An adjuvant is an ingredient used in some vaccines that helps create a stronger immune response in people receiving the vaccine. In other words, adjuvants help vaccines work better. Some vaccines that are made from weakened or killed germs contain naturally occurring adjuvants and help the body produce a strong protective immune response. However, most vaccines developed today include just small components of germs, such as their proteins, rather than the entire virus or bacteria. Adjuvants help the body to produce an immune response strong enough to protect the person from the disease he or she is being vaccinated against. Adjuvanted vaccines can cause more local reactions (such as redness, swelling, and pain at the injection site) and more systemic reactions (such as fever, chills and body aches) than non-adjuvanted vaccines.
By design then, vaccine adjuvants are necessarily toxic. This is because their purpose is to induce an immune response stronger than the immune response to the antigen component alone. The adjuvant is what makes the challenge to the immune system from the vaccine strong enough to induce immunological memory.
This is why the safety of vaccine adjuvants is so important to the safety of vaccines as a whole; the adjuvant is by design the most toxic part of the vaccine.
The CDC writes that:
Aluminum salts, such as aluminum hydroxide, aluminum phosphate, and aluminum potassium sulfate have been used safely in vaccines for more than 70 years. Aluminum salts were initially used in the 1930s, 1940s, and 1950s with diphtheria and tetanus vaccines after it was found they strengthened the body’s immune response to these vaccines.
Aluminum salts have indeed been used since the 1930s, as described in the earliest papers about aluminum adjuvants, such as Glenny 1926 and Volk 1942. Whether they have been used safely is the question at hand.
There were no safety studies done on the effects of Aluminum-Containing Vaccines (ACVs) prior to their routine use in infants. The only studies were of efficacy, tested by measuring antibodies. The first routine ACVs were the diphtheria, tetanus, and pertussis vaccines. These were later given as a combined vaccine, known as DTP, and then DTaP (the ‘a’ referring to acellular pertussis).
Up until 1990, the DTP vaccine was the only ACV given routinely to infants. After 1990 however, the number of ACVs on the recommended infant vaccine schedule started increasing. The Hib and Hepatitis B vaccines were added in 1991 and the Pneumococcal vaccine in 2000. They started being given in more doses and at a younger age. Infants today are considerably more exposed to aluminum from vaccines than infants of 30 years ago.
Aluminum and Neurodevelopmental Disorders
Aluminum is classified by the US Agency for Toxic Substances and Disease Registry (ATSDR) as a neurotoxin. This means the first harmful effects of aluminum overdose occur in the brain. The ATSDR describe aluminum as having “subtle neurological effects”, even at low doses. A steady increase in neurodevelopmental disorders, e.g. ASD, ADHD, and intellectual disability, is to be expected if aluminum adjuvants in infant vaccines are altering neurodevelopment.
We have seen an increase in neurodevelopmental disorders in line with increasing exposure to aluminum from vaccines. The increase cannot be explained away as expanded definitions, better diagnosis, or increased awareness. This ecological observation – the temporal correlation between ACV use and neurological disorders – calls for further research. As a neurotoxin, aluminum has to be a prime suspect in the search for causes of neurodevelopmental disorders.
The CDC writes that:
Newer adjuvants have been developed to target specific components of the body’s immune response, so that protection against disease is stronger and lasts longer.
None of the newer adjuvants are yet used in infant or childhood vaccines. They are thus of no relevance to an investigation of adjuvant safety in the infant and childhood vaccine schedule. The safety of adjuvants in this context means the safety of aluminum salts.
Safety Testing – Clinical Trials
The CDC writes that:
In all cases, vaccines containing adjuvants are tested for safety and effectiveness in clinical trials before they are licensed for use in the United States, and they are continuously monitored by CDC and FDA once they are approved.
In pre-approval clinical trials, potential new vaccines are tested against a placebo injection. The primary focus of the trials is efficacy; a vaccine is judged as successful when it results in the development of antibodies. Safety is a secondary consideration. Typically, immediate adverse reactions are recorded, there is active follow-up for only a week, and passive follow-up for only a year.
Pre-approval clinical trials will not pick up long-term adverse reactions, chronic conditions or altered neurodevelopment. No rare adverse events are picked up in the clinical trials, due to the small sample size.
For assessing adjuvant safety, pre-approval clinical trials are entirely useless. This is because both the vaccine being tested and the placebo contain adjuvant! The placebo is either a solution of adjuvant only, or an already-licensed vaccine. There are no safety tests of vaccine adjuvants in pre-approval clinical trials of ACVs.
Safety Testing – Monitoring
The post-approval monitoring systems of the CDC are the Vaccine Adverse Event Reporting System (VAERS) and the Vaccine Safety Datalink (VSD). VAERS is a system is for reporting immediate and short-term vaccine reactions. It cannot be used to monitor, for example, whether ACVs are causing autism. The VSD is a database that could easily and cheaply be used to conduct large-scale epidemiological studies. It is a data goldmine.
