IDEAL: A Community-Academic-Governmental Collaboration Toward Improving Evidence-Based Data Collection on Race and Ethnicity

• Published in: Preventing chronic disease Vol. 20; p. E90
• Main Authors: Kader, Farah, Ðoàn, Lan N, Chin, Matthew K, Scherer, Maya, Cárdenas, Luisa, Feng, Lloyd, Leung, Vanessa, Gundanna, Anita, Lee, Matthew, Russo, Rienna, Ogedegbe, Olugbenga G, John, Iyanrick, Cho, Ilseung, Kwon, Simona C, Yi, Stella S
• Format: Journal Article
• Language: English
• Published: United States Centers for Disease Control and Prevention 12-10-2023
• Series: Peer Reviewed
• Subjects: Asian Americans; Collaboration; Community; Data Collection; Datasets; Ethnicity; Evidence-based practice; Focus groups; Health disparities; Humans; Inclusion; Interdisciplinary aspects; Minority & ethnic groups; Polls & surveys; Questionnaires; Race; Racial Groups; Statistical methods; New York; United States > US
• ISSN: 1545-1151; 1545-1151
• DOI: 10.5888/pcd20.230029

Furthermore, the aggregation of diverse groups into a single racial category obscures within-group disparities and homogenizes people on the basis of unclear, inconsistent definitions of race and ethnicity (9,10). [...]the federal standard for reporting race and ethnicity in the US perpetuates racism through systemic erasure and invisibility of racial and ethnic minority communities from data, resulting in nonrandom exclusion and suppression of minoritized racial and ethnic groups, which then affects resource allocation and policies. IDEAL Overview and Objectives A cross-sector, interdisciplinary collaborative, IDEAL aims to 1) strengthen community–academic–government collaborations in discussions of best practices and implementation processes, 2) document and evaluate the process of implementing modified race and ethnicity questions across multiple sectors, 3) update race and ethnicity questionnaires that rely on OMB minimum categories to more accurately capture more granular data at state and city levels, 4) apply innovative statistical methods to improve classification and reclassification of race and ethnicity in existing data, and 5) provide detailed technical assistance on implementation of race and ethnicity data collection, analysis, and reporting procedures. [...]IDEAL has worked closely with leaders of NYU Langone Health (NYULH) to pilot collection of updated race and ethnicity questions and new data disaggregation practices within the health system. A summary report provided IDEAL with information on model policies, data governance systems, and data collection methods to better understand current efforts to disaggregate race and ethnicity data and other forms of data reformation.