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Disaggregating Health Conditions, Outcomes, and Service Access & Implications for COVID-19 Among Filipino Adults in California, 2011–2017
Presented by: 
(Poster pending)
Alexander C. Adia, MPH
Masters Student
Brown University
Alexander C. Adia MPH; Jennifer Nazareno PhD, MSW; Don Operario PhD; and Ninez A. Ponce PhD, MPP


Philippine Health Initiative for Research, Service, & Training, Brown University School of Public Health

UCLA Department of Health Services

UCLA Center for Health Policy Research, Fielding School of Public Health


Objectives. To determine the impact of data disaggregation on the ability to identify health disparities and needs for future research for Filipino adults in California, with an additional focus on factors (fair/poor health, obese/overweight, hypertension, diabetes, asthma, heart disease) that could impact outcomes with COVID-19.

Methods. Using available data from the 2011–2017 California Health Interview Survey, we conducted bivariate and multivariable analyses to assess disparities in health conditions, outcomes, and service access compared with non-Hispanic Whites for Asians as an overall group and for Filipinos, specifically

Results. As an aggregate category, Asians appeared healthier than did non-Hispanic Whites on most indicators. However, every Asian subgroup had at least 1 disparity disguised by aggregation. Filipinos had the most disparities, with higher prevalence of fair or poor health, being obese or overweight, and having high blood pressure, diabetes, or asthma compared with non-Hispanic Whites (P < .05) in multivariable analyses, which would all could drive worse COVID-19 outcomes

Conclusions. Failure to disaggregate health data for individual Asian subgroups disguises disparities and leads to inaccurate conclusions about needs for interventions and research. Filipino Americans may be particularly impacted by this practice, particularly when it comes to tracking COVID-19 outcomes.

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