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Genetic datasets lack diversity and include very few data from Indigenous populations. Research models based on equitable partnership have the potential to increase Indigenous participation and have led to successful collaborations. We report here on a meeting of participants in four Indigenous community-university partnerships pursuing research on precision medicine. The goal of the meeting was to define values and practices that strengthen opportunities for genetic research. The group accorded the highest priority to developing trusting relationships, ensuring respect for Indigenous community authority, and pursuing research that has the potential to lead to community benefit. Supporting priorities included incorporation of Indigenous expertise in research planning, transparent communication, and development of community capacity, including capacity to participate in formulating research questions, informing research methodology, and leading research projects. Participants also noted the importance of attention to social determinants of health so that genetic contributors to health are evaluated in the appropriate context.

This paper introduces a special issue of the American Journal of Bioethics focused on work presented at the 5th ELSI Congress: Innovating for a Just and Equitable Future. We call for ELSI scholars to tackle the difficult moral and empirical questions about what it means to represent the fullness of “the public”, how their engagement should be facilitated, and how we should interpret and apply their recommendations to maximally enhance equity and justice.

As genomic technologies rapidly develop, polygenic scores (PGS) are entering into a growing conversation on how to improve precision in public health and prevent chronic disease. However, although the integration of PGS into public health clinical services raises potential benefits, it also introduces potential harms. In particular, there is a high level of uncertainty about how to incorporate PGS into clinical settings in a manner that is equitable, just, and aligned with the long-term goals of most healthcare systems to support person-centered and value-based care. This paper argues that any conversation about whether and how to design and implement PGS clinical services requires dynamic engagement with local communities, patients, and families. These parties often face the consequences, both positive and negative, of such uncertainties and should therefore drive clinical translation. As a collaborative effort between hospital stakeholders, community partners, and researchers, this paper describes a community-empowered co-design process for addressing uncertainty and making programmatic decisions about the implementation of PGS into clinical services. We provide a framework for others interested in designing clinical programs that are responsive to, and inclusive and respectful of, local communities.

Machine learning predictive analytics (MLPA) are utilized increasingly in health care, but can pose harms to patients, clinicians, health systems, and the public. The dynamic nature of this technology creates unique challenges to evaluating safety and efficacy and minimizing harms. In response, regulators have proposed an approach that would shift more responsibility to MLPA developers for mitigating potential harms. To be effective, this approach requires MLPA developers to recognize, accept, and act on responsibility for mitigating harms. In interviews of 40 MLPA developers of health care applications in the United States, we found that a subset of ML developers made statements reflecting moral disengagement, representing several different potential rationales that could create distance between personal accountability and harms. However, we also found a different subset of ML developers who expressed recognition of their role in creating potential hazards, the moral weight of their design decisions, and a sense of responsibility for mitigating harms. We also found evidence of moral conflict and uncertainty about responsibility for averting harms as an individual developer working in a company. These findings suggest possible facilitators and barriers to the development of ethical ML that could act through encouragement of moral engagement or discouragement of moral disengagement. Regulatory approaches that depend on the ability of ML developers to recognize, accept, and act on responsibility for mitigating harms might have limited success without education and guidance for ML developers about the extent of their responsibilities and how to implement them.

n/a.

Data sharing must be accompanied by responsibility sharing

Asian Americans have been understudied in the literature on genetic and genomic services. The current study systematically identified, evaluated, and summarized findings from relevant qualitative and quantitative studies on genetic health care for Asian Americans. A search of five databases (1990 to 2018) returned 8,522 unique records. After removing duplicates, abstract/title screening, and full text review, 47 studies met inclusion criteria. Data from quantitative studies were converted into “qualitized data” and pooled together with thematic data from qualitative studies to produce a set of integrated findings. Synthesis of results revealed that (1) Asian Americans are under-referred but have high uptake for genetic services, (2) linguistic/communication challenges were common and Asian Americans expected more directive genetic counseling, and (3) Asian Americans’ family members were involved in testing decisions, but communication of results and risk information to family members was lower than other racial groups. This study identified multiple barriers to genetic counseling, testing, and care for Asian Americans, as well as gaps in the research literature. By focusing on these barriers and filling these gaps, clinical genetic approaches can be tailored to meet the needs of diverse patient groups, particularly those of Asian descent.

