Which curve are we flattening? The disproportionate impact of COVID-19 among economically marginalized communities in Ontario, Canada, was unchanged from wild-type to omicron

Economically marginalized communities have faced disproportionately higher risks for infection and death from COVID-19 across Canada. It was anticipated that health disparities would dissipate over time and during subsequent waves. We used person-level surveillance and neighbourhood-level income data to explore, using Lorenz curves and Gini coefficients, magnitude of inequalities in COVID-19 hospitalizations and deaths over five waves of COVID-19 in Ontario, Canada (population 14 million) between February 26, 2020 and February 28, 2022. We found that despite attempts at equity-informed policies alongside fluctuating levels of public health measures, inequalities in hospitalizations and deaths by income remained at levels observed during the first wave - prior to vaccination, discussion or implementation of equity-informed policies - and despite rising levels of hybrid immunity. There was no change in the magnitude of inequalities across all waves evaluated. Our findings indicate that interventions did not sufficiently address differential exposure risks amplified at the intersections of household crowding and size, workplace exposures, and systemic barriers to prevention and care (including access to therapeutics). Equity and effectiveness of programs are inherently linked and ongoing evaluation of both is central to inform the public health response to future waves of COVID-19 and other rapidly emergent pandemics.

Area-level social and structural inequalities determine mortality related to COVID-19 diagnosis in Ontario, Canada: a population-based explanatory modeling study of 11.8 million people

ImportanceSocial determinants of health (SDOH) play an important role in COVID-19 outcomes. More research is needed to quantify this relationship and understand the underlying mechanisms. ObjectivesTo examine differential patterns in COVID-19-related mortality by area-level SDOH accounting for confounders; and to compare these patterns to those for non-COVID-19 mortality, and COVID-19 case fatality (COVID-19-related death among those diagnosed). Design, setting, and participantsPopulation-based retrospective cohort study including all community living individuals aged 20 years or older residing in Ontario, Canada, as of March 1, 2020 who were followed through to March 2, 2021. ExposureSDOH variables derived from the 2016 Canada Census at the dissemination area-level including: median household income; educational attainment; proportion of essential workers, racialized groups, recent immigrants, apartment buildings, and high-density housing; and average household size. Main outcomes and measuresCOVID-19-related death was defined as death within 30 days following, or 7 days prior to a positive SARS-CoV-2 test. Cause-specific hazard models were employed to examine the associations between SDOH and COVID-19-related mortality, treating non-COVID-19 mortality as a competing risk. ResultsOf 11,810,255 individuals included, 3,880 (0.03%) died related to COVID-19 and 88,107 (0.75%) died without a positive test. After accounting for demographics, baseline health, and other SDOH, the following SDOH were associated with increased hazard of COVID-19-related death (hazard ratios [95% confidence intervals]) comparing the most to least vulnerable group): lower income (1.30[1.09-1.54]), lower educational attainment (1.27[1.10-1.47]), higher proportion essential workers (1.28[1.10-1.50]), higher proportion racialized groups (1.42[1.16-1.73]), higher proportion apartment buildings (1.25[1.11-1.41]), and larger vs. medium household size (1.30[1.13-1.48]). In comparison, areas with higher proportion racialized groups were associated with a lower hazard of non-COVID-19 mortality (0.88[0.85-0.92]). With the exception of income, SDOH were not independently associated with COVID-19 case fatality. Conclusions and relevanceArea-level social and structural inequalities determine COVID-19-related mortality after accounting for individual demographic and clinical factors. COVID-19 has reversed the pattern of lower non-COVID-19 mortality by racialized groups. Pandemic responses should include prioritized and community-tailored intervention strategies to address SDOH that mechanistically underpin disproportionate acquisition and transmission risks and shape barriers to the reach of, and access to prevention interventions. Key pointsO_ST_ABSQuestionC_ST_ABSAre area-level social determinants of health factors independently associated with coronavirus disease 2019 (COVID-19)-related mortality after accounting for demographics and clinical factors? FindingsIn this population-based cohort study including 11.8 million adults residing in Ontario, Canada and 3,880 COVID-19-related death occurred between Mar 1, 2020 and Mar 2, 2021, we found that areas characterized by lower SES (including lower income, lower educational attainment, and higher proportion essential workers), greater ethnic diversity, more apartment buildings, and larger vs. medium household size were associated with increased hazard of COVID-19-related mortality compared to their counterparts, even after accounting for individual-level demographics, baseline health, and other area-level SDOH. MeaningPandemic responses should include prioritized and community-tailored intervention strategies to address SDOH that mechanistically underpin inequalities in acquisition and transmission risks, and in the reach of, and access to prevention interventions.

