Clinic-based interventions for improving access to care: a good start

Prior to 1980, breast cancer mortality rates were slightly lower in Black women than in White women in the United States (1). During that decade, effective early detection of breast cancer through regular mammography screening and adjuvant endocrine treatment became available. Since that time, Black women have had substantially higher breast cancer mortality rates than White women, partly because they have not had equal access to advancements in screening and treatment (2-7). Differences in screening and treatment are attributable to a host of factors including health insurance (8), access to care (9), social determinants of health and health-related social needs (10), structural racism (11), and tumor biology (12). Black women are more likely to experience delays in treatment initiation, and once initiated, they are more likely to prematurely discontinue treatment (13,14). Clinic-based approaches (eg, patient navigators, bias training) to reduce inequities in care by race are reported to improve equitable treatment (15). Therefore, one might speculate that if these types of interventions were implemented population wide, would they close the breast cancer mortality gap between Black and White women? This important question is the subject of a study published in this issue of the Journal by Yanguela and colleagues (16).

In their study, Yanguela et al. (16) simulated the population-level impact of statewide implementation of clinic-based inequity-reduction interventions that would reduce treatment inequities between Black and White women diagnosed with breast cancer. The interventions, relative to the medical (eg, new therapeutics) or policy interventions (eg, universal health insurance), are low cost and include patient navigation, provider bias training, and real-time tracking dashboards. If effective, these interventions may lead to more equitable treatment, potentially reducing disparities in survival outcomes. Yanguela et al. (16) used Markov models, a well-established method, to examine outcomes on a population level in North Carolina. They used race-stratified Markov models to simulate the increase in the proportion of patients who received endocrine therapy and chemotherapy. Both therapies are recommended by clinical guidelines for reducing the risk of recurrence and prolonging survival (17,18). By implementing these clinic-based interventions, greater guideline concordant care is expected and, subsequently, narrowing the breast cancer survival gap between Black and White women. Without such interventions, a 10% and 15% difference in 5- and 10-year survival, respectively, will persist.

Yanguela and colleagues’ (16) findings suggest that these clinic-based interventions can remove treatment inequities for receipt of endocrine therapy and chemotherapy, which is an important step toward reducing racial inequities in breast cancer outcomes. Disappointingly, these improvements in treatment did not translate into similar improvements in survival. Clinic-based interventions lowered the survival gap by roughly only half of a percentage point, leading the authors to conclude that additional measures are needed to eliminate racial inequities in breast cancer outcomes. In an important sensitivity analysis, the authors replicated their results on Medicaid-insured women to establish a socially vulnerable cohort (see Supplementary Table 8, available online). These women were disproportionately Black, and the overall sample size was small. In this cohort however, clinic-based interventions reduced or eliminated treatment inequities between Black and White women and narrowed but did not eliminate survival differences. Ultimately, Yanguela and colleagues (16) concluded that equalizing treatment is important but insufficient to close the breast cancer mortality gap between Black and White women.

All interventions considered in Yanguela and colleagues’ (16) simulation exercise are clinic based with the goal of improving receipt of treatment and do not focus on external factors such as screening, cancer diagnosis stage, health insurance coverage, access to care, and social needs and determinants of health. We note that although study findings reported that clinic-based interventions did not eliminate the survival gap, improvements in breast cancer survival by a small percentage could affect many Black women with breast cancer. Estimations for the number of women affected by a half of a percentage point in North Carolina were not provided. Even if this number is small, the reduction in risk of recurrence and cancer death is still relevant and important for those affected, especially because breast cancer is the second leading cause of cancer deaths among women nationally (19). Thus, the implementation of low-cost, clinic-based interventions may be an important first step absent of more widespread policy and societal changes. Although not considered in the models of Yanguela and colleagues’ (16) study, these clinic-based interventions may also be effective at ensuring women receive guideline-recommended surgery and/or complete radiation after breast-conserving surgery; both are shown to be critically important in reducing mortality gaps (20,21). The accumulated effect of clinic-based interventions may also spill over into other initiatives (eg, treatment of comorbid conditions, connection to other services such as nutrition counseling) that can have a greater impact over time. These initiatives should become more feasible to implement when the Centers for Medicare and Medicaid Services started reimbursing for patient navigation services in 2024 (22).

