Social and racial injustice and inequity plagued America long before the deep roots of systemic racism were underscored during the COVID-19 pandemic and the deployment of a vaccine to prevent it.
Systemic racism is not something that a few people or institutions choose to practice. Rather, it has been a component of the social, economic, and political systems in which we all exist, and it is a part of the data that inform decisions, policies, and funding within those systems. Choices about the collection and categorization of data affirm identities of some groups more than others, and this perpetuates unfair advantages and oppression. Yet, there continues to be pervasive lack of investment in producing adequately disaggregated data.
This isn’t just missed opportunity. It is systemic racism.
It is clear that an investment in more meaningful data collection is critical for the livelihood of our diverse communities, particularly those who have been oppressed in nearly all facets of society. Data disaggregation allows data to be divided into detailed sub-categories, shifting information from broad categories to reflect people’s actual experience. Its goal is to ensure that populations that have been historically excluded are visible, allowing for health and social services approaches that address specific needs and create solutions to eliminate health disparities.
The research community has long documented the importance of data disaggregation and continues to strive to make necessary changes that will lead us toward health equity. A first-of-its-kind National Commission to Transform Public Health Data Systems established by the Robert Wood Johnson Foundation (RWJF), for example, explored how current health data perpetuate structural racism in their lack of detail by race and ethnicity and their failure to make existing disaggregated data available. It released recommendations in Oc
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