Impact of a vaccine passport on first-dose COVID-19 vaccine coverage by age and area-level social determinants in the Canadian provinces of Quebec and Ontario: an interrupted time series analysis (preprint)

Background: In Canada, all provinces implemented vaccine passports in 2021 to increase vaccine uptake and reduce transmission in non-essential indoor spaces. We evaluate the impact of vaccine passport policies on first-dose COVID-19 vaccination coverage by age, area-level income and proportion racialized. Methods: We performed interrupted time-series analyses using vaccine registry data linked to census information in Quebec and Ontario (20.5 million people [≥]12 years; unit of analysis: dissemination area). We fit negative binomial regressions to weekly first-dose vaccination, using a natural spline to capture pre-announcement trends, adjusting for baseline vaccination coverage (start: July 3rd; end: October 23rd Quebec, November 13th Ontario). We obtain counterfactual vaccination rates and coverage, and estimated vaccine passports' impact on vaccination coverage (absolute) and new vaccinations (relative). Results: In both provinces, pre-announcement first-dose vaccination coverage was 82% ([≥]12 years). The announcement resulted in estimated increases in vaccination coverage of 0.9 percentage points (p.p.;95% CI: 0.4-1.2) in Quebec and 0.7 p.p. (95% CI: 0.5-0.8) in Ontario. In relative terms, these increases correspond to 23% (95% CI: 10-36%) and 19% (95% CI: 15-22%) more vaccinations. The impact was larger among people aged 12-39 (1-2 p.p.). There was little variability in the absolute impact by area-level income or proportion racialized in either province. Conclusions: In the context of high baseline vaccine coverage across two provinces, the announcement of vaccine passports led to a small impact on first-dose coverage, with little impact on reducing economic and racial inequities in vaccine coverage. Findings suggest the need for other policies to further increase vaccination coverage among lower-income and more racialized neighbourhoods and communities.

Higher contact among vaccinated can be a mechanism for negative vaccine effectiveness (preprint)

Evidence from early observational studies suggested negative vaccine effectiveness for the SARS-CoV-2 Omicron variant. Using transmission modeling, we illustrated how increased contact between vaccinated individuals, vaccinated contact heterogeneity, paired with lower vaccine efficacies could produce negative measurements and how we can identify this mechanism via a key temporal signature.

Predictors of all-cause mortality among patients hospitalized with influenza, respiratory syncytial virus, or SARS-CoV-2 (preprint)

Background: Identification of shared and divergent predictors of clinical severity across respiratory viruses may support clinical decision-making and resource planning in the context of a novel or re-emergent respiratory pathogen. Methods: We conducted a retrospective cohort study to identify predictors of 30-day all-cause mortality following hospitalization with influenza (N=45,749; 2011-09 to 2019-05), respiratory syncytial virus (RSV, N=24,345; 2011-09 to 2019-04), or SARS-CoV-2 (N=8,988; 2020-03 to 2020-12; pre-vaccine) using population-based health administrative data from Ontario, Canada. Multivariable modified Poisson regression was used to assess associations between potential predictors and mortality. We compared the direction, magnitude and confidence intervals of risk ratios to identify shared and divergent predictors of mortality. Results: 3,186 (7.0%), 697 (2.9%) and 1,880 (20.9%) patients died within 30 days of hospital admission with influenza, RSV, and SARS-CoV-2, respectively. Common predictors of increased mortality included: older age, male sex, residence in a long-term care home, and chronic kidney disease. Positive associations between age and mortality were largest for patients with SARS-CoV-2. Few comorbidities were associated with mortality among patients with SARS-CoV-2 as compared to those with influenza or RSV. Conclusions: Our findings may help identify patients at highest risk of illness secondary to a respiratory virus, anticipate hospital resource needs, and prioritize local preventions and therapeutics to communities with high prevalence of risk factors.

The Individual and Social Determinants of COVID-19 in Ontario, Canada: A Population-Wide Study (preprint)

Importance: Optimizing 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. Objective: Characterizing individual, environmental, and social determinants of SARS-CoV-2 testing and COVID-19 diagnosis. Design: We conducted cross-sectional analyses among 14.7 million people comparing individual, environmental, and social determinants among individuals who were tested versus not yet tested. Among those diagnosed, we used three analytic designs to compare predictors of: 1) individuals testing positive versus negative; 2) symptomatic individuals testing positive versus 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. Setting: Ontario, Canada. Participants: All individuals with [≥]1 healthcare system contact since March 2012, excluding individuals deceased before, or born after, March 1, 2020, or residing in a long-term care facility. Exposures: Individual-level characteristics (age, sex, underlying health conditions, prior healthcare use), area-based environmental (air pollution) exposures, and area-based social determinants of health (income, education, housing, marital status, race/ethnicity, and recent immigration). Main Outcomes and Measures: Odds of SARS-CoV-2 test, and of COVID-19 diagnosis. Results: Of a total of 14,695,579 individuals, 758,691 had been tested, 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 design. Residing in areas with highest household density (adjusted odds ratio: 2.08; 95%CI: 1.95-1.21), lowest educational attainment (adjusted odds ratio: 1.52; 95%CI: 1.44-1.60), and highest proportion of recent immigrants (adjusted odds ratio: 1.12; 95%CI: 1.07-1.16) were consistently related to increased odds of COVID-19 across analytic designs. Conclusions and Relevance: Where 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 and systemic racism.