Health and Economic Consequences of Universal Paid Sick Leave Policies During the COVID-19 Pandemic

ImportanceUniversal paid sick-leave (PSL) policies have been implemented in jurisdictions to mitigate the spread of SARS-CoV-2. However empirical data regarding health and economic consequences of PSL policies is scarce. ObjectiveTo estimate effects of a universal PSL policy in Ontario, Canadas most populous province. DesignAn agent-based model (ABM) to simulate SARS-CoV-2 transmission informed by data from Statistics Canada, health administrative sources, and from the literature. SettingOntario from January 1st to May 1st, 2021. ParticipantsA synthetic population (1 million) with occupation and household characteristics representative of Ontario residents (14.5 million). ExposureA base case of existing employer-based PSL alone versus the addition of a 3-or 10-day universal PSL policy to facilitate testing and self-isolation among workers infected with SARS-CoV-2 themselves or because of infected household members. Main Outcome(s) and Measure(s)Number of SARS-CoV-2 infections and COVID-19 hospitalizations, worker productivity, lost wages, and presenteeism (going to a workplace while infected). ResultsIf a 3- and 10-day universal PSL were implemented over the 4-month study period, then compared with the base-case, the PSL policies were estimated to reduce cumulative SARS-CoV-2 cases by 85,531 (95% credible interval, CrI -2,484; 195,318) and 215,302 (81,500; 413,742), COVID-19 hospital admissions by 1,307 (-201; 3,205) and 3,352 (1,223; 6,528), numbers of workers forgoing wages by 558 (-327;1,608) and 7,406 (6,764; 8,072), and numbers of workers engaged in presenteeism by 24,499 (216; 54,170) and 279,863 (262,696; 295,449). Hours of productivity loss were estimated to be 10,854,379 (10,212,304; 11,465,635) in the base case, 17,446,525 (15,934,321; 18,854,683) in the 3-day scenario, and 26,127,165 (20,047,239; 29,875,161) in the 10-day scenario. Lost wages were $5,256,316 ($4,077,280; $6,804,983) and $12,610,962 ($11,463,128; $13,724,664) lower in the 3 day and 10 day scenarios respectively, relative to the base case. Conclusions and RelevanceExpanded access to PSL is estimated to reduce total numbers of COVID-19 cases, reduce presenteeism of workers with SARS-CoV-2 at workplaces, and mitigate wage loss experienced by workers. Competing interestsThe authors have no competing interests relevant to this article to disclose. FundingSupported by COVID-19 Rapid Research Funding (C-291-2431272-SANDER). This research was further supported, in part, by a Canada Research Chair in Economics of Infectious Diseases held by Beate Sander (CRC-950-232429). The study sponsor had no role in the design, collection, analysis, interpretation of the data, manuscript preparation or the decision to submit for publication. Author ContributionsConceptualization: PP, JDR, BS, DN Data Curation: PP, JDR, BS, DN Formal Analysis: PP, JDR, DN Methodology: PP, JDR, BS, DN Supervision: PP, DN, BS Validation: PP, JDR, BS, DN First Draft: PP, JDR, BS, DN Review and Edit PP, JDR, BS, DN Key pointsO_ST_ABSQuestionC_ST_ABSWhat could be the health and economic consequence of more generous paid sick leave policies in the context of the COVID-19 pandemic? FindingsMore generous policies are estimated to reduce SARS-CoV-2 infections (and thus COVID-19 hospitalizations), lost wages and presence of individuals with infection at workplaces. MeaningMore generous paid sick leave can be a valuable addition to other COVID-19 public health interventions.

Mortality trends and lengths of stay among hospitalized COVID-19 patients in Ontario and Quebec (Canada): a population-based cohort study of the first three epidemic waves

