Economics of pandemics

Economics in the Time of COVID-19

There is a new free ebook available on the economics of current pandemics that was finished on March 5h. These are the contents:
           Introduction: Richard Baldwin and Beatrice Weder di Mauro

1 Macroeconomics of the flu
Beatrice Weder di Mauro
2 Tackling the fallout from COVID-19
Laurence Boone
3 The economic impact of COVID-19
Warwick McKibbin and Roshen Fernando
4 Novel coronavirus hurts the Middle East and North Africa through many channels
Rabah Arezki and Ha Nguyen
5 Thinking ahead about the trade impact of COVID-19
Richard Baldwin and Eiichi Tomiura
6 Finance in the times of coronavirus
Thorsten Beck
7 Contagion: Bank runs and COVID-19
Stephen G. Cecchetti and Kermit L. Schoenholtz
8 Real and financial lenses to assess the economic consequences of COVID-19
Catherine L. Mann
9 As coronavirus spreads, can the EU afford to close its borders?
Raffaella Meninno and Guntram Wolff
10 Trade and travel in the time of epidemics
Joachim Voth
11 On plague in a time of Ebola
Cormac O Grada
12 Coronavirus monetary policy
John H. Cochrane
13 The economic effects of a pandemic
Simon Wren-Lewis
14 The good thing about coronavirus
Charles Wyplosz

From Joachim Voth chapter:

First, is a massive restriction of mobility desirable? And second, is it feasible at all? An economically rational answer to the first question should begin with the value of a human life. With all the reservations that one can have against such calculations from a philosophical point of view, cost-benefit considerations without numbers for the value of a human life are not feasible. However, estimates regularly show an enormous range; the average is around US$10 million per person (Viscusi and Masterman 2017). This means that even before the epidemic has peaked, COVID-19 caused an immediate cost of $26 billion in deaths. If the epidemic ends with a maximum of 10,000 deaths (four times the current value), the value of life destroyed would be approximately $100 billion.
The costs must be compared with the enormous gains in economic performance that the free exchange of goods and people has made possible. In China alone, hundreds of millions of people have escaped deepest poverty during the past 20 years. In 1980, more than half of the Chinese population lived on less than $2 a day; in 1998, it was less than a quarter (Sala-i-Martin 2006). Around the world, people have escaped the poverty trap wherever the free movement of goods and people has become possible. And richer regions also benefit massively, often in surprising ways.

Up to now,this is the only chapter I've read from these promising book.

To test or not to test (for coronavirus) (2)

Escaping epidemics through migration? Quarantine measures under incomplete

information about infection risk

Some days ago I was explaining the rationale for coronavirus testing regarding clinical decision making. However, as we all know, individual behavior is also capable to produce health and disease contagion. Therefore, in case of coronavirus, behavioral externalities are crucial and nowadays we have denominated them "social distancing". 

Having said that, there is an additional behavioral value from testing to take into account. If all individuals in a population have access to the test, maybe everybody is aware of social distancing than in a situation than only suspected cases receive the test. Behaviors may change, and quarantine strategies more successful. In such situation it is much more feasible to restrict mobility. Let's take for example what this article explains:

This paper studies the effect of public policies to restrict migration by individuals suspected of carrying disease, when those individuals do not know for certain whether they have the disease but may have more information than the authorities about their probability of being carriers. It has long been known that migration affects the spread of
disease, and this influence has for centuries been used to justify placing restrictions on the movement of individuals suspected of carrying infections.

 Epidemiological studies have addressed how individual behaviour, among other factors, affects the spread of infections. However, the study of how individual behaviour in turn
changes in response to the new incentives created by the occurrence of a disease is much less developed. The principal contribution of our paper is to bring the study of strategic behavior under uncertainty into the domain of epidemiology, and to analyze its impact, in interaction with public policies, on the overall impact of epidemic disease.

Migration as a form of preventive behaviour has received very little attention, although evidence has accumulated that migration behaviour and epidemics are intrinsically linked. Migration behaviour can respond very rapidly to changes in the health  environment, in particular when it suddenly deteriorates through epidemics.

