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Influenza vaccines do not reduce hospitalisations or deaths (Part 2)
Confirmation of Anderson et al. 2020 findings that on average, vaccination of 65 year olds does not directly help them.
This article is an extension of my 2021-12-24 article:
This illustration is adapted from Fig. 1 of Chen et al. 2020 Better influenza vaccines: an industry perspective jbiomedsci.biomedcentral.com/articles/10.1186/s12929-020-0626-6
Below is a detailed discussion for researchers and researchophile fusspots such as myself. The key points are:
Hoffman et al. 2020 found influenza vaccination did not reduce absence from work during the flu season. They also found an increase in disease risk increasing behaviour among those who were vaccinated - which is likely to counter any physically beneficial effects of the vaccination.
Van Ourti & Bouckaert 2020 found that influenza vaccination of Dutch 65 year olds was associated with a slight but statistically insignificant increase on hospitalisations, while finding other evidence of their effectiveness.
Courtney Ward 2014 analyses 2000 to 2006 influenza vaccination rates in Ontario and concludes that the benefits to older people, who are at greatest risk of harm and death from the flu, primarily result from vaccinating younger people - especially those who, in the absence of vaccination, in the current epoch of generally terribly low 25-hydroxyvitamin D levels, cause most influenza transmission.
Alberto Donzelli 2021 sci-hub.se/https://doi.org/10.7326/L20-0828 suggests that influenza vaccination reduces hospitalisations in people at risk of cardiovascular disease and increases hospitalisations among those without such risks. This is really interesting and likely to be important to the question of influenza vaccine effectiveness in the current situation of very low 25-hydroxyvitamin D levels. However, the solution to the influenza problem, as with COVID-19, does not lie in increased or fine-tuned vaccination programs. It can most easily be fixed, all year round, by proper vitamin D supplementation for most people.
No-one seems to be arguing against the validity of Anderson et al. 2020’s research methodology.
No-one except Alberto Donzelli seems to be arguing that this broad, widely accepted, extraordinarily expensive, influenza vaccination program be halted or drastically revised. Anderson et al. are not antivaxxers - they support the current programs continuing, despite their finding that vaccination made no difference to hospitalisation and that, in their unrealistic quantitative analysis, the vaccination increased the risk of hospitalisation.
All the evidence points to Anderson et al.’s conclusion that at the level of the individual - at least for 65 year olds in the UK who accepted government invitation to begin annual influenza vaccination - influenza vaccination and likely associated changes has no overall beneficial effect on the whole cohort.
However, in the current low 25-hydroxyvitamin D situation, influenza vaccination of younger people probably does protect older people from infection and so from harm and death.
Furthermore, it may be that a subset of older people - such as those with cardiovascular disease - are significantly protected at an individual level by influenza vaccination, but that this benefit is not visible in Anderson et al.’s age cohort wide hospitalisation and death figures due to this being counteracted by behavioural changes in others in this cohort, who derive no physical protection against severe symptoms from the vaccine itself, increasing their risks due to these higher risk behavioural changes.
The following is what I found after reading, at least partially, the English articles which Google Scholar lists as being relevant to Anderson et al. 2020 as well as the earlier Courtney Ward article.
I am partly concerned about what seems to be the generally undesirable nature of these broad, uncontroversial, influenza vaccination programs. I am also discussing this because it illustrates one aspect of the frequently unrealistic beliefs many people hold about the benefits of vaccination in general.
One such unrealistic belief is that the mRNA and adenovirus vector COVID-19 prophylactic treatments are widely known “vaccines”. They are not, by prior definitions, vaccines at all. For brevity I often refer to them as “vaccines”, but this only contributes the problem, which is in part deceptive advertising. I will adopt the term “quasi-vaccine” used by David Wiseman et al. in a 2022-01-05 submission discussed at: trialsitenews.com/dr-wisemans-damning-quasi-vaccine-efficacy-data-ignored-by-the-fda-and-the-cdc/. The Novavax protein sub-unit vaccine is a true vaccine, as are the inactivated virus vaccines such as Coronavac/Sinovax [WP].
These unrealistic beliefs regarding COVID-19 quasi-vaccines are a major driving force behind the current man-made disaster of the COVID-19 pandemic response to date, which denies the validity of nutritional and early treatment interventions (except those such as molnupiravir which are patented and highly profitable, even if they are unsafe and not very effective) and which therefore relies solely on vaccination, lockdowns and masks to protect people from disease severe enough to require hospital treatment.
