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Record W2118961649 · doi:10.1093/infdis/jiu192

Reply to Wilson et al

2014· letter· fr· W2118961649 on OpenAlexaboutno aff
Cécile Viboud, Jana Eisenstein, Ann Reid, Thomas A. Janczewski, David M. Morens, Jeffery K. Taubenberger

Bibliographic record

VenueThe Journal of Infectious Diseases · 2014
Typeletter
Languagefr
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicDemographyInfluenza pandemicPopulationExcess mortalityCoronavirus disease 2019 (COVID-19)MedicineSociologyDiseaseInfectious disease (medical specialty)

Abstract

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To theEditor— We thank Wilson et al [1] for their comments on our study of the 1918–1919 influenza pandemic in Kentucky [2]. Our key findings were to provide evidence of break points in the mortality profile of the fall 1918 pandemic wave among individuals aged 9–10 years (ie, cohorts born during 1908–1909) and those aged 24–26 years (ie, cohorts born during 1892–1894) [2]. These age break points do not strictly align with known dates of past pandemic events, and, hence, it is not intuitive that they support or refute the hypothesis proposed by Wilson et al, in which aberrant immune responses mediated by CD8+ T cells were more frequent among young adults whose first influenza virus exposures were to the 1889 pandemic virus [1]. In their letter, Wilson et al contribute interesting data on influenza mortality patterns in New Zealand [1]. They report a mortality peak at ages 29–30 years in males and females (ie, cohorts born during 1888–1889), which is more in line with hypotheses invoking the 1889 pandemic than our Kentucky data. These findings could reflect true differences in the pandemic experience of geographically distant populations. Alternatively, the mortality profile in New Zealand could be influenced by a preponderance of influenza risk factors in aboriginal Maori populations or by high variance in mortality estimates due to small population size (approximately 0.85 million). Methodological factors may also contribute to the observed differences between the 2 studies, as our Kentucky analysis relied on “above baseline” excess mortality in the lethal months of October–December 1918 [2]. Future studies could compare the risks profiles of different populations via the excess mortality approach to help tease out influenza-specific effects from unrelated background mortality [3]. Age-specific patterns of mortality due to respiratory disease during the October–December 1918 influenza pandemic wave in Kentucky. A, Data obtained using a regression model to estimate the influenza-related mortality rate (ie, the excess mortality rate above a seasonal baseline, as in the original study [2]). B, Results of a crude analysis of death counts that did not rely on population size estimates or baseline models. Blue dots represent observations, black lines are smoothing splines, and grey-shaded areas are 95% confidence intervals (CIs). Vertical grey bars mark the age break points (ie, extrema) and associated birth years identified in the age-specific mortality curves. Both graphs identify a clear mortality peak among young adults aged 24.6–25.6 years (ie, cohorts born during 1892–1893), which is confirmed in analysis of female-specific death counts (for which there was peak at ages 24.3–24.4 years, corresponding to the cohort born during 1893). Analysis of male death counts indicates an earlier peak, at ages 19.5–19.7 years (ie, the cohort born during born 1898), even more inconsistent with hypotheses invoking the 1889–1892 pandemic. Overall, our historical analysis provides unprecedented detail on the age and sex patterns of the pandemic in Kentucky [2]. Similar to Wilson et al [1], we found a substantially increased risk of death among military populations, perhaps due to crowding and/or increased circulation of coinfecting pathogenic bacteria. However, these factors alone cannot account for the mortality risk of the 1918 pandemic among young adult civilian females. While the reasons for the unusual age patterns of the 1918 pandemic are difficult to elucidate from epidemiological data alone, the steep rise in the risk of mortality among individuals aged 10–20 years during the influenza pandemic is worth noting, especially as it consistent in data from New Zealand, Canada, and the United States [1, 2, 4]. Historical morbidity surveys indicate that clinical attack rates were similar in these age cohorts [5], suggesting that the severity of influenza-related infection increased sharply between ages 10 and 20 years, likely because of a heightened risk of pneumonia caused by common bacterial respiratory pathogens (especially pneumococci, streptococci, and staphylococci) [5–7]. People aged 10–20 years had not lived through the 1889 pandemic, although they could have been infected by descendants of the 1889 influenza virus that persisted during 1892–1918. Although the reasons for the atypical mortality risk profile of the 1918–1919 pandemic may remain elusive, it is important to pursue efforts to analyze archival mortality records from a variety of locations, building upon the work by Wilson et al and others [1, 2, 4, 5, 8, 9]. A systematic epidemiological description of the pandemic in a variety of globally sampled populations may provide unique insights into the host and geographic factors responsible for the unusual severity of disease associated with the 1918 pandemic virus. Financial support. This work was supported by the Fogarty International Center (in-house research program); the National Institute for Allergy and Infectious Diseases, National Institutes of Health (intramural research program); the International Influenza Unit, Office of Global Affairs, US Department of Health and Human Services (to the Fogarty International Center). Potential conflicts of interest. All authors: No reported 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.

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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)0.007
metaresearch head score (Gemma)0.059
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 score0.040
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.059
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.001
Science and technology studies0.0040.005
Scholarly communication0.0050.008
Open science0.0050.003
Research integrity0.0400.053
Insufficient payload (model declined to judge)0.0100.009

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 head0.040
GPT teacher head0.366
Teacher spread0.326 · 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
Published2014
Admission routes1
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