In our model we show that:
• First, when the disease is concentrated in one place (the epicentre of an epidemic for instance), a decision to migrate away from the epicentre brings a potentially infected individual in contact with more uninfected individuals than she would have met had she
remained where she was. Thus the typical migrant imposes a net negative externality as a result of her decision to migrate, and the marginal migrant (for whom, by definition, private benefits of migrating just equal the private costs of doing so) has a negative
impact on social welfare. Laissez faire will therefore lead to excessive migration. This provides a rationale for the frequent (and frequently justified) public policy response to epidemics, which is to attempt to restrict migration away from the epicentre by those who may be infected.
• Secondly, and less obviously, not all policies that aim to restrict migration in fact do so. In particular, we distinguish two effects of quarantine policies. The first is that they raise migration costs, which lowers migration. For example, mandatory health certificates or test results may be required by health authorities to leave the epicentre of the disease.We call this a “type 1” effect of quarantine measures. The second effect is that they impose a utility cost on individuals of remaining in the city where quarantine measures are effective, since they face a chance of being subjected to awkward and possibly
dangerous restrictions on their movements. We call this a “type 2” effect of quarantine measures. Such measures impose a welfare cost on those who suffer them, which tends to increase migration by those who are not currently subject to quarantine but fear they may  become so if they remain where they are. Policies implemented without taking type 2 effects into account may therefore have results that are opposite from those intended.
• Thirdly, even policies that actually reduce migration may have an adverse impact on social welfare if they reduce migration “too much”, and specifically if they discourage those intra-marginal migrants whose private benefits from migration substantially
exceed their private costs of migration, by enough to outweigh the negative externality they impose on others. Overall disease prevalence may even increase if in the name of avoiding negative externalities the authorities discourage relatively low-risk individuals
from escaping the epicentre of the disease, thereby increasing the probability that they will catch the disease there from infected individuals.

When people have imperfect information about their own infection status, migration imposes net negative externalities by increasing the rate of exposure faced by the uninfected outside the epicentre of the epidemic. In and of itself, this our paper has highlighted the fact that although quarantine of individuals who have been identified as sick reduces (obviously) the propensity of these individuals to migrate and spread the disease, the threat of quarantine increases the propensity to migrate of other individuals who have not yet been fallen sick but who know themselves to be at risk.

Quarantine measures have all these effects. However, if information about contagion is confirmed, then behaviors may change, and mandatory health certificates can be issued. The case of the italian village of Vò confirms that population screening has been successful in stopping the outbreak. This could have been done at the beginning if diagnostics kits had been available. Right now it seems an unfeasible strategy. We know now that there is a behavioral value of test information, beyond the clinical value. And in the case of coronavirus, confirmatory tests provide only partial information. In case of non infection, incubation period is uncertain, and some days after can be confirmed. Therefore, quarantine measures have to be mandatory and strict for the whole population and for specific areas.

Current coronavirus pandemic in context

The Pandemic Century. One Hundred Years of Panic, Hysteria and Hubris

Some months ago in the best books of FT 2019, I listed this one. and the summary is:

The Pandemic Century exposes the limits of science against nature, and how these crises are shaped by humans as much as microbes.

This is exactly what is happening right now!!! Contagion is shaped also by politicians.
Just have a look at what's going on in Madrid. This is horrendous! And politicians don't want to close Madrid! And they have allowed to spread contagion outside. Somebody should say it louder. International health rules should prevail over spanish rulers.

Beyond politicians, there are experts.

Battered by their repeated failure to predict deadly outbreaks of infectious disease, even the experts have come to recognize the limits of medical prognostication. This is not only because microbes are highly mutable—that has been known since Pasteur’s time—but because we are continually lending them a helping a hand. Time and again, we assist microbes to occupy new ecological niches and spread to new places in ways that usually only become apparent after the event. And to judge by the recent run of pandemics and epidemics the process seems to be speeding up.

