tetano
Editor, Senior Moderator
J Theor Biol
. 2024 Mar 5:111771.
doi: 10.1016/j.jtbi.2024.111771. Online ahead of print. Decomposing mechanisms of COVID-19 mortality in empirical datasets: A modeling study
Tong Zhang[SUP] 1 [/SUP], Jiaying Qiao[SUP] 2 [/SUP], Katsuma Hayashi[SUP] 3 [/SUP], Hiroshi Nishiura[SUP] 4 [/SUP]
Affiliations
Our objective was to decompose mortality mechanisms during the coronavirus disease 2019 (COVID-19) pandemic to estimate direct, indirect, and associated deaths from COVID-19. Given the confirmatory diagnosis of COVID-19, a death event that was not necessarily caused by respiratory complications but stemmed from other complications was categorized as an indirect death from COVID-19. Associated deaths occurred in patients who did not have COVID-19 but died during the surge in COVID-19 cases when overwhelming pressure was exerted on the healthcare system. Analyzing the sixth wave (i.e., the first epidemic wave of the Omicron B.1.1.529 variant from January to May 2022), decomposition was achieved using the binomial and Poisson sampling process models fitted to two pieces of data (i.e., COVID-19 death certificate and excess data by major cause of death). The total numbers of direct, indirect, and associated deaths during the sixth wave in Osaka were estimated at 1,071; 948; and 2,157; respectively. The number of associated deaths was greater than the sum of direct and indirect deaths. We further observed that the composition of indirect and associated deaths differed by major cause of death, and deaths caused by circulatory disease included a greater proportion of indirect deaths compared with deaths by other causes. The goals of healthcare services for endemic COVID-19 include the sustainable provision of services to avoid preventable deaths. Therefore, gaining an in-depth understanding of mechanisms that lead to excess death is vital for improving future pandemic response efforts.
Keywords: Associated death; Circulatory disease; Indirect death; Statistical modeling.
. 2024 Mar 5:111771.
doi: 10.1016/j.jtbi.2024.111771. Online ahead of print. Decomposing mechanisms of COVID-19 mortality in empirical datasets: A modeling study
Tong Zhang[SUP] 1 [/SUP], Jiaying Qiao[SUP] 2 [/SUP], Katsuma Hayashi[SUP] 3 [/SUP], Hiroshi Nishiura[SUP] 4 [/SUP]
Affiliations
- PMID: 38452809
- DOI: 10.1016/j.jtbi.2024.111771
Our objective was to decompose mortality mechanisms during the coronavirus disease 2019 (COVID-19) pandemic to estimate direct, indirect, and associated deaths from COVID-19. Given the confirmatory diagnosis of COVID-19, a death event that was not necessarily caused by respiratory complications but stemmed from other complications was categorized as an indirect death from COVID-19. Associated deaths occurred in patients who did not have COVID-19 but died during the surge in COVID-19 cases when overwhelming pressure was exerted on the healthcare system. Analyzing the sixth wave (i.e., the first epidemic wave of the Omicron B.1.1.529 variant from January to May 2022), decomposition was achieved using the binomial and Poisson sampling process models fitted to two pieces of data (i.e., COVID-19 death certificate and excess data by major cause of death). The total numbers of direct, indirect, and associated deaths during the sixth wave in Osaka were estimated at 1,071; 948; and 2,157; respectively. The number of associated deaths was greater than the sum of direct and indirect deaths. We further observed that the composition of indirect and associated deaths differed by major cause of death, and deaths caused by circulatory disease included a greater proportion of indirect deaths compared with deaths by other causes. The goals of healthcare services for endemic COVID-19 include the sustainable provision of services to avoid preventable deaths. Therefore, gaining an in-depth understanding of mechanisms that lead to excess death is vital for improving future pandemic response efforts.
Keywords: Associated death; Circulatory disease; Indirect death; Statistical modeling.