Community consent: neither a ceiling nor a floor

Article by Jasmine McNealy: “The 23andMe breach and the Golden State Killer case are two of the more “flashy” cases, but questions of consent, especially the consent of all of those affected by biodata collection and analysis in more mundane or routine health and medical research projects, are just as important. The communities of people affected have expectations about their privacy and the possible impacts of inferences that could be made about them in data processing systems. Researchers must, then, acquire community consent when attempting to work with networked biodata. 

Several benefits of community consent exist, especially for marginalized and vulnerable populations. These benefits include:

  • Ensuring that information about the research project spreads throughout the community,
  • Removing potential barriers that might be created by resistance from community members,
  • Alleviating the possible concerns of individuals about the perspectives of community leaders, and 
  • Allowing the recruitment of participants using methods most salient to the community.

But community consent does not replace individual consent and limits exist for both community and individual consent. Therefore, within the context of a biorepository, understanding whether community consent might be a ceiling or a floor requires examining governance and autonomy…(More)”.

Precision public health in the era of genomics and big data

Paper by Megan C. Roberts et al: “Precision public health (PPH) considers the interplay between genetics, lifestyle and the environment to improve disease prevention, diagnosis and treatment on a population level—thereby delivering the right interventions to the right populations at the right time. In this Review, we explore the concept of PPH as the next generation of public health. We discuss the historical context of using individual-level data in public health interventions and examine recent advancements in how data from human and pathogen genomics and social, behavioral and environmental research, as well as artificial intelligence, have transformed public health. Real-world examples of PPH are discussed, emphasizing how these approaches are becoming a mainstay in public health, as well as outstanding challenges in their development, implementation and sustainability. Data sciences, ethical, legal and social implications research, capacity building, equity research and implementation science will have a crucial role in realizing the potential for ‘precision’ to enhance traditional public health approaches…(More)”.

AI: a transformative force in maternal healthcare

Article by Afifa Waheed: “Artificial intelligence (AI) and robotics have enormous potential in healthcare and are quickly shifting the landscape – emerging as a transformative force. They offer a new dimension to the way healthcare professionals approach disease diagnosis, treatment and monitoring. AI is being used in healthcare to help diagnose patients, for drug discovery and development, to improve physician-patient communication, to transcribe voluminous medical documents, and to analyse genomics and genetics. Labs are conducting research work faster than ever before, work that otherwise would have taken decades without the assistance of AI. AI-driven research in life sciences has included applications looking to address broad-based areas, such as diabetes, cancer, chronic kidney disease and maternal health.

In addition to increasing the knowledge of access to postnatal and neonatal care, AI can predict the risk of adverse events in antenatal and postnatal women and their neonatal care. It can be trained to identify those at risk of adverse events, by using patients’ health information such as nutrition status, age, existing health conditions and lifestyle factors. 

AI can further be used to improve access to women located in rural areas with a lack of trained professionals – AI-enabled ultrasound can assist front-line workers with image interpretation for a comprehensive set of obstetrics measurements, increasing quality access to early foetal ultrasound scans. The use of AI assistants and chatbots can also improve pregnant mothers’ experience by helping them find available physicians, schedule appointments and even answer some patient questions…

Many healthcare professionals I have spoken to emphasised that pre-existing conditions such as high blood pressure that leads to preeclampsia, iron deficiency, cardiovascular disease, age-related issues for those over 35, various other existing health conditions, and failure in the progress of labour that might lead to Caesarean (C-section), could all cause maternal deaths. Training AI models to detect these diseases early on and accurately for women could prove to be beneficial. AI algorithms can leverage advanced algorithms, machine learning (ML) techniques, and predictive models to enhance decision-making, optimise healthcare delivery, and ultimately improve patient outcomes in foeto-maternal health…(More)”.

Exploring Digital Biomarkers for Depression Using Mobile Technology

Paper by Yuezhou Zhang et al: “With the advent of ubiquitous sensors and mobile technologies, wearables and smartphones offer a cost-effective means for monitoring mental health conditions, particularly depression. These devices enable the continuous collection of behavioral data, providing novel insights into the daily manifestations of depressive symptoms.

