House, and we have a committed Republican Senate lead. Now, we need a Democratic Senator to co-lead and help move this bipartisan effort forward. Time is short, and we are racing to pass the bill this Congressional session. 36 organizations already support the EKDA. Join the movement and help end preventable kidney deaths. Visit EndKidneyDeaths.org to help us get to the finish line. Elaine and her org have been working extremely hard on this; you can read a Vox article on their campaign here. If you want to sign up for her email list and get updates any time there is a representative you can contact or meeting you can join in, go here. 43: Genetic Hack To Prevent Suffering In the estimate of multiple team members, the ACX grant was “worth it” - it likely had a counterfactual net positive impact, even though we had to pivot from our initial fast-track plans for developing the precision anti-suffering therapy. We identify three primary streams of value: a) reducing uncertainty in the emerging field through early exploratory research, helping with the identification of dead ends and promising R&D trajectories; b) a wide range of downstream effects (beyond the “raising awareness” cliché), including talent mobilization and rekindled interest in suffering abolitionism as a distinct cause area; and c) certain developments that cannot yet be publicly disclosed. In December 2024, Marcin Kowrygo (Acting CEO & volunteering contributor), David Pearce (Director of Bioethics), Aatu Koskensilta (President), and a few other team members decided to leave The Far Out Initiative. They look forward to collaborating and applying their experience to advance the suffering abolitionist lineage in the spirit of open science, public good, and thoughtfully decentralized governance. Feel free to reach out to us at suffab at protonmail dot com to discuss collaboration opportunities! I wrote a post profiling the Far Out Initiative here. Unfortunately there were some internal disagreements, and the people ACX Grants was closest to left the organization. I plan to continue to monitor whatever they do next. 44: Advocate For Pandemic Response Team At FDA This team prefers has asked me not to discuss their progress publicly, but you can probably guess what their lives are like right now, and your guess would be correct. 45: Anti-Mosquito Drones We developed a cheap sonar that is able to detect, track and classify the ultrasonic echoes of mosquito wings at more than three meters. I believe it’s a world first! We also have control algorithms that take the sonar data and output control commands that both ram into mosquitoes and avoid the walls of a simulated environment. Our current work is on integrating both components on a real drone, and we expect to be able to kill mosquitoes by June. We’ve also made an internal impact study (napkin-sized) that shows we’ll be more cost-effective than ITNs in urban to periurban environments. So, we’re super excited with what comes next and can’t wait to share the videos of our first interceptions! More information [in the video below] and on our website, https://tornyol.com 46: Tarbell Fellowship For AI Journalism No update received, but they have a public website. I can’t find the Voices program in particular, but the overall fellowship completed their first class of seven fellows and is working on their second. 47: Germicidal UV Lamp Study The research has successfully demonstrated the ability of off the shelf ozone scrubbers to mitigate the ozone production of far-UVC lamps, is now available as a preprint (https://chemrxiv.org/engage/chemrxiv/article-details/67e4cde76dde43c9084d88b7). The paper has been submitted for publication and is currently undergoing peer review. Any ideas you have for potential funders we can approach to help execute our six-year plan to accelerate far-UVC would be appreciated https://blueprintbiosecurity.org/introducing-project-air/ 48: Technological Solutions To Animal Welfare Challenges Directly because of Innovate Animal Ag's work, the first U.S. egg producer publicly announced in the New York Times their adoption of in-ovo sexing technology, eliminating the need to cull day-old male chicks. The initial in-ovo sexing machine began operating in the U.S. at the end of 2024, with the first eggs from these hens expected on shelves in mid-2025. External evaluations estimate our work accelerated U.S. adoption of this technology by over seven years, meaning that once fully implemented, more than 2 billion chicks will have been spared. In addition to continuing to support the rollout of in-ovo sexing in the US and globally, we're now exploring other technologies and paths to impact. Current promising projects include developing humane slaughter methods for fish and advocating for USDA approval of a poultry vaccine against bird