A survey study of Alzheimer's stigma among Black adults: intersectionality of Black identity and biomarker diagnosis.

OBJECTIVE: We urgently need to understand Alzheimer's disease (AD) stigma among Black adults. Black communities bear a disproportionate burden of AD, and recent advances in early diagnosis using AD biomarkers may affect stigma associated with AD. The goal of our study is to characterize AD stigma within our cohort of self-identified Black participants and test how AD biomarker test results may affect this stigma. DESIGN: We surveyed a sample of 1,150 self-identified Black adults who were randomized to read a vignette describing a fictional person, who was described as either having a positive or negative biomarker test result. After reading the vignette, participants completed the modified Family Stigma in Alzheimer's Disease Scale (FS-ADS). We compared FS-ADS scores between groups defined by age, gender, and United States Census region. We examined interactions between these groupings and AD biomarker test result. RESULTS: Participants over age 65 had lower scores (lower stigma) on all 7 FS-ADS domains compared to those under 65: structural discrimination, negative severity attributions, negative aesthetic attributions, antipathy, support, pity, and social distance. In the biomarker positive condition, worries about structural discrimination were greater than in the biomarker negative condition and statistically similar in the two age groups (DOR, 0.39 [95%CI, 0.22-0.69]). This pattern of results was similar for negative symptom attributions (DOR, 0.51 [95%CI, 0.28-0.90]). CONCLUSION: While older adults reported less AD stigma than younger adults, AD biomarker testing caused similarly high concerns about structural discrimination and negative severity attributions. Thus, use of AD biomarker diagnosis may increase AD stigma and exacerbate healthcare disparities known to effect AD diagnosis in some Black adults. Advances in AD diagnosis may interact with social and structural factors to differentially affect groups of Black adults.

How reactions to a brain scan result differ for adults based on self-identified Black and White race.

INTRODUCTION: How do reactions to a brain scan result differ between Black and White adults? The answer may inform efforts to reduce disparities in Alzheimer's disease (AD) diagnosis and treatment. METHODS: Self-identified Black (n = 1055) and White (n = 1451) adults were randomized to a vignette of a fictional patient at a memory center who was told a brain scan result. Measures of stigma and diagnosis confidence were compared between-groups. RESULTS: Black participants reported more stigma than White participants on four of seven domains in reaction to the patient at a memory center visit. Black participants' confidence in an AD diagnosis informed by a brain scan and other assessments was 72.2 points (95% confidence interval [CI] 70.4 to 73.5), which was lower than the respective rating for White participants [78.1 points (95%CI 77.0 to 79.3)]. DISCUSSION: Equitable access to early AD diagnosis will require public outreach and education that address AD stigma associated with a memory center visit.

Developer Perspectives on Potential Harms of Machine Learning Predictive Analytics in Health Care: Qualitative Analysis.

BACKGROUND: Machine learning predictive analytics (MLPA) is increasingly used in health care to reduce costs and improve efficacy; it also has the potential to harm patients and trust in health care. Academic and regulatory leaders have proposed a variety of principles and guidelines to address the challenges of evaluating the safety of machine learning-based software in the health care context, but accepted practices do not yet exist. However, there appears to be a shift toward process-based regulatory paradigms that rely heavily on self-regulation. At the same time, little research has examined the perspectives about the harms of MLPA developers themselves, whose role will be essential in overcoming the "principles-to-practice" gap. OBJECTIVE: The objective of this study was to understand how MLPA developers of health care products perceived the potential harms of those products and their responses to recognized harms. METHODS: We interviewed 40 individuals who were developing MLPA tools for health care at 15 US-based organizations, including data scientists, software engineers, and those with mid- and high-level management roles. These 15 organizations were selected to represent a range of organizational types and sizes from the 106 that we previously identified. We asked developers about their perspectives on the potential harms of their work, factors that influence these harms, and their role in mitigation. We used standard qualitative analysis of transcribed interviews to identify themes in the data. RESULTS: We found that MLPA developers recognized a range of potential harms of MLPA to individuals, social groups, and the health care system, such as issues of privacy, bias, and system disruption. They also identified drivers of these harms related to the characteristics of machine learning and specific to the health care and commercial contexts in which the products are developed. MLPA developers also described strategies to respond to these drivers and potentially mitigate the harms. Opportunities included balancing algorithm performance goals with potential harms, emphasizing iterative integration of health care expertise, and fostering shared company values. However, their recognition of their own responsibility to address potential harms varied widely. CONCLUSIONS: Even though MLPA developers recognized that their products can harm patients, public, and even health systems, robust procedures to assess the potential for harms and the need for mitigation do not exist. Our findings suggest that, to the extent that new oversight paradigms rely on self-regulation, they will face serious challenges if harms are driven by features that developers consider inescapable in health care and business environments. Furthermore, effective self-regulation will require MLPA developers to accept responsibility for safety and efficacy and know how to act accordingly. Our results suggest that, at the very least, substantial education will be necessary to fill the "principles-to-practice" gap.

