Precision Public Health and Structural Racism in the United States: Promoting Health Equity in the COVID-19 Pandemic Response

The COVID-19 pandemic has revealed deeply entrenched structural inequalities that resulted in an excess of mortality and morbidity in certain racial and ethnic groups in the United States. Therefore, this paper examines from the US perspective how structural racism and defective data collection on racial and ethnic minorities can negatively influence the development of precision public health (PPH) approaches to tackle the ongoing COVID-19 pandemic. Importantly, the effects of structural and data racism on the development of fair and inclusive data-driven components of PPH interventions are discussed, such as with the use of machine learning algorithms to predict public health risks. The objective of this viewpoint is thus to inform public health policymaking with regard to the development of ethically sound PPH interventions against COVID-19. Particular attention is given to components of structural racism (eg, hospital segregation, implicit and organizational bias, digital divide, and sociopolitical influences) that are likely to hinder such approaches from achieving their social justice and health equity goals.

Automated summary

This paper examines how structural racism and defective data collection on racial and ethnic minorities can negatively impact the development of precision public health approaches in the United States to address the ongoing COVID-19 pandemic. It discusses the effects of structural and data racism on the development of fair and inclusive data-driven interventions, such as using machine learning algorithms to predict public health risks.