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Record W2626503626 · doi:10.1093/infdis/jix287

Reply to Chung et al

2017· letter· fr· W2626503626 on OpenAlexaff
Danuta M. Skowronski, Catharine Chambers, Gaston De Serres

Bibliographic record

VenueThe Journal of Infectious Diseases · 2017
Typeletter
Languagefr
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsUniversité LavalInstitut National de Santé Publique du QuébecBC Centre for Disease ControlUniversity of British Columbia
Fundersnot available
KeywordsMedicine

Abstract

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To the Editor—In their correspondence [1], Chung et al respond to 2014–2015 findings from the Canadian Sentinel Practitioner Surveillance Network that showed increased influenza A(H3N2) risk among repeatedly vaccinated compared with consistently unvaccinated individuals across 3 consecutive seasons [2]. Although they credit us with first report of such an association for A(H3N2), Hoskins et al [3] and Keitel et al [4], decades earlier, showed similar effects with repeat vaccination, as outlined in our article [2]. Chung et al provide an update to the 2014–2015 vaccine effectiveness (VE) estimates from the United States FluVE Network previously reported by Zimmerman et al [5]. In their updated analysis, Chung et al consider 3 consecutive seasons’ vaccine history (current and 2 prior), whereas Zimmerman et al considered only 2 consecutive seasons’ vaccine history (current and 1 prior) from the same 2014–2015 dataset. Chung et al restricted vaccination history to participants with medical record documentation in all 3 seasons. Zimmerman et al also relied upon medical record documentation for prior season’s vaccination, but in some states accepted plausible self-report for defining current vaccination. In the reanalysis by Chung et al, VE estimates were highest in those vaccinated in 2014–2015 only, at 26% (95% confidence interval [CI], 0–46%) and slightly more divergent compared to repeat vaccine recipients (–2% [95% CI, −24% to 15%]) than those originally reported by Zimmerman et al (8% [95% CI, –14% to 26%] vs –2% [95% CI, –20% to 13%]). They interpret the updated findings as consistent with VE observations from Canada indicating pronounced negative interference with repeat vs current-season-only vaccination (–47% [95% CI, –101% to –8%] vs 63% [95% CI, 14%–84%]) [1, 2]. However, confidence intervals broadly overlap in both US analyses, and it is only the smaller subset of participants vaccinated in the current-season only that differed meaningfully from those reported by Zimmerman et al. Ultimately, it is unclear whether the updated findings from the United States reflect random variation, varying method(s) of vaccine status ascertainment and its potential misclassification, or negative interference based on the number of consecutive vaccine doses considered. In relying upon medical record documentation for vaccine status ascertainment, Chung et al caution that patients may overreport vaccination by approximately 10% relative to registry data, citing Irving et al [6]. Comparing self-report to registry data for current season’s vaccination, Irving et al reported sensitivity and specificity of 95% and 90%, respectively, across all age groups (Table 2 in [6]). However, among participants aged ≥5 years, Irving et al also showed that registry data missed approximately 10% of patient/guardian-reported vaccinations that were subsequently confirmed by the provider to have been received (ie, registry sensitivity = 90%) (Figure 2 in [6]). When the incompleteness of registry data was corrected, concordance was improved, with overall sensitivity and specificity of 95% and 95%, respectively (Table 3 in [6]). Sensitivity of patient/guardian report in relation to the adjudicated registry would have exceeded 97% if children <5 years old were excluded, which is relevant as repeat VE assessments were restricted to participants ≥9 years old in both Canada and the United States [1, 2]. Recall of prior seasons’ vaccination may be less accurate than for the current season, but is not expected to vary systematically by outcome, and is anticipated to be most reliable for those with habitual vaccination behaviors. In Canada, vaccination status—both current and prior—is consistently based on patient/guardian report and practitioner documentation before either knows the outcome status (ie, influenza test result). This minimizes the likelihood of differential exposure misclassification—in particular because illness severity and the pretest likelihood of influenza are further standardized in Canada by a consistent influenza-like illness syndrome as a study eligibility criterion—requiring a specific combination of respiratory and systemic symptoms. Exposure misclassification that is nondifferential and independent is more likely to obscure than to exaggerate differences between vaccinated and unvaccinated groups [7], attenuating VE estimates from either direction of protective or negative effect. VE estimates are most reliable for the largest subgroup of repeatedly vaccinated participants, whereas sparse data considerations mean that smaller subgroups (such as current-season only recipients) are more susceptible to the impact of measurement error. In that regard, the most contentious finding of negative VE (increased risk) with repeated vaccination is also anticipated to be the most robust among vaccine subgroups in Canada. Nondifferential exposure misclassification may partially explain attenuated protective effects of vaccination in the current season only in the United States compared with Canada (26% vs 63%) [1, 2], or attenuated negative effects of repeated vaccination in the United States compared with Canada (–2% vs –47%) [1, 2], but does not otherwise provide reassurance against the Canadian findings. We agree with Chung et al that the impact of exposure misclassification from both self-report and registry-based documentation warrants further reflection. However, as elaborated in our article, other variability should also be taken into account, including the broader incorporation of other methodological and immunoepidemiological considerations to explain repeat vaccination effects [2]. Potential conflicts of interest. G. D. S. has received grants unrelated to influenza from GSK and Pfizer and travel reimbursement to attend an ad hoc advisory board meeting of GSK also unrelated to influenza; and has provided paid expert testimony in a grievance against a vaccinate-or-mask healthcare worker influenza vaccination policy for the Ontario Nurses’ Association. All other authors report no potential conflicts. All authors have 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)0.004
metaresearch head score (Gemma)0.049
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.105
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0080.005
Scholarly communication0.0070.004
Open science0.0030.004
Research integrity0.1050.062
Insufficient payload (model declined to judge)0.0150.012

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 head0.028
GPT teacher head0.363
Teacher spread0.336 · 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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Citations0
Published2017
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