From its earliest days, the field of human genetics has had a complex, and at times troubling, connection with racist ideologies. Although the modern field of human genetics and genomics has come a long way from those earlier errors, systemic racism remains ingrained in its institutions and practices. Although a variety of efforts are needed to excise systemic racism, we focus in this commentary on the work that must be done in scientific publishing in genetics and genomics. We propose eight principles that are both scientifically grounded and antiracist that we hope will serve as a foundation for the development of policies by publishers and editorial boards that address the unique needs of the field of genetics and genomics. Publishers and journals must go beyond mere policies, however. Editors and reviewers will need training on these policies and principles, and will benefit from resources like rubrics that can be used for evaluating the adherence of submissions to these guidelines.

As genetics is increasingly used across clinical settings, there is a need to understand the impact and experiences of diverse patients. This review systematically examined research literature on Latinx experiences with genetic counseling and genetic testing (GC/GT) in the United States, synthesizing key themes and knowledge gaps pertaining to both patient experience and hypothetical scenarios. Findings were based on a systematic search, inclusion, and thematic analysis of 81 empirical peer-reviewed articles published from January 1990 to July 2019 pertaining to Latinx populations and GC/GT. Studies most commonly addressed Latinas' perspectives on GC/GT in prenatal settings or for hereditary breast and ovarian cancer (HBOC). Costs, referrals, and communication were significant barriers to accessing genetic services for many Latinx patients, particularly those with low English proficiency (LEP). Studies highlighted difficulties accessing and communicating in healthcare settings, and how medical context and prior experience with healthcare workers and institutions influenced GC/GT decision-making. Providers' implicit biases about Latinx patients negatively impacted their care and impeded communication. Despite low awareness of cancer GT, Latinx patients often reported interest in learning more about GC/GT or unmet needs for GT discussion and provider involvement. This systematic review identified areas where providers can take action to improve Latinx experiences with GC/GT. Clinicians should elicit and respond to patient preferences about shared decision-making. For patients with low numeracy or LEP, providers should consider tailored educational and communication techniques. Most studies focused on HBOC and prenatal testing, and Latinx patients are heterogeneous, leaving many research questions about Latinx experience with GT/GC in other clinical areas.

More than thirty years ago in the United States, the National Center for Human Genome Research (NCHGR) at the National Institutes of Health (NIH) and its partner in the Human Genome Project (HGP), the Department of Energy (DOE), called for proposals from social scientists, ethicists, lawyers, and others to explore the ethical, legal, and social implications (ELSI) of mapping and sequencing the human genome. Today, nearly twenty years after the completion of the HGP, the ELSI Research Program of the National Human Genome Research Institute (NHGRI) continues this support. It has fostered the growth of ELSI research into a global field of study, uniquely positioned at the nexus of many academic disciplines and in proximity to basic and applied scientific research. We examine the formation of the first ELSI program and consider whether science policy in the public interest can exist within the confines of a set-aside from the NHGRI budget.

Diversity, equity, and inclusion efforts in academia are leading publishers and journals to re-examine their use of terminology for commonly used scientific variables. This reassessment of language is particularly important for human genetics, which is focused on identifying and explaining differences between individuals and populations. Recent guidance on the use of terms and symbols in clinical practice, research, and publications is beginning to acknowledge the ways that language and concepts of difference can be not only inaccurate but also harmful. To stop perpetuating historical wrongs, those of us who conduct and publish genetic research and provide genetic health care must understand the context of the terms we use and why some usages should be discontinued. In this article, we summarize critiques of terminology describing disability, sex, gender, race, ethnicity, and ancestry in research publications, laboratory reports, diagnostic codes, and pedigrees. We also highlight recommendations for alternative language that aims to make genetics more inclusive, rigorous, and ethically sound. Even though norms of acceptable language use are ever changing, it is the responsibility of genetics professionals to uncover biases ingrained in professional practice and training and to continually reassess the words we use to describe human difference because they cause harm to patients.

Summary Precision mental health holds great potential for revolutionizing care and reducing the burden of mental illness. All races and ethnicities such as Asian Americans, the fastest growing racial group in the United States (U.S.), need to be engaged in precision mental health research. Owing to its global popularity, Korean drama (‘K-drama’) television shows may be an effective educational tool to increase precision mental health knowledge, attitudes and behaviors among Asian Americans. This qualitative study examined the participants’ perspectives about and acceptance of using K-dramas to educate and engage Asian Americans about precision mental health. Twelve workshops were conducted in English, Vietnamese and Korean with a convenience sample in the San Francisco Bay Area in the U.S. (n = 122). Discussions were coded for themes. Findings revealed that all language groups reported positive reactions to using K-dramas to learn about precision health, genetics and mental health. Overall, participants shared that they learned about topics that are not generally talked about (e.g. precision health; genetic testing; mental health), from other people’s perspectives, and the importance of mental health. Participants expressed how much they enjoyed the workshop, how they felt relieved due to the workshop, thought the workshop was interesting, and had an opportunity for self-reflection/healing. This pilot test demonstrated that K-dramas has promise to be used as a health educational tool in a workshop format focused on mental health among a diverse group of Asian Americans. Given the widespread access to K-dramas, they present a scalable opportunity for increasing awareness about specific health topics.

