Population-estimable frailty using 'big data' to predict Covid-19 infection and illness severity

About the Project Early studies have indicated that older persons are at high risk of severe COVID-19 infection, but it is not known if frailty is more important risk factor than age alone. It is important for older individuals to know if they are at increased risk of infection from COVID-19, to prevent delays in seeking medical care when early symptoms occur. In this study, we will determine if frailty is an important predictor of COVID-19 infection and adverse outcomes using 'big data' and artificial intelligence-based methods. We will also determine if patients that are frail were further impacted because of the restrictions to care that were imposed upon the population in response to the pandemic. Over the two-year duration of the proposal, our team of investigators will study health data on the population of all residents of Ontario, and determine the frailty status of all persons in the province. We will analyze COVID-19 testing data that is being collected right now, and available to the research team, linked to the hospitalization and vital status data available at ICES. We will collaborate with artificial intelligence researchers at the Vector Institute to determine if frailty and the other associated epiphenomena are also associated with COVID-19 infection and outcomes. We will compare access to virtual and ambulatory care for vulnerable, individuals that are frail during the Covid-19 pandemic using sophisticated statistical and temporal analyses. The knowledge that is gained from is important because we need to be better able to identify those who are at high risk during the first and subsequent waves of COVID-19. If frailty is a predictor, it can guide educational and preventative strategies to protect vulnerable individuals.