Andrew Berchuck

Supplemental Figure 1: Participant flowchart for Non-Hispanic Black, Hispanic and Non-Hispanic White ovarian cancer patients. Supplemental Table 1: Diagnosis Codes for patient comorbid conditions. Supplemental Methods: This supplemental material provides expanded descriptions of covariate creation, data linkages, and previously conducted factor analysis work. Supplemental Table 2: All HCA measures included in exploratory factor analysis. Supplemental Table 3: Any Ovarian cancer surgery coding definitions. Supplemental Figure 2: Indirect effects of black race on receipt of surgery.

Background:Ovarian cancer survival disparities have persisted for decades, driven by lack of access to quality treatment. We conducted structural equation modeling (SEM) to define latent variables representing three healthcare access (HCA) domains: affordability, availability, and accessibility, and evaluated the direct and indirect associations between race and ovarian cancer treatment mediated through the HCA domains.Methods:Patients with ovarian cancer ages 65 years or older diagnosed between 2008 and 2015 were identified from the SEER-Medicare dataset. Generalized SEM was used to estimate latent variables representing HCA domains by race in relation to two measures of ovarian cancer-treatment quality: gynecologic oncology consultation and receipt of any ovarian cancer surgery.Results:A total of 8,987 patients with ovarian cancer were included in the analysis; 7% were Black. The affordability [Ω: 0.876; average variance extracted (AVE) = 0.689], availability (Ω: 0.848; AVE = 0.636), and accessibility (Ω: 0.798; AVE = 0.634) latent variables showed high composite reliability in SEM analysis. Black patients had lower affordability and availability, but higher accessibility compared with non-Black patients. In fully adjusted models, there was no direct effect observed between Black race to receipt of surgery [β: −0.044; 95% confidence interval (CI), −0.264 to 0.149]; however, there was an inverse total effect (β: −0.243; 95% CI, −0.079 to −0.011) that was driven by HCA affordability (β: −0.025; 95% CI, −0.036 to −0.013), as well as pathways that included availability and consultation with a gynecologist oncologist.Conclusions:Racial differences in ovarian cancer treatment appear to be driven by latent variables representing healthcare affordability, availability, and accessibility.Impact:Strategies to mitigate disparities in multiple HCA domains will be transformative in advancing equity in cancer treatment.

Kaplan–Meier curves of OS in (A) main tumor groups (n = 582)—MBOT, MOC, LGI, and UGI; (B) patients with MOC by FIGO stage (n = 184); (C) patients with MOC by pattern of invasion in all stages (n = 178); and (D) patients with stage I MOC (n = 134), by pattern of invasion.

Heat map of unsupervised clustering analysis. Contains all samples with a concordant pathology diagnosis (n = 497), with MOC grouped by FIGO stage. Labels show main clusters and diagnoses. Gene-expression values are normalized and logarithm base 2 transformed. dx, diagnosis.

Associations between gene expression and stage group and OS in MOC.

Overall survival in MOC by pattern of invasion.

Schema of study numbers for each analysis to describe different cohort numbers due to pathology review and missing data. MOC, mucinous ovarian carcinoma; MBOT, mucinous borderline ovarian tumor; LGI, lower gastrointestinal; UGI, upper gastrointestinal; SISH, silver in situ hybridization.