Methods - We identified Black and White men aged ≥ 40 years with metastatic or locally advanced PCa (cN+ cM+ and/or T3/4) between 2004 and 2010 using the National Cancer Database. We employed sequential propensity score weighting procedures to generate simulated cohorts of Black and White patients with equal demographics, access to care, treatment, and tumor characteristics. Adjusted survival analyses were used to compare survival in these simulated cohorts. The changes in relative survival after each weighting procedure were used to infer the contribution of each set of variables on the excess risk of mortality in Blacks.
Results - In total, 35,611 men met inclusion criteria, 5927 (16.77%) of whom were Black. Survival was significantly worse for Black men after adjusting for demographics and comorbidities (hazard ratio (HR) 1.27, 95%-confidence interval (95%- CI) 1.2–1.34, p < 0.001). After simulating equal access to care, there was no significant difference in survival between races (HR 1.04, 95%-CI 0.97–1.12, p = 0.276), despite worse tumor characteristics in Blacks. After simulating equal treatment and equivalent tumor characteristics, Black men had a better survival than Whites (HR 0.93, 95%-CI 0.86–1.01, p = 0.071 and HR 0.92, 95%-CI 0.84–1.00, p = 0.043, respectively). Overall, access-related variables explained 84.7% of the excess risk of death in Black men.
Conclusion - Our analysis of men with advanced PCa revealed worse OS among Blacks. However, when access to care, treatment, and cancer characteristics are accounted for, Black race was associated with better OS. These findings suggest that initiatives to improve access to care may represent an effective tool to reduce disparities in PCa outcomes.
Introduction
Prostate cancer (PCa) is the most common non-skin malignancy among men in western countries and is the second most frequent cause of cancer death among US men.1 There are well-known race-based differences in PCa care2 and outcomes as Black individuals present with higher cancer stages and experience worse survival.3-6 The root causes for these disparities is thought to include differences in tumor and host biology, as well as differences in factors related to treatment and access to care.
There is good evidence that both biological and health systems factors may play a role in race-based disparities in PCa. For example, Black men with equivalent stage, grade, and PSA tend to have worse outcomes after radical prostatectomy7 and may exhibit a distinct zonal distribution of disease foci within the prostate.8 Additionally, specific genetic features associated with more aggressive PCa have been observed at higher rates in Black men.9,10 On the other hand, a large number of studies have shown that Black men have poorer access to PCa screening,11 appropriate follow-up,12 and receipt of definitive and high-quality therapy.13 Further strengthening the case that access to care is a significant determinant in racial differences in outcomes, several ecological studies have also shown that the racial differences in PCa outcomes are eliminated in settings where Black and White men have access to the same care (for example, among Medicare beneficiaries and within the military health system).14,15
Given the complex interplay of biological, socioeconomic, and health systems factors in health outcomes, it can be difficult to study the root cause of race-based differences in outcomes for PCa. Furthermore, given that patients cannot be “randomized” to one race or another, observational techniques must be used. Given that race is associated with a large number of potential confounders such as education, insurance coverage, and income, it may be difficult to infer the specific causal relationships at play.
On the basis of these considerations, we designed a study to compare overall survival (OS) between Black and White men diagnosed with advanced or metastatic PCa and to assess the relative contribution of demographics, access to care, treatment, and tumor characteristics on the excess risk of death among Black men, it has recently been done in other cancers.16,17 We hypothesized that Black men have worse OS than White men and that access-related variables (insurance, education, income, and distance to hospital) would explain a significant portion of the excess mortality in Black men with PCa.
Materials and methods
Data source
Data were obtained from the National Cancer Database (NCDB), a National Cancer Registry established by the Commission on Cancer (CoC) of the American Cancer Society. It includes patients seen at 1 of 1500 participating CoC-accredited hospitals for any portion of their diagnosis or treatment.18 The NCDB registry captures approximately 60% of PCa cases in the United States.19,20 Trained data abstractors employ standardized methodology to collect sociodemographic and clinical data, including tumor type, stage, grade, and treatments.
