Evidence map›Paper›PMID 26409276›Full record

ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2015

The Distribution of Fitness Costs of Resistance-Conferring Mutations Is a Key Determinant for the Future Burden of Drug-Resistant Tuberculosis: A Model-Based Analysis.

Gwenan M Knight, Caroline Colijn, Sourya Shrestha, Mariam Fofana, Frank Cobelens, Richard G White, David W Dowdy, Ted Cohen

Abstract read
In one paragraph

Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

28 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  11. Pathogens (Basel, Switzerland) · 2022
    Review
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  14. Article
  15. Evolution ofFrontiers in cellular and infection microbiology · 2022
    Review
  16. Article
  17. Heterogeneous Streptomycin Resistance Level AmongFrontiers in microbiology · 2021
    Article
  18. Article
  19. Article
  20. The Importance of Heterogeneity to the Epidemiology of Tuberculosis.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2019
    Article
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

8 authors.

Gwenan M KnightTuberculosis Modelling Group, Centre for the Mathematical Modelling of Infectious Diseases, Tuberculosis Centre, Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine.
Caroline ColijnDepartment of Mathematics, Imperial College London, United Kingdom.
Sourya ShresthaDepartment of Epidemiology, Johns Hopkins School of Public Health, Baltimore, Maryland.
Mariam FofanaDepartment of Epidemiology, Johns Hopkins School of Public Health, Baltimore, Maryland.
Frank CobelensAmsterdam Institute for Global Health and Development, Academic Medical Center KNCV Tuberculosis Foundation, The Hague, The Netherlands.
Richard G WhiteTuberculosis Modelling Group, Centre for the Mathematical Modelling of Infectious Diseases, Tuberculosis Centre, Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine.
David W DowdyDepartment of Epidemiology, Johns Hopkins School of Public Health, Baltimore, Maryland.
Ted CohenDepartment of Epidemiology of Microbial Diseases, School of Public Health, Yale University, New Haven, Connecticut.

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007309 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI COX, ANDREA L · 1985 to 2019
$43.2M
Medical Research Council MR/J005088/1NIGMS NIH HHS T32 GM007309PEPFARPHS HHS U2GPS0008111
6 · The paper itself

Abstract

backgroundDrug resistance poses a serious challenge for the control of tuberculosis in many settings. It is well established that the expected future trend in resistance depends on the reproductive fitness of drug-resistant Mycobacterium tuberculosis. However, the variability in fitness between strains with different resistance-conferring mutations has been largely ignored when making these predictions.

methodsWe developed a novel approach for incorporating the variable fitness costs of drug resistance-conferring mutations and for tracking this distribution of fitness costs over time within a transmission model. We used this approach to describe the effects of realistic fitness cost distributions on the future prevalence of drug-resistant tuberculosis.

resultsThe shape of the distribution of fitness costs was a strong predictor of the long-term prevalence of resistance. While, as expected, lower average fitness costs of drug resistance-conferring mutations were associated with more severe epidemics of drug-resistant tuberculosis, fitness distributions with greater variance also led to higher levels of drug resistance. For example, compared to simulations in which the fitness cost of resistance was fixed, introducing a realistic amount of variance resulted in a 40% increase in prevalence of drug-resistant tuberculosis after 20 years.

conclusionsThe differences in the fitness costs associated with drug resistance-conferring mutations are a key determinant of the future burden of drug-resistant tuberculosis. Future studies that can better establish the range of fitness costs associated with drug resistance-conferring mutations will improve projections and thus facilitate better public health planning efforts.

Indexed as

Genetic FitnessMutationAntitubercular AgentsDrug Resistance, Multiple, BacterialHumansModels, MolecularMycobacterium tuberculosisPrevalenceTuberculosis, Multidrug-ResistantUnited StatesAntitubercular Agentsantibiotic resistancefitness costsmathematical modelingtuberculosis

Identifiers

PMID26409276
PMCPMC4583567

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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.