Reviewing the last hundred years of epidemic outbreaks, the only thing that is certain is that there will be new plagues and new pandemics. It is not a question of if, we are told, but when. Pestilences may be unpredictable but we should expect them to recur. However, what Camus could not have foreseen is that the attempt to anticipate disaster also creates new distortions and introduces new uncertainties. Twice this pandemic century, in 1976 and again in 2003, scientists thought the world was on the brink of a new influenza pandemic, only to realize that the outbreaks were false alarms and that the real danger lay elsewhere. Then in 2009 the WHO declared that the Mexican swine flu, a ressortment of two well-known H1N1 swine-lineage viruses that had circulated separately for over a decade, met the criterion of a pandemic virus, triggering the activation of global pandemic preparedness plans. On paper, this was the first pandemic of the twenty-first century and the first influenza pandemic in forty-one years. The fact that the swine flu was an H1N1, just like the Spanish flu, raised the prospect that this might be the Big One and that governments should expect a wave of illness and deaths similar to that in 1918–1919. But though the WHO’s declaration sparked widespread panic, the anticipated viral Armageddon never materialized. Instead, when it was realized that the Mexican swine flu was no more severe than a seasonal strain of flu, the WHO was accused of “faking” the pandemic for the benefit of vaccine manufacturers and other special interests. The result is what Susan Sontag calls “a permanent modern scenario: apocalypse looms . . . and it doesn’t occur.” As we look to the next one hundred years of infectious disease outbreaks, let us hope that is one prognostication that turns out to be true.

Modeling coronavirus pandemic

Charting the Next Pandemic
Modeling Infectious Disease Spreading in the Data Science Age

This book was published last year and is a reference for pandemic modeling and in one chapter says:

In the case of coronaviruses, we selected scenarios referring to a case with a transmission rate and natural history of the disease similar to the SARS virus. Thus we assume that the infectiousness of individuals starts only after the onset of clinical symptoms, and we consider the absence of asymptomatic infections. Although we take into account a relatively high reproduction number R0=2.7, the absence of asymptomatic transmission makes all containment measures based on the timely isolation of cases viable and very effective. We therefore consider, for each possible initial condition of the outbreak, two scenarios with a transmissibility reduction, due to prompt case isolation, of 30% and 50% after the first 4 weeks and 50% and 90% after the seventh week, respectively.
Contrary to influenza, we do not consider seasonal variations because seasonal forcing does not appear to have a large impact on SARS. For this reason, we consider only one starting date during the calendar year for each geographical location. In total we provide six scenarios summarized in the infographic charts. The typical coronavirus chart layout and the “how to read it” guide can be found on the following pages.

STARTING CONDITIONS
• R0 = 2.7
• Starting date: varies by geographical location
SCENARIOS
Reduction of transmissibility:
• 30% after 4 weeks; 50% after 7 weeks
• 50% after 4 weeks, 90% after 7 weeks
Origin:
Barcelona, Spain
15 million
passengers/year
Guangzhou, China
Jeddah,
Saudi Arabia
21 million
passengers/year

You may find a recent example of its application in this Science article.
There is only one minor thing that the model can't consider. It is the politicans' and citizens decisions:

Different strategies in a globalized world are the seed of caos beyond the pandemic. Nowadays, this is the current situation. However, as this blogs explains:

There is not a single “one-size-fits-all” approach that allows to respond effectively to the ongoing and rapidly evolving situation. Each country needs to tailor its response in accordance with the capacities of its health systems, its economic resources and infrastructure, and the degree of collective and individual responsibility and compliance with recommendations issued by the authorities. The next generation of health professionals will look back at the different responses to COVID-19 described above and hopefully draw lessons for future infectious disease epidemics.

Meanwhile modeling provides little help.

