Results of the Genetic Counselor SARS-CoV-2 Impact Survey from the National Society of Genetic Counselors: Progress and penalty during the COVID-19 pandemic.

The Genetic Counselor SARS-CoV-2 Impact Survey (GCSIS) describes the impact of the pandemic on genetic counselors and genetic counseling services. With this information, the National Society of Genetic Counselors (NSGC) can better: (1) support advocacy and access efforts for genetic counseling services at both federal- and state-level; (2) promote effective billing and reimbursement for genetic counseling services provided via telemedicine; and (3) make decisions about how to best support genetic counselors. The survey was hosted on a novel data collection and analysis platform from LunaDNA and was open to all genetic counselors (n = 5,531 based on professional society membership). Survey response rate was approximately 3.8% (n = 212/5,531), with a demographic distribution broadly representative of the North American genetic counseling field. Genetic counselors remained largely employed, providing genetic counseling services throughout the pandemic, although almost one in five respondents (17%, n = 35/211) reported experiencing some degree of pandemic-related financial hardship. Nearly all respondents (90%, n = 104/115) transitioned, at least in part, to remote work settings, with about half (47%. n = 88/189) reporting restrictions in the care they were able to provide. These shifts came at a cost: existing gaps in Medicare status for genetic counselors and attendant reimbursement concerns led to uncertainty about whether genetic counselors' work will be reimbursed. Outside of work, caregiving responsibilities increased for 34% (n = 74/212) of respondents. The results of the GCSIS amplify the importance of federal- and state-level advocacy efforts for genetic counselors and their employers. They also highlight the impact of broader cultural intransigence on our majority-female profession. During the pandemic, genetic counselors continued to provide care, but without consistent financial support or expectation of reimbursement. The ability to attract and retain talented professionals to the genetic counseling field will hinge on the success of continued advocacy efforts.

All things considered, my risk for diabetes is medium: A risk personalization process of familial risk for type 2 diabetes.

BACKGROUND: A positive family history of type 2 diabetes (T2D) has been associated with risk awareness and risk-reducing behaviours among the unaffected relatives. Yet, little is known about how people with a positive family history for diabetes develop and manage their personal sense of risk. OBJECTIVE: To characterize two key concepts, salience and vulnerability, within the familial risk perception (FRP) model among unaffected individuals, at increased familial risk for T2D. DESIGN: We conducted a mixed method study. Descriptions of salience and vulnerability were collected through semi-structured interviews. Participant's perception of self-reported risk factors (family history, age, race/ethnicity, medical history, weight and exercise) was measured using the Perceived Risk Factors for T2D Tool and was compared to a clinical evaluation of the same risk factors. RESULTS: We identified two components of salience: (a) concern for developing T2D and (b) risk awareness triggers, and two features of vulnerability: (a) statement of risk and (b) risk assessment devices. Although few participants (26%) were concordant between their perceived and clinical overall T2D risk, concordance for individual risk factors was higher, ranging from 42% (medical history) to 90% (family history). DISCUSSION AND CONCLUSION: Both familial and non-familial events lead people to contemplate their T2D risk, even among people who have a positive family history. Participants often downplayed their overall risk and underestimated their overall risk compared to a clinical risk assessment of the same self-reported risk factors. Clinicians could leverage key components of the FRP process as way to engage patients in risk reduction strategies earlier.

Connected Medical Technology and Cybersecurity Informed Consent: A New Paradigm.

BACKGROUND: Connected medical technology is increasingly prevalent and offers both a host of new therapeutic potentials and cybersecurity-related considerations. Current practice largely does not include discussions of cybersecurity issues when clinicians obtain informed consent. OBJECTIVE: This paper aims to raise awareness about cybersecurity considerations for connected medical technology as they relate to informed consent discussions between patients and clinicians. METHODS: Clinicians, health care cybersecurity researchers, and informed consent experts propose the concept of a cybersecurity informed consent for connected medical technology. RESULTS: This viewpoint discusses concepts designed to facilitate further discussion on the need, development, and execution of cybersecurity informed consent. CONCLUSIONS: Cybersecurity informed consent may be a necessary component of informed consent practices, as connected medical technology proliferates in the health care environment.

