ASPO Abstracts
Applying thick data to advance the science of Asian American health disparities: the value of a mixed methods approach
Category: Cancer Health Disparities
Conference Year: 2020
Abstract Body:
Purpose: Cancer is the leading cause of mortality among Asian Americans, currently accounting for 27% of
deaths. For some types of cancers, Asian Americans have the lowest screening rates and the highest proportion
of late-stage diagnoses. In particular, Asian Americans experience proportionally more cancers of infectious origin
than other racial/ethnic groups, including HPV-induced cervical cancer, HBV-induced liver cancer, and H. pylori-
associated stomach cancer. The umbrella term “Asian American,” encompasses a diverse mix of people, with
distinct genetic traits, environmental exposures, and cultural histories that differentially impact their cancer risk
profiles. Over 60 nationalities that speak over 100 languages may be grouped into one US Census category
(“Asian American/Pacific Islander”), masking marked differences in socio-demographic characteristics known to be
associated with cancer risk factors. For example, low English proficiency (LEP) rates, poverty levels, household
income, and educational attainment vary substantially across subgroups.
Methods: Our research approach is focused on determining, confronting and minimizing these disparities through
the adoption of a mixed-method strategy that emphasizes the value of big data, via large pooled public data sets
and Electronic Health Records, and thick data, in the form of qualitative interviews, focus groups, and digital
stories. Our research portfolio includes the exploration of big data, where large samples are accumulated and
provide stable results of phenomena for Asian American subgroups. The disaggregation of data also facilitates a
focus on precision medicine in an effort to understand the needs of specific populations on a granular level.
Results & Conclusion: The analysis of thick data provides a deeper dive into matters difficult to assess with big
data alone, such as cultural or linguistic reasons for an interventions’ successful adoption or failure. Our inclusion
of qualitative methods allows us to comprehensively examine gaps in large quantitative datasets and understand
how contextual and environmental issues interact with culture and drive health outcomes.
Keywords: Asian American, cancer disparities, mixed methods