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A locational analytics approach to COVID-19 discrimination and inequality against minorities across the United States
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 discrimi...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Elsevier Ltd.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9760597/ https://www.ncbi.nlm.nih.gov/pubmed/36586212 http://dx.doi.org/10.1016/j.socscimed.2022.115618 |
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author | Meng, Qingmin |
author_facet | Meng, Qingmin |
author_sort | Meng, Qingmin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9760597 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97605972022-12-19 A locational analytics approach to COVID-19 discrimination and inequality against minorities across the United States Meng, Qingmin Soc Sci Med Article 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. Elsevier Ltd. 2023-02 2022-12-19 /pmc/articles/PMC9760597/ /pubmed/36586212 http://dx.doi.org/10.1016/j.socscimed.2022.115618 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Meng, Qingmin A locational analytics approach to COVID-19 discrimination and inequality against minorities across the United States |
title | A locational analytics approach to COVID-19 discrimination and inequality against minorities across the United States |
title_full | A locational analytics approach to COVID-19 discrimination and inequality against minorities across the United States |
title_fullStr | A locational analytics approach to COVID-19 discrimination and inequality against minorities across the United States |
title_full_unstemmed | A locational analytics approach to COVID-19 discrimination and inequality against minorities across the United States |
title_short | A locational analytics approach to COVID-19 discrimination and inequality against minorities across the United States |
title_sort | locational analytics approach to covid-19 discrimination and inequality against minorities across the united states |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9760597/ https://www.ncbi.nlm.nih.gov/pubmed/36586212 http://dx.doi.org/10.1016/j.socscimed.2022.115618 |
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