Bringing Code to Data: Do Not Forget Governance.

Developing or independently evaluating algorithms in biomedical research is difficult because of restrictions on access to clinical data. Access is restricted because of privacy concerns, the proprietary treatment of data by institutions (fueled in part by the cost of data hosting, curation, and distribution), concerns over misuse, and the complexities of applicable regulatory frameworks. The use of cloud technology and services can address many of the barriers to data sharing. For example, researchers can access data in high performance, secure, and auditable cloud computing environments without the need for copying or downloading. An alternative path to accessing data sets requiring additional protection is the model-to-data approach. In model-to-data, researchers submit algorithms to run on secure data sets that remain hidden. Model-to-data is designed to enhance security and local control while enabling communities of researchers to generate new knowledge from sequestered data. Model-to-data has not yet been widely implemented, but pilots have demonstrated its utility when technical or legal constraints preclude other methods of sharing. We argue that model-to-data can make a valuable addition to our data sharing arsenal, with 2 caveats. First, model-to-data should only be adopted where necessary to supplement rather than replace existing data-sharing approaches given that it requires significant resource commitments from data stewards and limits scientific freedom, reproducibility, and scalability. Second, although model-to-data reduces concerns over data privacy and loss of local control when sharing clinical data, it is not an ethical panacea. Data stewards will remain hesitant to adopt model-to-data approaches without guidance on how to do so responsibly. To address this gap, we explored how commitments to open science, reproducibility, security, respect for data subjects, and research ethics oversight must be re-evaluated in a model-to-data context.

Developing pathways for community-led research with big data: a content analysis of stakeholder interviews.

BACKGROUND: Big data (BD) informs nearly every aspect of our lives and, in health research, is the foundation for basic discovery and its tailored translation into healthcare. Yet, as new data resources and citizen/patient-led science movements offer sites of innovation, segments of the population with the lowest health status are least likely to engage in BD research either as intentional data contributors or as 'citizen/community scientists'. Progress is being made to include a more diverse spectrum of research participants in datasets and to encourage inclusive and collaborative engagement in research through community-based participatory research approaches, citizen/patient-led research pilots and incremental research policy changes. However, additional evidence-based policies are needed at the organisational, community and national levels to strengthen capacity-building and widespread adoption of these approaches to ensure that the translation of research is effectively used to improve health and health equity. The aims of this study are to capture uses of BD ('use cases') from the perspectives of community leaders and to identify needs and barriers for enabling community-led BD science. METHODS: We conducted a qualitative content analysis of semi-structured key informant interviews with 16 community leaders. RESULTS: Based on our analysis findings, we developed a BD Engagement Model illustrating the pathways and various forces for and against community engagement in BD research. CONCLUSIONS: The goal of our Model is to promote concrete, transparent dialogue between communities and researchers about barriers and facilitators of authentic community-engaged BD research. Findings from this study will inform the subsequent phases of a multi-phased project with the ultimate aims of organising fundable frameworks and identifying policy options to support BD projects within community settings.

Engaging populations underrepresented in research through novel approaches to consent.

The lack of diversity of populations included in genomics databases is an important inhibitor of genomic discovery from bench to bedside. One way to increase the diversity of participants is to ensure that informed consent processes are designed for cultural and linguistic concordance for non-majority populations. This article describes two case studies of genomics research studies that are using novel approaches to informed consent to increase recruitment and retention of participants from traditionally underrepresented populations: The Cancer Health Assessments Reaching Many (CHARM) study, part of the Clinical Sequencing Evidence-Generating Research (CSER) consortium, and the All of Us Research Program, part of the Precision Medicine Initiative. We conclude by proposing a community of practice among researchers seeking to improve informed consent to increase diversity in genomics research.

Review and Comparison of Electronic Patient-Facing Family Health History Tools.

