ABSTRACT
Ethnic and Racial minorities have been disproportionally affected by COVID-19 which can be seen from a significantly higher hospitalization rate, ICU-admission rate, and mortality rate [1]. This disparity has posed a great burden to certain racial groups, urging more studies to determine their vulnerability and the corresponding risk factors. This research used data from the Centers for Disease Control and Prevention, and chose patients from the US as a racially diverse sample. Logistic regression and categorical regression were conducted to identify the association between their race and clinical outcomes (ICU-admission and Death). A randomized sample with a total of 80204 patients proportionally from “Asian, Non-Hispanic”, “Black, Non-Hispanic”, “Hispanic/Latino”, and “White, Non-Hispanic” are included. This study shows that, given the same conditions of gender, hospitalization and medical condition, Asian and Black racial minorities have a higher mortality rate (“Asian, Non-Hispanic”: 0, “Black, Non-Hispanic”: 95%, -0.370 - -0.209), and are more likely to be ICU-admitted (“Asian, Non-Hispanic”: 0, “Black, Non-Hispanic”: 95%, -0.364 - -0.0.176) compared to White, Non-Hispanic individuals (Death: 95%, -0.725 - -0.495; ICU-admission: 95%, -0.788 - -0.0.580,). This result shows its consistency with the existing studies, and it also demonstrates the importance of rebuilding the medical system and more realistically reallocating resources as an agenda during and after the COVID-19 pandemic.
- Johns Hopkins University of Medicie Coronavirus Resource Center. COVID-19 dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). https://coronavirus.jhu.edu/map.html (accessed Mar 15th, 2021).Google Scholar
- Sze S, Pan D, Nevill CR, Ethnicity and clinical outcomes in COVID-19: A systematic review and meta-analysis. The Lancet, Vol 29, 2020.Google ScholarCross Ref
- Singh U, Wurtele ES. Differential expression of COVID-19-related genes in Europe Americans and African Americans. biorxiv 2020.Google Scholar
- Nguyen, Long H Racial and ethnicity differences in COVID-19 vaccine hesitancy and uptake. medRxiv: the preprint server for health sciences 2021.Google Scholar
- Surendra H, Elyazar IRF, Djaafara BA, Clinical characteristics and mortality associated with COVID-19 in Jakarta, Indonesia: A hospital-based retrospective cohort study. The Lancet Regional Health - Western Pacific vol 9, 2021.Google Scholar
- U.S. Department of Health and Human Services. Centers for Disease Control and Prevention. National Diabetes Statistics report 2020. Estimates of Diabetes and Its Burden in the United State. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdfGoogle Scholar
- Kibria GMA. Racial/ethnic disparities in prevalence, treatment, and control of hypertension among US adults following application of the 2017 American College of Cardiology/American Heart Association guideline. Preventive Medicine Reports vol. 14, 2019.Google Scholar
- Fang FC, Schooley RT. Treatment of COVID-19-Evidence-Based or Personalized Medicine. Clinical Infectious Diseases, 2020.Google Scholar
- Martin CA, Jenkins DR, Socio-demographic heterogeneity in the prevalence of COVID-19 during lockdown is associated with ethnicity and household size: Results from an observational cohort study. The Lancet, Vol 25, 2020.Google ScholarCross Ref
- James MK, Kishore M, Lee SW. Demographic and Socioeconomic Characteristics of COVID-19 Patients Treated in the Emergency Department of a New York Hospital. Journal of Community Health, 2020: 1-8.Google Scholar
Recommendations
Information networks for COVID-19 according to race/ethnicity
AbstractThis study highlights information networks for COVID-19 according to race/ethnicity by employing social network analysis for Twitter. First, this study finds that racial/ethnic groups are differently dependent on racial/ethnic key players. Whites ...
Social Perceptions in Computer Science and Implications for Diverse Students
ICER '17: Proceedings of the 2017 ACM Conference on International Computing Education ResearchThe barriers to diversity in computer science (CS) are complex, consisting of both structural and social barriers. In this paper, we focus on social perceptions for students in grades 7-12 in the U.S. using surveys of nationally representative samples ...
Diversity Barriers in K-12 Computer Science Education: Structural and Social
SIGCSE '17: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science EducationAs computer science (CS) education expands at the K-12 level, we must be careful to ensure that CS neither exacerbates existing equity gaps in education nor hinders efforts to diversify the field of CS. In this paper, we discuss structural and social ...
Comments