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. 2020 Nov 2;192(44):E1347-E1356.
doi: 10.1503/cmaj.201521. Epub 2020 Sep 1.

Clearing the surgical backlog caused by COVID-19 in Ontario: a time series modelling study

Affiliations

Clearing the surgical backlog caused by COVID-19 in Ontario: a time series modelling study

Jonathan Wang et al. CMAJ. .

Abstract

Background: To mitigate the effects of coronavirus disease 2019 (COVID-19), jurisdictions worldwide ramped down nonemergent surgeries, creating a global surgical backlog. We sought to estimate the size of the nonemergent surgical backlog during COVID-19 in Ontario, Canada, and the time and resources required to clear the backlog.

Methods: We used 6 Ontario or Canadian population administrative sources to obtain data covering part or all of the period between Jan. 1, 2017, and June 13, 2020, on historical volumes and operating room throughput distributions by surgery type and region, and lengths of stay in ward and intensive care unit (ICU) beds. We used time series forecasting, queuing models and probabilistic sensitivity analysis to estimate the size of the backlog and clearance time for a +10% (+1 day per week at 50% capacity) surge scenario.

Results: Between Mar. 15 and June 13, 2020, the estimated backlog in Ontario was 148 364 surgeries (95% prediction interval 124 508-174 589), an average weekly increase of 11 413 surgeries. Estimated backlog clearance time is 84 weeks (95% confidence interval [CI] 46-145), with an estimated weekly throughput of 717 patients (95% CI 326-1367) requiring 719 operating room hours (95% CI 431-1038), 265 ward beds (95% CI 87-678) and 9 ICU beds (95% CI 4-20) per week.

Interpretation: The magnitude of the surgical backlog from COVID-19 raises serious implications for the recovery phase in Ontario. Our framework for modelling surgical backlog recovery can be adapted to other jurisdictions, using local data to assist with planning.

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Conflict of interest statement

Competing interests: Maria Eberg is currently an employee of IQVIA, Canada; all work performed for the submitted manuscript was done while employed at Ontario Health (Cancer Care Ontario). The work performed for the submitted manuscript is unrelated to any of her duties as an employee of IQVIA. Frances Wright reports being an employee of Ontario Health (Cancer Care Ontario), as the Skin Cancer Lead and Surgical Oncology Quality and Knowledge Transfer Lead. This article is a result of some of the work performed at Ontario Health (Cancer Care Ontario). Jonathan Irish reports receiving salary support from Ontario Health (Cancer Care Ontario) as the Provincial Head, Surgical Oncology Program. No other competing interests were declared.

Figures

Figure 1:
Figure 1:
Schematic of the approach to model the surgical backlog. Note: ICU = intensive care unit, LOS = length of stay, OR = operating room.
Figure 2:
Figure 2:
Monthly volume trends for surgical procedures in Ontario from 2017 to 2020 (up to and including April 2020) including oncology, vascular, cardiac, transplant and other surgeries. Report date: May 20, 2020. Data source: Wait Times Information System, Ontario Health (Cancer Care Ontario); Trillium Gift of Life Network Organ and Tissue Allocation System, Ontario Health — Trillium Gift of Life Network; CorHealth Ontario Cardiac Registry. Note: Data are from calendar years 2017 to 2020, inclusive of April 2020; P2–P4 indicates priority level 2 to 4. The grey lines represent monthly volume trends from 2017 to 2019 and the blue line represents the monthly trends for 2020 up to April 2020. CABG = coronary artery bypass graft.
Figure 3:
Figure 3:
Weekly volumes versus forecasts for surgical procedures in Ontario from 2017 to 2020 (up to and including June 13, 2020) including cancer, vascular, pediatric, transplant and other surgeries. Report date: June 23, 2020. Data source: Wait Times Information System, Ontario Health (Cancer Care Ontario). Note: Data are from calendar year 2017 to 2020, inclusive of June 13, 2020; P2–P4 indicates priority level 2 to 4.
Figure 4:
Figure 4:
Week-over-week accumulation of the mean provincial backlog for all surgery types from Mar. 15, 2020, to June 13, 2020, for a total mean backlog size of 148 364 surgeries.
Figure 5:
Figure 5:
Provincial results from the probabilistic sensitivity analysis for (A) clearance time, (B) operating room (OR) time per week, (C) patients per week, (D) ward beds per week and (E) intensive care unit (ICU) beds per week. The analyses were run 1000 times, and the frequencies add up to 1000. Note: CI = confidence interval.

Comment in

References

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