Family health history (FHx) is one of the most important pieces of information available to help genetic counselors and other clinicians identify risk and prevent disease. Unfortunately, the collection of FHx from patients is often too time consuming to be done during a clinical visit. Fortunately, there are many electronic FHx tools designed to help patients gather and organize their own FHx information prior to a clinic visit. We conducted a review and analysis of electronic FHx tools to better understand what tools are available, to compare and contrast to each other, to highlight features of various tools, and to provide a foundation for future evaluation and comparisons across FHx tools. Through our analysis, we included and abstracted 17 patient-facing electronic FHx tools and explored these tools around four axes: organization information, family history collection and display, clinical data collected, and clinical workflow integration. We found a large number of differences among FHx tools, with no two the same. This paper provides a useful review for health care providers, researchers, and patient advocates interested in understanding the differences among the available patient-facing electronic FHx tools.

Understanding the contribution of family history to colorectal cancer risk and its clinical implications: A state-of-the-science review.

Persons with a family history (FH) of colorectal cancer (CRC) or adenomas that are not due to known hereditary syndromes have an increased risk for CRC. An understanding of these risks, screening recommendations, and screening behaviors can inform strategies for reducing the CRC burden in these families. A comprehensive review of the literature published within the past 10 years has been performed to assess what is known about cancer risk, screening guidelines, adherence and barriers to screening, and effective interventions in persons with an FH of CRC and to identify FH tools used to identify these individuals and inform care. Existing data show that having 1 affected first-degree relative (FDR) increases the CRC risk 2-fold, and the risk increases with multiple affected FDRs and a younger age at diagnosis. There is variability in screening recommendations across consensus guidelines. Screening adherence is <50% and is lower in persons under the age of 50 years. A provider's recommendation, multiple affected relatives, and family encouragement facilitate screening; insufficient collection of FH, low knowledge of guidelines, and poor family communication are important barriers. Effective interventions incorporate strategies for overcoming barriers, but these have not been broadly tested in clinical settings. Four strategies for reducing CRC in persons with familial risk are suggested: 1) improving the collection and utilization of the FH of cancer, 2) establishing a consensus for screening guidelines by FH, 3) enhancing provider-patient knowledge of guidelines and communication about CRC risk, and 4) encouraging survivors to promote screening within their families and partnering with existing screening programs to expand their reach to high-risk groups. Cancer 2016. © 2016 American Cancer Society. Cancer 2016;122:2633-2645. © 2016 American Cancer Society.

Translational Research and Communities.

Impactful translational research requires new approaches to computational analysis and bioethics, both of which have been advanced by adoption of community-engagement strategies. Community knowledge and experience will hone data collection, research, and insights and accelerate the impact of derived translational applications to improve individual health, medical decision-making, and public health policy. In the context of translational research with big health data, meaningful community-researcher engagement will require developing and deploying coengagement tools across the research life cycle and developing approaches for novel coproduction.

Young people's data governance preferences for their mental health data: MindKind Study findings from India, South Africa, and the United Kingdom.

Mobile devices offer a scalable opportunity to collect longitudinal data that facilitate advances in mental health treatment to address the burden of mental health conditions in young people. Sharing these data with the research community is critical to gaining maximal value from rich data of this nature. However, the highly personal nature of the data necessitates understanding the conditions under which young people are willing to share them. To answer this question, we developed the MindKind Study, a multinational, mixed methods study that solicits young people's preferences for how their data are governed and quantifies potential participants' willingness to join under different conditions. We employed a community-based participatory approach, involving young people as stakeholders and co-researchers. At sites in India, South Africa, and the UK, we enrolled 3575 participants ages 16-24 in the mobile app-mediated quantitative study and 143 participants in the public deliberation-based qualitative study. We found that while youth participants have strong preferences for data governance, these preferences did not translate into (un)willingness to join the smartphone-based study. Participants grappled with the risks and benefits of participation as well as their desire that the "right people" access their data. Throughout the study, we recognized young people's commitment to finding solutions and co-producing research architectures to allow for more open sharing of mental health data to accelerate and derive maximal benefit from research.

Big Health Data Research and Group Harm: The Scope of IRB Review.

