Covid-19 and the business of vaccines
The FT explains the business models behind vaccines and asks if the Covid-19 pandemic will fundamentally change the vaccine market. This short documentary features global experts including Bill Gates, the CEOs of Moderna and Gavi, and the lead scientist behind the Oxford/AstraZeneca vaccine
Assessing the Added Therapeutic Benefit of Ultra-Expensive Drugs
In US:
The number of ultra-expensive drugs and Medicare beneficiaries taking these drugs has grown significantly, resulting in a very high concentration of Medicare Part D spending on ultraexpensive drugs. Between 73% and 85% of these drugs assessed in France, Canada, or Germany received a low added therapeutic benefit rating. Policy reforms to address drug prices in the United States should consider developing an assessment framework for added therapeutic benefit to incentivize and reward the development of drugs that offer a significant clinical improvement over the current standard of care. In the interim, use of international assessments would be possible.
However, it may seem weird to our eyes, but:
Medicare Part D in particular has a problem with ultra-expensive drugs, since it pays nearly 80% of the cost of these drugs, and by law Medicare cannot directly negotiate the price for these drugs with the drug companies
Public funding without the possibility to set the contract for low marginal benefit drugs! The result:
Medicare Part D spending on brand-name drugs for these ultra-expensive drugs increased from 1.5% in 2012 to 19% in 2018
The answer is change the law and set benefits package according to added value.
CRISPR People. The Science and Ethics of Editing Humans
A book on the ethics of CRISPR, without a clear prescription, only a review of He horrendous experiment and a personal view of the current situation.
This has been a cautionary tale about scienceand scientists. People can overreach. He Jiankui, driven as far as I can tell by what Macbeth called “vaulting ambition, which o’erleaps itself,”1 and aided by a serious lack of scruples, behaved terribly. He put three lives at needless and reckless risk (and tried to do the same to more). The story is also a cautionary tale for Science. He Jiankui damaged Science by reinforcing the Victor Frankenstein image—the mad, uncontrolled scientist. Most scientists, and hence most science, are much more rule bound, constrained by the needs of getting and keeping jobs, tenure, and most importantly grants, as well as wrapped, in most countries, in many bureaucratic threads of control. But He happened, and Science needs both to act to minimize the harm he has caused and to be seen to be doing so.
A global consensus is a chimera. Seven and a half billion people are not going to agree on this issue—nor will the fraction of those who understand it. Neither will the roughly 200 countries of the world, at least at anything other than a lowest common denominator. Not all countries have agreed to various nuclear weapons or climate change treaties in spite of the apparently obvious need for them. If somehow something close to unanimity were achieved, it would probably be at the cost of precision. Thus, the Council of Europe enacted a convention that banned human cloning, but effectively left open the then-hot question whether it covered just reproductive cloning or “research cloning” (of human embryos for only ex vivo use) by not defining a “human being.” It also left the implantation and enforcement of such a ban up to the member nations, some of whom were probably happy, for political reasons, to sign it but will have little interest in enforcing it.
And this is the controversial position of the author:
One might argue that human germline genome changes are irreversible, or less reversible, than some other interventions. But is that true? A mistaken genome change could presumably be reedited, in a living person or in that person’s germline (or embryos), to reverse the error. Or it could be selected against in the individual’s offspring, through PGD or otherwise. It could be too late to avoid harm to the edited person, but that mistake will not have to pass on from generation to generation. Human germline genome editing does raise important questions about safety, coercion, equity, diversity, and enhancement. These are not unique to editing the human germline genome. They also apply to somatic cell DNA editing, to new drugs, to smartphones, to climate change, and to many other changes from technologies. Questions about reversibility also apply; the social effects of cell phones are probably less reversible than genome edits. The fact that a technological change is in “the human germline genome” should have no special ethical weight.
Really? Security in CRISPR is not unique???
The Cas proteins behind CRISPR diagnostics
CRISPR diagnostics explained in few words:
There are many protein tools in the CRISPR toolbox. Each is suited to a particular suite of uses. For example, the common CRISPR protein Cas9 is well suited for genome editing. It is not suited for CRISPR diagnostics. In this blog post, we’ll introduce you to the proteins behind CRISPR diagnostics: Cas12, Cas13, and Cas14.
CRISPR diagnostics have two key components:
Why Does the Pandemic Seem to Be Hitting Some Countries Harder Than Others?
And there lies an epidemiological mystery. The usual trend of death from infectious diseases—malaria, typhoid, diphtheria, H.I.V.—follows a dismal pattern. Lower-income countries are hardest hit, with high-income countries the least affected. But if you look at the pattern of covid-19 deaths reported per capita—deaths, not infections—Belgium, Italy, Spain, the United States, and the United Kingdom are among the worst off.
This is the unfixed conundrum explained by Siddhartha Mukherjee in The New Yorker, And no clear answer, you may read the whole article to confirm it.
The covid-19 pandemic will teach us many lessons—about virological surveillance, immunology, vaccine development, and social policy, among other topics. One of the lessons concerns not just epidemiology but also epistemology: the theory of how we know what we know. Epidemiology isn’t physics. Human bodies are not Newtonian bodies. When it comes to a crisis that combines social and biological forces, we’ll do well to acknowledge the causal patchwork. What’s needed isn’t Ockham’s razor but Ockham’s quilt.
Above all, what’s needed is humility in the face of an intricately evolving body of evidence. The pandemic could well drift or shift into something that defies our best efforts to model and characterize it.
Great documentary, on Knoedle gallery fake art
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.
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
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
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: 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.
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.
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.
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
