Increasing the number of African American (AA) women who are diagnosed with breast cancer at early stages
is a public health priority. Patient navigation has been developed to address this problem and is increasingly
widespread in public health practice. This individual-level strategy may have greater impacts than have been
previously estimated. Based on diffusion of innovation theories and our previous research, we hypothesize that
navigation may indirectly benefit patients' female relatives and non-relative friends (?alters?) and, subsequently,
population-level stage at diagnosis. Briefly, navigators coordinate care and provide informational, emotional,
and logistic support to patients. Receiving these additional supports has been associated with greater breast
cancer knowledge, medical system trust/knowledge, and breast cancer-specific communication self-efficacy
among navigated women. Empowering breast cancer patients through navigation may manifest in greater
survivor-driven dissemination to individuals known pre-diagnosis (induction). Survivors may also have greater
motivation to engage new individuals as a breast cancer leader (node addition, key network position). These
network changes and greater dissemination may result in greater rates of breast cancer-related shared
decision making practices, risk assessment, and screening among the alters of navigated women compared to
alters of non-navigated women. This may result in improved stage at diagnosis at the population-level.
Including network effects in economic evaluations may also reveal lower incremental costs for implementing
patient navigation than previously estimated. We will leverage system science methodologies (social network
analysis, agent-based modeling) to test these hypotheses and model how patient navigation may inadvertently
improve the likelihood of early stage diagnoses at the population-level. In Aim 1, we will recruit AA breast
cancer patients from a completed, NIH-funded randomized controlled trial in South Chicago to compare
patterns of breast cancer communication among 50 navigated and 50 non-navigated women using standard
egocentric network instruments. The primary outcomes will be the number of women to whom AA breast
cancer patients initiated conversations about breast cancer and the frequency of these conversations. Next, we
will compare breast cancer care among 150 family and friends identified by navigated and non-navigated
women. The primary outcomes will be breast cancer-related shared decision making practices with primary
care providers, risk assessment (genetic counseling, genetic testing), and breast cancer screening uptake.
Finally, we will determine the incremental costs of navigation compared to standard care for each additional
stage at diagnosis (including patients and their family/friends). In Aim 2, we will use published literature and
other local data sources to model improved population-level breast health as an emergent property from one
agent (the breast cancer patient) to other agents (relative/non-relative) using an agent-based model that
incorporates biological, intrapersonal, interpersonal, and network-level characteristics.
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