One of the most important truths that are obfuscated by Pharmafia’s propaganda machine: not only vaccine safety is very poorly studied, but there has been done precisely 0 (zero) research on the cumulative and synergetic effect of all the shots in the various vaccine schedules enforced by governments around the world.
Meaning that we know jack-shizzle about their combined effect.
Here’s an example of how that plays out against vaccines and serial dupes.
Serial Vaccination and the Antigenic Distance Hypothesis: Effects on Influenza Vaccine Effectiveness During A(H3N2) Epidemics in Canada, 2010–2011 to 2014–2015
Danuta M. Skowronski, Catharine Chambers, Gaston De Serres, Suzana Sabaiduc, Anne-Luise Winter, James A. Dickinson, Jonathan B. Gubbay, Kevin Fonseca, Steven J. Drews, Hugues Charest, Christine Martineau, Mel Krajden, Martin Petric, Nathalie Bastien, Yan Li, Derek J. SmithAuthor NotesThe Journal of Infectious Diseases, Volume 215, Issue 7, 1 April 2017, Pages 1059–1099, https://doi.org/10.1093/infdis/jix074
Published: 09 February 2017 Article history
The antigenic distance hypothesis (ADH) predicts that negative interference from prior season’s influenza vaccine (v1) on the current season’s vaccine (v2) protection may occur when the antigenic distance is small between v1 and v2 (v1 ≈ v2) but large between v1 and the current epidemic (e) strain (v1 ≠ e).Methods.
Vaccine effectiveness (VE) against medically attended, laboratory-confirmed influenza A(H3N2) illness was estimated by test-negative design during 3 A(H3N2) epidemics (2010–2011, 2012–2013, 2014–2015) in Canada. Vaccine effectiveness was derived with covariate adjustment across v2 and/or v1 categories relative to no vaccine receipt among outpatients aged ≥9 years. Prior vaccination effects were interpreted within the ADH framework.Results.
Prior vaccination effects varied significantly by season, consistent with the ADH. There was no interference by v1 in 2010–2011 when v1 ≠ v2 and v1 ≠ e, with comparable VE for v2 alone or v2 + v1: 34% (95% confidence interval [CI] = −51% to 71%) versus 34% (95% CI = −5% to 58%). Negative interference by v1 was suggested in 2012–2013 with nonsignificant reduction in VE when v1 ≈ v2 and v1 ≠ e: 49% (95% CI = −47% to 83%) versus 28% (95% CI = −12% to 54%). Negative effects of prior vaccination were pronounced and statistically significant in 2014–2015 when v1 ≡ v2 and v1 ≠ e: 65% (95% CI = 25% to 83%) versus −33% (95% CI = −78% to 1%).Conclusions.
Effects of repeat influenza vaccination were consistent with the ADH and may have contributed to findings of low VE across recent A(H3N2) epidemics since 2010 in Canada.
Read the body of the research on its site if you need, we’re jumping to the final discussion, with our emphasis added. It’s stuff that goes against the new terms and conditions set up by the sellout social media / Big Tech, who are cancelling the science that’s not fitting their narrative
Using databases of the Canadian SPSN, we explored the extent to which repeat vaccination effects may have contributed to suboptimal influenza vaccine performance during recent A(H3N2) epidemics in Canada. We interpret our findings within the framework of the ADH, comparing observed effects measured by the TND with predicted patterns based on the antigenic relatedness between prior season’s vaccine (v1), current season’s vaccine (v2), and the circulating epidemic strain (e). This is the first modern attempt to directly correlate AD metrics with epidemiological observations of v1 effects and their overall fit within the ADH paradigm since it was first formulated nearly 2 decades ago.
Across the 3 A(H3N2) epidemics since 2010–2011 in Canada, no adjusted seasonal VE estimate exceeded 40%, even among mostly healthy, working-age adults. Each of these epidemics was associated with a vaccine-mismatched strain (v2≠e), although variation in VE was not obviously correlated with the AD (or match) between v2 and e. Adjusted VE was highest in 2010–2011 (40%; 95% CI = 9% to 60%), similar in 2012–2013 (31%; 95% CI = −4% to 55%), but dramatically lower in 2014–2015 (−12%; 95% CI = −47% to 15%) despite comparable v2–e ADs ranging 4–6. In the original report of the ADH, Smith et al also highlighted a lack of correlation between VE and the v2–e distance in first-time vaccines . Because A(H3N2) epidemics are associated with the greatest influenza disease burden , understanding the agent–host factors that contribute to low VE is critical. Our findings suggest that prior vaccination may modify current VE and that this effect may vary by season according to the ADH. Given heterogeneity in the conditions of vaccine–virus relatedness, we should expect v1 effects on current season’s VE to vary by season. Pooling or averaging across seasons may enhance statistical power but at the risk of masking meaningful variation and insights to inform mechanisms and implications; further explorations of prior influenza vaccination effects should stratify results by season and subtype.
