Vaccines and autism: A thorough review of the evidence (2026 update)

Posted on October 26, 2026 by Fallacy Man

One of the most common concerns that people have about vaccines is that they might cause (or exacerbate) autism. This idea is perpetuated by celebrities and innumerable websites, and it has become one of the cornerstone arguments of the anti-vaccine movement, but is there any truth to it? Numerous websites and viral social media posts have assembled lists of papers that supposedly demonstrate that vaccines cause autism, but few people actually take the time to carefully examine those lists.

In science, quality is often far more important than quantity, and you have to critically examine studies rather than blindly believing them (details here; here and here; examples here and here). So that is what I want to do in this post. I am going to walk you through both the anti-vaccine lists of papers and the body of evidence supporting vaccines.

This is the third version of this post. When I wrote the first version in 2016, the largest anti-vaxxer list contained 124 papers. That list had grown to 157 papers when I wrote an update in 2019, and it has now grown to 228 studies, plus an additional 19 studies that I have seen cited elsewhere. On top of this, the USA currently has an administration that is constantly making false or misleading claims about vaccines, all of which makes me feel like it is time for an update.

Also, since my last update, several additional large studies failing to find a link between vaccines and autism have also been published. Therefore, I have decided to once again update this post and systematically go through this entire body of literature. You can also find excellent debunkings of the anti-vaccine lists at Stories from the trauma bay and I Speak of Dreams (here, here, here, here, and here). I highly recommend reading those posts in addition to my own.

I have three key target audiences here. First, to any parents who are concerned about vaccines and are truly and sincerely looking for good information, I hope that this post will be a helpful tool for you and will dispel much of the nonsense on the internet. There are so many frightening stories and claims out there that I fully understand why you would be concerned. So, I have done my best to thoroughly cover all of the evidence, and I hope that you will carefully consider it.

Second, for those who have already reviewed the evidence, but are tired of explaining it over and over again in debates, I hope that this post will provide a resource that will save you some time.

Third, for those who are not particularly interested in the autism/vaccine debate, I hope that this post will provide a nice, worked example of how to critically analyze a large body of literature. There are, sadly, a lot of bad scientific publications out there, and it is important that you know how to sift through them and separate the high-quality studies from the statistical noise.

Finally, to those who are already convinced that vaccines cause autism, although you are not my target audience, I do hope that you will read this, but I have one simple request to make of you. If you choose to continue reading, then I want you to seriously consider the possibility that you might be wrong. I want you to actually examine the evidence presented here rather than blindly ignoring it. If you aren’t willing to do that, then there is really no point in you continuing to read.

This post is necessarily long, so for your convenience, I have included a short summary section at the beginning which condenses the entire post into a few paragraphs. If you really want to understand this topic, however, I suggest that you skip past this section and read the detailed analyses of both the pro-vaccine and anti-vaccine literature. I have made a list of hyperlinks (below) that you can use to jump to any particular sections that interest you. You can also download an excel sheet with citations for the list of anti-vaccine papers along with how I categorized them. I have included references to the scientific literature with hyperlinks throughout, but please realize that hyperlinks are not always stable. Therefore, I have included a full Literature Cited section at the end which you can use to look up papers if the links break (also see this post for ways to get past paywalls).

Note: Throughout this study, I will refer to “pro-vaccine studies” and “anti-vaccine studies.” I am simply using those terms as an easy way to distinguish between studies that failed to find an association between vaccines and autism and studies that found an association (or are cited by anti-vaccers as evidence). Please do not misconstrue those terms or view them as implications that the research was biased or agenda driven.

Table of contents

TL;DR (summary)

Overview of studies. Note that some studies are in multiple categories.

Out of a combined 247 studies that anti-vaccers cite as evidence of vaccines causing autism, an astounding 126 (51%) aren’t even about vaccines! Another 49 aren’t about autism. 77 papers are about methyl mercury (which was never in vaccines) or ethyl mercury (which hasn’t been in childhood vaccines since the early 2000s). There are also 58 opinionated reviews/hypothesis papers which simply present proposals for a link between autism and vaccines rather than actually presenting experimental evidence. Another 45 are in vitro (petri dish) or animal studies, which have limited applicability to humans, and 28 are case reports, opinions, or other forms of non-research (note that some papers are in multiple categories). Also, seven of the papers have been retracted or withdrawn due to serious issues with them.

In short, these lists are full of totally irrelevant papers, thus clearly demonstrating that they were not carefully constructed by people who were systematically examining the evidence but were, instead, thrown together with little to no quality control and are intended to stoke fear rather than to educate. Many of the papers in these lists are of low quality, while others are fine papers; they just have nothing to do with vaccines.

After filtering out all this nonsense, there are only 13 papers that haven’t been retracted, are relevant to the current schedule, and present actual human research proposing a link between vaccines and autism. However, not a single one of those papers is a large, robust study that can establish causation. Every last one of them is a small, correlation-only study using weak designs (at best, case-control studies), and they are riddled with methodological flaws that have been widely criticized in the peer-reviewed literature. Additionally, many of them relied on the idea that autism rates are increasing, but there is a substantial amount of evidence that the increase is largely due to changes in how autism is diagnosed rather than an actual increase in the number of people with autism (Rutter 2005; Taylor 2006; Bishop et al. 2008; Baxter et al. 2015; Hansen et al. 2015; Russell et al. 2022; Grosvenor et al. 2024). Additionally, anti-vaccers often ignore the fact that we know autism is predominantly genetic (Tick et al. 2015; Sandin et al. 2017; Bai et al. 2019; Sandin et al. 2024).

In contrast to the anti-vaccer lists, the body of literature showing that vaccines do not cause autism contains many large studies using powerful designs, the largest of which is a cohort study with over 2.5 million children (Burstain et al. 2026)! This sample size provides enormous power, and the lack of association between vaccines and autism in this study is very strong evidence. Several other large and very powerful cohort studies have similarly failed to find a link (Hviid et al. 2019 [657,461 children]; Madsen et al. 2002 [440,654 children]; Andrews et al. 2004 [109,863 children]; and Jain et al. 2015 [95,727 children]).

There have also been multiple large case-control studies that found no evidence of vaccines being associated with autism. (DeStefano et al. 2004 [642 people with autism; 1824 people without autism]; Smeeth et al. 2004 [991 with; 4469 without]; DeStefano et al. 2013 [256 with; 752 without]; Uno et al. 2015 [189 with, 224 without]). Case-control studies start with two groups (one with and one without the effect of interest [in this case autism]) then work backwards to test a potential cause. This design does not allow them to assign causation, but they are a very powerful tool for detecting associations, and a lack of association is good evidence of a lack of causation (more details here).

Similarly, several studies have either looked for general correlations between autism rates and vaccines (Fombonne et al. 2006; Dales et al. 2001) or have looked for changes in autism rates following either the introduction (Taylor et al. 1999; Chen et al. 2004) or removal (Honda et al. 2005) of the MMR vaccine. None of these studies found a significant relationship.

There are also many studies looking at more focused possibilities, such as specifically at whether vaccines cause regressive autism (Richler et al. 2006; Uchiyama et al. 2007; Goin-Kochel et al. 2016), whether receiving vaccines while pregnant leads to autism  (Zerbo et al. 2017; Becerra-Culqui et al. 2018; Ludvigsson et al. 2022; Becerra-Culqui et al. 2022), whether aluminum in vaccines causes autism (Andersson et al. 2025; reviewed in Nirenberg et al. [2026]), whether vaccines cause autism in children with a higher genetic risk (Hviid et al. 2019), the possibility that age at vaccination matters (Uno et al. 2015; DeStefano et al. 2004; Smeeth et al. 2004; Madsen et al. 2002; Burstain et al. 2026), and many other specific hypotheses, and in every case, the answer is that there is no effect of vaccination on autism.

At this point, it should be very clear that the evidence is extremely one-sided: vaccines do not cause autism. However, I don’t expect you to take my word for it. Scientists have used the most powerful tool we have (a meta-analysis) to pool the results of multiple studies and look for central patterns. This resulted in a sample size of over 1.2 million children (Taylor et al. 2014). The larger the sample size, the easier it is to detect trends, yet even with this sample size, there was no association. Other scientists have systematically reviewed the literature, with the consistent conclusion that the evidence does not support a link between vaccines and autism (Jefferson et al. 2003; Klein and Diehl 2004; Parker et al. 2004; Hurley et al. 2010; Stratton et al. (eds). 2011; Demicheli et al. 2012; Maglione et al. 2014; Mohammed et al. 2022).

In short, we know that the “rise” in autism rates is mostly due to diagnostic changes. We know that autism is mostly genetic, and we have tested vaccines over and over and over again, including conducting multiple truly massive studies, and not a single large, high-quality study has produced any evidence of vaccines causing autism.

Please read the full post for more details about all of these studies.

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About this update

When I updated this post in 2019, Ginger Taylor’s infamous list of “research papers supporting the vaccine/autism link” contained 157 papers. It has since grown substantially. According to her list’s title, there are 230 papers, but her numbering goes to 233, and she included several papers twice, so the true number is 228 (not a lot of quality control goes into this list).

Previously, I had also included papers from websites claiming to have “30 solid scientific studies that prove vaccines cause autism” and “22 medical studies that show vaccines can cause autism.” Those websites are now defunct, but all the papers in them overlap with Taylor’s current list. There is also a new player in the form of a viral Facebook post that starts, “There is no science that shows vaccines cause Autism, except in these published studies which show vaccines cause Autism,” then regurgitates a list of URLs. Most of those papers were also in Taylor’s list, but 17 weren’t, so I have included those papers as well (this excel file shows which papers are in each list and how I categorized them).

Taylor has also removed two papers from her original list (Hewitson et al. 2008 and Inbar et al. 2016); I have, however, retained them in my master list because old versions of her list can be found in various places online. This brings the total to 247 studies. Most of the newly added studies are not new studies that were recently conducted (though there are exceptions). Rather, Taylor keeps scraping further and further into the bottom of the barrel adding more low-quality studies that don’t show what she thinks they do.

Out of the 73 new papers Taylor added, an astounding 43 are literally not about vaccines! Twelve aren’t about autism! Her additions include studies like Tan et al. (2020), which is about strokes and gut microbiomes and never even mentions vaccines or autism. The additions also include 10 papers on methyl mercury (which has never been in vaccines) or ethyl mercury (aka thimerosal, which hasn’t been in childhood vaccines for over 2 decades). There were also five animal or in vitro studies and 18 opinionated (non-systematic) reviews or opinion papers.

Out of all 73 new papers, only three were actual studies on humans looking at an association between vaccines and autism, all of them were small association studies with serious issues or limitations, and one of them (Sefrioui et al. 2023) explicitly argued against a causal link between vaccines and autism.

It’s also worth briefly mentioning what Taylor didn’t do with the list. Namely, remove the irrelevant papers. As Liz Ditz, Doc Bastard, and I have been pointing out for years, many of the papers on the list simply have nothing to do with vaccines, autism, or both. Also, several of the papers I criticized in the past have now been retracted because they were crap studies. The fact that Taylor refuses to remove them (and continues to add more irrelevant studies) shows a shocking level of either ignorance or intellectual dishonesty (most likely both).

To be fair, she did remove Inbar, et al. (2016) from the list after that paper was withdrawn from publication, and she removed Hewitson et al. (2008) which was only a conference abstract, but that doesn’t change the fact that she refuses to critically examine the vast majority of the papers on her list and is clearly more interested in having a high paper count than in accurately reporting the state of the literature.

On the other side of things, several important new papers have been published. First, Burstain et al. (2026) published the largest study of vaccines and autism to date, with a truly incredible sample size of over 2.5 million children! As with previous work, it found no association between vaccines and autism, including when looking at different age brackets to see if age at vaccination mattered.

Second, Andersson et al. (2025) published a massive 1.2 million-child study looking at aluminum in vaccines and failed to find evidence of harm (including no evidence of it causing autism).

Third, Mohammed et al. (2022) conducted an updated systematic review of the literature which, once again, failed to show a link between vaccines and autism.

Additionally, I have added a section (within the section on cohort studies) describing studies looking specifically at vaccination while pregnant. I have also expanded the “The Autism/Vaccine Hypothesis and what we know about autism” section to discuss our current knowledge of autism in general, and I have made other updates and revisions throughout. So even if you’ve read the 2019 version, hopefully there will be some new and useful information for you here.

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The autism/vaccine hypothesis and what we know about autism

Science is all about making hypotheses and testing whether or not their predictions come true. Therefore, it is always a good idea to establish exactly what you are asking before you look at the literature. In this case, the question is whether or not vaccines cause autism, but that needs to be nuanced a bit. According to the CDC, about 1 in 31 children in the US have autism, with other developed countries reporting varying, but largely comparable levels (Elsabbagh et al. 2012). Those numbers have gone up over time, which has led anti-vaccers to refer to the situation as an “autism epidemic.” Anti-vaccers insist that vaccines are the key factor driving autism rates, with the MMR vaccine being a particularly prominent target. If vaccines are driving an “autism epidemic,” then when we do studies comparing children who did and did not receive a given vaccine, we should see that autism rates are substantially higher among the vaccinated. In contrast, if vaccines are not a cause, then we should not see a statistically significant difference. Importantly, the larger the study, the more power we have to detect rare effects. So even if vaccines only cause a small subset of autism cases (rather than an “epidemic”), we should still be able to see that effect with a large enough sample size.

Before looking at the literature specifically on vaccines, it is worth spending a minute looking at the broader literature to see if the vaccine hypothesis is even plausible. This is a topic I talk about frequently known as “prior probability.” This takes an inherently Bayesian approach to science, and rather than relying on individual results in isolation, it incorporates our broader knowledge about the system in question.

When we take that broader approach, one of the first things we see is that the supposed “epidemic” is mostly not a real increase. The diagnostic criteria for autism have changed over time, and more people are getting diagnosed as we become more aware. The result is that the majority of the “increase” is actually just a change in diagnostic criteria and better screening, rather than a true increase in prevalence  (Rutter 2005; Taylor 2006; Bishop et al. 2008; Baxter et al. 2015; Hansen et al. 2015; Russell et al. 2022; Grosvenor et al. 2024). In other words, autism rates are higher now than they were in 1990 because people who would not have been considered autistic in 1990 are considered autistic today (Dr. Novella wrote a good post on this several years ago that includes some additional sources). Indeed, large studies like Russell et al. (2022) and Grosvenor et al. (2024) showed that the biggest change in autism rates over time is actually among adults, not children. This isn’t because adults are suddenly becoming autistic, but rather because we are doing a better job of diagnosing autism in people who were previously missed.

The second thing to note is that autism is primarily genetic. We know that genetics are, by far, the biggest factor in causing autism. We measure this as “heritability,” which is the proportion of a trait that is caused by genetics rather than the environment, and heritability scores for autism are generally around 80%. In a 2015 meta-analysis, heritability scores ranged from 64-91% (Tick et al. 2015). Subsequent massive studies found rates of 83% (Sandin et al. 2017), 80% (Bai et al. 2019), and 87.0% for men and 75.7% for women (Sandin et al. 2024). In other words, right off the bat, we know that vaccines are not the key factor causing autism, because we know that genetics is the key factor.

Additionally, there are strong associations between various aspects of maternal condition while pregnant and subsequent autism (Khachadourian et al. 2025). To give just a few examples, diabetes, obesity, and parents’ age all appear to increase autism risk (note as well that those are also things that are on the rise; Xiang et al. 2015; Wu et al. 2016; Cortese et al. 2022; Dehesh et al. 2024). This narrows any potential role of vaccines even further. Similarly, certain conditions at birth like low child weight and being born preterm are strongly associated with autism (Guo et al. 2022; Ma et al. 2022), which again narrows any potential role of vaccines. Indeed, when you look at all the factors known to be associated with autism, the majority of them are conditions while pregnant or at the time of birth, rather than environmental conditions after birth (Karimi et al. 2016).