This paper reports our analysis of the ELSI Virtual Forum: 30 Years of the Genome: Integrating and Applying ELSI Research, an online meeting of scholars focused on the ethical, legal, and social implications (ELSI) of genetics and genomics. The proceedings and published literature demonstrate how justice in the implementation of genome science can be realized in two contexts: 1) by attending to equity in our decisions about how we allocate clinical and research resources, and 2) by attending to the equitable distribution of the benefits and risks of sharing genomic data. We conclude that members of the ELSI and genomics communities must work to ensure that human biases are not implemented along with genomic medicine.

Background: In order for precision health to address health disparities, engagement of diverse racial/ethnic minority communi­ties and the physicians that serve them is critical.Methods: A community-based participatory research approach with mixed methods was employed to gain a deeper understanding of precision health research and practice among American Indian, African American, Latino, Chinese, and Vietnamese groups and physicians that serve these communi­ties. A survey assessed demographics and opinions of precision health, genetic testing, and precision health research. Focus groups (n=12) with each racial/ethnic minority group and physicians further explored at­titudes about these topics.Results: One hundred community mem­bers (American Indian [n=17], African American [n=13], Chinese [n=17], Latino [n=27], and Vietnamese [n=26]) and 14 physicians completed the survey and participated in the focus groups. Familiarity with precision health was low among com­munity members and high among physi­cians. Most groups were enthusiastic about the approach, especially if it considered influences on health in addition to genes (eg, environmental, behavioral, social fac­tors). Significant concerns were expressed by African American and American Indian participants about precision health practice and research based on past abuses in bio­medical research. In addition, physician and community members shared concerns such as security and confidentiality of genetic information, cost and affordability of genetic tests and precision medicine, discrimina­tion and disparities, distrust of medical and research and pharmaceutical institutions, language barriers, and physician’s specialty.Conclusions: Engagement of racial/ethnic minority communities and the providers who serve them is important for advancing a precision health approach to addressing health disparities.Ethn Dis. 2020;30(Suppl 1):137-148; doi:10.18865/ed.30.S1.137

Artificial intelligence (AI) systems have increasingly achieved expert-level performance in medical imaging applications. However, there is growing concern that such AI systems may reflect and amplify human bias, and reduce the quality of their performance in historically under-served populations such as female patients, Black patients, or patients of low socioeconomic status. Such biases are especially troubling in the context of underdiagnosis, whereby the AI algorithm would inaccurately label an individual with a disease as healthy, potentially delaying access to care. Here, we examine algorithmic underdiagnosis in chest X-ray pathology classification across three large chest X-ray datasets, as well as one multi-source dataset. We find that classifiers produced using state-of-the-art computer vision techniques consistently and selectively underdiagnosed under-served patient populations and that the underdiagnosis rate was higher for intersectional under-served subpopulations, for example, Hispanic female patients. Deployment of AI systems using medical imaging for disease diagnosis with such biases risks exacerbation of existing care biases and can potentially lead to unequal access to medical treatment, thereby raising ethical concerns for the use of these models in the clinic., Artificial intelligence algorithms trained using chest X-rays consistently underdiagnose pulmonary abnormalities or diseases in historically under-served patient populations, raising ethical concerns about the clinical use of such algorithms.

Without proper recognition of the dual pandemics of COVID-19 and racism that Asian Americans and other racial minorities in the United States are facing, we cannot successfully address structural b...

Morain et al. (2020) tackle the difficult and fascinating question of how researchers and health systems should manage individual-level “collateral” (i.e., incidental or secondary) findings (CFs) a...

In response to genetic testing being widely ordered by nongenetics clinicians, the Consent and Disclosure Recommendations (CADRe) Workgroup of the Clinical Genome Resource (ClinGen; clinicalgenome.org ) developed guidance to facilitate communication about genetic testing and efficiently improve the patient experience. Considering ethical, legal, and social implications, and medical factors, CADRe developed and pilot tested two rubrics addressing consent for genetic testing and results disclosure. The CADRe rubrics allow for adjusting the communication approach based on circumstances specific to patients and ordering clinicians. We present results of a formative survey of 66 genetics clinicians to assess the consent rubric for nine genes (MLH1, CDH1, TP53, GJB2, OTC; DMD, HTT, and CYP2C9/VKORC1). We also conducted interviews and focus groups with family and patient stakeholders (N = 18), nongenetics specialists (N = 27), and genetics clinicians (N = 32) on both rubrics. Formative evaluation of the CADRe rubrics suggests key factors on which to make decisions about consent and disclosure discussions for a “typical” patient. We propose that the CADRe rubrics include the primary issues necessary to guide communication recommendations, and are ready for pilot testing by nongenetics clinicians. Consultation with genetics clinicians can be targeted toward more complex or intensive consent and disclosure counseling.