Hoffman et al. 2020 - Ecuadorian bank staff in 2017-2018
This article is published by the IZA Institute of Labor Economics and is also available as the second version of two SSRN preprints.
IZA DP No. 12939: Vaccines at Work
Manuel Hoffmann, Roberto Mosquera and Adrian Chadi
January 2020, but the PDF creation date is 2020-08-04.
This research concerns the staff of a bank in Ecuador in which influenza vaccination rates were increased, for randomly chosen groups of individuals, by various experimental techniques, the most effective of which was to offer vaccination as part of the working week, rather than via visit to a clinic on Saturday. I found this research most interesting.
The bank’s management clearly had a genuine interest in health outcomes and an understanding of the importance of subtle research such as this. The bank enabled the researchers to experimentally alter vaccination incentives and analyse data in novel and constructive ways which are not possible in traditional RCTs [WP]. This enabled investigation of behavioural changes associated with vaccination which are rarely considered in the medical literature.
From the introduction:
Contrary to the company’s expectation, vaccination did not reduce sickness absence during the flu season. Getting vaccinated was ineffective with no measurable health externalities from coworker vaccination. We rule out meaningful individual health effects when considering several thresholds of expected vaccine effectiveness.
From the conclusion:
Regarding the health benefits of the intervention, flu vaccination did not have a significant effect on any of our outcomes. While we cannot rule out that the flu vaccine was medically ineffective, we find evidence consistent with moral hazard, i.e., individuals adopting riskier behaviors after getting vaccinated. Moral hazard constitutes a second way through which individual behavior can limit the effectiveness of health interventions.
. . . the proportion of vaccinated peers does not affect the probability of being diagnosed sick.
. . . getting vaccinated did not affect the probability of having a sick day. [This] increased the probability of having a sick day by 1.3 percentage points (5% of the baseline), which is insignificant at conventional levels. From an overall perspective of the firm, the results suggest that the investment in the health campaign was not worthwhile.
Note that sick diagnoses include severe illnesses, such as cancer, which leads to large numbers of sick days not related to the flu. If we exclude outliers with more than 100 sick days, the coefficient of the reduced-form is insignificantly positive, in line with our finding.
Note also that our results for sickness and sick days do not change if we take out the proportion of peers and estimate only the individual effect of vaccination.
. . . the main result is robust to the inclusion of controls (gender, age, tenure and income) and to using a broader and narrower definition of flu-related illness.
Page 23 - 24:
These results imply that we can safely rule out meaningful health benefits of the flu vaccination based on public health figures provided [by the US Center for Disease Control and Prevention] to policymakers from this intervention. However, the confidence interval in Figure 2 does not rule out potentially large positive values, which would suggest that getting vaccinated might increase illness [due to behavioural changes which they refer to as moral hazard].
The researchers consider (page 25) the possibility that vaccine recipients were less likely to seek medical help for any disease which resembled influenza. In January 2018, in response to high rates of influenza infection, the Ecuadorian government strongly encouraged anyone with flu-like symptoms to see their doctor. The workers who accepted the experimental encouragement to be vaccinated were no more or less likely to be diagnosed with flu in of the four months studied. The same was true in November, December and February for diagnoses of non-flu diagnoses. However, in January, these workers, who had chosen to accept the experimentally offered vaccination, were 7.2% less likely to be diagnosed with a non-flu illness - many of which would have been respiratory illnesses with symptoms similar to those of influenza.
This result suggests that employees [whose vaccination appointment which was] assigned to the workweek, who were more likely to get vaccinated [this was part of the experimental design, since more chose this than those who were offered appointments at the weekend], felt protected, and went less to the doctor when they felt flu-like symptoms. These estimates are consistent with the hypothesis of riskier behavior among vaccinated individuals, as they appeared to think that they are protected against the flu.
This has obvious implications for COVID-19 vaccination campaigns in which vaccination (in Western countries with quasi-vaccines) is promoted as reducing the risk of infection and/or of serious disease: the benefits of the physical vaccination are reduced by behaviours such as increased social mixing, and transmission to the person, or to others if they are infected, and longer delays in seeking medical attention when symptoms warrant it.