Much of precision medicine is driven by big health data research-the analysis of massive datasets representing the complex web of genetic, behavioral, environmental, and other factors that impact human well-being. There are some who point to the Common Rule, the regulation governing federally funded human subjects research, as a regulatory panacea for all types of big health data research. But how well does the Common Rule fit the regulatory needs of this type of research? This article suggests that harms that may arise from artificial intelligence and machine-learning technologies used in big health data research-and the increased likelihood that this research will affect public policy-mean it is time to consider whether the current human research regulations prohibit comprehensive, ethical review of big health data research that may result in group harm.

U.S. Adult Perspectives on Facial Images, DNA, and Other Biometrics.

Applications of biometrics in various societal contexts have been increasing in the United States, and policy debates about potential restrictions and expansions for specific biometrics (such as facial recognition and DNA identification) have been intensifying. Empirical data about public perspectives on different types of biometrics can inform these debates. We surveyed 4048 adults to explore perspectives regarding experience and comfort with six types of biometrics; comfort providing biometrics in distinct scenarios; trust in social actors to use two types of biometrics (facial images and DNA) responsibly; acceptability of facial images in eight scenarios; and perceived effectiveness of facial images for five tasks. Respondents were generally comfortable with biometrics. Trust in social actors to use biometrics responsibly appeared to be context specific rather than dependent on biometric type. Contrary to expectations given mounting attention to dataveillance concerns, we did not find sociodemographic factors to influence perspectives on biometrics in obvious ways. These findings underscore a need for qualitative approaches to understand the contextual factors that trigger strong opinions of comfort with and acceptability of biometrics in different settings, by different actors, and for different purposes and to identify the informational needs relevant to the development of appropriate policies and oversight.

Assessment of the All of Us research program's informed consent process.

Informed consent is the gateway to research participation. We report on the results of the formative evaluation that follows the electronic informed consent process for the All of Us Research Program. Of the nearly 250,000 participants included in this analysis, more than 95% could correctly answer questions distinguishing the program from medical care, the voluntary nature of participation, and the right to withdraw; comparatively, participants were less sure of privacy risk of the program. We also report on a small mixed-methods study of the experience of persons of very low health literacy with All of Us informed consent materials. Of note, many of the words commonly employed in the consent process were unfamiliar to or differently defined by informants. In combination, these analyses may inform participant-centered development and highlight areas for refinement of informed consent materials for the All of Us Research Program and similar studies.

A survey of U.S. public perspectives on facial recognition technology and facial imaging data practices in health and research contexts.

Facial imaging and facial recognition technologies, now common in our daily lives, also are increasingly incorporated into health care processes, enabling touch-free appointment check-in, matching patients accurately, and assisting with the diagnosis of certain medical conditions. The use, sharing, and storage of facial data is expected to expand in coming years, yet little is documented about the perspectives of patients and participants regarding these uses. We developed a pair of surveys to gather public perspectives on uses of facial images and facial recognition technologies in healthcare and in health-related research in the United States. We used Qualtrics Panels to collect responses from general public respondents using two complementary and overlapping survey instruments; one focused on six types of biometrics (including facial images and DNA) and their uses in a wide range of societal contexts (including healthcare and research) and the other focused on facial imaging, facial recognition technology, and related data practices in health and research contexts specifically. We collected responses from a diverse group of 4,048 adults in the United States (2,038 and 2,010, from each survey respectively). A majority of respondents (55.5%) indicated they were equally worried about the privacy of medical records, DNA, and facial images collected for precision health research. A vignette was used to gauge willingness to participate in a hypothetical precision health study, with respondents split as willing to (39.6%), unwilling to (30.1%), and unsure about (30.3%) participating. Nearly one-quarter of respondents (24.8%) reported they would prefer to opt out of the DNA component of a study, and 22.0% reported they would prefer to opt out of both the DNA and facial imaging component of the study. Few indicated willingness to pay a fee to opt-out of the collection of their research data. Finally, respondents were offered options for ideal governance design of their data, as "open science"; "gated science"; and "closed science." No option elicited a majority response. Our findings indicate that while a majority of research participants might be comfortable with facial images and facial recognition technologies in healthcare and health-related research, a significant fraction expressed concern for the privacy of their own face-based data, similar to the privacy concerns of DNA data and medical records. A nuanced approach to uses of face-based data in healthcare and health-related research is needed, taking into consideration storage protection plans and the contexts of use.

