Spatial patterns of racial disparities in breast cancer mortality in Georgia: insights for targeted interventions

Breast cancer remains the most commonly diagnosed malignancy among women in the United States and is the second leading cause of cancer-related mortality (1). Despite advances in early detection and treatment over the past several decades, significant racial disparities in outcomes persist (2). Non-Hispanic Black (NHB) women experience disproportionately higher mortality from breast cancer compared with non-Hispanic White (NHW) women, even when controlling for stage at diagnosis, tumor subtype, or socioeconomic status (SES) (2). National-level statistics report a 40% higher breast cancer mortality rate among NHB women compared to NHW women, despite similar incidence rates, highlighting the persistence of these inequities and the insufficiency of purely clinical explanations (3).

Traditional approaches to monitoring breast cancer disparities often rely on aggregated state- or national-level data, which can obscure important local variations. Yet, the geography of breast cancer outcomes is complex, and disparities are not uniformly distributed across the United States (4). For example, southeastern states, including Georgia, have been shown to exhibit more pronounced racial mortality disparities, whereas northeastern states tend to show lower differences (5). Understanding the local-level distribution of disparities is critical, as it can illuminate underlying drivers and guide targeted interventions where they are most needed.

The current study by Nash et al. (6) seeks to address this gap by leveraging county-level data from the Georgia Cancer Registry (GCR), applying a Bayesian spatial modeling approach to estimate racial disparities in 5-year breast cancer mortality across counties. By focusing on the place of diagnosis rather than the place of death, this study offers an opportunity to identify points of intervention where timely detection and treatment may reduce mortality disparities. This approach provides a more granular understanding of racial disparities and the social, geographic, and structural factors that shape them. Importantly, the study’s use of multiple disparity measures allows for nuanced insights into both proportional and absolute differences, as well as the interaction between race and geography, offering a framework for equity-focused public health strategies.

The study population comprised 11,782 NHB and 29,222 NHW women aged 20 years or older, diagnosed with localized or regional first primary breast cancer between 2005 and 2013 in Georgia. Overall, 5-year mortality was 43% higher among NHB women compared to NHW women, consistent with national trends.

Geographic analysis revealed substantial heterogeneity in the magnitude of racial disparity. Using relative, absolute, and interaction contrast (IC) measures, counties in the central and southeastern regions of Georgia—particularly Fulton, Cobb, Henry, Putnam, Toombs, and Chatham—demonstrated the most pronounced disparities. For instance, Fulton County had a relative disparity of 1.61, indicating that NHB women were over 60% more likely to die within 5 years of diagnosis than their NHW counterparts. Notably, disparities were also observed in metropolitan areas, challenging the assumption that urban counties uniformly confer an advantage in access to high-quality care.

Conversely, some counties, particularly Dougherty and Rockdale, exhibited narrower racial disparities. These narrower gaps often reflected elevated mortality among NHW women rather than reductions among NHB women, underscoring the importance of examining absolute differences and ICs to fully understand the dynamics of disparity.

Area-level characteristics were associated with the magnitude of disparity. Rurality was linked to higher relative disparities once other covariates—education, poverty, and housing—were accounted for. Housing stability, operationalized as the proportion of owner-occupied units, was inversely associated with disparity, suggesting that more stable housing environments may confer protective effects that reduce mortality inequities. Importantly, these findings indicate that social determinants do not act uniformly across space, and interventions may need to be tailored not only to racial groups but also to the unique social and geographic context of each county.

This study leveraged several methodological strengths that enhance both the validity and utility of its findings. First, the use of Bayesian spatial modeling enabled the estimation of racial disparities even in counties with sparse data or small NHB populations, overcoming limitations of prior studies that suppressed or excluded low-count areas. This modeling approach “borrows strength” from neighboring counties, producing more stable and reliable estimates of local mortality rates.

Second, focusing on the place of diagnosis rather than the place of death provides a more actionable understanding of disparities. Interventions targeting diagnosis and early treatment can reduce mortality before cancer progression, highlighting counties where improvements in screening, care navigation, and treatment adherence could have the greatest impact.

Third, the study incorporates multiple measures of disparity—relative differences, absolute differences, and the IC. Relative measures provide insight into proportional risk, absolute measures quantify the public health burden, and IC highlights the interplay between race and geographic context. This multidimensional approach allows for a nuanced interpretation of disparities, capturing both the strength of association and the population-level consequences.

In addition, rigorous assessment of model fit using the deviance information criterion (DIC) and the Watanabe-Akaike information criterion (WAIC) ensured the robustness of model estimates. Spatial mapping of disparities allowed for visual representation of heterogeneity, making findings accessible for policymakers, community stakeholders, and healthcare providers. These maps are particularly useful for identifying high-priority counties where intervention efforts may yield the greatest benefit.