The Institute of Medicine, in their 2013 report “The Childhood Immunization Schedule and Safety: Stakeholder Concerns, Scientific Evidence, and Future Studies” recommended using the VSD to assess vaccine safety:
The most feasible approach to studying the safety of the childhood immunization schedule is through analyses of data obtained by VSD. VSD is a collaborative effort between CDC and 9 managed care organizations that maintain a large database of linked data for monitoring immunization safety and studying potential rare and serious adverse events. VSD member sites include data for more than 9 million children and adults receiving vaccinations on a variety of immunization schedules.
No studies have been on VSD data looking for a possible association between ACVs and neurodevelopmental conditions. The IOM’s call for more safety studies of the vaccine schedule has gone unheeded, so far.
The VSD has great potential to increase vaccine take-up by allaying many concerns people about vaccine safety. For reasons unknown, the CDC refuses to allow researchers to access the VSD data to perform these kinds of studies. Nor has it produced any studies of its own using the VSD.
Aluminum Adjuvants used in Vaccines
The CDC provides a table showing which vaccines use aluminum-based adjuvants, other adjuvants, or no adjuvant.
Four different aluminum salt forms are listed in the table: AAHS, AH, AP and Alum. Three of these are in vaccines on the CDC’s infant vaccination schedule.
Aluminum Salts in Infant Vaccines
The following table shows the ACVs on the CDC’s infant vaccine schedule, with the most popular brands chosen where applicable. The aluminum-weight in each dose is given, along with the aluminum salt form they use, and the ages at which doses are given.
Al Content (mcg)
Doses Given At (months)
The highest single day exposure is at 2 months old, when 1,225 mcg is injected at once. The total aluminum content of the vaccines given to infants in the first six months is 3,450 mcg (3.45 mg).
Is this a “small” amount?
The CDC writes that:
Aluminum-containing adjuvants are vaccine ingredients that have been used in vaccines since the 1930s. Small amounts of aluminum are added to help the body build stronger immunity against the germ in the vaccine.
The term “small” requires some reference value. Is 3.45 mg in six months a “small” or “large” amount of exposure to aluminum for an infant?
One way to answer this question is to compare it to aluminum exposure from other sources.
Aluminum from Vaccines vs Other Sources
The CDC writes that:
Aluminum is one of the most common metals found in nature and is present in air, food, and water.
We are indeed all exposed to aluminum in our food, drinking water, and air. By knowing the aluminum content of our food, drinking water, and air, we can estimate our total intake. Using estimates of gut and lung bioavailability, we can calculate our total uptake, i.e. how much aluminum is entering our blood.
It can be seen that for some infants, 98% of aluminum exposure over the year comes from their vaccines. If they are exclusively breastfed they will only uptake 50 mcg through their gut over the entire year. Exposure from city air of average quality is only 6 mcg. Even infants fed high-aluminum soy formula or grain-based baby foods will uptake no more than 2,000 mcg of aluminum through their gut. Still a lot less than the 3,450 mcg aluminum they received from their vaccines.
Since vaccines are the primary source of aluminum exposure to infants, it cannot be claimed that the amount of aluminum in vaccines is “small”.
Absorption from Vaccines
The CDC writes that:
Scientific research has shown the amount of aluminum exposure in people who follow the recommended vaccine schedule is low and is not readily absorbed by the body.
The phrase “not readily absorbed by the body” is certainly true in the case of aluminum exposure in food, drinking water, and air. Gut bioavailability (the percentage of intake that enters the blood) is less than 0.3% and lung bioavailability is only 2.0%. Hardly any of the intake from diet and air enters the body; aluminum is not readily absorbed through the guts or lungs.
Vaccines are injected into muscle. There is no way for the aluminum in vaccines to leave the injection site except by entering the systemic circulation. Thus, intramuscular bioavailability of aluminum is necessarily 100%. It is all absorbed by the body, sooner or later.
By “not readily”, the CDC means that it is absorbed slowly. There have been two studies looking at the rate of aluminum absorption from ACVs: Flarend 1997 and Weisser 2019. Both tested both aluminum hydroxide (AH) and aluminum phosphate (AP) injected intramuscularly in animals. They measured blood concentration to estimate the speed of aluminum absorption.
At the rates observed by Flarend, assuming zero-order absorption, AP vaccines take 56 days to be fully absorbed and AH vaccines take 165 days. At the rates observed by Weisser, AP vaccines take 94 days to be fully absorbed and AH vaccines take 369 days. Based on the absorption rates observed by Flarend, all aluminum from infant ACVs is fully absorbed by one year of age.
Daily Aluminum Uptake in Infancy
Is uptake of aluminum from vaccines “low” because of the slow absorption?
