Increased Self-Reported Discrimination and Concern for Physical Assault Due to the COVID-19 Pandemic in Chinese, Vietnamese, Korean, Japanese, and Filipino Americans

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Abstract

Objectives

To investigate self-reported discrimination and concern for physical assault due to the COVID-19 pandemic among disaggregated Asian subgroups in the US.

Methods

We conducted a nationwide survey to assess self-reported discrimination and concern for physical assault due to COVID-19 across racial/ethnic groups, including diverse subgroups of Asians.

Results

Chinese respondents experienced the largest change (15% increase) in proportion of respondents reporting discrimination from 2019 to 2020 (P<.01). Chinese, Korean, Japanese, Vietnamese, and Other API showed up to 3.9 times increased odds of self-reported racial/ethnic discrimination due to COVID-19 and, with the addition of Filipino, experienced up to 5.4 times increased odds of concern for physical assault due to COVID-19 compared to Whites.

Conclusions

Our study is the first to examine self-reported discrimination and concern for physical assault due to COVID-19 in subgroups of Asian Americans, finding that East (Chinese, Korean, Japanese) and Southeast (Vietnamese, Filipino) Asian Americans have been disproportionately affected. Future studies should disaggregate Asian subgroups to fully understand experiences of discrimination in diverse populations in the US.

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  1. SciScore for 10.1101/2020.09.15.20194720: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: IRB: The study was considered exempt by Stanford University Institutional Review Board (Protocol: 56235) Funding: The study was funded by the Stanford Center for Asian Health Research and Education.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Analyses were performed using RStudio (Version 1.2.5001, Boston, MA).
    RStudio
    suggested: (RStudio, RRID:SCR_000432)

    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:
    While the current study presented robust evidence for the experiences of Asian Americans related to COVID-19, several limitations should be considered in the interpretation of these findings. Firstly, our use of a novel measurement of self-reported racial/ethnic discrimination due to COVID-19 has not been validated. The survey, which was advertised and administered via online platforms including email listservs, social media, and Pollfish, limited our sample to technologically literate populations, leading to a lack of older respondents (Table 1) and relatively few respondents with limited English proficiency, despite the fact that alternative language surveys were offered. Additionally, given the cross-sectional, retrospective, and survey nature of our current study, selection, recall, and social desirability biases may have all played a role in overrepresenting or underrepresenting participants experiencing discrimination. There is no evidence to suggest that these biases are differential by race/ethnicity, though these racial/ethnic comparisons should be viewed with these caveats in mind. While this is the largest sample of COVID-19 discrimination survey data in Asian Americans published to date, each Asian subgroup displayed a relatively small sample size. Future studies should strive to disaggregate Asian subgroups and achieve higher sample sizes to further explore trends that were observed in our study. There are also numerous factors not controlled for in this study th...

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).


    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.