Abstract
BACKGROUND: Multiracial adults represent a growing U.S. population but are often grouped together or reassigned to single-race categories in public health data. Aggregation can obscure important variation across subgroups, limiting opportunities for targeted prevention. METHODS: We analyzed 2014-2023 California Behavioral Risk Factor Surveillance System data (n=100,177) to estimate prevalence of 28 health indicators across racial and ethnic groups, including disaggregated Multiracial subgroups. We categorized participants based on all self-identified races and aggregated subgroups with N<50. We standardized prevalence by age and sex using 2020 California census data, calculated relative standard errors, and used survey-weighted methods to compare prevalence and subgroup differences. RESULTS: Among 100,177 participants, Multiracial subgroups had the highest prevalence for 24 of 28 outcomes. American Indian or Alaska Native-Black and Hispanic-Black-White adults had the highest prevalence of chronic conditions, poor general health, and disability. In contrast, Asian Multiracial subgroups (e.g., Asian-Black, Asian-Pacific Islander) more often had the lowest prevalence, though Asian-White adults were not consistently the healthiest subgroup. Differences across Multiracial subgroups exceeded 20 percentage points for nearly half of all outcomes. DISCUSSION: Wide health variation among Multiracial adults is masked by common aggregation practices. Subgroups with the highest burden may be overlooked if data are not routinely disaggregated. Public health surveillance systems should expand capacity to collect and report disaggregated race and ethnicity data to better inform prevention strategies.