To test or not to test (for coronavirus)

A Framework for Comprehensive Assessment of the Value of Diagnostic Test

The Value of Diagnostic Information A comprehensive concept of “Value” for diagnostics

A framework to understand value of lab tests is the following one:

Key principles:

• Apply broad array of patient centric value drivers from various perspectives (Comprehensiveness principle)
• Utilize appropriate range of available evidence, reflecting test type and potential risks-benefits (Evidentiary principle)
• Consider reporting direct and indirect costs incurred and avoided over timeframe appropriate for the test (Cost principle)
• Account for immediate and longer-term test impact and patient benefits in representative patient populations (Specificity principle)
• Include quantified estimates as well as qualitative analyses as appropriate (Flexibility principle)
• Incorporate multiple stakeholders’ perspectives (Engagement principle)
• Disclose why the assessment was initiated, who was involved, its purpose, and the decision-making process (Transparency principle)
• Update assessments regularly to keep up with the rapid technological and clinical changes (Relevancy principles)

 Value drivers can come from four major sources:

• Clinical impact: clinical utility and health outcomes associated with the diagnostic technology. The test needs to measure accurately and reliably the analyte/biomarker of interest (analytical validity); detect, predict the outcomes of interest in a patient population (clinical validity) and inform an appropriate clinical decision (clinical utility). Improved patient safety, tolerability, compliance and physical and psychological wellbeing shall be also taken into account.
• Non clinical patient impact breaks down to patient experience, and patient economics, such as proximity of test delivery, reduced follow-up visits, repeat procedures, improved care plan compliance and reduced burden on care givers.
• Care delivery revenue, and cost impact mostly refer to quality of care metrics and more efficient resource utilization (e.g. readmissions; follow-ups, length of stay, wait times)
• Public and population impact refer to macro implications mainly from population health, burden of disease, patient and caregiver productivity perspectives

AdvaMedDx’s Approach for Effective Value Assessment

Source: A Framework for Comprehensive Assessment of the Value of Diagnostic Tests, AdvaMed, 2017

And clinical impact depends on analytical validity, clinical validity, clinical utility, patient safety and patient response. If you have only one strategy for all the patients, like social distancing in the case of coronavirus, then the information post-test will not change the therapeutic strategy. If the test tries to prevent contagion when social distancing can't be applied (health professionals, politicians, journalists, executives, essential services), than you have to test them if there are symptoms. If the test will add information to existing comorbidities to differentiate from other symptoms, then it makes sense. Therefore, this is the current situation in my country. Test, test, test when it adds value.

Climate change and health

Enviromedics: The Impact of Climate Change on Human Health

These are tough times for the relationship between mankind and the planet. Therefore, this is a good reason to know better the relationship between climate change and health. In this book you'll find the details on each topic.
These are the key issues:

Part I. Climate Change Cascade
2 Climate Change 101: A Primer
3 Heat Waves and Heat Stress
4 Extreme Weather
5 Vector-Borne Diseases
6 Mental Health
Part II. Clear and Present Pathogens
7 Air Degradation
8 Water Security
9 Food Security
10 Allergens
11 Harmful Algal Blooms 

 Many of these modern sources of environmental hazards share a common feature—they derive from human activity as much as or more than from nonhuman sources. Radiation exists in nature, but its concentrated forms on Earth are created by humans. Industries produce the goods that support modern life, while they spin off by-products that can harm the environment and humans. We celebrate the productivity of modern agriculture, but if the runoff of pesticides and antibiotics pollutes the water supply and encourages antimicrobial resistance, we pay a higher price than we realize for food.

Balancing this tradeoff is complicated by the fact that the individuals and interests who typically stand to benefit from a polluting activity are not the same as the ones who will suffer the adverse health and other consequences.

Global externalities and how to fix them. This is one of the greatest challenges nowadays.

A controversial view on confidence with medicine

Medical Nihilism

On Therapeutical  Nihilism and effectiveness (Ch. 11), a philosophical view:

The confidence that a medical intervention is effective ought to be low, even when presented with evidence for that intervention’s effectiveness. How low? I do not think that there can be a precise or general answer. It is enough to say: lower, often much lower, than our confidence on average now appears to be. There is surprisingly little direct study of the confidence that physicians or patients or policy-makers have regarding the effectiveness of medical interventions. However, the confidence typically
placed in medical interventions can be gauged by the resources dedicated to developing, marketing, and consuming such interventions.