Despite these strengths, certain limitations should be acknowledged. First, analyses were conducted at the county level, which may mask sub-county variation and create potential ecological fallacies. Wealth, healthcare access, and social determinants vary substantially within counties, particularly in urban areas, suggesting that finer-scale analyses—at the census tract or neighborhood level—may be needed for targeted interventions.

Second, the study lacked individual-level socioeconomic data, comorbidity profiles, and measures of healthcare access, as expected with registry data. While area-level covariates like rurality and housing stability provide valuable context, these proxies cannot fully capture patient-level risk factors or the complex mechanisms underlying racial disparities. As digital connectivity capacity continues to improve, future studies that link registry data with electronic health records, insurance claims, or census-derived measures to enhance explanatory power.

Third, the study was limited to Georgia, and findings may not generalize to other states or regions with different population structures, healthcare systems, or social contexts. States with smaller NHB populations, different patterns of urbanization, or unique healthcare access challenges may demonstrate different patterns of disparity.

Finally, while the IC is informative for understanding the interplay between race and place, it may be challenging for non-technical audiences to interpret. Translating these findings into policy-relevant recommendations will require clear communication strategies and engagement with stakeholders to ensure that the evidence informs actionable interventions.

This study has several implications for research, policy, and clinical practice. First, the Bayesian modeling framework can be applied to other states to identify local disparities and guide resource allocation. Cancer registries should integrate individual- and neighborhood-level social determinants to provide actionable data for policymakers and healthcare providers (7).

Second, targeted interventions should be developed in high-disparity counties. These interventions could include expanding access to screening, ensuring timely follow-up care, and addressing structural barriers such as transportation, insurance coverage, and housing instability (8). Strengthening patient navigation programs, as demonstrated by Freeman’s initial program at Harlem Hospital—which significantly increased breast cancer 5-year survival rates from 39% to 70%—can help guide patients through complex healthcare systems, improve adherence to recommended care, and ultimately reduce disparities in breast cancer outcomes (9,10).

Third, public health efforts should recognize that urban areas are not uniformly advantaged. Within-county variation in resources, access, and outcomes underscores the need for geographically precise interventions rather than broad urban-rural dichotomies (11).

Additionally, housing stability’s inverse association with disparity highlights the importance of addressing social determinants of health in equity-focused cancer control. Policies promoting housing security, neighborhood investment, and community support may indirectly improve breast cancer survival outcomes, illustrating the interconnection between social policy and health equity (12,13).

Finally, the study’s methodological approach provides a model for how spatial epidemiology can guide evidence-based, place-specific interventions. By combining statistical rigor with geographic visualization, stakeholders can identify priority areas, track progress over time, and allocate resources efficiently (14).

The findings of this study are consistent with prior research documenting persistent racial disparities in breast cancer outcomes in different geographic locations. Prior studies have demonstrated higher recurrence and mortality among Black women in Memphis, Tennessee, even after adjusting for tumor biology and socioeconomic factors, highlighting that clinical characteristics alone cannot explain disparities (15). Similarly, it was observed in New York City that Black and Latina women presented with more advanced-stage disease and higher mortality-to-incidence ratios compared with White women, emphasizing the role of screening access and timely treatment (16).

Local-level disparities have also been documented in healthcare systems. McClintock et al. [2022] reported that minority patients at safety-net hospitals in Los Angeles County presented with more aggressive tumors and advanced disease at diagnosis than those treated at private hospitals, reflecting systemic factors that influence racial differences in outcomes (17). Neighborhood SES is also a known determinant of survival; Shariff-Marco et al. [2015] found that lower neighborhood SES predicted poorer breast cancer survival across racial and ethnic groups (18).

Complementing these findings, the Boston-based assessment of patient navigation programs highlighted substantial variability in how navigation services are implemented across academic hospitals, with differences in staffing, patient eligibility, and screening for social needs (19). Despite the presence of navigation programs at all sites, gaps in care coordination and systematic assessment of social determinants were evident, particularly for patients at the highest risk of delays in treatment. These results underscore that even when supportive interventions exist, local implementation and resource allocation critically shape whether disparities are mitigated, reinforcing the importance of integrating structural and community-level factors into efforts to reduce inequities.

The current study builds on this literature by integrating spatial Bayesian methods to capture local heterogeneity, explicitly estimating the effect of county-level social factors like rurality and housing stability. This allows for the identification of geographic “hotspots” of disparity and contributes to a growing recognition that race, place, and social context interact in complex ways to shape cancer outcomes. By providing granular county-level estimates, this study equips researchers, clinicians, and policymakers with actionable data to design interventions that target both structural inequities and local resource gaps.

This study provides a compelling example of how local-level data and spatial modeling can illuminate racial disparities in breast cancer mortality. The findings demonstrate substantial geographic heterogeneity, with certain urban and rural counties experiencing particularly pronounced disparities. Factors such as rurality and housing stability influence these disparities, suggesting avenues for policy and public health interventions.