We can use the results of Flarend on vaccine absorption to calculate daily aluminum uptake from vaccines throughout infancy. In the chart below, daily uptake of aluminum from vaccines is plotted against uptake from diet and air. The input scenario is full vaccination on the CDC schedule, typical formula-feeding and average urban air quality.
The slow absorption of the aluminum in vaccines does not alter the fact that vaccines are the biggest source of aluminum exposure in infants. At the peak, just after the 6 month shots, infants are exposed to nearly 20 mcg per day. This is the amount entering their bloodstream from each of their vaccine injection sites, like a nicotine patch. Most infants will never be exposed to more than 2 mcg per day from diet and air.
Since vaccines are the primary source of aluminum exposure to infants, it cannot be claimed that the amount of aluminum exposure is “low”.
Uptake from Vaccines v Safety Levels
Perhaps “low” is meant as compared to a safety level. Unfortunately, there are no uptake safety levels for chronic exposure to aluminum.
There are safety levels for oral intake. These have been derived from observations in animal experiments. They are the highest dose of aluminum for which no adverse effects were observed, converted to a Human-Equivalent Dose (HED). The US ATSDR set an oral intake Minimal Risk Level (MRL) of 1 mg/kgbw/day. The WHO JECFA set an oral intake safety level of 0.3 mg/kgbw/day.
These oral intake safety levels can be converted into a uptake safety levels by multiplying them by an assumed gut bioavailability of 0.3%. The units of mcg/kgbw can be converted into mcg using CDC average infant weight charts. The two derived uptake safety levels can then be added to our daily uptake chart:
Uptake from vaccines alone far exceeds the safety level from the JECFA, and gets very close to the safety level from ATSDR. The cumulative impact of vaccines, diet, and air does exceed the ATSDR safety level for most infants. This is especially true of underweight infants and those on a high-aluminum diet.
The Mitkus Paper – The CDC’s Only Defense
The CDC ends it’s paragraph of information about aluminum adjuvant safety with this:
The first link is to a 2011 paper by Robert Mitkus and four of his colleagues at the US FDA. The paper describes a pharmacokinetic model developed for assessing the safety of ACVs in infants. The second link is to a webpage of the FDA, which refers only to the same Mitkus 2011 paper.
The CDC claims that aluminum adjuvant use is safe. Since Mitkus 2011 is the only scientific paper cited by the CDC in regard to aluminum in vaccines, this is presumably the strongest paper they can cite to support their claim. If this paper is shown to have serious flaws, it would be a crippling blow to the scientific case for vaccine adjuvant safety.
I have studied the Mitkus paper in great depth, along with the papers cited therein. I have reconstructed his whole model. I have expanded it to be able to cover more scenarios, i.e. variations in diet, air quality, vaccine schedules, etc. By doing that, I have identified many weaknesses, faulty assumptions, and outright errors.
I will explain the many flaws in the Mitkus model in future posts. In the meantime, here is an infographic explaining the most outrageous error. Mitkus used the wrong retention equation in his model!
The only scientific paper cited by the CDC in defense of their claim that aluminum adjuvants are safe contains this outrageous error, and there are many more.
There are good reasons to be concerned about aluminum adjuvant safety. Aluminum is a neurotoxin that can subtly alter neurodevelopment. It’s use in vaccines has increased over the years, in line with the increase in neurodevelopmental disorders.
The CDC is incorrect in describing the amount of aluminum in vaccines as “small” or total exposure from vaccines as “low”. Vaccines are in fact the primary source of aluminum exposure in infants. The CDC should update their website to remove these inaccurate descriptions.
Aluminum exposure from the vaccines on the CDC’s infant schedule exceeds the safety level established by the WHO. This calls for a reappraisal of the vaccine schedule: delaying or avoiding ACVs to reduce the risk of adverse neurodevelopmental effects from vaccines.
The CDC’s claim that aluminum exposure from vaccines is low enough to be considered safe relies on one single paper: a deeply flawed study by the FDA, presenting a pharmacokinetic model with many serious errors.
As both sides acknowledge, the text of the page is unchanged and still says that “vaccines do not cause ASD”, and that “there is no link between receiving vaccines and developing ASD” and “no links have been found between any vaccine ingredients and ASD”.
ICAN intend to pressure the CDC to remove these statements, too, because they say they are not supported by the scientific evidence.
Let us assess whether ICAN are right by looking at each of the sources cited by the CDC on this page to support their claim that vaccines do not cause autism.
Institute of Medicine, 2012
Under the heading “There is no link between vaccines and autism”, the CDC writes that:
Some people have had concerns that ASD might be linked to the vaccines children receive, but studies have shown that there is no link between receiving vaccines and developing ASD. In 2011, an Institute of Medicine (IOM) report on eight vaccines given to children and adults found that with rare exceptions, these vaccines are very safe.
