Observed versus expected rates of myocarditis after SARS-CoV-2 vaccination: a population-based cohort study.

BACKGROUND: Postmarketing evaluations have linked myocarditis to SARS-CoV-2 mRNA vaccines. We sought to estimate the incidence of myocarditis after mRNA vaccination against SARS-CoV-2, and to compare the incidence with expected rates based on historical background rates in British Columbia. METHODS: We conducted an observational study using population health administrative data from the BC COVID-19 Cohort from Dec. 15, 2020, to Mar. 10, 2022. The primary exposure was any dose of an mRNA vaccine against SARS-CoV-2. The primary outcome was incidence of hospital admission or emergency department visit for myocarditis or myopericarditis within 7 and 21 days postvaccination, calculated as myocarditis rates per 100 000 mRNA vaccine doses, expected rates of myocarditis cases and observedto-expected ratios. We stratified analyses by age, sex, vaccine type and dose number. RESULTS: We observed 99 incident cases of myocarditis within 7 days (0.97 cases per 100 000 vaccine doses; observed v. expected ratio 14.81, 95% confidence interval [CI] 10.83-16.55) and 141 cases within 21 days (1.37 cases per 100 000 vaccine doses; observed v. expected ratio 7.03, 95% CI 5.92-8.29) postvaccination. Cases of myocarditis per 100 000 vaccine doses were higher for people aged 12-17 years (2.64, 95% CI 1.54-4.22) and 18-29 years (2.63, 95% CI 1.94-3.50) than for older age groups, for males compared with females (1.64, 95% CI 1.30-2.04 v. 0.35, 95% CI 0.21-0.55), for those receiving a second dose compared with a third dose (1.90, 95% CI 1.50-2.39 v. 0.76, 95% CI 0.45-1.30) and for those who received the mRNA-1273 (Moderna) vaccine compared with the BNT162b2 (Pfizer-BioNTech) vaccine (1.44, 95% CI 1.06-1.91 v. 0.74, 95% CI 0.56-0.98). The highest observed-to-expected ratio was seen after the second dose among males aged 18-29 years who received the mRNA-1273 vaccine (148.32, 95% CI 95.03-220.69). INTERPRETATION: Although absolute rates of myocarditis were low, vaccine type, age and sex are important factors to consider when strategizing vaccine administration to reduce the risk of postvaccination myocarditis. Our findings support the preferential use of the BNT162b2 vaccine over the mRNA-1273 vaccine for people aged 18-29 years.

Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques.

Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in mutant prevalence in situations where clinical sequencing is unavailable.

Comparative Risk of Myocarditis/Pericarditis Following Second Doses of BNT162b2 and mRNA-1273 Coronavirus Vaccines.

BACKGROUND: Postmarketing evaluations have linked myocarditis to COVID-19 mRNA vaccines. However, few population-based analyses have directly compared the safety of the 2 mRNA COVID-19 vaccines. OBJECTIVES: This study aimed to compare the risk of myocarditis, pericarditis, and myopericarditis between BNT162b2 and mRNA-1273. METHODS: We used data from the British Columbia COVID-19 Cohort (BCC19C), a population-based cohort study. The exposure was the second dose of an mRNA vaccine. The outcome was diagnosis of myocarditis, pericarditis, or myopericarditis during a hospitalization or an emergency department visit within 21 days of the second vaccination dose. We performed multivariable logistic regression to assess the association between vaccine product and the outcomes of interest. RESULTS: The rates of myocarditis and pericarditis per million second doses were higher for mRNA-1273 (n = 31, rate 35.6; 95% CI: 24.1-50.5; and n = 20, rate 22.9; 95% CI: 14.0-35.4, respectively) than BNT162b2 (n = 28, rate 12.6; 95% CI: 8.4-18.2 and n = 21, rate 9.4; 95% CI: 5.8-14.4, respectively). mRNA-1273 vs BNT162b2 had significantly higher odds of myocarditis (adjusted OR [aOR]: 2.78; 95% CI: 1.67-4.62), pericarditis (aOR: 2.42; 95% CI: 1.31-4.46) and myopericarditis (aOR: 2.63; 95% CI: 1.76-3.93). The association between mRNA-1273 and myocarditis was stronger for men (aOR: 3.21; 95% CI: 1.77-5.83) and younger age group (18-39 years; aOR: 5.09; 95% CI: 2.68-9.66). CONCLUSIONS: Myocarditis/pericarditis following mRNA COVID-19 vaccines is rare, but we observed a 2- to 3-fold higher odds among individuals who received mRNA-1273 vs BNT162b2. The rate of myocarditis following mRNA-1273 receipt is highest among younger men (age 18-39 years) and does not seem to be present at older ages. Our findings may have policy implications regarding the choice of vaccine offered.

Social Contacts and Transmission of COVID-19 in British Columbia, Canada.

Background: Close-contact rates are thought to be a driving force behind the transmission of many infectious respiratory diseases. Yet, contact rates and their relation to transmission and the impact of control measures, are seldom quantified. We quantify the response of contact rates, reported cases and transmission of COVID-19, to public health contact-restriction orders, and examine the associations among these three variables in the province of British Columbia, Canada. Methods: We derived time series data for contact rates, daily cases and transmission of COVID-19 from a social contacts survey, reported case counts and by fitting a transmission model to reported cases, respectively. We used segmented regression to investigate impacts of public health orders; Pearson correlation to determine associations between contact rates and transmission; and vector autoregressive modeling to quantify lagged associations between contacts rates, daily cases, and transmission. Results: Declines in contact rates and transmission occurred concurrently with the announcement of public health orders, whereas declines in cases showed a reporting delay of about 2 weeks. Contact rates were a significant driver of COVID-19 and explained roughly 19 and 20% of the variation in new cases and transmission, respectively. Interestingly, increases in COVID-19 transmission and cases were followed by reduced contact rates: overall, daily cases explained about 10% of the variation in subsequent contact rates. Conclusion: We showed that close-contact rates were a significant time-series driver of transmission and ultimately of reported cases of COVID-19 in British Columbia, Canada and that they varied in response to public health orders. Our results also suggest possible behavioral feedback, by which increased reported cases lead to reduced subsequent contact rates. Our findings help to explain and validate the commonly assumed, but rarely measured, response of close contact rates to public health guidelines and their impact on the dynamics of infectious diseases.

Mathematical modeling of COVID-19 in British Columbia: An age-structured model with time-dependent contact rates.

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.