Effectiveness of Coronavirus Disease 2019 Vaccines Against Hospitalization and Death in Canada: A Multiprovincial, Test-Negative Design Study

Leveraging a clinical emergency department dataset to estimate two-dose COVID-19 vaccine effectiveness and duration of protection in Canada

BACKGROUND

During the COVID-19 pandemic, clinical care shifted toward virtual and Emergency Department care. We explored the feasibility of mRNA vaccine effectiveness (VE) estimation against SARS-CoV-2-related Emergency Department visits and hospitalizations using prospectively collected Emergency Department data.

METHODS

We estimated two-dose VE using a test-negative design and data from 10 participating sites of the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN). We included Emergency Department patients presenting with COVID-19 symptoms and nucleic acid amplification testing for SARS-CoV-2 between July 19 and December 31, 2021. We excluded patients with unclear vaccination and one or more than 2 vaccine doses by their Emergency Department visit.

RESULTS

Among 3,405 eligible patients, adjusted two-dose mRNA VE against SARS-CoV-2-related Emergency Department visits was 93.3 % (95 % CI 87.9-96.3 %) between 7-55 days, sustained over 80 % through 139 days post-vaccination. In stratified analyses, VE was similar among patients with select immune-compromising conditions, chronic kidney disease, lung disease, unstable housing, and reported illicit substance use.

CONCLUSIONS

Two-dose mRNA VE against SARS-CoV-2-related Emergency Department visit was high and sustained, including among vulnerable subgroups. Compared to administrative datasets, active Emergency Department enrolment enables standardization for testing access and indication and supports separate VE assessment among special population subgroups. Compared to other active enrolment settings, Emergency Departments more consistently function during crises when alternate healthcare sectors become variably closed.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT0470294.

Risk factors for severe outcomes of coronavirus disease 2019 through the waves of the pandemic: Comparing patients with and without solid organ transplantation

BACKGROUND

While coronavirus disease 2019 (COVID-19) is no longer a public health emergency, certain patients remain at risk of severe outcomes. To better understand changing risk profiles, we studied the risk factors for patients with and without solid organ transplantation (SOT) through the various waves of the pandemic.

METHODS

Using the National COVID Cohort Collaborative we studied a cohort of adult patients testing positive for COVID-19 between January 1, 2020, and May 2, 2022. We separated the data into waves of COVID-19 as defined by the Centers for Disease Control. In our primary outcome, we used multivariable survival analysis to look at various risk factors for hospitalization in those with and without SOT.

RESULTS

A total of 3,570,032 patients were captured. We found an overall risk attenuation of adverse COVID-19-associated outcomes over time. In both non-SOT and SOT populations, diabetes, chronic kidney disease, and congestive heart failure were risk factors for hospitalization. For SOT specifically, longer time periods between transplant and COVID-19 were protective and age was a risk factor. Notably, asthma was not a risk factor for major adverse renal cardiovascular events, hospitalization, or mortality in either group.

CONCLUSIONS

Our study provides a longitudinal view of the risks associated with adverse COVID-related outcomes amongst SOT and non-SOT patients, and how these risk factors evolved over time. Our work will help inform providers and policymakers to better target high-risk patients.

Deviation from the recommended schedule: optimal dosing interval for a two-dose vaccination programme

Optimizing vaccination impact during an emerging disease becomes crucial when vaccine supply is limited, and robust protection requires multiple doses. Facing this challenge during the early stages of the COVID-19 vaccine deployment, a pivotal policy question arose: whether to administer a single dose to a larger proportion of the population by deferring the second dose, or to prioritize stronger protection for a smaller subset of the population with the established dosing interval from clinical trials. Using a delay-differential model and considering waning immunity and distribution capacity, we compared these strategies. We found that the efficacy of the first dose significantly influences the impact of delaying the second dose. Even for a relatively low efficacy of the first dose, a delayed strategy may outperform vaccination with the recommended dosing interval in reducing short-term hospitalizations and deaths despite increase in infections. The optimal delay, however, depends on the specific outcome measured and timelines within which the vaccination strategy is evaluated. We found transition lines for the relative reduction of infection, hospitalization and death below which vaccination with the recommended schedule is the preferred strategy. In a realistic parameter space, our results highlight scenarios in which the conclusions of previous studies are invalid.

Cohort profile: the British Columbia COVID-19 Cohort (BCC19C)-a dynamic, linked population-based cohort

Purpose

The British Columbia COVID-19 Cohort (BCC19C) was developed from an innovative, dynamic surveillance platform and is accessed/analyzed through a cloud-based environment. The platform integrates recently developed provincial COVID-19 datasets (refreshed daily) with existing administrative holdings and provincial registries (refreshed weekly/monthly). The platform/cohort were established to inform the COVID-19 response in near "real-time" and to answer more in-depth epidemiologic questions.

