Latent Class Analysis: An Innovative Approach for Revealing Profiles of Health Among Asian Americans

Purpose: Much empirical research documents racial and ethnic diversity in health outcomes, but is limited in its ability to reveal heterogeneity within groups. Most studies of Asian American health utilize variable-centered analyses and thus, do not take into account heterogeneity within and between groups to better understand variation in health outcomes. A person-centered approach to the study of race, ethnicity and health allows for the identification of distinct health profiles within the broader Asian American population, which can lead to more targeted intervention and prevention efforts to better meet the needs of particular subgroups. This presentation will demonstrate the utility of a person-centered approach to understanding how socio-demographic characteristics and health status combine to generate distinct health profiles among a nationally representative sample of Asian Americans. Specifically, this study will address the following research questions:
1) Are there distinct profiles of socio-demographic characteristics and health among Asian Americans?
2) If so, what is the composition of the profiles and how can they be utilized to inform interventions and policy to better meet the needs of Asian Americans?

Methods: Latent class analysis was used to identify homogeneous subgroups within a heterogeneous population of Asian American adults. Data come from the National Latino and Asian American Study (NLAAS), the first nationally representative epidemiological study of Asian Americans (N= 2,083). Covariates include: chronic health condition (0 vs. 1 or more), gender, age, ethnic group (Chinese, Filipino, Vietnamese, and Other Asian), duration of residence in the U.S., years of education, and household income. Analyses were conducted using Mplus Version 6.12 and were weighted to adjust for sample selection probabilities.

Results: Four distinct classes were identified – “excellent,” “good,” “fair” and “poor” health classes. In addition to distinct patterns of ethnic group composition, each class had a distinct composition of socio-demographic characteristics that highlights the heterogeneity within the Asian American population and clearly identifies at-risk groups.

Implications: Findings of distinct health profile groups demonstrate the practical utility of using a latent class approach to uncover heterogeneity that would have otherwise been obscured. These distinct profiles are useful for targeting culturally appropriate interventions to individuals with particular characteristics. Rather than programs designed for all Asians, more targeted prevention and intervention efforts can be made to address the needs of those members in the “fair health” and “poor health” classes, while also identifying potential protective factors in the “excellent” and “good” health classes. Clearly, insights regarding risk and protective factors revealed by using a latent class approach can provide a foundation for developing targeted social programs and health interventions.