To be clear, there do appear to be some risk factors during childhood (such as air pollution; Amnuaylojaroen et al. 2024), but to a large extent, it appears that whether or not a child will develop autism is determined before or at birth. Note also that most of the studies on potential risks after birth are association only, and the direction of causality is not clear. There are, for example, multiple studies showing high levels of some heavy metals in the blood or hair of autistic children, but it is not clear if those metals contribute to autism or if the physiology of autistic children makes it more difficult for them to eliminate those metals from their bodies.

All of this matters because it means that before we even look at the literature specifically on vaccines, we know that vaccines aren’t going to be the major factor driving autism trends. Anti-vaccers often act like autism is a total mystery, and I frequently receive comments like, “well if it isn’t vaccines then what could it possibly be!?” In reality, while there are still unknowns, we do actually know a great deal about the risk factors for autism. Autism is predominantly genetic, with additional large risk factors during pregnancy and birth as well as some during early childhood.

Just to be clear, I’m not saying that this automatically eliminates any role of vaccines. Based only on this information it would still be possible that vaccines are interacting with other factors and playing a small role in autism, but they simply can’t have the type of major role that anti-vaccers claim, and the prior probability that vaccines are an important factor is very low.

Now, with that said, let’s look at the studies specifically on vaccines, because there are a lot of them.

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Studies that failed to find evidence of vaccines causing autism

I want to start with the pro-vaccine studies, and there are quite a few of them. Indeed, you can find several lists on the internet that boast over 100 studies demonstrating that vaccines do not cause autism (for example, here and here). I am, however, a bit cautious about lists like this. They can be quite useful, and I have linked to them multiple times on this blog, but a quick examination of these lists will reveal that they do actually contain quite a few low-quality studies with weak designs or tiny sample sizes, as well as opinion papers and other non-research publications. So, I am not going to talk about all of the studies in these lists. Rather, I have painstakingly gone through them to eliminate all of the studies with really weak designs (like animal studies), tiny sample sizes, or questionable statistics. This type of filtering is a really good idea when you are examining a topic because it weeds out the statistical noise and leaves you with the reliable studies (importantly, however, you need to have a good understanding of experimental design, statistics, etc. before you can do this properly). My goal here is to present an honest evaluation of the evidence, rather than trying to simply dazzle you with a large quantity of low-quality studies.

hierarchy of evidence
Not all study designs are equal (details here)

In presenting these studies, I have decided to start in the middle of the scientific hierarchy of evidence (I eliminated the bottom levels) and work my way to the top. Not all scientific study designs are equal. As I explained in detail here, some designs are more powerful and give more reliable results than others, and it is often the case that a body of evidence begins with the lower-level studies (which are often cheaper and easier to run) and builds its way up to higher levels. So, I have structured this post in the same way.

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Correlation/time-series

First, we have studies that looked for broad associations between vaccination and autism rates. These studies are inherently correlative and cannot establish causation because correlation does not automatically equal causation. The fact that two things occur together does not mean or even suggest that they are causally related. Further, these studies are often limited by looking at broad patterns, rather than vaccine/autism rates of individuals. Nevertheless, if vaccines were really causing an “autism epidemic,” as anti-vaccers claim, then there should be a strong correlation between vaccination and autism, and failing to find that correlation is reasonable evidence that vaccines aren’t causing autism.

Several different designs were used for these studies. One of them simply looked for correlations between vaccine coverage and autism rates (Dales et al. 2001). If vaccines cause autism, we would, of course, expect there to be higher autism rates when vaccine coverage is higher; however, this study failed to find that trend (i.e., vaccination rates and autism rates were not correlated). Similarly, Fombonne et al. (2006) used data on 27,749 children in Montreal, Canada from 1987 to 1998 to look at pervasive developmental disorders (including autism) over time and how they associated with MMR vaccine coverage, MMR dose (one vs two), and thimerosal exposure. There were no associations in any comparison, and total developmental disorders were actually highest when MMR coverage was the lowest.

Another pair of studies specifically looked for changes in autism rates when the MMR vaccine was introduced to the UK (Taylor et al. 1999; Chen et al. 2004). The MMR vaccine is a favorite target of anti-vaccers, and if it actually causes autism, then we should see a spike in autism rates immediately following the introduction of that vaccine into a population, but neither study detected a significant change.

Yet another study (Honda et al. 2005) took the opposite approach. In 1993, Japan abruptly stopped using the MMR vaccine, so Honda et al. (2005) examined the autism rates across the entire city of Yokohama (roughly 300,000 people) from 1988-1996. This study provides a nice balance to Taylor et al. (1999) and Chen et al. (2004), because just as you would expect autism rates to spike following the introduction of MMR (if MMR caused autism), you would also expect the rates to drop after the vaccine is removed. Just like the previous studies, however, Honda et al. (2005) failed to find any evidence of the vaccine causing autism (i.e., autism rates did not drop when the MMR vaccine was removed).

Finally, Makela et al. (2002) took a very different approach and took a group of 535,544 children who received the MMR vaccine and followed them to see if and when they developed autism. The idea was that if the anecdotes about vaccines quickly leading to autism were correct, then autism diagnoses should have clustered shortly after receiving the vaccine. As you might have guessed, they did not cluster (I talked about why we expect some cases to follow vaccines just by chance here).

All of this shows that one of anti-vaccers’ core claims isn’t actually true. Autism rates are not correlated with vaccination.

Note: this type of time-series design was not included in the original image on the hierarchy of evidence that I have included throughout this post, but these studies would usually rank about the same as a cross sectional study.

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Case-control studies

Case-control studies are another type of correlation study, but rather than simply looking for correlations in a population, they take two groups that are similar except for an outcome (e.g., autism) then work backwards to test for a potential cause of that outcome (e.g., you compare groups with and without autism to see if they differ in their vaccination rates). This design can only show association rather than causation, but it is a very powerful way of looking at relatively rare outcomes, and it allows you to get very robust results out of relatively small sample sizes (compared to other designs), making it an excellent method for looking for associations between vaccines and autism.

There are tons of small case-control studies that failed to find an association between vaccines and autism, but I want to focus on the large ones (DeStefano et al. 2004 [642 people with autism; 1824 people without autism]; Smeeth et al. 2004 [991 with*; 4469 without]; DeStefano et al. 2013 [256 with; 752 without]; Uno et al. 2015 [189 with, 224 without]). Again, if vaccines actually caused autism, we really should see higher vaccination rates among the “cases” (group with autism) than among the “controls” (group without autism). The fact that vaccination rates are the same among those with and without autism is very good evidence that vaccines aren’t causing autism.

As an example to help explain this study design, imagine we took 1000 people with lung cancer (cases) and matched them with 1000 people who had similar demographics (age, sex, region of the world, economic status, etc.) then looked at the rates of smoking in both groups. We would almost certainly find that smoking was much more common in the group with lung cancer than in the group without lung cancer, right? That’s how this design works, and the same should be true for autism and vaccines. If vaccines really cause autism, then vaccination rates should be higher among those who have autism. The fact that they aren’t strongly suggests that vaccines do not cause autism.

Additionally, although all of these studies addressed the question of vaccines and autism, several of them were focused on a particular facet of the question or examined multiple sub-questions. For example, DeStefano et al. (2004), Smeeth et al. (2004), and Uno et al. (2015) looked specifically at the MMR vaccine, whereas DeStefano et al. (2013) took an entirely different approach and looked at antigen exposure. This is really important because one of the most common tropes of the anti-vaccine argument is the “too many too soon” argument, which argues that the antigens in vaccines will overwhelm a child’s immune system and lead to problems like autism. This study directly addressed that concern.

Several of these studies also looked at whether or not the age at vaccination was important for the development of autism, thus addressing the argument that vaccines should be delayed (DeStefano et al. 2004; Smeeth et al. 2004; Uno et al. 2015). In all fairness, DeStefano et al. (2004) did find slightly higher vaccination rates among the autistic children for their 36 month age-group (93.4% vs 90.6%), but there were no differences at 18 or 24 months, and in the case of the 36 month-olds, many of them had started to show signs of autism before receiving the vaccine, so the vaccine was clearly not at fault.

Another study (Goin-Kochel et al. 2016) took a different approach. Many anti-vaccers have shifted the goal posts from saying that vaccines cause autism to saying that vaccines specifically cause regressive autism. They use this to try to dismiss many of the existing studies by arguing that they looked at autism generally, rather than specifically looking at regressive autism. This argument is a logically invalid ad hoc fallacy (i.e., it is an assumption being made to patch a hole in an argument), but Goin-Kochel et al. (2016) tested it anyway. They took a large group of people with autism (2,755 people), grouped them by type of autism, then compared vaccination rates among the groups. If vaccines specifically cause regressive autism (as anti-vaccers suggest), then the vaccination rates should have been higher in the regressive autism group, but the study found no consistent significant differences among groups. This conclusion has also been supported by other studies (Richler et al. 2006; Uchiyama et al. 2007).

Finally, I want to briefly mention Price et al. (2010). This case control study (256 with autism; 752 without) looked specifically at thimerosal exposure from vaccines and immunoglobulin injections. It also found no associations. I have not included it in my main list of studies, however, because thimerosal was removed from childhood vaccines nearly two decades ago (the exact date varies by country, but it’s usually 1999-2001; certain formulations of the flu vaccine still have thimerosal, and in Australia, the Hep B vaccine still has it). Thus, thimerosal studies are no longer relevant for the current childhood vaccine schedule.

*Note: Smeeth et al. (2004) also did a larger analysis of 1294 people diagnosed with “pervasive developmental disorders” which included those diagnosed with autism and those diagnosed with other developmental disorders, and that analysis also failed to find a significant effect of vaccines.

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Cohort studies

Case-controlled studies can be very powerful (especially with large sample sizes) and can actually show causation (details here).

Next, we have the cohort studies. These work in the opposite direction from case-control studies. They start with a group of people (cohort) that are similar except for their exposure to some potential cause (e.g., vaccines). Then, the researchers track these individuals to see if they eventually differ in some outcome (e.g., autism). In other words, if vaccines cause autism, then you expect the members of the cohort that received vaccines to develop autism significantly more frequently than the members of the cohort that were unvaccinated.

I found many small cohort studies that are too small to be worth discussing in detail, as well as six truly enormous studies that provide very convincing evidence that vaccines do not cause autism. In the largest study of vaccines and autism to date, Burstain et al. (2026) examined medical records for an astounding 2.5 million children and found no evidence of an association between MMR vaccines and autism. Andersson et al. (2025) took a different approach and used a cohort of over 1.2 million children to look for associations between cumulative aluminum exposure from vaccines and autism. Once again, there was no association. Likewise, Andrews et al. (2004) used 109,863 children to study the DTP/DT vaccine, Hviid et al. 2019, Madsen et al. (2002) and Jain et al. (2015) used 657,461 children, 440,654 children, and 95,727 children (respectively) to examine the MMR vaccine, and none of them found significant associations. Really think about those numbers for a minute. Those sample sizes are extraordinary and gave the researchers tremendous power to detect significant trends, yet none were found. If vaccines are really causing thousands of cases of autism each year, then why isn’t that showing up in any of these studies?

Further, as with the case-control studies, several of these studies also examined additional aspects of the topic. For example, Burstain et al. 2026 and Madsen et al. (2002) also examined the effects of age at vaccination and did not find a significant effect. This is important, because many argue that the problem is that too many vaccines are given too soon and the schedule should be spaced out or delayed. These studies show that “too many too soon” is not actually a problem.

Hviid et al. (2019) also examined several other factors, such as the timing of an autism diagnosis following a vaccination (like Makela et al. 2002, which I discussed earlier). They also tried grouping children by previous vaccination history (i.e., vaccines other than the MMR) to see if the cumulative effect of lots of vaccines plus the MMR would make a difference, and the results were still negative. This is a very useful result because anti-vaccers often criticize these studies for only looking at a single vaccine, but this study included vaccine history in its analyses and still failed to find evidence of vaccines causing autism.

Perhaps most importantly, Jain et al. (2015) also looked specifically at a subset of 1,929 children who had a sibling with autism. This is a brilliant design because we know that autism has a genetic component. Even most anti-vaccers agree with that; they just argue that the genes make you more susceptible to the effects of vaccines. If that was the case, however, then it should be much easier to detect vaccine-associated autism in children who have a sibling with autism. In other words, infants who have an older sibling with autism have a higher risk of developing autism than infants whose siblings do not have autism (i.e., if your sibling has autism, then it is likely that the genes that predispose you to it are in your family). This means that by examining siblings, you are looking at a “high risk” group, thus maximizing your statistical power. Even with this design, however, they failed to find any significant effects of vaccines.

Hviid et al. (2019) also incorporated this sibling design and failed to find a significant effect, but their sample size for this subset test (37 children) was admittedly too small to be compelling. However, they also used other factors that are thought to increase a child’s risk of autism to create subgroups of children with a moderate risk (2312 children) or high risk (1048 children), then they looked for associations between autism and vaccination in those subgroups. Once again, there were no significant effects of vaccines. This lack of association in high-risk groups is very strong evidence that vaccines are not a cause of autism.

In addition to these studies looking at vaccination in children, several large cohort studies have looked at the effects of vaccinating while pregnant. Zerbo et al. (2017), Ludvigsson et al. (2022), Becerra-Culqui et al. 2022 all looked at the effects of influenza (flu) vaccination while pregnant with sample sizes of 196929, 29293, 84739, respectively, while Becerra-Culqui et al. (2018) looked at the DTaP vaccine (81,993 participants). None of these studies found a statistically significant association.

I also want to briefly talk about Hviid et al. (2003) and Verstraeten et al. (2003). I did not include those studies in my main list because unlike all of the other studies that I have been talking about, they looked specifically at thimerosal (which is no longer in childhood vaccines). Verstraeten et al. (2003) used a cohort of 124,170 to look at cumulative thimerosal exposure, while Hviid et al. (2003) used a cohort of 446,695 children to compare vaccines with thimerosal to vaccines without thimerosal. Discussions of thimerosal are fairly pointless since it was removed from childhood vaccines almost two decades ago. Nevertheless, I want to make a few brief points.

First, anti-vaccers often accuse thimerosal of being the causative agent of autism (despite its absence from the current schedule), and these massive studies provide powerful evidence against that hypothesis. Second, Hviid et al. (2003) also looked at dose effects and failed to find any evidence of increasing autism with increasing vaccine dose. This is significant, because even if there was something in vaccines other than thimerosal that was causing autism, you would still expect that to show up in this study. The inclusion of dose once again provides evidence against the “too many too soon” argument.

Finally, I want to spend a bit more time discussing Andersson et al. (2025), the 1.2 million-child Denmark cohort study looking for relationships between aluminium exposure in vaccines and autism. This paper is really important because it looked at 50 different chronic conditions (including autism) and failed to find any associations between the aluminium in vaccines. They did a really thoroughgoing set of sub-analyses (e.g., looking at aluminium doses either as a continuous variable or a categorical variable) with highly consistent results. Following the removal of thimerosal from vaccines, aluminum (or aluminium if you prefer) has become anti-vaccers’ new favorite scapegoat, despite the fact that the amount of aluminum in vaccines is tiny and the fact that aluminum is everywhere and children get far more aluminum from other sources (see Nirenberg et al. [2026] for a review of the literature). So, this study is a very serious blow to that argument.

