Age-Based Disparities in Hospitalizations and Mortality for Coronavirus Disease 2019 (COVID-19)

  1. Lauren E. Wisk
  2. Santi K.M. Bhagat
  3. Niraj Sharma

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Abstract

Purpose

Evidence suggests that older adults, racial/ethnic minorities, and those with comorbidities all face elevated risk for morbidity and mortality from COVID-19; but there are limited reports describing the potential for interactions between these factors.

Methods

We sought to evaluate age-based heterogeneity in observed disparities in hospitalization, ICU admission, and mortality related to COVID-19 using CDC public use surveillance data on 3,662,325 COVID-19 cases reported from January 1 to August 30, 2020.

Results

Racial/ethnic and comorbidity disparities in hospitalization were most pronounced during ages 20-29 and ages 10-19, with similar elevation seen for disparities in ICU risk.

Racial/ethnic disparities in mortality were most pronounced during ages 20-29 while risk from comorbidity peaks among ages 10-39.

Conclusions

As COVID-19 continues to affect younger populations, special attention to the implications for the most vulnerable subgroups are clearly warranted.

Implications and Contribution

Adolescents and young adults appear to have experienced the greatest inequities in COVID-19 outcomes by race/ethnicity and comorbidity. Careful monitoring of trends in this population is warranted as they re-enter school, work, and social settings while being the last group to receive priority for vaccination.

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  3. ScreenIT

    SciScore for 10.1101/2021.06.14.21258919: (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: We detected the following sentences addressing limitations in the study:
    However, despite considerable limitations with CDC surveillance data, this dataset currently represents the best national accounting of COVID-19 cases in the US. Inequalities are likely explained by residual confounding from root causes not captured by public-use surveillance data, such as socioeconomic disparities and the experience of systemic racism, and are thus proxied by the use of racial/ethnic groups (e.g., Black-white disparities are due to conscious and unconscious biases and discrimination that predispose Black individuals to worse outcomes and prohibit equitable treatment). The use of ten-year age brackets could mask patterns for subgroups, such as college students. Analyses only involve reported case data and cannot account for potential disparities in testing that could introduce selection biases. Finally, cases are temporally and geo-spatially evolving, thus findings may change over time and location.

    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.

  4. Version published to 10.1101/2021.06.14.21258919v1 on medRxiv