Evidence map›Paper›PMID 25376480›Full record

ArticleEvolution, medicine, and public health2014

Timing of antimicrobial use influences the evolution of antimicrobial resistance during disease epidemics.

Mark M Tanaka, Benjamin M Althouse, Carl T Bergstrom

Open access · goldAbstract read
In one paragraph

Article in Evolution, medicine, and public health, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 26% 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
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  3. Article
  4. Seasonality of respiratory viruses causing hospitalizations for acute respiratory infections in children in Nha Trang, Vietnam.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2018
    Article
  5. Article
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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 2 institutions in 2 countries.

Mark M TanakaSchool of Biotechnology and Biomolecular Sciences, University of New South Wales, Kensington NSW 2052, Australia; Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA; Department of Biology, University of Washington, Seattle, WA 98195-1800, USA m.tanaka@unsw.edu.au.
Benjamin M AlthouseSchool of Biotechnology and Biomolecular Sciences, University of New South Wales, Kensington NSW 2052, Australia; Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA; Department of Biology, University of Washington, Seattle, WA 98195-1800, USA.
Carl T BergstromSchool of Biotechnology and Biomolecular Sciences, University of New South Wales, Kensington NSW 2052, Australia; Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA; Department of Biology, University of Washington, Seattle, WA 98195-1800, USA.
UNSW Sydney · AUUniversity of Washington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough the emergence and spread of antibiotic resistance have been well studied for endemic infections, comparably little is understood for epidemic infections such as influenza. The availability of antimicrobial treatments for epidemic diseases raises the urgent question of how to deploy treatments to achieve maximum benefit despite resistance evolution. Recent simulation studies have shown that the number of cases prevented by antimicrobials can be maximized by delaying the use of treatments during an epidemic. Those studies focus on indirect effects of antimicrobial use: preventing disease among untreated individuals. Here, we identify and examine direct effects of antimicrobial use: the number of successfully treated cases. METHODOLOGY: We develop mathematical models to study how the schedule of antiviral use influences the success or failure of subsequent use due to the spread of resistant strains.

resultsDirect effects are maximized by postponing drug use, even with unlimited stockpiles of drugs. This occurs because the early use of antimicrobials disproportionately drives emergence and spread of antibiotic resistance, leading to subsequent treatment failure. However, for antimicrobials with low effect on transmission, the relative benefit of delaying antimicrobial deployment is greatly reduced and can only be reaped if the trajectory of the epidemic can be accurately estimated early. CONCLUSIONS AND IMPLICATIONS: Health planners face uncertainties during epidemics, including the possibility of early containment. Hence, despite the optimal deployment time near the epidemic peak, it will often be preferable to initiate widespread antimicrobial use as early as possible, particularly if the drug is ineffective in reducing transmission.

Indexed as

antiviraldrug resistanceevolutioninfluenzaoseltamivirzanamivir

Identifiers

PMID25376480
PMCPMC4246056
OpenAlexW2128711378

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.