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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.
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BackgroundThere are currently no effective treatments for outpatients with coronavirus disease 2019 (COVID-19). Interferon-lambda-1 is a Type III interferon involved in the innate antiviral response with activity against respiratory pathogens. MethodsIn this double-blind, placebo-controlled trial, outpatients with laboratory-confirmed COVID-19 were randomized to a single subcutaneous injection of peginterferon-lambda 180g or placebo within 7 days of symptom onset or first positive swab if asymptomatic. The primary endpoint was proportion negative for SARS-CoV-2 RNA on Day 7 post-injection. FindingsThere were 30 patients per arm, with median baseline SARS-CoV-2 viral load of 6.71 (IQR 1.3-8.0) log copies/mL. The decline in SARS-CoV-2 RNA was greater in those treated with peginterferon-lambda than placebo (p=0.04). On Day 7, 24 participants (80%) in the peginterferon-lambda group had an undetectable viral load compared to 19 (63%) in the placebo arm (p=0.15). After controlling for baseline viral load, peginterferon lambda treatment resulted in a 4.12-fold (95CI 1.15-16.7, p=0.029) higher likelihood of viral clearance by Day 7. Of those with baseline viral load above 10E6 copies/mL, 15/19 (79%) in the peginterferon-lambda group were undetectable on Day 7 compared to 6/16 (38%) in the placebo group (p=0.012). Adverse events were similar between groups with only mild reversible transaminase elevations more frequently observed in the peginterferon-lambda group. InterpretationPeginterferon-lambda accelerated viral decline in outpatients with COVID-19 resulting in a greater proportion with viral clearance by Day 7, particularly in those with high baseline viral load. Peginterferon-lambda may have potential to prevent clinical deterioration and shorten duration of viral shedding. (NCT04354259) FundingThis study was supported by the Toronto COVID-19 Action Initiative, University of Toronto and the Ontario First COVID-19 Rapid Research Fund. Medication was supplied by Eiger BioPharma. Research in ContextTreatment trials for COVID-19 have largely focused on hospitalized patients and no treatments are approved for people with mild to moderate disease in the outpatient setting. A number of studies in ambulatory populations have been registered but no controlled studies in the outpatient setting have been reported to date (Pubmed Search October 20, 2020, COVID-19 treatment; controlled trials). Uncontrolled case series of hydroxychloroquine with or without azithromycin have been reported with mixed results but no clear signal of efficacy and some concerns raised about cardiac toxicity. Treamtent in the outpatient setting has potential to prevent infected individuals from deteriorating and perhaps more importantly, may shorten the duration of viral shedding, reducing the risk of transmission and the duration required for self-isolation, with significant public health and societal impact. Added value of this studyThis is the first study to show an antiviral effect in outpatients with COVID-19. After controlling for baseline viral load, those treated with peginterferon-lambda had a 4.12-fold (95%CI 1.15-16.7, p=0.029) higher odds of viral clearance by Day 7 compared to those who received placebo. The viral load decline was faster with pegterferon-lambda and the effect was most pronounced in those with high viral loads. In individuals with a baseline viral load of 10E6 copies/mL or higher, 15/19 (79%) in the peginterferon-lambda arm cleared by Day 7 compared to 6/16 (38%) (p=0.012) in the placebo arm (OR 6.25, 95%CI 1.49-31.1, p=0.012), translating to a median time to viral clearance of 7 days (95%CI 6.2-7.8 days) with peginterferon-lambda compared to 10 days (95%CI 7.8-12.2 days) with placebo (p=0.038). Those with low viral loads (<10E6 copies/mL) cleared quickly in both groups. Peginterferon-lambda was well-tolerated with a similar side effect profile to placebo and no concerning laboratory adverse events. Implications of all available evidenceThere is no currently approved therapy for outpatients with COVID-19. This study showed that peginterferon-lambda accelerated viral clearance, particularly in those with high baseline viral loads, highlighting the importance of quantitative viral load testing in the evaluation of antiviral agents for COVID-19. Treatment early in the course of disease may prevent clinical deterioration and shorenting of the duration of viral shedding may have important public health impact by limiting transmission and reducing the duration required for self-isolation. Additional trials of peginterferon-lambda and other antiviral strategies in the outpatient setting are required.
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BackgroundOptimizing the public health response to reduce coronavirus disease 2019 (COVID-19) burden necessitates characterizing population-level heterogeneity of COVID-19 risks. However, heterogeneity in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing may introduce biased estimates depending on analytic design. MethodsWe explored the potential for collider bias and characterized individual, environmental, and social determinants of testing and diagnosis using cross-sectional analyses among 14.7 million community-dwelling people in Ontario, Canada. Among those diagnosed, we used separate analytic designs to compare predictors of: 1) individuals testing positive versus negative; 2) symptomatic individuals only testing positive versus testing negative; and 3) individuals testing positive versus individuals not testing positive (i.e., testing negative or not being tested). Analyses included tests conducted between March 1 and June 20, 2020. ResultsOf a total of 14,695,579 individuals, 758,691 were tested for SARS-CoV-2, of whom 25,030 (3.3%) tested positive. The further the odds of testing from the null, the more variability observed in the odds of diagnosis across analytic design, particularly among individual factors. There was less variability in testing by social determinants across analytic designs. Residing in areas with highest household density (adjusted odds ratio [aOR]: 1.86; 95%CI: 1.75-1.98), highest proportion of essential workers (aOR: 1.58; 95%CI: 1.48-1.69), lowest educational attainment (aOR: 1.33; 95%CI: 1.26-1.41), and highest proportion of recent immigrants (aOR: 1.10; 95%CI: 1.05-1.15) were consistently related to increased odds of SARS-CoV-2 diagnosis regardless of analytic design. InterpretationWhere testing is limited, risk factors may be better estimated using population comparators rather than test-negative comparators. Optimizing COVID-19 responses necessitates investment and sufficient coverage of structural interventions tailored to heterogeneity in social determinants of risk, including household crowding, occupation, and structural racism.
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BackgroundA hospital-level pandemic response involves anticipating local surge in healthcare needs. MethodsWe developed a mechanistic transmission model to simulate a range of scenarios of COVID-19 spread in the Greater Toronto Area. We estimated healthcare needs against 2019 daily admissions using healthcare administrative data, and applied outputs to hospital-specific data on catchment, capacity, and baseline non-COVID admissions to estimate potential surge by day 90 at two hospitals (St. Michaels Hospital [SMH] and St. Josephs Health Centre [SJHC]). We examined fast/large, default, and slow/small epidemics, wherein the default scenario (R0 2.4) resembled the early trajectory in the GTA. ResultsWithout further interventions, even a slow/small epidemic exceeded the citys daily ICU capacity for patients without COVID-19. In a pessimistic default scenario, for SMH and SJHC to remain below their non-ICU bed capacity, they would need to reduce non-COVID inpatient care by 70% and 58% respectively. SMH would need to create 86 new ICU beds, while SJHC would need to reduce its ICU beds for non-COVID care by 72%. Uncertainty in local epidemiological features was more influential than uncertainty in clinical severity. If physical distancing reduces contacts by 20%, maximizing the diagnostic capacity or syndromic diagnoses at the community-level could avoid a surge at each hospital. InterpretationAs distribution of the citys surge varies across hospitals over time, efforts are needed to plan and redistribute ICU care to where demand is expected. Hospital-level surge is based on community-level transmission, with community-level strategies key to mitigating each hospitals surge.
