1. Epidemiology and Global Health
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A user-friendly, open-source tool to project impact and cost of diagnostic tests for tuberculosis

  1. David W Dowdy  Is a corresponding author
  2. Jason R Andrews
  3. Peter J Dodd
  4. Robert H Gilman
  1. Johns Hopkins Bloomberg School of Public Health, United States
  2. Massachusetts General Hospital, United States
  3. University of Sheffield, United Kingdom
Research Article
  • Cited 13
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Cite this article as: eLife 2014;3:e02565 doi: 10.7554/eLife.02565

Abstract

Most existing models of infectious diseases, including tuberculosis (TB), do not allow end-users to customize results to local conditions. We created a dynamic transmission model to project TB incidence, TB mortality, multidrug-resistant (MDR) TB prevalence, and incremental costs over five years after scale-up of nine alternative diagnostic strategies including combinations of sputum smear microscopy, Xpert MTB/RIF, microcolony-based culture, and same-day diagnosis. We developed a corresponding web-based interface that allows users to specify local costs and epidemiology. Full model code - including the ability to change any input parameter - is also included. The impact of improved diagnostic testing was greater for mortality and MDR-TB prevalence than TB incidence, and was maximized in high-incidence, low-HIV settings. More costly interventions generally had greater impact. In settings with little capacity for up-front investment, same-day microscopy had greatest impact on TB incidence and became cost-saving within five years if feasible to deliver at $10/test. In settings where more initial investment was possible, population-level scale-up of either Xpert MTB/RIF or microcolony-based culture offered substantially greater benefits, often averting ten times more TB cases than narrowly-targeted diagnostic strategies at minimal incremental long-term cost. Where containing MDR-TB is the overriding concern, Xpert for smear-positives has reasonable impact on MDR-TB incidence, but at substantial price and little impact on overall TB incidence and mortality. This novel, user-friendly modeling framework improves decision-makers' ability to evaluate the impact of TB diagnostic strategies, accounting for local conditions.

Article and author information

Author details

  1. David W Dowdy

    Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
    For correspondence
    ddowdy@jhsph.edu
    Competing interests
    The authors declare that no competing interests exist.

  2. Jason R Andrews

    Massachusetts General Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. Peter J Dodd

    University of Sheffield, Sheffield, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  4. Robert H Gilman

    Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.

Reviewing Editor

  1. Sema Sgaier, Bill & Melinda Gates Foundation, India

Publication history

  1. Received: February 17, 2014
  2. Accepted: May 31, 2014
  3. Accepted Manuscript published: June 4, 2014 (version 1)
  4. Version of Record published: July 8, 2014 (version 2)

Copyright

© 2014, Dowdy et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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