Evidence map›Paper›PMID 35581650›Full record

ArticleBMC medicine2022

Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis.

Juan F Vesga, Christian Lienhardt, Placide Nsengiyumva, Jonathon R Campbell, Olivia Oxlade, Saskia den Boon, Dennis Falzon, Kevin Schwartzman, Gavin Churchyard, Nimalan Arinaminpathy

Open access · goldAbstract read
In one paragraph

Article in BMC medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.8field-weighted citation impact, top 28% 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

8 citing papers in PubMed, 8 citations in OpenAlex.

  1. Factors Associated With Discontinuation of Tuberculosis Preventive Treatment: Post Hoc Analysis of 2 Randomized, Controlled Trials.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023
    Trial
  2. Article
  3. Preventing Multidrug-Resistant Tuberculosis: The Dawn of a New Era.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026
    Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. 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

10 authors at 5 institutions in 5 countries.

Juan F VesgaDepartment of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College London, London, UK. juanovesga@gmail.com.ORCID 0000-0003-1103-9587
Christian LienhardtInstitut de recherche pour le développement, Montpellier, France.
Placide NsengiyumvaMcGill International Tuberculosis Centre, McGill University, Montreal, Canada.
Jonathon R CampbellMcGill International Tuberculosis Centre, McGill University, Montreal, Canada.
Olivia OxladeMcGill International Tuberculosis Centre, McGill University, Montreal, Canada.
Saskia den BoonGlobal TB Programme, World Health Organization, Geneva, Switzerland.
Dennis FalzonGlobal TB Programme, World Health Organization, Geneva, Switzerland.
Kevin SchwartzmanMcGill International Tuberculosis Centre, McGill University, Montreal, Canada.
Gavin ChurchyardThe Aurum Institute, Parktown, South Africa.
Nimalan ArinaminpathyDepartment of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College London, London, UK.
McGill University · CAImperial College London · GBWorld Health Organization · CHInstitut de Recherche pour le Développement · FRUniversity of the Witwatersrand · ZA

Funding

Aurum Clinical Trials UnitUM1AI154463 · NIAID · AURUM INSTITUTE NPC · PI Gavin John Churchyard · 2021 to 2026
$8.1M
Medical Research Council MR/R015600/1NIAID NIH HHS UM1 AI154463World Health Organization 001
6 · The paper itself

Abstract

backgroundRecent years have seen important improvements in available preventive treatment regimens for tuberculosis (TB), and research is ongoing to develop these further. To assist with the formulation of target product profiles for future regimens, we examined which regimen properties would be most influential in the epidemiological impact of preventive treatment.

methodsFollowing expert consultation, we identified 5 regimen properties relevant to the incidence-reducing impact of a future preventive treatment regimen: regimen duration, efficacy, ease-of-adherence (treatment completion rates in programmatic conditions), forgiveness to non-completion and the barrier to developing rifampicin resistance during treatment. For each regimen property, we elicited expert input for minimally acceptable and optimal (ideal-but-feasible) performance scenarios for future regimens. Using mathematical modelling, we then examined how each regimen property would influence the TB incidence reduction arising from full uptake of future regimens according to current WHO guidelines, in four countries: South Africa, Kenya, India and Brazil.

resultsOf all regimen properties, efficacy is the single most important predictor of epidemiological impact, while ease-of-adherence plays an important secondary role. These results are qualitatively consistent across country settings; sensitivity analyses show that these results are also qualitatively robust to a range of model assumptions, including the mechanism of action of future preventive regimens.

conclusionsAs preventive treatment regimens against TB continue to improve, understanding the key drivers of epidemiological impact can assist in guiding further development. By meeting these key targets, future preventive treatment regimens could play a critical role in global efforts to end TB.

Indexed as

TuberculosisAntitubercular AgentsClinical ProtocolsHumansIncidenceIndiaRifampinAntitubercular AgentsRifampinMathematical modellingPreventive therapyTuberculosis

Identifiers

PMID35581650
PMCPMC9115962
OpenAlexW4280491411

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.