Potential magnitude of COVID-19-induced healthcare resource depletion in Ontario, Canada

  1. Kali Barrett
  2. Yasin A. Khan
  3. Stephen Mac
  4. Raphael Ximenes
  5. David MJ Naimark
  6. Beate Sander

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Abstract

Background

The global spread of coronavirus disease 2019 (COVID-19) continues in several jurisdictions, causing significant strain to healthcare systems. The purpose of our study is to predict the impact of the COVID-19 pandemic on patient outcomes and the healthcare system in Ontario, Canada.

Methods

We developed an individual-level simulation to model the flow of COVID-19 patients through the Ontario healthcare system. We simulated different combined scenarios of epidemic trajectory and healthcare capacity. Outcomes include numbers of patients needing admission to the ward, Intensive Care Unit (ICU), and requiring ventilation; days to resource depletion; and numbers of patients awaiting resources and deaths associated with limited access to resources.

Findings

We demonstrate that with effective early public health measures system resources need not be depleted. For scenarios considering late or ineffective implementation of physical distancing, health system resources would be depleted within 14-26 days. Resource depletion was also avoided or delayed with aggressive measures to rapidly increase ICU, ventilator, and acute care hospital capacity.

Interpretation

We found that without aggressive physical distancing measures the Ontario healthcare system would have been inadequately equipped to manage the expected number of patients with COVID-19, despite the rapid capacity increase. This overall lack of resources would have led to an increase in mortality. By slowing the spread of the disease via ongoing public health measures and having increased healthcare capacity, Ontario may have avoided catastrophic stresses to its health care system.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.19.20071712: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study has several limitations. The model currently relies on forecasting COVID-19 cases based on reported data from Ontario and other countries, projections, and assumption on physical distancing effectiveness. Also, since we assumed that patients requiring ward beds will not die from COVID-19 based on the current literature, we may underestimate the number of overall deaths. Given current data, we assumed a fixed number of ward beds, ICU beds, and ventilators. However, we simulated scenario analyses to demonstrate the impact of increasing availability of existing resources as well as adding additional resources. Currently available data that informs our acute care and ICU length of stay estimates, or the effect of insufficient resources on mortality is limited for COVID-19 patients. Priority setting is modeled so that ICU patients requiring ward beds have access to any ward bed ahead of incoming patients, regardless of their wait time. Other resources are available based on time of admission and not time since the resource was first needed. Our model does not incorporate COVID-19 transmission in the hospital, potentially underestimating resource need. Lastly, we do not explicitly consider health human resource constraints or the availability of adequate personal protective equipment, and assume that all hospital resources are appropriately staffed and have necessary supplies. Our study considers epidemic growth and resources for the whole of Ontario and does not consider...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  2. Version published to 10.1101/2020.04.19.20071712v1 on medRxiv