The "Spectrum" of Breast Cancer Disparities

Principal Investigator: Jeanne S. Mandelblatt
Institution: Georgetown University Medical Center
Grant Number: 2U01CA088283-05

Awarded under CA-05-018
Originally funded under CA-99-013 (view abstract)

Abstract: Breast cancer remains the second most common cancer in women, and is one of the few cancer sites for which incidence rates continue to rise. In addition, despite improvements in breast cancer survival, racial disparities in mortality remain pervasive. These mortality disparities have been widening over the last two decades, with the odds of dying from breast cancer for Black vs. White women increasing from 1.27 in 1984 to 1.85 in 1999. This racial inequality in breast cancer outcomes is cast on the backdrop of an obesity epidemic. We have assembled an experienced multidisciplinary team from Georgetown University, Einstein College of Medicine, Erasmus Medical Center, and RAND to use two established models (SPECTRUM and MISCAN) to develop a mini "base case" with common parameters to simulate how obesity (defined as BMI >30) interacts with risk, screening outcomes, and treatment effectiveness to effect trends in overall and race-specific US breast cancer incidence and mortality from 1975 to 2015. We will also project results to 2025 to capture the lag time in effects of obesity (and screening). We have selected obesity as a key modifiable risk factor to be examined since it has been identified as a proximate target for change to achieve the Healthy People 2010 targets, disproportionately effects Black women, and impacts breast cancer incidence and mortality through several mechanisms with potentially competing effects. As secondary goals, we will also examine how screening policies and treatment improvements are likely to impact future differences in rates of breast cancer between Black and White women. Our objectives will be accomplished in three phases: In the first phase, we will focus on developing the common data inputs needed to address our research questions. In phase two, we will use the models to synthesize the data and conduct analyses. Finally, in phase three, we will use the results to inform policy and practice relevant discussions and make the models available to others to address emerging research questions. Throughout, we will also collaborate with other CISNET modeling groups. By working together with two models, we can achieve important economies of scale, test the impact of model structure on results, provide a range of plausible projections, and contribute to a better understanding of the science of modeling. Overall, the information generated by these models will provide a framework to inform policy debates about equity in care and how to best achieve targeted reductions in breast cancer morbidity and mortality for all US women.

Awarded under CA-99-013
Title: Outcomes Across the Spectrum of Breast Cancer Care

Abstract: The US Department of Health and Human Services has renewed its "war on cancer" by declaring the elimination of race and age disparities in cancer screening and management as a key public health priority for the next century; the National Cancer Institute Year 2010 goals echo this priority. The importance of these cancer control objectives is cast on the backdrop of the changing demographic profile of the US: by the year 2030, one in five women will be 65 years or older, and 40 percent will be from minority groups. Thus, successful achievement of these objectives will require application of effective interventions to diverse populations, and integration of evolving paradigms of breast cancer care into public health initiatives. Modeling can evaluate the success of such initiatives. However, the majority of existing models have focused on a single dimension of breast cancer care, and generally lack flexibility to study trends in outcomes among population subgroups. To address this gap, Lombardi Cancer Center, in collaboration with MEDTAP International, has constituted a multi-disciplinary team of demographers, epidemiologists, oncologists, genetics, behavioral science, and health services researchers, and economists to develop a novel discrete-event, stochastic population forecasting simulation model. Our overarching goal is to extend and use our existing model to develop an integrated model of disease history linked to sub-models portraying modifiable points in the cancer control process, including primary prevention, early detection, methods to enhance diagnosis, and improvements in treatment quality and practice (ie, models within a model). We will use this model to evaluate the impact of changes in behaviors, practice patterns, and interventions on intermediate outcomes and incidence and mortality trends. Innovative features of our model include the integration of epidemiological and biological representations of the disease process with the screening, diagnostic, and treatment, portrayal of disease in Whites and Blacks, and incorporation of the effects of comorbidities on effectiveness and quality-adjusted survival. We will test hypotheses about which services will be most effective, in which population-, age-, and health-groups, for which phase of care, in reducing overall breast cancer mortality. Secondary objectives include using existing utility data to identify areas where preferences change conclusions about effectiveness, and to use cost data to evaluate which strategies yield the maximal improvement in outcomes at the most reasonable costs. Overall, the data from the model will provide a framework for setting cancer control priorities in the next century.