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Background: Mortality 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., Methods: Age-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., Results: It 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., Conclusions: Deconvolution 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.

Background: Longitudinal studies are critical to informing evolving responses to COVID-19 but can be hampered by attrition bias, which undermines their reliability for guiding policy and practice. We describe recruitment and retention in the Optimise Study, a longitudinal cohort and social networks study that aimed to inform public health and policy responses to COVID-19. Methods: Optimise recruited adults residing in Victoria, Australia September 01 2020–September 30 2021. High-frequency follow-up data collection included nominating social networks for study participation and completing a follow-up survey and four follow-up diaries each month, plus additional surveys if they tested positive for COVID-19 or were a close contact. This study compared number recruited to a-priori targets as of September 302,021, retention as of December 31 2021, comparing participants retained and not retained, and follow-up survey and diary completion October 2020–December 2021. Retained participants completed a follow-up survey or diary in each of the final three-months of their follow-up time. Attrition was defined by the number of participants not retained, divided by the number who completed a baseline survey by September 302,021. Survey completion was calculated as the proportion of follow-up surveys or diaries sent to participants that were completed between October 2020–December 2021. Results: At September 302,021, 663 participants were recruited and at December 312,021, 563 were retained giving an overall attrition of 15% (n = 100/663). Among the 563 retained, survey completion was 90% (n = 19,354/21,524) for follow-up diaries and 89% (n = 4936/5560) for monthly follow-up surveys. Compared to participants not retained, those retained were older (t-test, p < 0.001), and more likely to be female (χ2, p = 0.001), and tertiary educated (χ2, p = 0.018). Conclusion: High levels of study retention and survey completion demonstrate a willingness to participate in a complex, longitudinal cohort study with high participant burden during a global pandemic. We believe comprehensive follow-up strategies, frequent dissemination of study findings to participants, and unique data collection systems have contributed to high levels of study retention. [ABSTRACT FROM AUTHOR]