Unsurprisingly, anti-vaccers were not pleased, and a firestorm of arguments erupted around this paper, largely driven by prominent anti-vaccers such as Robert F. Kennedy Jr., Brian Hooker, Christopher Shaw, Lucija Tomljenovic, and Karl Jablonowski. That obviously doesn’t automatically make their criticisms wrong, but it is important to note that they aren’t exactly objective. The core of the arguments actually took place in comments on the paper itself. JAMA has an unusual (and I think very good) format of allowing scientists to have a back-and-forth exchange directly on the paper. So, if you go to Andersson et al. (2025) and scroll down to the very bottom (past the literature cited) you will find a long series of comments (you may need to click a “comments” button to see them). You can also read statements/articles against the paper by RFK Jr., Jablonowski and Hooker (2026), and Crepaux et al. (2025; which also includes Jablonowski and Hooker as authors). After which, you should read the rebuttals such as the authors’ own comments on the paper itself, as well as responses here, here, here, and here.

The arguments against this paper range wildly from the totally unreasonable and completely wrong to claims that have some merit but are overblown. No study is perfect, and there are important caveats and valid criticism of this study, but it is not as fundamentally flawed as anti-vaccers argue. I had originally written some of my own responses to key points, but the section was just too long. So, I have simply put it in the comments at the end of the post for anyone who wants additional details.

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Systematic reviews

At the start of this section, I want to distinguish between a systematic review and a general or non-systematic review. A systematic review looks for papers using a predefined set of search terms and databases. For example, you might search PubMed and Web of Science for papers containing both the terms “vaccine” and “autism.” Then, you take that list of papers and filter by some other pre-defined criteria. For example, you might be interested only in studies that were case-control, cohort, or randomized controlled trials. Then, you write the review on the papers that are left.

Setting up reviews this way with pre-defined search terms and inclusion criteria is a really powerful way to comb through the scientific literature because it avoids bias. If a study meets your criteria, then it gets included regardless of what its conclusions were. Thus, you get a fair and accurate representation of the literature. To be clear, you can still bias these if you use absurd search terms (like, “vaccines do not cause autism,” for example), so you should always check systematic reviews to see what their inclusion criteria were.

In contrast, non-systematic reviews use any papers that the authors thought should be included. These reviews can still be quite good, but they also can be very problematic because the authors’ biases can have major influences over the papers that get included. If you are anti-vaccine, for example, you can write a “review” that only includes anti-vaccine papers and ignores all of the pro-vaccine papers, thus creating the illusion that there is an overwhelming amount of evidence against vaccines (we’ll encounter some of those later in this post). The inverse is, of course, also true. A pro-vaccine scientist can bias a review just as easily as an anti-vaccine scientist can. This is why systematic reviews are much better than regular reviews, and when you have multiple systematic reviews that all agree with each other, you can be fairly confident that the literature is pointing in a consistent direction.

In the case of autism, that direction is most decidedly away from a link between autism and vaccines. I found eight different systematic reviews of the topic, and they all said the same thing: the evidence does not support an association between vaccines and autism (Jefferson et al. 2003; Klein and Diehl 2004; Parker et al. 2004; Hurley et al. 2010; Stratton et al. (eds). 2011; Demicheli et al. 2012; Maglione et al. 2014; Mohammed et al. 2022). I also found multiple non-systematic reviews that concluded that vaccines don’t cause autism, but I decided not to include them for the reasons explained above.

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Meta-analysis

Systematic reviews and meta-analysis are the highest category of evidence because they combine the results of multiple studies, which makes them less prone to false conclusions (details here).

Finally, we have a meta-analysis. Meta-analyses are the single most powerful tool available to scientists because they actually pool the results of multiple studies and run statistics on that pooled data set. This provides much larger sample sizes than you could normally achieve, and it largely overcomes the fact that sometimes a study reaches the wrong conclusion just by chance (i.e., the odds of the pooled data from multiple studies producing an erroneous conclusion is much, much lower than the odds of a single study being wrong). As a result, these studies are considered to be the highest level of evidence.

There is only one large meta-analysis for vaccines and autism, but it’s a big one (Taylor et al. 2014). It had a sample size of over 1.2 million children, which is an extraordinarily large sample size that provides tremendous statistical power. Nevertheless, this study did not find any associations between autism and vaccines (or thimerosal or mercury for that matter). That is as conclusive of an answer as you could ever hope to have.

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Randomized-controlled trials

There are no large randomized controlled trials on vaccines and autism, and there are two very good reasons for that. First, the benefits of vaccines have been established beyond any shadow of a doubt, so it would be unethical to deliberately give people placebos rather than vaccines.

Second, we don’t need randomized controlled trials to show that vaccines don’t cause autism. Randomized controlled trials are the most powerful experimental tool for establishing causation, but the other methods are perfectly capable of showing a lack of causation. For example, case-control trials can only show correlation, not causation, but since a lack of correlation also generally means a lack of causation, they can be very powerful tools for showing that two things are not causally related. So, if you were arguing that vaccines cause autism, then a lack of randomized controlled trials would be potentially problematic (depending on the strength of the other studies, especially the cohort studies), but the lack of randomized controlled trials is really irrelevant for this negative result. I explained all of this in much greater detail here.

Stated another way, if vaccines actually caused autism, then that would still have shown up in the large cohort and case-control studies. Having a placebo control wouldn’t change that.

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Regressive autism, multiple doses, age at vaccination and other sub-hypotheses

At this point, I want to deal with some common criticisms and point out just how broad the literature is. Anti-vaccers are masters at shifting goal posts, and, as a result, scientists are forced to study a constantly shifting hypothesis. It’s like fighting the hydra. Every time we falsify one hypothesis, anti-vaccers invent two more, rather than simply accepting the results.

For example, a common criticism I hear is that many of the studies I’ve been talking about focused specifically on the MMR vaccine. However, this criticism ignores the fact that scientists have focused on the MMR vaccine precisely because that is the vaccine that anti-vaccers singled out as the cause of autism! In other words, scientists tested anti-vaccers’ hypothesis, but anti-vaccers didn’t like the answer to those tests, so they changed the hypothesis, then accused scientists of not addressing the “real issue.” That’s just not how this works. You don’t get to say, “the MMR vaccine causes autism,” then respond to studies on the MMR vaccine with, “well you only looked at MMR. The real cause is…” (also, there have been large studies that looked at vaccines other than the MMR, e.g., Andrews et al. (2004)).

I’ve tried to point out some of these additional hypotheses as I’ve gone through the studies, but to make it easy, here is a non-exhaustive list of sub-hypotheses that scientists have looked at and falsified:

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Conflicts of interest

At this point, you may be thinking, “but all of those studies were conducted/paid for by pharmaceutical companies, so they can’t be trusted!” I anticipated that argument and checked for conflicts of interest as I went through these papers. I only found ten, and only two of them were serious enough to be at all concerning.

First, for two of the case-control studies (Smeeth et al. 2004; Price et al. 2010) and two of the studies on vaccination while pregnant (Zerbo et al. 2017; Ludvigsson et al. 2022), some of the authors had previously received funding from pharmaceutical companies for other projects. In other words, these studies were not funded by pharmaceutical companies, and the authors don’t work for those companies, but the companies had funded some other projects that were conducted by some of the researchers. That’s hardly damning evidence of corruption.

Two of the other conflicts surrounded Dr. Jefferson. In 1999, he worked as “an ad hoc consultant for a legal team advising MMR manufacturers.” This was acknowledged in his review (Jefferson et al. 2003) and in Demicheli et al. (2012). Jefferson was not an author on the final paper for Demicheli et al. (2012), but the paper stated, “The review authors wish to acknowledge Tom Jefferson and Deirdre Price as previous authors.” In other words, he was involved at some stage, but not with the final product. Once again, showing that one of the researchers involved used to be a consultant for a legal team hardly indicates that all of his research has been corrupted.

Similarly, Dr Fombonne, who wrote Fombonne et al. (2006), has given advice to pharmaceutical companies, has been on government panels, and has been an expert witness in vaccine lawsuits, but has never received research funding from pharmaceutical companies.

Another one of the conflicts also comes from a review. Hurley et al. 2010 stated, “All authors are employed by MED Communications, Inc., which provides medical and drug information services to multiple pharmaceutical firms, including several manufacturers of various vaccines.” So, they were employed by a company that provides info to vaccine manufacturers, but they were not employed by the vaccine manufacturers themselves. Again, that’s not really concerning.

Finally, the two potentially serious conflicts are from the funding for Hviid et al. (2019) and Makela et al. (2002). Hviid et al. was partially funded by the Novo Nordisk Foundation. This foundation funds a large number of research projects, but it also owns several pharmaceutical companies (the rest of the funding came from the Danish Ministry of Health). However, as the paper says, “The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.” The conflict in Makela et al. was more direct. It was partially funded by Merck.

To be clear, conflicts of interest do not automatically negate papers. Conflicts of interest should make you scrutinize a paper more closely, not automatically reject it.

Nevertheless, let’s say that you did want to completely reject those ten studies. That would still leave you with four correlation/time-series studies, three case-control studies, five cohort studies (including studies with 2.5 and 1.2 million children), four systematic reviews, and one massive meta-analysis of 1.2 million children, all of which provide compelling evidence that vaccines are safe. As far as I can tell, none of those studies have conflicts of interest. They were funded by numerous different agencies, and the researchers worked for various universities, hospitals, and government agencies from all over the world (you can find more details about the funding for some of these studies here). So if it is all a coverup by “big pharma” why did all of these independent studies fail to find a relationship between vaccines and autism?

I’ll also briefly add here that I have never received any money from pharmaceutical companies, and I do not get paid to write this blog. In fact, it costs me money, because I am using a non-commercial WordPress account and I pay an annual fee for a domain name and an ad-free version (you’re welcome).

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Sample size and statistical power

Before I leave this section, I want to make a very important point about the nature of negative results in science. Technically speaking, it is not actually possible to demonstrate that vaccines don’t cause autism, but it is possible to demonstrate that if they cause it, they do so at a very low rate. In other words, if you have a sample size of 100 people, you haven’t tested the possibility that vaccines cause it at a rate of 1 in 1000, and if you have 1000 people, you haven’t tested the possibility that vaccines cause it at a rate of 1 in 10,000, etc. (note: those numbers are not precise). So, no matter how large your sample size is, it is always technically possible that the effect is just smaller than what you were able to detect.

Now, when we apply that to vaccines and autism, what we see is that if vaccines do cause autism they do so at an absurdly low rate. Scientists have conducted a meta-analysis with over 1.2 million children, cohort studies with 2.5 and 1.2 million children, several cohort studies with tens or even hundreds of thousands of children, several large case-control studies, and studies that looked specifically at children who were at a high risk of developing autism. This means that we could have detected even a very low rate of vaccines causing autism. It’s a bit difficult to calculate exactly what that rate is, but even a rate of about 1 in 10,000 would likely show up in the larger studies.

Remember, however, that the claim being made by anti-vaccers is that vaccines are causing an “autism epidemic,” and that claim is clearly false. Even if vaccines were only responsible for 1% of autism cases, that would be a rate of roughly 1 in 3,100 people, which is well within the range that these studies had the power to detect.

My point here is that these studies had an extraordinary power to detect even very tiny effects of vaccines, yet they failed to find any evidence of vaccines causing autism. This means that if vaccines do cause autism, they do so at an incredibly low rate that shouldn’t be concerning (remember, every decision has risks, including the decision not to vaccinate).

To be 100% clear here, I am not in any way shape or form suggesting that vaccines actually do cause autism at a very low rate. There is utterly no evidence to think that they do (thus assuming that they do is an argument from ignorance fallacy). Rather, I am bringing this up because I am trying my best to give a fair and honest representation of the current state of our knowledge, and it is not technically correct to say that we have demonstrated that vaccines don’t cause autism, because what we have actually done is demonstrate that it is very unlikely that vaccines cause autism at a meaningful rate, or, to put it another way, if they cause autism, they do so at an undetectably low rate.

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Anti-vaccers’ lists of papers
Introduction to anti-vaccers’ papers

Before I go into the lists of papers that, according to the anti-vaccers, demonstrate that vaccines cause autism, I want to specify exactly what we would need to find in order for anti-vaccers’ claims to be legitimate. Remember, sample size and experimental design are extremely important in determining the quality of a study. Therefore, given that we have very large case-control and cohort studies (including studies with 2.5 and 1.2 million children), as well as a meta-analysis with over 1.2 million children, all of which found no evidence of vaccines causing autism, for the claim that vaccines do cause autism to be plausible, we would need multiple studies of similar size and power. That’s how science works. You don’t get to trump a massive meta-analysis with a tiny association study. So, with that in mind, let’s look at anti-vaccers’ lists of papers, and see if there are any large, high-quality studies.

The largest list (to my knowledge) was compiled by Ginger Taylor and currently contains 228 papers “supporting the vaccine/autism link” (previous versions of the list have contained 91, 124, 130 papers, and 157; note that the current title claims to have 230 papers, but some are in there twice). There’s also a viral list that keeps showing up on Facebook and starts with, “There is no science that shows vaccines cause Autism, except in these published studies which show vaccines cause Autism.” In my previous versions of this post, I also included links to web articles titled “30 solid scientific studies that prove vaccines cause autism” and “22 medical studies that show vaccines can cause autism.” Mercifully, those links are now broken and I can’t find the original articles, but should you find them lurking somewhere on the internet, all of the studies they cited are redundant with the other two lists.

All of these lists have been widely shared, so you may find them in different places with different names (as well as older versions with fewer studies). For the most part, they are redundant with each other, but I still painstakingly went through each of them one paper at a time to make sure that I hadn’t missed anything. This produced a total of 247 papers.

To get that number down to something manageable, I did a series of filtering steps to get rid of the papers that weren’t worth talking about (you can download an Excel file showing how I categorized them here). Now, before you accuse me of cherry-picking, please remember that I did the same thing for the pro-vaccine lists. I took lists of over 100 papers, and I whittled them down to just the ones that were worth talking about. Further, as you’ll see in a minute, I was far more generous to the anti-vaccine lists than I was to the pro-vaccine lists. For the pro-vaccine list, I filtered by content, study design, sample size, and study quality. In contrast, for the anti-vaccine lists, I only filtered by content and study design, because if I had filtered by the same sample size and quality standards that I applied to the pro-vaccine lists, I would have had exactly ZERO studies left to talk about. In other words, none of the studies on those lists are large, well-conducted studies that used a powerful design and provide good evidence that the current vaccine schedule causes autism.

Note: some of the papers that I am not going to talk about were perfectly fine studies, they just have no relevance to the topic at hand. Many of them were, however, seriously flawed. You can find more details about these studies at I Speak of Dreams and Stories from the trauma bay.

Note: many papers fit into multiple categories, so if you simply try to add up the numbers from each of the following categories, it is going to look like I suck at math.

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Studies that weren’t about vaccines or weren’t about autism

It may or may not surprise you to learn that an enormous portion of the papers in these lists had absolutely nothing to do with vaccines and autism. Of the 247 papers in the lists, there were 49 that were not about autism and 126 that were not about vaccines! I’m really curious about how a study that never even uses the words “autism” or “vaccine” can prove that vaccines cause autism. To be clear, I was generous in assigning papers to those categories. I did not score animal studies in that category (even though demonstrating autism in animals is essentially impossible) and I didn’t include papers on thimerosal (even though thimerosal is no longer in childhood vaccines). So right off the bat, we have a really good reason to be skeptical of these lists. They were clearly made by people who are either ignorant or deceptive (or both), because they are full of irrelevant papers.

