Counting Everyone, Counting Correctly: Case Studies in Data Disaggregation, Inclusive Data Visualization, and Reducing Misclassification of American Indian and Alaska Native Populations
Overview
November 20, 2025 | 12:00 p.m. – 1:00 p.m. CT
Public health decisions rely on accurate data, yet broad racial and ethnic categories often conceal meaningful differences within communities and perpetuate inequities. This webinar introduces the foundational principles of data disaggregation and highlights how inclusive practices can improve the visibility of health disparities and prevent the statistical erasure of small or historically marginalized groups, particularly American Indian and Alaska Native (AI/AN) populations.
The presenters will share strategies for visualizing and tabulating more inclusive data that better represents the full identities of individuals and examine how racial misclassification in Medicaid data contributes to underreporting of AI/AN populations in Oklahoma. Together, these discussions emphasize the importance of data disaggregation to ensure accurate and equitable data to inform policy and strengthen health outcomes.
By attending this webinar, you will:
- Obtain an understanding of the foundations of data disaggregation and how it can promote health equity.
- Learn about the challenges associated with aggregated race and ethnicity data and its impacts.
- Obtain strategies for more inclusive data collection, tabulation, and visualization.
- Learn about the impact of racial misclassification on marginalized populations and American Indian and Alaska Native data accuracy and reporting.
Presenters:
- Brian Hui, Ph.D., Center for Health Equity, LA County Department of Public Health
- Jamie Piatt, M.P.H, Epidemiologist, Southern Plains Tribal Health Board
- Shannan Piccolo, J.D., Senior Attorney, Mid-States Region, Network for Public Health Law
Moderator:
Meghan Mead, J.D., Deputy Director, Mid-States Region, Network for Public Health Law