Toward better data disaggregation: A person-centered approach to understanding AANHPI sociodemographic diversity in resource constrained times - Lu Dong; Jaimie Shaff; Douglas Yeung; Ruolin Lu; Delia Bugliari; Anthony Rodriguez; Anita Chandra (PLoS ONE, 2025)
Background Each year, the United States loses billions of dollars due to health inequities. Data disaggregation is essential for understanding the health status and needs of populations to identify these inequities and inform efficient resource allocation. For example, aggregating data from people identifying with Asian, Native Hawaiian, and other Pacific Islander (AANHPI) communities may inhibit the identification of important health challenges within this large and diverse community, impeding meaningful progress toward reducing differences in health outcomes. Methods This study employed Latent Class Analysis (LCA) to identify meaningful subgroups within the AANHPI population. Two studies were conducted: Study 1 analyzed data from the Amplify AAPI Survey, which included 1,026 AANHPI adults, while Study 2 utilized the 2023 National Survey of Health Attitudes (NSHA) with a sample of 318 AANHPI respondents. Both studies collected comprehensive sociodemographic measures, including educational attainment, household income, and employment status. Results Study 1 identified four latent classes, revealing heterogeneity within the AANHPI sample based on income, education, language use, and generational status. Class characteristics highlighted variations in age, marital status, and employment. Study 2 identified two classes: high socioeconomic status (SES) and low SES. Class characteristics demonstrated differences in age distribution, homeownership, and perceptions of community well-being. Conclusion This study demonstrated the feasibility and utility of a person-centered analytic approach like LCA to identify meaningful subgroups within an aggregated population. These findings join a growing body of evidence that emphasizes the complexity within the AANHPI population and the importance of data disaggregation in public health. These insights are crucial for informing targeted interventions and optimizing resource allocation to effectively address health disparities.