Participants

The surveillance platform facilitates the creation of large, up-to-date analytic cohorts of people accessing COVID-19 related services and their linked medical histories. The program of work focused on creating/analyzing these cohorts is referred to as the BCC19C. The administrative/registry datasets integrated within the platform are not specific to COVID-19 and allow for selection of "control" individuals who have not accessed COVID-19 services.

Findings to date

The platform has vastly broadened the range of COVID-19 analyses possible, and outputs from BCC19C analyses have been used to create dashboards, support routine reporting and contribute to the peer-reviewed literature. Published manuscripts (total of 15 as of July, 2023) have appeared in high-profile publications, generated significant media attention and informed policy and programming. In this paper, we conducted an analysis to identify sociodemographic and health characteristics associated with receiving SARS-CoV-2 laboratory testing, testing positive, and being fully vaccinated. Other published analyses have compared the relative clinical severity of different variants of concern; quantified the high "real-world" effectiveness of vaccines in addition to the higher risk of myocarditis among younger males following a 2nd dose of an mRNA vaccine; developed and validated an algorithm for identifying long-COVID patients in administrative data; identified a higher rate of diabetes and healthcare utilization among people with long-COVID; and measured the impact of the pandemic on mental health, among other analyses.

Future plans

While the global COVID-19 health emergency has ended, our program of work remains robust. We plan to integrate additional datasets into the surveillance platform to further improve and expand covariate measurement and scope of analyses. Our analyses continue to focus on retrospective studies of various aspects of the COVID-19 pandemic, as well as prospective assessment of post-acute COVID-19 conditions and other impacts of the pandemic.

A proportional incidence rate model for aggregated data to study the vaccine effectiveness against COVID-19 hospital and ICU admissions

We develop a proportional incidence model that estimates vaccine effectiveness (VE) at the population level using conditional likelihood for aggregated data. Our model assumes that the population counts of clinical outcomes for an infectious disease arise from a superposition of Poisson processes with different vaccination statuses. The intensity function in the model is calculated as the product of per capita incidence rate and the at-risk population size, both of which are time-dependent. We formulate a log-linear regression model with respect to the relative risk, defined as the ratio between the per capita incidence rates of vaccinated and unvaccinated individuals. In the regression analysis, we treat the baseline incidence rate as a nuisance parameter, similar to the Cox proportional hazard model in survival analysis. We then apply the proposed models and methods to age-stratified weekly counts of COVID-19-related hospital and ICU admissions among adults in Ontario, Canada. The data spanned from 2021 to February 2022, encompassing the Omicron era and the rollout of booster vaccine doses. We also discuss the limitations and confounding effects while advocating for the necessity of more comprehensive and up-to-date individual-level data that document the clinical outcomes and measure potential confounders.

"Flying a plane and building it at the same time": Lessons learned from the dynamic implementation of mass vaccination clinics in the Region of Waterloo, Ontario, Canada

BACKGROUND

Vaccination plays a critical role during pandemics, and mass vaccination clinics are often an imperative public health measure. These clinics usually consist of multi-disciplinary teams, which can pose significant coordination challenges, yet also present an opportunity for collectively contributing towards mitigating the impact of infection within communities. This study explores the coordination dynamics of the Region of Waterloo's coronavirus disease of 2019 (COVID-19) mass vaccination clinics in Ontario, Canada, between July 2021 and April 2022.

METHODS

This qualitative study included 16 purposively selected participants working in mass vaccination clinics. Participants were individually interviewed for 40-60 min. An inductive and iterative thematic analysis was undertaken, including open coding, grouping, labelling, regrouping and making sense of the themes.

RESULTS

Three interrelated themes were created: (1) unpredictable work environment, which was comprised of changing clinic processes and the impact of clinic adjustments to the running of the clinics; (2) clinic cohesion challenges, which included staff role disparities, limited job preparation and clinic system silos; and (3) adaptable and supportive work environment, which was comprised of staff adaptability, dispositional flexibility and a supportive work environment. While the first two themes created a precarious situation in the clinics, the third countered it, leading to a largely successful clinic implementation.

CONCLUSIONS

The rapid evolution and high transmissibility of COVID-19 in communities required a public health response that felt like flying and building a plane simultaneously - a seemingly impossible yet necessary task. However, an adaptable and supportive work environment was critical for establishing an atmosphere that can overcome challenges from a constantly changing pandemic and the guidance of public health officials. Such lessons gained from understanding the dynamic experiences in mass vaccination clinics are essential for improving the development and operation of future immunization campaigns.