flu. They add: If you ever meet folks that are interested animal welfare and are partial to more technocratic and practical solutions, please continue to pass them our way, or connect them directly to me. 49: Assurance Contract Website www.Spartacus.app is an ACX grantee that created a platform to help solve coordination and collective action problems. It enables the creation of campaigns that build critical mass through conditional commitments, which only activate when a sufficient number of people join, converting risk and uncertainty into a higher probability of successful outcomes. They are currently facilitating several projects that leverage conditional commitments, including a dominant assurance contract interface for fashion pop-ups, accelerating a community business association's membership drive, and helping an AI safety organization organize petitions and events, among others. They have pivoted from an emphasis on high-stakes coordination problems requiring anonymity (because they occur too infrequently) to a broader range of more common use cases and have successfully run small-scale campaigns, but are still working toward product-market fit. Despite resource constraints and split time commitments that have impeded faster progress, they remain dedicated to the project's growth and success. You can follow its progress on X or Substack, or email Jordan directly here. 50: Cause Prioritization @ Center For Exploratory Altruism Research Moderately good progress on a salt reduction policy advocacy project we funded; informal commitments have been made by the Ministry of Health, and we're awaiting the publication of a formal administrative order. The official description sounds maximally generic, but this is an EA charity with a broad mandate whose current thesis is that dietary guidelines in developing countries can have outsized effects in saving lives. They’re making some progress on a salt reduction campaign in a developing country they prefer not to name publicly. 51: Mark Webb Studying Land Reform The purpose of this project was to identify specific farmland that could be acquired and transferred to the farmers already working the land. This has been difficult to achieve. I have been able to connect with other charities and landless farmers, and was able to interview a number of people about what their situation looks like, as well as what it would look like to them personally if they owned, rather than rented, their farmland. All this was immensely helpful in pushing this long-term project forward, even if I was unable to identify a specific plot of land that could be used to try the experiment. I intend to continue this project. If you have any insights or connections, I am interested. 52: More AI Advocacy In Australia Good Ancestors is focused on AI safety policy in Australia. Middle powers might be a useful path to influence as the US and China focus on racing, rather than safety. The ACX grant helped us give testimony about AI safety to the Australian Senate alongside Google, Microsoft and Facebook (We were the only nonprofit to give oral evidence to the inquiry. We also engaged government on other AI-related issues, including cybersecurity, biosecurity, consumer law and automated decision making (https://www.goodancestors.org.au/ai-safety). We’re currently working to inform voters about where parties stand on AI safety for the election, ahead of engaging on a likely Australian AI Act in 2025 (https://www.australiansforaisafety.com.au/). This is the same Australian lobbying organization we founded in Year 1, after a change in name and leadership. I continue to be excited about AI safety in middle-tier countries for a few reasons. First, these countries have some power in international organizations to set international standards. Second, companies will usually comply with any not-excessively-burdensome regulation set by any country with a significant market. Third, AI safety is underfunded by the standard of government programs, so Australia setting up a national AI Safety Institute would significantly expand the field. It’s kind of crazy that ACX Grants tier levels of money can have significant effects at this scale, but GA continues to do a great job and we continue to be proud to support them. 53: Campus For African School Of Economics At Zanzibar Charter City The ACX grant helped launch the first research center at the African School of Economics-Zanzibar, which is a main anchor of the Fumba Town charter city project in Zanzibar. This research center is called the Africa Urban Lab (AUL), focused on rapid urbanization across Africa. The AUL launched its first Diploma program in Urban Development with 38 students in our first cohort (now graduated!), including mayors, and deputy mayor, a director of a national Ministry of urban