Seminar by Ping Yan

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

It is important to assess the effectiveness of vaccines for the SARS-Cov2 virus in preventing severe illness from Covid-19 in the Canadian population. We develop a proportional incidence model that estimates vaccine effectiveness (VE) at the population level using conditional likelihood based on aggregated data. Our model assumes that the population counts of clinical outcomes for an infectious disease arise from a superposition of Poisson processes for persons with differing vaccination statuses.

The intensity function in the model is 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 for relative risk, defined as the ratio of the per capita incidence rates of vaccinated and unvaccinated individuals. In the regression analysis, we treat the baseline incidence rate as a nuisance parameter, like the Cox proportional hazard model in survival analysis. We apply the proposed models and methods to age- stratified weekly counts of COVID-19–related hospital and ICU admissions among adults in Ontario, Canada from January 2021 to February 2022; this period encompasses the Omicron era and the rollout of booster vaccine doses. We also discuss limitations and confounding effects and the need for comprehensive and timely individual-level data that documents clinical outcomes and includes potential confounders.