A Typology of Existing Machine Learning-Based Predictive Analytic Tools Focused on Reducing Costs and Improving Quality in Health Care: Systematic Search and Content Analysis.

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.

Bipolar Disorder, Genetic Risk, and Reproductive Decision-Making: A Qualitative Study of Social Media Discussion Boards.

In this study, we present views on bipolar disorder and reproductive decision-making through an analysis of posts on Reddit™, a major Internet discussion forum. Prior research has shown that the Internet is a useful source of data on sensitive topics. This study used qualitative textual analysis to analyze posts on Reddit™ bipolar discussion boards that dealt with genetics and related topics. All thread titles over 4 years were reviewed (N = 1,800). Genetic risk was often raised in the context of Redditors' discussions about whether or not to have children. Reproductive decision-making for Redditors with bipolar was complex and influenced by factors from their past, present, and imagined future. These factors coalesced under a summative theme: for adults with bipolar disorder, what was the manageability of parenting a child? Reproductive decisions for individuals with bipolar disorder are complex, and Reddit™ is a novel source of information on their perspectives.

Normative and conceptual ELSI research: what it is, and why it's important.

The Ethical, Legal, and Social Implications (ELSI) Research Program of the National Human Genome Research Institute sponsors research examining ethical, legal, and social issues arising in the context of genetics/genomics. The ELSI Program endorses an understanding of research not as the sole province of empirical study, but instead as systematic study or inquiry, of which there are many types and methods. ELSI research employs both empirical and nonempirical methods. Because the latter remain relatively unfamiliar to biomedical and translational scientists, this paper seeks to elucidate the relationship between empirical and nonempirical methods in ELSI research. It pays particular attention to the research questions and methods of normative and conceptual research, which examine questions of value and meaning, respectively. To illustrate the distinct but interrelated roles of empirical and nonempirical methods in ELSI research, including normative and conceptual research, the paper demonstrates how a range of methods may be employed both to examine the evolution of the concept of incidental findings (including the recent step toward terming them 'secondary findings'), and to address the normative question of how genomic researchers and clinicians should manage incidental such findings.

Identifiable Characteristics and Potentially Malleable Beliefs Predict Stigmatizing Attributions Toward Persons With Alzheimer's Disease Dementia: Results of a Survey of the U.S. General Public.

The general public's views can influence whether people with Alzheimer's disease (AD) experience stigma. The purpose of this study was to understand what characteristics in the general public are associated with stigmatizing attributions. A random sample of adults from the general population read a vignette about a man with mild Alzheimer's disease dementia and completed a modified Family Stigma in Alzheimer's Disease Scale (FS-ADS). Multivariable ordered logistic regressions were used to examine relationships between personal characteristics and FS-ADS ratings. Older respondents expected that persons with AD would receive less support (OR = 0.82, p = .001), have social interactions limited by others (OR = 1.13, p = .04), and face institutional discrimination (OR = 1.13, p = .04). Females reported stronger feelings of pity (OR = 1.57, p = .03) and weaker reactions to negative aesthetic features (OR = 0.67, p = .05). Those who believed strongly that AD was a mental illness rated symptoms more severely (OR = 1.78, p = .007). Identifiable characteristics and beliefs in the general public are related to stigmatizing attributions toward AD. To reduce AD stigma, public health messaging campaigns can tailor information to subpopulations, recognizable by their age, gender, and beliefs.