development, and many others. We published our research framing papers for the AUL's research agenda. We raised funding to launch an Urban Expansion Program that's now selecting 15 African cities to support in implementing urban expansion planning on the urban periphery. We held two Public Talks by renowned cities scholars and practitioners. We received additional funding from Emergent Ventures and from the Templeton Foundation. And we've partnered with 8 universities across the region, and with one of these universities (Ardhi) we'll be working with them to update their urban planning and urban economics curriculum (amplifying AUL's impact beyond our own organization). A longer update from end of 2024 is here: https://www.aul.city/blog/reflecting-on-africa-urban-lab-s-inaugural-year-2024-highlights) 54: Online Training Program For Health Workers In Developing Countries To date, over 11,000 health workers in Nigeria have completed our course on basic, life-saving newborn care. ACX funding was catalytic for helping us secure government approvals and complete an evaluation of the impact of our training on health workers' clinical practices. The evaluation shows that birth attendants provide better birth care after taking the course. We fed the evaluation results into an updated model, which suggests the program is 24 times more cost-effective than direct cash transfers (a widely recognized benchmark for cost-effectiveness). The program is likely to become even more cost-effective as we scale up. https://healthlearn.org/blog/updated-impact-model 55: Smartphone Pupillometry To Diagnose Neurological Conditions We have continued to expand our work in the smartphone pupillometry space and the development of our application, PupilScreen (https://www.apertur.ai/). We have expanded our pilot/research program to include new sites across the United States (Missouri, New Jersey, Kentucky, USAC racing, PitFit driver performance training in Indiana) and the world (Nepal, Taiwan, South Africa). We continue to publish at the leading edge of the pupillometry literature as well looking at concussion (https://neuro.jmir.org/2024/1/e58398 and https://pubmed.ncbi.nlm.nih.gov/39682632/), cerebral vasospasm (https://pubmed.ncbi.nlm.nih.gov/39128501/), and stroke (https://pubmed.ncbi.nlm.nih.gov/39674431/ and https://pubmed.ncbi.nlm.nih.gov/39561861/). Currently, we are raising a $3 million seed round via a SAFE to fund the expansion of our work into the hands of healthcare workers and the general public. We will first focus on traumatic brain injury for clinical use and develop a neuro-monitoring wellness application utilizing our technology for the general public. They add: “We would welcome connections to anyone that you think might be interested in supporting our work further by investing in our $3M seed round of funding.” 56: Mike Saint-Antoine’s Biology Tutorial Videos Since getting the grant, I've continued to make Youtube tutorials as planned. One series that I'm especially proud of is about how to make a neural network in the Julia programming language completely from scratch, with no imports, up to the point of being able to solve MNIST (https://www.youtube.com/playlist?list=PLWVKUEZ25V97tNULapu07DhWv6_W4NfpE). Also, a college student in Pakistan came across my videos and invited me to give a virtual Zoom-lecture to her department, so I ended up teaching a 6-hour "Python-for-Biologists" workshop to more than a hundred college students in Pakistan over Zoom. So that was pretty awesome. Also, lately I've been teaching some in-person classes too, mostly at Fractal University in NYC, and I also recently organized a day-long, in-person Beginner Python class for people in my local area (Philly suburbs) who wanted to learn some basic programming. I'm having a lot of fun with this project, and am grateful to Scott and the grant funders for their generosity! 57: Conceptual Boundaries Workshop On AI Safety The workshop was completed successfully; you can read a writeup here. 58: Apart Research To Incubate AI Safety Scientists No update received, but they have a public website, and you can see their impact metrics here. They seem to be in urgent need of more funding. 