Objective:
To analyze the association between residential context and survival outcomes among Black and White women with epithelial ovarian cancer.
Approach:
- Study Design: Investigators analyzed overall survival outcomes among 509 Black women and 2,035 White women treated for epithelial ovarian cancer from January 2000 to May 2023, with follow-up through June 2024.
- Assessment Method: Residential context was assessed using the 2020 Social Vulnerability Index (SVI) at the census-tract level.
- Statistical Analysis: Models accounted for race, age at diagnosis, decade of diagnosis, cancer stage, and histologic type.
Key Findings:
- Black women had 45% higher adjusted odds of mortality compared to White women.
- Black women were more likely to live in areas with greater social vulnerability, with a median SVI of 0.78 vs 0.48 for White women.
- Residence in a high-SVI area was associated with 20% higher adjusted odds of mortality compared to low-SVI areas.
- Black women in high-SVI areas had 77% higher adjusted odds of mortality compared to White women in low-SVI areas.
- The combination of identifying as Black and having high social vulnerability was associated with 33% greater odds of mortality.
- Black women in low- and high-SVI areas had 20% and 60% higher adjusted odds of mortality compared to White women in similar areas.
Limitations:
- The study was observational, preventing the establishment of causality.
- Data was sourced from a single tertiary cancer center, with 97% of patients from Alabama, limiting generalizability.
- The study included only Black and White women, restricting applicability to other racial groups.
- Historical neighborhood conditions may have been misclassified due to applying 2020 SVI data to patients diagnosed between 2000 and 2023.
- Lack of data on treatment regimens, tumor mutations, comorbidities, and patient-level factors such as employment and education.
- Experiences like discrimination and clinician bias were not captured, leaving room for residual confounding.
Sources:
This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.