The Precision Medicine Initiative's All of Us Research Program: an agenda for research on its ethical, legal, and social issues.

The Precision Medicine Initiative (PMI) is an innovative approach to developing a new model of health care that takes into account individual differences in people's genes, environments, and lifestyles. A cornerstone of the initiative is the PMI All of Us Research Program (formerly known as PMI-Cohort Program) which will create a cohort of 1 million volunteers who will contribute their health data and biospecimens to a centralized national database to support precision medicine research. The PMI All of US Research Program is the largest longitudinal study in the history of the United States. The designers of the Program anticipated and addressed some of the ethical, legal, and social issues (ELSI) associated with the initiative. To date, however, there is no plan to call for research regarding ELSI associated with the Program-PMI All of Us program. Based on analysis of National Institutes of Health (NIH) funding announcements for the PMI All of Us program, we have identified three ELSI themes: cohort diversity and health disparities, participant engagement, and privacy and security. We review All of Us Research Program plans to address these issues and then identify additional ELSI within each domain that warrant ongoing investigation as the All of Us Research Program develops. We conclude that PMI's All of Us Research Program represents a significant opportunity and obligation to identify, analyze, and respond to ELSI, and we call on the PMI to initiate a research program capable of taking on these challenges.Genet Med advance online publication 01 December 2016.

Factors affecting willingness to share electronic health data among California consumers.

BACKGROUND: Robust technology infrastructure is needed to enable learning health care systems to improve quality, access, and cost. Such infrastructure relies on the trust and confidence of individuals to share their health data for healthcare and research. Few studies have addressed consumers' views on electronic data sharing and fewer still have explored the dual purposes of healthcare and research together. The objective of the study is to explore factors that affect consumers' willingness to share electronic health information for healthcare and research. METHODS: This study involved a random-digit dial telephone survey of 800 adult Californians conducted in English and Spanish. Logistic regression was performed using backward selection to test for significant (p-value ≤ 0.05) associations of each explanatory variable with the outcome variable. RESULTS: The odds of consent for electronic data sharing for healthcare decreased as Likert scale ratings for EHR impact on privacy worsened, odds ratio (OR) = 0.74, 95% CI [0.60, 0.90]; security, OR = 0.80, 95% CI [0.66, 0.98]; and quality, OR = 0.59, 95% CI [0.46-0.75]. The odds of consent for sharing for research was greater for those who think EHR will improve research quality, OR = 11.26, 95% CI [4.13, 30.73]; those who value research benefit over privacy OR = 2.72, 95% CI [1.55, 4.78]; and those who value control over research benefit OR = 0.49, 95% CI [0.26, 0.94]. CONCLUSIONS: Consumers' choices about electronically sharing health information are affected by their attitudes toward EHRs as well as beliefs about research benefit and individual control. Design of person-centered interventions utilizing electronically collected health information, and policies regarding data sharing should address these values of importance to people. Understanding of these perspectives is critical for leveraging health data to support learning health care systems.

Reflections on the cost of "low-cost" whole genome sequencing: framing the health policy debate.

The cost of whole genome sequencing is dropping rapidly. There has been a great deal of enthusiasm about the potential for this technological advance to transform clinical care. Given the interest and significant investment in genomics, this seems an ideal time to consider what the evidence tells us about potential benefits and harms, particularly in the context of health care policy. The scale and pace of adoption of this powerful new technology should be driven by clinical need, clinical evidence, and a commitment to put patients at the centre of health care policy.

Engineering Values Into Genetic Engineering: A Proposed Analytic Framework for Scientific Social Responsibility.

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.

"What is the FDA Going to Think?": Negotiating Values through Reflective and Strategic Category Work in Microbiome Science.