59: Primer On How To Achieve Political Change No update received and I can’t find anything about this. 60: Research IVF Clinic Success Rates We've built a predictive model that estimates the odds of having a child at different IVF clinics across the country while controlling for factors like patient age and infertility differences that can falsely make some clinics look better than others. We found that an average patient can increase their odds of having a kid by 43% just by going to a top 10% clinic. Patients unlucky enough to go to a bottom 10% clinic will reduce their odds of having a kid by 40%. Next month, we're adding several more clinics, 2023 data, additional procedural controls, and donor/gestational carrier models, which should push our accuracy beyond state-of-the-art models in this space and better isolate clinic impact on patient outcomes. We've launched ivf.clinic, a website where patients can access personalized IVF reports and browse our clinic rankings (though we're still squashing some bugs). Currently, we're expanding our research to include comprehensive insurance coverage and pricing data across clinics nationwide. If anyone has insights on automating the collection of IVF clinic pricing information, I'd love to hear from you at scelarek@gmail.com. 61: Replicate Study On Brain Wave Synchronization For Speeding Learning We have acquired and configured the OpenBCI UltraCortex Mark IV 8-channel EEG headset and a clinical-grade Biosemi 32-channel EEG system. We’ve implemented the required components for the experimental pipeline (computing alpha from EEG, flashing bright white light, presenting stimulus images). We are currently putting them together into a single system that we’ll use to collect the data from several participants. We are aiming to gather data on several participants in late June / early July and complete the pilot of the replication in July 2025. If you’d like to be a participant in the study, [they might announce a link once they have it]. 62: Advocate Repeal Of Interstate Runaway Compact No update received and I can’t find anything about this. 63: Animal Welfare (Especially Fish) In Turkiye Future For Fish asks companies to sign up to FFF's fish welfare commitment, which requires producers to certify their facilities and enforce specific standards for stocking density and harvest. Luckyfish, İlknak, Divan (35 restaurants, 17 hotels) and NG Hotels (5 hotels) have signed and published FFF's fish welfare commitment with İlknak publishing the commitment on their website. Kılıç published its first sustainability report detailing fish welfare policies, including enforcing a maximum stocking density of 10 kg/m³ and confirmation of electrical stunning practices. Longer version with some caveats: https://manifund.org/projects/improving-fish-w From the longer document, these commitments involve things like reducing overcrowding, or stunning fish before killing them. Over 30 million fish were affected just from their single largest commitment, and they say 100 fish are helped per dollar spent. 64: More Georgism Advocacy Lars and Will used the 2021 grant to co-found ValueBase. Will remained with the company, and Lars left to do advocacy work at the Center For Land Economics. Here’s their summary of how things are going: [Our] organization transitioned leadership with Greg Miller, a former Program Analyst at the US Department of Housing and Urban Development, and Lars Doucet, author of Land is A Big Deal and Co-Founder of Valuebase, working full time and Joe Caissie stepping aside. This transition happened naturally as the next career transition for each respective person. Since then, progress has been made on pushing forward legislation. Maryland had two bills introduced to give Baltimore and counties the ability to enact split-rate taxes. One of the bills passed the state senate and would allow Baltimore to enact land value taxes within one mile of rail corridors–this contains 50% of Baltimore’s land value. However, the legislative session ended. We expect the bill to revive next session. The Center for Land Economics has been actively working to help efforts to get this bill passed the line. At the same time, we have uncovered systematic undervaluing of vacant land in assessments. We are writing a report on the assessment issues in Maryland with actionable steps to resolve them.

Inline links: Codebuff, website, Substack, survives, a public Substack, in Rwanda, Growth Summit, Stanford Social Innovation Review, ProMarket, Global Prosperity Institute, Saharon, this page, eadbatteries.substack.com, here, here, a post profiling the Far Out Initiative here, https://tornyol.com, a public website, https://chemrxiv.org/engage/chemrxiv/article-details/67e4cde76dde43c9084d88b7, https://blueprintbiosecurity.org/introducing-project-air/, our way, connect them directly to me, www.Spartacus.app, X, Substack, here, https://www.goodancestors.org.au/ai-safety, https://www.australiansforaisafety.com.au/, https://www.aul.city/blog/reflecting-on-africa-urban-lab-s-inaugural-year-2024-highlights, https://healthlearn.org/blog/updated-impact-model, https://www.youtube.com/playlist?list=PLWVKUEZ25V97tNULapu07DhWv6_W4NfpE, here, public website, here, in urgent need, https://manifund.org/projects/improving-fish-w

Astral Codex Ten

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Astral Codex Ten

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