Manual on Collection, Analysis, and Reporting Of Asian American Health Data (2023)
Manual on Collection, Analysis, and Reporting Of Asian American Health Data
In 2023, the Center for the Study of Asian American Health (CSAAH) at NYU Langone Health and the Coalition of Asian American Children and Families (CACF) collaboratively developed a new resource to support individuals and teams working with quantitative health data pertaining to Asian Americans. The Manual on Collection, Analysis, and Reporting Of Asian American Health Data provides a series of best practices for collecting, analyzing, and reporting Asian American health data. This PDF resource is divided into three sections presenting guidance for: 1) collecting, 2) analyzing, and 3) reporting Asian American data.
The recommendations outlined in the manual allow researchers and practitioners to contribute to a more accurate and comprehensive understanding of Asian American health experiences and may help to foster equitable healthcare practices and interventions for this diverse population.
The manual offers a Revised Race/Ethnicity Form, presenting our suggested revisions to a commonly asked question used to gather race/ethnicity information. National policy recommendations, Census Data, and community focus group feedback informed the development of the revised framing and formatting of the question. While the form question is specifically tailored for use with the New York metropolitan population, we present detailed description of the question’s revision and development process, to support adaptation and use of the question with other target geographies in the U.S.
To access the manual, CLICK HERE. Our manual resource is a product of NYU Langone’s Innovations in Data Equity For All Laboratory (IDEAL), which aims to address health equity through racial/ethnic data infrastructure.
The manual is complemented by a series of Race/Ethnicity Data Disaggregation Toolkits, a series of downloadable resources that you can easily tailor to your needs to help to communicate the importance of, and best practices for disaggregating race/ethnicity data, in different work or community settings. View the toolkits here.