This study found large differences by R/E in breast cancer survival for elderly women. Screening mammography, tumor severity, biology, treatment, comorbidities, and demographics all contributed to these differences. Controlling for these variables reduced nearly all of the differences between AA and white women in the all stages analysis and reduced, but did not eliminate, disparities in the stage II/III analysis.
In contrast, A/PI women had a consistently better survival profile than white women in all analyses that remained with the addition of predictor variables. This may be because there were no large differences between A/PI women and white women with regard to the variables measured. No statistically significant differences in survival were observed between H and white women, although the overall pattern demonstrated that H women have better survival than white women after full adjustment in both all stages and stage II/III analyses.
All women diagnosed with stage IV breast cancers had a similar risk of death regardless of R/E. This likely reflects the poor prognosis and small number of women in this group.
Screening mammography is known to reduce mortality from breast cancer in the general U.S. population31
with disparities in screening mammography heavily contributing to R/E disparities in breast cancer survival.6,32,33
Our findings show persistent differences by R/E in the utilization of screening mammography. These results are consistent with recent reports21,34
and may be due to persistent underutilization of mammography in this elderly population.
The findings of the current study demonstrate that, although the use of adequate screening will reduce differences in survival between AA and white women, it will not eliminate them. Despite not being the sole remedy, screening nevertheless remains important. It is possible that if H women had greater utilization of adequate screening, then their risk of breast cancer death could drop further below white women, as seen for A/PI women. AA differences in screening interval accounted for a considerable portion of the mortality difference with white women. Addressing this disparity should therefore reduce differences between AA and white women with regard to breast cancer survival.35
Stage at diagnosis, the strongest predictor of breast cancer survival, is known to contribute to R/E survival disparities.4
Our findings support this hypothesis, given the importance of tumor severity in reducing mortality disparities between AA and white women.
Tumor grade and estrogen receptor status are known to be different between AA and white women, suggesting that they may also contribute to differences in survival.18
In this study, biologic markers were found to have an effect of similar magnitude on survival results as did screening, tumor characteristics, and treatment. In exploring the role of biology further, it is important to understand the determinants of tumor biology given the ongoing debate as to whether biology reflects genetic causes versus exposure to less favorable environmental conditions.36,37
We found persistent differences in treatment by R/E, with AA women more likely to have poor-quality treatment. Adjusting for differences in treatment receipt reduced differences between AA and white women with regard to survival. This finding has been observed elsewhere and may reflect differential access to optimal care, including receipt of adjuvant radiotherapy with breast-conserving surgery.21,26,38,39
R/E disparities in access to optimal treatment, and therefore survival, may reflect differences in healthcare access, regional variations, or a higher disease burden.5 However, our findings among a Medicare population accounted for insurance type, SEER site, type of community, comorbidities, and tumor severity and still found differences.
It is interesting to note that comorbidities and demographics appeared to contribute relatively little to R/E differences noted. However, these factors could each act in a common pathway to other factors, as underserved/poor women are less likely to undergo mammography.40
Because the exact contribution of each variable to the reduction in hazard ratio remains dependent on the model order, it is important to consider these findings within the larger context that highlights a similar reduction in hazard risk for screening, biology, and treatment.
Despite the large number of variables considered in our model, there were persistent differences in stage II/III disease. Other untested hypotheses should be explored, particularly the possibility of R/E differences in the timeliness and quality of breast cancer care.41
Such differences may reflect the patient-physician relationship, patient preferences, and/or institutional and interpersonal racism across healthcare service provision.41–43
Differences in quality may occur in some contexts more often than others, partially explaining why differences persist within stage II/III only for AA and white women.41
For example, R/E differences in the quality of breast cancer care are unlikely to occur when all women are expected to do well or less well (ie, early/late stage) but become unmasked in situations in which timeliness and quality are more likely to affect outcomes (ie, stage II/III cancers).
Due to small numbers of mortality events we chose R/ E categories that may unfortunately mask disparities within each category. This is of particular concern for A/PI, H, and Other categories because subgroups within these categories (ie, Native American, Hawaiian, Mexican, South/Central American, Puerto Rican) have greater breast cancer mortality and poorer survival than white women.4,44
The potential for underserved subgroups within the AA population also exists, with Caribbean women having a different screening profile than other women identified as AA.45,46
Future analyses should disaggregate the R/E categories so that we can better understand these findings.11,44
The use of aggregate-based measures to assign individual SES status is suboptimal.20
However, aggregate measures of income at the community level remains valuable because community-level predictors of SES have been shown to be a strong predictor of healthcare services and health outcomes for all individuals living in that community (both high and low income).47
Our population all have health insurance and we expect that the importance of demographics may differ in a less insurance homogenous sample.
Unfortunately, data regarding hormonal therapy is not available in SEER or Medicare data and information on chemotherapy has not been included in the SEER public use dataset and is of uncertain validity within Medicare.48
Quality indicators such as timeliness are also difficult to measure within the SEER-Medicare database.19
Additional biologic markers were not available (ie, HER-2 status, CYP1A1, or P53 mutations), although there is no conclusive evidence of marker differences between AA and white women.9
This analysis improves on studies using SEER data alone because a more accurate assessment of screening exposure was used (rather than self-report that overestimates exposure), and comorbidity and treatment information were included.21,49,50
Therefore, the current study responds to calls in the literature for analyses that include these variables.7,9,10,37,51
Understanding the reasons for R/E disparities in breast cancer survival remains complex. Despite the complexity, there are still areas in which health policy should clearly intervene. Enough evidence now exists such that policy initiatives should urgently be applied to improve access to adequate screening for AA and H women and increase access to appropriate treatment for AA women in particular. Further multidisciplinary investigation into the role of biology, demographics, and potential disparities in quality of care are required in both young and elderly cohorts of women.