IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-41937-9.html
   My bibliography  Save this article

Global burden of disease due to rifampicin-resistant tuberculosis: a mathematical modeling analysis

Author

Listed:
  • Nicolas A. Menzies

    (Harvard T. H. Chan School of Public Health
    Harvard T. H. Chan School of Public Health)

  • Brian W. Allwood

    (Stellenbosch University & Tygerberg Hospital)

  • Anna S. Dean

    (World Health Organization)

  • Pete J. Dodd

    (University of Sheffield)

  • Rein M. G. J. Houben

    (London School of Hygiene and Tropical Medicine
    London School of Hygiene and Tropical Medicine)

  • Lyndon P. James

    (Harvard T. H. Chan School of Public Health
    Harvard University)

  • Gwenan M. Knight

    (EPH, London School of Hygiene and Tropical Medicine)

  • Jamilah Meghji

    (Imperial College London)

  • Linh N. Nguyen

    (World Health Organization)

  • Andrea Rachow

    (Medical Centre of the University of Munich (LMU)
    Partner Site Munich
    German Research Center for Environmental Health (HMGU))

  • Samuel G. Schumacher

    (World Health Organization)

  • Fuad Mirzayev

    (World Health Organization)

  • Ted Cohen

    (Yale School of Public Health)

Abstract

In 2020, almost half a million individuals developed rifampicin-resistant tuberculosis (RR-TB). We estimated the global burden of RR-TB over the lifetime of affected individuals. We synthesized data on incidence, case detection, and treatment outcomes in 192 countries (99.99% of global tuberculosis). Using a mathematical model, we projected disability-adjusted life years (DALYs) over the lifetime for individuals developing tuberculosis in 2020 stratified by country, age, sex, HIV, and rifampicin resistance. Here we show that incident RR-TB in 2020 was responsible for an estimated 6.9 (95% uncertainty interval: 5.5, 8.5) million DALYs, 44% (31, 54) of which accrued among TB survivors. We estimated an average of 17 (14, 21) DALYs per person developing RR-TB, 34% (12, 56) greater than for rifampicin-susceptible tuberculosis. RR-TB burden per 100,000 was highest in former Soviet Union countries and southern African countries. While RR-TB causes substantial short-term morbidity and mortality, nearly half of the overall disease burden of RR-TB accrues among tuberculosis survivors. The substantial long-term health impacts among those surviving RR-TB disease suggest the need for improved post-treatment care and further justify increased health expenditures to prevent RR-TB transmission.