Study population
Men with PCa between 2004 and 2010 were identified using the International Classification of Diseases for Oncology, 3rd edition. We chose this observation period to prolong the median follow-up because many men with locally advanced PCa will still be alive after 5 years from diagnosis. We selected men with locally advanced or metastatic PCa (cN+ or cM+ and/or T3-4) based on the 7th edition of the American Joint Committee on Cancer staging system. We evaluated men with advanced disease to reduce the effect of lead-time bias and because overall mortality may be more likely due to cancer-specific mortality in advanced disease. Additionally, these men would be less subject to the lead-time bias seen in screening-detected low-risk disease. Since our method entails pairwise comparisons, we focused on Black men because these are known to have lower survival rates in PCa21 compared to White men. We excluded individuals who were missing follow-up or staging information, as well as those diagnosed age < 40 years, as facility information on these patients is censored by NCDB for confidentiality purposes.
Covariates
Baseline sociodemographic and health status covariates were age, geographical region in the United States, county (metropolitan, urban, rural, and unknown), year of diagnosis, and general health status estimated by using the Charlson comorbidity index.
Access-related variables included insurance status and type (Private, Medicare, Medicaid/other Gov’t, none, unknown), ZIP code level median household income, percentage of adults within patient’s ZIP code without a high school diploma, and distance to the treating hospital.
Treatment-related variables included surgery (radical prostatectomy or other surgeries), radiation, and systemic 126 M. J. Krimphove et al. chemotherapy (both hormonal and cytotoxic chemotherapy). To calculate facility case volume, we determined the number of men treated for advanced PCa at that institution in the year that he was treated; institutions were then ranked and divided into quartiles. This was done because there are known differences in access to high-volume care between Black and White patients.22
Cancer characteristics included clinical tumor, nodal, and metastatic (TNM) staging information, grade (Gleason score), as well as PSA level at diagnosis.
Outcome variable
Our primary outcome was OS, from diagnosis to the NCDB-recorded date of death or last follow-up.
Statistical analysis
Descriptive statistics were reported using frequencies and proportions for categorial variables. Significance was evaluated using Chi-square test.
To assess the contribution of demographics and health status, access, treatment, and tumor-related factors, we generated a series of four sequential propensity score models for each patient. For each propensity score model, a binomial logistic regression model was fit in order to predict each subject's probability (or propensity) of being Black or White as a function of available covariates.23 At each stage, an additional set of covariates was added as follows: (A) demographics and health status; (B) demographics and health status and access-related variables; (C) demographics and health status, access and treatment characteristics; and (D) demographics and health status, access, treatment and tumor characteristics.
After each propensity score was generated, men were weighted by the inverse of their propensity of being Black or White in order to generate cohorts of Black and White men that were balanced with respect to included covariates (the inverse probability of treatment weighting [IPTW] approach).18,24 To quantify the differences in distribution of covariates between Black and White cohorts, mean standardized differences (%) were reported for all covariates after each weighting procedure in which standardized differences of < 10% indicate well-balanced groups.25
After generating propensity scores, we performed four separate weighted Cox regression analyses to compare survival between synthetically weighted cohorts of White and Black men. Clustering was performed at the level of the hospital to account for correlation between patients treated at the same hospitals. By assessing the marginal change in excess mortality of Black men after applying each sequential propensity score, we were able to estimate the contribution of these variables on excess mortality.
All statistical analyses were performed using Stata v.13.0 (StataCorp, College Station, TX, USA). Codes can be provided via email upon request. Two-sided statistical significance was defined as p < 0.05. An institutional review board waiver was obtained before the study was conducted.
Results
The overall cohort comprised 35 611 men with locally advanced or metastatic PCa. The comparison groups consisted of unweighted cohorts of 5 927 (16.77%) Black and 29 639 (83.23%) White men. The cohort selection is depicted in Fig. 1. The median follow-up was 91.43 months and the median survival was 82.73 months. Baseline characteristics are summarized in Table 1.
Figure 1. Cohort selection
On average, Black men were younger, more often lived in metropolitan areas (88.46 versus 77.91%, p < 0.001) and had proportionally more metastatic disease (46.99 versus 34.77%, p < 0.001). Black patients less often received radical prostatectomy (16.98 versus 24.22%, p < 0.001) or radiation (39.5 versus 44.81%, p < 0.001). Finally, Black men were more often uninsured compared with their White counterparts (7.05 versus 2.69%, p < 0.001); Black men more often presented with the lowest quartile of income (43.42 versus 13.72%, p < 0.001) and were more likely to have no high school diploma (35.26 versus 13.66%, p < 0.001), they were more often treated at hospitals closer to their home address (74.25 versus 54.29%, p < 0.001).
