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结构性种族主义、健康不平等以及数据的双刃剑:结构性问题需要结构性解决方案。

Structural Racism, Health Inequities, and the Two-Edged Sword of Data: Structural Problems Require Structural Solutions.

机构信息

Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, MA, United States.

出版信息

Front Public Health. 2021 Apr 15;9:655447. doi: 10.3389/fpubh.2021.655447. eCollection 2021.

Abstract

Analyzing the myriad ways in which structural racism systemically generates health inequities requires engaging with the profound challenges of conceptualizing, operationalizing, and analyzing the very data deployed-i. e., racialized categories-to document racialized health inequities. This essay, written in the aftermath of the January 6, 2021 vigilante anti-democratic white supremacist assault on the US Capitol, calls attention to the two-edged sword of data at play, reflecting long histories of support for and opposition to white supremacy and scientific racism. As illustrated by both past and present examples, including COVID-19, at issue are both the non-use () and problematic use () of data on racialized groups. Recognizing that structural problems require structural solutions, in this essay I propose a new two-part institutional mandate regarding the reporting and analysis of publicly-funded work involving racialized groups and health data and documentation as to why the proposed mandates are feasible. is to implement enforceable requirements that all US health data sets and research projects supported by government funds . is that any individual-level health data by membership in racialized groups . A new opportunity arises as US government agencies re-engage with their work, out of the shadow of white grievance politics cast by the Trump Administration, to move forward with this structural proposal to aid the work for health equity.

摘要

分析结构性种族主义如何系统性地产生健康不平等,需要应对概念化、操作化和分析用于记录种族化健康不平等的非常数据(即种族分类)的深刻挑战。本文是在 2021 年 1 月 6 日,美国国会大厦遭到支持民主的白人至上主义暴徒袭击之后撰写的,它提请注意数据的双刃剑,反映了长期以来对白人至上主义和科学种族主义的支持和反对。正如过去和现在的例子所表明的,包括 COVID-19,问题既在于对种族群体数据的不使用(),也在于有问题地使用()。认识到结构性问题需要结构性解决方案,在本文中,我提出了一个新的两部分机构任务,涉及报告和分析涉及种族群体和健康数据的公共资助工作,并说明提出的任务为何可行。第一个任务是实施可执行的要求,即所有由政府资金支持的美国卫生数据集和研究项目都应报告和分析。第二个任务是根据成员种族分类对任何个体层面的健康数据进行分类。随着美国政府机构摆脱特朗普政府的白人不满政治阴影,重新投入工作,这为推进这一结构性建议以促进健康公平提供了一个新的机会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9799/8082016/7274f6164365/fpubh-09-655447-g0001.jpg

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