Dataset Information

Weeks of life lost to COVID-19, the case of the United States.

ABSTRACT:

Background

Years of life lost (YLL) is a preferable indicator to assess the mortality impact of COVID-19. This indicator still has limits, however. Therefore, a new approach and its early-death weeks (eDW) index has been recently proposed to alter YLL. This study aims to add a new approach, the moving excess-deficit mortality model, and its method, the weeks of life lost (WLL) index. The new method was then used to measure WLL associated with COVID-19 in the United States (US).

Methods

The natural mortality law and the random pattern of spreading COVID-19 were employed to support calculating WLL. The natural mortality law implied that under the same living conditions and the weaker would die earlier. The random spreading of COVID-19 assumed that COVID-19 causes the weekly number of early deaths in equal proportions from all of those who would have died eventually distributed through the pandemic.

Results

From Week 02 of 2020 to Week 44 of 2021, we found that the US population has lost 56,270,300 weeks to COVID-19; the average WLL per COVID-19-related death is 74 or 1.4 in the unit of years.

Conclusions

The results do not depend on the high heterogeneity of deaths (e.g., age, gender, health status) and on whether COVID-19 is the main cause of death. The moving excess-deficit mortality model and WLL index can be applied promptly at any time and anywhere once excess deaths occurred during the pandemic. The index also provides critical insights into COVID-19, which can support making public health policies and decisions.

SUBMITTER: Vu Manh C

PROVIDER: S-EPMC9208283 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:With the onset of COVID-19 and the resulting shelter in place guidelines combined with remote working practices, human mobility in 2020 has been dramatically impacted. Existing studies typically examine whether mobility in specific localities increases or decreases at specific points in time and relate these changes to certain pandemic and policy events. However, a more comprehensive analysis of mobility change over time is needed. In this paper, we study mobility change in the US through a five-step process using mobility footprint data. (Step 1) Propose the Delta Time Spent in Public Places (ΔTSPP) as a measure to quantify daily changes in mobility for each US county from 2019-2020. (Step 2) Conduct Principal Component Analysis (PCA) to reduce the ΔTSPP time series of each county to lower-dimensional latent components of change in mobility. (Step 3) Conduct clustering analysis to find counties that exhibit similar latent components. (Step 4) Investigate local and global spatial autocorrelation for each component. (Step 5) Conduct correlation analysis to investigate how various population characteristics and behavior correlate with mobility patterns. Results show that by describing each county as a linear combination of the three latent components, we can explain 59% of the variation in mobility trends across all US counties. Specifically, change in mobility in 2020 for US counties can be explained as a combination of three latent components: 1) long-term reduction in mobility, 2) no change in mobility, and 3) short-term reduction in mobility. Furthermore, we find that US counties that are geographically close are more likely to exhibit a similar change in mobility. Finally, we observe significant correlations between the three latent components of mobility change and various population characteristics, including political leaning, population, COVID-19 cases and deaths, and unemployment. We find that our analysis provides a comprehensive understanding of mobility change in response to the COVID-19 pandemic.