Table 1. Baseline characteristics of Black and White patients and their unadjusted standardized differences
Table 2 summarizes the results of our four propensity score weighting procedures. Each of the four vertical columns represents a separate weighting procedure. After weighting, the mean standardized differences for the covariates included in each respective propensity score were below 10%, suggesting adequate balance between Black and White groups.
Table 2. Cohort balance expressed as standardized differences between Black and White patients after four sequential IPTW procedures
The results of IPTW-adjusted Cox regression models and Kaplan–Meier survival analyses are presented in Table 3 and Fig. 2a–d. After balancing for demographic and comorbidities only, Black race was significantly associated with worse OS (hazard ratio (HR) 1.27, 95%-confidence interval (95%-CI) 1.2–1.34, p < 0.001). After weighting the cohorts to simulate equivalent access to care among Black and White patients, the Black/White survival difference was eliminated (HR 1.04, 95%-CI 0.97–1.12, p = 0.276)— regardless of there being proportionally more high-stage and high-grade disease in the Black men.
Table 3. Hazard ratios and explainable excess risk of mortality (Black versus White) after four sequential propensity score weighting procedures
Next, after weighting the cohorts to include treatment modalities and then tumor characteristics, the survival reversed towards a significant OS benefit of Black (HR 0.93, 95%-CI 0.86– 1.01, p = 0.071 and HR 0.92, 95%-CI 0.84–1.00, p = 0.043, respectively). Overall, access-related factors explained 84.7% of the excess risk of death among Black men versus 4.7% from tumor-related factors.
Figure 2 a–d. IPTW-adjusted Kaplan–Meier curves of overall survival of White and Black men including a demographics; b demographics and access to care; c demographics, access to care, and treatment; d demographics, access to care, treatment, and cancer characteristics
Discussion
In this retrospective study comparing OS of Black and White men with locally advanced and metastatic PCa, we found statistically significant worse OS among demographically balanced cohorts Black men as compared with White men. After weighting the study subjects to simulate equivalent access to care, this survival difference between Black and White men was eliminated. Addition of treatment and tumor characteristics to our propensity score model resulted in an inversion of the survival difference—with a statistically significant better survival in Black men. The findings of this simulated model are consistent with a major role for access-related variables in Black/White disparities in PCa outcomes.
Finding strategies to overcome differences in access to care is subject of recent discussions and political interest. According to the National Center for Health Statistics, adults with any period without health insurance in the past year were more likely to have had an unmet medical need due to cost than those with coverage for more than a year.26 Several practices have been shown to be associated with better access to care especially for underserved populations. These comprised primarily of educational endeavors and identification of low-cost options for both, medical treatment and insurance27—all of which are considerable for further efforts.
The authors of two recent studies from the American Cancer Society further emphasize the importance of equal access to care not only in PCa. They employed a similar approach to infer the relative contribution of demographic, access, and treatment-related variables on Black–White disparities in breast and colorectal cancer. Jemal et al. found a that lack of insurance coverage accounted for 37% of excess risk of mortality in Black women with breast cancer, compared with 23% due to differences in tumor characteristics.14 In colorectal cancer, Sineshaw et al. showed that a lack of insurance coverage accounted for 54% of the excess risk of death in Black patients, versus 27% from tumor characteristics.16,17
Although there are well-known studies highlighting racial differences in the biology of PCa seen in Black and White men, our results are consistent with a growing body of work suggesting that survival between Black and White men with PCa may be more related to inequalities in access28,29 and receipt of care.30 Several studies support the concept that providing equal access to care will mitigate racial differences in outcomes.14,15
Our study showed that after controlling for all factors, Black men do even better than Whites. Although controversial, our study broadly corroborate observations made in two recent studies, which showed that Black men with castration-resistant PCa have a greater and more durable PSA response to secondary hormonal treatment,31 as well as significantly better OS than their White counterparts.32
