Category: Scientific Evidence

New York, NY/USA - 08.31.2018: Overdose Awareness March

Bold Steps Needed to Correct Course in US Drug Policies

The Petrie-Flom Center Staff Leave a comment

By Leo Beletsky, Dan Werb, Ayden Scheim, Jeanette Bowles, David Lucas, Nazlee Maghsoudi, and Akwasi Owusu-Bempah

The accelerating trajectory of the overdose crisis is an indictment of the legal and policy interventions deployed to address it. Indeed, at the same time as the U.S. has pursued some of the most draconian drug policies in the world, it has experienced one of the worst drug crises in its history.

The legal and institutional system of U.S. drug control remains defined by its racist, xenophobic, and colonialist roots. It is no surprise, then, that current policy approaches to drug use have amplified inequities across minoritized and economically marginalized Americans. Reliance on the criminal-legal system and supply-side interventions have disproportionately devastated Black and brown communities, while failing to prevent drug-related harms on the population level.

The Biden-Harris Administration has an unprecedented opportunity to chart a different path. The priorities for the Administration’s approach should flow directly from its stated principles: emphasis on scientific evidence and a focus on equity.

The following key areas require immediate, bold, and evidence-grounded action.

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Consider the Fundamentals of Viruses When Crafting Law and Policy Responses

Jennifer S. Bard Leave a comment

By Jennifer S. Bard

Lawyers and law professors are very much part of the ongoing efforts to make policy in response to the COVID-19 pandemic. Like everyone else involved, we face the particular challenge of being confronted daily with what seems to be an ever-changing flow of information about a newly emerged and rapidly mutating virus.

But what may help us better make or evaluate policy is a better understanding of some typical characteristics of viruses that make all of them very difficult to contain, rather than just the unique features of the one threatening us now.

Knowing more about the ways that viruses spread could help us avoid the pitfalls of declaring victory too early, rolling back existing infection control measures, and ending up worse off than we have been at any stage of this pandemic.

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The Future of Race-Based Clinical Algorithms

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By Jenna Becker

Race-based clinical algorithms are widely used. Yet many race-based adjustments lack evidence and worsen racism in health care. 

Prominent politicians have called for research into the use of race-based algorithms in clinical care as part of a larger effort to understand the public health impacts of structural racism. Physicians and researchers have called for an urgent reconsideration of the use of race in these algorithms. 

Efforts to remove race-based algorithms from practice have thus far been piecemeal. Medical associations, health systems, and policymakers must work in tandem to rapidly identify and remove racist algorithms from clinical practice.

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Deep Phenotyping Could Help Solve the Mental Health Care Crisis

The Petrie-Flom Center Staff Leave a comment

By Justin T. Baker

The United States faces a growing mental health crisis and offers insufficient means for individuals to access care.

Digital technologies — the phone in your pocket, the camera-enabled display on your desk, the “smart” watch on your wrist, and the smart speakers in your home — might offer a path forward.

Deploying technology ethically, while understanding the risks of moving too fast (or too slow) with it, could radically extend our limited toolkit for providing access to high-quality care for the many individuals affected by mental health issues for whom the current mental health system is either out of reach or otherwise failing to meet their need.

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Incidental Findings in Deep Phenotyping Research: Legal and Ethical Considerations

The Petrie-Flom Center Staff Leave a comment

By Amanda Kim, M.D., J.D.‡, Michael Hsu, M.D. ‡, Amanda Koire, M.D., Ph.D. ‡, Matthew L. Baum, M.D., Ph.D., D.Phil. ‡

What obligations do researchers have to disclose potentially life-altering incidental findings (IFs) as they happen in real time?

Deep phenotyping research in psychiatry integrates an individual’s real-time digital footprint (e.g., texts, GPS, wearable data) with their biomedical data (e.g., genetic, imaging, other biomarkers) to discover clinically relevant patterns, usually with the aid of machine learning. Findings that are incidental to the study’s objectives, but that may be of great importance to participants, will inevitably arise in deep phenotyping research.

The legal and ethical questions these IFs introduce are fraught. Consider three hypothetical cases below of individuals who enroll in a deep phenotyping research study designed to identify factors affecting risk of substance use relapse or overdose:

A 51-year-old woman with alcohol use disorder (AUD) is six months into sobriety. She is intrigued to learn that the study algorithm will track her proximity to some of her known triggers for alcohol relapse (e.g., bars, liquor stores), and asks to be warned with a text message when nearby so she can take an alternative route. Should the researchers share that data?

A 26-year-old man with AUD is two years into sobriety. Three weeks into the study, he relapses. He begins arriving to work inebriated and loses his job. After the study is over, he realizes the researchers may have been able to see from his alcohol use surveys, disorganized text messages, GPS tracking, and sensor data that he may have been inebriated at work, and wishes someone had reached out to him before he lost his job. Should they have?

A 35-year-old man with severe opioid use disorder experiences a near-fatal overdose and is discharged from the hospital. Two weeks later, his smartphone GPS is in the same location as his last overdose, and his wearable detects that his respiratory rate has plummeted. Should researchers call EMS? Read More

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Unique Challenges to Informed Consent in Deep Phenotyping Research

The Petrie-Flom Center Staff Leave a comment

By Benjamin C. Silverman

Deep phenotyping research procedures pose unique challenges to the informed consent process, particularly because of the passive and boundless nature of the data being collected and how this data collection overlaps with our everyday use of technology.