The researchers found the same effect regarding cost-free, workplace time, visits to the bank’s in-house health center in January, and some evidence of reduction in infection protective behaviours (such as using an umbrella) among those who accepted the experimental invitation to be vaccinated.
The researchers were able to measure the average increase in vaccination rate which occurred when there was a 10% increase in the vaccination rate of the each individual’s workplace peers: 7.9% (page 18). They further determined that the primary impetus for this pro-vaccination choice was the desire to conform with the work group rather than due to change in belief about the personal or wider benefits of vaccination.
This seems to me like a powerful amplification effect which should be considered when trying to understand the current situation of majority adoption (some of it forced or unreasonably encouraged) of COVID-19 vaccination in many Western countries, and how this evident desire to follow social norms regarding an ostensibly self- and other-protective behavioural choice, amplifies within a population, potentially to the point of snowballing adoption rates among the subset of the population who are amenable to such influence.
I think that subset is the majority. Almost all of us are influenced by social pressure and our perception of the behavior of others. Dear reader: If you and I were walking along Bourke St Melbourne one day, and everyone around us fell to the ground, on their backs, and waved their arms and legs in the air and emitted vocalisations resembling those of male koalas seeking mates, I don’t know about you, but I would probably do the same with only a moment’s thought. Likewise if I hallucinated that this was what everyone else was doing.
Hoffman et al. cited Ward 2014 (see below) in the following interpretation:
In a study on flu vaccines, Ward (2014) finds that flu vaccination increased sickness absences in years when the flu vaccine had a bad match with the prevalent flu viruses, and it had no effect in years when the vaccine had a good match. The difference between these two results, which would control for moral hazard, points to the medical benefits of the vaccine.
I think they are considering only moral hazard (infection-increasing riskier behaviour) in vaccinated people and the physical benefits of the vaccine which varied from one year to the next, with the two approximately canceling when the vaccine was a good match for that year’s virus. However, an additional consideration is that the vaccinations may cause adverse effects which result in sickness absences.
Van Ourti & Bouckaert 2020: 65 year olds in The Netherlands, 1996 to 2008
This is research relies on data which is 25 to 13 years old, and so is not necessarily relevant to current influenza vaccines. It is of interest because it uses a similar methodology to Anderson et al. 2020, although using data from ages 64 to 66 only and without any graphs.
The Dutch influenza vaccination policy and medication use, outpatient visits, hospitalization and mortality at age 65
Tom Van Ourti and Nicolas Bouckaert
European Journal of Public Health 2020-02-14
. . . the effectiveness of vaccination for people aged 65+ has come under scrutiny during the last decade. A [Cochrane 2018] meta-analysis of randomized controlled trials (RCT) concluded that good evidence of the impact of vaccines on pneumonia, hospitalization and mortality is lacking for this fragile age group . . .
The researchers did not know which subjects were vaccinated, but they knew that all individuals aged 65 and above received an annual invitation for free influenza vaccination and that individuals who turned 65 were “9.8% more likely to vaccinate”. However, I have not been able to understand what the vaccination rates were for the four age brackets, other than from their statement that “Between 1996 and 2008, vaccination rates of the target group reached 75–80% in The Netherlands and were among the highest in Europe.”
So the results below were presumably due to a relatively small number of people who were generally not vaccinated at 64 choosing do do so at 65 and 66. The researchers found that in epidemic months, the increase in vaccination rates (and any other effects) which resulted from the 65 and above annual free vaccination letter, was associated with the following four observations during pandemic months:
15% lower probability to use prescribed medicines (95% CI = 28 to 3; P = 0.02).
0.13 fewer GP visits (95% CI = 0.28 to 0.02; P = 0.09) per month.
Table 2’s caption states “corresponding to relative reductions of 20% and 26%” referring to these two items. However, after scrutinising the supplementary data, I was unable to clearly understand the baselines which the statement implies.
No evidence of an age discontinuity at 65 in epidemic and non-epidemic months for hospitalisation or respiratory deaths.
Their assessment of this reduction in deaths is:
The estimated mortality reduction is very small, also in contrast to previous findings.
None of these articles mention vitamin D. Influenza transmission, case numbers and severity of those infected could all be drastically reduced if everyone had the 50 ng/mL 25-hydroxyvitamin D their immune system needs to function properly.
Ward 2014: 2000 to 2006 increase in influenza vaccination for people younger than 65 in Ontario
This data is now rather old. The analysis is novel and highly complex, but seems to me to be suitable for the task of researching total vaccination benefits due to direct effects and other effects, while eliminating numerous confounders.