Just to be clear here, when I say that these papers are irrelevant, for the most part, I mean really well and truly, 100% irrelevant. Some of these papers were so off topic that I have absolutely no clue why they were included. For example, Guy et al. (2015) was on the relationship between pre-term birth and autism, and it presented evidence that infants who are born prematurely have a higher risk of developing autism. I have absolutely no clue why Ginger thought that paper was evidence that vaccines cause autism (since it’s not about vaccines), and there were lots of papers like that in the lists. For example, Carvalho et al. 2011 was on methods of treating mercury poisoning. How on earth does a paper on methods for treating mercury poisoning show that vaccines cause autism?

Perhaps most amusingly of all, Ginger’s list includes a few gems like Khaled et al. (2016). This was not about vaccines and never mentioned them. Rather, it looked for biomarkers of autism that were present at birth, and it found them! This study clearly doesn’t suggest that vaccines cause autism and, in fact, it suggests the opposite. If biomarkers can be used to detect autism at birth (i.e., before vaccines) then clearly vaccines aren’t the problem! I think it is pretty clear that Ginger doesn’t understand the scientific literature.

Other papers were on extremely tangentially related issues. For example, there were several studies on methyl-mercury (e.g., Rice 1989; Charleston et al. 1994), but methyl-mercury has never been in vaccines. The mercury that used to be in vaccines was ethyl-mercury, which is a very different chemical with different properties. You can’t say, “methyl-mercury does X, therefore ethyl-mercury also does X.” That’s not how chemistry works (also most of those mercury studies weren’t about autism either). Similarly, there were a few papers showing that some components of vaccines were toxic in very high doses, but the dose makes the poison. The fact that chemical Y is dangerous at a very high level is irrelevant to whether or not it is dangerous at the low levels present in vaccines.

Others were about general neurotoxic effects or effects other than autism. Many of these had specific problems that I won’t go into here, but the point that I want to make is that you can’t jump from, “vaccines cause X” to “vaccines cause autism.” Even if these studies had successfully demonstrated that vaccines cause neurological problems other than autism, that wouldn’t indicate or even suggest that vaccines actually cause autism. Each disorder is different with its own set of causes and triggers, and you can’t just assume that the same causal relationships exist for all of them.

The remainder of the studies were, I think, intended to demonstrate various components of hypothetical pathways that supposedly lead from vaccines to autism. For example, there were numerous papers showing associations between oxidative stress and autism, and the argument (I assume) is that vaccines cause oxidative stress, and therefore vaccines can cause autism. There are several problems with that approach, however.

First, the papers on associations between autism and factors other than vaccines were all over the map. They didn’t paint a consistent picture that points towards vaccines. There were studies on associations with the microbiome, associations with oxidative stress, associations with autoimmune disorders, associations with febrile seizures, associations with mitochondrial disorders, etc. Indeed, Ginger seems to have deluded herself into thinking that any paper on any association with autism is somehow evidence that vaccines cause autism. The list seriously looks like she googled, “causes of autism” then included any studies she found regardless of what they showed.

Second, correlation isn’t causation, and none of those papers established causation, so it could, for example, be the case that some aspect of autism causes oxidative stress rather than the other way around (further, the evidence for vaccines causing oxidative stress is extremely weak).

To put this another way, what the anti-vaccers are doing is laying out a hypothetical pathway in which, for example, they argue that vaccines cause oxidative stress which in turn causes autism, but we don’t know if either of those steps are actually true. Further, even if each step was true independently, you couldn’t assume that one will lead to the other in actual patients. The human body is remarkably complex, and biochemical pathways and interactions are complicated and difficult to predict. So, it’s often the case that under the right circumstances, A causes B, and under the right circumstances B causes C, but that doesn’t necessarily mean that A will go all the way to C in an actual person.

Finally, and most importantly, in science, you use hypothetical pathways to design experiments, but the pathway itself is not evidence. In other words, you do large studies to see whether or not the pathway is true, and, in the case of vaccines, if this hypothetical pathway actually worked, then the large case-control and cohort studies should have found a significant difference between vaccinated and unvaccinated children, but they didn’t. You absolutely cannot present a hypothesis as evidence for your position, but that is exactly what is happening here. These hypotheses have been thoroughly refuted by the large studies that I discussed earlier, and, therefore, they must be rejected.

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In vitro trials and animal studies

All of the anti-vaccine papers fall into the lowest categories of evidence, and none of them were capable of showing causal relationships (details here).

The anti-vaccine lists contained 25 animal studies and 20 in vitro studies (in vitro studies are done on cells and tissues in a petri dish; one study used both methods). There are two important reasons why I am filtering these out (remember that I also eliminated animal trials and in vitro trials from the pro-vaccine lists).

First, and most importantly, these types of studies are always at the bottom of the evidence pyramid. The human body is obviously vastly more complicated than a dish of cells, and in the human body, there are far more chemicals for a drug to interact with, not to mention the fact that you have the kidneys and liver which are actively filtering harmful compounds from your body. As a result, it is very often the case that a chemical will behave one way when directly exposed to a plate of cells (such as nerve cells), but behave very differently in the body where it must travel to the cells without being filtered out or becoming overly diluted, avoid interacting with other chemicals, etc. Similarly, humans, mice, monkeys, etc. are all biochemically different, and chemicals don’t always behave the same way in each species.

Because of all of these factors, in vitro studies and animal trials are preliminary studies that are used as a first pass filtering mechanism. In other words, they are used to decide which topics merit further research, and you absolutely cannot use them as evidence against large epidemiological trials. When an animal trial says X and a cohort study says Y, you go with the cohort study (unless, of course, you can actually find real problems with the cohort study).

The second reason that I am filtering out these trials is that they have very limited applicability to the topic of autism. You cannot diagnose a petri dish as having autism. The best you could do is say, “after being exposed to chemical X, the cells had characteristics that were similar to those of an autistic patient,” but again, association is not causation. So, unless you know that those cellular characteristics cause autism, you can’t really say much. Further, showing that a chemical damages a nerve cell does not mean that it will specifically cause autism. Similarly, how would you diagnose autism in a rat? You can say, “the rat is behaving differently,” but again that doesn’t necessarily mean that it specifically has autism.

Finally, I do want to make a few brief comments about a particular set of monkey trials. Anti-vaccers are very fond of citing Hewitson et al. (2008) and Hewitson et al. (2010), which claimed to find evidence of vaccines causing neurological damage in rhesus macaques (anti-vaccers also sometimes cite Hewitson et al. 2009, but that study was withdrawn). All of these were preliminary studies based on ongoing research. Hewitson et al. 2010 literally has the words “pilot study” in the title, and Hewitson et al. 2008 was a conference abstract, not a peer-reviewed paper. Further, the sample sizes were laughably small.

When the full study with a more proper sample size finally came out, it found that the preliminary data were wrong. In the full study, there was no evidence of vaccines causing neurological problems (Gadad et al. 2015). Also, it is worth mentioning that Hewitson is an author on the Gadad paper, and, amusingly, the study was actually funded by an anti-vaccine group!

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Mercury and thimerosal studies

Mercury is by far the most common vaccine component to be accused of causing autism. Therefore, it should not be surprising that 77 of the papers were specifically about it. However, I am not going to talk much about those papers for several reasons.

First, many of these papers were on elemental mercury (Hg) or methyl-mercury, but the preservative used in some vaccines (thimerosal) is actually ethyl-mercury. The mercury in thimerosal does not behave like other types of mercury because it is bound to an ethyl group (just like the chlorine in table salt does not behave like chlorine because it is bound to sodium; details here).

Second, even for the studies on thimerosal, none of them were large human trials that were capable of establishing causation. There were lots of small trials, animal trials, in vitro trials, association studies, etc., but large cohort studies were completely lacking. In contrast, remember that several of the papers in the pro-vaccine list specifically looked at thimerosal (as well as the whole vaccine) and failed to find any relationship. These pro-vaccine studies included a meta-analysis with 1.2 million children (Taylor et al. 2014), a cohort study with 446,695 children (Hviid et al. 2003), another cohort study with 124,170 children (Verstraeten et al. 2003), and a case-control trial with over 1,000 children (Price et al. 2010; several of the other studies also used vaccines that contained thimerosal, but they did not explicitly test that component). These massive, high-quality studies completely obliterate the small low-quality studies presented by the anti-vaccers. That is how the hierarchy of evidence works.

Finally, and perhaps most importantly, most industrialized countries do not currently have thimerosal in their childhood vaccines, and, in most cases, thimerosal has been absent for over 20 years! In the USA, Canada, and countries in the EU, for example, currently only certain strains of the flu vaccine contain thimerosal. Similarly, in Australia it is absent from all childhood vaccines except for certain Hep B vaccines.

This removal of thimerosal is important for two important reasons. First, if you live in a developed country (which is where most of my readers are) then thimerosal in childhood vaccines is a non-issue for you. Even if early exposure to thimerosal did cause autism (which it doesn’t), thimerosal isn’t in childhood vaccines, so you have nothing to worry about.

Second, the nearly world-wide removal of thimerosal from vaccines provides an excellent test of the hypothesis. If thimerosal in vaccines was actually causing autism, then there should have been an obvious drop in autism rates following its removal, but there wasn’t (Madsen et al. 2003; Schechter and Grether 2008). Look at the autism rates over time for any of these countries, and you will not find a noticeable difference following the removal of thimerosal. That is extremely clear evidence that thimerosal does not cause autism.

Now, you may protest and say, “but aren’t trace amounts of it still present in some vaccines?” Yes, trace amounts are present in some vaccines, but think about what you just said, “trace amounts are present.” We are talking about less than 1 microgram of thimerosal per dose. Let me put that in perspective, a small paper clip weighs about 1,000,000 micrograms! We are talking about a dose that is much smaller than any study has ever found to be harmful, and, of course, the dose makes the poison. Finally, if thimerosal caused autism, then the shift to only trace amounts in a few vaccines should still have resulted in lower autism rates.

To quote Orac/Monty Python,

“The hypothesis that mercury in vaccines causes autism is about as dead a hypothesis as there can be. It’s passed on. The hypothesis is no more. It has ceased to be! It’s expired and gone to meet its maker. It’s a stiff. Bereft of life, it rests in peace. It’s shuffled off this mortal coil, run down the curtain and joined the bleedin’ choir invisible! This is an ex-hypothesis!”

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Conference abstracts, case series opinions, other non-research

The lists contained several conference abstracts (which should always be considered preliminary and should not be used as evidence), case reports/series (which are glorified collections of anecdotes, and anecdotes aren’t evidence of causation), opinion papers, and other non-research papers (such as a bizarre, non-peer-reviewed student paper on court cases [Holland et al. 2011]).

I do, however, want to briefly talk about one of the case reports (Poling 2006), because it is very famous in anti-vaccine circles. The paper documents the story of a girl who developed normally until she received a vaccine, then she regressed into autism. So, it is every anti-vaccer’s story, just published in a scientific paper. However, the fact that it was published does not make a causal conclusion any more legitimate than if someone had said it on the internet. Saying, “X happened before Y, therefore X caused Y” is a logical fallacy known as post hoc ergo propter hoc. It does not prove or even suggest that X caused Y. There are also some other really disturbing aspects of this paper. For example, the subject of this study was Hannah Poling. That’s right, she is the author’s daughter (conflict of interest anyone?). In my opinion, parents publishing about their children’s health is probably not a great idea, especially when that parent proceeds to seek a financial settlement for their child’s health (as happened in this case).

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Actual human studies on current vaccines and autism

After all of that filtering, we are down to just 18 studies and 12 non-systematic reviews/opinion papers that are actually on humans and are relevant to both autism and the current vaccine schedule. However, three of those studies and one of those reviews have actually been retracted, and two of the remaining studies actually argue against vaccines causing autism! So, it’s really only 13 studies.

Nevertheless, let’s look at them. Some of these are grouped together, and I have preceded each cluster with the title(s) of the paper(s) contained therein (titles are underlined and in quotes).

“A positive association found between autism prevalence and childhood vaccination uptake across the U.S. population”
“Empirical data confirm autism symptoms related to aluminum and acetaminophen exposure”
“Impact of environmental factors on the prevalence of autistic disorder after 1979”
“A comparison of temporal trends in United States autism prevalence to trends in suspected environmental factors”

These four were association studies that looked for correlations between autism rates and vaccination rates. That’s automatically a problem, because association does not mean causation, so even if these studies were good, they would not present evidence that vaccines cause autism. Also, there are additional problems. For example, DeLong (2011) used very crude state-wide data, failed to account for all possible confounders, and lumped speech disabilities in with autism for the analysis (more details on the problems with this paper here).

Similarly, Seneff et al. (2012) didn’t actually measure autism rates. Instead, they looked for mentions of autism in the self-reported VAERS database (which went up over time), then tried to match that to the amount of aluminum in vaccines. Because it is entirely self-reported, VAERS is notoriously unreliable, especially in this case because there is so much evidence that autism rates aren’t actually increasing (Rutter 2005; Taylor 2006; Bishop et al. 2008; Baxter et al. 2015; Hansen et al. 2015; Russell et al. 2022; Grosvenor et al. 2024). Think about it, autism has become an increasingly hot topic over time, so you would naturally expect it to show up more often in VAERS over time, thus completely negating the study. VAERS is meant as an early-warning system. It’s simply not suitable for the type of analysis Seneff et al. (2012) conducted.

Next, we have Deisher et al. (2014). This study attempted to look for correlations between the release of certain vaccines and increases in autism rates, and it is honestly a very hard paper to read because it is so horrible. The writing is nearly impossible to follow, the methods are nonsense, the statistics are total crap, the conclusions aren’t merited by those statistics, and the fundamental premises of the paper are refuted by numerous other studies (i.e., it assumes that autism rates are actually going up). In short, what they did was take a data set that would normally be analyzed by linear regression, then chop it up based on cherry-picked data points. Other authors have explained the problems in detail, so I’ll defer to them (here and here).

Finally, we have Nevison (2014). In short, this study claimed to find an increase in autism rates over time (which is debatable), and it found that the increase correlates with glyphosate use and aluminum in vaccines. Once again, however, correlation does not indicate causation.

In short, all four of these papers used weak, correlation-only designs, and they were riddled with problems.

“Do aluminum vaccine adjuvants contribute to the rising prevalence of autism?”

Now we arrive at the infamous Tomljenovic and Shaw (2011) study. It’s a paper that is so fundamentally flawed that I don’t even know where to begin, and it would take me an entire (and lengthy) post to go over all of the problems with it. Fortunately, others have done that for me, so I will defer you to them and just hit the highlights.

First, this study looked at two regressions: autism and aluminum in the US, and autism and aluminum across countries. We have lots of problems here. First, as Orac explains in more detail, you have a problem known as an ecological fallacy where you lump a very large data set (i.e., a country) into a single data point. In other words, they aren’t showing that individuals who receive vaccines have higher autism rates. Rather, they are showing that countries with lots of vaccines have higher autism rates. That is a huge problem because there are obviously tons of factors other than just vaccination rates that differ among countries (which means that we can’t be sure that vaccines are the thing that is causing the difference in autism rates). Additionally, as explained here, the sources of the data for different countries varied widely and involved different cohorts, and it appears that the authors cherry-picked their sources.

Even if we zoom in on the correlation in the US, we have serious problems. To assess autism rates, they looked at the number of autistic children who were reported via the Individuals with Disabilities Education Act (IDEA) database, which is an extremely problematic and inappropriate way to measure autism levels because it is affected by diagnostic changes, and, once again, the increase in autism seems to be largely due to diagnostic changes rather than actual changes (Rutter 2005; Taylor 2006; Bishop et al. 2008; Baxter et al. 2015; Hansen et al. 2015; Russell et al. 2022; Grosvenor et al. 2024).

