County-level racial disparities in prostate cancer-specific mortality from 2005 to 2020

JNCI Cancer Spectr. 2024 Nov 1;8(6):pkae109. doi: 10.1093/jncics/pkae109.

Abstract

Background: Local conditions where people live continue to influence prostate cancer outcomes. By examining local characteristics associated with trends in Black-White differences in prostate cancer-specific mortality over time, we aim to identify factors driving county-level prostate cancer-specific mortality disparities over a 15-year period.

Methods: We linked county-level data (Area Health Resource File) with clinicodemographic data of men with prostate cancer (Surveillance, Epidemiology, and End Results registry) from 2005 to 2020. Generalized linear mixed models evaluated associations between race and county-level age-standardized prostate cancer-specific mortality, adjusting for age; year of death; rurality; county-level education; income; uninsured rates; and densities of urologists, radiologists, primary care practitioners, and hospital beds.

Results: In 1085 counties, 185 390 patients were identified of which 15.8% were non-Hispanic Black. Racial disparities in prostate cancer-specific mortality narrowed from 2005 to 2020 (25.4 per 100 000 to 19.2 per 100 000 overall, 57.9 per 100 000 to 38 per 100 000 for non-Hispanic Black patients, and 23.4 per 100 000 to 18.3 per 100 000 for non-Hispanic White patients). For non-Hispanic Black and non-Hispanic White patients, county prostate cancer-specific mortality changes varied greatly (-65% to +77% and -61% to +112%, respectively). From 2016 to 2020, non-Hispanic Black patients harbored greater prostate cancer-specific mortality risk (relative risk = 2.09, 95% confidence interval [CI] = 2.01 to 2.18); higher radiation oncologist density was associated with lower mortality risk (relative risk = 0.93, 95% CI = 0.89 to 0.98), while other practitioner densities were not.

Conclusion: Although overall rates improved, specific counties experienced worsening race-based disparities over time. Identifying locations of highest (and lowest) mortality disparities remains critical to development of location-specific solutions to racial disparities in prostate cancer outcomes.

MeSH terms

  • Aged
  • Black or African American* / statistics & numerical data
  • Educational Status
  • Health Status Disparities*
  • Hispanic or Latino / statistics & numerical data
  • Humans
  • Income
  • Linear Models
  • Male
  • Medically Uninsured / statistics & numerical data
  • Middle Aged
  • Prostatic Neoplasms* / ethnology
  • Prostatic Neoplasms* / mortality
  • Rural Population / statistics & numerical data
  • SEER Program*
  • United States / epidemiology
  • White
  • White People* / statistics & numerical data