Health disparities within Asian American, Native Hawaiian, and Pacific Islander (AA & NH/PI) communities are often masked due to issues with health data aggregation. At the 2026 APAICS Health Summit, leaders in health policy, advocacy, and clinical research highlighted the need for disaggregated data to reveal and address the unique challenges faced within one of the most culturally, linguistically, and ethnically diverse populations in the U.S.

Why aggregated data is insufficient

As Congressman Ted Lieu noted during his keynote speech at the summit, health databases that categorize all Asians together fail to account for differences between subgroups. Diseases and health risks impact various AA & NH/PI communities differently, and without disaggregated data, vital insights are lost. A study published in Nature emphasized that social stress impacts these groups uniquely:

• Chinese Americans under stress show an increased risk of diabetes.
• Filipino Americans see higher rates of hypertension.
• Asian Indians are more prone to poor sleep and reduced physical activity.

These findings underscore why breaking data into subgroup-specific information is crucial to delivering equitable healthcare solutions.

The impact of the model minority myth and invisibility

Tanisha Carino, the panel’s moderator, discussed how the "model minority" myth perpetuates harmful stereotypes, presenting AA & NH/PI communities as uniformly healthy and economically secure.

This myth has real consequences. It shapes research questions, policy funding decisions, and resource allocations, often leaving significant disparities unaddressed. Without disaggregated health data, policymakers risk overlooking critical trends, such as higher liver and stomach cancer rates in Asian Americans compared to their white counterparts.

Challenges in clinical research representation

Juliet Choi from the Asian and Pacific Islander American Health Forum highlighted an alarming gap in clinical research representation. Between 2010 and 2022, Asian Americans constituted less than 1% of U.S.-based cancer trial participants, while Native Hawaiians and Pacific Islanders represented fewer than 0.002%. These low participation rates limit the understanding of how treatments affect different groups. Cancer remains the leading cause of death for AA & NH/PI individuals, yet research largely neglects how these communities experience the disease differently.

Fibby Daniel, representing Amgen’s RISE team, expanded on this by stressing that clinical trials and treatments are often based on data that fail to capture subgroup nuances. Participation in research is essential to developing therapies tailored to the community’s unique needs.

Public health policy implications

Disaggregated data also plays a pivotal role in shaping public health policies. Juliet Choi stressed that Medicaid reforms introduced by HR1 and OB3 threaten accessible healthcare, potentially displacing over a million AA & NH/PI individuals from Medicaid coverage. Without concrete data to assess which subgroups would face the greatest impact, targeted policy interventions are virtually impossible.

Moreover, panelist Baojian spoke about the need for disaggregated data to address wider determinants of health, such as food security and housing stability. In Minnesota, efforts to pass community impact notes for legislative bills highlight how integrating detailed demographic data ensures policies consider human costs, not just fiscal ones.

How AI could exacerbate disparities

As AI becomes a fixture in healthcare, its accuracy and fairness are determined largely by the quality of the data it uses. AI systems trained on aggregated datasets may replicate or even amplify existing inequities.

For example, predictive models that fail to distinguish between AA & NH/PI subgroups might underrepresent the risk factors for diseases prevalent in some communities. Addressing data gaps now is essential to prevent marginalization in AI-driven healthcare systems.

Practical solutions moving forward

To address these issues, panelists at the summit proposed several practical steps:

• Increasing investment in disaggregated systems: Prioritize funding for public health data infrastructure that collects and analyzes subgroup-specific information.
• Expanding community-based research: Encourage partnerships between healthcare organizations, local governments, and advocacy groups to improve research participation.
• Making clinical research more accessible: Overcome systemic barriers like language access and cultural stigma to improve representation in clinical trials.
• Developing standardized reporting protocols: Require health institutions and agencies to adopt consistent methods for collecting and reporting data segmented by ethnicity and cultural background.
• Integrated policymaking: Apply disaggregated data in decision-making processes beyond healthcare, such as housing, education, and environmental policies.

Case study: Tackling tobacco control disparities

Baojian, representing Blue Cross and Blue Shield of Minnesota, shared insights from tobacco control initiatives. The commercial tobacco industry has long applied rigorous data segmentation to target marginalized communities effectively. Public health sectors can learn from this approach by investing in similarly detailed analyses. Targeted interventions based on disaggregated data have the potential to reduce disparities in smoking rates among AA & NH/PI subgroups.

Conclusion

The lack of disaggregated health data for AA & NH/PI communities has perpetuated disparities in healthcare and beyond. By advocating for data systems that reflect the diverse realities of these populations, policymakers, researchers, and healthcare providers can create more equitable systems. Recognizing the stakes — from the integration of AI to the representation in clinical research — makes clear that addressing these challenges isn’t just necessary; it’s urgent.

Disaggregated data allows for targeted interventions, equitable policy reforms, and improved representation in research. As highlighted at the APAICS Health Summit, bridging these gaps could save lives and create systems that serve all communities fairly.