Finally, this is yet another association study. It cannot demonstrate causation, but that doesn’t stop the authors from trying. They misappropriate Hill’s criteria, which is a series of nine diagnostic criteria used to assess whether or not causation is likely. I will outline and discuss them below.

  1. Strength (robustness): Although they did get low P values, their methods have multiple problems and confounding factors that they did not account for. So, this study fails the strength test.
  2. Consistency (consistent with other results): This study is extremely inconsistent with all of the large studies discussed earlier.
  3. Specificity (if you are dealing with a very specific, isolated event, causation is more likely): This was done at the country level. It is as far from specific as you can get.
  4. Temporality (cause happens before the effect): This could not be assessed by this study.
  5. Biological gradient (i.e., higher dose = stronger effect): This was not evaluated by this study, but I cited several studies earlier that failed to find a relationship between the number of vaccine doses and autism.
  6. Plausibility: Once upon a time, the vaccine/autism hypothesis was plausible, but now that it has been so thoroughly tested, it is no longer plausible.
  7. Coherence (agreement of laboratory and epidemiological findings): There are lots of in vitro and animal trials that have found vaccines to be safe.
  8. Experiment: not relevant for this particular topic
  9. Analogy: not relevant for this particular topic

It’s pretty obvious that this paper epically fails Hill’s criteria. So, in short, this paper showed some shoddy correlations that were based on crude, inappropriate, and cherry-picked data sources. It absolutely is not evidence that vaccines cause autism.

“Hepatitis B triple series vaccine and developmental disability in US children aged 1-9 years”
(RETRACTED) “Hepatitis B vaccination of male neonates and autism diagnosis, NHIS 1997–2002.”

Next, I want to look at two studies by Gallagher and Goodman. Gallagher and Goodman (2008) is another study that used general developmental disorders, rather than autism specifically (which automatically makes it problematic and means that we can’t reach any conclusions specifically about autism). Further, it was yet another association study, and it relied on parental surveys (which are often prone to biases). Additionally, it had fairly small sample sizes (228 unvaccinated boys, 678 vaccinated boys, 217 unvaccinated girls, and 571 vaccinated girls). Most importantly though, its results are entertainingly problematic. It found higher levels of EIS (special education services) in vaccinated boys than unvaccinated boys (7% vs 3%), but it also found significantly lower levels of EIS in vaccinated girls than in unvaccinated girls (2% vs 6%). In other words, if you want to use this study as evidence that vaccines cause autism in boys, you must simultaneously use it as evidence that they prevent autism in girls.

Now, let’s look at Gallagher and Goodman 2010. It was a cross-sectional study, which is a study design that looks at the rate of something in a population, then looks for possible causes of that thing. This is a very weak type of association study which cannot establish causal relationships and is easily biased by numerous factors, especially when sample sizes are small, as was the case here (31 boys with autism). Further, other scientists identified issues in the analyses ultimately resulting in the study being retracted. So, this was a flawed association-only study with a tiny sample size that produced unreliable results and was ultimately retracted.

“Serological association of measles virus and human herpesvirus-6 with brain autoantibodies in Autism”
“Abnormal measles-mumps rubella antibodies and CNS autoimmunity in children with autism”
“Elevated levels of measles antibodies in children with autism”

Next, let’s talk about three papers for which Vijendra Singh was the primary author. First, we have Singh et al. 1998, which is another really sorry excuse for a paper. It is yet another association study, and it also had tiny sample sizes (48 with autism and 43 controls; you should be detecting a theme by now). Most importantly, their numbers are so far off that I am willing to label it “fraudulent.” The authors looked at serum levels of HHV-6-IgG, measles-IgG, anti-MBP, and anti-NAFP, and found that 70% of the autistic children were positive for anti-MBP. Simple math tells us that 70% of 48 is 34 (after rounding). So, 34 of their autistic children had anti-MBP; however, in table 1, where they are presenting the “associations” on which their entire paper is based, they reported that 37 autistic children had both measles IgG antibodies and anti-MBP. That is not possible if only 34 autistic children had anti-MBP.

Similarly, they said that 57% of autistic children were positive for anti-NAFP. So, 57% of 48 is 27 autistic children with anti-NAFP. Yet they claim that 30 autistic children had both measles IgG and anti-NAFP. Whenever you find inconsistencies of this magnitude in the core results of a paper, you should toss out the whole paper, because at that point, you don’t have any reason to trust anything in it. There are also lots of other problems with this study, such as the fact that there was no significant difference in viral levels in the autistic and non-autistic group (which is the opposite of what you would expect if exposure to the virus caused autism), but the numerical inconsistencies are so great that I don’t feel the need to go any further.

The next study from this group, (Singh et al. 2002) also looked at serum levels. Specifically, it was looking for antibodies produced by the MMR vaccine as well as anti-MBP. Once again, it is a small association study (125 autistic children, 92 controls), and the results are, unsurprisingly, a bit suspicious. They used two different techniques for detecting the antibodies, and the main one (which was used for the primary comparisons) found MMR antibodies in 60% of autistic children, or at least that’s what the text says. According to figure 5, it was only about 55%. Again, inconsistencies like that in the main results are enormous red flags. At best, they mean that the authors were really sloppy and, at worst, they were dishonest and fudged the results. Additionally, this technique did not detect MMR antibodies in any of the control children. This is extremely surprising, because all of the control children were vaccinated, which means that most of them should have had those antibodies. Further, their other technique (which was used on a subset of samples) did detect MMR antibodies in some of the controls, which means that either their primary method was not sensitive enough to be useful, or they lied. Either way, this paper is busted.

Finally, Singh and Jensen (2003) looked at measles antibodies in a small sample (88 with autism, 47 without) and found that they were higher among those with autism. Again, that doesn’t mean anything other than that the antibodies were higher. This in no way demonstrates that the MMR vaccine is causing autism. Further, even the result that antibody levels were higher is questionable because, as other scientists pointed out in a rebuttal (Sweeten and Fujinami 2004), antibody rates decline over time, so for this analysis to be even remotely meaningful, age has to be included as a covariate in the analysis. This was not done, which renders the analysis meaningless.

“Possible immunological disorders in autism: concomitant autoimmunity and immune tolerance”

Next, I want to talk about Kawashti (2006). The inclusion of this paper in the anti-vaccers’ list amuses me to no end, because it is designed very similarly to the Singh et al. papers (i.e., it looked for antibodies from the MMR vaccine in children with and without autism), but it found the exact opposite! It found the antigens for measles, mumps, and rubella were present in 100% of non-autistic children, but only 50%, 73.3%, and 53.3% of autistic children.

Further, it said, “At this stage, we can conclude that M.M.R. vaccine may not be a cause of autism.” That’s right, this is not actually an anti-vaccine paper. This, once again, shows just how little quality control went into constructing the anti-vaccine lists. This paper directly contradicts other papers in the list, yet it was still included (note: this is another small association study, so I’m not touting it as evidence that vaccines don’t cause autism; rather, the point is simply that the anti-vaccers screwed up and put a pro-vaccine paper in their list).

“Profile of autism spectrum disorders in Morocco: cross-sectional retrospective study of parents of children with autism”

Sefrioui et al. (2023) was a simple parental survey to gather information about parents’ observations of their autistic children. So, once again, it is not a study design that could possibly demonstrate causation. Further, while multiple parents did report vaccination prior to noticing autism, the authors of the study explicitly stated that this was likely a bias due to the amount of attention the supposed link between vaccines and autism has received, and they argued that vaccines do not cause autism, citing many of the same papers I have been citing. So, once again, if anti-vaccers actually bothered to read the study, they would see that it does not actually support their views.

(RETRACTED) “Preterm birth, vaccination and neurodevelopmental disorders: a cross-sectional study of 6- to 12-year-old vaccinated and unvaccinated children”
(RETRACTED) “Pilot comparative study on the health of vaccinated and unvaccinated 6- to 12- year old U.S. children”

Next, let’s talk about a particularly awful pair of studies: Mawson et al. (2017a, b). These papers aren’t actually published in real, respected journals. Rather, they both appeared in the predatory journal, “Journal of Translational Science,” and they were so bad that even that fake journal decided to retract them. Both “studies” are based on an online survey that used a method known as “convenience sampling.” They simply sent the survey to homeschool groups (which tend to be dominated by anti-vaccers), and they did not use any of the controls or randomizations that are necessary for a proper survey. In statistics courses, literally the first thing that you learn about surveys is that you shouldn’t use convenience sampling.

Essentially, what these studies did was poll anti-vaccers about their children’s medical history. Unsurprisingly, when you poll people who are convinced that vaccines cause autism, you get the result that vaccines cause autism. These papers are complete nonsense (which is why they were in a predatory journal instead of real journals and why they have been retracted). You can read more about these studies at Science-Based Medicine, Vaccines Work, Retraction Watch, and Respectful Insolence.

“Can awareness of medical pathophysiology in autism lead to primary care autism prevention strategies?”

Now, I want to shift gears a bit and talk about Mumper (2013). This is a truly bizarre “study.” It was published in the North American Journal of Medicine and Science, which is a journal that is so minor that I couldn’t even find an impact factor (in laymen’s terms, that means that no one cites this journal or takes it seriously). Further, the paper itself doesn’t follow any of the standard conventions for a scientific paper (it includes bizarre sections like anecdotes of the author’s travels to other countries). It looks more like an undergraduate report than a scientific paper.

It may seem like I am being nitpicky, but all of those things are actually clues that this is a subpar paper that did not go through a proper review process. You should learn to watch out for flags like that as you read scientific papers. Then, of course, we have the actual experimental design (and I’m using that term very loosely). The author works at a private pediatric clinic that stresses a lot of different things like breastfeeding, probiotics, nutritional counseling, flexible vaccine schedules, etc. Some of those are good, some are questionable, some are bad, but what she did was go back through her records since 2005 to see if any of their infants had been diagnosed with autism while under their care. She found that out of 294 infants, none had been diagnosed with autism. This was lower than the national background rate at the time (1 in 59), so she made an astronomical jump to the conclusion that her clinic’s practices prevent autism (or at least avoid the causes of autism). That’s clearly an absurd conclusion (especially given that we know autism is largely genetic). What she did is not even close to a proper study. Nevertheless, let’s assume for a second that it was. Let’s assume that there is actually something happening at that clinic that prevents autism. You still can’t jump to the conclusion that it was the lack of vaccines, because there are so many factors. Maybe it was breastfeeding. Maybe it was probiotics, etc. This is not a real study. It is an anecdote that is cosplaying as a study.

“Detection and sequencing of measles virus from peripheral mononuclear cells from patients with inflammatory bowel disease and autism.”

Kawashima et al. (2000) simply found DNA from the measles vaccine in the colons of nine autistic patients, which of course doesn’t mean anything other than that DNA from the measles vaccine was in their colons. That result is not even remotely evidence that the vaccine does anything harmful.

“Epidemiologic and molecular relationship between vaccine manufacture and autism spectrum disorder prevalence.”

This paper (Deisher et al. 2015) was thoroughly debunked on Science-Based Medicine, so I will defer to that post, but in short, it is yet another correlation study attempting to show that the “increase” in autism diagnoses is correlated with an increase in vaccination. It is on an implausible hypothesis, the data were collected in a very questionable way, and the data were presented deceptively to show trends that aren’t really there. Additionally, this paper was on the MMR vaccine, which is also the vaccine that was specifically examined by most of the large cohort studies, the massive meta-analysis, etc. All of those studies were far more powerful than this study, so you really can’t use this paper as evidence that those papers are wrong. Finally, this paper was published in an extremely minor journal (the impact factor is roughly 0), which is a huge red flag that it is junk science.

Acetaminophen (paracetamol) use, measles-mumps-rubella vaccination, and autistic disorder: the results of a parent survey.

Schultz et al. (2008) is another very small association only study (83 cases, 80 controls). Basically, after some statistical rigmarole to subset the data even further, they found an association between paracetamol use after vaccination and autism. However, as other scientists pointed out (Cox and McDowell 2009), this study was based entirely on parental surveys, which are notoriously riddled with biases.

Overview

That’s it. That’s the entirety of the evidence supposedly supporting a link between vaccines and autism: a bunch of tiny, methodologically-flawed studies showing some dodgy correlations. Please compare that to the multiple, utterly enormous, methodologically rigorous studies failing to find a link between vaccines and autism.

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Non-systematic reviews/hypothesis papers

At this point, I have gone through the relevant anti-vaccine literature and shown that all of the studies are small and incapable of demonstrating causation. So, you may be wondering, “what could the reviews possibly be on?” Well, this is where we come back to the importance of systematic reviews. If you recall, in the pro-vaccine section, all of the reviews that I cited were systematic, meaning that they used pre-defined search terms and criteria. The anti-vaccine reviews, however, are not systematic. Thus, they completely ignored all of the large studies that have discredited the vaccine/autism hypothesis, they ignored all of the problems with the anti-vaccine studies, and they used all of those tiny, problematic studies to spin a fanciful tale in which, through a complex, unlikely, and thoroughly discredited series of events, vaccines cause autism. In other words, all that these “reviews” do is set forth a hypothesis. Doing that would be fine if it wasn’t for the fact that the hypothesis has been discredited by numerous massive studies. Again, you always reject a hypothesis based on the evidence. You never reject the evidence based on a hypothesis, but that is exactly what these papers are doing. They are deceptively only showing the papers that support their position while ignoring all of the papers that refute it.

Here is the list of reviews: Rimland and McGinnis 2002 [retracted], Blaylock 2008; Singh 2009, Ratajczak 2011 (details of problems here and here), Sienkiewicz et al. 2012 (details of problems here) Shaw and Tomljenovic 2013b, Shaw et al. 2014a, Morris et al. 2017a and Lyons-Weiler 2018 (details of problems here; Giannotta and Giannotta 2019; Boretti 2021; Angrand et al. 2022). Most of these contain numerous blatantly false (or at least highly misleading) statements, but the main point that I want to drive home is simply that they are stating hypotheses rather than presenting evidence of causation, and they are ignoring the fact that those hypotheses have already been tested and refuted.

Amusingly, there was one relevant systematic review in Ginger Taylor’s list (Piyasirisilp and Hemachudha 2002), but it actually concluded that there was no evidence of vaccines causing autism! To be fair, this review was conducted before most of the studies failing to find a link between vaccines and autism had been published, so its conclusion was based simply on a lack of studies; therefore, it is not good evidence that vaccines are safe, nor did I include it in my list of pro-vaccine systematic reviews. However, it clearly does not support anti-vaccers’ views, and once again, anti-vaccers clearly don’t actually read their own lists and are just trying to deceive you by making it sound like a huge number of papers are on their side.

Note: There were actually 58 reviews/hypothesis papers in all, but most of them were not about vaccines, were about thimerosal, etc. So only the 12 listed here were actually relevant for the current vaccine schedule.

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Conflicts of interest

Anti-vaccers are very quick to point out conflicts of interest in pro-vaccine papers (or, more often, assume that they exist before they even check for them), but they are very slow to acknowledge them in their own papers. So, I want to briefly provide a few examples to show that they do in fact exist (I did not check for conflicts in all of the anti-vaccine papers).

First, it is worth mentioning that this whole vaccine/autism mess got started when Andrew Wakefield falsified evidence against the MMR vaccine and conspired with lawyers to sue pharmaceutical companies, all while covertly working on a patent for his own vaccine which he planned to replace the MMR vaccine with (details here and here). Similarly, Geier, who is the author on a bunch of anti-vaccine papers, has had his medical license suspended for unethical behavior and, “incompetence or multiple instances of negligence.” The heroes of the anti-vaccine movement leave much to be desired. Further, those two are far from alone. As I mentioned earlier, Dr. Poling published his infamous case report while in the process of seeking a financial settlement for his daughter’s “vaccine injury.”

