Isolating the Drivers of Racial Inequities in Prostate Cancer Treatment

Abstract

Background: Black individuals in the United States are less likely than White individuals to receive curative therapies despite a 2-fold higher risk of prostate cancer death. While research has described treatment inequities, few studies have investigated underlying causes.

Methods: We analyzed a cohort of 40,137 Medicare beneficiaries (66 and older) linked to the Surveillance Epidemiology and End Results (SEER) cancer registry who had clinically significant, non-metastatic (cT1-4N0M0, grade group 2-5) prostate cancer (diagnosed 2010-2015). Using the Kitagawa-Oaxaca-Blinder decomposition, we assessed the contributions of patient health and health care delivery on the racial difference in localized prostate cancer treatments (radical prostatectomy or radiation). Patient health consisted of comorbid diagnoses, tumor characteristics, SEER site, diagnosis year, and age. Health care delivery was captured as a prediction model with these health variables as predictors of treatment, reflecting current treatment patterns.

Results: A total of 72.1% and 78.6% of Black and White patients received definitive treatment, respectively, a difference of 6.5 percentage points. An estimated 15% [95% confidence interval (CI): 6-24] of this treatment difference was explained by measured differences in patient health, leaving the remaining estimated 85% (95% CI: 74-94) attributable to a potentially broad range of health care delivery factors. Limitations included insufficient data to explore how specific health care delivery factors, including structural racism and social determinants, impact differential treatment.

Conclusions: Our results show the inadequacy of patient health differences as an explanation of the treatment inequity.

Impact: Investing in studies and interventions that support equitable health care delivery for Black individuals with prostate cancer will contribute to improved outcomes.

Figure 1.. Example scenarios of the roles of patient health and healthcare in the decomposition of prostate cancer treatment differences.

The three panels represent three different possible conclusions about the roles of patient health and healthcare that depend on whether the counterfactual definitive treatment prediction for Black men ‘treated like’ White men is: Scenario 1) the same percentage currently received by Black men – the entirety of the difference is driven by patient health; Scenario 2) between the percentage currently received by White and Black men – the portion above the counterfactual prediction is driven by healthcare while the portion below is driven by patient health; and Scenario 3) the same percentage currently received by White men – the entirety of the difference is driven by healthcare.

Figure 2.. Observed and counterfactual treatment rates of Black and White individuals with ‘Clinically Significant’ prostate cancer.

Each panel presents the counterfactual predictions that result from inputting the levels of important patient health factors that lead to treatment for one patient group into the modeled association between those factors and treatment of the other patient group. Panel A presents the White counterfactual, the main analysis of the paper. Panel B presents the Black counterfactual to demonstrate robustness This allows for the visualization of the treatment rates of one patient group treated like the other patient group over the years available in the SEER-Medicare data sample. The comparison of the counterfactual predictions to the observed average treatment rates illustrates the clear differences between these different rates.

Figure 3.. Results from the Kitagawa-Oaxaca-Blinder decomposition of the drivers of the prostate cancer inequity between Black and White individuals.

This approach decomposes the 6.5 pp difference in treatment rates by first forming the counterfactual prediction. Here, we present the decomposition that uses the White individuals’ relationship between health predictors and treatment to form the counterfactual prediction if Black individuals were treated like (had the same relationship between predictors and treatment) White individuals. The decomposition compares this counterfactual prediction to observed rates to find the portion of the overall inequity that is driven by differences in patient health and healthcare. The figure presents the base treatment inequity on the left, the 3 potential result scenarios from Figure 1 in the middle, and the result from the decomposition on the right. 95% confidence intervals from the percentile bootstrap are presented in brackets.