The US National Institute of Health's Human Microbiome Project aims to use genomic techniques to understand the microbial communities that live on the human body. The emergent field of microbiome science brought together diverse disciplinary perspectives and technologies, thus facilitating the negotiation of differing values. Here, we describe how values are conceptualized and negotiated within microbiome research. Analyzing discussions from a series of interdisciplinary workshops conducted with microbiome researchers, we argue that negotiations of epistemic, social, and institutional values were inextricable from the reflective and strategic category work (i.e., the work of anticipating and strategizing around divergent sets of institutional categories) that defined and organized the microbiome as an object of study and a potential future site of biomedical intervention. Negotiating the divergence or tension between emerging scientific and regulatory classifications also activated "values levers" and opened up reflective discussions of how classifications embody values and how these values might differ across domains. These data suggest that scholars at the intersections of science and technology studies, ethics, and policy could leverage such openings to identify and intervene in the ways that ethical/regulatory and scientific/technical practices are coproduced within unfolding research.

Reporting Race and Ethnicity in Genetics Research: Do Journal Recommendations or Resources Matter?

Appeals to scrutinize the use of race and ethnicity as variables in genetics research notwithstanding, these variables continue to be inadequately explained and inconsistently used in research publications. In previous research, we found that published genetic research fails to follow suggestions offered for addressing this problem, such as explaining the basis on which these labels are assigned to populations. This study, an analysis of genetic research articles using race or ethnicity terms, explores possible features of journals that are associated with improved reporting of race and ethnicity in genetic research. A journal's expressed commitment to improving how race and ethnicity are used in genetic research, demonstrated by an editorial or in its instructions to authors, was the strongest predictor of following recommendations about reporting race and ethnicity. Journal impact factor had only a limited positive effect on attention to these issues, suggesting that editorial resources associated with higher impact factor journals are not sufficient to improve practices. Our findings reiterate that race and ethnicity variables are used inconsistently in genetic research, but also shed light on how journals might improve practices by highlighting the need for scientists to carefully scrutinize the use of these variables in their work.

Moral Engagement and Disengagement in Health Care AI Development.

BACKGROUND: Machine learning (ML) is utilized increasingly in health care, and can pose harms to patients, clinicians, health systems, and the public. In response, regulators have proposed an approach that would shift more responsibility to ML developers for mitigating potential harms. To be effective, this approach requires ML developers to recognize, accept, and act on responsibility for mitigating harms. However, little is known regarding the perspectives of developers themselves regarding their obligations to mitigate harms. METHODS: We conducted 40 semi-structured interviews with developers of ML predictive analytics applications for health care in the United States. RESULTS: Participants varied widely in their perspectives on personal responsibility and included examples of both moral engagement and disengagement, albeit in a variety of forms. While most (70%) of participants made a statement indicative of moral engagement, most of these statements reflected an awareness of moral issues, while only a subset of these included additional elements of engagement such as recognizing responsibility, alignment with personal values, addressing conflicts of interests, and opportunities for action. Further, we identified eight distinct categories of moral disengagement reflecting efforts to minimize potential harms or deflect personal responsibility for preventing or mitigating harms. CONCLUSIONS: These findings suggest possible facilitators and barriers to the development of ethical ML that could act by encouraging moral engagement or discouraging 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.

Double-Edged Sword: A Positive Brain Scan Result Heightens Confidence in an Alzheimer's Diagnosis But Also Leads to Higher Stigma Among Older Adults in a Vignette-Based Experiment.

OBJECTIVES: Early diagnosis of Alzheimer's disease (AD) using brain scans and other biomarker tests will be essential to increasing the benefits of emerging disease-modifying therapies, but AD biomarkers may have unintended negative consequences on stigma. We examined how a brain scan result affects AD diagnosis confidence and AD stigma. METHODS: The study used a vignette-based experiment with a 2 × 2 × 3 factorial design of main effects: a brain scan result as positive or negative, treatment availability and symptom stage. We sampled 1,283 adults ages 65 and older between June 11and July 3, 2019. Participants (1) rated their confidence in an AD diagnosis in each of four medical evaluations that varied in number and type of diagnostic tools and (2) read a vignette about a fictional patient with varied characteristics before completing the Modified Family Stigma in Alzheimer's Disease Scale (FS-ADS). We examined mean diagnosis confidence by medical evaluation type. We conducted between-group comparisons of diagnosis confidence and FS-ADS scores in the positive versus negative brain scan result conditions and, in the positive condition, by symptom stage and treatment availability. RESULTS: A positive versus negative test result corresponds with higher confidence in an AD diagnosis independent of medical evaluation type (all p < .001). A positive result correlates with stronger reactions on 6 of 7 FS-ADS domains (all p < .001). DISCUSSION: A positive biomarker result heightens AD diagnosis confidence but also correlates with more AD stigma. Our findings inform strategies to promote early diagnosis and clinical discussions with individuals undergoing AD biomarker testing.