Reducing racial disparities in breast cancer outcomes requires attention to the intersection of geography, policy, and structural inequities. Place-based interventions targeting high-disparity counties, improvements in healthcare access, and integration of social determinants into cancer control strategies are essential. This study lays the groundwork for informed, equity-focused interventions that can help close the racial mortality gap in breast cancer. Moreover, it illustrates the potential of combining advanced statistical methods with geographic data to produce meaningful, actionable insights, highlighting the critical role of spatial epidemiology in addressing health inequities.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Annals of Cancer Epidemiology. The article has undergone external peer review.

Peer Review File: Available at https://ace.amegroups.com/article/view/10.21037/ace-2025-10/prf

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://ace.amegroups.com/article/view/10.21037/ace-2025-10/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Houghton SC, Hankinson SE. Cancer Progress and Priorities: Breast Cancer. Cancer Epidemiol Biomarkers Prev 2021;30:822-44. [Crossref] [PubMed]
2. Akinyemiju T, Moore JX, Ojesina AI, et al. Racial disparities in individual breast cancer outcomes by hormone-receptor subtype, area-level socio-economic status and healthcare resources. Breast Cancer Res Treat 2016;157:575-86. [Crossref] [PubMed]
3. DeSantis CE, Ma J, Goding Sauer A, et al. Breast cancer statistics, 2017, racial disparity in mortality by state. CA Cancer J Clin 2017;67:439-48. [Crossref] [PubMed]
4. Chien LC, Yu HL, Schootman M. Efficient mapping and geographic disparities in breast cancer mortality at the county-level by race and age in the U.S. Spat Spatiotemporal Epidemiol 2013;5:27-37. [Crossref] [PubMed]
5. Giaquinto AN, Sung H, Newman LA, et al. Breast cancer statistics 2024. CA Cancer J Clin 2024;74:477-95. [Crossref] [PubMed]
6. Nash R, Switchenko JM, Ward KC, et al. A registry-based approach for estimating county-level race disparities in breast cancer mortality: an analysis in Georgia. Am J Epidemiol 2025;194:2698-704. [Crossref] [PubMed]
7. Alcaraz KI, Wiedt TL, Daniels EC, et al. Understanding and addressing social determinants to advance cancer health equity in the United States: A blueprint for practice, research, and policy. CA Cancer J Clin 2020;70:31-46. [Crossref] [PubMed]
8. Brawley OW, Lansey DG. Disparities in Breast Cancer Outcomes and How to Resolve Them. Hematol Oncol Clin North Am 2023;37:1-15. [Crossref] [PubMed]
9. Chan RJ, Milch VE, Crawford-Williams F, et al. Patient navigation across the cancer care continuum: An overview of systematic reviews and emerging literature. CA Cancer J Clin 2023;73:565-89. [Crossref] [PubMed]
10. Joseph KA. Addressing breast cancer surgical disparities using a community-clinical linkage patient navigation model strategy in a NYC hospital system. Am J Surg 2022;223:1228-9. [Crossref] [PubMed]
11. McGee-Avila JK, Richmond J, Henry KA, et al. Disparities in geospatial patterns of cancer care within urban counties and structural inequities in access to oncology care. Health Serv Res 2023;58:152-64. [Crossref] [PubMed]
12. Fan Q, Keene DE, Banegas MP, et al. Housing Insecurity Among Patients With Cancer. J Natl Cancer Inst 2022;114:1584-92. [Crossref] [PubMed]
13. Coughlin SS. Social determinants of breast cancer risk, stage, and survival. Breast Cancer Res Treat 2019;177:537-48. [Crossref] [PubMed]
14. Duncan EW, Cramb SM, Aitken JF, et al. Development of the Australian Cancer Atlas: spatial modelling, visualisation, and reporting of estimates. Int J Health Geogr 2019;18:21. [Crossref] [PubMed]
15. Lamb EP, Pritchard FE, Nouer SS, et al. Understanding Disparities in Breast Cancer Care in Memphis, Tennessee. Am Surg 2018;84:620-7.
16. Nobel TB, Asumeng CK, Jasek J, et al. Disparities in mortality-to-incidence ratios by race/ethnicity for female breast cancer in New York City, 2002-2016. Cancer Med 2020;9:8226-34. [Crossref] [PubMed]
17. McClintock NC, Ayabe RI, Salas Parra RD, et al. A Microcosm of Disparities in Breast Cancer: Comparison Between a Private Hospital and a Safety-Net County Hospital Within Los Angeles County. Am Surg 2022;88:1653-6. [Crossref] [PubMed]
18. Shariff-Marco S, Yang J, John EM, et al. Intersection of Race/Ethnicity and Socioeconomic Status in Mortality After Breast Cancer. J Community Health 2015;40:1287-99. [Crossref] [PubMed]
19. LeClair AM, Battaglia TA, Casanova NL, et al. Assessment of patient navigation programs for breast cancer patients across the city of Boston. Support Care Cancer 2022;30:2435-43. [Crossref] [PubMed]