CRISPR: from lab to the clinic

 This is the year that CRISPR moves from lab to clinic

Jennifer Doudna says:

In 2021, researchers will use CRISPR to enhance our medical response to the Covid-19 pandemic. Teams will continue to collaborate and bring to market vital CRISPR-based diagnostic tools that are accurate, rapid and painless. One currently being developed and scaled by Mammoth Biosciences, a company I co-founded, along with partners at the University of California, San Francisco and the pharmaceutical company GSK, can detect and indicate the presence of SARS-CoV-2 RNA in a similar fashion to a pregnancy test.

 CRISPR will also have an important effect on the way we treat other diseases. In 2021, we will see increased use of CRISPR-Cas enzymes to underpin a new generation of cost-effective, individualised therapies. With CRISPR enzymes, we can cut DNA at precise locations, using specifically designed proteins, and insert or delete pieces of DNA to correct mutations.

This is precisely what is going on. 

 

COVID-19 innovation response

 The COVID-19 Innovation System

EIT Community COVID-19 Response

 The COVID-19 innovation system represents a departure from business as usual. Considering the remarkable progress to date, especially on vaccine development, this raises the question of whether this model is useful only for crisis times, or whether biomedical innovation policy in “normal” times might productively incorporate some elements of the COVID-19 model as well.

The largest funding response has been from the US government. Roughly $14–$15 billion of the $4 trillion allocated to the COVID-19 response was for R&D for vaccines and treatments.8,9 Though this increased US federally funded biomedical R&D by about one-third (compared to previous NIH funding; see “NIH Data” in the online appendix),10 it is small compared with the potential value of these interventions for ameliorating or preventing the disease and securing a return to normalcy

 

 

 

Profit over health

CODE BLUE.  Inside America’s Medical Industrial Complex

An interesting and persusive book by Mike Magee, an insider talking about details of how to create the worst health care system in developed countries and how to avoid it!. A clear message for European countries and current misguided policies. 

Ten Reasons Why Consolidating Oversight under a Single Centralized Authority Works So Well:

  1. It provides unimpeded universal access to coverage.

  2. It lowers administrative costs by at least 50 percent, and overall health care expenses by 15 percent, according to an extensive economic health policy research study published in BMC Health Services Research in 2014.15

  3. It is portable, allowing people to change jobs or geographic locations without worry.

  4. It can emphasize prevention by promoting health planning and budgeting priorities.

  5. It provides a standard basic benefit package for all, with flexibility for an individual to purchase additional services through private supplemental insurance.

   6. It offers a wider choice of doctors and hospitals, with coverage guaranteed, and limits “balanced billing” on essential services that are covered.

  7. It prohibits insurers from obstructing access to care with administrative obstacles to payment.

  8. By prioritizing care, the system increases wellness, therefore decreasing the cost of sickness.

  9. It forces transparent budgeting for outcomes, with a clear definition of goals and priorities, and promotes performance-based payments.

 10. It inserts public oversight of patient databases, preventing MIC use of data to maximize profits by steering patient choices and managing opaque MIC profit sharing

 

COVID-19 and the surge of populism

 Populism and the Politicization of the COVID-19 Crisis in Europe

Chapter 3. Spain: Is Ideology Back in Populist Discourse? by Jaume Magre, Lluís Medir, and Esther Pano

Risk stratification, objective and subjective

 Risk Stratification: A Two-Step Process for Identifying Your Sickest Patients

A proposal in two steps:

Step one involves sorting patients into one of three risk groups (high, medium, and low) based on objective data, which we take from claims or our electronic health record (EHR). We make our determinations based on the presence or absence of such factors as chronic conditions, advanced age, multiple comorbidities, physical limitations, substance abuse, a lack of health insurance, low health literacy, frequent hospitalizations or emergency department (ED) visits, recent major surgery or brain trauma, polypharmacy, or difficulty following a treatment plan. Some EHRs will calculate a risk score automatically based on this data. In either case, it is important to adjust the score based on additional, subjective considerations, which are the focus of step two.