Purpose To determine whether patients distinguish between biospecimens and electronic health records (EHRs) when considering research participation to inform research protections. Methods We conducted 20 focus groups with individuals who identified as African American, Hispanic, Chinese, South Asian and non-Hispanic White on the collection of biospecimens and EHR data for research. Results Our study found that many participants did not distinguish between biospecimens and EHR data. However, some participants identified specific concerns about biospecimens. These included the need for special care and respect for biospecimens due to enduring connections between the body and identity; the potential for unacceptable future research, specifically the prospect of human cloning; heightened privacy risks; and the potential for unjust corporate profiteering. Among those who distinguished biospecimens from EHR data, many supported separate consent processes and would limit their own participation to EHR data. Conclusion Considering that the potential misuse of EHR data is as great, if not greater than for biospecimens, more research is needed to understand how attitudes differ between biospecimens and EHR data across diverse populations. Such research should explore mechanisms beyond consent that can address diverse values, perspectives and misconceptions about sources of patient information to build trust in research relationships.

Advances in artificial intelligence (AI) bring with them new regulatory, social, and ethical challenges. At the level of data collection an increase in the ability to identify previously deidentified health data and questions overreporting of incidental findings call for a new model of data stewardship. In designing new AI tools, recognition of biases and competing interests in algorithm design, in the data in which algorithms learn, and in society itself will all be necessary in avoiding unjust or suboptimal outcomes in application. And at the level of regulation, there is a need for a system that can verify the quality of algorithm design to improve trust in these tools. Moving forward, a new regulatory model that focuses on regulating the process, rather than the product, of AI design, as well as a reconceptualization of the traditional physician–patient dyad to incorporate AI, is needed to help overcome these challenges.

BACKGROUND Considerable effort has been devoted to the development of artificial intelligence, including machine learning–based predictive analytics (MLPA) for use in health care settings. The growth of MLPA could be fueled by payment reforms that hold health care organizations responsible for providing high-quality, cost-effective care. Policy analysts, ethicists, and computer scientists have identified unique ethical and regulatory challenges from the use of MLPA in health care. However, little is known about the types of MLPA health care products available on the market today or their stated goals. OBJECTIVE This study aims to better characterize available MLPA health care products, identifying and characterizing claims about products recently or currently in use in US health care settings that are marketed as tools to improve health care efficiency by improving quality of care while reducing costs. METHODS We conducted systematic database searches of relevant business news and academic research to identify MLPA products for health care efficiency meeting our inclusion and exclusion criteria. We used content analysis to generate MLPA product categories and characterize the organizations marketing the products. RESULTS We identified 106 products and characterized them based on publicly available information in terms of the types of predictions made and the size, type, and clinical training of the leadership of the companies marketing them. We identified 5 categories of predictions made by MLPA products based on publicly available product marketing materials: disease onset and progression, treatment, cost and utilization, admissions and readmissions, and decompensation and adverse events. CONCLUSIONS Our findings provide a foundational reference to inform the analysis of specific ethical and regulatory challenges arising from the use of MLPA to improve health care efficiency.

From its earliest days, the field of human genetics has had a complex, and at times troubling, connection with racist ideologies. Although the modern field of human genetics and genomics has come a long way from those earlier errors, systemic racism remains ingrained in its institutions and practices. Although a variety of efforts are needed to excise systemic racism, we focus in this commentary on the work that must be done in scientific publishing in genetics and genomics. We propose eight principles that are both scientifically grounded and antiracist that we hope will serve as a foundation for the development of policies by publishers and editorial boards that address the unique needs of the field of genetics and genomics. Publishers and journals must go beyond mere policies, however. Editors and reviewers will need training on these policies and principles, and will benefit from resources like rubrics that can be used for evaluating the adherence of submissions to these guidelines.

During the COVID-19 pandemic, ethicists and others have worked to allocate scarce resources in the clinical setting, including intensive care beds, ventilators, and personal protective equipment, i...

