Social Vulnerability May Contribute to Disparities in Ovarian Cancer Survival - Summary - MDSpire
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Social Vulnerability May Contribute to Disparities in Ovarian Cancer Survival

  • By

  • Andrea Surnit

  • August 17, 2026

  • 3 min

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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:

Original Source(s)

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