The Mutually Reinforcing Cycle Of Poor Data Quality And Racialized Stereotypes That Shapes Asian American Health

Abstract

The Asian American health narrative reflects a long history of structural racism in the US and the complex interplay of racialized history, immigrant patterns, and policies regarding Asians in the US. Yet owing to systematic issues in data collection including missing or misclassified data for Asian Americans and practices that lead to indiscriminate grouping of unlike individuals (for example, Chinese, Vietnamese, and Bangladeshi) together in data systems and pervasive stereotypes of Asian Americans, the drivers and experiences of health disparities experienced by these diverse groups remain unclear. The perpetual exclusion and misrepresentation of Asian American experiences in health research is exacerbated by three racialized stereotypes-the model minority, healthy immigrant effect, and perpetual foreigner-that fuel scientific and societal perceptions that Asian Americans do not experience health disparities. This codifies racist biases against the Asian American population in a mutually reinforcing cycle. In this article we describe the poor-quality data infrastructure and biases on the part of researchers and public health professionals, and we highlight examples from the health disparities literature. We provide recommendations on how to implement systems-level change and educational reform to infuse racial equity in future policy and practice for Asian American communities.