Ambient intelligence is increasingly finding applications in health-care settings, such as helping to ensure clinician and patient safety by monitoring staff compliance with clinical best practices or relieving staff of burdensome documentation tasks. Ambient intelligence involves using contactless sensors and contact-based wearable devices embedded in health-care settings to collect data (eg, imaging data of physical spaces, audio data, or body temperature), coupled with machine learning algorithms to efficiently and effectively interpret these data. Despite the promise of ambient intelligence to improve quality of care, the continuous collection of large amounts of sensor data in health-care settings presents ethical challenges, particularly in terms of privacy, data management, bias and fairness, and informed consent. Navigating these ethical issues is crucial not only for the success of individual uses, but for acceptance of the field as a whole.

With the growth of precision medicine research on health data and biospecimens, research institutions will need to build and maintain long-term, trusting relationships with patient-participants. While trust is important for all research relationships, the longitudinal nature of precision medicine research raises particular challenges for facilitating trust when the specifics of future studies are unknown. Based on focus groups with racially and ethnically diverse patients, we describe several factors that influence patient trust and potential institutional approaches to building trustworthiness. Drawing on these findings, we suggest several considerations for research institutions seeking to cultivate long-term, trusting relationships with patients: (1) Address the role of history and experience on trust, (2) engage concerns about potential group harm, (3) address cultural values and communication barriers, and (4) integrate patient values and expectations into oversight and governance structures.

The increasing complexity of human subjects research and its oversight has prompted researchers, as well as institutional review boards (IRBs), to have a forum in which to discuss challenging or novel ethical issues not fully addressed by regulations. Research ethics consultation (REC) services provide such a forum. In this article, we rely on the experiences of a national Research Ethics Consultation Collaborative that collected more than 350 research ethics consultations in a repository and published 18 challenging cases with accompanying ethical commentaries to highlight four contexts in which REC can be a valuable resource. REC assists: 1) investigators before and after the regulatory review; 2) investigators, IRBs, and other research administrators facing challenging and novel ethical issues; 3) IRBs and investigators with the increasing challenges of informed consent and risk/benefit analysis; and 4) in providing flexible and collaborative assistance to overcome study hurdles, mediate conflicts within a team, or directly engage with research participants. Institutions that have established, or plan to establish, REC services should work to raise the visibility of their service and engage in open communication with existing clinical ethics consult services as well as the IRB. While the IRB system remains the foundation for the ethical review of research, REC can be a valuable service for investigators, regulators, and research participants aligned with the goal of supporting ethical research.

Background Digital phenotyping (also known as personal sensing, intelligent sensing, or body computing) involves the collection of biometric and personal data in situ from digital devices, such as smartphones, wearables, or social media, to measure behavior or other health indicators. The collected data are analyzed to generate moment-by-moment quantification of a person’s mental state and potentially predict future mental states. Digital phenotyping projects incorporate data from multiple sources, such as electronic health records, biometric scans, or genetic testing. As digital phenotyping tools can be used to study and predict behavior, they are of increasing interest for a range of consumer, government, and health care applications. In clinical care, digital phenotyping is expected to improve mental health diagnoses and treatment. At the same time, mental health applications of digital phenotyping present significant areas of ethical concern, particularly in terms of privacy and data protection, consent, bias, and accountability. Objective This study aims to develop consensus statements regarding key areas of ethical guidance for mental health applications of digital phenotyping in the United States. Methods We used a modified Delphi technique to identify the emerging ethical challenges posed by digital phenotyping for mental health applications and to formulate guidance for addressing these challenges. Experts in digital phenotyping, data science, mental health, law, and ethics participated as panelists in the study. The panel arrived at consensus recommendations through an iterative process involving interviews and surveys. The panelists focused primarily on clinical applications for digital phenotyping for mental health but also included recommendations regarding transparency and data protection to address potential areas of misuse of digital phenotyping data outside of the health care domain. Results The findings of this study showed strong agreement related to these ethical issues in the development of mental health applications of digital phenotyping: privacy, transparency, consent, accountability, and fairness. Consensus regarding the recommendation statements was strongest when the guidance was stated broadly enough to accommodate a range of potential applications. The privacy and data protection issues that the Delphi participants found particularly critical to address related to the perceived inadequacies of current regulations and frameworks for protecting sensitive personal information and the potential for sale and analysis of personal data outside of health systems. Conclusions The Delphi study found agreement on a number of ethical issues to prioritize in the development of digital phenotyping for mental health applications. The Delphi consensus statements identified general recommendations and principles regarding the ethical application of digital phenotyping to mental health. As digital phenotyping for mental health is implemented in clinical care, there remains a need for empirical research and consultation with relevant stakeholders to further understand and address relevant ethical issues.