BackgroundEpidemic waves of COVID-19 strained hospital resources. We describe temporal trends in mortality risk and length of stay in intensive cares units (ICUs) among COVID-19 patients hospitalized through the first three epidemic waves in Canada. MethodsWe used population-based provincial hospitalization data from Ontario and Quebec to examine mortality risk and lengths of ICU stay. For each province, adjusted estimates were obtained using marginal standardization of logistic regression models, adjusting for patient-level characteristics and hospital-level determinants. ResultsUsing all hospitalizations from Ontario (N=26,541) and Quebec (N=23,857), we found that unadjusted in-hospital mortality risks peaked at 31% in the first wave and was lowest at the end of the third wave at 6-7%. This general trend remained after controlling for confounders. The odds of in-hospital mortality in the highest hospital occupancy quintile was 1.2 (95%CI: 1.0-1.4; Ontario) and 1.6 (95%CI: 1.3-1.9; Quebec) times that of the lowest quintile. Variants of concerns were associated with an increased in-hospital mortality. Length of ICU stay decreased over time from a mean of 16 days (SD=18) to 15 days (SD=15) in the third wave but were consistently higher in Ontario than Quebec by 3-6 days. ConclusionIn-hospital mortality risks and lengths of ICU stay declined over time in both provinces, despite changing patient demographics, suggesting that new therapeutics and treatment, as well as improved clinical protocols, could have contributed to this reduction. Continuous population-based monitoring of patient outcomes in an evolving epidemic is necessary for health system preparedness and response.

MITIGATING THE 4th WAVE OF THE COVID-19 PANDEMIC IN ONTARIO

BackgroundThe goal of this study was to project the number of COVID-19 cases and demand for acute hospital resources for Fall of 2021 in a representative mid-sized community in southwestern Ontario. We sought to evaluate whether current levels of vaccine coverage and contact reduction could mitigate a potential 4th wave fueled by the Delta variant, or whether the reinstitution of more intense public health measures will be required. MethodsWe developed an age-stratified dynamic transmission model of COVID-19 in a mid-sized city (population 500,000) currently experiencing a relatively low, but increasing, infection rate in Step 3 of Ontarios Wave 3 recovery. We parameterized the model using the medical literature, grey literature, and government reports. We estimated the current level of contact reduction by model calibration to cases and hospitalizations. We projected the number of infections, number of hospitalizations, and the time to re-instate high intensity public health measures over the fall of 2021 under different levels of vaccine coverage and contact reduction. ResultsMaintaining contact reductions at the current level, estimated to be a 17% reduction compared to pre-pandemic contact levels, results in COVID-related admissions exceeding 20% of pre-pandemic critical care capacity by late October, leading to cancellation of elective surgeries and other non-COVID health services. At high levels of vaccination and relatively high levels of mask wearing, a moderate additional effort to reduce contacts (30% reduction compared to pre-pandemic contact levels), is necessary to avoid re-instating intensive public health measures. Compared to prior waves, the age distribution of both cases and hospitalizations shifts younger and the estimated number of pediatric critical care hospitalizations may substantially exceed 20% of capacity. DiscussionHigh rates of vaccination coverage in people over the age of 12 and mask wearing in public settings will not be sufficient to prevent an overwhelming resurgence of COVID-19 in the Fall of 2021. Our analysis indicates that immediate moderate public health measures can prevent the necessity for more intense and disruptive measures later.

Variant-of-concern-attributable health and health system-related outcomes: a population-level propensity-score matched cohort study

BackgroundAs the transmission of SARS-CoV-2 variants intensifies globally, the burden of COVID-19 on the already strained health systems is becoming increasingly concerning. While there is growing literature on the effects of various variants-of-concern (VOC) on increased transmission, the extent to which VOCs may lead to more severe disease remains debated. MethodsIn the current analysis, we use a population-based propensity-score matched cohort study of all incident laboratory-confirmed COVID-19 cases with VOC testing in Ontario, Canada to estimate healthcare resource use and health outcomes attributable to VOCs introduced to Ontario between January 1 and April 9, 2021, relative to the previously circulating wild-type strain. ResultsWe find that VOCs are associated with a higher odds of hospitalisation (odds ratio [OR], 2.25; 95% confidence interval [CI], 2.10-2.40) and ICU admission (OR, 3.31; 95%CI, 2.84-3.86); as well as with a higher odds of mortality for both the general COVID-19 population (OR 1.75; 1.47-2.09) and hospitalised cases (OR, 1.62; 95%CI, 1.23-2.15). ConclusionTaken together, these findings suggest that health systems may face increased demand for healthcare resources as VOCs predominate worldwide in view of low global vaccination coverage.

