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Cost Effectiveness of Strategies for Caring for Critically Ill Patients with COVID-19 in Tanzania

Author

Listed:
  • Hiral Anil Shah

    (Center for Global Development)

  • Tim Baker

    (London School of Hygiene and Tropical Medicine
    Ifakara Health Institute
    Muhimbili University of Health and Allied Sciences
    Karolinska Institutet)

  • Carl Otto Schell

    (Karolinska Institutet
    Uppsala University
    Nyköping Hospital)

  • August Kuwawenaruwa

    (Ifakara Health Institute)

  • Khamis Awadh

    (Ifakara Health Institute)

  • Karima Khalid

    (Ifakara Health Institute
    Muhimbili University of Health and Allied Sciences)

  • Angela Kairu

    (KEMRI Wellcome Trust Research Programme)

  • Vincent Were

    (KEMRI Wellcome Trust Research Programme)

  • Edwine Barasa

    (KEMRI Wellcome Trust Research Programme
    University of Oxford)

  • Peter Baker

    (Center for Global Development)

  • Lorna Guinness

    (Center for Global Development
    Global Health Economics Centre, London School of Hygiene and Tropical Medicine)

Abstract

Background The resources for critical care are limited in many settings, exacerbating the significant morbidity and mortality associated with critical illness. Budget constraints can lead to choices between investing in advanced critical care (e.g. mechanical ventilators in intensive care units) or more basic critical care such as Essential Emergency and Critical Care (EECC; e.g. vital signs monitoring, oxygen therapy, and intravenous fluids). Methods We investigated the cost effectiveness of providing EECC and advanced critical care in Tanzania in comparison with providing ‘no critical care’ or ‘district hospital-level critical care’ using coronavirus disease 2019 (COVID-19) as a tracer condition. We developed an open-source Markov model ( https://github.com/EECCnetwork/POETIC_CEA ) to estimate costs and disability-adjusted life-years (DALYs) averted, using a provider perspective, a 28-day time horizon, patient outcomes obtained from an elicitation method involving a seven-member expert group, a normative costing study, and published literature. We performed a univariate and probabilistic sensitivity analysis to assess the robustness of our results., Results EECC is cost effective 94% and 99% of the time when compared with no critical care (incremental cost-effectiveness ratio [ICER] $37 [−$9 to $790] per DALY averted) and district hospital-level critical care (ICER $14 [−$200 to $263] per DALY averted), respectively, relative to the lowest identified estimate of the willingness-to-pay threshold for Tanzania ($101 per DALY averted). Advanced critical care is cost effective 27% and 40% of the time, when compared with the no critical care or district hospital-level critical care scenarios, respectively. Conclusion For settings where there is limited or no critical care delivery, implementation of EECC could be a highly cost-effective investment. It could reduce mortality and morbidity for critically ill COVID-19 patients, and its cost effectiveness falls within the range considered ‘highly cost effective’. Further research is needed to explore the potential of EECC to generate even greater benefits and value for money when patients with diagnoses other than COVID-19 are accounted for.

Suggested Citation

  • Hiral Anil Shah & Tim Baker & Carl Otto Schell & August Kuwawenaruwa & Khamis Awadh & Karima Khalid & Angela Kairu & Vincent Were & Edwine Barasa & Peter Baker & Lorna Guinness, 2023. "Cost Effectiveness of Strategies for Caring for Critically Ill Patients with COVID-19 in Tanzania," PharmacoEconomics - Open, Springer, vol. 7(4), pages 537-552, July.
  • Handle: RePEc:spr:pharmo:v:7:y:2023:i:4:d:10.1007_s41669-023-00418-x
    DOI: 10.1007/s41669-023-00418-x
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    References listed on IDEAS

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    1. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
    2. Jessica Ochalek & James Lomas & Karl Claxton, 2015. "Cost per DALY averted thresholds for low- and middle-income countries: evidence from cross country data," Working Papers 122cherp, Centre for Health Economics, University of York.
    3. Frank A. Sonnenberg & J. Robert Beck, 1993. "Markov Models in Medical Decision Making," Medical Decision Making, , vol. 13(4), pages 322-338, December.
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