Grant Number:	5R37CA233777-06 Interpret this number
Primary Investigator:	Akinyemiju, Tomi
Organization:	Duke University
Project Title:	A Role of Multilevel Healthcare Access Dimensions in Ovarian Cancer Disparities
Fiscal Year:	2023

Less than 40% of ovarian cancer (OC) patients in the US receive stage-appropriate guideline- adherent surgery and chemotherapy; Black OC patients are even less likely to receive such treatment. While 5-year relative survival for White OC patients improved by 47% between 1975 and 2009, it declined by 27% for Black patients during this same period. Among cancer survivors, Black patients are also observed to have significantly higher depression, pain, and fatigue than White survivors. These racial disparities are likely due largely to differences in healthcare access – specifically, access to high quality initial treatment and post- treatment supportive care. Healthcare access is a complex subject; however, the Penchansky healthcare access framework proposed that it comprises of five specific dimensions: availability, affordability, accessibility, accommodation and acceptability of health care services. Our study will comprehensively evaluate all five dimensions of healthcare access (HCA) among Black and White patients to identify and quantify the specific factors contributing to the striking racial differences in OC care and survival. More specifically, we will utilize data from SEER-Medicare (8,060 OC patients) along with primary survey data from a population-based sample of 1,010 OC patients, linked with several existing datasets (e.g., American Community Survey, Area Healthcare Resource File), to characterize racial differences in associations between each HCA dimension and three outcomes: quality of initial treatment and supportive care, quality of life based on patient-reported outcomes in prevalent yet manageable symptoms, and survival. We will evaluate HCA dimensions across patient, neighborhood, provider and hospital levels, and utilize hierarchical regression models with random effects to account for clustering, and multilevel structural equation models to estimate the total, direct and indirect effect of race on treatment mediated through HCA dimensions. Our preliminary studies suggest that certain under-studied dimensions (e.g., acceptability) may outweigh other dimensions (e.g., availability) in determining quality of care. Moreover, the impact of the various HCA dimensions may vary by race. By analyzing high-quality multilevel datasets with Black and White patients, we can fully characterize the nature of racial disparities, assess the relative importance of race-specific barriers to care, and identify race-specific modifiable factors. Our study will provide novel, empirical, and generalizable insights regarding the distinct and collective influence of HCA dimensions on OC outcomes. These insights will help identify and prioritize specific modifiable factors that can then be targeted to reduce disparities and improve care for all patients.





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