1. Epidemiology and Global Health
Download icon

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
  • Views 1,804
  • Annotations
Cite this article as: eLife 2014;3:e02565 doi: 10.7554/eLife.02565


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
    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)


© 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.


  • 1,804
    Page views
  • 151
  • 13

Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Epidemiology and Global Health
    2. Medicine
    ISARIC Clinical Characterisation Group et al.
    Research Article

    Background: There is potentially considerable variation in the nature and duration of the care provided to hospitalised patients during an infectious disease epidemic or pandemic. Improvements in care and clinician confidence may shorten the time spent as an inpatient, or the need for admission to an intensive care unit (ICU) or high density unit (HDU). On the other hand, limited resources at times of high demand may lead to rationing. Nevertheless, these variables may be used as static proxies for disease severity, as outcome measures for trials, and to inform planning and logistics.

    Methods: We investigate these time trends in an extremely large international cohort of 142,540 patients hospitalised with COVID-19. Investigated are: time from symptom onset to hospital admission, probability of ICU/HDU admission, time from hospital admission to ICU/HDU admission, hospital case fatality ratio (hCFR) and total length of hospital stay.

    Results: Time from onset to admission showed a rapid decline during the first months of the pandemic followed by peaks during August/September and December 2020. ICU/HDU admission was more frequent from June to August. The hCFR was lowest from June to August. Raw numbers for overall hospital stay showed little variation, but there is clear decline in time to discharge for ICU/HDU survivors.

    Conclusions: Our results establish that variables of these kinds have limitations when used as outcome measures in a rapidly-evolving situation.

    Funding: This work was supported by the UK Foreign, Commonwealth and Development Office and Wellcome [215091/Z/18/Z] and the Bill and Melinda Gates Foundation [OPP1209135]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

    1. Epidemiology and Global Health
    Stephanie M Holm, John Balmes

    Pollution from landscape fires, which are increasing with climate change, leads to babies being born with lower birthweights in low- and middle-income countries.