Background: Mediation by multiple agents can affect the relation between neighborhood deprivation and segregation indices and ovarian cancer survival. In this paper, we examine a variety of potential clinical mediators in the association between deprivation indices (DIs) and segregation indices (SIs) with all-cause survival among women with ovarian cancer in the African American Cancer Epidemiology Study (AACES).
Methods: We use novel Bayesian multiple mediation structural models to assess the joint role of mediators (stage at diagnosis, histology, diagnostic delay) combined with the DIs and SIs (Yost, ADI, Kolak's URB, ICE-income) and a set of confounders with survival. The confounder set is selected in a preliminary step, and each DI or SI is included in separate model fits.
Results: When multiple mediators are included, the total impact of DIs and SIs on survival is much reduced. Unlike the single mediator examples previously reported, the Yost, ADI and ICE-income indices do not display significant direct effects. This suggests that when important clinical mediators are included, the impact of neighborhood SES indices is significantly attenuated. It is also clear that certain behavioral and demographic measures such as physical activity, smoking, or adjusted family income do not have a significant role in survival when mediated by clinical factors.
Conclusion: Multiple mediation via clinical and diagnostic-related measures reduces the contextual effects of neighborhood measures on ovarian cancer survival. The robust association of the Kolak URB index on survival may be due to its relevance to access to care, unlike SES-based indices whose impact was significantly reduced when important clinical mediators were included.
Keywords: African American Cancer Epidemiology Study (AACES); Bayesian Mixed effect models; Deprivation; Multiple mediation; Neighborhood; Ovarian cancer; Segregation; Survival.
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