An Application of Evidence-Based Approaches to Engage Young People in the Design of a Global Mental Health Databank.

INTRODUCTION: Engaging youth in mental health research and intervention design has the potential to improve their relevance and effectiveness. Frameworks like Roger Hart's ladder of participation, Shier's pathways to participation and Lundy's voice and influence model aim to balance power between youth and adults. Hart's Ladder, specifically, is underutilized in global mental health research, presenting new opportunities to examine power dynamics across various contexts. Drawing on Hart's ladder, our study examined youth engagement in mental health research across high- and middle-income countries using Internet-based technologies, evaluating youth involvement in decision-making and presenting research stages that illustrate these engagements. METHODS: We conducted a directed content analysis of youth engagement in the study using primary data from project documents, weekly AirTable updates and discussions and interviews with youth and the research consortium. Using Hart's Ladder as a framework, we describe youth engagement along rungs throughout different research stages: cross-cutting research process, onboarding, formative research and quantitative and qualitative study designs. RESULTS: Youth engagement in the MindKind study fluctuated between Rung 4 ('Assign, but informed') and Rung 7 ('Youth initiated and directed') on Hart's Ladder. Engagement was minimal in the early project stages as project structures and goals were defined, with some youth feeling that their experiences were underutilized and many decisions being adult-led. Communication challenges and structural constraints, like tight timelines and limited budget, hindered youth engagement in highest ladder rungs. Despite these obstacles, youth engagement increased, particularly in developing recruitment strategies and in shaping data governance models and the qualitative study design. Youth helped refine research tools and protocols, resulting in moderate to substantial engagement in the later research stages. CONCLUSION: Our findings emphasize the value of youth-adult partnerships, which offer promise in amplifying voices and nurturing skills, leadership and inclusiveness of young people. Youth engagement in project decision-making progressed from lower to higher rungs on Hart's Ladder over time; however, this was not linear. Effective youth engagement requires dynamic strategies, transparent communication and mutual respect, shaping outcomes that authentically reflect diverse perspectives and mental health experiences. PATIENT OR PUBLIC CONTRIBUTION: There was substantial patient and public involvement in this study. This paper reports findings on youth engagement conducted with 35 young people from India, South Africa and the United Kingdom, all of whom had lived experience of mental health challenges. Youth engagement in the MindKind study was coordinated and led by three professional youth advisors (PYAs) in these contexts, who were also young people with lived experience of mental health challenges. Each of the three study sites embedded a full-time, community-based PYA within their study team to inform all aspects of the research project, including the development of informational materials and the facilitation of Young People's Advisory Group (YPAG) sessions referenced in this paper. Each PYA also consulted with a site-specific YPAG that met bi-monthly throughout the project, shaping the formation of study materials and serving as a test group in both the quantitative and qualitative studies. Youth participants in this study also contributed extensively, engaging in data collection and manuscript writing. The following youth advisory panels members (J.B., L.B., D.O.J., M.V.) and all PYAs (E.B., S.R., R.S.) in the MindKind study contributed to the writing of this manuscript and are acknowledged as co-authors.

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.

AI Governance: A Challenge for Public Health.

Unlabelled: The rapid evolution of artificial intelligence (AI) is structuralizing social, political, and economic determinants of health into the invisible algorithms that shape all facets of modern life. Nevertheless, AI holds immense potential as a public health tool, enabling beneficial objectives such as precision public health and medicine. Developing an AI governance framework that can maximize the benefits and minimize the risks of AI is a significant challenge. The benefits of public health engagement in AI governance could be extensive. Here, we describe how several public health concepts can enhance AI governance. Specifically, we explain how (1) harm reduction can provide a framework for navigating the governance debate between traditional regulation and "soft law" approaches; (2) a public health understanding of social determinants of health is crucial to optimally weigh the potential risks and benefits of AI; (3) public health ethics provides a toolset for guiding governance decisions where individual interests intersect with collective interests; and (4) a One Health approach can improve AI governance effectiveness while advancing public health outcomes. Public health theories, perspectives, and innovations could substantially enrich and improve AI governance, creating a more equitable and socially beneficial path for AI development.

Synthetic Health Data: Real Ethical Promise and Peril.

Researchers and practitioners are increasingly using machine-generated synthetic data as a tool for advancing health science and practice, by expanding access to health data while-potentially-mitigating privacy and related ethical concerns around data sharing. While using synthetic data in this way holds promise, we argue that it also raises significant ethical, legal, and policy concerns, including persistent privacy and security problems, accuracy and reliability issues, worries about fairness and bias, and new regulatory challenges. The virtue of synthetic data is often understood to be its detachment from the data subjects whose measurement data is used to generate it. However, we argue that addressing the ethical issues synthetic data raises might require bringing data subjects back into the picture, finding ways that researchers and data subjects can be more meaningfully engaged in the construction and evaluation of datasets and in the creation of institutional safeguards that promote responsible use.

The Key Features of a Genetic Nondiscrimination Policy: A Delphi Consensus Statement.

Importance: Governments worldwide have become increasingly cognizant of the spread of genetic discrimination (negative treatment or harm on the basis of actual or presumed genetic characteristics). Despite efforts by a number of governments to establish regulations addressing this phenomenon, public concern about genetic discrimination persists. Objective: To identify key elements of an optimal genetic nondiscrimination policy and inform policymakers as they seek to allay genetic nondiscrimination and related public anxieties. Evidence Review: Sixty multidisciplinary experts from 20 jurisdictions worldwide were consulted to understand their views on effective genetic nondiscrimination policies. Following standard requirements of the Delphi method, 3 rounds of surveys over the course of 1.5 years were conducted. Round 1 focused on assessing participants' understanding of the intricacies of existing genetic nondiscrimination policies, while rounds 2 and 3 invited participants to reflect on specific means of implementing a more effective regime. A total of 60 respondents participated in the first round, 53 participated in round 2, and 43 participated in round 3. Findings: While responses varied across disciplines, there was consensus that binding regulations that reach across various sectors are most useful in preventing genetic discrimination. Overall, experts agreed that human rights-based approaches are well suited to preventing genetic discrimination. Experts also agreed that explicit prohibition of genetic discrimination within nondiscrimination policies can highlight the importance of genetic nondiscrimination as a fundamental right and ensure robust protection at a national level. While most participants believed the international harmonization of genetic nondiscrimination laws would facilitate data sharing worldwide, they also recognized that regulations must reflect the sociocultural differences that exist among regions. Conclusions and Relevance: As the reach of genetic discrimination continues to evolve alongside developments in genomics, strategic policy responses that are harmonious at the international and state levels will be critical to address this phenomenon. In seeking to establish comprehensive frameworks, policymakers will need to be mindful of regional and local circumstances that influence the need for and efficacy of unique genetic nondiscrimination approaches across diverse contexts.

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.