Evidence map›Paper›PMID 30349811›Full record

ReviewFrontiers in cellular and infection microbiology2018

Back to the Future: Lessons Learned From the 1918 Influenza Pandemic.

Kirsty R Short, Katherine Kedzierska, Carolien E van de Sandt

Registry-linked trialOpen access · goldAbstract readHistorical ArticleReview
In one paragraph

Review in Frontiers in cellular and infection microbiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04359095 (Effectiveness and Safety of Medical Treatment for SARS-CoV-2), which is not on this map. Cited by 136 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
136citing papers in PubMed, 6 pooled it
25.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04359095 phase2 / phase3completednot on this mapstarted 2020, after this paper: background citation

Effectiveness and Safety of Medical Treatment for SARS-CoV-2 (COVID-19) in Colombia: A Pragmatic Randomized Controlled Trial

TypeinterventionalSponsorUniversidad Nacional de ColombiaRan2020 to 2021Enrolled650ConditionsCOVID-19ArmsEmtricitabine/tenofovir, Colchicine Pill, Rosuvastatin, Standard treatment
3 · Its place in the literature

Who cites it

136 citing papers in PubMed, 6 syntheses or guidelines pooled it, 280 citations in OpenAlex.

  1. Pooled it
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  8. The ageing immune system and its battle with viruses.The Journal of general virology · 2026
    Review
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76 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 2 countries.

Kirsty R ShortSchool of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia.
Katherine KedzierskaDepartment of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, VIC, Australia.
Carolien E van de SandtDepartment of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, VIC, Australia.
Peter Doherty Institute · AUSanquin · NLUniversity of Queensland · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

2018 marks the 100-year anniversary of the 1918 influenza pandemic, which killed ~50 million people worldwide. The severity of this pandemic resulted from a complex interplay between viral, host, and societal factors. Here, we review the viral, genetic and immune factors that contributed to the severity of the 1918 pandemic and discuss the implications for modern pandemic preparedness. We address unresolved questions of why the 1918 influenza H1N1 virus was more virulent than other influenza pandemics and why some people survived the 1918 pandemic and others succumbed to the infection. While current studies suggest that viral factors such as haemagglutinin and polymerase gene segments most likely contributed to a potent, dysregulated pro-inflammatory cytokine storm in victims of the pandemic, a shift in case-fatality for the 1918 pandemic toward young adults was most likely associated with the host's immune status. Lack of pre-existing virus-specific and/or cross-reactive antibodies and cellular immunity in children and young adults likely contributed to the high attack rate and rapid spread of the 1918 H1N1 virus. In contrast, lower mortality rate in in the older (>30 years) adult population points toward the beneficial effects of pre-existing cross-reactive immunity. In addition to the role of humoral and cellular immunity, there is a growing body of evidence to suggest that individual genetic differences, especially involving single-nucleotide polymorphisms (SNPs), contribute to differences in the severity of influenza virus infections. Co-infections with bacterial pathogens, and possibly measles and malaria, co-morbidities, malnutrition or obesity are also known to affect the severity of influenza disease, and likely influenced 1918 H1N1 disease severity and outcomes. Additionally, we also discuss the new challenges, such as changing population demographics, antibiotic resistance and climate change, which we will face in the context of any future influenza virus pandemic. In the last decade there has been a dramatic increase in the number of severe influenza virus strains entering the human population from animal reservoirs (including highly pathogenic H7N9 and H5N1 viruses). An understanding of past influenza virus pandemics and the lessons that we have learnt from them has therefore never been more pertinent.

Indexed as

Host-Pathogen InteractionsInfluenza Pandemic, 1918-1919Age FactorsCoinfectionGenetic Predisposition to DiseaseGlobal HealthHistory, 20th CenturyHumansInfluenza A Virus, H1N1 SubtypeInfluenza, Human1918external factorshost factorsinfluenzapandemicpreventionsocietal factorsviral factors

Identifiers

PMID30349811
PMCPMC6187080
OpenAlexW2895226241

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.