Modelling the impact of extending dose intervals for COVID-19 vaccines in Canada

BackgroundDual dose SARS-CoV-2 vaccines demonstrate high efficacy and will be critical in public health efforts to mitigate the COVID-19 pandemic and its health consequences; however, many jurisdictions face very constrained vaccine supply. We examined the impacts of extending the interval between two doses of mRNA vaccines in Canada in order to inform deliberations of Canadas National Advisory Committee on Immunization. MethodsWe developed an age-stratified, deterministic, compartmental model of SARS-CoV-2 transmission and disease to reproduce the epidemiologic features of the epidemic in Canada. Simulated vaccination comprised mRNA vaccines with explicit examination of effectiveness against disease (67% [first dose], 94% [second dose]), hospitalization (80% [first dose], 96% [second dose]), and death (85% [first dose], 96% [second dose]) in adults aged 20 years and older. Effectiveness against infection was assumed to be 90% relative to the effectiveness against disease. We used a 6-week mRNA dose interval as our base case (consistent with early program rollout across Canadian and international jurisdictions) and compared extended intervals of 12 weeks, 16 weeks, and 24 weeks. We began vaccinations on January 1, 2021 and simulated a third wave beginning on April 1, 2021. ResultsExtending mRNA dose intervals were projected to result in 12.1-18.9% fewer symptomatic cases, 9.5-13.5% fewer hospitalizations, and 7.5-9.7% fewer deaths in the population over a 12-month time horizon. The largest reductions in hospitalizations and deaths were observed in the longest interval of 24 weeks, though benefits were diminishing as intervals extended. Benefits of extended intervals stemmed largely from the ability to accelerate coverage in individuals aged 20-74 years as older individuals were already prioritized for early vaccination. Conditions under which mRNA dose extensions led to worse outcomes included: first-dose effectiveness < 65% against death; or protection following first dose waning to 0% by month three before the scheduled 2nd dose at 24-weeks. Probabilistic simulations from a range of likely vaccine effectiveness values did not result in worse outcomes with extended intervals. ConclusionUnder real-world effectiveness conditions, our results support a strategy of extending mRNA dose intervals across all age groups to minimize symptomatic cases, hospitalizations, and deaths while vaccine supply is constrained.

Increasing concentration of COVID-19 by socioeconomic determinants and geography in Toronto, Canada: an observational study

BackgroundInequities in the burden of COVID-19 observed across Canada suggest heterogeneity within community transmission. ObjectivesTo quantify the magnitude of heterogeneity in the wider community (outside of long-term care homes) in Toronto, Canada and assess how the magnitude in concentration evolved over time (January 21 to November 21, 2020). DesignRetrospective, population-based observational study using surveillance data from Ontarios Case and Contact Management system. SettingToronto, Canada. ParticipantsLaboratory-confirmed cases of COVID-19 (N=33,992). MeasurementsWe generated epidemic curves by SDOH and crude Lorenz curves by neighbourhoods to visualize inequities in the distribution of COVID-19 cases by social determinants of health (SDOH) and estimated the crude Gini coefficient. We examined the correlation between SDOH using Pearson correlation coefficients. ResultsThe Gini coefficient of cumulative cases by population size was 0.41 (95% CI: 0.36-0.47) and were estimated for: household income (0.20, 95%CI: 0.14-0.28); visible minority (0.21, 95%CI: 0.16-0.28); recent immigration (0.12, 95%CI: 0.09-0.16); suitable housing (0.21, 95%CI: 0.14-0.30); multi-generational households (0.19, 95%CI: 0.15-0.23); and essential workers (0.28, 95% CI: 0.23-0.34). Most SDOH were highly correlated. Locally acquired cases were concentrated in higher income neighbourhoods in the early phase of the epidemic, and then concentrated in lower income neighbourhoods. Mirroring the trajectory of epidemic curves by income, the Lorenz curve shifted over time from below to above the line of equality with a similar pattern across SDOH. LimitationsStudy relied on area-based measures of the SDOH and individual case counts of COVID-19. We cannot infer concentration of cases by specific occupational exposures given limitation to broad occupational categories. ConclusionCOVID-19 is increasingly concentrated by SDOH given socioeconomic inequities and structural racism. Primary Funding SourceCanadian Institutes of Health Research.

