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The ophthalmic surgical backlog associated with the COVID-19 pandemic: a population-based and microsimulation modelling study

Tina Felfeli, Raphael Ximenes, David M.J. Naimark, Philip L. Hooper, Robert J. Campbell, Sherif R. El-Defrawy and Beate Sander
November 23, 2021 9 (4) E1063-E1072; DOI: https://doi.org/10.9778/cmajo.20210145
Tina Felfeli
Department of Ophthalmology and Vision Sciences (Felfeli), University of Toronto; Toronto Health Economics and Technology Assessment (THETA) Collaborative (Ximenes), University Health Network; Sunnybrook Health Sciences Centre (Naimark), Toronto, Ont.; Ivey Eye Institute (Hooper), Western University, London, Ont.; Department of Ophthalmology (Campbell), Queen’s University, Kingston, Ont.; Kensington Vision and Research Centre (El-Defrawy), Kensington Eye Institute, and Institute of Health Policy, Management and Evaluation (Sander), University of Toronto, Toronto, Ont.
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Raphael Ximenes
Department of Ophthalmology and Vision Sciences (Felfeli), University of Toronto; Toronto Health Economics and Technology Assessment (THETA) Collaborative (Ximenes), University Health Network; Sunnybrook Health Sciences Centre (Naimark), Toronto, Ont.; Ivey Eye Institute (Hooper), Western University, London, Ont.; Department of Ophthalmology (Campbell), Queen’s University, Kingston, Ont.; Kensington Vision and Research Centre (El-Defrawy), Kensington Eye Institute, and Institute of Health Policy, Management and Evaluation (Sander), University of Toronto, Toronto, Ont.
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David M.J. Naimark
Department of Ophthalmology and Vision Sciences (Felfeli), University of Toronto; Toronto Health Economics and Technology Assessment (THETA) Collaborative (Ximenes), University Health Network; Sunnybrook Health Sciences Centre (Naimark), Toronto, Ont.; Ivey Eye Institute (Hooper), Western University, London, Ont.; Department of Ophthalmology (Campbell), Queen’s University, Kingston, Ont.; Kensington Vision and Research Centre (El-Defrawy), Kensington Eye Institute, and Institute of Health Policy, Management and Evaluation (Sander), University of Toronto, Toronto, Ont.
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Philip L. Hooper
Department of Ophthalmology and Vision Sciences (Felfeli), University of Toronto; Toronto Health Economics and Technology Assessment (THETA) Collaborative (Ximenes), University Health Network; Sunnybrook Health Sciences Centre (Naimark), Toronto, Ont.; Ivey Eye Institute (Hooper), Western University, London, Ont.; Department of Ophthalmology (Campbell), Queen’s University, Kingston, Ont.; Kensington Vision and Research Centre (El-Defrawy), Kensington Eye Institute, and Institute of Health Policy, Management and Evaluation (Sander), University of Toronto, Toronto, Ont.
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Robert J. Campbell
Department of Ophthalmology and Vision Sciences (Felfeli), University of Toronto; Toronto Health Economics and Technology Assessment (THETA) Collaborative (Ximenes), University Health Network; Sunnybrook Health Sciences Centre (Naimark), Toronto, Ont.; Ivey Eye Institute (Hooper), Western University, London, Ont.; Department of Ophthalmology (Campbell), Queen’s University, Kingston, Ont.; Kensington Vision and Research Centre (El-Defrawy), Kensington Eye Institute, and Institute of Health Policy, Management and Evaluation (Sander), University of Toronto, Toronto, Ont.
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Sherif R. El-Defrawy
Department of Ophthalmology and Vision Sciences (Felfeli), University of Toronto; Toronto Health Economics and Technology Assessment (THETA) Collaborative (Ximenes), University Health Network; Sunnybrook Health Sciences Centre (Naimark), Toronto, Ont.; Ivey Eye Institute (Hooper), Western University, London, Ont.; Department of Ophthalmology (Campbell), Queen’s University, Kingston, Ont.; Kensington Vision and Research Centre (El-Defrawy), Kensington Eye Institute, and Institute of Health Policy, Management and Evaluation (Sander), University of Toronto, Toronto, Ont.
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Beate Sander
Department of Ophthalmology and Vision Sciences (Felfeli), University of Toronto; Toronto Health Economics and Technology Assessment (THETA) Collaborative (Ximenes), University Health Network; Sunnybrook Health Sciences Centre (Naimark), Toronto, Ont.; Ivey Eye Institute (Hooper), Western University, London, Ont.; Department of Ophthalmology (Campbell), Queen’s University, Kingston, Ont.; Kensington Vision and Research Centre (El-Defrawy), Kensington Eye Institute, and Institute of Health Policy, Management and Evaluation (Sander), University of Toronto, Toronto, Ont.
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  • Figure 1:
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    Figure 1:

    Model schematic depicting patient flow for cases requiring subspecialty ophthalmic surgery. Two entry streams for patients include urgent cases and surgical wait-list (consists of existing wait-list before the pandemic and daily additions following declaration of the pandemic). The stop node (red symbol) represents resource constraint for ophthalmic subspecialty surgery. For patients in semiurgent and nonurgent classifications, there is a deterioration and increase in urgency priority (as indicated by the dashed line) for surgery as the maximum wait time is reached (highest priority given to “Level 1”). This was done to account for the risk of vision impairment associated with delays in surgical repair. Patients move to the “Outcomes” health states after surgery only when resources become available. Those requiring additional surgical interventions will re-enter the model (as indicated by the dotted line). Note: P1–4 = priority level 1–4.

