Aimee M Near1, Jeanne S Mandelblatt, Clyde B Schechter, Michael A Stoto. 1. Department of Oncology, Georgetown University Medical Center and Cancer Control Program, Lombardi Comprehensive Cancer Center, 3300 Whitehaven St, NW, Suite 4100, Washington, DC 20007.
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 vs. 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.
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 vs. 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.
Authors: Kathleen A Cronin; Binbing Yu; Martin Krapcho; Diana L Miglioretti; Michael P Fay; Grant Izmirlian; Rachel Ballard-Barbash; Berta M Geller; Eric J Feuer Journal: Cancer Causes Control Date: 2005-08 Impact factor: 2.506
Authors: Nicolien T van Ravesteyn; Clyde B Schechter; Aimee M Near; Eveline A M Heijnsdijk; Michael A Stoto; Gerrit Draisma; Harry J de Koning; Jeanne S Mandelblatt Journal: Cancer Epidemiol Biomarkers Prev Date: 2010-11-30 Impact factor: 4.254
Authors: E Paci; R Boer; M Zappa; H J de Koning; G J van Oortmarssen; E Crocetti; D Giorgi; M Rosselli del Turco; J D Habbema Journal: Eur J Cancer Date: 1995 Impact factor: 9.162
Authors: Oguzhan Alagoz; Donald A Berry; Harry J de Koning; Eric J Feuer; Sandra J Lee; Sylvia K Plevritis; Clyde B Schechter; Natasha K Stout; Amy Trentham-Dietz; Jeanne S Mandelblatt Journal: Med Decis Making Date: 2018-04 Impact factor: 2.583
Authors: Clyde B Schechter; Aimee M Near; Jinani Jayasekera; Young Chandler; Jeanne S Mandelblatt Journal: Med Decis Making Date: 2018-04 Impact factor: 2.583