Tag: Asian American and Pacific Islander Heritage Month

In an effort to remain accountable to communities who have been negatively impacted by past and present medical injustices, the staff at Himmelfarb Library is committed to the work of maintaining an anti-discriminatory practice. We will uplift and highlight diverse stories throughout the year and not shy away from difficult conversations necessary for health sciences education. To help fulfill this mission, today’s blog post will highlight data disaggregation and how it can address health disparities within the Asian-American, Native Hawaiin and Pacific Islander communities. 

As health researchers and medical professionals, data collection and management is necessary for discovering emerging health trends and understanding how behavioral changes can impact a patient's quality of life. But the way data is collected and interpreted can generate misleading information for certain communities. 

When filling out surveys or federal documents, for example new patient intake forms, job applications, or the U.S. Census survey, there’s a section that asks for race and ethnicity. There are typically a minimum of five selections for race : American Indian or Alaska Native, Black or African American, Asian, Native Hawaiian or Other Pacific Islands, and White. These categories are the minimum requirement as established by the Office of Management and Budget’s (OMB) 1997 ‘Revisions to the Standards for Classification of Federal Data on Race and Ethnicity.’ According to the standards, “Data were needed to monitor equal access in housing, education, employment, and other areas, for populations that historically had experienced discrimination and differential treatment because of their race or ethnicity. The standards are used not only in the decennial census…but also in household surveys, on administrative forms (e.g., school registration and mortgage lending applications), and in medical and other research.” (Office of Management and Budget (OMB), 1997, p. 58782) Within health sciences research, these racial categories allow researchers to understand health concerns within specific communities and can lead to preventative health measures that are tailored to a community’s concerns. But many researchers are pushing for data disaggregation which can highlight disparities that are otherwise overlooked when using broad racial categories such as ‘Asian’ or ‘Pacific Islander.’ 

“Asia consists of over forty countries, and the Pacific Islands are grouped by three subregions of Oceania (including Native Hawaiians); both have a diaspora spread across the globe. Due to differences in social, economic, and environmental factors, it is erroneous to assume that health disparities for this population do not exist.” (Bhakta, 2022, p. 133)

Adia et. al examined the results of a California Health Interview Survey (CHIS) conducted from 2011-2017 and found that while the aggregated data suggested Asian Americans in the state appeared healthier than non-Hispanic Whites, when the data was broken into specific subgroups that fall under the Asian category many health disparities, such as high blood pressure, diabetes or asthma, were uncovered. For example, when examining the rates of high blood pressure among survey responders, 31.0% of Non-Hispanic White respondents reported having high blood pressure compared to 22.9% of All Asian respondents. But when examining specific subgroups, the researchers found that 32.3% of Filipino and Japanese respondents reported having high blood pressure. (Adia et al., 2020) “Overall, these findings support further data disaggregation in other large-scale research efforts to support interventions tailored specifically to Asian subpopulations in need…Disaggregation showed that each Asian subgroup faced disparities in health condition, outcomes, and service access that would have been masked.” (Adia et al., 2020, p. 525) When health data is disaggregated, researchers may be alert to concerning medical trends in specific communities and can work with local community partners to implement preventative screenings or devise treatment plans that allow patients to receive the best care possible. Adia et al. also noted that their findings are not applicable to Asian and Pacific Islander populations in other parts of the United States as the makeup of these populations will differ from state to state, which further highlights the need to conduct research in other communities across the country. 

In order to gather accurate data and combat health inequities within the Asian American and Pacific Islander communities, researchers will need to partner with local community members and find solutions that prevent people from accessing proper care. In a 2020 article for Cronkite News, Laura Makaroff, Senior Vice President for Prevention and Early Detection at the American Cancer Society, said,  “To make a big difference and seriously impact and reduce health inequities in Asian American populations…we need to address language access, be culturally competent, really support and engage partnerships and collaborations, include communities and people in all of research, and really be responsive and accountable to all of the different Asian American communities we serve…We need to begin and end with the community.”(Gu, 2020) Like other communities of color in the country, some sections of the Asian American and Pacific Islander communities do not fully trust the medical community. To bridge that divide, researchers will need to partner with local leaders and trusted institutions, such as religious centers, community centers, public libraries or cultural organizations, who are embedded in these communities and have a deep understanding of community members’ concerns. There are numerous ways to conduct medical research that is accessible and the local leaders and institutions can provide valuable insight to researchers. 

To learn more about data disaggregation as it relates to the Asian American and Pacific Islander communities, please read any of the works cited in this article or listed in the reference section below. The importance of data disaggregation is an ongoing conversation and we hope this article will encourage you to think critically about this topic and share your ideas and solutions with your colleagues. 

References

Gu, Y. (2020, September 8). ‘A lot of differences’: Experts address health disparities among Asian American subgroups. Cronkite News|Arizona PBS. https://cronkitenews.azpbs.org/2020/09/28/experts-address-health-disparities-among-asian-americans/

Yeung, D. & Dong, L. (2021, December 13). The health of Asian Americans depends on not grouping communities under the catch-all term. NBC News|Think. https://www.nbcnews.com/think/opinion/health-asian-americans-depends-not-grouping-communities-under-catch-all-ncna1285849

 Yi, S.S. (2020). Taking Action to Improve Asian American Health. American Journal of Public Health (1971), 110(4), 435–437. https://doi.org/10.2105/AJPH.2020.305596

 Le, Cha, L., Han, H.-R., & Tseng, W. (2020). Anti-Asian Xenophobia and Asian American COVID-19 Disparities. American Journal of Public Health (1971), 110(9), 1371–1373. https://doi.org/10.2105/AJPH.2020.305846

Adia, Nazareno, J., Operario, D., & Ponce, N. A. (2020). Health Conditions, Outcomes, and Service Access Among Filipino, Vietnamese, Chinese, Japanese, and Korean Adults in California, 2011-2017. American Journal of Public Health (1971), 110(4), 520–526. https://doi.org/10.2105/AJPH.2019.305523

Bhakta, S. (2022). Data disaggregation: the case of Asian and Pacific Islander data and the role of health sciences librarians. Journal of the Medical Library Association, 110(1), 133–138. https://doi.org/10.5195/jmla.2022.1372

Panapasa, Jackson, J., Caldwell, C. H., Heeringa, S., McNally, J. W., Williams, D. R., Coral, D., Taumoepeau, L., Young, L., Young, S., & Fa’asisila, S. (2012). Community-Based Participatory Research Approach to Evidence-Based Research: Lessons From the Pacific Islander American Health Study. Progress in Community Health Partnerships, 6(1), 53–58. https://doi.org/10.1353/cpr.2012.0013

Executive Office of the President, Office of Management and Budget (OMB), Office of Information and Regulatory Affairs. (1997). Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity. https://www.govinfo.gov/content/pkg/FR-1997-10-30/pdf/97-28653.pdf