Characterizing the disproportionate burden of SARS-CoV-2 variants of concern among essential workers in the Greater Toronto Area, Canada

STRUCTURED ABSTRACTO_ST_ABSImportanceC_ST_ABSThe emergence of SARS-CoV-2 Variants of Concern (VOC) across North America has been associated with concerns of increased COVID-19 transmission. Characterizing the distribution of VOCs can inform the development and implementation of policies and programs to address the prevention needs of disproportionately affected communities. ObjectiveWe compared per-capita rates of COVID-19 cases (overall and VOC) from February 3, 2021 to March 10, 2021, across neighborhoods in the health regions of Toronto and Peel, Ontario, by proportion of the population working in essential services and income. DesignDescriptive epidemiological analysis, integrating COVID-19 surveillance and census data. Per-capita daily epidemic curves were generated using 7-days rolling averages for cases and deaths. Cumulative per-capita rates were determined using census-reported population of each neighbourhood. SettingThe study setting was the city of Toronto and the region of Peel (the City of Brampton, Mississauga, and Caledon), Canadas largest cities with a combined population of 4.3 million. This area of Canada has had one of the highest incident rates of COVID-19 throughout the pandemic. ParticipantsWe used person-level data on laboratory-confirmed COVID-19 community cases (N=22,478) and census data for neighborhood-level attributes. ExposuresWe stratified neighbourhood using dissemination areas which represent geographic areas of approximately 400-700 individuals, into tertiles by ranking the proportion of population in each neighbourhood working in essential services (health, trades, transport, equipment, manufacturing, utilities, sales, services, agriculture); and the per-person equivalent household income. Main Outcome(s) and Measure(s)The primary outcomes were laboratory-confirmed COVID-19 cases overall and VOC positives by neighbourhood. ResultsDuring the study period, VOC cases emerged faster in groups with lowest income (growth rate 43.8%, 34.6% and 21.6% by income tertile from lowest to highest), and most essential work (growth rate 18.4%, 30.8% and 50.8% by tertile from lowest tertile of essential workers to highest tertile of essential workers). Conclusions and RelevanceThe recent introduction of VOC in the large urban area of Toronto has disproportionately affected neighbourhoods with the most essential workers and lowest income levels. Notably, this is consistent with the increased burden of non-VOC COVID-19 cases suggesting shared risk factors. To date, restrictive public health strategies have been of limited impact in these communities suggesting the need for complementary and well-specified supportive strategies including vaccine prioritization to address disparities and overall incidence of both VOC and non-VOC COVID-19. KEY POINTS QuestionDoes the emergence of Variants of Concern (VOC) in urban centers in Canada affect groups based on income and essential work status disproportionally? FindingsWe found a significantly higher cumulative case count and rate of increase of VOC cases amongst those with lowest income and highest essential work status. MeaningThe distribution of VOC and COVID-19 in general is disproportionately distributed, and mitigation measures, including vaccines should be targeted to the highest risk groups.

Predicting the Ophthalmic Surgical Backlog as a Result of the COVID-19 Pandemic: A population-based study and microsimulation model to inform surgical recovery plans

BackgroundJurisdictions worldwide ramped down ophthalmic surgeries to mitigate the effects of coronavirus disease 2019 (COVID-19), creating a global surgical backlog. We sought to predict the long-term impact of COVID-19 on ophthalmology surgical care delivery. MethodsThis is a population-based and a microsimulation modelling study. Provincial administrative data from January 2019 to May 2021 was used to estimate the backlog size and wait-times following the COVID-19 pandemic. For the post-pandemic recovery phase, we estimated the resources required to clear the backlog of patients accumulated on the waitlist during the pandemic. ResultsA total of 56,923 patients were on the waitlist in the province of Ontario awaiting non-emergency ophthalmic surgery as of March 15, 2020. The number of non-emergency surgeries performed in the province decreased by 45-98% from March to May 2020, and 48-80% from April to May 2021 compared to the same months in 2019. By 2 years and 3 years, the overall estimated number of patients awaiting surgery grew by 129% and 150%, respectively. The estimated mean wait-time for patients for all subspecialty surgeries increased to 282 (SD 91) in March 2023 compared to 94 days (SD 97) in 2019. The provincial monthly additional resources required to clear the backlog by March 2023 was estimated to be a 34% escalation from the pre-pandemic volumes (4,626 additional surgeries). InterpretationThe magnitude of the ophthalmic surgical backlog from COVID-19 has important implications for the recovery phase. The estimates from this microsimulation modelling can be adapted to other jurisdictions to assist with recovery planning for vision saving surgeries.