That Institute of Medicine (IOM) report was called “Adverse Effects of Vaccines: Evidence and Causality” (2012) and is a systematic review of all the scientific literature looking adverse effects of vaccines. The eight vaccines included in the study were MMR, Varicella, Influenza, Hepatitis A, Hepatitis B, HPV, DTaP, and Meningococcal. An impressive 76 different health outcomes were included in the study, one of which was autism.
However, only two vaccines – MMR and DTaP – were even examined in relation to the autism health outcome. Presumably, this is because there are no studies to examine. This alone makes the report insufficient evidence to claim that vaccines don’t cause autism. For all but two vaccines, according to the IOM, there have been no studies looking at associations to autism.
Regarding DTaP, the IOM concluded that:
The evidence is inadequate to accept or reject a causal relationship between DTaP and autism.
Their epidemiological assessment found the evidence “insufficient” (just one single study, rejected due to being based on data from a passive reporting system). Their mechanistic assessment found the evidence “lacking” (no studies at all).
The MMR is the only vaccine where the IOM made a conclusive statement:
The evidence favors rejection of a causal relationship between MMR and autism.
At best, therefore, this report supports the claim that the MMR does not cause autism. It cannot possibly support the bigger claim that vaccines do not cause autism.
In their epidemiological assessment, the IOM reviewed an impressive 22 studies looking for an association between MMR and autism. 12 of them were dismissed for being based on data from a passive surveillance system lacking an unvaccinated comparison population, or for being an ecological comparison study lacking individual-level data. A further 5 were dismissed as having “very serious methodological limitations”.
In their mechanistic assessment, the IOM reviewed 6 studies, but dismissed them all for not providing evidence beyond temporality, concluding that the mechanistic evidence is “lacking” when it comes to assessing a causal association between MMR and autism.
This left just 5 epidemiological studies that the IOM considered good enough to be used to conclude the lack of a causal association between MMR and autism. These were the studies by Taylor 1999, Farrington 2001, Madsen 2002, Smeeth 2004, and Mrozek-Budzyn 2010. The Mrozek-Budzyn study was acknowledged by the IOM as having “serious limitations”, and the Farrington study is based on the same data as the Taylor study.
I will review each of these MMR-autism studies in a future post.
The CDC writes that:
A 2013 CDC study added to the research showing that vaccines do not cause ASD. The study looked at the number of antigens (substances in vaccines that cause the body’s immune system to produce disease-fighting antibodies) from vaccines during the first two years of life. The results showed that the total amount of antigen from vaccines received was the same between children with ASD and those that did not have ASD.
The De Stefano study is a helpful addition to the research, in that it looks at the cumulative effects of multiple vaccines, rather than at vaccines in isolation as in the IOM report.
However, the study is deeply flawed, because no one who claims that vaccines cause autism says that it should be related to the number of antigens received. It is the other ingredients of vaccines that are of greater concern, especially the aluminum salts used as adjuvants. Nevertheless, grouping by number of antigens has the potential to act as a proxy for distinguishing fully vaccinated, partially vaccinated, and unvaccinated, so it may not be an entirely worthless measurement.
Unfortunately, the results in the study are skewed by the presence of three vaccines with 3000 antigens (DTP, DTP-Hib, and Typhoid), and then a large drop down to vaccines with 69 antigens (Varicella), 24 antigens (MMR), and all other vaccines having less than 15 antigens. Total number of antigens is therefore merely a proxy for number of doses of high-antigenic vaccines. From the chart below it can be seen that most subjects had either zero, three, or four doses of these high-antigenic vaccines, and this alone determines the groups used in the analysis.
The chart also shows that there were no unvaccinated subjects in the study. Nobody received less than 50 antigens. The group that received zero high-antigenic vaccines received many other vaccines, because most of them had between 151 and 311 antigens. During the study period, the high-antigenic DTP vaccines were replaced by low-antigentic DTaP vaccines, so most of those in the “low antigens” group were fully vaccinated, just like most of the subjects in all the other groups.
At best, this study can be used to support the claim that high-antigenic vaccines do not cause autism any more than low-antigenic vaccines do. Since there are no high-antigenic vaccines used anymore, this is a moot conclusion. This study certainly cannot be used to support the claim that vaccines do not cause autism, because there were no unvaccinated subjects in the study.
The CDC then has a heading of “Vaccine ingredients do not cause autism” and writes that:
One vaccine ingredient that has been studied specifically is thimerosal, a mercury-based preservative used to prevent contamination of multidose vials of vaccines. Research shows that thimerosal does not cause ASD. In fact, a 2004 scientific review by the IOM concluded that “the evidence favors rejection of a causal relationship between thimerosal–containing vaccines and autism.” Since 2003, there have been nine CDC-funded or conducted studies that have found no link between thimerosal-containing vaccines and ASD, as well as no link between the measles, mumps, and rubella (MMR) vaccine and ASD in children.