ObjectiveTo better understand diverse experts’ views about the ethical implications of ongoing research funded by the National Institutes of Health that uses machine learning to predict HIV/AIDS risk in sub-Saharan Africa (SSA) based on publicly available Demographic and Health Surveys data.DesignThree rounds of semi-structured surveys in an online expert panel using a modified Delphi approach.ParticipantsExperts in informatics, African public health and HIV/AIDS and bioethics were invited to participate.MeasuresPerceived importance of or agreement about relevance of ethical issues on 5-point unipolar Likert scales. Qualitative data analysis identified emergent themes related to ethical issues and development of an ethical framework and recommendations for open-ended questions.ResultsOf the 35 invited experts, 22 participated in the online expert panel (63%). Emergent themes were the inclusion of African researchers in all aspects of study design, analysis and dissemination to identify and address local contextual issues, as well as engagement of communities. Experts focused on engagement with health and science professionals to address risks, benefits and communication of findings. Respondents prioritised the mitigation of stigma to research participants but recognised trade-offs between privacy and the need to disseminate findings to realise public health benefits. Strategies for responsible communication of results were suggested, including careful word choice in presentation of results and limited dissemination to need-to-know stakeholders such as public health planners.ConclusionExperts identified ethical issues specific to the African context and to research on sensitive, publicly available data and strategies for addressing these issues. These findings can be used to inform an ethical implementation framework with research stage-specific recommendations on how to use publicly available data for machine learning-based predictive analytics to predict HIV/AIDS risk in SSA.

Background Considerable effort has been devoted to the development of artificial intelligence, including machine learning–based predictive analytics (MLPA) for use in health care settings. The growth of MLPA could be fueled by payment reforms that hold health care organizations responsible for providing high-quality, cost-effective care. Policy analysts, ethicists, and computer scientists have identified unique ethical and regulatory challenges from the use of MLPA in health care. However, little is known about the types of MLPA health care products available on the market today or their stated goals. Objective This study aims to better characterize available MLPA health care products, identifying and characterizing claims about products recently or currently in use in US health care settings that are marketed as tools to improve health care efficiency by improving quality of care while reducing costs. Methods We conducted systematic database searches of relevant business news and academic research to identify MLPA products for health care efficiency meeting our inclusion and exclusion criteria. We used content analysis to generate MLPA product categories and characterize the organizations marketing the products. Results We identified 106 products and characterized them based on publicly available information in terms of the types of predictions made and the size, type, and clinical training of the leadership of the companies marketing them. We identified 5 categories of predictions made by MLPA products based on publicly available product marketing materials: disease onset and progression, treatment, cost and utilization, admissions and readmissions, and decompensation and adverse events. Conclusions Our findings provide a foundational reference to inform the analysis of specific ethical and regulatory challenges arising from the use of MLPA to improve health care efficiency.

Background To describe the types of research being conducted and the availability of research ethics training and research ethics review in Thailand and Vietnam. Methods An English survey with four major domains, Research Area, Societal Conditions, Research Ethics, and Basic Information was translated into Thai and Vietnamese by native training partners from the NIH Fogarty Research Ethics Training Program. Setting/Participants The survey was administered in two modes - an online survey distributed via an email link in Thailand, and an onsite paper survey in Vietnam. Participants were Thai and Vietnamese trainees and investigators from prestigious universities. Results In Thailand, there were 363 respondents (9.3% online response rate); in Vietnam there were 117 survey participants. Among those who conduct research, 81% in Thailand and 92% percent in Vietnam reported that their research involves human subjects. Among human subject researchers, 83% in Thailand reported having ethics training, and among these, only 44% reported having formal training. In Vietnam, 66% reported having research ethics training; among them, 72% had formal training. Human subject research reported include clinical observations (26% from Thailand and 26% from Vietnam) and clinical interventions (29% from Thailand and 26% from Vietnam). Significant proportion of respondents reported that their institutions have guidelines (97% in Thailand; 89% from Vietnam) and have established Institutional Review Boards (92% in Thailand; 77% in Vietnam). 76% and 79% of respondents in Thailand and Vietnam respectively reported no experience in teaching research ethics. Lack of trained research ethics teachers (38% in Thailand and 59% in Vietnam), training materials (34% in Thailand; 43% in Vietnam), and an adequate curricular “delivery platform” (58% in Thailand; 49% in Vietnam) are most pressing issues. Conclusions We identified gaps in research ethics training in these two South East Asian countries undergoing rapid socioeconomic transition and identified future curricular focus opportunities.

