Evidence map›Paper›PMID 28273116›Full record

ArticlePloS one2017

MDR-TB treatment as prevention: The projected population-level impact of expanded treatment for multidrug-resistant tuberculosis.

Emily A Kendall, Andrew S Azman, Frank G Cobelens, David W Dowdy

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04208789 (Artificial Neural Network as Diagnostic Tools For Rifampicin-Resistant Tuberculosis In Indonesia), which is not on this map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
3.2field-weighted citation impact, top 8% 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.

NCT04208789 completednot on this mapstarted 2020, after this paper: background citation

Artificial Neural Network as Diagnostic Tools For Rifampicin-Resistant Tuberculosis In Indonesia: A Predictive Model Study and Economic Evaluation

TypeobservationalSponsorHasanuddin UniversityRan2020 to 2020Enrolled524ConditionsMDR Tuberculosis, Resistance to Tuberculostatic DrugsArmsRapid Molecular Drug-Resistant Tuberculosis Test, Artificial Intelligent Model, Drug Susceptibility Test
3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 44 citations in OpenAlex.

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  17. Distribution of Antibiotic Resistance Genes in Three Different Natural Water Bodies-A Lake, River and Sea.International journal of environmental research and public health · 2020
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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

4 authors at 2 institutions in 2 countries.

Emily A KendallDivision of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.ORCID http://orcid.org/0000-0002-0083-422X
Andrew S AzmanDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Frank G CobelensAmsterdam Institute for Global Health and Development, Academic Medical Centre, Amsterdam, Netherlands.
David W DowdyDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Johns Hopkins University · USAmsterdam Institute for Global Health and Development · NL

Funding

RESEARCH TRAINING IN MICROBIAL DISEASEST32AI007291 · NIAID · JOHNS HOPKINS UNIVERSITY · PI JOEL N BLANKSON, Sara Elizabeth Cosgrove · 1986 to 2026
$9.1M
NIAID NIH HHS T32 AI007291
6 · The paper itself

Abstract

backgroundIn 2013, approximately 480,000 people developed active multidrug-resistant tuberculosis (MDR-TB), while only 97,000 started MDR-TB treatment. We sought to estimate the impact of improving access to MDR-TB diagnosis and treatment, under multiple diagnostic algorithm and treatment regimen scenarios, on ten-year projections of MDR-TB incidence and mortality.

methodsWe constructed a dynamic transmission model of an MDR-TB epidemic in an illustrative East/Southeast Asian setting. Using approximate Bayesian computation, we investigated a wide array of potential epidemic trajectories consistent with current notification data and known TB epidemiology.

resultsDespite an overall projected decline in TB incidence, data-consistent simulations suggested that MDR-TB incidence is likely to rise between 2015 and 2025 under continued 2013 treatment practices, although with considerable uncertainty (median 17% increase, 95% Uncertainty Range [UR] -38% to +137%). But if, by 2017, all identified active TB patients with previously-treated TB could be tested for drug susceptibility, and 85% of those with MDR-TB could initiate MDR-appropriate treatment, then MDR-TB incidence in 2025 could be reduced by 26% (95% UR 4-52%) relative to projections under continued current practice. Also expanding this drug-susceptibility testing and appropriate MDR-TB treatment to treatment-naïve as well as previously-treated TB cases, by 2020, could reduce MDR-TB incidence in 2025 by 29% (95% UR 6-55%) compared to continued current practice. If this diagnosis and treatment of all MDR-TB in known active TB cases by 2020 could be implemented via a novel second-line regimen with similar effectiveness and tolerability as current first-line therapy, a 54% (95% UR 20-74%) reduction in MDR-TB incidence compared to current-practice projections could be achieved by 2025.

conclusionsExpansion of diagnosis and treatment of MDR-TB, even using current sub-optimal second-line regimens, is expected to significantly decrease MDR-TB incidence at the population level. Focusing MDR diagnostic efforts on previously-treated cases is an efficient first-step approach.

Indexed as

Antitubercular AgentsBayes TheoremHumansIncidenceMycobacterium tuberculosisPreventive Health ServicesPrimary PreventionTuberculosis, Multidrug-ResistantAntitubercular Agents

Identifiers

PMID28273116
PMCPMC5342197
OpenAlexW2594008718

What OpenQuestion holds

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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.