RESUMO
BackgroundPatient age is the most salient clinical indicator of risk from COVID-19. Age-specific distributions of known SARS-CoV-2 infections and COVID-19-related deaths are available for most countries. However, relatively little attention has been given to the age distributions of hospitalizations and serious healthcare interventions administered to COVID-19 patients. We examined these distributions in Ontario, Canada, in order to quantify the age-related impacts of COVID-19, and to identify potential risks should the healthcare system become overwhelmed with COVID-19 patients in the future. MethodsWe analysed known SARS-CoV-2 infection records from the integrated Public Health Information System (iPHIS) and the Toronto Public Health Coronavirus Rapid Entry System (CORES) between 23 January 2020 and 17 June 2020 (N = 30,546), and estimated the age distributions of hospitalizations, ICU admissions, intubations, and ventilations. We quantified the probability of hospitalization given known SARS-CoV-2 infection, and of survival given COVID-19-related hospitalization. ResultsThe distribution of COVID-19-related hospitalizations peaks with a wide plateau covering ages 54-90, whereas deaths are sharply concentrated in very old ages, with a maximum at age 90. The estimated probability of hospitalization given known SARS-CoV-2 infection reaches a maximum of 32.0% at age 75 (95% CI 27.5%-36.7%). The probability of survival given COVID-19-related hospitalization is uncertain for children (due to small sample size), and near 100% for adults younger than 40. After age 40, survival of hospitalized COVID-19 patients declines substantially; for example, a hospitalized 50-year-old patient has a 90.4% chance of surviving COVID-19 (95% CI 81.9%-95.7%). InterpretationConcerted efforts to control the spread of SARS-CoV-2 have kept prevalence of the virus low in the population of Ontario. The healthcare system has not been overstretched, yet the probability of survival given hospitalization for COVID-19 has been lower than is generally recognized for patients over 40. If prevalence of the virus were to increase and healthcare capacities were to be exceeded, survival of individuals in the broad age range requiring acute care would be expected to decrease, potentially expanding the distribution of COVID-19-related deaths toward younger ages.
RESUMO
A novel coronavirus (SARS-CoV-2) has recently emerged as a global threat. As the epidemic progresses, many disease modelers have focused on estimating the basic reproductive number[R] 0- the average number of secondary cases caused by a primary case in an otherwise susceptible population. The modeling approaches and resulting estimates of[R] 0 vary widely, despite relying on similar data sources. Here, we present a novel statistical framework for comparing and combining different estimates of[R] 0 across a wide range of models by decomposing the basic reproductive number into three key quantities: the exponential growth rate r, the mean generation interval [Formula], and the generation-interval dispersion{kappa} . We then apply our framework to early estimates of[R] 0 for the SARS-CoV-2 outbreak. We show that many early[R] 0 estimates are overly confident. Our results emphasize the importance of propagating uncertainties in all components of[R] 0, including the shape of the generation-interval distribution, in efforts to estimate[R] 0 at the outset of an epidemic.