Precision mental health (MH) holds great potential for revolutionizing MH care and reducing the burden of mental illness. Efforts to engage Asian Americans in precision MH research is necessary to help reduce MH disparities. Korean drama (&ldquo, K-drama&rdquo, ) television shows may be an effective educational tool to increase precision MH knowledge, attitudes, and behaviors (KAB) among Asian Americans. This study determined whether KAB improved after participating in a K-drama precision MH workshop, and examined the participants&rsquo, perspectives about K-dramas&rsquo, utility as an educational tool. A K-drama precision MH workshop in English/Vietnamese/Korean was conducted with a convenience sample (n = 122). Pre-/post-tests on precision MH KAB (genetics and genetic testing, and MH and help-seeking) and a survey on K-dramas&rsquo, utility as an educational tool were administered. Findings revealed a significant difference in the pre- and post-test KAB scores overall, by genetics and genetic testing, and by MH and help-seeking. There were also significant increases in the overall post-test KAB scores by workshop (language) participation. Overall, participants responded positively on the utility of K-dramas as a precision MH educational tool. This study demonstrates the feasibility of K-drama as an innovative and widely available health education tool to educate communities about precision MH.

Biobanks and human genomics applications are key for understanding health, disease and heredity in Africa and globally. Growing interest in these technologies calls for strengthening relevant legal, ethical and policy systems to address knowledge disparities and ensure protection of society, while supporting advancement of science. In Zimbabwe there is limited understanding of ethical, legal, and societal issues (ELSI) for biobanking and genomics. The Genomics Inheritance Law Ethics and Society (GILES) initiative was established in 2015 to explore the current status and gaps in the ethical and legal frameworks, knowledge among various stakeholders, and to establish capacity for addressing ELSI of biobanking and genomics as applied in biomedical and population research, and healthcare. A multi-methods approach was applied including document reviews, focus group discussions and in-depth interviews among health and research professionals, and community members in six provinces comprising urban, peri-urban and rural areas. Emerging findings indicates a need for updating guidelines and policies for addressing ELSI in biobanking and genomics research in Zimbabwe. Emerging terminologies such as biobanking and genomics lack clarity suggesting a need for increased awareness and educational tools for health professionals, research scientists and community members. Common concerns relating to consent processes, sample and data use and sharing, particularly where there is trans-national flow of biospecimens and data, call for nationally tailored ELSI frameworks aligned to regional and international initiatives. This paper describes the strategy undertaken for the development and implementation of the GILES project and discusses the importance of such an initiative for characterisation of ELSI of human biobanking and genomics in Zimbabwe and Africa. Conducting this explorative study among a wide range of stakeholders over a countrywide geographical regions, established one of the most comprehensive studies for ELSI of human biobanking and genomics in Africa.

Studies have established the importance of physical activity and fitness for long-term cardiovascular health, yet limited data exist on the association between objective, real-world large-scale physical activity patterns, fitness, sleep, and cardiovascular health primarily due to difficulties in collecting such datasets. We present data from the MyHeart Counts Cardiovascular Health Study, wherein participants contributed data via an iPhone application built using Apple’s ResearchKit framework and consented to make this data available freely for further research applications. In this smartphone-based study of cardiovascular health, participants recorded daily physical activity, completed health questionnaires, and performed a 6-minute walk fitness test. Data from English-speaking participants aged 18 years or older with a US-registered iPhone who agreed to share their data broadly and who enrolled between the study’s launch and the time of the data freeze for this data release (March 10 2015–October 28 2015) are now available for further research. It is anticipated that releasing this large-scale collection of real-world physical activity, fitness, sleep, and cardiovascular health data will enable the research community to work collaboratively towards improving our understanding of the relationship between cardiovascular indicators, lifestyle, and overall health, as well as inform mobile health research best practices., Design Type(s)observation design · source-based data analysis objective · data collection and processing objectiveMeasurement Type(s)physical activity · sleepTechnology Type(s)crowd-sourced data generationFactor Type(s)sex · height · weight · age · smoking status measurement · employment statusSample Characteristic(s)Homo sapiens · United States of America Machine-accessible metadata file describing the reported data (ISA-Tab format)

In their Policy Forum "Toward unrestricted use of public genomic data" (25 January, p. 350), R. I. Amann et al. argue that once data has been cleared for release to the public domain by institutions, it should be open for use without further restrictions. However, they neglect the key point that researchers and their institutions are entrusted by research participants, funders, and others with weighing the pros and cons of public data release. By suggesting that informed consent can provide a straightforward path to data release, they overlook evidence that once people understand their options, only a little more than half opt for open data sharing, and some refuse data sharing altogether.

