IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0196480.html
   My bibliography  Save this article

Choice of time horizon critical in estimating costs and effects of changes to HIV programmes

Author

Listed:
  • Nicky McCreesh
  • Ioannis Andrianakis
  • Rebecca N Nsubuga
  • Mark Strong
  • Ian Vernon
  • Trevelyan J McKinley
  • Jeremy E Oakley
  • Michael Goldstein
  • Richard Hayes
  • Richard G White

Abstract

Background: Uganda changed its antiretroviral therapy guidelines in 2014, increasing the CD4 threshold for antiretroviral therapy initiation from 350 cells/μl to 500 cells/μl. We investigate what effect this change in policy is likely to have on HIV incidence, morbidity, and programme costs, and estimate the cost-effectiveness of the change over different time horizons. Methods: We used a complex individual-based model of HIV transmission and antiretroviral therapy scale-up in Uganda. 100 model fits were generated by fitting the model to 51 demographic, sexual behaviour, and epidemiological calibration targets, varying 96 input parameters, using history matching with model emulation. An additional 19 cost and disability weight parameters were varied during the analysis of the model results. For each model fit, the model was run to 2030, with and without the change in threshold to 500 cells/μl. Results: The change in threshold led to a 9.7% (90% plausible range: 4.3%-15.0%) reduction in incidence in 2030, and averted 278,944 (118,452–502,790) DALYs, at a total cost of $28M (-$142M to +$195M). The cost per disability adjusted life year (DALY) averted fell over time, from $3238 (-$125 to +$29,969) in 2014 to $100 (-$499 to +$785) in 2030. The change in threshold was cost-effective (cost

Suggested Citation

  • Nicky McCreesh & Ioannis Andrianakis & Rebecca N Nsubuga & Mark Strong & Ian Vernon & Trevelyan J McKinley & Jeremy E Oakley & Michael Goldstein & Richard Hayes & Richard G White, 2018. "Choice of time horizon critical in estimating costs and effects of changes to HIV programmes," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-10, May.
    • Handle: RePEc:plo:pone00:0196480
    DOI: 10.1371/journal.pone.0196480
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0196480
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0196480&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0196480?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. Ioannis Andrianakis & Ian R Vernon & Nicky McCreesh & Trevelyan J McKinley & Jeremy E Oakley & Rebecca N Nsubuga & Michael Goldstein & Richard G White, 2015. "Bayesian History Matching of Complex Infectious Disease Models Using Emulation: A Tutorial and a Case Study on HIV in Uganda," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-18, January.
    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. Sean L Wu & Héctor M Sánchez C. & John M Henry & Daniel T Citron & Qian Zhang & Kelly Compton & Biyonka Liang & Amit Verma & Derek A T Cummings & Arnaud Le Menach & Thomas W Scott & Anne L Wilson & St, 2020. "Vector bionomics and vectorial capacity as emergent properties of mosquito behaviors and ecology," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-32, April.
    2. Chaitanya Kaligotla & Jonathan Ozik & Nicholson Collier & Charles M. Macal & Kelly Boyd & Jennifer Makelarski & Elbert S. Huang & Stacy T. Lindau, 2020. "Model Exploration of an Information-Based Healthcare Intervention Using Parallelization and Active Learning," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(4), pages 1-1.
    3. Christopher N Davis & T Deirdre Hollingsworth & Quentin Caudron & Michael A Irvine, 2020. "The use of mixture density networks in the emulation of complex epidemiological individual-based models," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-16, March.
    4. Jackson Samuel E. & Vernon Ian & Liu Junli & Lindsey Keith, 2020. "Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(2), pages 1-33, April.
    5. Josie McCulloch & Jiaqi Ge & Jonathan A. Ward & Alison Heppenstall & J. Gareth Polhill & Nick Malleson, 2022. "Calibrating Agent-Based Models Using Uncertainty Quantification Methods," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 25(2), pages 1-1.
    6. Evan Baker & Peter Challenor & Matt Eames, 2021. "Future proofing a building design using history matching inspired level‐set techniques," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 335-350, March.
    7. Gyanendra Pokharel & Rob Deardon, 2022. "Emulation‐based inference for spatial infectious disease transmission models incorporating event time uncertainty," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 455-479, March.
    8. Si Chen & Daniel Friedrich & Zhibin Yu & James Yu, 2019. "District Heating Network Demand Prediction Using a Physics-Based Energy Model with a Bayesian Approach for Parameter Calibration," Energies, MDPI, vol. 12(18), pages 1-19, September.

    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:plo:pone00:0196480. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.