We found several significant links between depression severity and various behavioral biomarkers: elevated depression levels were associated with diminished sleep quality (assessed through Fitbit metrics), reduced sociability (approximated by Bluetooth), decreased levels of physical activity (quantified by step counts and GPS data), a slower cadence of daily walking (captured by smartphone accelerometers), and disturbances in circadian rhythms (analyzed across various data streams).
Leveraging digital biomarkers for assessing and continuously monitoring depression introduces a new paradigm in early detection and development of customized intervention strategies. Findings from these studies not only enhance our comprehension of depression in real-world settings but also underscore the potential of mobile technologies in the prevention and management of mental health issues…(More)”

Are We Ready for the Next Pandemic? Navigating the First and Last Mile Challenges in Data Utilization

Blog by Stefaan Verhulst, Daniela Paolotti, Ciro Cattuto and Alessandro Vespignani:

“Public health officials from around the world are gathering this week in Geneva for a weeklong meeting of the 77th World Health Assembly. A key question they are examining is: Are we ready for the next pandemic? As we have written elsewhere, regarding access to and re-use of data, particularly non-traditional data, for pandemic preparedness and response: we are not. Below, we list ten recommendations to advance access to and reuse of non-traditional data for pandemics, drawing on input from a high-level workshop, held in Brussels, within the context of the ESCAPE program…(More)”


How this mental health care app is using generative AI to improve its chatbot

Interview by Daniela Dib: “Andrea Campos struggled with depression for years before founding Yana, a mental health care app, in 2017. The app’s chatbot provides users emotional companionship in Spanish. Although she was reluctant at first, Campos began using generative artificial intelligence for the Yana chatbot after ChatGPT launched in 2022. Yana, which recently launched its English-language version, has 15 million users, and is available in Latin America and the U.S.

This interview has been edited for clarity and brevity.

How has your product evolved since you introduced generative AI to it?

At first, we didn’t use generative AI because we believed it was far from ready for mental health support. We designed and guardrailed our chatbot’s responses with decision trees. But when ChatGPT launched and we saw what it could do, it wasn’t a question of whether to use generative AI or not, but how soon — we’d fall behind otherwise. It’s been a challenge because everyone quickly began developing with generative AI, but our advantage was that, having operated our chatbot for a while, we had gathered over 2 billion data points that have been invaluable for our app’s fine-tuning. One thing is clear: It’s crucial to have a model tailored to the specific needs of our product…(More)”.

AI Chatbot Credited With Preventing Suicide. Should It Be?

Article by Samantha Cole: “A recent Stanford study lauds AI companion app Replika for “halting suicidal ideation” for several people who said they felt suicidal. But the study glosses over years of reporting that Replika has also been blamed for throwing users into mental health crises, to the point that its community of users needed to share suicide prevention resources with each other.

The researchers sent a survey of 13 open-response questions to 1006 Replika users who were 18 years or older and students, and who’d been using the app for at least one month. The survey asked about their lives, their beliefs about Replika and their connections to the chatbot, and how they felt about what Replika does for them. Participants were recruited “randomly via email from a list of app users,” according to the study. On Reddit, a Replika user posted a notice they received directly from Replika itself, with an invitation to take part in “an amazing study about humans and artificial intelligence.”

Almost all of the participants reported being lonely, and nearly half were severely lonely. “It is not clear whether this increased loneliness was the cause of their initial interest in Replika,” the researchers wrote. 

The surveys revealed that 30 people credited Replika with saving them from acting on suicidal ideation: “Thirty participants, without solicitation, stated that Replika stopped them from attempting suicide,” the paper said. One participant wrote in their survey: “My Replika has almost certainly on at least one if not more occasions been solely responsible for me not taking my own life.” …(More)”.

A New National Purpose: Harnessing Data for Health

Report by the Tony Blair Institute: “We are at a pivotal moment where the convergence of large health and biomedical data sets, artificial intelligence and advances in biotechnology is set to revolutionise health care, drive economic growth and improve the lives of citizens. And the UK has strengths in all three areas. The immense potential of the UK’s health-data assets, from the NHS to biobanks and genomics initiatives, can unlock new diagnostics and treatments, deliver better and more personalised care, prevent disease and ultimately help people live longer, healthier lives.

