Objectives. Given the importance of the dissemination of accurate family history to assess disease risk, we characterized the gatherers, disseminators, and blockers of health information within families at high genetic risk of cancer. Methods. A total of 5466 personal network members of 183 female participants of the Breast Imaging Study from 124 families with known mutations in the BRCA1/2 genes (associated with high risk of breast, ovarian, and other types of cancer) were identified by using the Colored Eco-Genetic Relationship Map (CEGRM). Hierarchical nonlinear models were fitted to characterize information gatherers, disseminators, and blockers. Results. Gatherers of information were more often female (P < .001), parents (P < .001), and emotional support providers (P < .001). Disseminators were more likely female first- and second- degree relatives (both P < .001), family members in the older or same generation as the participant (P < .001), those with a cancer history (P < .001), and providers of emotional (P < .001) or tangible support (P < .001). Blockers tended to be spouses or partners (P < .001) and male, first-degree relatives (P < .001). Conclusions. Our results provide insight into which family members may, within a family-based intervention, effectively gather family risk information, disseminate information, and encourage discussions regarding shared family risk.

Objective: We investigated the association between psychological distress and indices of social integration and communal coping among sisters from hereditary breast and ovarian cancer (HBOC) families. Sample and methods: Sixty‐five sisters from 31 HBOC families completed the Brief Symptom Inventory‐18 and the Colored Eco‐Genetic Relationship Map, which identified members of participants' social support networks. Hierarchical linear models were used for all analyses to account for the clustering of sisters within families. Results: Intra‐family correlation coefficients suggested that sisters shared perceptions of breast cancer risk and worry, but not ovarian cancer risk and worry. Further, sisters demonstrated shared levels of anxiety and somatization, but not depressive symptoms. Communal coping indices quantifying shared support resources were negatively related to anxiety and somatization. The number of persons with whom cancer risk information was shared exhibited a positive trend with somatization. Social integration, as measured by the size of participants' emotional support network, was negatively associated with anxiety. Lower depression scores were observed among participants with more persons playing multiple support roles and fewer persons providing tangible assistance. Conclusion: Understanding how support relationships impact well‐being among persons adjusting to HBOC risk, and the particular role of family in that process, will facilitate developing appropriate management approaches to help cancer‐prone families adjust to their cancer risk. Published in 2008 by John Wiley & Sons Ltd.

To determine how three dimensions of genetic literacy (familiarity, skills, and factual knowledge) fit the hierarchy of knowledge outlined in E.M. Rogers’ Diffusion of Innovations to better conceptualize lay understandings of genomics. A consumer panel representing the US adult population (N = 1016) completed an electronic survey in November 2013. Adjusting for education, we used correlations, principle components analysis, Mokken Scale tests, and linear regressions to assess how scores on the three genetic literacy sub-dimensions fit an ordered scale. The three scores significantly loaded onto one factor, even when adjusting for education. Analyses revealed moderate strength in scaling (0.416, p<0.001) and a difficulty ordering that matched Rogers’ hierarchy (knowledge more difficult than skills, followed by familiarity). Skills scores partially mediated the association between familiarity and knowledge with a significant indirect effect (0.241, p<0.001). We established an ordering in genetic literacy sub-dimensions such that familiarity with terminology precedes skills using information, which in turn precedes factual knowledge. This ordering is important to contextualizing previous findings, guiding measurement in future research, and identifying gaps in the understanding of genomics relevant to the demands of differing applications.

This study demonstrates the added value to caregiving research by using a multi-informant social network approach within the context of Alzheimer’s disease and related dementia. Sixty-six informants from 24 families enumerated caregiving network members in 2012. Comparisons were made between networks based on a single informant versus multiple informants in terms of network composition and caregiving roles, core-periphery structure, and identification of “missed opportunities” in recruitment. On average, each informant beyond the index enumerated 6.2 new members, resulting in about 10 new members per family network when the multiple-informant approach is used. Compared with index informants’ networks, multi-informant networks showed an 85% increase in identification of direct care providers (1.71 compared with 3.42) and a 48% increase in identification of those involved in care decision making (3.33 compared with 4.92). Informants from the same network generally showed agreement in reported participation in caregiving activities. However, the reports of non-participation in these roles were less consistent among the informants. Resulting structure indicated a core caregiving network (M = 6.12 members), with semi-peripheral and peripheral members (M = 5.19 and M = 14.83 members, respectively). Results suggest that an iterative, targeted sampling approach with at least three informants allows for a more comprehensive assessment of caregiving processes. Applying this approach in future research will greatly enhance our knowledge and better inform future interventions to facilitate family adaptation.

Common diseases such as heart disease, diabetes, and cancer are etiologically complex with multiple risk factors (e.g., environment, genetic, lifestyle). These risk factors tend to cluster in families, making families an important social context for intervention and lifestyle-focused disease prevention. The Families Sharing Health Assessment and Risk Evaluation (SHARE) workbook was designed as an educational tool outlining family health history based risk of heart disease, type 2 diabetes, breast cancer, and colorectal cancer. The current paper describes the steps taken to develop and evaluate the workbook employing a user-centered design approach. The workbook was developed in four steps, culminating in an evaluation focusing on understanding and usability of the tool. The evaluation was based on two Phases of data collected from a sample of mothers of young children in the Washington, D.C., area. A baseline assessment and follow-up approximately two weeks after receipt of the workbook were conducted, as well as focus groups with participants. The design of the workbook was refined in response to participant feedback from the first evaluation Phase and subsequently re-evaluated with a new sample. After incorporating user-based feedback and revising the workbook, Phase 2 evaluation results indicated that understanding of the workbook components improved for all sections (from 6.26 to 6.81 on a 7-point scale). In addition, 100 % of users were able to use the algorithm to assess their disease risk and over 60 % used the algorithm to assess family members’ disease risk. At follow-up, confidence to increase fruit, vegetable and fiber intake improved significantly, as well. The Families SHARE workbook was developed and evaluated resulting in a family health history tool that is both understandable and usable by key stakeholders. This educational tool will be used in intervention studies assessing the effectiveness of family genomics health educators who use the Families SHARE workbook to disseminate family risk information and encourage risk reducing behaviors. ClinicalTrials.gov, NCT01498276 . Registered 21 December 2011

Multi-layer networks arise when more than one type of relation is observed on a common set of actors. Modeling such networks within the exponential-family random graph (ERG) framework has been previously limited to special cases and, in particular, to dependence arising from just two layers. Extensions to ERGMs are introduced to address these limitations: Conway–Maxwell–Binomial distribution to model the marginal dependence among multiple layers; a “layer logic” language to translate familiar ERGM effects to substantively meaningful interactions of observed layers; and nondegenerate triadic and degree effects. The developments are demonstrated on two previously published datasets.