Our study benefits from several noteworthy strengths. Although the previous studies made inferences based on analyses of group-level outcomes, we analyzed individual patient-level survival outcomes and thus our work is less subject to the ecological fallacy.33 Because we focused only on locally advanced and metastatic cancer, it is likely that the substantial effect of “access to care” is not simply an artifact of large numbers of insured men diagnosed with indolent, screening-detected disease. An additional strength lies in leaving out tumor characteristics until the last weighting procedure. What this means is that even despite worse tumor characteristics, Black men had equal OS than White men after weighting to simulate equal access to care. Although other authors looked at survival among separate care settings34,35 to support the hypothesis that access related parameters affect racial disparities; our study uniquely focused on a patient-level analysis across multiple care settings. Thus, we were able to infer their individual and specific contribution of each group of variables on excess. By applying propensity weighting rather than propensity matching, we could increase sample size without any reduction in the ability to account for confounders.24
Despite these strengths, the study is not without limitations. First, despite the use of propensity score weighting, there may be aspects of care access not captured by our included variables. For example, Black and White men with “private insurance” may in fact have different barriers to access and different insurance quality, but would be treated the same way in our model. Similarly, Black and White men who both receive a “radical prostatectomy” may have surgeons with different skill and quality of follow-up. Similarly, to assess tumor biology we looked at available variables such as stage, grade, and PSA, however, there may be aspects of tumor biology, which are not captured in these variables such as genomic markers. Third, by investigating advanced and metastatic cancer stages, we selected more homogenous groups in terms of tumor characteristics, which may reduce their relative contribution to survival. Furthermore, the NCDB only includes CoC-accredited hospitals; thus, patients managed in non-CoC hospitals or those who move their care to non-CoC facilities during treatment are not accounted for in our study. Fourth, the NCDB does not provide the cause of death—we chose to focus on advanced disease because we felt that overall mortality would therefore be more likely related to cancer specific mortality. Finally, we could not account for granular effects of race on clinical care: for example, physician and patients’ belief systems, trust and other possible sources of differential care. Some aspects of treatment (e.g., the specific type of hormonal therapy) were also not available.
Overall, these limitations are important but are largely unavoidable in studies of racial disparities in cancer—which obviously do not lend themselves to prospective clinical trials. Although our approach does not rule out race-based differences in tumor biology between Black and White men, our study does highlight the critical role of access to care (and to a lesser extent, treatment) for disparities PCa outcomes that were even better for Black men after simulating equal conditions as White men. Although policy efforts are demanded to increase access to care to dramatically reduce race-based disparities, there is also a need of endeavors to include Black individuals to a greater proportion into clinical trials to better understand the underlying reasons of differing outcomes.
Conclusion
Black men with locally advanced and metastatic PCa had significantly worse OS compared with White men. After performing a statistical technique to simulate equal access to care, this survival difference was eliminated, and even reversed after accounting for all parameters. Besides improving access to care for Blacks, further studies with a larger proportion of Black individuals are needed to better understand this finding.
Conflict of interest - Q-DT reports honoraria from Bayer and Astellas. ASK reports consulting fees from Sanofi, Dendreon, Tokai, and Profound. PLN consulted for Ferring, Bayer, Astellas, Janssen, Blue Earth, Dendreon and received research funding from Janssen and Astellas. JCH is on the speakers’ bureau for Genomic Health. These financial relationships are outside this submitted work.
Authors: Marieke J. Krimphove1,2, Alexander P. Cole1, Sean A. Fletcher1, Sabrina S. Harmouch1, Sebastian Berg1,3, Stuart R. Lipsitz4, Maxine Sun1,5, Junaid Nabi1, Paul L. Nguyen6, Jim C. Hu7, Adam S. Kibel1, Toni K. Choueiri5, Luis A. Kluth2, Quoc-Dien Trinh1
1. Division of Urological Surgery and Center for Surgery and Public Health, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
2. Department of Urology, University Hospital Frankfurt, Frankfurt am Main, Germany
3. Department of Urology, Marien Hospital Herne, Ruhr-University Bochum, Herne, Germany
4. Division of General Internal Medicine and Center for Surgery and Public Health, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
5. Lank Center for Genitourinary Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
6. Department of Radiation Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
7. Department of Urology, Weill Cornell Medicine–New York Presbyterian Hospital, New York, NY, USA
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Read an Editorial by Andrew J. Armstrong, MD, ScM, FACP: Initiatives to Improve Access to Care and their Effect on Reducing Disparities in Prostate Cancer Outcomes - Editorial