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Title: Collection, Analysis, and Reporting of Asian American Health Data

Citation Type: Miscellaneous

Publication Year: 2023

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Abstract: he Center for the Study of Asian American Health (CSAAH) within the NYU Grossman School of Medicine at NYU Langone Health and the Coalition of Asian American Children and Families (CACF) have created a manual to promote best practices for collecting, analyzing, and reporting Asian American health data. We recommend anyone who is working with quantitative data that pertains to Asian Americans to review this guide in order to promote an accurate and nuanced characterization of Asian American health.Data Collection The current national standards for collecting racial/ethnic data were issued by the Office of Man-agement and Budget’s 1997 Directive 15 though leaders in civil rights and academia have voiced concerns over their definition and application. CSAAH, CACF and the New York Academy of Med-icine (NYAM) developed a race/ethnicity form for collecting disaggregated race/ethnicity infor-mation in the New York metropolitan area which was informed by three main sources: 1) national policy recommendations by the State Data Collaborative;1 2) American Community Survey 2019 data; and 3) feedback from community focus groups and trusted subject matter experts. Our Revised Race/Ethnicity Form asks participants to identify as one or more of seven aggregate race/ethnicity groups – American Indian/Alaska Native (AI/AN), Asian, Black or African American, Hispanic/Latino, Middle Eastern or North African (MENA), Native Hawaiian or Pacific Islander (NH/PI), or White – and then asks participants to identify as one or more of 70+ detailed race/ethnicity categories. The form can be adapted in format and to include different detailed categories based on project or research focus and needs. When conducting primary data collection among Asian American populations, there are several factors to consider regarding the study population of focus and survey design. These factors include but are not limited to the study population: age, income, immigration history, education level, and English proficiency. Data from the U.S. Census Bureau like the Decennial Census and American Community Survey can be used for information on the demographic makeup of the study population in a specific geographic region or neighborhoods where the population resides for targeted recruitment efforts. Data Analysis When constructing racial/ethnic group variables for analysis we recommend creating: 1) A mutually exclusive aggregate race/ethnicity variable where individuals who identify as more than one aggregate racial/ethnic identities are labeled ‘Multiracial’ (AI/AN, Asian, Black, Hispanic/Latino, MENA, NH/PI, White, Multiracial, Other); 2) A mutually exclusive detailed race/ethnicity variable where individuals who identify as more than one detailed racial/ethnic identifies are labeled ‘Mixed’ (e.g., Chinese, Asian Indian, Filipino, Japanese, Asian Mixed, Detailed Other, etc.); 3) If appropriate, for Asian populations, a mutually exclusive Asian regional race/ethnicity variable (e.g., East Asian, South Asian, Southeast Asian, Central Asian); 4) If appropriate, a non-mutually exclusive race/ethnicity variable for each unique aggregate and detailed race/ethnicity category; and 5) If appropriate, further granular variables for Multiracial/Mixed individuals. When conducting secondary data analysis, it is important to consider how Asian American race/ethnicity data has been collected in the original dataset. Race/ethnicity data may be missing, unknown or other. Detailed race/ethnicity data may be limited or nonexistent. Additionally, many datasets are not always designed to be inclusive of non-English speakers, and sampling bias may result from higher rates of English proficient Asian Americans who tend to have more favorable socioeconomic indicators.2 Data quality and therefore representation should be qualified in Methods, Discussion and Limitations sections. Sensitivity analyses should be conducted for more precise evaluation of the heterogeneity of the Asian American sample. Careful consideration of who was included or not included in the data being analyzed will help researchers evaluate biases in data collection and recruitment and the generalizability of the analyses to Asian American populations. Partial missingness of data using complete case analysis, imputation and advanced imputation and total missingness using standardization, simulation, and sensitivity analyses should be performed. In the case studies, we examine the impact of undercounting in minority populations. The U.S. Census has a history of undercounting immigrant and minority populations, which can impact the accuracy of public health statistics. The 2020 U.S. Census