Absence of Excess Mortality in a Highly Vaccinated Population During the Initial Covid-19 Delta Period

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Background

All-cause excess mortality (the number of deaths that exceed projections in any period) has been widely reported during the Covid-19 pandemic. Whether excess mortality has occurred during the Delta wave is less well understood.

Methods

We performed an observational study using data from the Massachusetts Department of Health. Five years of US Census population data and CDC mortality statistics were applied to a seasonal autoregressive integrated moving average (sARIMA) model to project the number of expected deaths for each week of the pandemic period, including the Delta period (starting in June 2021, extending through August 28th 2021, for which mortality data are >99% complete). Weekly Covid-19 cases, Covid-19-attributed deaths, and all-cause deaths are reported. County-level excess mortality during the vaccine campaign are also reported, with weekly rates of vaccination in each county that reported 100 or more all-cause deaths during any week included in the study period.

Results

All-cause mortality was not observed after March 2021, by which time over 75% of persons over 65 years of age in Massachusetts had received a vaccination. Fewer deaths than expected (which we term ‘deficit mortality’) occurred both during the summer of 2020, the spring of 2021 and during the Delta wave (beginning June 13, 2021 when Delta isolates represented >10% of sequenced cases). After the initial wave in the spring of 2020, more Covid-19-attributed deaths were recorded that all-cause excess deaths, implying that Covid-19 was misattributed as the underlying cause, rather than a contributing cause of death in some cases.

Conclusion

In a state with high vaccination rates, excess mortality has not been recorded during the Delta period. Deficit mortality has been recorded during this period.

Article activity feed

  1. SciScore for 10.1101/2021.09.16.21263477: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.


    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.


    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.