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The Associations Between Racially/Ethnically Stratified COVID-19 Tweets and COVID-19 Cases and Deaths: Cross-sectional Study.
Liu, Xiaohui; Kar, Bandana; Montiel Ishino, Francisco Alejandro; Onega, Tracy; Williams, Faustine.
  • Liu X; National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, United States.
  • Kar B; Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States.
  • Montiel Ishino FA; National Security Sciences Directorate, Oak Ridge National Lab, Knoxville, TN, United States.
  • Onega T; National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, United States.
  • Williams F; Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States.
JMIR Form Res ; 6(5): e30371, 2022 05 30.
Article in English | MEDLINE | ID: covidwho-1875269
ABSTRACT

BACKGROUND:

The COVID-19 pandemic exacerbated existing racial/ethnic health disparities in the United States. Monitoring nationwide Twitter conversations about COVID-19 and race/ethnicity could shed light on the impact of the pandemic on racial/ethnic minorities and help address health disparities.

OBJECTIVE:

This paper aims to examine the association between COVID-19 tweet volume and COVID-19 cases and deaths, stratified by race/ethnicity, in the early onset of the pandemic.

METHODS:

This cross-sectional study used geotagged COVID-19 tweets from within the United States posted in April 2020 on Twitter to examine the association between tweet volume, COVID-19 surveillance data (total cases and deaths in April), and population size. The studied time frame was limited to April 2020 because April was the earliest month when COVID-19 surveillance data on racial/ethnic groups were collected. Racially/ethnically stratified tweets were extracted using racial/ethnic group-related keywords (Asian, Black, Latino, and White) from COVID-19 tweets. Racially/ethnically stratified tweets, COVID-19 cases, and COVID-19 deaths were mapped to reveal their spatial distribution patterns. An ordinary least squares (OLS) regression model was applied to each stratified dataset.

RESULTS:

The racially/ethnically stratified tweet volume was associated with surveillance data. Specifically, an increase of 1 Asian tweet was correlated with 288 Asian cases (P<.001) and 93.4 Asian deaths (P<.001); an increase of 1 Black tweet was linked to 47.6 Black deaths (P<.001); an increase of 1 Latino tweet was linked to 719 Latino deaths (P<.001); and an increase of 1 White tweet was linked to 60.2 White deaths (P<.001).

CONCLUSIONS:

Using racially/ethnically stratified Twitter data as a surveillance indicator could inform epidemiologic trends to help estimate future surges of COVID-19 cases and potential future outbreaks of a pandemic among racial/ethnic groups.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study / Randomized controlled trials Language: English Journal: JMIR Form Res Year: 2022 Document Type: Article Affiliation country: 30371

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study / Randomized controlled trials Language: English Journal: JMIR Form Res Year: 2022 Document Type: Article Affiliation country: 30371