Grant Number:	5R21CA215252-02 Interpret this number
Primary Investigator:	Molina, Yamile
Organization:	University Of Illinois At Chicago
Project Title:	Assessing and Modeling Network-Level Consequences of Patient Navigation
Fiscal Year:	2019

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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