Digital phenotyping uses smartphone and wearable signals to measure cognition, mood, and behavior. This promising new approach has been developed as an objective, passive assessment tool for the diagnosis and treatment of mental illness. Digital phenotyping is currently used with informed consent in research studies but is expected to expand to broader uses in healthcare and direct-to-consumer applications. Digital phenotyping could involve the collection of massive amounts of individual data and potential creation of new categories of health and risk assessment data. Because existing ethical and regulatory frameworks for the provision of mental healthcare do not clearly apply to digital phenotyping, it is critical to consider its possible ethical, legal, and social implications. This paper addresses four major areas where guidelines and best practices will be helpful: transparency, informed consent, privacy, and accountability. It will be important to consider these issues early in the development of this new approach so that its promise is not limited by harmful effects or unintended consequences.

Recent experiments have been used to "edit" genomes of various plant, animal and other species, including humans, with unprecedented precision. Furthermore, editing the Cas9 endonuclease gene with a gene encoding the desired guide RNA into an organism, adjacent to an altered gene, could create a "gene drive" that could spread a trait through an entire population of organisms. These experiments represent advances along a spectrum of technological abilities that genetic engineers have been working on since the advent of recombinant DNA techniques. The scientific and bioethics communities have built substantial literatures about the ethical and policy implications of genetic engineering, especially in the age of bioterrorism. However, recent CRISPr/Cas experiments have triggered a rehashing of previous policy discussions, suggesting that the scientific community requires guidance on how to think about social responsibility. We propose a framework to enable analysis of social responsibility, using two examples of genetic engineering experiments.

ObjectiveWe sought to present a model of privacy disposition and its development based on qualitative research on privacy considerations in the context of emerging health technologies.Materials and MethodsWe spoke to 108 participants across 44 interviews and 9 focus groups to understand the range of ways in which individuals value (or do not value) control over their health information. Transcripts of interviews and focus groups were systematically coded and analyzed in ATLAS.ti for privacy considerations expressed by respondents.ResultsThree key findings from the qualitative data suggest a model of privacy disposition. First, participants described privacy related behavior as both contextual and habitual. Second, there are motivations for and deterrents to sharing personal information that do not fit into the analytical categories of risks and benefits. Third, philosophies of privacy, often described as attitudes toward privacy, should be classified as a subtype of motivation or deterrent.DiscussionThis qualitative analysis suggests a simple but potentially powerful conceptual model of privacy disposition, or what makes a person more or less private. Components of privacy disposition are identifiable and measurable through self-report and therefore amenable to operationalization and further quantitative inquiry.ConclusionsWe propose this model as the basis for a psychometric instrument that can be used to identify types of privacy dispositions, with potential applications in research, clinical practice, system design, and policy.

To investigate the potential impacts of whole-genome sequencing (WGS) in the pediatric critical-care context, we examined how clinicians caring for critically ill children with congenital heart disease (CHD) anticipate and perceive the impact of WGS on their decision-making process and treatment recommendations. We conducted semistructured in-person and telephone interviews of clinicians involved in the care of critically ill children with CHD at a high-volume pediatric heart center. We qualitatively analyzed the transcribed interviews. In total, 34 clinicians were interviewed. Three themes emerged: (i) uncertainty about the accuracy of WGS testing and adequacy of testing validation; (ii) the use of WGS to facilitate life-limiting decisions such as futility, rationing, and selective prenatal termination; and (iii) moral distress over using WGS with a lack of decision support. Despite uncertainty about WGS testing, the interviewed clinicians were using, and anticipated expanding the use of, WGS results to justify declarations of futility, withdrawal of care, and rationing in critically ill children with CHD. This situation is causing moral distress in providers who have to make high-stakes decisions involving WGS results, with only partial understanding of them. Decision support for clinicians, and discussion with families of the risks of using WGS for rationing or withdrawal, is needed.

The Open Peer Commentaries on “The Emergence of Clinical Research Ethics Consultation: Insights from a National Collaborative201D highlight the many ways in which the practice of ethics consultation for clinical research can be further advanced. We respond here to a number of key considerations highlighted by commentators, including the role and scope of research ethics consultation (REC), relationships with other institutional services and programs, efforts to ensure the quality of consultations provided, and the feasibility of widespread REC services.

We conducted focus groups to assess patient attitudes towards research on medical practices in the context of usual care. We found that patients focus on the implications of this research for their relationship with and trust in their physicians. Patients view research on medical practices as separate from usual care, demanding dissemination of information and in most cases, individual consent. Patients expect information about this research to come through their physician, whom they rely on to identify and filter associated risks. In general, patients support this research, but worry that participation in research involving randomization may undermine individualized care that acknowledges their unique medical histories. These findings suggest the need for public education on variation in practice among physicians and the need for a collaborative approach to the governance of research on medical practices that addresses core values of trust, transparency and partnership.