Abstract
BackgroundMortality is a key component of the natural history of COVID-19 infection. Surveillance data on COVID-19 deaths and case diagnoses are widely available in the public domain, but they are not used to model time to death because they typically do not link diagnosis and death at an individual level. This paper demonstrates that by comparing the unlinked patterns of new diagnoses and deaths over age and time, age-specific mortality and time to death may be estimated using a statistical method called deconvolution.MethodsAge-specific data were analysed on 816 deaths among 6235 cases over age 50 years in Victoria, Australia, from the period January through December 2020. Deconvolution was applied assuming logistic dependence of case fatality risk (CFR) on age and a gamma time to death distribution. Non-parametric deconvolution analyses stratified into separate age groups were used to assess the model assumptions.ResultsIt was found that age-specific CFR rose from 2.9% at age 65 years (95% CI:2.2 – 3.5) to 40.0% at age 95 years (CI: 36.6 – 43.6). The estimated mean time between diagnosis and death was 18.1 days (CI: 16.9 – 19.3) and showed no evidence of varying by age (heterogeneity P = 0.97). The estimated 90% percentile of time to death was 33.3 days (CI: 30.4 – 36.3; heterogeneity P = 0.85). The final age-specific model provided a good fit to the observed age-stratified mortality patterns.ConclusionsDeconvolution was demonstrated to be a powerful analysis method that could be applied to extensive data sources worldwide. Such analyses can inform transmission dynamics models and CFR assessment in emerging outbreaks. Based on these Australian data it is concluded that death from COVID-19 occurs within three weeks of diagnosis on average but takes five weeks in 10% of fatal cases. Fatality risk is negligible in the young but rises above 40% in the elderly, while time to death does not seem to vary by age.
Highlights
Mortality is a key component of the natural history of COVID-19 infection
The Victorian population as of 10 December had no active cases, which means that the crude case fatality risk, calculated as the ratio of total deaths to total cases, is not subject to the usual underestimation bias arising from right-censoring of death times in active cases [21]
At the end of the case series in Panel A there is a period of six consecutive weeks of zero cases ending in early December, signifying elimination of COVID-19 from the Victorian population at that point in time
Summary
Surveillance data on COVID19 deaths and case diagnoses are widely available in the public domain, but they are not used to model time to death because they typically do not link diagnosis and death at an individual level. In this paper we analyse age-specific surveillance data from Australia on new COVID-19 case diagnoses and deaths over time. Despite their wide availability globally, surveillance data are not used for assessing time to death, because they do not link time of diagnosis and time of death at an individual level. We overcome this complexity by using an analysis method called deconvolution. The method relies on comparing the unlinked patterns of new diagnoses and deaths within a population
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