Literature DB >> 26633767

Understanding the incremental value of novel diagnostic tests for tuberculosis.

Nimalan Arinaminpathy1, David Dowdy2.   

Abstract

Tuberculosis is a major source of global mortality caused by infection, partly because of a tremendous ongoing burden of undiagnosed disease. Improved diagnostic technology may play an increasingly crucial part in global efforts to end tuberculosis, but the ability of diagnostic tests to curb tuberculosis transmission is dependent on multiple factors, including the time taken by a patient to seek health care, the patient's symptoms, and the patterns of transmission before diagnosis. Novel diagnostic assays for tuberculosis have conventionally been evaluated on the basis of characteristics such as sensitivity and specificity, using assumptions that probably overestimate the impact of diagnostic tests on transmission. We argue for a shift in focus to the evaluation of such tests' incremental value, defining outcomes that reflect each test's purpose (for example, transmissions averted) and comparing systems with the test against those without, in terms of those outcomes. Incremental value can also be measured in units of outcome per incremental unit of resource (for example, money or human capacity). Using a novel, simplified model of tuberculosis transmission that addresses some of the limitations of earlier tuberculosis diagnostic models, we demonstrate that the incremental value of any novel test depends not just on its accuracy, but also on elements such as patient behaviour, tuberculosis natural history and health systems. By integrating these factors into a single unified framework, we advance an approach to the evaluation of new diagnostic tests for tuberculosis that considers the incremental value at the population level and demonstrates how additional data could inform more-effective implementation of tuberculosis diagnostic tests under various conditions.

Entities:  

Mesh:

Year:  2015        PMID: 26633767     DOI: 10.1038/nature16045

Source DB:  PubMed          Journal:  Nature        ISSN: 0028-0836            Impact factor:   49.962


  10 in total

1.  The impact of changing the diagnostic algorithm for TB in Manicaland, Zimbabwe.

Authors:  K Zvinoera; I D Olaru; P Khan; J Mutsvangwa; C M Denkinger; V Kampira; D Coutinho; H Mutunzi; M Pepukai; E Chikaka; S Zinyowera; S Mharakurwa; K Kranzer
Journal:  Public Health Action       Date:  2021-12-21

2.  Blood transcriptomic diagnosis of pulmonary and extrapulmonary tuberculosis.

Authors:  Jennifer K Roe; Niclas Thomas; Eliza Gil; Katharine Best; Evdokia Tsaliki; Stephen Morris-Jones; Sian Stafford; Nandi Simpson; Karolina D Witt; Benjamin Chain; Robert F Miller; Adrian Martineau; Mahdad Noursadeghi
Journal:  JCI Insight       Date:  2016-10-06

3.  Clinical information systems for the management of tuberculosis in primary health care.

Authors:  Eliabe Rodrigues de Medeiros; Sandy Yasmine Bezerra E Silva; Cáthia Alessandra Varela Ataide; Erika Simone Galvão Pinto; Maria de Lourdes Costa da Silva; Tereza Cristina Scatena Villa
Journal:  Rev Lat Am Enfermagem       Date:  2017-12-11

4.  Modelling the impact of effective private provider engagement on tuberculosis control in urban India.

Authors:  Nimalan Arinaminpathy; Sarang Deo; Simrita Singh; Sunil Khaparde; Raghuram Rao; Bhavin Vadera; Niraj Kulshrestha; Devesh Gupta; Kiran Rade; Sreenivas Achuthan Nair; Puneet Dewan
Journal:  Sci Rep       Date:  2019-03-07       Impact factor: 4.379

5.  Potential population level impact on tuberculosis incidence of using an mRNA expression signature correlate-of-risk test to target tuberculosis preventive therapy.

Authors:  Tom Sumner; Thomas J Scriba; Adam Penn-Nicholson; Mark Hatherill; Richard G White
Journal:  Sci Rep       Date:  2019-07-31       Impact factor: 4.379

6.  Performance of novel antibodies for lipoarabinomannan to develop diagnostic tests for Mycobacterium tuberculosis.

Authors:  Jason L Cantera; Lorraine M Lillis; Roger B Peck; Emmanuel Moreau; James A Schouten; Paul Davis; Paul K Drain; Alfred Andama; Abraham Pinter; Masanori Kawasaki; Gunilla Källenius; Christopher Sundling; Karen M Dobos; Danara Flores; Delphi Chatterjee; Eileen Murphy; Olivia R Halas; David S Boyle
Journal:  PLoS One       Date:  2022-09-30       Impact factor: 3.752

Review 7.  Molecular bacterial load assay versus culture for monitoring treatment response in adults with tuberculosis.

Authors:  Bibie Said; Loveness Charlie; Emnet Getachew; Catherine Lydiah Wanjiru; Mekdelawit Abebe; Tsegahun Manyazewal
Journal:  SAGE Open Med       Date:  2021-07-17

8.  Patients direct costs to undergo TB diagnosis.

Authors:  Rachel M Anderson de Cuevas; Lovett Lawson; Najla Al-Sonboli; Nasher Al-Aghbari; Isabel Arbide; Jeevan B Sherchand; Emenyonu E Nnamdi; Abraham Aseffa; Mohammed A Yassin; Saddiq T Abdurrahman; Joshua Obasanya; Oladimeji Olanrewaju; Daniel Datiko; Sally J Theobald; Andrew Ramsay; S Bertel Squire; Luis E Cuevas
Journal:  Infect Dis Poverty       Date:  2016-03-24       Impact factor: 4.520

9.  Do Xpert MTB/RIF Cycle Threshold Values Provide Information about Patient Delays for Tuberculosis Diagnosis?

Authors:  Willy Ssengooba; Durval Respeito; Edson Mambuque; Silvia Blanco; Helder Bulo; Inacio Mandomando; Bouke C de Jong; Frank G Cobelens; Alberto L García-Basteiro
Journal:  PLoS One       Date:  2016-09-09       Impact factor: 3.240

10.  SeeTB: A novel alternative to sputum smear microscopy to diagnose tuberculosis in high burden countries.

Authors:  Vikas Pandey; Pooja Singh; Saumya Singh; Naresh Arora; Neha Quadir; Saurabh Singh; Ayan Das; Mridu Dudeja; Prem Kapur; Nasreen Zafar Ehtesham; Ravikrishnan Elangovan; Seyed E Hasnain
Journal:  Sci Rep       Date:  2019-11-12       Impact factor: 4.379

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.