PT  - JOURNAL ARTICLE
AU  - Wang, Jonathan
AU  - Vahid, Saba
AU  - Eberg, Maria
AU  - Milroy, Shannon
AU  - Milkovich, John
AU  - Wright, Frances C.
AU  - Hunter, Amber
AU  - Kalladeen, Ryan
AU  - Zanchetta, Claudia
AU  - Wijeysundera, Harindra C.
AU  - Irish, Jonathan
TI  - Clearing the surgical backlog caused by COVID-19 in Ontario: a time series modelling study
AID  - 10.1503/cmaj.201521
DP  - 2020 Nov 02
TA  - Canadian Medical Association Journal
PG  - E1347--E1356
VI  - 192
IP  - 44
4099  - http://www.cmaj.ca/content/192/44/E1347.short
4100  - http://www.cmaj.ca/content/192/44/E1347.full
SO  - CMAJ2020 Nov 02; 192
AB  - 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.