Population simulation modeling of disparities in US breast cancer mortality

Author:

Mandelblatt Jeanne S1ORCID,Schechter Clyde B2,Stout Natasha K3,Huang Hui4ORCID,Stein Sarah3,Hunter Chapman Christina5,Trentham-Dietz Amy6ORCID,Jayasekera Jinani7,Gangnon Ronald E8,Hampton John M6,Abraham Linn9,O’Meara Ellen S9,Sheppard Vanessa B10,Lee Sandra J4

Affiliation:

1. Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program at Georgetown Lombardi Comprehensive Cancer Center , Washington, DC, USA

2. Departments of Family and Social Medicine and of Epidemiology and Population Health, Albert Einstein College of Medicine , Bronx, NY, USA

3. Department of Population Sciences, Harvard Medical School and Harvard Pilgrim Health Care Institute , Boston, MA, USA

4. Department of Data Science, Dana-Farber Cancer Institute and Harvard Medical School , Boston, MA, USA

5. Department of Radiation Oncology, Section of Health Services Research, Baylor College of Medicine and Health Policy, Quality and Informatics Program at the Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center , Houston, TX, USA

6. Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison , Madison, WI, USA

7. Health Equity and Decision Sciences Research Lab, National Institute on Minority Health and Health Disparities, Intramural Research Program, National Institutes of Health , Bethesda, MD, USA

8. Departments of Population Health Sciences and of Biostatistics and Medical Informatics and Carbone Cancer Center, University of Wisconsin-Madison , Madison, WI, USA

9. Kaiser Permanente Washington Health Research Institute , Seattle, WA, USA

10. Department of Health Behavior and Policy and Massey Cancer Center, Virginia Commonwealth University , Richmond, VA, USA

Abstract

Abstract Background Populations of African American or Black women have persistently higher breast cancer mortality than the overall US population, despite having slightly lower age-adjusted incidence. Methods Three Cancer Intervention and Surveillance Modeling Network simulation teams modeled cancer mortality disparities between Black female populations and the overall US population. Model inputs used racial group–specific data from clinical trials, national registries, nationally representative surveys, and observational studies. Analyses began with cancer mortality in the overall population and sequentially replaced parameters for Black populations to quantify the percentage of modeled breast cancer morality disparities attributable to differences in demographics, incidence, access to screening and treatment, and variation in tumor biology and response to therapy. Results Results were similar across the 3 models. In 2019, racial differences in incidence and competing mortality accounted for a net ‒1% of mortality disparities, while tumor subtype and stage distributions accounted for a mean of 20% (range across models = 13%-24%), and screening accounted for a mean of 3% (range = 3%-4%) of the modeled mortality disparities. Treatment parameters accounted for the majority of modeled mortality disparities: mean = 17% (range = 16%-19%) for treatment initiation and mean = 61% (range = 57%-63%) for real-world effectiveness. Conclusion Our model results suggest that changes in policies that target improvements in treatment access could increase breast cancer equity. The findings also highlight that efforts must extend beyond policies targeting equity in treatment initiation to include high-quality treatment completion. This research will facilitate future modeling to test the effects of different specific policy changes on mortality disparities.

Funder

National Institutes of Health

NIH

National Institute on Aging

Georgetown University Lombardi Cancer Center Support grant

NIH under NCI

Division of Intramural Research at the National Institute on Minority Health and Health Disparities of the NIH and the NIH Distinguished Scholars Program

Publisher

Oxford University Press (OUP)

Subject

Cancer Research,Oncology

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