Engaged genomic science produces better and fairer outcomes: an engagement framework for engaging and involving participants, patients and publics in genomics research and healthcare implementation.

Genomic science is increasingly central to the provision of health care. Producing and applying robust genomics knowledge is a complex endeavour in which no single individual, profession, discipline or community holds all the answers.  Engagement and involvement of diverse stakeholders can support alignment of societal and scientific interests, understandings and perspectives and promises better science and fairer outcomes. In this context we argue for F.A.I.R.E.R. data and data use that is Findable, Accessible, Interoperable, Reproducible, Equitable and Responsible. Yet there is a paucity of international guidance on how to engage publics, patients and participants in genomics. To support meaningful and effective engagement and involvement we developed an Engagement Framework for involving and engaging participants, patients and publics in genomics research and health implementation. The Engagement Framework is intended to support all those working in genomics research, medicine, and healthcare to deliberatively consider approaches to participant, patient and public engagement and involvement in their work. Through a series of questions, the Engagement Framework prompts new ways of thinking about the aims and purposes of engagement, and support reflection on the strengths, limitations, likely outcomes and impacts of choosing different approaches to engagement. To guide genomics activities, we describe four themes and associated questions for deliberative reflection: (i) fairness; (ii) context; (iii) heterogeneity, and (iv) recognising tensions and conflict. The four key components in the Engagement provide a framework to assist those involved in genomics to reflect on decisions they make for their initiatives, including the strategies selected, the participant, patient and public stakeholders engaged, and the anticipated goals. The Engagement Framework is one step in an actively evolving process of building genomics research and implementation cultures which foster responsible leadership and are attentive to objectives which increase equality, diversity and inclusion in participation and outcomes.

GA4GH: International policies and standards for data sharing across genomic research and healthcare.

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

Research ethics in a pandemic: considerations for the use of research infrastructure and resources for public health activities.

The number and size of existing research studies with massive databases and biosample repositories that could be leveraged for public health response against SARS-CoV-2 (or other infectious disease pathogens) are unparalleled in history. What risks are posed by coopting research infrastructure-not just data and samples but also participant recruitment and contact networks, communications, and coordination functions-for public health activities? The case of the Seattle Flu Study highlights the general challenges associated with utilizing research infrastructure for public health response, including the legal and ethical considerations for research data use, the return of the results of public health activities relying upon research resources to unwitting research participants, and the possible impacts of public health reporting mandates on future research participation. While research, including public health research, is essential during a pandemic, careful consideration should be given to distinguishing and balancing the ethical mandates of public health activities against the existing ethical responsibilities of biomedical researchers.

Who Are the People in Your Neighborhood? Personas Populating Unregulated mHealth Research.

A key feature of unregulated mHealth research is the diversity of participants in this space. Applying an approach drawn from user experience design, we describe a set of archetypal unregulated mHealth researcher "personas," which range from individuals who seek empowerment or have philanthropic objectives to those who are primarily motivated by financial gain or have misanthropic objectives. These descriptions are useful for evaluating policies applicable to mHealth to understand how they will impact various stakeholders.

Navigating ethical quandaries with the privacy dilemma of biomedical datasets.

With decreasing cost of biomedical technologies, the scale of the genetic and healthcare data have exponentially increased and become available to wider audiences. Hence, privacy of patients and study participants has garnered the attention of researchers and regulators alike. Availability of genetic and health care information for uses not anticipated at the time of collection gives rise to privacy concerns such that people suffer dignitary harm when their data is used in ways they did not desire or intend, even if no concrete economic damage results. In this workshop, we explore the issues surrounding data use to advance human health from a privacy perspective. Broadly this field can be considered in two encompassing areas: (1) Ethics and regulation of privacy: The ethical and regulatory frames through which we can consider privacy, the existing regulations regarding privacy and what is on the horizon, and implementation of such ethical considerations for data with the new Common Rule. (2) Approaches to ensuring privacy using technology: The technologies that allow responsible use and sharing of data such as encryption and the quantification of privacy leakages in publicly available data through privacy attacks for better risk-assessment tools.