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Record W2123288913 · doi:10.1093/infdis/jiu651

How Can We Solve the Enigma of Influenza Vaccine-Induced Protection?

2014· letter· en· W2123288913 on OpenAlexaboutno aff
Kathleen M. Neuzil

Bibliographic record

VenueThe Journal of Infectious Diseases · 2014
Typeletter
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVaccinationInfluenza vaccineCohortVaccine efficacyGold standard (test)Cohort studyOddsImmunologyInfluenza seasonEnvironmental healthVirologyIntensive care medicineInternal medicineLogistic regression

Abstract

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(See the major article by Ohmit et al on pages 1519–28.) For the past decade, on an annual basis, multiple sites across the United States have estimated the effectiveness of seasonal influenza vaccine to prevent influenza-associated, medically attended acute respiratory illness [1]. The standard design involves selecting persons who present to medical facilities with acute respiratory infection and are laboratory-test positive for influenza virus (defined as cases) and comparing their odds of having been vaccinated to those of persons who are laboratory-test negative for influenza virus (controls). Such studies are now done routinely in the United States, Canada, and several other countries [1–4]. This design has been validated using data sets from the gold standard, randomized, placebo-controlled clinical trials of influenza vaccines [4]. Beginning in the 2010–2011 influenza season, Ohmit et al assessed annual influenza vaccine effectiveness by using a prospective household cohort study design [5, 6]. While consuming more time and resources than the case-control approach, this method has the advantage of capturing multiple outcomes (including less severe illness), establishing denominator-based attack rates, and allowing for collection of influenza serology prior to the influenza season. In this issue of The Journal of Infectious Diseases, Ohmit et al provide effectiveness estimates from this Michigan household cohort for the 2012–2013 influenza season [7]. As previously demonstrated in this cohort, there was a detrimental effect of prior vaccination on current vaccine performance. The authors appropriately highlight that comparisons are complex, since vaccination during the prior year may provide some residual disease protection, particularly if the vaccine strain does not change between seasons. Therefore, the use of “neither year” vaccination status as the reference group, in this and other studies, is appropriate. This phenomenon that vaccination against influenza in the prior year is significantly associated with a modestly lower level of clinical protection in the current year has now been reported from multiple populations during multiple influenza seasons, using both the prospective household design, as well as the test-negative case-control design. In addition to the single season effect, McLean et al recently reported on a cohort in Wisconsin with 5 years of historical vaccination data. They found that vaccine effectiveness was significantly higher among individuals with no prior vaccination history, compared with individuals with a frequent vaccination history [8]. Understanding influenza vaccine–induced immunity and protection is complex. In these cohorts, it is not possible to fully account for the interaction between past exposure to naturally circulating influenza virus, past exposure to influenza vaccines, strain match, and age and immunocompetence of each individual. Further, unmeasured confounding remains a concern for any observational study. However, one might postulate that individuals who are not vaccinated in the prior year, or years, would be more likely to have an influenza virus infection and that natural immunity resulting from this infection would be superior to vaccine-induced immunity in preventing influenza. In the study by McLean et al, for example, cases of influenza were less likely to have been vaccinated but also less likely to have received a previous diagnosis of influenza, compared to controls [8]. Further, in a landmark study by Hoskins et al, in a boys' boarding school in England in the 1970s, attack rates during influenza outbreaks were lowest in boys with documented prior influenza virus infection [9]. Understanding the immunologic mechanisms underlying this clinical observation of attenuation of influenza vaccine effectiveness with prior vaccination is important. Vaccine-induced and naturally induced serum antibody responses correlate with protection against influenza. Because of the repeated exposure to influenza virus infection and influenza vaccination, the B-cell response to influenza vaccination is an antigen-recall response in most older children and adults [10]. The emergence of the 2009 pandemic influenza A(H1N1) strain (A[H1N1]pdm09) in 2009 provided an opportunity to compare B-cell responses induced by natural infection and vaccine in naive adults, without the confounder of previous exposure. Infection with A(H1N1)pdm09, as compared to vaccination with inactivated A(H1N1)pdm09 vaccines, resulted in an increased breadth and magnitude of B-cell responses in adults. Wild-type A(H1N1)pdm09 infection did not affect the antigen-recall response to subsequent inactivated vaccines with the same strain, whereas receipt of A(H1N1)pdm09 inactivated vaccines reduced the B-cell response to repeated vaccination [11]. Ideally, such measures of immunologic responses would be incorporated into multiyear, prospective studies of vaccine effectiveness. In the meantime, what do these observations tell us about the value of repeated vaccinations? Table 1 uses estimates from the Michigan household cohort to highlight the current findings from a disease-prevention standpoint [7]. Based on this scenario, over a 2-year period, the highest number of influenza cases will occur in the population that does not receive any influenza vaccine. Likewise, a single vaccination is better than no vaccination. In support of the current influenza vaccine policy, vaccination in both years prevents more disease than vaccination in a single year, although depending on the assumptions, that benefit may be minimal. While looking at vaccination in a single year may illustrate an apparent negative effect, compared with either year alone, the overall public health impact must be considered over all years, not just the current year. The hypothetical scenario in Table 1 assumes that at entry into the first year of the cohort, all individuals are vaccine naive, which is likely not a reasonable assumption. The lifetime history of influenza virus infection and influenza vaccination will be unique for each person in the cohort. Only through multiyear prospective studies, with evaluation of subclinical infections, will we be able to solve the enigma of vaccine-induced influenza virus immunity. In the meantime, the current policy to administer influenza vaccine every year should be maintained. Cases of Influenza Prevented in a Hypothetical Cohort in Which the Influenza Attack Rate for Symptomatic Acute Respiratory Illness Is Held Constant at 10% Each Year and Using Point Estimates of Effectiveness in Table 4 From the Associated Article by Ohmit et al [7] Cases of Influenza Prevented in a Hypothetical Cohort in Which the Influenza Attack Rate for Symptomatic Acute Respiratory Illness Is Held Constant at 10% Each Year and Using Point Estimates of Effectiveness in Table 4 From the Associated Article by Ohmit et al [7] Potential conflict of interest. Author certifies no potential conflicts of interest. The author has submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.026
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.055
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0020.001
Science and technology studies0.0020.010
Scholarly communication0.0060.025
Open science0.0040.004
Research integrity0.0110.024
Insufficient payload (model declined to judge)0.0170.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.060
GPT teacher head0.332
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations10
Published2014
Admission routes1
Has abstractyes

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