Riti Shimkhada

The U.S. has recently made progress in collecting better data on race, ethnicity, and gender identity to ensure all communities are fairly represented in research and policy. However, new executive orders from President Trump in 2025 have halted many of these efforts, including stopping data collection on transgender people and removing key datasets from public websites. These actions threaten the availability of accurate, inclusive data that supports health, equity, and civil rights.

Authors explain what’s at stake and offer suggestions to encourage and protect robust data collection that represents everyone. While legal challenges are underway, state and local groups, along with researchers, are stepping up to protect and continue this vital work to reflect and protect data that reflects the nation's full diversity.
 

The U.S. Office of Management and Budget (OMB) categorizes Filipino Americans as Asian; however, many may not identify as such, opting instead for "other" or Pacific Islander (PI). This study examines the extent to which Filipino Americans select PI or "other" rather than Asian, using a large population-based survey of Californians.

Authors analyzed data from the 2019, 2020, and 2021 California Health Interview Survey (CHIS), the largest state health survey in the U.S. that includes write-in prompts for detailed race and ethnicity data. The focus was on participants who identified as Pacific Islanders and wrote in 'Filipino,' those who selected 'Asian' and specified 'Filipino,' and respondents who chose 'other' and wrote in 'Filipino.'

Findings: The analysis included 1,859 Filipino respondents, revealing that 8.8% identified as Pacific Islander, 85.2% as Asian, and 6.1% as "other." Those identifying as PI were more likely to also identify as Latino/Hispanic, be older, and possess U.S. citizenship compared to those identifying as Asian.

Disaggregating Filipino Americans from the broader Asian category in surveys is vital for accurately identifying the community's unique needs. Authors recommend incorporating open-ended write-in prompts in surveys that ask respondents to first identify their broader race category (e.g. Asian). These prompts help identify and reclassify Filipino respondents who may have identified as PI. Such prompts are also important for other racial/ethnic communities who may be uncertain about how to categorize themselves. Ongoing, dynamic community-driven research is essential for understanding identities and effectively categorizing Filipino Americans and other communities.
 

This study explores the association between climate change stress and psychological distress and suicide ideation among adolescents in California and factors associated with climate change stress.  Study authors use cross-sectional data from the 2021 and 2022 California Health Interview Survey (CHIS) to explore differences between adolescents reporting climate change stress or not. Association between climate stress and psychological distress and suicide ideation were explored.

Findings: In this study, 38.1% of California’s adolescents report climate change stress. Adolescents who report this stress are more likely to be female, gender nonconforming, white, from higher-income families, have adverse childhood experiences, frequently use social media, be involved in their community, and feel civic efficacy. Climate change stress is associated with 2.395 greater odds of psychological distress in the past month and 1.853 greater odds of suicide ideation in the past year.
 

Achieving health equity necessitates high-quality data to address disparities that have remained stagnant or even worsened over time despite public health interventions. Data disaggregation, the breakdown of data into detailed subcategories, is crucial in health disparities research. It reveals and contextualizes hidden trends and patterns about marginalized populations and guides resource allocation and program development for specific needs in these populations.

Data disaggregation underpins data equity, which uses community engagement to democratize data and develop better solutions for communities. Years of research on disaggregation show that researchers must collaborate closely with communities for adequate representation. However, despite generally positive support for this approach in health disparities research, data disaggregation faces methodological and political challenges.

This review offers a framework for understanding data disaggregation in the context of data equity and highlights critical aspects of implementation, including challenges, opportunities, and recent policy and community-based efforts to address hurdles.