Suggested Citation

  • Nicolas A. Menzies & Brian W. Allwood & Anna S. Dean & Pete J. Dodd & Rein M. G. J. Houben & Lyndon P. James & Gwenan M. Knight & Jamilah Meghji & Linh N. Nguyen & Andrea Rachow & Samuel G. Schumacher, 2023. "Global burden of disease due to rifampicin-resistant tuberculosis: a mathematical modeling analysis," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41937-9
    DOI: 10.1038/s41467-023-41937-9
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-41937-9
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-41937-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ronald L. Iman & Jon C. Helton, 1988. "An Investigation of Uncertainty and Sensitivity Analysis Techniques for Computer Models," Risk Analysis, John Wiley & Sons, vol. 8(1), pages 71-90, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    2. Lee, Sang-Hee & Park, Cheol-Min, 2022. "The effect of hunter-wild boar interactions and landscape heterogeneity on wild boar population size: A simulation study," Ecological Modelling, Elsevier, vol. 464(C).
    3. Shabbir Ahmed Osmani & Foysol Mahmud, 2021. "An integrated approach of machine algorithms with multi-objective optimization in performance analysis of event detection," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(2), pages 1976-1993, February.
    4. Emanuele Borgonovo & Gordon B. Hazen & Elmar Plischke, 2016. "A Common Rationale for Global Sensitivity Measures and Their Estimation," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1871-1895, October.
    5. Alan H. Stern, 1993. "Re‐evaluation of the Reference Dose for Methylmercury and Assessment of Current Exposure Levels," Risk Analysis, John Wiley & Sons, vol. 13(3), pages 355-364, June.
    6. Wout Slob, 1994. "Uncertainty Analysis in Multiplicative Models," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 571-576, August.
    7. James K. Hammitt, 1990. "Subjective‐Probability‐Based Scenarios for Uncertain Input Parameters: Stratospheric Ozone Depletion," Risk Analysis, John Wiley & Sons, vol. 10(1), pages 93-102, March.
    8. Yizhen Wang & Menglei Cui & Jiong Guo & Han Zhang & Yingjie Wu & Fu Li, 2023. "Decay Branch Ratio Sampling Method with Dirichlet Distribution," Energies, MDPI, vol. 16(4), pages 1-17, February.
    9. Gorka Merino & Hilario Murua & Josu Santiago & Haritz Arrizabalaga & Victor Restrepo, 2020. "Characterization, Communication, and Management of Uncertainty in Tuna Fisheries," Sustainability, MDPI, vol. 12(19), pages 1-22, October.
    10. Messan, Komi & Rodriguez Messan, Marisabel & Chen, Jun & DeGrandi-Hoffman, Gloria & Kang, Yun, 2021. "Population dynamics of Varroa mite and honeybee: Effects of parasitism with age structure and seasonality," Ecological Modelling, Elsevier, vol. 440(C).
    11. Ronald L. Iman & Mark E. Johnson & Charles C. Watson, 2005. "Uncertainty Analysis for Computer Model Projections of Hurricane Losses," Risk Analysis, John Wiley & Sons, vol. 25(5), pages 1299-1312, October.
    12. M. Kadohira & M. A. Stevenson & H. R. Høgåsen & A. de Koeijer, 2012. "A Quantitative Risk Assessment for Bovine Spongiform Encephalopathy in Japan," Risk Analysis, John Wiley & Sons, vol. 32(12), pages 2198-2208, December.
    13. Liu, Zicheng & Lesselier, Dominique & Sudret, Bruno & Wiart, Joe, 2020. "Surrogate modeling based on resampled polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    14. John H. Miller, 1996. "Active Nonlinear Tests (ANTs) of Complex Simulation Models," Working Papers 96-03-011, Santa Fe Institute.
    15. Pang, Zhihong & O'Neill, Zheng, 2018. "Uncertainty quantification and sensitivity analysis of the domestic hot water usage in hotels," Applied Energy, Elsevier, vol. 232(C), pages 424-442.
    16. Yan Yang & Guoqiang Wang & Lijing Wang & Jingshan Yu & Zongxue Xu, 2014. "Evaluation of Gridded Precipitation Data for Driving SWAT Model in Area Upstream of Three Gorges Reservoir," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-15, November.
    17. Victor R. Vasquez & Wallace B. Whiting, 2005. "Accounting for Both Random Errors and Systematic Errors in Uncertainty Propagation Analysis of Computer Models Involving Experimental Measurements with Monte Carlo Methods," Risk Analysis, John Wiley & Sons, vol. 25(6), pages 1669-1681, December.
    18. Thomas E. McKone, 1994. "Uncertainty and Variability in Human Exposures to Soil Contaminants Through Home‐Grown Food: A Monte Carlo Assessment," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 449-463, August.
    19. Maxine E. Dakins & John E. Toll & Mitchell J. Small & Kevin P. Brand, 1996. "Risk‐Based Environmental Remediation: Bayesian Monte Carlo Analysis and the Expected Value of Sample Information," Risk Analysis, John Wiley & Sons, vol. 16(1), pages 67-79, February.
    20. Yang Yang & Haiyan Liu, 2022. "Sensitivity analysis of disease-information coupling propagation dynamics model parameters," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-15, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41937-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.