Forensic genetics and ethical, legal and social implications beyond the clinic.

Data on human genetic variation help scientists to understand human origins, susceptibility to illness and genetic causes of disease. Destructive episodes in the history of genetic research make it crucial to consider the ethical and social implications of research in genomics, especially human genetic variation. The analysis of ethical, legal and social implications should be integrated into genetic research, with the participation of scientists who can anticipate and monitor the full range of possible applications of the research from the earliest stages. The design and implementation of research directs the ways in which its results can be used, and data and technology, rather than ethical considerations or social needs, drive the use of science in unintended ways. Here we examine forensic genetics and argue that all geneticists should anticipate the ethical and social issues associated with nonmedical applications of genetic variation research.

How Clinicians Conceptualize "Actionability" in Genomic Screening.

Over the last decade, the concept of actionability has become a primary framework for assessing whether genetic data is useful and appropriate to return to patients. Despite the popularity of this concept, there is little consensus about what should count as "actionable" information. This is particularly true in population genomic screening, where there is considerable disagreement about what counts as good evidence and which clinical actions are appropriate for which patients. The pathway from scientific evidence to clinical action is not straightforward-it is as much social and political as it is scientific. This research explores the social dynamics shaping the integration of "actionable" genomic data into primary care settings. Based on semi-structured interviews with 35 genetics experts and primary care providers, we find that clinicians vary in how they define and operationalize "actionable" information. There are two main sources of disagreement. First, clinicians differ on the levels and types of evidence required for a result to be actionable, such as when we can be confident that genomic data provides accurate information. Second, there are disagreements about the clinical actions that must be available so that patients can benefit from that information. By highlighting the underlying values and assumptions embedded in discussions of actionability for genomic screening, we provide an empirical basis for building more nuanced policies regarding the actionability of genomic data in terms of population screening in primary care settings.

Not in my AI: Moral engagement and disengagement in health care AI development.

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.

The relative contributions of biomarkers, disease modifying treatment, and dementia severity to Alzheimer's stigma: A vignette-based experiment.

OBJECTIVE: The symptoms and prognosis of Alzheimer's disease (AD) dementia contribute to the public's negative reactions toward individuals with AD dementia and their families. But what if, using AD biomarker tests, diagnosis was made before the onset of dementia, and a disease-modifying treatment was available? This study tests the hypotheses that a "preclinical" diagnosis of AD and treatment that improves prognosis will mitigate stigmatizing reactions. METHODS: A sample of U.S. adults were randomized to receive one vignette created by a 3 × 2 × 2 vignette-based experiment that described a person with varied clinical symptom severity (Clinical Dementia Rating stages 0 (no dementia), 1 (mild), or 2 (moderate)), AD biomarker test results (positive vs negative), and disease-modifying treatment (available vs not available). Between-group comparisons were conducted of scores on the Modified Family Stigma in Alzheimer's Disease Scale (FS-ADS). RESULTS: The sample of 1,817 adults had a mean age two years younger than that of U.S. adults but was otherwise similar to the general adult population. The response rate was 63% and the completion rate was 96%. In comparisons of randomized groups, mild and moderate symptoms of dementia evoked stronger reactions on all FS-ADS domains compared to no dementia (all p < 0.001). A positive biomarker test result evoked stronger reactions on all but one FS-ADS domain (negative aesthetic attributions) compared to a negative biomarker result (all p < 0.001). Disease-modifying treatment had no measurable influence on stigma (all p > 0.05). CONCLUSIONS: The stigmas of dementia spill over into preclinical AD, and availability of treatment does not alter that stigma. Translation of the preclinical AD construct from research into practice will require interventions that mitigate AD stigma to preserve the dignity and identity of individuals living with AD.