Applying thick data to advance the science of Asian American health disparities: the value of a mixed methods approach

Authors: Kwon SC, Kranick J, Yi S, Foster V, Wyatt L, Trinh-Shevrin C

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