Table of Contents
Epidemiology Research International
Volume 2012, Article ID 241340, 10 pages
http://dx.doi.org/10.1155/2012/241340
Research Article

Using Simulation Modeling to Inform Strategies to Reduce Breast Cancer Mortality in Black Women in the District of Columbia

1Department of Oncology, Georgetown University Medical Center and Cancer Control Program, Lombardi Comprehensive Cancer Center, 3300 Whitehaven Street NW, Suite 4100, Washington, DC 20007, USA
2Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Mazer Building 406, Bronx, NY 10461, USA
3Department of Health Systems Administration, Georgetown University, 3700 Reservoir Road, NW Room 236, Washington, DC 20057-1107, USA

Received 30 October 2011; Accepted 26 April 2012

Academic Editor: Carolyn Rutter

Copyright © 2012 Aimee M. Near et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background. Black women in the District of Columbia (DC) have the highest breast cancer mortality in the US. Local cancer control planners are interested in how to most efficiently reduce this mortality. Methods. An established simulation model was adapted to reflect the experiences of Black women in DC and estimate the past and future impact of changes in use of screening and adjuvant treatment. Results. The model estimates that the observed reduction in mortality that occurred from 1975 to 2007 attributable to screening, treatment, and both was 20.2%, 25.7%, and 41.0% respectively. The results suggest that, by 2020, breast cancer mortality among Black women in DC could be reduced by 6% more by initiating screening at age 40 versus age 50. Screening annually may also reduce mortality to a greater extent than biennially, albeit with a marked increase in false positive screening rates. Conclusion. This study demonstrates how modeling can provide data to assist local planners as they consider different cancer control policies based on their individual populations.