Understanding Decision Making about Breast Cancer Prevention in Action: The Intersection of Perceived Risk, Perceived Control, and Social Context: NRG Oncology/NSABP DMP-1.

BACKGROUND: Literature on decision making about breast cancer prevention focuses on individual perceptions and attitudes that predict chemoprevention use, rather than the process by which women decide whether to take risk-reducing medications. This secondary analysis aimed to understand how women's perceptions of breast cancer risk and locus of control influence their decision making. METHODS: Women were accrued as part of the NRG Oncology/National Surgical Adjuvant Breast and Bowel Project Decision-Making Project 1, a study aimed at understanding contributors to chemoprevention uptake. Thirty women participated in qualitative in-depth interviews after being counseled about chemoprevention. Deductive codes grouped women based on dimensions of risk perception and locus of control. We used a constant comparative method to make connections among inductive themes focused on decision making, deductive codes for perceived risk and perceived locus of control, and the influence of explanatory models within and across participants. RESULTS: Participants were predominantly non-Hispanic white (63%), with an average age of 50.9 years. Decision making varied across groups: the high-perceived risk/high-perceived control group used "social evidence" to model the behaviors of others. High-perceived risk/low-perceived control women made decisions based on beliefs about treatment, rooted in the experiences of social contacts. The low-perceived risk/low-perceived control group interpreted signs of risk as part of the normal continuum of bodily changes in comparison to others. Low-perceived risk/high-perceived control women focused on maintaining a current healthy trajectory. CONCLUSION: "Social evidence" plays an important role in the decision-making process that is distinct from emotional aspects. Attending to patients' perceptions of risk and control in conjunction with social context is key to caring for patients at high risk in a way that is evidence based and sensitive to patient preferences.

Exploring Explanatory Models of Risk in Breast Cancer Risk Counseling Discussions: NSABP/NRG Oncology Decision-Making Project 1.

BACKGROUND: Explanatory models represent patient understanding of etiology, pathophysiology, illness, symptoms, and treatments, but little attention has been paid to how they are used by patients "at risk" for future disease. OBJECTIVE: The aims of this study were to elucidate what constitutes an explanatory model of risk and to describe explanatory models of risk related to developing breast cancer. METHODS: Thirty qualitative interviews with women identified as at an increased risk for breast cancer were conducted. Interviews were coded to identify domains of explanatory models of risk using a priori codes derived from the explanatory model of illness framework. Within each domain, a grounded thematic analysis described participants' explanatory models related to breast cancer risk. RESULTS: The domains of treatment and etiology remained similar in a risk context compared with illness, whereas course of illness, symptoms, and pathophysiology differed. We identified a new, integrative concept relative to other domains within explanatory models of risk: social comparisons, which was dominant in risk perhaps due to the lack of physical experiences associated with being "at risk." CONCLUSIONS: Developing inclusive understandings of risk and its treatment is key to developing a framework for the care of high-risk patients that is both evidence based and sensitive to patient preferences. IMPLICATIONS FOR PRACTICE: The concept of "social comparisons" can assist healthcare providers in understanding women's decision making under conditions of risk. Ensuring that healthcare providers understand patient perceptions of risk is important because it relates to patient decision making, particularly due to an increasing focus on risk assessment in cancer.

Social Network Structures of Breast Cancer Patients and the Contributing Role of Patient Navigators.

BACKGROUND: Minority women in the U.S. continue to experience inferior breast cancer outcomes compared with white women, in part due to delays in care delivery. Emerging cancer care delivery models like patient navigation focus on social barriers, but evidence demonstrating how these models increase social capital is lacking. This pilot study describes the social networks of newly diagnosed breast cancer patients and explores the contributing role of patient navigators. MATERIALS AND METHODS: Twenty-five women completed a one hour interview about their social networks related to cancer care support. Network metrics identified important structural attributes and influential individuals. Bivariate associations between network metrics, type of network, and whether the network included a navigator were measured. Secondary analyses explored associations between network structures and clinical outcomes. RESULTS: We identified three types of networks: kin-based, role and/or affect-based, or heterogeneous. Network metrics did not vary significantly by network type. There was a low prevalence of navigators included in the support networks (25%). Network density scores were significantly higher in those networks without a navigator. Network metrics were not predictive of clinical outcomes in multivariate models. CONCLUSION: Patient navigators were not frequently included in support networks, but provided distinctive types of support. If navigators can identify patients with poorly integrated (less dense) social networks, or who have unmet tangible support needs, the intensity of navigation services could be tailored. Services and systems that address gaps and variations in patient social networks should be explored for their potential to reduce cancer health disparities. IMPLICATIONS FOR PRACTICE: This study used a new method to identify the breadth and strength of social support following a diagnosis of breast cancer, especially examining the role of patient navigators in providing support. While navigators were only included in one quarter of patient support networks, they did provide essential supports to some individuals. Health care providers and systems need to better understand the contributions of social supports both within and outside of health care to design and tailor interventions that seek to reduce health care disparities and improve cancer outcomes.