may have had a greater undercount due to a potential citizenship question and the COVID-19 pandemic.3,4 Correcting for the undercount requires specific birth, death, and migration data, making it challenging for most researchers. In the case of New York City (NYC) COVID-19 vaccination statistics, the implications of an undercount for Asian American and NH/PI populations were examined. Collection, Analysis, and Reporting of Asian American Health Data5Data Reporting When reporting Asian American data, descriptions should include all eligibility and exclusion criteria, including explicit details about the disaggregated Asian racial/ethnic group(s) sampled, the dates of the study period, the type of study design, and the number of individuals surveyed or enrolled. Having this information can help researchers better determine next steps for research and resource allocation by contextualizing the population sample.The demographic details of the study population play a crucial role in preventing harmful stereotypes and generalizing data. These details should include racial/ethnic information, income, education, English language proficiency, language spoken at home, and nativity. It is important to be transparent about any groupings, such as aggregating into an “Other” category or combining smaller Asian race/ethnicity categories into regional groupings. When describing multiracial or Asian mixed populations, it is useful to include details of the combinations of race/ethnicities. Reporting race and ethnicity to meet OMB guidelines is required for federal agencies as well as other state and local organizations that model their reporting guidelines on the OMB’s 1997 directive. The Agency for Healthcare Research and Quality (AHRQ) provides a guideline for roll-up of detailed race/ethnicity categories while the Institute of Medicine (IOM) provides further granular guidance. The process of rolling up detailed race/ethnicity categories into aggregate categories has gray areas and can lead to mismatches between aggregate categories and self-identification of race/ethnicity. This can result in individuals who identify as one aggregate category but are categorized differently based on their detailed race/ethnicity. It is recommended that participants’ reported aggregate race/ethnicity should always reflect their selected aggregate category and not be changed. In cases where only detailed race/ethnicity information is provided, researchers should detail their assignment rules for ambiguous categories. The representation of Asian American experiences in data can be distorted by three racialized stereotypes - the model minority, healthy immigrant effect, and perpetual foreigner. These stereotypes can be a result of implicit biases in researchers – embedded in their own lived experiences – and should be considered when reporting Asian American data.5 Recommendations for considerations when discussing results from Asian American data: 1) Discuss who is represented in the data to provide context to research findings and avoid creating generalizations about the Asian American population. 2) Be cautious when discussing Asian multiracial and mixed data as these categories are diverse. 3) Hypothesize the potential impact of missing data in the analysis. 4) Consider complementing statistics from large databases with community-based research to uncover health problems that may be hidden. 5) Seek and include community feedback to enhance trust and cultural resonance. Collection, Analysis, and Reporting of Asian American Health Data66) If Asian data is not available, discuss the implications and provide recommendations for additional research that includes Asian Americans. This manual provides recommendations for public health practitioners, allied health professionals, health equity researchers, and data managers to employ in their own research with immigrant and minority groups in the U.S. Understanding and addressing the nuances of data collection, analysis, and reporting is the cornerstone to accurate representation of groups in data. Suggested Citation: Chin MK, Yusuf Y, Wyatt LC, Ðoàn LN, Russo RG, Kader F, Feng L, Fu L, Kwon, SC, and Yi SS. Collection, Analysis, and Reporting of Asian American Health Data. E-published by Center for the Study of Asian American Health at NYU Langone; 2023. Available from: https://aanhpihealth.org/resource/asian-american-manual-202

Url: https://aanhpihealth.org/wp-content/uploads/2023/06/Data_Disaggregation_Manual_052323.pdf

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Authors: Chin, Matthew K; Yusuf, Yousra; Wyatt, Laura C; Đoàn, Lan N; Russo, Rienna G; Kader, Farah; Feng, Lloyd; Fu, Lauren; Kwon, Simona C; Yi, Stella S

Publisher: NYU Center for the Study of Asian American Health

Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Race and Ethnicity

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