During the 3 A(H3N2) epidemics presented here, observed v1 effects included no modification, as well as significant negative interference; we did not observe positive interference (ie, enhanced protection), also possible within the ADH framework but under specific conditions not found during epidemics included here . In 2010–2011, when v1 and v2 were antigenically distinct (v1 ≠ v2), minimal or no interaction was expected or observed. Conversely, with closer but nonhomologous v1 and v2 relatedness in 2012–2013 (v1 ≈ v2), the expected pattern of negative interference was apparent, although, with limited sample size, effect modification was not statistically significant. As anticipated based on the ADH, the negative effects of prior vaccination on the current season’s VE were most pronounced and statistically significant in 2014–2015 with homologous v1 and v2 antigens (v1 ≡ v2) and antigenically distinct circulating epidemic virus relative to v1 (v1 ≠ e).
Although antigenic drift has been widely emphasized to explain the historically low VE in 2014–2015, the AD between v2 and e was not estimated to be dramatically different from recent prior seasons [10, 11, 22–24]. Conversely, prior vaccination had marked effects, negating the otherwise moderate VE observed among v2-only recipients despite vaccine mismatch. A similar pattern of moderate VE among v2-only recipients, substantially reduced with receipt of the prior season’s homologous vaccine, was also reported for 2014–2015 in multicountry analysis from Europe  but not from the United States, where VE against A(H3N2) was negligible in all categories of current and prior vaccine recipients . In the Canadian data, a dramatic increase in the distribution of influenza A(H3N2) cases reporting prior vaccination was observed in 2014–2015 whereas controls showed the expected trajectory of gradual increase, reflecting vaccine coverage trends in the general source population [25, 26]. In all seasons, vaccination status was based on patient self-report and practitioner documentation before either knew the patient’s case versus control status (ie, influenza test positivity result), minimizing differential recall bias and heightening the plausibility of the observation particular to cases in 2014–2015.
In 2014–2015 in Canada, under the specific conditions of v0 ≈ v1 ≡ v2 ≠ e, serial vaccination was associated with a nearly 50% increased risk of medically attended A(H3N2) illness relative to participants who were consistently unvaccinated. Statistically significant increased risk (OR = 1.85; 95% CI = 1.17–2.90) of A(H3N2) illness in 2014–2015 was also reported from Italy, where vaccinated participants were also mostly repeat recipients . The 2014–2015 epidemic is the first season in more than a decade of annual VE monitoring for which the Canadian SPSN reported vaccine-associated increased risk, and caution is warranted in its interpretation. However, increased risk was previously reported by multiple studies from Canada and elsewhere during the 2009 A(H1N1)pdm09 pandemic in association with prior receipt of mismatched 2008–2009 seasonal vaccine, replicated also in at least 1 randomized controlled study in ferrets [27–31]. Influenza vaccine-associated enhanced respiratory disease (VAERD) is a well-recognized phenomenon following heterologous challenge in vaccinated swine, most of whom recover . Although animal experiments may not be directly relevant to human experience, elements of involved mechanistic pathways may overlap and inform biological plausibility.
The ADH is a useful conceptualization but is not amenable to exact extrapolation . The originally published simulations were based on AD between v2 and e set at 2 with variability explored around v1–v2 and v1–e. Sensitivity analyses explored effects of homologous vaccination ranging up to a v2–e distance of 3, but not greater. Emphasis was placed on the prior season’s vaccination; the effects of earlier or multiple prior virus or vaccine exposures were not considered. The ADH predicts relative, but not absolute, VE, and the possibility that serial vaccine receipt might be associated with increased risk under some conditions was not considered, although such signals may have already been evident in the studies by both Hoskins and Keitel under specific conditions of multiple repeat vaccinations and v1, v2, and e relatedness [3, 4] (Supplementary Figures 1 and 2). The ADH is predicated on the HI assay, but variability in HI results by assay conditions must be acknowledged [16, 20]. For example, in 2 of 3 epidemics analyzed here (2010–2011, 2012–2013), Canada’s national influenza reference laboratory characterized all viruses as well-matched to the WHO-recommended v2 reference strain (AD < 3) [8, 9, 33, 34]. Those characterizations, however, were in relation to the cell-passaged v2 referent (whereas manufacturers use an egg-adapted reassortant), included varying animal-source erythrocytes, and did not include oseltamivir to address neuraminidase (NA)-mediated effects [8, 9]. We based our AD calculations on HI assays standardized for these conditions by the WHO Collaborating Centre for Reference and Research on Influenza (London) . Even so, further variability in the mix of variants by setting, the representativeness of selected reference strains, and changes induced by laboratory passaging complicates AD derivation, interpretation, and generalization. Future evaluations and their extrapolation would benefit from the assembly of a standard and definitive library of HI characterizations and ADs between specific egg-passaged vaccine strains and circulating genetic variants each season. The incorporation of modern genomic, bioinformatic mapping and antibody landscape approaches could also improve resolution in the understanding of vaccine-virus relatedness and response [35, 36].