That Institute of Medicine (IOM) report was called “Immunization Safety Review: Vaccines and Autism” (2004) and is a systematic review of all the epidemiology studies looking at associations between vaccines and autism. The report only examines studies of thimerosal-containing vaccines (TCVs) and the MMR vaccine (superceded by their 2012 review).
The IOM reviewed 12 studies looking for an association between TCVs and autism. 6 of them were dismissed for being based on data from a passive surveillance system lacking an unvaccinated comparison population, or for being an ecological comparison study lacking individual-level data. One study based on Vaccine Safety Datalink (VSD) data was dismissed as “uninterpretable”.
This left 5 studies on which the IOM relied for their conclusion that TCVs do not cause autism: Hviid 2003, Miller 2004, Verstraeten 2003, Madsen 2003, Stehr-Green 2003.
The CDC cites a two-page PDF that lists and briefly summarises eight further studies to support their claim TCVs do not cause autism: Barile 2011, Price 2010, Tozzi 2009, DeStefano 2009, McMahon 2008, Thompson 2007, Verstraeten 2003, Stehr-Green 2003 (included twice).
I will review each of these TCV-autism studies in a future post.
Thimerosal Removed From Vaccines
The CDC writes that:
Between 1999 and 2001, thimerosal was removed or reduced to trace amounts in all childhood vaccines except for some flu vaccines. This was done as part of a broader national effort to reduce all types of mercury exposure in children before studies were conducted that determined that thimerosal was not harmful. It was done as a precaution. Currently, the only childhood vaccines that contain thimerosal are flu vaccines packaged in multidose vials. Thimerosal-free alternatives are also available for flu vaccine.
Thus, the question of whether TCVs cause autism is now only of historical interest within the wider context of the question of whether vaccines cause autism. The studies cited to support that the claim that TCVs do not cause autism have significant weaknesses, and there is considerable evidence suggesting that TCVs do cause autism.
Since they are now rarely used, TCVs are clearly not an ingredient of concern to those who claim that vaccines cause autism today. Therefore, whether TCVs do cause autism is a moot point in respect of this present-tense claim.
Besides thimerosal, no other vaccine ingredients are named. All the ingredients in vaccines today are addressed by the CDC with a single sentence:
Besides thimerosal, some people have had concerns about other vaccine ingredients in relation to ASD as well. However, no links have been found between any vaccine ingredients and ASD.
No studies at all are cited to support this claim. The link goes to a page merely listing types of vaccine ingredients and their purpose:
Aluminum salts are the vaccine ingredient of biggest concern when it comes to autism, because aluminum is a neurotoxin that has been observed to induce subtle neurological effects and autism-like behaviour in animals exposed to it. The toxic nature of aluminum is why aluminum salt works as a vaccine adjuvant, ensuring that the activation of the immune system is strong enough to result in immunological memory.
There have also been health concerns raised about formaldehyde, glutamate, polysorbate-80, antibiotics, animal cells and fetal cells.
No studies are cited on this page to support the claim that vaccine ingredients do not cause autism.
The first link is to a meta-analysis that combined the results of five MMR-autism studies, and five TCV-autism studies, all of which individually found no associations. Most of the studies in this Taylor 2014 meta-analysis are the same papers reviewed by the IOM, cited above (including some they rejected for having serious methodological limitations); there is one new TCV study (Andrews 2004) and one new MMR study (Uno 2012).
The second link is to an ecological study, Schechter 2008, showing the removal of TCVs did not coincide with any reduction of cases of autism in California. The third link is to the IOM 2004 Report cited above. The fourth link is to the Hviid 2003 study of TCVs-autism. The fifth link is to the Madsen 2002 study of MMR-autism, both in the IOM Reports cited above. The sixth link is to an outdated review of TCVs that does not reference autism. The seventh link is to a statement by the AAP regarding removing thimerosal from vaccines.
There is no evidence cited on the CDC’s “Autism and Vaccines” page to support their claim that vaccines do not cause autism. The only evidence cited relates either to one single vaccine: the MMR, or to one single vaccine ingredient: thimerosal. Even if this evidence is accepted, it does not follow that vaccines do not cause autism.
More studies are needed in order to determine whether vaccines cause autism, starting with the most basic kind: epidemiological studies that compare health outcomes in vaccinated and unvaccinated children.
Interestingly, this was pointed out by the IOM itself in their 2013 report “The Childhood Immunization Schedule and Safety”:
Without any studies looking at autism as a health outcome and comparing vaccinated and unvaccinated groups, there is no support for the CDC’s claim that vaccines do not cause autism. They were right to change the title of their page. They now need to correct the rest of it.