Potentiating the Cross-Reactive IFN-γ T Cell and Polyfunctional T Cell Responses by Heterologous GX-19N DNA Booster in Mice Primed with Either a COVID-19 mRNA Vaccine or Inactivated Vaccine

Waning vaccine-induced immunity, coupled with the emergence of SARS-CoV-2 variants, has inspired the widespread implementation of COVID-19 booster vaccinations. Here, we evaluated the potential of the GX-19N DNA vaccine as a heterologous booster to enhance the protective immune response to SARS-CoV-2 in mice primed with either an inactivated virus particle (VP) or an mRNA vaccine. We found that in the VP-primed condition, GX-19N enhanced the response of both vaccine-specific antibodies and cross-reactive T Cells to the SARS-CoV-2 variant of concern (VOC), compared to the homologous VP vaccine prime-boost. Under the mRNA-primed condition, GX-19N induced higher vaccine-induced T Cell responses but lower antibody responses than the homologous mRNA vaccine prime-boost. Furthermore, the heterologous GX-19N boost induced higher S-specific polyfunctional CD4⁺ and CD8⁺ T cell responses than the homologous VP or mRNA prime-boost vaccinations. Our results provide new insights into booster vaccination strategies for the management of novel COVID-19 variants.

Maternal mRNA covid-19 vaccination during pregnancy and delta or omicron infection or hospital admission in infants: test negative design study

OBJECTIVE

To estimate the effectiveness of maternal mRNA covid-19 vaccination during pregnancy against delta and omicron severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection and hospital admission in infants.

DESIGN

Test negative design study.

SETTING

Community and hospital testing in Ontario, Canada.

PARTICIPANTS

Infants younger than six months of age, born between 7 May 2021 and 31 March 2022, who were tested for SARS-CoV-2 between 7 May 2021 and 5 September 2022.

INTERVENTION

Maternal mRNA covid-19 vaccination during pregnancy.

MAIN OUTCOME MEASURES

Laboratory confirmed delta or omicron infection or hospital admission of the infant. Multivariable logistic regression estimated vaccine effectiveness, with adjustments for clinical and sociodemographic characteristics associated with vaccination and infection.

RESULTS

8809 infants met eligibility criteria, including 99 delta cases (4365 controls) and 1501 omicron cases (4847 controls). Infant vaccine effectiveness from two maternal doses was 95% (95% confidence interval 88% to 98%) against delta infection and 97% (73% to 100%) against infant hospital admission due to delta and 45% (37% to 53%) against omicron infection and 53% (39% to 64%) against hospital admission due to omicron. Vaccine effectiveness for three doses was 73% (61% to 80%) against omicron infection and 80% (64% to 89%) against hospital admission due to omicron. Vaccine effectiveness for two doses against infant omicron infection was highest with the second dose in the third trimester (53% (42% to 62%)) compared with the first (47% (31% to 59%)) or second (37% (24% to 47%)) trimesters. Vaccine effectiveness for two doses against infant omicron infection decreased from 57% (44% to 66%) between birth and eight weeks to 40% (21% to 54%) after 16 weeks of age.

CONCLUSIONS

Maternal covid-19 vaccination with a second dose during pregnancy was highly effective against delta and moderately effective against omicron infection and hospital admission in infants during the first six months of life. A third vaccine dose bolstered protection against omicron. Effectiveness for two doses was highest with maternal vaccination in the third trimester, and effectiveness decreased in infants beyond eight weeks of age.

Risk factors for COVID-19 hospitalization after COVID-19 vaccination: a population-based cohort study in Canada

OBJECTIVES

With the uptake of COVID-19 vaccines, there is a need for population-based studies to assess risk factors for COVID-19-related hospitalization after vaccination and how they differ from unvaccinated individuals.

METHODS

We used data from the British Columbia COVID-19 Cohort, a population-based cohort that includes all individuals (aged ≥18 years) who tested positive for SARS-CoV-2 by real-time reverse transcription-polymerase chain reaction from January 1, 2021 (after the start of vaccination program) to December 31, 2021. We used multivariable logistic regression models to assess COVID-19-related hospitalization risk by vaccination status and age group among confirmed COVID-19 cases.

RESULTS

Of the 162,509 COVID-19 cases included in the analysis, 8,546 (5.3%) required hospitalization. Among vaccinated individuals, an increased odds of hospitalization with increasing age was observed for older age groups, namely those aged 50-59 years (odds ratio [OR] = 2.95, 95% confidence interval [CI]: 2.01-4.33), 60-69 years (OR = 4.82, 95% CI: 3.29, 7.07), 70-79 years (OR = 11.92, 95% CI: 8.02, 17.71), and ≥80 years (OR = 24.25, 95% CI: 16.02, 36.71). However, among unvaccinated individuals, there was a graded increase in odds of hospitalization with increasing age, starting at age group 30-39 years (OR = 2.14, 95% CI: 1.90, 2.41) to ≥80 years (OR = 41.95, 95% CI: 35.43, 49.67). Also, comparing all the age groups to the youngest, the observed magnitude of association was much higher among unvaccinated individuals than vaccinated ones.

CONCLUSION

Alongside a number of comorbidities, our findings showed a strong association between age and COVID-19-related hospitalization, regardless of vaccination status. However, age-related hospitalization risk was reduced two-fold by vaccination, highlighting the need for vaccination in reducing the risk of severe disease and subsequent COVID-19-related hospitalization across all population groups.