Neighborhood-level vaccine impact on COVID-19 infection and hospital admission in Quebec, Canada, during the Delta and early Omicron periods.

OBJECTIVE: To assess the impact of COVID-19 vaccination on COVID-19 infection and hospitalisation at the population-level, and to assess the indirect effects of vaccination in the province of Quebec, Canada. METHODS: We performed a time-stratified, neighborhood-level ecologic study. The exposure was neighborhood-level vaccination (primary series) coverage; outcomes were COVID-19 infection and hospitalisation rates. We used robust Poisson regression to estimate weekly relative rates of infection and hospitalisation versus vaccination. RESULTS: Higher vaccination coverage was associated with lower COVID-19 infection rates from July 18-December 4 for the year 2021 (Delta period) (RR≈0.46 [0.39; 0.54] - 0.94 [0.83; 1.05], 85-100% vs. 60-74% coverage). From December 5-December 25, this association reversed (RR≈1.28 [1.16; 1.41] - 1.41 [1.31; 1.52]), possibly due to the Omicron variant, social behaviors and accumulation of susceptibles in more vaccinated neighborhoods. Vaccine impact against hospitalisation was maintained throughout (RR≈0.43 [0.29; 0.65] - 0.88 [0.64; 1.22]). Vaccination provided substantial indirect protection (RR≈0.43 [0.34; 0.54] - 0.81 [0.65; 1.03]). CONCLUSIONS: This study confirmed the protective impact of vaccination against severe disease regardless of variant, at the population level. Ecological analyses are a valuable strategy to evaluate vaccination programs. Population-level effects can have substantial effects and should be accounted for in public health and vaccination program planning.

Evolution of illness severity in hospital admissions due to COVID-19, Québec, Canada, January to April 2022.

Background: The coronavirus disease 2019 (COVID-19) severity is influenced by multiple factors, such as age, underlying medical conditions, individual immunity, infecting variant, and clinical practice. The highly transmissible Omicron variants resulted in decreased COVID-19 screening capacity, which limited disease severity surveillance. Objective: To report on the temporal evolution of disease severity among patients admitted to Québec hospitals due to COVID-19 between January 2, 2022, and April 23, 2022, which corresponded to the peak period of hospitalizations due to Omicron. Methods: Retrospective population-based cohort study of all hospital admissions due to COVID-19 in Québec, between January 2, 2022, and April 23, 2022. Study period was divided into four-week periods, corresponding roughly to January, February, March and April. Regression using Cox and Poisson generalized estimating equations (GEEs) was used to quantify temporal variations in length of stay and risk of complications (intensive care admission or in-hospital death) through time, using the Omicron peak (January 2022) as reference. Measures were adjusted for age, sex, vaccination status, presence of chronic diseases, and clustering by hospital. Results: During the study period, 9,178 of all 18,272 (50.2%) patients hospitalized with a COVID-19 diagnosis were admitted due to COVID-19. Of these, 1,026 (11.2%) were admitted to intensive care and 1,523 (16.6%) died. Compared to January, the risk of intensive care admission was 25% and 31% lower in March and April respectively, while in-hospital fatality continuously decreased by 45% lower in April. The average length of stay was temporarily lower in March (9%). Conclusion: Severity of admissions due to COVID-19 decreased in the first months of 2022, when predominant circulating variants were considered to be of similar severity. Monitoring hospital admissions due to COVID-19 can contribute to disease severity surveillance.

Evaluation of influenza case definitions for use in real-world evidence research.

Background: Laboratory confirmation of influenza is not routinely done in practice. With the advent of big data, it is tempting to use healthcare administrative databases for influenza vaccine effectiveness studies, which often rely on clinical diagnosis codes. The objective of this article is to compare influenza incidence curves using international case definitions derived from clinical diagnostic codes with influenza surveillance data from the United States (US) Centers for Disease Control and Prevention (CDC). Methods: This case series describes influenza incidence by CDC week, defined using International Classification of Disease diagnostic codes over four influenza seasons (2015-2016 to 2018-2019) in a cohort of US individuals three years of age and older who consulted at least once per year between 2015 and 2019. Results were compared to the number of influenza-positive specimens or outpatient visits for influenza-like illness obtained from the CDC flu surveillance data. Results: The incidence curves of influenza-related medical encounters were very similar to the CDC's surveillance data for laboratory-confirmed influenza. Conversely, the number of influenza-like illness encounters was high when influenza viruses started to circulate, leading to a discrepancy with CDC-reported data. Conclusion: A specific case definition should be prioritized when data for laboratory-confirmed influenza are not available, as a broader case definition would conservatively bias influenza vaccine effectiveness toward the null.