Primary Submission Category: Methodological approaches to studying public health
Ensuring racial equity in data collection, analysis and reporting for underrepresented communities
Authors: Lan Ðoàn Matthew Chin Yousra Yusuf Farah Kader Laura Wyatt Rienna Russo Lloyd Feng Vanessa Leung Anita Gundanna Simona Kwon Stella Yi
Presenting Author: Lan Ðoàn*
Federal standards on racial/ethnic data were last updated in 1997. Proposals to update race/ethnicity questions for the 2030 Census were released in January 2022, to better reflect US demographic shifts. The quality of race/ethnicity data has profound impacts on how health inequities are monitored and how resources and money are and will be distributed to communities.
We will discuss lessons learned from the Innovations in Data Equity for All Laboratory (IDEAL) initiative, a collaboration led by the Center for the Study of Asian American Health at NYU Grossman School of Medicine, with the Coalition for Asian American Children and Families, New York Academy of Medicine, and New York State Department of Health.
We will articulate our comprehensive process rooted in the peer-reviewed literature, present multisector perspectives and lessons learned from efforts to transform race/ethnicity data collection in health systems, and share best practices for advancing data equity for underrepresented populations. We present Asian Americans as a case study and include: 1) data-driven approaches to propose updated race/ethnicity questions for use in electronic health systems and state agencies; 2) scan of state-level data disaggregation efforts to identify facilitators and barriers for implementation in other states and settings; and 3) focus groups with community members to understand perceptions and self-report of racial/ethnic identity in health settings. Collectively, these efforts informed the development of: 1) a manual for the data collection, analysis and reporting of Asian American health data, and 2) tailored toolkits on data disaggregation for community members, health providers and allied health professionals.
These resources can serve as a blueprint for other cross-sector collaboratives seeking to foster inclusion and representation, and to reimagine health data systems that intentionally center communities and advance data equity for underrepresented populations.