A disproportionate epidemic: COVID-19 cases and deaths among essential workers in Toronto, Canada

Shelter-in-place mandates and closure of non-essential businesses have been central to COVID-19 response strategies including in Toronto, Canada. Approximately half of the working population in Canada are employed in occupations that do not allow for remote work suggesting potentially limited impact of some of the strategies proposed to mitigate COVID-19 acquisition and onward transmission risks and associated morbidity and mortality. We compared per-capita rates of COVID-19 cases and deaths from January 23, 2020 to January 24, 2021, across neighborhoods in Toronto by proportion of the population working in essential services. We used person-level data on laboratory-confirmed COVID-19 community cases (N=74,477) and deaths (N=2319), and census data for neighborhood-level attributes. Cumulative per-capita rates of COVID-19 cases and deaths were 3-fold and 2.5-fold higher, respectively, in neighborhoods with the highest versus lowest concentration of essential workers. Findings suggest that the population who continued to serve the essential needs of society throughout COVID-19 shouldered a disproportionate burden of transmission and deaths. Taken together, results signal the need for active intervention strategies to complement restrictive measures to optimize both the equity and effectiveness of COVID-19 responses.

Using Administrative Data to Incorporate Age and Sex-Dependent Resource Use for COVID-19 Acute Care Resource Use Simulations in Ontario, Canada

As the COVID-19 pandemic has progressed, more local data has become available, enabling a more granular modeling approach. In March 2020, we developed a COVID-19 Resource Estimator (CORE) model to estimate the acute care resource use in Ontario, Canada. In this paper, we describe the evolution of CORE2.0 to incorporate age, sex, and time-dependent acute care resource use, length of stay, and mortality to simulate hospital occupancy. Demographics (e.g., age and sex) of infected cases are informed by 4-month averages between March-June, and July-October using 10-year age groups. The probability of hospitalization, ICU admission, and requiring mechanical ventilation are all age and sex-dependent. LOS for each acute care level ranges from 5.7 to 16.15 days in the ward, 6.5 to 10.7 days in the ICU without ventilation, and 14.8 to 21.6 days on the ventilator, depending on month of infection. We calibrated some LOS components to reported ward and ICU occupancy between June 15 and October 31, 2020. Furthermore, we demonstrate the use of CORE2.0 for a regional analysis of Region of Waterloo, Ontario, Canada to simulate the ward bed, ICU bed, and ventilator occupancies for 30 days starting December 2020 for three case trajectory scenarios. Moving forward, this model has become highly flexible and customizable to data updates, and can better inform acute care planning and public measures as the pandemic progresses.

The potential impact of School Closure Relative to Community-based Non-pharmaceutical Interventions on COVID-19 Cases in Ontario, Canada

ImportanceResurgent COVID-19 cases have resulted in the re-institution of nonpharmaceutical interventions, including school closure, which can have adverse effects on families. Understanding the impact of schools on the number of incident and cumulative COVID-19 cases is critical for decision-making. ObjectiveTo determine the quantitative effect of schools being open or closed relative to community-based nonpharmaceutical interventions on the number of COVID-19 cases. DesignAn agent-based transmission model. SettingA synthetic population of one million individuals based on the characteristics of the population of Ontario, Canada. ParticipantsMembers of the synthetic population clustered into households, neighborhoods or rural districts, cities or a rural region, day care facilities, classrooms - primary, elementary or high school, colleges or universities and workplaces. ExposureSchool reopening on September 15, 2020, versus schools remaining closed under different scenarios for nonpharmaceutical interventions. Main Outcome and MeasuresIncident and cumulative COVID-19 cases between September 1, 2020 and October 31, 2020. ResultsThe percentage of infections among students and teachers acquired within schools was less than 5% across modelled scenarios. Incident case numbers on October 31, 2020, were 4,414 (95% credible interval, CrI: 3,491, 5,382) and 4,740 (95% CrI 3,863, 5,691), for schools remaining closed versus reopening, respectively, with no other community-based nonpharmaceutical intervention; 714 (95%, CrI: 568, 908) and 780 (95% CrI 580, 993) for schools remaining closed versus reopening, respectively, with community-based nonpharmaceutical interventions implemented; 777 (95% credible CrI: 621, 993) and 803 (95% CrI 617, 990) for schools remaining closed versus reopening, respectively, applied to the observed case numbers in Ontario in early October 2020. Contrasting the scenarios with implementation of community-based interventions versus not doing so yielded a mean difference of 39,355 cumulative COVID-19 cases by October 31, 2020, while keeping schools closed versus reopening them yielded a mean difference of 2,040 cases. Conclusions and relevanceOur simulations suggest that the majority of COVID-19 infections in schools were due to acquisition in the community rather than transmission within schools and that the effect of school reopening on COVID-19 case numbers is relatively small compared to the effects of community-based nonpharmaceutical interventions. KEY POINTSO_ST_ABSQuestionC_ST_ABSWith resurgence of COVID-19, reinstitution of school closure remains a possibility. Given the harm that closures can cause to children and families, the expected quantitative effect of school reopening or closure on incident and cumulative COVID-19 case numbers is an important consideration. FindingRelative to community-based nonpharmaceutical interventions, school closure resulted in a small change in COVID-19 incidence trajectories and cumulative case counts. MeaningCommunity-based interventions should take precedence over school closure.

