ABSTRACT
ABSTRACT Background A major goal of COVID-19 vaccination is to prevent severe outcomes (hospitalizations and deaths). We estimated the effectiveness of mRNA and ChAdOx1 COVID-19 vaccines against severe outcomes in four Canadian provinces between December 2020 and September 2021. Methods We conducted this multiprovincial retrospective test-negative study among community-dwelling adults aged ≥18 years in Ontario, Quebec, British Columbia, and Manitoba using linked provincial databases and a common study protocol. Multivariable logistic regression was used to estimate province-specific vaccine effectiveness against COVID-19 hospitalization and/or death. Estimates were pooled using random effects models. Results We included 2,508,296 tested subjects, with 31,776 COVID-19 hospitalizations and 5,842 deaths. Vaccine effectiveness was 83% after a first dose, and 98% after a second dose, against both hospitalization and death (separately). Against severe outcomes (hospitalization or death), effectiveness was 87% (95%CI: 71%–94%) ≥84 days after a first dose of mRNA vaccine, increasing to 98% (95%CI: 96%–99%) ≥112 days after a second dose. Vaccine effectiveness against severe outcomes for ChAdOx1 was 88% (95%CI: 75%–94%) ≥56 days after a first dose, increasing to 97% (95%CI: 91%–99%) ≥56 days after a second dose. Lower one-dose effectiveness was observed for adults aged ≥80 years and those with comorbidities, but effectiveness became comparable after a second dose. Two doses of vaccines provided very high protection for both homologous and heterologous schedules, and against Alpha, Gamma, and Delta variants. Conclusions Two doses of mRNA or ChAdOx1 vaccines provide excellent protection against severe outcomes of hospitalization and death.
Subject(s)
COVID-19ABSTRACT
Following the emergence of COVID-19 at the end of 2019, several mathematical models have been developed to study the transmission dynamics of this disease. Many of these models assume homogeneous mixing in the underlying population. However, contact rates and mixing patterns can vary dramatically among individuals depending on their age and activity level. Variation in contact rates among age groups and over time can significantly impact how well a model captures observed trends. To properly model the age-dependent dynamics of COVID-19 and understand the impacts of interventions, it is essential to consider heterogeneity arising from contact rates and mixing patterns. We developed an age-structured model that incorporates time-varying contact rates and population mixing computed from the ongoing BC Mix COVID-19 survey to study transmission dynamics of COVID-19 in British Columbia (BC), Canada. Using a Bayesian inference framework, we fit four versions of our model to weekly reported cases of COVID-19 in BC, with each version allowing different assumptions of contact rates. We show that in addition to incorporating age-specific contact rates and mixing patterns, time-dependent (weekly) contact rates are needed to adequately capture the observed transmission dynamics of COVID-19. Our approach provides a framework for explicitly including empirical contact rates in a transmission model, which removes the need to otherwise model the impact of many non-pharmaceutical interventions. Further, this approach allows projection of future cases based on clear assumptions of age-specific contact rates, as opposed to less tractable assumptions regarding transmission rates.
Subject(s)
COVID-19ABSTRACT
Background This study identified factors associated with hospital admission among people with laboratory-diagnosed COVID-19 cases in British Columbia. Methods This study was performed using the BC COVID-19 Cohort, which integrates data on all COVID-19 cases, hospitalizations, medical visits, emergency room visits, prescription drugs, chronic conditions and deaths. The analysis included all laboratory-diagnosed COVID-19 cases in British Columbia as of January 15 th , 2021. We evaluated factors associated with hospital admission using multivariable Poisson regression analysis with robust error variance. Findings From 56,874 COVID-19 cases included in the analyses, 2,298 were hospitalized. Models showed significant association of the following factors with increased hospitalization risk: male sex (adjusted risk ratio (aRR)=1.27; 95%CI=1.17-1.37), older age (p-trend <0.0001 across age groups with a graded increase in hospitalization risk with increasing age [aRR 30-39 years=3.06; 95%CI=2.32-4.03, to aRR 80+years=43.68; 95%CI=33.41-57.10 compared to 20-29 years-old]), asthma (aRR=1.15; 95%CI=1.04-1.26), cancer (aRR=1.19; 95%CI=1.09-1.29), chronic kidney disease (aRR=1.32; 95%CI=1.19-1.47), diabetes (treated without insulin aRR=1.13; 95%CI=1.03-1.25, requiring insulin aRR=5.05; 95%CI=4.43-5.76), hypertension (aRR=1.19; 95%CI=1.08-1.31), injection drug use (aRR=2.51; 95%CI=2.14-2.95), intellectual and developmental disabilities (aRR=1.67; 95%CI=1.05-2.66), problematic alcohol use (aRR=1.63; 95%CI=1.43-1.85), immunosuppression (aRR=1.29; 95%CI=1.09-1.53), and schizophrenia and psychotic disorders (aRR=1.49; 95%CI=1.23-1.82). Among women of reproductive age, in addition to age and comorbidities, pregnancy (aRR=2.69; 95%CI=1.42-5.07) was associated with increased risk of hospital admission. Interpretation Older age, male sex, substance use, intellectual and developmental disability, chronic comorbidities, and pregnancy increase the risk of COVID-19-related hospitalization. Funding BC Centre for Disease Control, Canadian Institutes of Health Research. Research in context Evidence before this study Factors such as older age, social inequities and chronic health conditions have been associated to severe COVID-19 illness. Most of the evidence comes from studies that don’t include all COVID-19 diagnoses in a jurisdiction), focusing on in-hospital mortality. In addition, mental illness and substance use were not evaluated in these studies. This study assessed factors associated with hospital admission among people with laboratory-diagnosed COVID-19 cases in British Columbia. Added value of this study In this population-based cohort study that included 56,874 laboratory-confirmed COVID-19 cases, older age, male sex, injection drug use, problematic alcohol use, intellectual and developmental disability, schizophrenia and psychotic disorders, chronic comorbidities and pregnancy were associated with the risk of hospitalization. Insulin-dependent diabetes was associated with higher risk of hospitalization, especially in the subpopulation younger than 40 years. To the best of our knowledge this is the first study reporting this finding, (insulin use and increased risk of COVID-19-related death has been described previously). Implications of all the available evidence Prioritization of vaccination in population groups with the above mentioned risk factors could reduce COVID-19 serious outcomes. The findings indicate the presence of the syndemic of substance use, mental illness and COVID-19, which deserve special public health considerations.