What explains the disparity between the confidence placed in medical interventions and the lower confidence that I have argued we ought to have? The ingenious techniques that companies use to market their products—paying celebrities to publicly praise their products, funding consumer advocacy groups, sponsoring medical conferences,  influencing medical education, direct-to-consumer advertising—have been extensively discussed by others. The promise of scientific breakthroughs partly explains this disparity—scientists seeking support for their research programs, and companies building hype for their products, often make bold predictions about the promise of the experimental interventions they are researching, and this can sound convincing when it is put in the language of genomics, proteomics, precision medicine, personalized medicine, and evidence-based medicine. Unwarranted optimism may be based in part on a history of a few successful magic bullets, such as penicillin and insulin—magic bullet thinking gets inappropriately adopted in premature proclamations of game-changing medical interventions, which media outlets promulgate.

Medical nihilism is not the thesis that there are no effective medical interventions. Please do not confuse this. Medical nihilism is, rather, the thesis that there are fewer effective medical interventions than most people assume and that our confidence in medical interventions ought to be low, or at least much lower than is now the case.

As I said, an unconventional and controversial view. We do need measures to assess facts and knowledge, philosophy is not enough. Anyway, I recommend its reading.

Are Pharmaceutical Companies Earning Too Much?

Are Pharmaceutical Companies Earning Too Much?

Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018

The debate about pharmaceutical companies earnings is a never ending story. Now you can find in JAMA an article that reflects the cost of a new drug: $1336 million. This is the summary:

The FDA approved 355 new drugs and biologics over the study period. Research and development expenditures were available for 63 (18%) products, developed by 47 different companies. After accounting for the costs of failed trials, the median capitalized research and development investment to bring a new drug to market was estimated at $985.3 million (95% CI, $683.6 million-$1228.9 million), and the mean investment was estimated at $1335.9 million (95% CI, $1042.5 million-$1637.5 million) in the base case analysis. Median estimates by therapeutic area (for areas with ≥5 drugs) ranged from $765.9 million (95% CI, $323.0 million-$1473.5 million) for nervous system agents to $2771.6 million (95% CI, $2051.8 million-$5366.2 million) for antineoplastic and immunomodulating agents.

Why this new figure is relevant? Because previous estimates said that it was the more than the double!

The mean estimate of $1.3 billion in the present study was lower than the $2.8 billion (in 2018 US dollars) reported by DiMasi et al,

And   my impression is that we have entered in a difficult world to estimate the real cost. Right now many firms are buying research (buying firms that have already a product close to be commercialised) and they are paying a premium for outsourcing research. Therefore, how to estimate the cost in this situations? Uncertain.

David Cutler asks about the earnings of pharma firms and says:

Ledley showed that from 2000 to 2018, the median net income margin in the pharmaceutical industry was 13.8% annually, compared with 7.7% in the S&P 500  sample. This difference was statistically significant, even with controls, although earnings seemed to be declining over time.

Is this positive return differential evidence of too high a return? Not necessarily. The economics of pharmaceuticals are important to consider. Like several other industries (eg, software and motion picture production), the pharmaceutical industry has very high fixed cost and very low marginal cost. It takes substantial investment to discover a drug or develop a complex computer code, but the cost of producing an extra pill or allowing an extra download is minimal. The way that firms recoup these fixed costs is by charging above cost for the product once it is made. If these upfront costs are not accounted for, the return on the marketed good will look very high.

 Paying more than a drug is worth clinically is not a good strategy. Even if a drug is worth a high price socially, pricing patients who need the drug out of the market is a real loss, even if it leads to more innovation in the future. In still another case, price increases for older, generic drugs serve no innovation purpose. But, as a general rule, it is important to be wary of blunt “lower all drug prices” policies.

Cutler doesn't say too much on price according value and about public funding of research. It leaves the initial question open and waiting for adhoc answers. That's it , it's a complicated issue, no general prescriptions, they need to be adjusted to specific conditions without a captured regulator. This last point is the most difficult one to overcome.