Atladottir, 2010: admission to hospital due to maternal viral infection in the first trimester and maternal bacterial infection in the second trimester were found to be associated with diagnosis of ASDs in the offspring https://www.ncbi.nlm.nih.gov/pubmed/20414802
Vargas, 2005: The brains of people with ASD show a marked activation of microglia and astroglia, and cytokine profiling indicated that MCP-1 and TGF- β1, derived from neuroglia, were the most prevalent cytokines. Cerebrospinal fluid showed a unique proinflammatory profile of cytokines, including a marked increase in MCP-1. https://www.ncbi.nlm.nih.gov/pubmed/15546155
Choi, 2016: Either MIA or direct administration to the fetal brain of mice of inflammatory cytokine IL-17a promotes abnormal cortical development and ASD-like behaviors in offspring https://www.ncbi.nlm.nih.gov/pubmed/26822608
This infographic demonstrates that the majority of exposure to aluminum in infants comes from vaccines.
Children fully vaccinated using the CDC schedule for the first 6 months will receive 3,450mcg of aluminum into the blood from those vaccines, as the aluminum salts used as vaccine adjuvants slowly dissolve at the intramuscular injection site and enter the bloodstream. This process takes no more than 6 months, so 100% will have been absorbed by 1 year of age.
Breast milk only contains an average of 23.9 mcg/L, and infants drink an average of 0.74 L/day, for a total dietary intake of 6,455mcg of aluminum in the first 12 months. Most of this will be defecated out, but up to 0.78% could leak across the gut lining and enter the bloodstream. This would amount to 50mcg of aluminum over the year. Formula milk contains an average of 226 mcg/L and soy formula can contain up to 930 mcg/L. With the same amount drunk and same gut bioavailability, this would be 476mcg and 1959mcg respectively entering the gut. Infants on a semi-solid food diet will be getting an average of 767mcg. There is no diet that exposes infants to more aluminum through the gut than they are from vaccines.
The third biggest normal exposure is air, but the amounts here are insignificant. Multiplying average tidal volume by average respiratory rate results in 730 m3 of air being breathed by an infant by 1 year of age. Normal city air has around 0.4 mcg/m3 of aluminum, resulting in an intake of 292mcg. Lung bioavailability has been estimated at 2%, so only 6mcg of aluminum enters the bloodstream through the lungs during infancy. Even in a city with high pollution or industrial air, with up to 8 mcg/m3 of aluminum, that uptake figure only reaches 116mcg. Infant exposure to aluminum via the air is insignificant.
Sources for all these figures are listed and linked below.
In this post I will explore whether there is a correlation between Covid-19 deaths and elderly influenza vaccination rates across countries.
At least two studies have found an association between flu vaccines and susceptibility to other non-influenza respiratory diseases, including other coronaviruses:
Cowling, 2012 – a randomised controlled trial (RCT) in which 69 children were given a flu shot and 46 a placebo. 20 vaccinated children (29%) got sick with a non-flu virus and only 3 unvaccinated children (6.5%) got sick with a non-flu virus, a statistically significant result. The most common non-flu viruses detected were rhinoviruses and coxsackie/echoviruses. For both these virus types, a significant association was found between getting a flu shot and susceptibility to the virus. The sample size was too small to identify any association between flu shots and coronaviruses.
Wolff, 2020 – a retrospective study of the illness and vaccination records of 9469 individuals working for the Department of Defense, of which 6541 had received the 2017/8 seasonal flu shot and 2928 had not. The flu shot was associated with reduced risk of getting the flu but an increased risk of non-influenza illnesses, including specifically coronaviruses. 507 (7.8%) of the vaccinated and 170 (5.8%) of the unvaccinated tested positive for a coronavirus, resulting a significant relationship with an odds ratio of 1.36 [1.14-1.63 95% CI].
The above studies suggest that the flu shot may increase susceptibility to coronaviruses, possibly by a mechanism known as viral interference. It is hypothesised that the flu shot may increase susceptibility to SARS-CoV-2 via the same mechanism. Hence, there are reasons to expect a correlation between flu shot rates and Covid-19 death rates.
Correlation does not imply causation, obviously. No ecological study, no matter how strong the correlation, can ever be strong evidence of causation. They can merely give us a clue about where to look and what further studies to do. Ecological studies are low on the evidence hierarchy (beneath RCTs, cohort and case-control studies) but they are quick, cheap and easy, which is why these studies are often first to emerge in cases like this.
Since we have no studies of the stronger types yet, ecological studies are the best we can do for Covid-19 at the moment. One study comparing regions of Italy can be found here; a significant negative correlation was found, but no confounders were examined. Here I will present my analysis of the international data we have for flu vaccination rates and Covid-19 death rates, and then examine six possible confounders.
Covid-19 death rates per million people by country are available from Our World In Data. In this study, death rates as at 31st July 2020 were used.