However, realising this potential is not without its challenges. The complex and fragmented nature of the current health-data landscape, coupled with legitimate concerns around privacy and public trust, has made for slow progress. The UK has had a tendency to provide short-term funding across multiple initiatives, which has led to an array of individual projects – many of which have struggled to achieve long-term sustainability and deliver tangible benefits to patients.

To overcome these challenges, it will be necessary to be bold and imaginative. We must look for ways to leverage the unique strengths of the NHS, such as its nationwide reach and cradle-to-grave data coverage, to create a health-data ecosystem that is much more than the sum of its many parts. This will require us to think differently about how we collect, manage and utilise health data, and to create new partnerships and models of collaboration that break down traditional silos and barriers. It will mean treating data as a key health resource and managing it accordingly.

One model to do this is the proposed sovereign National Data Trust (NDT) – an endeavour to streamline access to and curation of the UK’s valuable health-data assets…(More)”.

How the war on drunk driving was won

Blog by Nick Cowen: “…Viewed from the 1960s it might have seemed like ending drunk driving would be impossible. Even in the 1980s, the movement seemed unlikely to succeed and many researchers questioned whether it constituted a social problem at all.

Yet things did change: in 1980, 1,450 fatalities were attributed to drunk driving accidents in the UK. In 2020, there were 220. Road deaths in general declined much more slowly, from around 6,000 in 1980 to 1,500 in 2020. Drunk driving fatalities dropped overall and as a percentage of all road deaths.

The same thing happened in the United States, though not to quite the same extent. In 1980, there were around 28,000 drunk driving deaths there, while in 2020, there were 11,654. Despite this progress, drunk driving remains a substantial public threat, comparable in scale to homicide (of which in 2020 there were 594 in Britain and 21,570 in America).

Of course, many things have happened in the last 40 years that contributed to this reduction. Vehicles are better designed to prioritize life preservation in the event of a collision. Emergency hospital care has improved so that people are more likely to survive serious injuries from car accidents. But, above all, driving while drunk has become stigmatized.

This stigma didn’t come from nowhere. Governments across the Western world, along with many civil society organizations, engaged in hard-hitting education campaigns about the risks of drunk driving. And they didn’t just talk. Tens of thousands of people faced criminal sanctions, and many were even put in jail.

Two underappreciated ideas stick out from this experience. First, deterrence works: incentives matter to offenders much more than many scholars found initially plausible. Second, the long-run impact that successful criminal justice interventions have is not primarily in rehabilitation, incapacitation, or even deterrence, but in altering the social norms around acceptable behavior…(More)”.

On the Meaning of Community Consent in a Biorepository Context

Article by Astha Kapoor, Samuel Moore, and Megan Doerr: “Biorepositories, vital for medical research, collect and store human biological samples and associated data for future use. However, our reliance solely on the individual consent of data contributors for biorepository data governance is becoming inadequate. Big data analysis focuses on large-scale behaviors and patterns, shifting focus from singular data points to identifying data “journeys” relevant to a collective. The individual becomes a small part of the analysis, with the harms and benefits emanating from the data occurring at an aggregated level.

Community refers to a particular qualitative aspect of a group of people that is not well captured by quantitative measures in biorepositories. This is not an excuse to dodge the question of how to account for communities in a biorepository context; rather, it shows that a framework is needed for defining different types of community that may be approached from a biorepository perspective. 

Engaging with communities in biorepository governance presents several challenges. Moving away from a purely individualized understanding of governance towards a more collectivizing approach necessitates an appreciation of the messiness of group identity, its ephemerality, and the conflicts entailed therein. So while community implies a certain degree of homogeneity (i.e., that all members of a community share something in common), it is important to understand that people can simultaneously consider themselves a member of a community while disagreeing with many of its members, the values the community holds, or the positions for which it advocates. The complex nature of community participation therefore requires proper treatment for it to be useful in a biorepository governance context…(More)”.