COVID-19 Demographics, Acute Care Resource Use and Mortality by Age and Sex in Ontario, Canada: Population-based Retrospective Cohort Analysis

BackgroundUnderstanding resource use for COVID-19 is critical. We conducted a population-based cohort study using public health data to describe COVID-19 associated age- and sex-specific acute care use, length of stay (LOS), and mortality. MethodsWe used Ontarios Case and Contact Management (CCM) Plus database of individuals who tested positive for COVID-19 in Ontario from March 1 to September 30, 2020 to determine age- and sex-specific hospitalizations, intensive care unit (ICU) admissions, invasive mechanical ventilation (IMV) use, LOS, and mortality. We stratified analyses by month of infection to study temporal trends and conducted subgroup analyses by long-term care residency. ResultsDuring the observation period, 56,476 COVID-19 cases were reported (72% < 60 years, 52% female). The proportion of cases shifted from older populations (> 60 years) to younger populations (10-39 years) over time. Overall, 10% of individuals were hospitalized, of those 22% were admitted to ICU, and 60% of those used IMV. Mean LOS for individuals in the ward, ICU without IMV, and ICU with IMV was 12.8, 8.5, 20.5 days, respectively. Mortality for individuals receiving care in the ward, ICU without IMV, and ICU with IMV was 24%, 30%, and 45%, respectively. All outcomes varied by age and decreased over time, overall and within age groups. InterpretationThis descriptive study shows acute care use and mortality varying by age, and decreasing between March and September in Ontario. Improvements in clinical practice and changing risk distributions among those infected may contribute to fewer severe outcomes among those infected with COVID-19.

Efficacy of stay-at-home policy and transmission of COVID-19 in Toronto, Canada: a mathematical modeling study

BackgroundIn many parts of the world, restrictive non-pharmaceutical interventions (NPI) that aim to reduce contact rates, including stay-at-home orders, limitations on gatherings, and closure of public places, are being lifted, with the possibility that the epidemic resurges if alternative measures are not strong enough. Here we aim to capture the combination of use of NPIs and reopening measures which will prevent an infection rebound. MethodsWe employ an SEAIR model with household structure able to capture the stay-at-home policy (SAHP). To reflect the changes in the SAHP over the course of the epidemic, we vary the SAHP compliance rate, assuming that the time to compliance of all the people requested to stay-at-home follows a Gamma distribution. Using confirmed case data for the City of Toronto, we evaluate basic and instantaneous reproduction numbers and simulate how the average household size, the stay-at-home rate, the efficiency and duration of SAHP implementation, affect the outbreak trajectory. FindingsThe estimated basic reproduction number R_0 was 2.36 (95% CI: 2.28, 2.45) in Toronto. After the implementation of the SAHP, the contact rate outside the household fell by 39%. When people properly respect the SAHP, the outbreak can be quickly controlled, but extending its duration beyond two months (65 days) had little effect. Our findings also suggest that to avoid a large rebound of the epidemic, the average number of contacts per person per day should be kept below nine. This study suggests that fully reopening schools, offices, and other activities, is possible if the use of other NPIs is strictly adhered to. InterpretationOur model confirmed that the SAHP implemented in Toronto had a great impact in controlling the spread of COVID-19. Given the lifting of restrictive NPIs, we estimated the thresholds values of maximum number of contacts, probability of transmission and testing needed to ensure that the reopening will be safe, i.e. maintaining an Rt < 1. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSA survey on published articles was made through PubMed and Google Scholar searches. The search was conducted from March 1 to August 13, 2020 and all papers published until the end of this research were considered. The following terms were used to screen articles on mathematical models: "household structure", "epidemic model", "SARS-CoV-2", "COVID-19", "household SIR epidemic", "household SIS epidemic", "household SEIR epidemic", "quarantine, isolation model", "quarantine model dynamics", "structured model isolation". Any article showing, in the title, application of epidemic models in a specific country/region or infectious diseases rather than SARS-CoV-2 were excluded. Articles in English were considered. Added value of this studyWe develop an epidemic model with household structure to study the effects of SAHP on the infection within households and transmission of COVID-19 in Toronto. The complex model provides interesting insights into the effectiveness of SAHP, if the average number of individuals in a household changes. We found that the SAHP might not be adequate if the size of households is relatively large. We also introduce a new quantity called symptomatic diagnosis completion ratio (d_c). This indicator is defined as the ratio of cumulative reported cases and the cumulative cases by episode date at time t, and it is used in the model to inform the implementation of SAHP. If cases are diagnosed at the time of symptom onset, isolation will be enforced immediately. A delay in detecting cases will lead to a delay in isolation, with subsequent increase in the transmission of the infection. Comparing different scenarios (before and after reopening phases), we were able to identify thresholds of these factors which mainly affect the spread of the infection: the number of daily tests, average number of contacts per individual, and probability of transmission of the virus. Our results show that if any of the three above mentioned factors is reduced, then the other two need to be adjusted to keep a reproduction number below 1. Lifting restrictive closures will require the average number of contacts a person has each day to be less than pre-COVID-19, and a high rate of case detection and tracing of contacts. The thresholds found will inform public health decisions on reopening. Implications of all the available evidenceOur findings provide important information for policymakers when planning the full reopening phase. Our results confirm that prompt implementation of SAHP was crucial in reducing the spread of COVID-19. Also, based on our analyses, we propose public health alternatives to consider in view of a full reopening. For example, for different post-reopening scenarios, the average number of contacts per person needs to be reduced if the symptomatic diagnosis completion ratio is low and the probability of transmission increases. Namely, if fewer tests are completed and the usage of NPIs decreases, then the epidemic can be controlled only if individuals can maintain contact with a maximum average number of 4-5 people per person per day. Different recommendations can be provided by relaxing/strengthening one of the above-mentioned factors.