Influenza vaccination rates in elderly people (defined for most countries as aged 65+) are available from The OECD. However, data from within the last 5 years is only available for 31 out of the 37 OECD countries. I could find no explanation why there is no recent data for the 6 other OECD countries (Austria, Australia, Colombia, Mexico, Poland and Switzerland). In this study, latest available vaccination rate data is used for each country (for most countries, this is the 2018/9 seasonal flu vaccine uptake rates).
There are 23 European countries in the OECD for which flu shot data is available. I have excluded two of them – Iceland and Luxembourg – for having a population of less than a million. The remaining 21 countries are shown in the following plot:
As indicated by the slope of the red line, there is a positive correlation between these two variables. A correlation coefficient is a measure of the degree to which a pair of variables are linearly related, between -1 and +1. As shown on the chart, the correlation coefficient R is 0.67, which is considered a moderate-to-high correlation. The p-value of <0.01 shows that the null hypothesis (i.e. no relationship between these variables) is falsified by the data, with a confidence level exceeding 99%.
There are 8 non-European OECD countries for which data is available: Canada, USA, Chile, Turkey, Israel, Korea, Japan and New Zealand. Adding these countries to the plot, we find:
The positive correlation is still present but is weakened, the correlation coefficient now 0.49. This is due to Korea, Japan and New Zealand all being outliers, having high vaccination rates and low Covid-19 death rates. The p-value of <0.01 shows that the null hypothesis of no relationship between these variables is falsified by the data, with a confidence exceeding 99%.
One way to improve an ecological study beyond a single-variable is to look for confounders. A confounder is a variable that influences both the dependent variable and independent variable, causing a spurious association. I have looked at all of the following six variables that have been suggested to me as possible confounders:
Income (as GDP-per-capita)
Elderly as a proportion of population
Climate (as average temperature in April)
Health of population (as life expectancy)
Healthcare system (as hospital beds per population)
Slight positive correlation: richer countries had more Covid-19 deaths. Not statistically significant.
No correlation: being more densely packed is not associated with more Covid-19 deaths.
No correlation: having more elderly people is not associated with having more Covid-19 deaths.
No correlation: being a colder country is not associated with more Covid-19 deaths.
Health of Population
Slight positive correlation: countries where people live longer had more Covid-19 deaths. Not statistically significant.
Here we have a weak but statistically significant correlation: countries with more capacity in the healthcare system had fewer Covid-19 deaths. The correlation coefficient is -0.37, and with a p-value of 0.049, the result is significant with 95% confidence but not 99% confidence.
Out of the 7 variables tested, two showed a significant relationship with Covid-19 death rates: flu shot rates and hospital beds. If we create a model based on these two variables, we obtain the following:
The p-value for the flu shot rate is still <0.05, so remains significant to a 95% confidence level, but the p-value for the hospital beds has gone to 0.068, so is no longer statistically significant at that level.
If we create a model based on all seven variables that we have, we obtain the following:
Here we have an opposite result to the two-variable model. The p-value for the hospital beds is still <0.05, so remains significant to a 95% confidence level, but the p-value for the flu shot rate has gone to 0.076, so is no longer statistically significant at that level.
Does the flu shot make us more vulnerable to Covid-19? Until we have better kinds of studies, ecological studies of Covid-19 death rates against flu vaccination rates are the best way to get an idea of whether the flu shot makes people more susceptible to Covid-19 due to viral interference, as seems to occur with other coronaviruses.
This ecological analysis found a correlation coefficient of 0.67 when only European OECD countries are included. This would be classified as a moderate-to-high positive correlation. With the addition of non-European OECD countries the correlation coefficient is 0.49, a low-to-moderate positive correlation. This contradicts the finding of the study of Italian regions, which found a negative correlation of -0.58, without looking for confounders. Confounders between Italian regions should be examined, and similar studies of regions within countries should be done to try and resolve this apparent contradiction.
Six possible confounders have been analysed and one (hospital beds per thousand population) was found to have a significant association to Covid-19 deaths, just like the flu shot rates. When combined into a multivariate model, these two variables seem to cancel out, to the extent that one of them becomes statistically significant, but which one is dependent on what other variables are included in the model. I cannot explain this behaviour – if you think you can, let me know!
Appendix: Paul’s Chart
This picture has been shared on social media:
It comes from this blog, authored by “Paul”, who created it in response to a “whimsical suggestion” in the BMJ (here) by Dr Allan Cunningham: to correlate influenza vaccine uptake with Covid-19 death rates. My post above was inspired by Dr Cunningham’s challenge and by seeing the flaws in Paul’s chart and wanting to dig into the data myself.
In his note, Dr Cunningham provided data on influenza vaccine coverage rates in the elderly and covid-19 death rates per million for 20 selected European countries. His source for covid deaths was Worldometers, accessed 21st May 2020. His source for flu shot rates was the OECD. There are 26 European members of the OECD… Dr Cunningham did not include Belgium, Greece or Iceland in his list of 20 countries despite the data being available at the same source he used – with no explanation given. As we have seen, data from Austria, Poland, and Switzerland are not available from the OECD.