Prix Pictet

How to stop ineffective and harmful medical practices

Ending Medical Reversal: Improving Outcomes, Saving Lives

What are medical reversals? We expect that medicine will progress in a generally orderly fashion, with good medical practices being replaced by better ones. We used to use cholestyramine—a horribly tolerated drug that had no effect on patients’ life expectancy—to lower cholesterol after heart attacks. Now we use atorvastatin, a well-tolerated drug backed by robust evidence that it saves lives. This is how medical practice should evolve. Reversal, however, is different. Reversal occurs when a currently accepted therapy is overturned, found to be no better than the therapy it replaced. This often occurs when a practice—a diagnostic tool, a medicine, a procedure, or a surgical technique—is adopted without a robust evidence base.

 Instead of the ideal, which is replacement of good medical practices by better ones, medical reversal occurs when a currently accepted therapy is overturned—found to be no better than the therapy it replaced. Now, you might argue that this is how science is supposed to proceed. In high school, we learned that the scientific method involves proposing a hypothesis and testing to see whether it is right. This is true. But what has happened in medicine is that the hypothesized treatment is often instituted in millions of people, and billions of dollars are spent, before adequate research is done. Not surprisingly, sometimes the research demonstrates that the hypothesis was incorrect and that the treatment, which is already being used, is ineffective or harmful.

So what?

Our medical system is too tolerant of unproven practices. Doctors are too comfortable recommending a practice without real knowledge of whether it is helping or hurting patients. People are too willing to accept practices that seem like they should help. When a medical reversal does occur, most physicians consider it an exception to the rule. 

We need a culture change in medicine. We need to recommit to evidence-based medicine and realize that it is the only rational way to provide care. In this book we have provided a few suggestions for ways we can improve. We do not advocate that these recommendations be immediately implemented but that they be carefully considered, alongside recommendations proposed by other thoughtful analysts, and tested in prospective trials. As we move forward, we must recognize that drastic and dramatic change can often be harmful. We acknowledge that there will be areas of medicine in which, for now, we must tolerate the status quo. As we go through the house of medicine and clean up each room, we have to prioritize.  

Well, let's say that the book focuses on the shadows of medicine, but this is only one part. Generalisations are inacurate. Anyway, good to review it. And medical education is not enough to solve the issue, incentives and culture play a crucial role.

The opportunity costs of excessive medical practice variations

 Atlas de utilización de procedimientos de dudoso valor. Actualización datos 2017

From the new report on practice variations:

La literatura científica abunda en estimaciones de la proporción de asistencia sanitaria cuyo valor para el paciente es cuando menos escaso. Este cuerpo de evidencia no ha hecho sino crecer en la última década, dando origen a varias iniciativas tanto académicas como gubernamentales para identificar y abordar lo que se considera uno de los principales problemas de los sistemas sanitarios modernos. Hay consenso: se trata de un fenómeno altamente prevalente que pone en cuestión el buen uso de los recursos sanitarios.
La actividad sanitaria de dudoso valor incluye tanto la utilización de procedimientos escasamente efectivos o para los que existen alternativas superiores, como el uso de intervenciones efectivas en indicaciones en las que los beneficios para el paciente son prácticamente nulos y en ocasiones incluso generan efectos negativos. Obviamente, para el sistema sanitario y la sociedad que destina los recursos necesarios, el coste oportunidad derivado de este tipo de actividad es sustancial.

So many years talking about it and nothing happens...

Great report, something should be done.
 Angulo-Pueyo E, Seral-Rodríguez M, Ridao-Lopez M, Estupiñán-Romero F, Martínez-Lizaga N, Comendeiro-Maaloe M, Ibañez-Beroiz B, Librero-López J, Millán-Ortuondo E, Peiró-Moreno S, Bernal-Delgado E, por el grupo Atlas VPM. Atlas de variaciones en la práctica médica en utilización de procedimientos de dudoso valor en el Sistema Nacional de Salud, 2017. Marzo 2020; Disponible en: www.atlasvpm.org/atlas/desinversion-2017

PS. Some books I'm waiting for.

