Evidence map›Paper›PMID 25189546›Full record

ArticleThe international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease2014

Impact and cost-effectiveness of current and future tuberculosis diagnostics: the contribution of modelling.

D W Dowdy, R Houben, T Cohen, M Pai, F Cobelens, A Vassall, N A Menzies, G B Gomez, I Langley, S B Squire and 2 more

Registry-linked trialAbstract readConference Proceedings
In one paragraph

Article in The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03934931 (Options for Delivering Isoniazid-Rifapentine), which is not on this map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 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.

NCT03934931 nacompletednot on this mapstarted 2020, after this paper: background citation

Options for Delivering Isoniazid-Rifapentine (3HP) for TB Prevention: the 3HP Options Implementation Trial

TypeinterventionalSponsorUniversity of California, San FranciscoRan2020 to 2025Enrolled1,656ConditionsTuberculosis, Latent Tuberculosis, HIV/AIDSArmsStreamlined weekly DOT visits, Weekly DOT visit reminders, Cost reimbursement DOT, 99DOTS, Weekly SAT dosing reminders/check-ins
3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Toward Alignment in the Reporting of Economic Evaluations of Diagnostic Tests and Biomarkers: The AGREEDT Checklist.Medical decision making : an international journal of the Society for Medical Decision Making · 2018
    Review
  6. Article
  7. Article
  8. Molecular Diagnosis of Tuberculosis.Chonnam medical journal · 2018
    Review
  9. Review
  10. Defining the needs for next generation assays for tuberculosis.The Journal of infectious diseases · 2015
    Review
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

12 authors.

D W DowdyDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
R HoubenDepartment of Infectious Disease Epidemiology and TB Modelling Group, London School of Hygiene & Tropical Medicine, London, UK.
T CohenDepartment of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.
M PaiDepartment of Epidemiology and Biostatistics & McGill International TB Centre, McGill University, Montreal, Quebec, Canada.
F CobelensDepartment of Global Health and Amsterdam Institute for Global Health and Development, Academic Medical Center, Amsterdam, The Netherlands.
A VassallSAME Modelling and Economics, Department of Global Health and Development, London School of Hygiene & Tropical Medicine, London, UK.
N A MenziesCenter for Health Decision Science, Harvard School of Public Health, Boston, Massachusetts, USA.
G B GomezDepartment of Global Health and Amsterdam Institute for Global Health and Development, Academic Medical Center, Amsterdam, The Netherlands.
I LangleyDepartment of Clinical Sciences and Centre for Applied Health Research & Delivery, Liverpool School of Tropical Medicine, Liverpool, UK.
S B SquireDepartment of Clinical Sciences and Centre for Applied Health Research & Delivery, Liverpool School of Tropical Medicine, Liverpool, UK.
R WhiteDepartment of Infectious Disease Epidemiology and TB Modelling Group, London School of Hygiene & Tropical Medicine, London, UK.
TB MAC meeting participants

Funding

Medical Research Council MR/J005088/1
6 · The paper itself

Abstract

The landscape of diagnostic testing for tuberculosis (TB) is changing rapidly, and stakeholders need urgent guidance on how to develop, deploy and optimize TB diagnostics in a way that maximizes impact and makes best use of available resources. When decisions must be made with only incomplete or preliminary data available, modelling is a useful tool for providing such guidance. Following a meeting of modelers and other key stakeholders organized by the TB Modelling and Analysis Consortium, we propose a conceptual framework for positioning models of TB diagnostics. We use that framework to describe modelling priorities in four key areas: Xpert(®) MTB/RIF scale-up, target product profiles for novel assays, drug susceptibility testing to support new drug regimens, and the improvement of future TB diagnostic models. If we are to maximize the impact and cost-effectiveness of TB diagnostics, these modelling priorities should figure prominently as targets for future research.

Indexed as

Health Care CostsAntitubercular AgentsBacteriological TechniquesBiomedical ResearchCost-Benefit AnalysisHealth PrioritiesHumansMicrobial Sensitivity TestsModels, EconomicPractice Guidelines as TopicPredictive Value of TestsPrognosisTuberculosisAntitubercular Agents

Identifiers

PMID25189546
PMCPMC4436823

What OpenQuestion holds

Textmetadata
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.