Here is a plot of the 20-countries data provided by Dr Cunningham:
A coefficient of determination, or R-squared, is the proportion of the variance in the dependent variable (covid deaths) that is predictable from the independent variable (flu shot rate). Using just Dr Cunningham’s 20 countries, we find an R-squared of 0.5327 under the assumption of a linear relationship, which corresponds to a correlation coefficient of 0.7299.
On Paul’s chart, this correlation coefficient is displayed prominently… but as we have seen, this value comes from Cunningham’s data as shown on the chart above. Paul’s chart is completely different, so the value of R he shows has no relation to his chart! Paul’s chart displays 27 data points (seven extra), the values plotted are different from Cunningham’s (due to a change to the data source), and the line drawn on the chart is exponential rather than linear (so it has nothing to do with the correlation coefficient of 0.7299, which assumes a linear relationship).
The extra seven countries that Paul added are curious. From among the three countries mysteriously omitted by Dr Cunningham, Paul rightly added back Belgium and Iceland, but not Greece, which would be a significant outlier, weaking the association. Paul adds Poland, which isn’t in the OECD data, and Romania and Croatia, which aren’t even in the OECD. Paul refers to the ECDC as a data source for these three and all other European countries. He may be referring to this publication, but I could not find the exact numbers he used. Adding Poland, Romania and Croatia strengthens the association because apparently they all have low vaccination rates and few Covid-19 deaths.
Strangest of all, Paul has added Canada and USA, being the only two countries on the chart outside of Europe. He uses OECD data for these countries, which makes it strange why he would omit other non-European OECD countries like Korea, New Zealand and Japan. These countries would all weaken the association, and their omission seems somewhat convenient for someone wishing to make the case that there is a strong correlation.
It is misleading to display on the chart an R value that has nothing to do with the data in the chart. It is misleading to cherry-pick countries and omit significant outliers without explanation. I think my charts give a more complete and honest picture than this chart by Paul.
The argument that the amount of aluminum in vaccines is safe relies on a 2011 paper by Mitkus, which presents an aluminum pharmacokinetic model. The paper is flawed. The model is flawed. Here is an example of an error in the Mitkus 2011 paper.
Priest 2004 provided the equation for aluminum retention used in Mitkus’s model. The equation and the half-lives stated in this paper are inconsistent.
1.4 days half-life => exponent of 0.495 40 days half-life => exponent of 0.0172 1727 days half life => exponent of 0.000401
0.595 as 0.495 and 0.172 as 0.0172 look like typos.
Mitkus took the equation and half-lives from Priest’s paper, and apparently did not notice the inconsistency. He not only failed to correct the typos in the equation, he added one of his own, mis-stating 11.4 as 11.
Newton, the original source of the equation and co-author of the 2004 paper by Priest, later authored a paper with the same equation… but with the none of the typos.
Mitkus used the wrong equation
How could Mitkus have made such a basic error?
How did none of the co-authors or peer reviewers spot it?
Why has the paper not been retracted in light of this error?
William Thompson is the CDC whistleblower who revealed that he had been involved in a cover-up of a key result in the vaccine-autism debate.
He was referring to the DeStefano 2004 study of MMR and autism, on which Thompson was a co-author, conducting the statistical analysis. Thompson claimed that an association between MMR and autism in African American boys was identified in the data, but that the finding was omitted from the final paper. He cited the pressure to show no association between MMR and autism, and explained how they tried various statistical techniques to try to hide the association.
The infographic above presents the data behind the debate. Brian Hooker’s 2014 re-analysis of the data shows there is indeed an association between MMR and autism in African American boys in the data.
Forget the politics; the science here is telling us there is an association between a vaccine and autism.
In 2012, the Institute of Medicine (IOM) released a comprehensive evidence review entitled “Adverse Effects of Vaccines: Evidence and Causality”.
They looked at 8 different vaccines and 76 different adverse events. One of these adverse events was autism.
For 1 vaccine (MMR), the IOM favored rejection of a causal relationship.
For 1 vaccine (DTaP), the IOM declared the evidence inadequate to accept or reject a causal relationship.
For the other 6 vaccines in the review, the IOM did not look for any evidence regarding a causal relationship.
Clearly then, the correct conclusion of this evidence is NOT that “vaccines do not cause autism”. There is not enough evidence to make that conclusion.
Even if a causal relationship between MMR and autism is rejected, it does not follow that “vaccine do not cause autism” because MMR is only one of 8 or more vaccines, and the evidence is inadequate to accept or reject a causal relationship for them. There have also been no studies looking for associations between cumulative vaccinations, or different timings, or different combinations of vaccines, and autism.
The CDC cites this IOM report for its claim that “vaccines do not cause autism” and yet this report does not support this claim.