Income inequality and treatment of African American men with high-risk prostate cancer

PURPOSE

Definitive treatment of high-risk prostate cancer with radical prostatectomy or radiation improves survival. We assessed whether racial disparities in the receipt of definitive therapy for prostate cancer vary by regional income.

PATIENTS AND METHODS

A cohort of 102,486 men (17,594 African American [AA] and 84,892 non-Hispanic white) with localized high-risk prostate cancer (prostate-specific antigen >20 ng/ml or Gleason ≥ 8 or stage ≥ cT2c) diagnosed from 2004 to 2010 was identified in the Surveillance, Epidemiology, and End Results database. Income was measured at the census-tract-level. We used multivariable logistic regression to assess patient and cancer characteristics associated with the receipt of definitive therapy for prostate cancer. Multivariable Fine and Gray competing risks analysis was used to evaluate factors associated with prostate cancer death.

RESULTS

Overall, AA men were less likely to receive definitive therapy than white men (adjusted odds ratio [AOR] = 0.51; 95% CI: 0.49-0.54; P<0.001), and there was a significant race/income interaction (Pinteraction = 0.016) such that there was a larger racial treatment disparity in the bottom income quintile (AOR = 0.49; 95% CI: 0.45-0.55; P<0.001) than in the top income quintile (AOR = 0.60; 95% CI: 0.51-0.71; P<0.001). After a median follow-up of 35 months, AA men in the bottom income quintile suffered the greatest prostate cancer mortality (adjusted hazard ratio = 1.47; 95% CI: 1.17-1.84; P = 0.001), compared with white men in the top income quintile.

CONCLUSIONS

Racial disparities in the receipt of definitive therapy for high-risk prostate cancer are greatest in low-income communities, suggesting that interventions to reduce racial disparities should target low-income populations first.

Scalable combinatorial tools for health disparities research

Despite staggering investments made in unraveling the human genome, current estimates suggest that as much as 90% of the variance in cancer and chronic diseases can be attributed to factors outside an individual’s genetic endowment, particularly to environmental exposures experienced across his or her life course. New analytical approaches are clearly required as investigators turn to complicated systems theory and ecological, place-based and life-history perspectives in order to understand more clearly the relationships between social determinants, environmental exposures and health disparities. While traditional data analysis techniques remain foundational to health disparities research, they are easily overwhelmed by the ever-increasing size and heterogeneity of available data needed to illuminate latent gene x environment interactions. This has prompted the adaptation and application of scalable combinatorial methods, many from genome science research, to the study of population health. Most of these powerful tools are algorithmically sophisticated, highly automated and mathematically abstract. Their utility motivates the main theme of this paper, which is to describe real applications of innovative transdisciplinary models and analyses in an effort to help move the research community closer toward identifying the causal mechanisms and associated environmental contexts underlying health disparities. The public health exposome is used as a contemporary focus for addressing the complex nature of this subject.

Individual- and neighborhood-level predictors of mortality in Florida colorectal cancer patients

Purpose

We examined individual-level and neighborhood-level predictors of mortality in CRC patients diagnosed in Florida to identify high-risk groups for targeted interventions.

Methods

Demographic and clinical data from the Florida Cancer Data System registry (2007–2011) were linked with Agency for Health Care Administration and US Census data (n = 47,872). Cox hazard regression models were fitted with candidate predictors of CRC survival and stratified by age group (18–49, 50–64, 65+).

Results

Stratified by age group, higher mortality risk per comorbidity was found among youngest (21%), followed by middle (19%), and then oldest (14%) age groups. The two younger age groups had higher mortality risk with proximal compared to those with distal cancer. Compared with private insurance, those in the middle age group were at higher death risk if not insured (HR = 1.35), or received healthcare through Medicare (HR = 1.44), Medicaid (HR = 1.53), or the Veteran’s Administration (HR = 1.26). Only Medicaid in the youngest (52% higher risk) and those not insured in the oldest group (24% lower risk) were significantly different from their privately insured counterparts. Among 18–49 and 50–64 age groups there was a higher mortality risk among the lowest SES (1.17- and 1.23-fold higher in the middle age and 1.12- and 1.17-fold higher in the older age group, respectively) compared to highest SES. Married patients were significantly better off than divorced/separated (HR = 1.22), single (HR = 1.29), or widowed (HR = 1.19) patients.

Conclusion

Factors associated with increased risk for mortality among individuals with CRC included being older, uninsured, unmarried, more comorbidities, living in lower SES neighborhoods, and diagnosed at later disease stage. Higher risk among younger patients was attributed to proximal cancer site, Medicaid, and distant disease; however, lower SES and being unmarried were not risk factors in this age group. Targeted interventions to improve survivorship and greater social support while considering age classification may assist these high-risk groups.