Estimating healthcare resource needs for COVID-19 patients in Nigeria

BackgroundPredicting potential healthcare resource use under different scenarios will help to prepare the healthcare system for a surge in COVID-19 patients. In this study, we aim to predict the effect of COVID-19 on hospital resources in Nigeria. MethodWe adopted a previously published discrete-time, individual-level, health-state transition model of symptomatic COVID-19 patients to the Nigerian healthcare system and COVID-19 epidemiology. We simulated different combined scenarios of epidemic trajectories and acute care capacity. Primary outcomes included expected cumulative number of cases, days until depletion resources, and number of deaths associated with resource constraints. Outcomes were predicted over a 60-day time horizon. ResultsIn our best-case epidemic trajectory, which implies successful implementation of public health measures to control COVID-19 spread, the current number of ventilator resources in Nigeria (conservative resources scenario), were expended within five days, and 901 patients may die while waiting for hospital resources in conservative resource scenario. In our expanded resource scenarios, ventilated ICU beds were depleted in all three epidemic trajectories within 60 days. Acute care resources were only sufficient in the best-case and intermediate epidemic scenarios, combined with a substantial increase in healthcare resources. ConclusionCurrent hospital resources are inadequate to manage the COVID-19 pandemic in Nigeria. Given Nigerias limited resources, it is imperative to increase healthcare resources and maintain aggressive public health measures to reduce COVID-19 transmission. KEY QUESTIONSO_ST_ABSWhat is already known on this subject?C_ST_ABSWhile western countries seem to be recovering from the COVID-19 pandemic, there is an increasing community spread of the virus in many African countries. The limited healthcare resources available in the region may not be sufficient to cope with increasing numbers of COVID-19 cases. What this study adds?Using the COVID-19 Resource Estimator (CORE) model, we demonstrate that implementing and maintaining aggressive public health measures to keep the epidemic growth at a low rate, while simultaneously substantially increasing healthcare resources is critical to minimize the impact of COVID-19 on morbidity and mortality. The impact of COVID-19 in low resource settings will likely overwhelm health system capacity if aggressive public health measures are not implemented. To mitigate the impact of COVID-19 in these settings, it is essential to develop strategies to substantially increase health system capacities, including hospital resources, personal protective equipment and trained healthcare personnel and to implement and maintain aggressive public health measures.

A model to estimate demand for personal protective equipment for Ontario acute care hospitals during the COVID-19 pandemic

In addition to instituting public health measures for COVID-19, managing healthcare resources is important for outcomes. The experiences in Italy and New York have shown that personal protective equipment (PPE) shortages can cause increased morbidity and mortality. We demonstrate a method to predict PPE demand across a health care system.

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

BackgroundThe 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. MethodsWe 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. FindingsWe 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. InterpretationWe 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.