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    Figure 2:

    Monthly surgical throughputs based on real data after the pandemic began (solid blue line = 2020; solid green line = 2021) compared with 2019 (solid grey line). The dotted line shows the model-estimated monthly increase in number of surgeries required to clear the backlog created as a result of COVID-19 over a 2-year period starting in September 2021 (recovery plan A). The dashed line shows the monthly increase in number of surgeries required to clear the backlog over a 1-year period starting in September 2021 (recovery plan B). These recovery plan results show the degree of escalation in resource availability required to return to the prepandemic wait-list queue and wait times for ophthalmic surgery. Note that only the first months of the recovery plans are depicted in the graph.

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    Figure 3:

    Monthly model-estimated accumulation of patients awaiting surgery for all ophthalmic surgeries and subspecialty types, including cataract surgery (A, cataract and combination cataract and other procedures), retina surgery (B, vitrectomy and other vitreoretinal surgery), corneal surgery (C, corneal transplant and other cornea surgery), glaucoma surgery (D, glaucoma filter or seton and other glaucoma surgeries), oculoplastics (E) and adult strabismus surgery (F) from March 2020 to March 2023. The simulations were run 50 times (variations in projected estimated represented by lighter blue lines) for a total of 240 000 patients. Note that the y-axis scale for cataract surgery (A) is different than that of the other subspecialty groups.

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    Table 1:

    Monthly real-data wait-list queue, wait time, newly added cases and cases completed for ophthalmic surgery in Ontario from January 2019 to February 2020, by subspecialty and priority level*

    VariableNo. or mean ± SD
    All subspecialtiesCataractRetinaGlaucomaCorneaOculoplasticsStrabismus
    Patients waiting for surgery
     All urgency levels†58 38054 03011545928331247524
     Semiurgent (P2–3)‡46883983364190456638
     Nonurgent (P4)§53 69250 0477894027881181486
    Wait time, d, mean ± SD
     All urgency levels†89.6 ± 95.993.1 ± 97.537.8 ± 58.952.0 ± 60.992.7 ± 93.573.2 ± 65.8109.3 ± 101.3
     Semiurgent (P2–3)‡62.8 ± 92.880.6 ± 103.223.6 ± 46.030.6 ± 35.8)37.6 ± 43.831.2 ± 33.684.6 ± 145.4
     Nonurgent (P4)§92.6 ± 95.894.1 ± 96.951.6 ± 64.367.5 ± 70.398.6 ± 95.879.9 ± 67.2109.1 ± 88.5
    Cases added per month
     All urgency levels†14 17612 697671230179279115
     Semiurgent (P2–3)‡1657115533010024369
     Nonurgent (P4)§12 52011 542341130155243106
    Cases completed per month
     All urgency levels†13 66112 271631220170254109
     Semiurgent (P2–3)‡13909233129117349
     Nonurgent (P4)§12 27111 348319129153220100
    • Note: P2–4 = priority level 2–4, SD = standard deviation.

    • ↵* Data source: Wait Times Information System, Ontario Health.

    • ↵† Includes semiurgent and nonurgent cases. Urgent cases are not included as they are not adequately documented through Ontario Health.

    • ↵‡ Moderate probability of disease progression. Low probability of disease occurrence or progression affecting morbidity or mortality.

    • ↵§ Minimal risk of disease progression affecting morbidity or mortality.

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    Table 2:

    Wait times for patients awaiting semiurgent and nonurgent ophthalmic subspecialty surgery for November 2019 and November 2020

    VariableWait time, d, mean ± SD
    All subspecialtiesCataractRetinaGlaucomaCorneaOculoplasticsStrabismus
    Real data*
     November 201994.4 ± 97.494.4 ± 99.137.7 ± 44.748.6 ± 49.896.4 ± 95.977.2 ± 70.2102.1 ± 94.4
     November 2020101.9 ± 137.2126.9 ± 135.747.9 ± 80.651.8 ± 78.9109.6 ± 114.2111.3 ± 126.0201.2 ± 190.7
    Projected model estimates†
     November 2020 to March 2021198.5 ± 81.4177.0 ± 101.5110.9 ± 68.3187.8 ± 67.6229.8 ± 78.3186.6 ± 99.4298.9 ± 64.8
     April 2021 to March 2022220.9 ± 116.1225.7 ± 115.0121.1 ± 69.4206.4 ± 63.8278.8 ± 79.6232.5 ± 114.2526.0 ± 90.8
     April 2022 to March 2023282.1 ± 91.4244.8 ± 121.2124.2 ± 69.3211.2 ± 61.6298.8 ± 81.6250.9 ± 120.1562.5 ± 75.6
    • Note: SD = standard deviation.

    • ↵* Data source: Wait Times Information System, Ontario Health.

    • ↵† Model-estimated projections for wait times up to 3 years after the pandemic. Scenarios were run 50 times for a total of 240 000 patients.

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CMAJ Open: 9 (4)
Vol. 9, Issue 4
1 Oct 2021
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The ophthalmic surgical backlog associated with the COVID-19 pandemic: a population-based and microsimulation modelling study
Tina Felfeli, Raphael Ximenes, David M.J. Naimark, Philip L. Hooper, Robert J. Campbell, Sherif R. El-Defrawy, Beate Sander
Oct 2021, 9 (4) E1063-E1072; DOI: 10.9778/cmajo.20210145

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The ophthalmic surgical backlog associated with the COVID-19 pandemic: a population-based and microsimulation modelling study
Tina Felfeli, Raphael Ximenes, David M.J. Naimark, Philip L. Hooper, Robert J. Campbell, Sherif R. El-Defrawy, Beate Sander
Oct 2021, 9 (4) E1063-E1072; DOI: 10.9778/cmajo.20210145
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