title: A new approach to understanding racial disparities in prostate  cancer treatment.
abstract: OBJECTIVE
  Previous studies addressing racial disparities in treatment for early-stage prostate cancer have focused on the etiology of undertreatment of black men. Our objective was to determine whether racial disparities are attributable to undertreatment, overtreatment, or both.
  
  
  METHODS
  Using the SEER-Medicare dataset, we identified men 67–84 years-old diagnosed with localized prostate cancer from 1998 to 2007. We stratified men into clinical benefit groups using tumor aggressiveness and life expectancy. Low-benefit was defined as low-risk tumors and life expectancy <10 years; high-benefit as moderate-risk tumors and life expectancy ≥10 years; all others were intermediate-benefit. Logistic regression modeled the association between race and treatment (radical prostatectomy or radiotherapy) across benefit groups.
  
  
  RESULTS
  Of 68,817 men (9.8% black and 90.2% white), 56.2% of black and 66.3% of white men received treatment (adjusted odds ratio (OR)=0.65; 95% CI, 0.62–0.69). The percent of low-, intermediate-, and high-benefit men who received treatment was 56.7%, 68.4%, and 79.6%, respectively (P=<0.001). In the low-benefit group, 51.9% of black vs. 57.2% of white patients received treatment (OR=0.74; 95% CI, 0.67–0.81) compared to 57.2% vs. 69.6% in the intermediate-benefit group (OR=0.64; 95% CI, 0.59–0.70). Racial disparity was largest in the high-benefit group (64.2% of black vs. 81.4% of white patients received treatment; OR=0.57; 95% CI, 0.48–0.68). The interaction between race and clinical benefit was significant (P<0.001).
  
  
  CONCLUSIONS
  Racial disparities were largest among men most likely to benefit from treatment. However, a substantial proportion of both black and white men with a low clinical benefit received treatment, indicating a high level of overtreatment.
authors.forename: Carolyn J
authors.initials: CJ
authors.lastname: Presley
authors.forename: Ann C
authors.initials: AC
authors.lastname: Raldow
authors.forename: Laura D
authors.initials: LD
authors.lastname: Cramer
authors.forename: Pamela R
authors.initials: PR
authors.lastname: Soulos
authors.forename: Jessica B
authors.initials: JB
authors.lastname: Long
authors.forename: James B
authors.initials: JB
authors.lastname: Yu
authors.forename: Danil V
authors.initials: DV
authors.lastname: Makarov
authors.forename: Cary P
authors.initials: CP
authors.lastname: Gross
mesh_terms: D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D044383:Black People / Q000208:ethnology*; D054625:Healthcare Disparities / Q000208:ethnology; D006801:Humans; D008297:Male; D011471:Prostatic Neoplasms / Q000208:ethnology / Q000628:therapy*; D012044:Regression Analysis; D012307:Risk Factors; D044465:White People / Q000208:ethnology*