Influenza Vaccination Campaigns: Is an Ounce of Prevention Worth a Pound of Cure?
Courtney J. Ward
American Economic Journal: Applied Economics, January 2014 PDF date 2013-12-18
In 2000, Ontario, like other provinces of Canada had high (~70%) rates of influenza vaccination for people aged 65 and over, and low (~16%) rates for younger people, both of which were somewhat higher than the rates for all other provinces. In 2000 to 2006, Ontario provided and promoted free influenza vaccination for all ages, which did not significantly alter rates for those 65 years and above, but increased rates for ages 10 to 64, reasonably evenly by age from (eyeballing Fig, 2B) ~10% to ~20%. This was a discontinuity in the overall trend to greater vaccination rates for both age groups in all provinces.
Table 4 shows that this change was associated with a significant increase in influenza hospital admissions (the natural logarithm [WP] of the change being 0.152 with p [statistical significance WP] between 0.05 and 0.1. However, this is may be a spurious result based on changed diagnostic biases in the post 2000 period, since overall hospital respiratory diagnoses fell (-0.087). Work absences also increased significantly.
In this analysis, further divination of underlying processes is claimed to be made possible by quantifying the degree to which the flu vaccine for each season matched the actual mix of strains encountered in that season. This seems reasonable to me, but I am way out of my comfort zone with the complexity of this analysis.
The peak case rate for each season is shown to anti-correlate with the match figure for each season’s vaccine over the 1995 to 2006 survey period, as would be expected if the vaccine suppressed transmission. (Fig. 3.)
Table 4 shows the strength of the post-2000 boost in 64 years and younger vaccination rate for seasons with a poor vaccine match and for those with high vaccine match. The post-2000 increase in work absences is shown to be almost entirely attributed to low match seasons. I suggest that this could be interpreted as this increase in <65 yo vaccination rates, when the vaccine was not very protective against the strains of that year, causing (physically or by some other mechanisms) a highly significant increase in flu season sick days, with any such undesirable impact in good match seasons being reduced and/or to some extent balanced by the vaccine doing a better job of reducing infections which were severe enough to cause absence from work. I can’t clearly interpret this, but it could be explained by the vaccines in all seasons causing some people to take time off work, to a greater degree than it reduces this due to combating total community-wide influenza transmission and individual severity, with the good match vaccines causing enough of a beneficial reduction in influenza illness to offset this.
Table 4 also indicates that most of the overall drop in hospital respiratory diagnoses (which includes influenza, non-influenza infections and any severe, perhaps partly bacterially caused, pneumonia cases - mainly among the elderly) is due to them decreasing strongly in high match seasons.
Non respiratory hospital diagnoses also dropped significantly in the post-2000 period, but this was equally true of good and poor match years.
The analysis apparently confirms (page 56 onwards) the significance of the above by showing that the effects occur only during flu season - but this is getting too complex for me to reliably evaluate.
Fig 4 depicts pneumonia rates among the elderly decreasing as for those under 65 years post-2000, despite the elderly’s static vaccination rates. Courtney Ward attributes this, potentially, to the elderly being less infected due to reduced infections in younger people. I wonder whether treatments improved, but I guess that her analysis is precisely targeted and so reflects the effects of higher vaccination rates. Since the reduction in infections is greater than the reduction in unvaccinated people, she proposes (page 65), that those who newly accepted vaccination post-2000, who were more likely to be poor, with poorer average health, and that prior to this these people collectively made an above average contribution to community transmission of influenza. This seems reasonable to me.
I feel that we are right out on a statistical limb with these analyses, compared to the simplicity of the Anderson et al. graph. However, Courtney Ward is much more ambitious in discerning underlying mechanisms, and I sense that her analysis is well targeted.
Are large external benefits to older adults realistic in the face of already high vaccination rates? One interpretation of these results is that vaccination of the young yields higher returns when compared to the “own” effects of vaccination for this group. This accords with the medical literature, which indicates that effectiveness of the vaccine among older groups is low due to poorer immune response and provides support for policies aimed at vaccinating close contacts of care residents.
This seems reasonable to me: The elderly suffer the most harm from influenza, but vaccinating them provides less direct protection against infection and severe illness than it does for younger people. By vaccinating younger people, we reduce overall transmission and so the level of infection amongst the elderly.