In step two we assign each patient to one of six risk levels based on how physicians and staff answer the following questions:

• Is the patient healthy with no medical problems? If so, are his or her biometrics in or out of range?
• Does the patient have chronic conditions but he or she is doing well?
• Does the patient have chronic conditions that are out of control but without complications?
• Does the patient have complications of chronic disease or high-risk social determinants of health? (If you or your care team are unsure how to assess or address a patient's social determinants of health, the AAFP's EveryONE Project1 includes tools and resources.)
• Is the patient potentially in danger of dying or being institutionalized within the next year?

I missed any reference to morbidity and episode measurement. My impression is that objective measure is the first mandatory step through groupers, and risk stratification improves with subjective sources of information. And levels can be allocated through probabilistic fuzzy systems.

Regulating Artificial Intelligence as a medical device

 The need for a system view to regulate artificial intelligence/machine learning-based software as medical device

The starting point:

AI/ML-based SaMD raise new challenges for regulators. As compared to typical drugs and medical devices, we argue that due to their systemic aspects, AI/ML-based SaMD will present more variance between performance in the artificial testing environment and in actual practice settings, and thus potentially more risks and less certainty over their benefits. Variance can increase due to human factors or the complexity of these systems and how they interact with their environment. Unlike drugs, the usage of software and generally Information Technologies (IT) is known to be highly affected by organizational factors such as resources, staffing, skills, training, culture, workflow, and processes (e.g., regarding data quality management)8. There is no reason to expect that the adoption and impact of AI/ML-based SaMD will be consistent, or even improve performance, across all settings.

on unlocked and adaptive algorithms,

 All AI/ML-based SaMD that the FDA has thus far reviewed have been cleared or approved as “locked” algorithms, which it defines as “an algorithm that provides the same result each time the same input is applied to it and does not change with use”. The agency is currently developing a strategy for how to regulate “unlocked” or “adaptive” AI/ML algorithms—algorithms that may change as they are applied to new data.

Therefore,

 AI/ML-based SaMD pose new safety challenges for regulators. They need to make a difficult choice: either largely ignore systemic and human factor issues with each approval and subsequent update or require the maker to conduct significant organizational and human factors validation testing with each update resulting in increased cost and time, which may, in turn, chill the desire of the maker to engage in potentially very beneficial innovations or possible updates. 

 

How does health affect happiness?

 An Economist’s Lessons on Happiness

The Easterlin paradox is a finding in happiness economics formulated in 1974 by Richard Easterlin, then professor of economics at the University of Pennsylvania, and the first economist to study happiness data. The paradox states that at a point in time happiness varies directly with income both among and within nations, but over time happiness does not trend upward as income continues to grow. It is the contradiction between the point-of-time and time series findings that is the root of the paradox.

Does money make you happy? This is one of the questions addressed in the book. 

Richard Easterlin says :

In considering the effect on happiness of increasing income, we saw that because of interpersonal comparison, the reference level for income (the incomes of others) tends to increase along with one’s actual income, and happiness remains unchanged. By contrast, when intrapersonal comparison chiefly determines the reference level, as it does for health, the happiness outcome is different. The reference level for health is rooted in past experience and usually changes much less than the reference level for income

And on the Happiness Revolution, 

 Here it is: The Happiness Revolution. Whereas the two prior revolutions, the Industrial Revolution and the Demographic Revolution, led to a transformation in people’s objective circumstances, as indexed by the multiplication of real GDP per capita and life expectancy, the principal concern of the Happiness Revolution is different and calls for a different kind of measure. Which is? “What people have to say about themselves,” Andy offers. “Specifically, people’s feelings about their lives as a whole.” Yes! This revolution centers on people’s feelings—how happy they are and how satisfied with their lives. It becomes a revolution, the Happiness Revolution, when the findings show a marked improvement in people’s feelings of well-being, i.e., their subjective well-being. And this is what’s happening now!

A controversial view. You may agree or not, anyway, a recommended reading. 

 

Vaccine access, now!