Dr. Shaw and Dr. Tomljenovic are also two fantastic examples of conflicts of interest. These are two of the most prominent anti-vaccine scientists, and many of the papers in the anti-vaccers’ lists were authored by at least one of them. However, both of them have served as consultants or expert witnesses in vaccine lawsuits, Shaw is the chair of the Scientific Advisory Board for an anti-vaccine group, and at least one of their studies was funded by members of the governing board of that group (more info here). Funding from an activist group that describes vaccines as, “a holocaust of poison on our children’s brains and immune systems” is just as big of a conflict of interest as funding from a pharmaceutical company. So, according to standard anti-vax reasoning, this should cast doubt on all of the authors’ work. They have also been authors on at least one paper that was retracted for data manipulation, so they clearly are not very trustworthy.

Similarly, Dr. Singh (who you may remember authored three of the papers and one of the reviews that I talked about) was funded by the Autism Research Institute, which, at the time that he received funding, ran a program called “Defeat Autism Now!,” which actively promoted the idea that vaccines cause autism. Further, remember that horrible DeLong (2011) paper that I talked about earlier? Well DeLong is a board member of the prominent anti-vaccine group “SafeMinds,” and, like Poling, is the parent of an autistic child. Similarly, those retracted Mawson surveys I talked about were funded by Generation Rescue, yet another anti-vaccine organization.

I could keep going, but I think that I have made my point clear. Anti-vaccers like to pretend that all of their studies are conflict free and represent true, unbiased research. In reality, there are plenty of anti-vaccine organizations that are happy to fund anti-vaccine studies, and many of the authors are deeply involved in the anti-vaccine movement. To be clear, I’m not suggesting that we should automatically reject these papers because of those conflicts (we should reject them because they are junk science), but I wanted to point out that, despite what anti-vaccers like to think, they are not free from conflicts of interest.

Note: please see these two posts for more info on when and how it is OK to attack a source.

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Studies that I did not include

You may have noticed that I did not talk about either the original Wakefield study or Hooker (2014). That is because both of these studies were so flawed that they were retracted (I talked more about Hooker’s study here).

Overview of anti-vaccine papers

The vast majority of papers cited by anti-vaccers aren’t even about vaccines and autism. Of the ones that are, many of them are animal trials and in vitro trials, or they are about thimerosal, which is no longer in vaccines and has been documented to be safe via several very large epidemiological studies. Indeed, there were only 18 studies on humans that were about both vaccines and autism and were relevant to the current vaccine schedule, but none of them were large, systematic studies and there were no meta-analyses or systematic reviews. The studies used small sample sizes and shoddy statistics to show crude correlations, and none of them had the ability to assign causation. Further, most of them were filled with problems (three were retracted), and two actually argued or presented data against vaccines causing autism. There were also 12 non-systematic reviews (one retracted), but these were essentially glorified opinion papers that ignored all of the literature against a link between autism and vaccines. They simply presented hypotheses rather than evidence, and those hypotheses have been thoroughly discredited by large studies.

In short, there is no credible evidence from large, properly controlled, well-conducted peer-reviewed studies showing that vaccines cause autism. All that anti-vaccers have is a bunch of meaningless correlations.

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Anecdotes, court cases, VAERS, etc.

At this point, it should be exceptionally clear that there is no good scientific evidence to suggest that vaccines cause autism, which means that this is usually the point at which people start trying to use non-scientific evidence. Therefore, I want to very briefly explain the problems with some common non-scientific arguments.

The most common response to the statement, “vaccines don’t cause autism” is probably the appeal to anecdotes. The internet is full of stories of people who vaccinated their child, then watched as the child regressed. The problem is, of course, that anecdotes aren’t evidence. The fact that X happened before Y does not mean that X caused Y. In fact, arguing that X caused Y is a logical fallacy known as post hoc ergo propter hoc. This is especially true for something like autism because the first signs of autism usually appear around the same age as many vaccinations. Therefore, given the large number of people who develop autism, and the large number of people who are vaccinated, you expect there to be many cases where vaccination and the onset of autism occur together just by chance. Really think about that for a minute. If 1 in 31 children will have autism, usually with the first obvious symptoms occurring around 2-3 years of age, and over 90% of children receive vaccines around 2-3 years of age, then there should be lots of cases where parents notice the signs of autism shortly after vaccinating, even if vaccines aren’t the cause (I actually ran the numbers on this here).

I fully understand why parents would blame vaccines. I understand why seeing your child develop autism shortly after receiving a vaccine would make you think that the vaccine was responsible, but you need to realize that lots of things happen together just by chance, and, as a result, anecdotes are not valid evidence. If vaccines actually did cause autism at the rates claimed by anti-vaccers, then the large, systematic studies should have found a significant difference between vaccinated and unvaccinated children. In the face of evidence like that, it is not logically or scientifically valid to cling to anecdotes.

An extension of the anecdote argument is to cite reports of vaccines causing autism in the Vaccine Adverse Event Reporting System (VAERS). VAERS is, however, a self-reported database. In other words, it is just a collection of anecdotes. The fact that someone reported an anecdote to VAERS doesn’t make the anecdote any more trustworthy than if it was on Facebook. The point of databases like this is to allow doctors and scientists to identify potential issues that need to be studied. It is not meant to be used as evidence of causation (more details about issues with using VAERS as evidence here).

A final line of anecdotal reasoning involves appealing to the vaccine package inserts (which is the one and only time that anti-vaccers trust pharmaceutical companies). The problem is, once again, that the lists of adverse reactions do not demonstrate causation. Those lists consist of any symptoms reported during clinical trials, most of which were almost certainly not caused by the vaccine. In fact, the package inserts even state that the list of adverse reactions is not a list of confirmed causal relationships.

Another common strategy is to appeal to court cases. There have been various court cases in various countries where money has been awarded to people who claim that vaccines gave their child autism (there have also been plenty that were thrown out of court or later overturned). To anti-vaccers, these are admissions of guilt by governments and confirm that vaccines are dangerous. In reality, they are nothing of the kind. The fact that a judge concluded that a vaccine caused autism does not mean that the vaccine actually did cause autism. This is a blatant appeal to authority fallacy. Judges aren’t infallible, and they usually aren’t even scientists. A judge or jury can be deceived about the current state of our scientific knowledge just as easily as anyone else. So, you absolutely cannot say, “This court gave money for an autism case, therefore all of those massive studies with hundreds of thousands of children must be wrong.” Hopefully you can see why statements like that are absurd (more on why court cases shouldn’t be used as scientific evidence here).

Additionally, there has been a great deal of fuss over the “CDC whistleblower.” I won’t go into the details because it is just too long of a story, but the short version is that there was no cover up and the CDC did not hide evidence or deceive the public. This argument is nothing more than typical conspiracy theorist ramblings. Many others have explained the situation in detail, so if you are prone to using this argument, please see their posts (for example, here, here, and here).

More recently, people have latched onto the CDC statement that, “The claim ‘vaccines do not cause autism’ is not an evidence-based claim because studies have not ruled out the possibility that infant vaccines cause autism.” First, this is coming from the CDC under the leadership of RFK Jr. as Secretary of Health and Human Services. RFK Jr. has a long history of anti-vax views, and there have been countless instances of him weakening the USA’s science and polluting it with his politically motivated, nonsense views. So, unfortunately, this statement is both unsurprising and meaningless. More importantly, it ignores all of the massive studies cited earlier. By any reasonable standard, those studies have ruled out the possibility of vaccines causing autism (also, see the section earlier on Sample size and statistical power).

Finally, when faced with the overwhelming evidence that vaccines are safe, many people try to argue that science shouldn’t be trusted because it has been wrong before (debunked here) or by citing the work of scientists like Dr. Ioannidis and claiming that most studies are wrong, therefore we can blindly ignore the studies on vaccine safety (debunked here). Both of these arguments are logically and scientifically invalid. The fact that science has been wrong before doesn’t mean that you can dismiss it anytime that you want to. Similarly, what Ioannidis’s work actually shows is exactly what I have argued in this post: on any topic there will be many small, poorly conducted, and unreliable studies, so we have to rely on a consensus of large, high-quality studies.

Conclusion

If you made it all the way here, then congratulations on reading the world’s longest blog post (or at least an unusually long post). It should now be clear to you that the evidence really is overwhelmingly supportive of vaccines. Even though anti-vaccers claim to have lengthy lists of papers supporting their position, most of those papers are irrelevant, used weak designs, and had small sample sizes. In contrast, the literature supporting vaccine safety consists of multiple exceptionally large and powerful studies. So there really is no good scientific evidence to suggest that vaccines cause autism.

Jump back to the top

Literature Cited

(Note that while I have included links, the links may break overtime, but you can easily look up the full paper using the citations provided. Additionally, some papers may be paywalled. In many cases, just pasting the title into Google Scholar will reveal a free PDF. If it doesn’t you can either email the corresponding author or try Sci Hub; more details here)

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COVID vaccines are safer than COVID itself

Posted on August 28, 2026 by Fallacy Man

Some of the most persistent myths and misconceptions around COVID19 vaccines concern the risk of myocarditis and pericarditis, particularly in young people. A massive new study has obliterated these myths using a very clever study design, and it’s worth diving into it.

First, as critical background information, numerous massive studies have confirmed that COVID vaccines are enormously beneficial and reduce the spread of COVID, reduce the risk of catching COVID, reduce the severity of COVID if you catch it, reduce hospitalization rates, and reduce mortality (Huang and Kuan 2022; Rahmani et al. 2022; Zheng et al. 2022; Wu et al. 2023). This has been studied over and over again with very consistent results. Indeed, COVID vaccines have been examined with some of the largest studies I have ever seen, such as this meta-analysis of over 24 million people (Ikeokwu et al. 2023). Anyone who says that COVID vaccines haven’t been well-studied or aren’t beneficial is simply wrong. The reality is that COVID vaccines have been extensively studied and have saved millions of lives (Watson et al. 2022; Mesle et al. 2024).

Nevertheless, nothing is risk free, and COVID vaccines (like all medications) do have risks and side effects. Most of those side effects are, however, very mild, and serious side effects are rare (Amanzio et al. 2022; Haas et al. 2022; Kouhpaye and Ansari 2022; Bello et al. 2023). This is true even when looking at subgroups like children (Piechotta et al. 2023). There is absolutely no evidence whatsoever of widespread deaths from COVID vaccines (Lamptey 2021; Abdel-Qadir et al. 2026).

With that said, there are occasional serious side effects, particularly in some subgroups. Heart issues (especially myocarditis and pericarditis) have received the most attention. These are anti-vaxxers’ go-to excuse for why the vaccines are actually dangerous. What vaccine opponents ignore is the fact that these side effects are very rare and are also caused by COVID itself. Indeed, multiple studies have shown that while your risk of myocarditis does increase after COVID vaccination, your total risk is still incredibly small (Juan Gao et al. 2023) and your risk is substantially higher from actually catching COVID (Patone et al. 2022; Voleti et al. 2022).

Despite this evidence, concerns have persisted, and there are countless myths out there like the notion that elite athletes are suddenly dropping dead, and people have continued to focus on the low risk of the vaccine while downplaying the higher risk from COVID itself. The new study I want to discuss address those concerns with a brilliant design.

Study design

The study is titled, “Cardiac outcomes following SARS-CoV-2 infection versus BNT162b2 vaccination in adolescents and young adults: a cohort study of 4 million individuals” and was published in the journal Vaccine by Toraih et al. (2026).

The authors took an enormous group of over 4 million people, split them into the following groups, and compared the risk of myocarditis, pericarditis, and all-cause mortality (i.e., any deaths):

  • People who were never vaccinated for COVID or infected with COVID
  • People who were never vaccinated but were infected
  • People who were vaccinated and never became infected
  • People who were vaccinated and became infected.

This study has a lot of strengths. First, it only included 16-25 year-olds. This is really important, because this is the age group with the highest risk of myocarditis from COVID vaccines, and it is a group that anti-vaccers routinely insist don’t need to be vaccinated. It is very common for them to claim that young adults simply don’t need the vaccine. Therefore, restricting the study to this age group creates something of a “worst-case scenario” for vaccines. It is a group with lower risk of COVID complications and higher risk of vaccine complications. So, if we were ever going to see a situation where the vaccines did more harm than good, this would be it.

Second, the study had a truly massive sample size. As I’ve written about before, cohort studies like this one are often better tools than placebo-controlled trials for studying vaccines, because they allow researchers to have massive sample sizes that are better at detecting relatively rare events. So, this is actually a really powerful design for comparing risk.

Third, the four-group design of the study is brilliant. It directly compares the risk under each of the four potential scenarios. Often, studies only look at groups like unvaccinated vs. vaccinated and suffer from confounding factors like COVID infection history. This study eliminates that problem.

Fourth, this study followed people for a full 6 months. Because of how the vaccines work (and how COVID causes heart issues), we really wouldn’t expect these issues beyond that period, so this is a good window and, importantly, it is a longer window than many previous studies have used.

Fifth, the authors used three different analytical approaches, and all three gave the same results. The statistical details are beyond what I want to get into here, but this agreement among approaches is good evidence that the results aren’t simply statistical flukes or the result of authors cherry-picking a biased approach (note that one of these approaches included stratifying by sex).

But what about conflicts of interest? One of the great things about cohort studies is that they are often retrospective and therefore require little or no funding. In this case, there was no funding for this study. It was not paid for by “big pharma.” That said, one of the four authors has previously worked on COVID vaccine trials and various safety monitoring boards. This is, in my assessment, not a very serious conflict of interest. This author is simply an expert in this area. Further, this author was not the project leader and was not the one leading the analyses (see the “author contribution statement”).

Graphical abstract from: Toraih, et al. 2026. Cardiac outcomes following SARS-CoV-2 infection versus BNT162b2 vaccination in adolescents and young adults: a cohort study of 4 million individuals. Vaccine 91: 129001

Results

So, what did the study actually find? In short, vaccines passed with flying colors. In every comparison, risk was lower with vaccination than without vaccination. When comparing those who were vaccinated and uninfected to those who were unvaccinated and infected, the vaccinated had:

  • 85% lower risk of myocarditis
  • 79% lower risk of pericarditis
  • 72% lower mortality

It is inescapably clear that infection from COVID is substantially more dangerous than COVID vaccination. Also, it is worth noting that many people simultaneously claim that COVID is a very mild disease and claim that the vaccines are extremely dangerous. That claim is clearly self-contradictory nonsense.

So being vaccinated and never catching COVID is clearly much better than being unvaccinated and never catching COVID. However, while vaccines do reduce your risk of catching COVID, some people who are unvaccinated will get lucky and never catch COVID. So what about them? In all cases, there was no increase in risk among the vaccinated compared to the unvaccinated. In fact, the vaccinated/uninfected had slightly lower risk than the unvaccinated/never infected. I’m a bit cautious about that result, because other studies have shown slight increases in rates of myocarditis and pericarditis (but not death) following vaccination, but what is clear is that the risk is not substantially higher following vaccination. In other words, I’m not going to hang my hat on this result as showing that the vaccines reduce your risk even if you don’t catch COVID, but they are compelling that any risk from the vaccines must be extremely low.

