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Tytuł pozycji:

A locational analytics approach to COVID-19 discrimination and inequality against minorities across the United States.

Tytuł:
A locational analytics approach to COVID-19 discrimination and inequality against minorities across the United States.
Autorzy:
Meng Q; Department of Geosciences, Mississippi State University, Mississippi State, MS 39762, USA. Electronic address: .
Źródło:
Social science & medicine (1982) [Soc Sci Med] 2023 Feb; Vol. 318, pp. 115618. Date of Electronic Publication: 2022 Dec 19.
Typ publikacji:
Journal Article
Język:
English
Imprint Name(s):
Original Publication: Oxford ; New York : Pergamon, c1982-
MeSH Terms:
COVID-19*/epidemiology
Health Status Disparities*
Minority Groups*
Humans ; Asian ; Hispanic or Latino ; United States/epidemiology ; Black or African American ; American Indian or Alaska Native ; Native Hawaiian or Other Pacific Islander
Contributed Indexing:
Keywords: COVID-19; Locational discrimination; Locational inequality; Minority vulnerability; Statistical significance; The United States
Entry Date(s):
Date Created: 20221231 Date Completed: 20230124 Latest Revision: 20230324
Update Code:
20240105
PubMed Central ID:
PMC9760597
DOI:
10.1016/j.socscimed.2022.115618
PMID:
36586212
Czasopismo naukowe
A major challenge in managing disasters during a pandemic is assessing the inequalities in society and protecting vulnerable people. The objective of this paper is to geographically understand the discrimination and inequality against minorities by COVID-19. This study designed a locational discrimination index (LDI) to measure COVID-19 discrimination against minorities at county-level in the US. LDI is the difference between the death proportion of a minority and the proportion of a minority's population. If LDI >0 is significant, COVID-19 discrimination is identified against a minority in a county. I further developed a locational minority inequality index (LMII), and LMII of a minority is directly quantified by comparing its LDI with the LDI of the majority population (i.e., the White population in the US). If LMII>0 is significant, a significant health inequality is confirmed against a minority in a county. In the US, I found 157 counties with significant discrimination against Black people, and 103 counties with significant inequality against Black people; 58 counties with significant discrimination against the American Indian population, but 38 counties with significant inequality against the American Indian population; 17 counties with significant discrimination against Native Hawaiians, but only 8 counties with significant inequality; for Hispanic people, 47 counties had significant discrimination, and 64 counties had significant inequality; for Asians, 7 counties had significant discrimination, but 28 had significant inequality. LDI, LMII, and the thematic mapping provide novel insight into COVID-19 discrimination and inequalities. To the best of our knowledge, this is the first time anyone has quantitatively and statistically defined and mapped COVID-19 discrimination and inequality against minorities at a county-level across the US. Based on this, governments and communities could make efficient decisions and take effective action to addressthe significant discrimination and inequality against Black, American Indian, Native Hawaiian, Hispanic, and Asian people, which can be applied to other pandemics or public health disasters in the USA or other countries.
(Copyright © 2022 Elsevier Ltd. All rights reserved.)

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