Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.09.10.20192369: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Software: All analyses were completed in R 3.6.3, using the rstanarm package16 for Bayesian regression analysis, the tidybayes package for post-processing17 and ggplot2 for visualization.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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: We detected the following sentences addressing limitations in the study:
    Nonetheless, there are some limitations that are important to highlight. First, our reliance on census-defined race/ethnicity as a proxy for exposure and mortality risk is necessarily reductive and does not shed enough light factors that can be modified to reduce these disparities. Numerous studies have highlighted the role of wealth and other markers of socioeconomic status (SES) such as educational attainment, as an important mediator of the effects of race on health outcomes. At the same time, even after adjustment for SES, there is often a residual effect of race which cannot be explained solely in terms of material wealth, and instead is likely accounted for by other factors including discrimination in healthcare settings, and the impact of cumulative stress associated with exposure to structural racism.24 Future analyses are necessary using either prospectively collected data inclusive of SES, or spatial analyses that join neighborhood-level information on wealth and other markers of SES with individual-level case data. In addition, although the disparities in our data likely mirror many of those nationwide, it is important to remember that these results reflect specifically patterns of infection and death in Michigan during the first wave of the COVID-19 pandemic in the U.S. Although its relatively large population size and socioeconomic and racial composition make the state a belwether of many national trends, patterns of racial residential segregation are more region...

    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.

    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.

  2. NCRC

    Our take

    This is a prospective study, available as a preprint and thus not yet peer reviewed, using case level data on 49,701 confirmed and probable cases of SARS-CoV-2 from Michigan, United States. Investigators found that racial minorities (except for Native Americans) had higher incidence and mortality rates compared to whites. The similarity in case fatality rates among racial groups suggests that greater exposure to the virus may be driving excess deaths in racial minority groups compared to whites. This could be indicative of various factors such as higher population density or a lack of ability to socially distance due to a higher number of essential workers in these communities. A major limitation is that the study does not include other variables (e.g. markers of socioeconomic status (SES) or neighborhood level SES) that may help explain underlying contributors to the disparities. However, this provides a basis for future studies to identify the mechanisms driving racial disparities in infection and mortality rates.

    Study design

    prospective-cohort

    Study population and setting

    Data were used from 49,701 individuals with confirmed or probable SARS-CoV-2 infection from the Michigan Disease Surveillance System (MDSS) from March 8 to May 20, 2020. The data included information regarding sex, age, race, date of death (if applicable), and date the case was referred to the Michigan Department of Health and Human Services (MDHHS). Investigators excluded individuals who did not reside in Michigan, had missing data on race/ethnicity, age, or sex at birth, or any duplicates in the data. Investigators reported comparisons of infection and mortality rates by race, age, and sex by using 2010 US Census data to represent Michigan’s population.

    Summary of main findings

    After standardizing by age and sex, Black [IRR: 5.6 (95% CI: 5.5, 5.7)], Latinx [IRR: 3.8 (95% CI: 3.7, 3.9)], Asian/Pacific Islander [IRR: 2.4 (95% CI: 2.2,2.5)], and other [IRR: 5.2 (95% CI: 4.9, 5.5)], race was associated with a higher SARS-CoV-2 incidence rate compared to white race. Overall within the population, Black, Latinx, Asian/Pacific Islander, and other race was also associated with higher mortality rates compared to whites. There were no statistically significant differences in the case fatality rate (I.e. the mortality rate only among cases) between racial groups, thus this suggests that higher overall mortality rates among racial minorities is due to increased risk of infection.

    Study strengths

    This study had a large sample size as it included all confirmed and probable cases that were reported to MDHHS. Reconciling these data with the 2010 US Census allowed for investigators to calculate incidence and mortality rates that could be representative of the Michigan population.

    Limitations

    The investigators did not include other variables (e.g. indicators of socioeconomic status) that may further help explain racial disparities in infection and mortality rates. Also, due to the fact that investigators used the 2010 US Census, the base population of Michigan in 2020 may be different from that of the 2010 U.S. Census, thus these figures may not be accurate if there were significant demographic changes over this 10-year period.

    Value added

    This study highlights the stark racial disparities in infection and mortality rates in Michigan, and suggests that excessive deaths among racial minorities is due to greater exposure to the virus rather than being more likely to die once one has the virus.

  3. Version 1 published on medRxiv