This is highly pertinent to one of the most intense debates as we enter 2022: to what extent is the extreme push, among some people and some authorities in Western nations, to vaccinate children, down to 5 years, due to the desire to protect adults and the elderly rather than the children themselves? (There now a vast amount of evidence that the harm caused by these quasi-vaccines far exceeds what is officially admitted. See, for instance, Steve Kirsch’s Substack and perhaps read some accounts of those who believe they have been vaccine injured: nomoresilence.world.)
Since children have relatively low risks of harm and death from COVID-19 (though perhaps Omicron BA.1.x and or BA.2 is worse for children) and since the current mRNA and adenovirus vector quasi-vaccines do (despite avoidance of this by many authorities) carry known risks for children of myocarditis AND unknown risks of unknown lifelong complications, to what extent is it morally justified to burden people at the very start of their still vulnerable lives with such risks for the benefit of those who have already lived much of their lives?
This vexed debate is generally conducted in complete ignorance of the fact that if everyone had 50 ng/mL 25-hydroxyvitamin D levels and access to early treatments, COVID-19 transmission would be too low to support the current pandemic, and that few of the small number of people who would be infected each year would suffer serious symptoms.
The practical answer is not to fine-tune vaccination policy but to fix the underlying nutritional deficiency which makes SARS-CoV-2 so transmissible and harmful. Then, vaccines - with their higher than officially acknowledged risks, need only be used for a subset of people with obesity and other co-morbidities who remain at significant risk even with good 25-hydroxyvitamin D levels and access to multiple early treatments.
Exactly the same arguments apply to influenza.
There is a vast effort, costing billions and billions of dollars, fighting influenza. The correct fix for this is boosting everyone’s 25-hydroxyvitamin D levels to at least 50 ng/mL, voluntarily, with easy access to inexpensive vitamin D tablets and capsules, and realistic information about the profound health benefits which result from supplementing with these in the right quantities, which are primarily a ratio of bodyweight: vitamindstopscovid.info/01-supp/ .
Then brilliant scientists such as Courtney Ward can devote their energies to other really serious problems and people like me won’t need to be up at 3AM trying to understand their articles about trivially easy to fix problems such as influenza, which they currently think are extremely difficult.
She concludes the raising influenza vaccination rates much above 35% of the general population is likely to result in little additional benefit, due to this level being sufficient to reduce transmission R0 below 1.0, and so greatly reduce the overall incidence of infection. This sounds right to me, especially if those being vaccinated are the ones who, without vaccination, are contributing most to the spread of the disease. This is primarily people under 65, right down to children and babies.
I do not support this broad-based vaccination approach to quelling influenza, because vitamin D supplementation is a far easier, safer, less expensive, non-invasive alternative with a huge range of other health benefits - and no need for medical staff, whose expertise and efforts should be reserved for the many problems which only they can properly attend to.
Both influenza and SARS-CoV-2 viruses mutate at a faster rate than vaccines - including easily customisable mRNA and adenovirus vector quasi-vaccines can be customised and deployed to whole populations. This makes vaccines a lousy tool for any population scale intervention against these diseases. Vitamin D has no such problems of being so narrowly targeted. It simply enables the immune system to work properly. Very few people understand this. Everyone needs to understand it. The best place to start is the Quraishi et al. 2015 graph at: https://vitamindstopscovid.info/05-mds/ .
With the current generally terribly low 25-hydroxyvitamin D levels, both influenza and COVID-19 are crapshoots [WP] with serious risks of harm and death. The response so far as to been to tackle them solely with vaccines. These sound powerful and specifically targeted, but most people have far too much faith in the power of vaccines to tackle these diseases. The vaccines themselves are a crapshoot - both influenza and COVID-19 vaccines are difficult or impossible to make effective for any actual occurrence of recently mutated variants. In addition, mRNA and adenovirus vector COVID-19 quasi-vaccines are a crapshoot regarding safety. They harm and kill far too many people.
Proper vitamin D supplementation for most people will mean that influenza and COVID-19 do not spread very widely at any time of the year, and that they are only a crapshoot in terms of severity, harm and death for a small proportion of the population with well-known co-morbidities. These people may benefit from the crapshoot vaccines - but there will be no reason to deploy these at population scale, as is currently widely believed to be the only way these diseases can be suppressed.