Nevertheless, no vaccine is 100% effective, and some people who were vaccinated still caught COVID. So, what happens if you are vaccinated but still catch COVID? In this case, the risk goes up compared to never catching COVID (either vaccinated or unvaccinated), but the risk is still lower from catching COVID without being vaccinated. This makes perfect sense since so many previous studies have shown that COVID vaccines reduce the severity of COVID infections.

Conclusions

This study gives extremely clear and compelling results. No matter how you slice the data, the worst-case scenario is being unvaccinated and catching COVID. That scenario has the highest risk of myocarditis, pericarditis, and death. Conversely, the best-case scenario is being vaccinated and never catching COVID. This had the lowest risk across the board, even when compared with people who were never vaccinated and never caught COVID. Being vaccinated and developing COVID resulted in a mid-level of risk, with higher risk than never catching COVID (either with or without vaccination) but lower risk than catching COVID without being vaccinated.

These results could not be more clear: COVID vaccines are very safe (with low risk) and provide a substantial level of protection. The risks from COVID itself far outweigh the risks from vaccines, and you are safer with the vaccine than you are without it. There is no evidence whatsoever to support the claims of widespread harm from COVID vaccines.

 Literature cited

  • Amanzio et al. 2022. Adverse events of active and placebo groups in SARS-CoV-2 vaccine randomized trials: A systematic review. The Lancet Regional Health 12:100253
  • Abdel-Qadir, et al. 2026. Association between COVID-19 vaccination and sudden death in apparently healthy younger individuals: A population-based case-control study. PLOS Medicine
  • Bello et al. 2023. Adverse Events Related to SARS-Cov-2 Vaccination: A Systematic Review and Meta-Analysis. Journal of Epidemiology and Public Health 8:284-297
  • Haas et al. 2022. Frequency of Adverse Events in the Placebo Arms of COVID-19 Vaccine Trials: A Systematic Review and Meta-analysis. JAMA Network Open 5:e2143955
  • Huang and Kuan 2022. Vaccination to reduce severe COVID-19 and mortality in COVID-19 patients: a systematic review and meta-analysis. European Review for Medical and Pharmacological Sciences 26: 1770-1776.
  • Ikeokwu et al. 2023. A Meta-Analysis To Ascertain the Effectiveness of COVID-19 Vaccines on Clinical Outcomes in Patients With COVID-19 Infection in North America. Cureus 15:e41053
  • Juan Gao et al. 2023. A Systematic Review and Meta-analysis of the Association Between SARS-CoV-2 Vaccination and Myocarditis or Pericarditis. American Journal of Preventative Medicine 64:275-284.
  • Kouhpaye and Ansari 2022. Adverse events following COVID-19 vaccination: A systematic review and meta-analysis. Int. Immunopharmacol. 109:108906
  • Lamptey 2021. Post-vaccination COVID-19 deaths: a review of available evidence and recommendations for the global population. Clin. Exp. Vaccine Res. 10:254-275
  • Mesle et al. 2024. Estimated number of lives directly saved by COVID-19 vaccination programmes in the WHO European Region from December, 2020, to March, 2023: a retrospective surveillance study. Lancet Respir. Med. 12:714-727
  • Patone et al. 2022. Risks of myocarditis, pericarditis, and cardiac arrhythmias associated with COVID-19 vaccination or SARS-CoV-2 infection. Nature Medicine 28: 410-422
  • Piechotta et al. 2023. Safety and effectiveness of vaccines against COVID-19 in children aged 5–11 years: a systematic review and meta-analysis. Lancet Child Adolsc. Health 7:379-91.
  • Rahmani et al. 2022. The effectiveness of COVID-19 vaccines in reducing the incidence, hospitalization, and mortality from COVID-19: A systematic review and meta-analysis. Frontiers in Public Health
  • Toraih, et al. 2026. Cardiac outcomes following SARS-CoV-2 infection versus BNT162b2 vaccination in adolescents and young adults: a cohort study of 4 million individuals. Vaccine 91: 129001
  • Voleti et al. 2022. Myocarditis in SARS-CoV-2 infection vs. COVID-19 vaccination: A systematic review and meta-analysis. Frontiers Cardiovascular Medicine 9
  • Watson et al. 2022. Global impact of the first year of COVID-19 vaccination: a mathematical modelling study. Lancet 22:1293-1302
  • Wu et al. 2023. Long-term effectiveness of COVID-19 vaccines against infections, hospitalisations, and mortality in adults: findings from a rapid living systematic evidence synthesis and meta-analysis up to December, 2022. The Lancet Respir. Med. 11:439-452
  • Zheng et al. 2022. Real-world effectiveness of COVID-19 vaccines: a literature review and meta-analysis. International Journal of Infectious Diseases 114:252-260.

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Anti-vaccers are fundamentally wrong about placebo-controlled trials

Posted on July 6, 2026 by Fallacy Man

Anti-vaxxers love to demand placebo-controlled trials and insist that nothing else will suffice for demonstrating vaccine safety. This approach to medical research is flawed for a number of reasons. First, as I’ve explained previously, new vaccines are, in fact, tested against placebos. Truly novel vaccines (e.g., COVID vaccines) get tested against an inert, saline placebo (e.g., Polack et al. 2020), while updates to existing vaccines typically get tested against the previous versions of the vaccine, because those versions already have known safety and effectiveness profiles.

There is, however, a more fundamental issue with this insistence on placebo-controlled trials. Namely, they are not actually the best tool for examining vaccine safety, and cohort and case-control trials are often more useful. I’ve talked about this a number of times before (here, here, and here), but in this post, I want to try a more visual approach and actually show some data to illustrate the point.

As a brief thesis statement, correlation doesn’t automatically equal causation, but a lack of correlation does suggest a lack of causation (see note at end). Therefore, you do not need a placebo-controlled trial to make a statement like “vaccines do not cause autism” (within the statistical limits described below), and other study designs like cohort studies and case-control studies are actually better.

Note: we usually use “correlated” for continuous variables that change together (regressions) and “associated” for discrete variables, but for simplicity, I will just use “correlated” throughout.

Types of studies

There are three types of studies that are most relevant to this post (more details are here).

The first is the classic randomized controlled trial (aka RCT). In medical research, these are usually also placebo-controlled. This design takes a group of subjects, randomizes them into a treatment group (i.e., the group that receives a medicine) and a control group that receives a placebo instead of medicine. Some outcome of interest (e.g., a side effect) is then recorded in each group, and the rates of that outcome are compared.

Next, we have cohort studies. These use the same basic design as randomized placebo-controlled studies, but they are not manipulative. So rather than randomizing people into groups, it simply looks at groups who did and did not take a treatment (often via medical records) then compares the rates of the outcome of interest.

Finally, case-control studies work backwards. They identify a group of people with the outcome of interest (the “cases”) then match them with a group of people who are similar demographically but don’t have the outcome of interest (the “controls”). Then, the rates of a potential cause are compared between the groups.

Why randomize?

At the outset, we need to briefly discuss why randomized controlled trials (which, in medicine, are typically placebo-controlled) are generally so useful, as well as the nature of causation (note that the explanation below is simplified for the sake of brevity).

When we say that two things are correlated, we mean that they change together. So, there is a relationship between the two variables. The problem is that simply being related doesn’t mean that one thing is causing the other. For example, they could both be being caused by a third factor.

To use a famous example, ice cream sales and drowning are positively correlated (they both increase and decrease together). That does not, however, mean that ice cream causes drowning. Rather, a third factor (high temperatures) causes people to buy more ice cream and spend more time swimming (which results in more drowning accidents).

When we are trying to test for causation scientifically, we need to be extremely confident that there is no third factor driving the relationship. This is where randomization comes in. By randomly dividing people into two groups, we spread out all additional factors between the two groups so that no third factor can drive the results. This is the reason why randomized trials can confidently assert causation, while cohort and case-control trials cannot. For those designs, we can do our best to account for confounding factors (third factors) in the models, but it is always possible that we missed something. We just don’t have the confidence that we do with randomization.

So, if you want to say that X causes Y, you need a randomized trial. However, you do not need a randomized trial to say that X does not cause Y.

If you think about this for a second, the reason should be obvious. If X causes Y, then they will inherently, by definition, be correlated. Therefore, if they are not correlated, then X cannot be causing Y. Any time that X causes Y, there will be a correlation. So, no correlation = no causation (see note at end).

This means that a statement like, “vaccines don’t cause autism” does not need randomized, placebo-controlled trials to back it up, and, as we’ll see below, other designs are actually better.

Finally, it is worth mentioning that the purpose of the placebo is to get a good measure of the background experimental error rates (e.g., regression to the mean). This is really important for something like measuring whether a drug improves symptoms. It is substantially less important for outcomes like whether or not patients get an emerging infectious disease or rare side effects (more on placebos here).

The power of cohort and case-control trials

The big limitation of placebo-controlled trials (beyond ethical issues) is that they are really expensive and hard to conduct. They are very time consuming, and it is difficult and costly to recruit participants and get them to stick with the program. As a result, controlled trials generally range from a few dozen to a few hundred participants. Sample sizes of a few thousand are rare (though COVID provided a few exceptions).

This is a huge drawback, because sample size is one of the key factors dictating the power of a test. The larger the sample size, the more power you have to detect the thing you’re testing.

Background rates and the strength of the association also affect a test’s power and determine the sample size needed to detect an effect. Let’s say that you are testing a vaccine to see if it causes side effect X. If that side effect is really common, then you can detect it with a small sample size, but if it is really rare, then you are going to need a much larger sample size to detect it. Further, how often X occurs on its own (for reasons other than the vaccine) also influences your power. If it is really common in the general population, then it is going to be hard to detect the signal from vaccines causing it and, once again, you need a very large sample size. In contrast, if it is rare, then you can detect it with a smaller sample size.

This is where cohort and case-control studies come in. Cohort studies follow the same basic power rules as randomized studies, but because they are cheap and easy to conduct, they can be quite large, often including tens of thousands or even hundreds of thousands of individuals.

Case-control studies are even more powerful because they start with the outcome of interest. So even if the outcome is quite rare, you can get records for a lot of people with that outcome. This makes them substantially more powerful for studying potential causes of rare side effects, and these studies often have samples sizes of thousands of participants.

Simulated results

To illustrate this, I ran a simple simulation. In short, for a given sample size, background rate (how often it occurs without the potential cause), and side effect rate (how often the treatment actually causes it), it ran 500 iterations of generating populations of people who did and did not receive the treatment with the background rates and side effect rates applied. It then analyzed the data using the approach of randomized/cohort studies or case-control studies and returned the percent of “studies” (the 500 runs) where a statistically significant result was found (P < 0.05; see more details below).

I realize the resulting graph can be a bit daunting, so let me walk you through it. Going from left to right, the columns have decreasing background rates. So, in the first column, the outcome is very common in the general population (occurs in 1 in 10 individuals without the treatment), and on the far right, it is rare (1 in 10,000). Going down the rows, the side effect rate from the treatment decreases. So, in the first row, the side effect is very common (the treatment causes it in 1 in 10 patients, in addition to the background rate), and by the last row, it is rare, only being caused in 1 in 1,000,000 patients.

Within each panel, we are seeing the percent of tests (out of 500) where we were able to detect a correlation (significant result) on the y-axis. Keeping in mind that there was a causal relationship in each test. So, anything less than 100% indicates a false negative (an under-powered test). On the x-axis, we have increasing sample sizes (on a log10 scale). Blue results are for randomize/cohort trials, and red indicates case-control trials.

Simulation results comparing the power of randomized controlled trials/cohort studies and case-control studies. See text for details. Note that the x-axis is on a log10 scale, so each tick is 10 times the sample size of the previous tick.

There are several important and clear patterns here:

  1. Within each row, the rate of positive results increases from left to right (i.e., the lower the background rate, the more powerful the test).
  2. Within each column, the rate of positive results decreases from top to bottom (i.e., the rarer the side effect, the harder it is to detect).
  3. Within each panel, the rate of positive results increases from left to right (i.e., the larger the sample size, the more power).
  4. Case-control studies consistently have more power than randomized/cohort studies, with particularly pronounced differences for rare background and side effect rates.

Before going any further, I want to pause and stress that these are simulated results based on a simplistic scenario. So, the four trends described above apply to the real world, but the exact numbers shown here don’t necessarily apply, and in the real world there would be confounding factors (sex, age, medical history, etc.) that would be built into the models.

Interpreting the negative results

Now we get to a really important caveat about how to interpret negative results (keeping in mind that in my simulation, there was an effect of treatment in each simulation, we just weren’t always able to detect it).

Technically speaking, science never proves anything, and it is particularly problematic to demonstrate a negative. So, when we fail to find a correlation, technically, we have not shown that there is NO effect. Rather we have shown that IF there is an effect, it was too rare to detect with our sample size.

Keep in mind that in real studies, we know both the sample size and the background rate (that’s just the rate in the control group), so the only unknown is the treatment effect (side effect rate, in our example). Therefore, while we cannot conclusively with 100% certainty say that there is no relationship, we can get a sense for how rare it would have to be if it occurred. Thus, we can use the study design, sample size, and background rate to judge how concerned we need to be about the possibility of an undetectable effect. If the study had low power, then it may still be a legitimate concern, but if the study had a high power, then the concern is greatly reduced.

Note that anti-vaccers love to abuse this reality and play word games like demanding to see a study that “proves that vaccines don’t cause autism.” That’s an impossible request. It is never possible to prove that X does not cause Y, but we can show that if X causes Y, it would have to be doing so at such an incredibly low rate that it’s not a big concern (more on that in a sec).

Applying this to vaccines

Finally, let’s bring this all back around to anti-vaccers’ original argument that only placebo-controlled trials are satisfactory for establishing vaccine safety. As you can hopefully now see, that is a really faulty claim, and placebo-controlled trials are actually badly under-powered because of their low sample sizes. If you showed me a randomized, placebo-controlled trial of 1,000 children (a pretty big trial) that failed to find a significant trend for autism, I’d actually agree with you that that study is weak evidence. We know autism is fairly common (it has a high background rate), so that study would only eliminate the possibility of vaccines causing autism at a really high rate. It would still be entirely possible for vaccines to be a substantial cause of autism. That test was just under-powered.

In contrast, there have been several cohort studies of vaccines and autism with sample sizes of several hundred thousand children (Anders et al. 2004; Hviid et al. 2019; Madsen et al. 2002; Jain et al. 2015). Now we are talking about tests with some power; tests that can confidently assert that IF there is a relationship between vaccines and autism, it is a very weak one, and autism is a very rare side effect.

Further, we have case-control studies on vaccines and autism with hundreds or even thousands of children (Destefano et al. 2004; Smeeth et al. 2004; DeStefano et al. 2013; Uno et al. 2015). Again, those are actually very powerful (way more powerful than a randomized trial).

Additionally, there is even a meta-analysis that combined the studies above into one uber study with some truly impressive power (Taylor et al. 2014). Guess what? There was still no correlation. So, we can confidently state that there is no evidence of vaccines causing autism and IF they do, they are doing so at an incredibly low rate.

Stated another way, anti-vaccers are technically correct that we cannot “prove” that vaccines don’t cause autism, BUT we can and have demonstrated that even IF vaccines cause autism, the rate is very, very low. Thus, the notion of vaccines causing an “autism epidemic” is completely falsified. You almost certainly don’t know anyone who has autism because of vaccines because even if that side effect ever occurs, it is very rare.