Assessing the intervention effects for the population is challenging because of large baseline differences in cancer survival (6.7 percentage points for the 10-year radiation cohort and 8.2 percentage points for the 10-year chemotherapy cohort), most likely driven by a substantially higher proportion of Black women diagnosed at later stages. Without findings stratified by cancer stage, we draw attention to the Medicaid cohort where the baseline differences in survival were smaller (2.3 percentage points for the 10-year radiation cohort and 0.8 percentage points for the 10-year chemotherapy cohort) relative to the differences in the entire population. In addition to making the baseline survival more comparable between Black and White women with breast cancer, the Medicaid cohort provides insight into the role of social and economic vulnerability in mortality differences. Many of these women may have been uninsured prior to diagnosis (23,24). If enrolled in Medicaid, they met stringent low-income thresholds. In 2018, Medicaid income eligibility in North Carolina was 43% of the federal poverty line for adults in a family of 3, which is approximately $9000 annually or $750 monthly. North Carolina has since expanded Medicaid income eligibility under the Affordable Care Act, providing greater financial and health insurance coverage security for its residents. Social and economic vulnerability is a considerable risk factor. Even so, clinic-based interventions narrowed the survival gap between Black and White women. The remaining gap was 2.3 percentage points in the 10-year radiation cohort and 0.4 in the 10-year chemotherapy cohort. Nonetheless, a survival gap remained, suggesting that additional efforts are needed to eliminate racial differences.

In our view, it would be surprising if clinic-based interventions alone were sufficient to eliminate survival differences, despite their ability to eliminate differences in receipt of endocrine therapy and chemotherapy between Black and White women—so much more than treatment factors into survival following breast cancer diagnosis. Research from the Cancer Intervention and Surveillance Modeling Network concluded that screening alone attributed to 25% of the reduction in breast cancer mortality over the past several decades (25), suggesting the critical role of reducing racial disparities in screening to achieve more equitable stage distribution between Black and White patients. Among women diagnosed with breast cancer, support at home, transportation, and food security, as well as underlying health status that includes not only comorbid conditions and aggressive cancer subtypes but also early childhood experiences can influence mortality (26-30). Housing insecurity has aslo been shown to adversely affect cancer outcomes (31). The Medicaid cohort in Yanguela and colleagues’ (16) simulation accounts for some of these factors, but even in a cohort of people with low-income, Black patients are more likely to experience health-related social needs than their White counterparts. They are also exposed to systemic racism that exacerbates all other negative social, biological, and economic exposures. Furthermore, higher comorbidities often observed among Black patients raised a question whether the reduction in the survival gap would be larger than estimated here had the authors accounted for competing risks in their model.

Taken together, the 2 essential findings of Yanguela and colleagues’ (16) simulation are that 1) treatment inequities can be addressed through low-cost, clinic-based interventions, and 2) despite increasing access to effective treatment, survival differences between Black and White women may persist. Given the narrowing of differences in the Medicaid cohort, the findings suggest that much, but not all, of the differences can be explained by social and economic vulnerability. These differences may be attributable to disparities in primary and secondary preventions and the accumulated inequities Black people in the United States experience. Our health systems should embrace interventions that reduce treatment inequities; it is their responsibility to do so. As noted, reducing these inequities may have larger mortality benefits over time. This is the optimistic interpretation of Yanguela and colleagues’ (16) study. Society should also embrace interventions to decrease inequities that stem from sources outside the clinic; it, too, is our responsibility to do so.

Data availability

No new data were generated or analyzed for this editorial.

Author contributions

Cathy Bradley, PhD, MPA (Conceptualization; Writing—original draft; Writing—review & editing), K. Robin Yabroff, PhD (Writing—original draft; Writing—review & editing), and Ya-Chen Tina Shih, PhD (Writing—original draft; Writing—review & editing).

Funding

No funding was use for this editorial.

Conflicts of interest

Bradley, Yabroff, and Shih are JNCI associate editors and authors of this editorial. Shih and Yabroff were not involved in the editorial review of the manuscript or decision to publish the editorial. Bradley was involved in the review of the manuscript that is the subject of the editorial and the decision to publish.

Acknowledgements

This editorial reflects the viewpoint of the authors and is not the official position of the University of Colorado, the American Cancer Society, or the University of California-Los Angeles.