What Makes Patient Navigation Most Effective: Defining Useful Tasks and Networks.

Given the momentum in adopting patient navigation into cancer care, there is a need to understand the contribution of specific navigator activities to improved clinical outcomes. A mixed-methods study combined direct observations of patient navigators within the Patient Navigation Research Program and outcome data from the trial. We correlated the frequency of navigator tasks with the outcome of rate of diagnostic resolution within 365 days among patients who received the intervention relative to controls. A focused content analysis examined those tasks with the strongest correlations between navigator tasks and patient outcomes. Navigating directly with specific patients (r = 0.679), working with clinical providers to facilitate patient care (r = 0.643), and performing tasks not directly related to their diagnostic evaluation for patients were positively associated with more timely diagnosis (r = 0.714). Using medical records for non-navigation tasks had a negative association (r = -0.643). Content analysis revealed service provision directed at specific patients improved care while systems-focused activities did not.

An assessment of patient navigator activities in breast cancer patient navigation programs using a nine-principle framework.

OBJECTIVE: To determine how closely a published model of navigation reflects the practice of navigation in breast cancer patient navigation programs. DATA SOURCE: Observational field notes describing patient navigator activities collected from 10 purposefully sampled, foundation-funded breast cancer navigation programs in 2008-2009. STUDY DESIGN: An exploratory study evaluated a model framework for patient navigation published by Harold Freeman by using an a priori coding scheme based on model domains. DATA COLLECTION: Field notes were compiled and coded. Inductive codes were added during analysis to characterize activities not included in the original model. PRINCIPAL FINDINGS: Programs were consistent with individual-level principles representing tasks focused on individual patients. There was variation with respect to program-level principles that related to program organization and structure. Program characteristics such as the use of volunteer or clinical navigators were identified as contributors to patterns of model concordance. CONCLUSIONS: This research provides a framework for defining the navigator role as focused on eliminating barriers through the provision of individual-level interventions. The diversity observed at the program level in these programs was a reflection of implementation according to target population. Further guidance may be required to assist patient navigation programs to define and tailor goals and measurement to community needs.

Patterns of task and network actions performed by navigators to facilitate cancer care.

BACKGROUND: Patient navigation is a widely implemented intervention to facilitate access to care and reduce disparities in cancer care, but the activities of navigators are not well characterized. PURPOSE: The aim of this study is to describe what patient navigators actually do and explore patterns of activity that clarify the roles they perform in facilitating cancer care. METHODOLOGY/APPROACH: We conducted field observations of nine patient navigation programs operating in diverse health settings of the national patient navigation research program, including 34 patient navigators, each observed an average of four times. Trained observers used a structured observation protocol to code as they recorded navigator actions and write qualitative field notes capturing all activities in 15-minute intervals during observations ranging from 2 to 7 hours; yielding a total of 133 observations. Rates of coded activity were analyzed using numerical cluster analysis of identified patterns, informed by qualitative analysis of field notes. FINDINGS: Six distinct patterns of navigator activity were identified, which differed most relative to how much time navigators spent directly interacting with patients and how much time they spent dealing with medical records and documentation tasks. Navigator actions reveal a complex set of roles in which navigators both provide the direct help to patients denoted by their title and also carry out a variety of actions that function to keep the health system operating smoothly. PRACTICE IMPLICATIONS: Working to navigate patients through complex health services entails working to repair the persistent challenges of health services that can render them inhospitable to patients. The organizations that deploy navigators might learn from navigators' efforts and explore alternative approaches, structures, or systems of care in addressing both the barriers patients face and the complex solutions navigators create in helping patients.