Conclusion

I have been using autism as an example here, but all of this applies generally to all vaccines and all medical research using these study designs. When we are talking about safety and making claims about side effect rates, randomized placebo-controlled studies are often under-powered and frequently aren’t the best tool. They are great during initial testing, because they will detect common side effects and allow us to assign causation, but once we are talking about side effects that only occur once in a few thousand people, randomized trials are grossly under-powered, and cohort and case-control trials are much better tools for detecting correlation/associations. Randomization is important for assigning causation, but it is not needed to detect correlation, and while correlation does not indicate causation, two things that are causally related will, by definition, be correlated (though the correlation may be hard to detect). As such, anti-vaccers are completely and totally wrong to insist that we don’t know vaccines are safe without placebo-controlled trials. A lack of correlation/association in large cohort and case-controlled studies are great evidence that even if there was a side effect, it would be extremely rare. Thus, anti-vaccers are fundamentally misunderstanding how study designs and statistical power work.

NOTE ON LACK OF CORRELATION: When I say that a lack of correlation indicates a lack of causation, this is true in the strictest sense that if two things are causally related, there will inherently be a relationship between them. However, that does not mean that failing to detect a correlation proves a lack of causation. As seen in the post, tests may simply have been under-powered. Further, there may be time-lag effects or third causes that are also important. So, sample size and study design are important considerations (i.e., did they correctly control for confounding factors?). Nevertheless, my fundamental point remains that a lack of correlation/association in large cohort/case-control studies is good evidence that if there is a causal relationship, it is a weak one with a small effect size.

MODEL DETAILS: This was a stochastic simulation, meaning that there was chance variation in the results, which accounts for some of the “waviness” in the figure. For example, if the background rate was 1 in 10, then each individual in the control population, had a 1 in 10 chance of developing the outcome, but this was determined stochastically. So, for runs with 100 individuals, on average, 10 would have the outcome, but sometimes it would be 9, sometimes it would be 11, etc. Also note that the side effect rate was additive to the background rate, so if background = 1 in 10, and side effect rate = 1 in 10, people who received the treatment had a 2 in 10 chance of developing the side effect. Also note that sample size is per group. So, n = 100 means 100 people in the control group and 100 people in the treatment/case group. For modeling simplicity for the case-control trials, it assumed that half of the total population received the treatment.

Related posts

Litterateur cited

  • Anders et al. 2004. Thimerosal exposure in infants and developmental disorders: a retrospective cohort study in the United Kingdom does not support a causal association. Pediatrics 114:584–591
  • DeStefano et al. 2004. Age at first measles-mumps-rubella vaccination in children with autism and school-matched control subjects: a population-based study in metropolitan Atlanta. Pediatrics 113:259–266
  • DeStefano et al. 2013. Increasing exposure to antibody-stimulating proteins and polysaccharides in vaccines is not associated with risk of autism. J Ped 163:561–567
  • Hviid et al. 2019. Measles, mumps, rubella vaccination and autism: A nationwide cohort study. Annals of Internal Medicine.
  • Jain et al. 2015. Autism occurrence by MMR vaccine status among US children with older siblings with and without autism. JAMA 313:1534–1540
  • Madsen et al. 2002. A population-based study of measles, mumps, and rubella vaccination and autism. New England Journal of Medicine 347:1477–1482
  • Polack et al. 2020. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. New England Journal of Medicine 383:2603-2615.
  • Smeeth et al. 2004. MMR vaccination and pervasive developmental disorders: a case-control study. Lancet 364:963–969
  • Taylor et al. 2014. Vaccines are not associated with autism: and evidence-based meta-analysis of case-control and cohort studies. Elsevier 32:3623-3629
  • Uno et al. 2015. Early exposure to the combined measles-mumps-rubella vaccine and thimerosal-containing vaccines and risk of autism spectrum disorder. Vaccine 33:2511–2516

Posted in Nature of Science, Vaccines/Alternative Medicine | Tagged , , , , , | 5 Comments

Understanding analogies in logical arguments

Posted on June 25, 2026 by Fallacy Man

Yesterday, on this blog’s Facebook page, I posted the stick figure comic on the right, lightly making fun of anti-vaccers and using analogies to demonstrate why they are wrong that 100% effectiveness is needed for vaccines to be useful. I love analogies. They are a great way to get people past their biases and show underlying flaws in their reasoning, but apparently many people don’t understand how analogies work, and the comments quickly filled with people saying that these were bad analogies/false equivalencies because (according to them) unlike seat belts, helmets, birth control, or air bags, vaccines cause serious injury, go inside you, and are protected from lawsuits (see examples below).

There is a lot wrong with these responses (e.g., serious side effects from vaccines are extremely rare, serious injuries can occur from the other things mentioned, etc.), but I want to focus on the big one. Namely, these responses totally misunderstand the nature of analogies and how to evaluate arguments.

This comic was about one specific argument: the argument that vaccines aren’t useful because they aren’t 100% effective. That’s it. Effectiveness is the only thing being discussed. As such, all other considerations are 100% irrelevant. Even if vaccines were horribly dangerous (they aren’t) that would not make this comic a bad analogy or a false equivalency because it is not about safety. It is about the effectiveness argument. The purpose of a good analogy is to get at the underlying logical structure of an argument, and nothing outside of that structure matters.

To illustrate this, let’s break the argument down into its syllogism:

  • Premise 1: Vaccines aren’t 100% effective
  • Conclusion: Therefore, vaccines should not be used.

Stated like that, it is clear that the conclusion does not follow necessarily from the premises, which is why there is actually a second, unstated premise.

  • Premise 1: Vaccines aren’t 100% effective.
  • Premise 2 (unstated): Things that aren’t 100% effective should not be used.
  • Conclusion: Therefore, vaccines should not be used.

That is the argument being addressed by the comic, and we can show that the argument clearly does not work by replacing vaccines with literally anything else that is useful despite not being 100% effective. For example:

  • Premise 1: Seat belts aren’t 100% effective.
  • Premise 2: Things that aren’t 100% effective should not be used.
  • Conclusion: Therefore, seat belts should not be used.

Do you see how that works? All of those other considerations (like safety) are totally irrelevant for this analogy, because they don’t matter for this one particular argument.

To be completely clear, things like safety are certainly important in a broader discussion of vaccines. If I had said that this comic proves that we should be using vaccines, then commenters would have been absolutely correct to point out that it doesn’t address safety, but no one ever claimed that this comic encompasses all considerations or proves that we should vaccinate. It was about one specific argument about one specific aspect of vaccines, and the examples used are completely analogous for that specific aspect. Thus, it is in no way a false equivalency.

Understanding this is really important, because our brains are very resistant to information that is contrary to our beliefs, and one of the tricks they play on us is to ignore an argument we are being presented with and shift to a different argument. That is exactly what happened here. Rather than take the time to consider this one particular defect in anti-vaccer reasoning, people ignored the argument at hand and jumped to a different argument. We all tend to do this frequently, and it is a cognitive trap that we need to be aware of and train ourselves to avoid.

When presented with an argument, train yourself to avoid the tendency to jump to “what about…” Stop, take a deep breath, and carefully consider the argument currently in front of you. Don’t let your brain jump to other topics. Force yourself to focus on the specific argument you have been presented and thoroughly analyze it before moving on to other considerations.

My point with all of this is two-fold. First, try to understand what analogies are actually intended to do in logical arguments. Analogies are used to illustrate underlying defects in the structure of an argument. As such, only the factors relevant to that structure matter and all other considerations are irrelevant to the argument at hand. Even if two things being compared are wildly different in every respect except for the one thing being discussed in the argument at hand, it can still be a good analogy as long as the specific thing being addressed by the argument is the same. Second, train yourself to take each argument seriously and carefully on its own terms. Avoid the mental trap of jumping to other considerations that are irrelevant for the specific argument being discussed.

SIDE NOTE ON STRAW MEN: Another common response was to accuse me of committing a strawman. This comic is not a strawman, however, because on countless occasions I have seen people make this exact argument. Indeed, many years ago, one of my first popular blog posts was debunking a popular article called “One hundred arguments against vaccines.” The second argument in that list was “NO vaccine is 100% effective.” In the years since, I have seen people comment with this argument more times than I can count, though admittedly it is often stated less bluntly. For example, when you hear someone say something like, “being vaccinated doesn’t even guarantee that you won’t get infected!” They are making this argument. If you break that down into a syllogism, it’s the same logical structure, and we can use the same analogies, such as “wearing a helmet doesn’t even guarantee you won’t get a head injury!”

Further, in situations like this, many people make the strawman response simply because it is not their personal reason for holding a position. I see this all the time on topics like GMOs where, for example, I’ll post about pesticides and someone will say, “that’s a strawman because the real issue is corporate control of food.” Just because it isn’t the issue that you personally care about doesn’t mean it’s not an issue that others care about, and that doesn’t make it a strawman.

Related posts

Posted in Rules of Logic, Vaccines/Alternative Medicine | Tagged , , , , | 1 Comment

Stop blaming China and India for climate change

Posted on May 13, 2026 by Fallacy Man

Over and over again when discussing climate change, I encounter Americans who insist that there is no point in doing anything because China and India are the real problem. This claim takes various forms but generally includes claiming that America’s emissions are tiny compared to China’s and India’s and that there is no point in the USA doing anything because countries like China and India will never change. When you start looking at the numbers and actually examining the facts, however, this argument utterly falls apart. It is simply a copout excuse for not taking action. I wrote about this several years ago, but the numbers have shifted since then, so it is time for an update.

At the start, I want to make it clear that I am not trying to “vilify” America or claim that countries like China don’t play a substantial role in climate change. There is plenty of blame to go around, and while China is taking action (more on that later), there is a lot more that they can and should do. However, America also plays a huge, outsized role in climate change, and it is disingenuous and dangerous to blame others rather than taking responsibility for our actions. All countries need to work together to solve this problem, but some countries (like the USA) have contributed an outsized proportion of the world’s greenhouse gas emissions.

With that said, let me outline some core points:

  • India produces far fewer emissions than the USA both in total and per capita. So, if you are claiming that India is worse than the USA, you are simply wrong on the facts.
  • China does contribute more than the USA in terms of total greenhouse gas emissions, but that is a fairly recent development and China lags way behind the USA in terms of per capita emissions.
  • China is building many new coal power plants and increasing their emissions, but they are also investing very heavily in renewable energy. So, the claim that they aren’t taking action is false.
  • Even if none of the points above were true, that would not absolve Americans of their duty to take responsibility for their own actions. “Other people were doing it too” has never been a valid excuse for unethical behavior.

Data source: For this post, I will be using: Crippa M., Guizzardi D., Pagani F., Banja M., Muntean M., Schaaf, E., Quadrelli, R., Risquez Martin, A., Taghavi-Moharamli, P., Grassi, G., Rossi, S., Melo, J., Oom, D., Branco, A., Suarez Moreno, M., Sedano, F. San-Miguel, J., Manca, G., Pisoni, E., Pekar, F., GHG emissions of all world countries – JRC/IEA 2025 Report, Luxembourg, 2025, https://data.europa.eu/doi/10.2760/9816914

Note: In this post, I am talking specifically about greenhouse gas emissions. Other topics such as plastic pollution and air quality in cities (e.g., the gases that cause smog) are separate issues that are irrelevant to the discussion at hand.

Total emissions over time

There are several ways we can look at the data, but let’s start by looking at total emissions over time. The three biggest emitters are China, the USA, and India, so I will focus on them while also including the countries currently in the European Union (EU) as a reference point (Figure 1). When we do that, several things become obvious.

First, China obviously has had a dramatic increase in greenhouse gas emissions. To that extent, there is some truth to the claim that they are having the biggest impact, but there are several other critical factors (like population size) that we have to take into account to get a full picture (more on that in a minute).

Second, shifting the blame from the USA to India makes no sense and is at odds with the facts. It is simply not true that India produces more greenhouse gases than the USA. Here again, India is admittedly increasing its greenhouse gas production, but as with China, there are other factors to consider (again, more in a minute).

Meanwhile, the USA has had a moderate emissions decrease since its peak in the 2000s, but it is still higher than its 1970 emissions level, whereas the EU has been consistently lower than the US, with a stronger decrease.

Figure 1: Greenhouse gas emissions per country from 1970 to 2024.

To really understand which countries have had the biggest role in climate change, however, we need to not simply look at the trends over time, but also at the total levels of contribution. So, let’s sum each country’s greenhouse gas emissions over time. When we do that (Figure 2), we find that India has actually only contributed 5.4% of the world’s total emissions. Meanwhile, China leads with 18.4% and the USA is only slightly behind at 17.2%. So, while China has produced more emissions than the USA, it is not even remotely true that they are the key polluter, and the USA is minuscule in comparison.

Figure 2: Percent of all of the world’s emissions from 1970-2024 produced by China, the United States of America, European unit (current countries), and India

Emissions per capita

So far, we have only looked at totals, but to get a complete picture, we have to look at emissions per person. Obviously, a larger country will be expected to produce more emissions.

Put another way, if two countries had identical environmental laws and regulations, but one country was twice the size of the other, we’d obviously expect the larger country to produce more emissions, even though the environmental policies were the same. That’s just simple math.

So, when looking at these emissions, we also have to account for the fact the USA is smaller than the EU and much smaller than India or China.

Looking at emissions per capita paints a very different story (Figure 3). For reference, I have included a line showing the global total emissions per capita (all countries combined). Any country above that line is contributing more than their fair share to climate change, while any country under that line is contributing less than expected based on population size. Compared to that line, the USA is an egregious offender. The emissions per capita are, fortunately, declining, but they are consistently way above the global average. Meanwhile, India consistently sits way below the global average, and the EU has declined to the point that it is just barely above the global average. China is, unfortunately increasing, but it has only recently risen above the global average and still lags well below the USA. Again, China’s increase is a problem, I’m not saying that it isn’t, but trying to place all the blame on China while ignoring the USA’s massive role is dishonest.

Figure 3: Emissions per capita for China, the United States of America, European unit (current countries), and India from 1970-2024. The per capita emissions for the entire planet are also shown as (“Global average”)

To put this another way, the USA has 4.2% of the world’s population but has produced 17.2% of the world’s total greenhouse emissions (since 1970). Meanwhile, China has 17.0% of the world’s population and has contributed 18.4% of total emissions. India lags way, way behind, with 17.7% of the world’s population, but only 5.4% of the world’s total emissions.

Stated yet another way, as of 2024 (the last year for which I have data), an average American produced 1.6x as many greenhouse gas emissions as someone in China, 2.4x as many as someone in the EU, and 5.7x someone in India! So don’t tell me that China and India are the “real” problem.

Again, I’m not saying that America is the only country to blame, but it is undeniable that it is playing an outsized role relative to its population size and it is silly and dishonest to pretend that other countries are the real problem. Further, all of this is before we even get into details like many of China’s emissions resulting from the production of products that are shipped overseas to satiate America’s rampant consumerism.

China is investing in renewable energy

Finally, while it is true that China is building more coal power plants, they are also one of the world leaders in investing in renewable energy. In 2024, China invested $625 billion in renewable energy, representing 31% of the world’s total investment (again, keeping in mind that they have 17.0% of the world’s population, thus representing an outsized investment). Indeed, in 2024, 84% of their electricity demand growth was met by their investment in renewables.

Here again, I’m not arguing that China is a shining example. Obviously, they still have a long way to go, and China is a massive contributor to climate change. However, it is completely dishonest to pretend that they aren’t taking steps to curb their emissions or that they are the “real” problem.

Americans often seem to think that the USA is the only country investing in fighting climate change and everyone else is to blame, but the actual facts and numbers paint a completely different picture. Even with China’s increasing fossil fuel use, its per capita emissions are still much lower than the USA’s, and China is investing heavily in renewable energy. The USA has contributed and continues to contribute a disproportionate amount of fossil fuel emissions and has a very, very long way to go before it can point fingers at other countries.

Posted in Global Warming | Tagged , | 4 Comments