Key Indicators for Health

Leading Health Indicators 2030: Advancing Health, Equity, and Well-Being

Hyper-personalized medicine is just starting

If DNA is like software, can we just fix the code?

 10 Breakthrough Technologies 2020

From technology Review:

Here is our annual list of technological advances that we believe will make a real difference in solving important problems. How do we pick? We avoid the one-off tricks, the overhyped new gadgets. Instead we look for those breakthroughs that will truly change how we live and work.

• Unhackable internet

• Hyper-personalized medicine

• Digital money

• Anti-aging drugs

• AI-discovered molecules

• Satellite mega-constellations

• Quantum supremacy

• Tiny AI

• Differential privacy

• Climate change attribution

What hyper-personalized medicine stands for?

Here’s a definition of a hopeless case: a child with a fatal disease so exceedingly rare that not only is there no treatment, there’s not even anyone in a lab coat studying it. “Too rare to care,” goes the saying.
That’s about to change, thanks to new classes of drugs that can be tailored to a person’s genes. If an extremely rare disease is caused by a specific DNA mistake—as several thousand are—there’s now at least a fighting chance for a genetic fix.
One such case is that of Mila Makovec, a little girl suffering from a devastating illness caused by a unique genetic mutation, who got a drug manufactured just for her. Her case made the New England Journal of Medicine in October, after doctors moved from a readout of her genetic error to a treatment in just a year. They called the drug milasen, after her.
The treatment hasn’t cured Mila. But it seems to have stabilized her condition: it has reduced her seizures, and she has begun to stand and walk with assistance.
Mila’s treatment was possible because creating a gene medicine has never been faster or had a better chance of working. The new medicines might take the form of gene replacement, gene editing, or antisense (the type Mila received), a sort of molecular eraser, which erases or fixes erroneous genetic messages. What the treatments have in common is that they can be programmed, in digital fashion and with digital speed, to correct or compensate for inherited diseases, letter for DNA letter.
How many stories like Mila’s are there? So far, just a handful.
But more are on the way. Where researchers would have once seen obstacles and said “I’m sorry,” they now see solutions in DNA and think maybe they can help.
The real challenge for “n-of-1” treatments (a reference to the number of people who get the drug) is that they defy just about every accepted notion of how pharmaceuticals should be developed, tested, and sold. Who will pay for these drugs when they help one person, but still take large teams to design and manufacture?
—Antonio Regalado

Allocating drugs by lottery

Health ministers condemn Novartis lottery for Zolgensma, the world’s most expensive drug

Novartis has held the first draw to choose four babies who will receive its one-shot treatment for the genetic disease spinal muscular atrophy, Zolgensma (onasemnogene abeparvovec), amid criticism of its lottery programme from patient groups and EU health ministers.
Priced in the United States at $2.1m (£1.6m; €1.9m), the most expensive drug course of treatment ever, Zolgensma is not yet approved elsewhere. In December the company announced a plan to give away 50 treatments in other countries over the next six months, the recipients to be chosen randomly from among applicants every two weeks.
Recipients must be under 2, the upper age limit for which the drug is approved in the US. Most of the children in the Zolgensma draw were registered by their doctors. About one child in every 8000 live births is born with spinal muscular atrophy. The most severe type, called type 1 or Werdnig-Hoffmann disease, usually causes death during early childhood if untreated.

Does this makes any sense? In my opinion is a perfect strategy (for Novartis) to create artificial  scarcity. It is a well known approach to increase willingness to access/ willingness to pay. It was described by Adam Brandenburger in a book long time ago: Coopetition.
I hope it will not succeed (at least in Europe).

David Hockney

Predictive modeling in health care (2)

Data-Driven Approaches for Health Care Machine Learning for Identifying High Utilizers

Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program.

Five years ago I explained in this blog our experience on predictive modeling. This a key reference book.