 Global equitable access to vaccines, medicines and diagnostics for COVID-19: The role of patents as private governance 

A compulsory licence allows a third party to produce a patented technology without the patent holder’s permission. Article 31 of the TRIPS Agreement allows all WTO States to issue compulsory licences subject to certain criteria.19 First, all cases are considered on their individual merits. Thus, a blanket compulsory licence for certain technologies, for example, medicines, is not possible. Second, prior attempts to negotiate a licence for the invention on reasonable terms with the patent holder must be evident. This requirement can be waived in ‘a national emergency or other circumstances of extreme urgency or in cases of public non-commercial use’ which would likely apply for COVID-19. Third, the scope/duration of the licence must be for the limited purpose it was authorised for. Fourth, the licence is non-exclusive so the patent holder can still enter into licensing agreements with others. Fifth, use of the licence is generally permitted predominantly for the supply of the domestic market of the State where the compulsory licence is granted. Finally, the patent holder must be paid ‘adequate renumeration’ for the compulsory licence.

So, 

 Crucially, it is only by starting a deeper conversation around the role of patent holders within the health context for COVID-19 and of the role of the public interest within patent law more generally that we can address and pre-empt some of the current obstacles posed by patents to equitable global access to healthcare. Given the significant health implications at stake it is vital that this conversation is informed by a global health and bioethics perspective

 

The blueprint of human life

 The Human Genome: Mapping the Blueprint of Human Life

Health at the centre of all policies

 The Lancet Planetary Health

The public health implications of the Paris Agreement: a modelling study

Great article, a must read

Compared with the current pathways scenario, the sustainable pathways scenario resulted in an annual reduction of 1·18 million air pollution-related deaths, 5·86 million diet-related deaths, and 1·15 million deaths due to physical inactivity, across the nine countries, by 2040. Adopting the more ambitious health in all climate policies scenario would result in a further reduction of 462 000 annual deaths attributable to air pollution, 572 000 annual deaths attributable to diet, and 943 000 annual deaths attributable to physical inactivity. These benefits were attributable to the mitigation of direct greenhouse gas emissions and the commensurate actions that reduce exposure to harmful pollutants, as well as improved diets and safe physical activity.

Though I'm not a fan of such predictions, these are some figures related to 9 countries only to take into account. More interesting articles released in the same issue.

Just take these ones, for instance, on diet:

Number of deaths avoided attributable to dietary risks in the year 2040, relative to CPS per 100 000 population, by scenario and country

The health impacts associated with the combination of all risks is smaller than the sum of individual risks because the former controls for co-exposure (ie, each death is attributed to only one risk factor). CPS=current pathways scenario. HPS=health in all climate policies. SPS=sustainable pathways scenario.

PS.Un año de pandemia y seguimos elucubrando respuestas

The response to public health crisis

  Pandemics, Publics, and Politics: Staging Responses to Public Health Crises

The inescapable architecture of everyday life

 Choice Architecture. A New Approach to Behavior, Design, and Wellness

The contents of the book:

1  The Inescapable Architecture of Everyday Life

2  A Framework for Architectural Interpretation

2.1 Rational Persons

2.2 Architects and Designers

2.3 Looking a Little More Closely at What Happens Inside Phil

2.6 The Architectural Problem

2.7 Phil Can Sometimes be Inconsistently Rational

2.8 How Tom’s Irrationality can Sometimes Help Him

2.9 The Architectural Problem Revisited

3  Rational and Irrational Behavior

3.1 Back to Consistent Rationality

3.2 Anchoring

3.3 Availability

3.4 The Cost of Zero Cost

3.5 Nonlinearity

3.6 Representativeness

3.7 Framing

3.8 Reference Point Shifts

3.9 An Overview of the Architectural Problem

4. Reflecting on choice architecture

4.1 Choice architecture is not a tree

4.2 The Structure of Architectural Experience

4.3 A Few Cautionary Remarks

4.4 Uncertainty

Behavior design

 Reset: An Introduction to Behavior Centered Design

A hot topic :

There are over 100 change theories in health psychology alone, and the field of behavioral economics has over 100 “nudges” for inspiring behavior change as well (just to mention the two most prominent fields dealing with this topic). This book is about a new, generic way of approaching behavior change called Behavior Centered Design (BCD).

The obesity industry

 The Hungry Gene. The Inside Story of the Obesity Industry