||1R01CA225478-01A1 Interpret this number
||University Of California, San Francisco
||Understanding the Multilevel Drivers of Liver Cancer Disparities
From 2000-2014, hepatocellular carcinoma, or HCC, incidence rates increased nearly 4% per year, while most
cancers in the United States were on the decline. HCC disproportionately impacts minority racial/ethnic groups
who are diagnosed at rates approximately twice that of non-Hispanic Whites. To inform primary prevention
strategies that will reduce disparities in HCC risk, we need to determine the relative contribution of well-
established and emerging (e.g., hepatitis B virus, hepatitis C virus, alcohol, smoking, cirrhosis, NAFLD,
metabolic disorders, diabetes, HIV infection), and novel (e.g., medications, comorbidities, neighborhood
attributes) risk factors to these disparities. To inform secondary and tertiary prevention strategies to reduce
disparities in HCC burden, we need to understand the multilevel factors that contribute to HCC surveillance
disparities. Answering these gaps in knowledge requires a robust high-quality study with a sample enriched for
racial/ethnic minorities. Thus, we propose to leverage existing multi-disciplinary collaborations to develop an
integrated dataset that includes electronic health records (EHR) data linked to population-based state cancer
registry data and geospatial contextual data. This multilevel resource will include data on nearly 2.3 million
individuals from three healthcare systems (mixed payer, integrated healthcare, federally qualified health
centers) in California and Hawaii, thus providing diversity in healthcare settings and enrichment for racial/ethnic
minorities: 59,400 are Black, 189,500 are Hispanic, and 441,700 are Asian American/Native Hawaiian/Pacific
Islander (AANHPI). With this resource, we specifically aim to: (1) assess the relative importance of established
and emerging examine the extent to which these factors independently and jointly contribute to racial/ethnic
disparities in HCC risk; (2) discover novel risk factors and assess their relative importance to HCC risk; and (3)
assess racial/ethnic disparities in adherence with surveillance for HCC as well as examine the extent to which
these disparities are attributable to modifiable individual-, clinician-, system-, and neighborhood factors (Aim 3).
For Aim 1, using prospective data, we will assess the relative importance of risk factors and their contribution
to racial/ethnic disparities in HCC risk with causal inference methods. For Aim 2, we will apply innovative
machine learning methods to identify novel factors and validate their associations with HCC risk using
modeling strategy from Aim 1. For Aim 3, we will use multilevel generalized linear regression to investigate the
patient, clinician, institutional and geographic factors that contribute to disparities in HCC surveillance. Given
the importance of sex and age/birth cohort for HCC risk, these social determinants will be considered together
with race/ethnicity using an intersectional approach. By applying a multilevel framework to understand how
biological, clinical, and social factors at multiple levels contribute to HCC disparities in incidence and
surveillance, the proposed study will identify modifiable factors that can be translated to the clinical and
community settings to collaboratively identify strategies to ameliorate racial/ethnic disparities in HCC.
If you are accessing this page during weekend or evening hours, the database may currently be offline for maintenance and should operational within a few hours. Otherwise, we have been notified of this error and will be addressing it immediately.
Please contact us
if this error persists.
We apologize for the inconvenience.
- The DCCPS Team.