Vaccine effects beyond those involving the HA1 (ie, HA2 or NA) and other agent-host immunological influences beyond (or complementary to) the ADH likely also play a role, including possible heterosubtypic effects of trivalent vaccine not otherwise considered. Original priming (eg, imprinting) and prominent recall (eg, back-boosting) responses to historic influenza exposures can shape hierarchical antibody responses, with either positive or negative implications [37–42]. Annually repeated vaccination, compared with less frequent infection exposures, may accelerate antibody refocusing toward prior versus evolved epitopes, with selection for cross-reactive but non-neutralizing memory responses . In the context of preexisting antibody, immune complex formation and Fc-receptor activation can suppress B-cell response to subsequent influenza vaccine doses . Antibody-dependent mechanisms may also suppress innate cytokine signaling pathways required for proinflammatory T-cell responses , and in children, annual repeat vaccination has been reported to hamper development of virus-specific CD8+ T-cell immunity . Repeat vaccination may also select for T-cell responses that are antagonistic, such as preferential activation and/or recruitment of regulatory cells upon reexposure . Such mechanisms may also modify risk in previous but not current vaccine recipients. Ultimately, the mechanisms to explain the potential negative effects of repeat vaccination remain unknown but are likely multifactorial, requiring a more complex systems approach to resolve .
Random and systematic error, including residual confounding and behavioral differences, may also contribute to findings. Few A(H3N2) epidemics were analyzed here, and each season represented a unique set of specific vaccine–virus relatedness conditions. Sample size in our indicator-variable analyses was also limited. Additional seasons are required before definitive conclusions can be drawn about correlation with the ADH. Population-based immunization registries are not available in Canada for the study period, but self-report is considered an accurate predictor of influenza vaccination status, as demonstrated in US analyses relative to registry data for both current  and prior season’s vaccination status (Ed Belongia Marshfield Clinic Research Foundation, personal communication), especially among adults who comprise the majority (86%) of our participants. We have the greatest confidence in VE estimates for repeatedly vaccinated relative to consistently unvaccinated participants, both in terms of reliable personal recall of vaccine history and also statistical certainty owing to sample size, but less confidence in smaller subsets of participants reporting more erratic vaccination behaviors. Change in vaccination habit may be correlated with influenza risk, a bias that has been raised previously in deriving VE estimates in elderly adults based on administrative datasets but also potentially relevant in assessing current/prior vaccination effects using an observational design . First-time vaccinees may have been newly motivated to receive influenza vaccine because of recent acute respiratory illness, possibly due to influenza. In the context of recent prior infection, vaccine responses may be enhanced  and/or VE may be overestimated through confounding by more durable and cross-protective infection-induced immunity. We did not have data available on prior infection history, but the proportion of newly vaccinated individuals with that recent history would have to be substantial to meaningfully influence VE estimates. Prior vaccination may have conversely blocked opportunity to acquire infection-induced immunity (ie, infection-block hypothesis), leading to underestimation of VE in the recurrently vaccinated—an indirect mechanism for repeat vaccination effects originally favored by Hoskins but insufficient to fully explain observed effects of vaccine-associated increased risk [3, 27, 31].
In summary, serial vaccination may have contributed to poor influenza vaccine performance during recent A(H3N2) epidemics in Canada. The ADH remains a useful framework for reconciling variability in repeat vaccination effects but requires update to incorporate recent epidemiological findings, modern and standardized laboratory approaches for monitoring vaccine–virus relatedness and response, and a broader understanding of immunological context and consequences. Integrated immuno-epidemiological evaluation across an extended horizon is needed to understand the spectrum of repeat vaccination effects and to determine whether annual influenza vaccination is likely to provide long-term advantage at the individual or population levels—a return to the question first posed by Hoskins 40 years ago .
To be continued?
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