As detailed elsewhere in this symposium, deep phenotyping in research involves the collection and analysis of multiple streams of behavioral (e.g., location, movement, communications, etc.) and biological (e.g., imaging, clinical assessments, etc.) data with the goal to better characterize, and eventually predict or intervene upon, a number of clinical conditions.

Obtaining voluntary competent informed consent is a critical aspect to conducting ethical deep phenotyping research. We will address here several challenges to obtaining informed consent in deep phenotyping research, and describe some best practices and relevant questions to consider.

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Lessons Learned from Deep Phenotyping Patients with Rare Psychiatric Disorders

The Petrie-Flom Center Staff Leave a comment

By Catherine A Brownstein and Joseph Gonzalez-Heydrich

Given the potential sensitivities associated with describing (i.e., phenotyping) patients with potentially stigmatizing psychiatric diagnoses, it is important to acknowledge and respect the wishes of the various parties involved.

The phenotypic description and depiction of a patient in the literature, although deidentified, may still be of great impact to a family.

By way of example, a novel genetic variant was identified as a likely explanation for the clinical presentation of a patient in a large cohort of individuals with neurodevelopmental and/or psychiatric phenotypes, a finding of great medical interest. The research team elected to further study this candidate and collected samples for functional evaluation of the gene variant and preparation of a case report.

Because the patient had a complicated phenotype, several physicians from various specialties were involved in the patient’s care. The paper draft was circulated amongst the collaborating clinicians and researchers and ultimately shared with the patient’s family by one of their involved caregivers. This is typically not a requirement of such studies, as the informed consent process includes the subjects’ understanding and consent for dissemination of deidentified results in the scientific literature. But as a general practice, families are informed about manuscripts in process, and in this case the family had requested to be kept abreast of ongoing developments.

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“Actionability” and the Ethics of Communicating Results to Study Participants

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By Patrick Monette

To what end does a physician have a responsibility toward a research participant? Specifically, what data may be considered “actionable” for the physician to disclose to the patient, and when and how might this be done?

In the clinical setting, contemporary medical ethics address a physician’s “fiduciary responsibility.” That is, there is a well-established professional expectation that the physician will place the patient’s interests above their own and advocate for their welfare. This post focuses on an alternative dyad, that of physician and research participant, to explore how the field has broached the topic of actionability in the setting of clinical research. Read More

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From 9/11 to COVID-19: A Brief History of FDA Emergency Use Authorization

The Petrie-Flom Center Staff Leave a comment

Cross-posted from COVID-19 and The Law, where it originally appeared on January 14, 2021. 

By Jonathan Iwry

The ongoing fight against COVID-19 has thrown a spotlight on the Food and Drug Administration (FDA) and its power to grant emergency use authorizations (EUAs). EUA authority permits FDA to authorize formally unapproved products for temporary use as emergency countermeasures against threats to public health and safety.

Under § 564 of the Food, Drug, and Cosmetic Act (FD&C Act), use of FDA’s EUA authority requires a determination that an emergency exists by secretaries of the Department of Homeland Security, the Department of Defense, or the Department of Health and Human Services (HHS), as well as a declaration by the HHS Secretary that emergency circumstances exist warranting the issuance of EUAs. Each issuance of an EUA requires that FDA conclude that:

  • it is reasonable to believe that a given product “may be effective” as an emergency countermeasure,
  • the known and potential benefits of authorization outweigh the known and potential risks, and
  • no formally approved alternatives are available at the time.

Annie Kapnick’s post on COVID-19 and FDA’s EUA authority provides a helpful overview of FDA’s emergency powers and their use in response to the pandemic. A brief look at the history of FDA’s emergency powers, including key events leading up to their enactment — Thalidomide, swine flu, AIDS, and 9/11 — offers perspective on the situation facing FDA today and its implications for the future. The history of EUA illustrates how its use today against COVID-19 involves fundamental questions about the role of public officials, scientific expertise, and administrative norms in times of crisis.

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Data Talking to Machines: The Intersection of Deep Phenotyping and Artificial Intelligence

Carmel Shachar Leave a comment

By Carmel Shachar

As digital phenotyping technology is developed and deployed, clinical teams will need to carefully consider when it is appropriate to leverage artificial intelligence or machine learning, versus when a more human touch is needed.

Digital phenotyping seeks to utilize the rivers of data we generate to better diagnose and treat medical conditions, especially mental health ones, such as bipolar disorder and schizophrenia. The amount of data potentially available, however, is at once both digital phenotyping’s greatest strength and a significant challenge.

For example, the average smartphone user spends 2.25 hours a day using the 60-90 apps that he or she has installed on their phone. Setting aside all other data streams, such as medical scans, how should clinicians sort through the data generated by smartphone use to arrive at something meaningful? When dealing with this quantity of data generated by each patient or research subject, how does the care team ensure that they do not miss important predictors of health?

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