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Estimating age-specific COVID-19 fatality risk and time to death by comparing population diagnosis and death patterns: Australian data
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 individu...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8215490/ https://www.ncbi.nlm.nih.gov/pubmed/34154563 http://dx.doi.org/10.1186/s12874-021-01314-w |
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author | Marschner, Ian C. |
author_facet | Marschner, Ian C. |
author_sort | Marschner, Ian C. |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-021-01314-w. |
format | Online Article Text |
id | pubmed-8215490 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82154902021-06-21 Estimating age-specific COVID-19 fatality risk and time to death by comparing population diagnosis and death patterns: Australian data Marschner, Ian C. BMC Med Res Methodol Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-021-01314-w. BioMed Central 2021-06-21 /pmc/articles/PMC8215490/ /pubmed/34154563 http://dx.doi.org/10.1186/s12874-021-01314-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Marschner, Ian C. Estimating age-specific COVID-19 fatality risk and time to death by comparing population diagnosis and death patterns: Australian data |
title | Estimating age-specific COVID-19 fatality risk and time to death by comparing population diagnosis and death patterns: Australian data |
title_full | Estimating age-specific COVID-19 fatality risk and time to death by comparing population diagnosis and death patterns: Australian data |
title_fullStr | Estimating age-specific COVID-19 fatality risk and time to death by comparing population diagnosis and death patterns: Australian data |
title_full_unstemmed | Estimating age-specific COVID-19 fatality risk and time to death by comparing population diagnosis and death patterns: Australian data |
title_short | Estimating age-specific COVID-19 fatality risk and time to death by comparing population diagnosis and death patterns: Australian data |
title_sort | estimating age-specific covid-19 fatality risk and time to death by comparing population diagnosis and death patterns: australian data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8215490/ https://www.ncbi.nlm.nih.gov/pubmed/34154563 http://dx.doi.org/10.1186/s12874-021-01314-w |
work_keys_str_mv | AT marschnerianc estimatingagespecificcovid19fatalityriskandtimetodeathbycomparingpopulationdiagnosisanddeathpatternsaustraliandata |