The value of life (4)

 How Much Is a Human Life Worth? A Systematic Review

The distinction between a statistical life and a human life is crucial. Generally, economic research and policy evaluation aims at eliciting the VSL. A common misconception is that the VSL expresses the value for which an individual would trade their life. It does not. The VSL identifies how people value a smallreduction in mortality risk. For instance, if each individual is willing to pay $1 to reduce the risk of dying by 1 in 1000 000, then a population of 1 million individuals would be willing to pay $1 million to save 1 statistical life – the VSL is $1 million. Even though valuation tasks in surveys/experiments are generally framed as a change in the participant’s own mortality risk, the objective is to elicit the VSL because it is unlikely that the participant’s life will be saved owing to the intervention considered. 

Closely related to the concept of a VSL, the value of a statistical life-year (VSLY) represents the value of one additional year of life. One of the benefits of using VSLY estimates instead of VSL estimates is that the age of individuals benefiting from an intervention is taken into account when performing an  economic evaluation. As such, a higher value would be placed on the life of a child than the life of an elderly person owing to the difference in remaining life expectancy.

Unfortunately, VSLY are currently not available, you'll find VSL in this table:

 

Table 3. Median of the midpoint of reported value of a statistical life (VSL) estimates in included studies.

Sector	Overall (no. studies)	Developed countries (∗no. studies)	Developing countries (∗no. studies)	Stated-preference studies (∗no. studies)	Revealed-preference studies (∗no. studies)	Human capital approach (∗no. studies)
Environment	$1 062 630 (6)	$5 146 850 (2)	$680 489 (4)	$1 381 201 (5)	NA (0)	$744 058 (1)
Health	$6 770 534 (33)	$8 989 328 (21)	$580 663 (12)	$6 770 534 (33)	NA (0)	NA (0)
Labor market	$8 740 231 (35)	$11 784 289 (22)	$1 430 105 (13)	NA (0)	$8 740 231 (35)	NA (0)
Safety	$3 010 740 (9)	$7 075 108 (5)	$409 110 (4)	$3 010 740 (7)	$2 942 773 (2)	NA (0)
Transportation safety	$5 335 248 (41)	$7 075 108 (28)	$403 798 (13)	$5 335 248 (37)	$5 383 706 (4)	NA (0)
All sectors	$5 716 830 (116)	$8 342 027 (73)	$858 599 (42)	$5 185 402 (74)	$7 940 006 (41)	$744 058 (1)

NA indicates not available.

∗

No. studies indicates the number of studies on which the calculations for the median of the midpoint estimates are based. The number of studies considering all sectors is not necessarily equal to the sum of studies across the single sectors owing to some studies reporting VSL estimates for multiple sectors.

Rewardable pharmaceutical innovation

A Review of Current Approaches to Defining and Valuing Innovation in Health Technology Assessment

Key messages, 

Although, formerly, denoting a new drug as innovative was based on the drug having received patent protection or being a new molecular entity, this criterion is no longer sufficient from the perspective of many stakeholders. Rather, it became widely accepted that the central criterion to identify a drug as truly innovative should be the drug’s benefit or usefulness. With usefulness being a relative quality though, innovation is often defined by the particular view of what is deemed important or valuable. 

On the one hand, there is the notion that the value of innovation derives exclusively from the effect that the innovative drug has on the therapeutic benefit. What matters most for patients is whether a drug is the best choice to achieve treatment goals. It is this basic principle that guides most of the work on how to define rewardable innovation in the context of drug value assessments. The main argument is that, assuming a limited budget for healthcare expenditures, valuing anything beyond this therapeutic benefit in pricing and reimbursement decisions would reduce population health by displacing more cost-effective therapies. Furthermore, as a consequence, pharmaceutical companies might be incentivized to increasingly invest in R&D of slightly modified but basically similar products as opposed to drugs with a potentially larger positive therapeutic impact on patients.

On the other hand, it is argued that there might also be other, less obvious benefits from innovation in a wider sense. First, there may be benefits related to health or well-being that are not captured in measures of health outcomes typically used in clinical trials. A novel drug with less side effects or one that allows a more convenient treatment, for example, oral versus intravenous administration, may have the same effect on the therapeutic outcome but still improve the subjective well-being of the patients.

 

Tuscany, man made landscape

Health expenditure before COVID

 Spending on health in Europe: entering a new era

This report analyses health spending in 53 countries in the WHO European Region from 2000 to 2018 (the latest year for which internationally comparable data are available). It reviews key patterns and trends in health spending over time and across countries

 

Pharmaceutical utilization research

Methods to analyse medicine utilization and expenditure to support pharmaceutical policy implementation 

Reproductive genomics paradigm

The End of Genetics. Designing Humanity's DNA

Although human genetics has already advanced far enough to provide parents with information on which they may choose to act, key gaps in our knowledge make it very difficult to anticipate the consequences of the actions likely to become possible. These gaps mean that any reproductive engineering that is performed beyond the most straightforward elimination of strongly acting disease-causing mutations will be performed without a complete understanding of the likely consequences of those changes. This is a prospect that I find deeply troubling, and this book above all represents my best effort to empower non-specialists to develop their own opinions about this most central question for the future of humanity.

In response to this deep uncertainty, I have developed a thought experiment in reproductive genomics to help illustrate the kind of genome engineering that could be entertained in the not too distant future. Throughout the book I will refer back to this thought experiment to help make clear that we will have the technological ability of making some kinds of adjustments to the genomes of children, without having a matching ability to accurately predict the consequences of those adjustments. You will be in a position to understand this thought experiment more fully later in the book, but as a motivation in the reading that follows, consider the following possibility.

A very controversial book, glups!

This is the outline, 

Introduction

Chapter 1. The Future of Reproduction

Chapter 2. Learning to Read the Human Genome

Chapter 3. The Nature of Human Genetic Variation

Chapter 4. DNA and Human Disease

Chapter 5. Writing the Genomes of Our Children

The financial crash of 2008 never really ended

 The Lords of Easy Money. How the Federal Reserve Broke the American Economy

Ten years on, the gap between the rich and poor has grown dramatically, stock prices are trading far above what’s justified by actual corporate profits, corporate debt in America is at an all-time high, and this debt is being traded by big banks on Wall Street, leaving them vulnerable—just as they were during the mortgage boom. Middle-class wages have barely budged in a decade, and consumers are buried under credit card debt, car loan debt, and student debt.

Finding network effects desperately

 The Cold Start Problem. How to start and scale network effects.

Precision medicine (2)

Discovering Precision Health: Predict, Prevent, and Cure to Advance Health and Well-Being

Introduction The Power of Precision Health 1

Chapter 1 The State of U.S. Health and Health Care Delivery 15

Chapter 2 There’s More to “Health” Than Health Care 33

Chapter 3 The Innovation and Disruption Powering Progress in Health 43

Chapter 4 Fundamental, Discovery‐Focused Research: The Foundation of Biomedical Breakthroughs 111

Chapter 5 Peering into the Future: Leveraging The Powers of Prediction to Help Prevent Illness 147

Chapter 6 Prevention as a Pathway to Health and Wellness 177

Chapter 7 Curing Disease with More Precise Medical Therapies 207

Conclusion Achieving Precision Health: The Opportunities—and Challenges—Ahead 237

Paying for healthcare

Better Ways to Pay for Health Care

Public health budget evolution

 Last September I posted the data on per capita health expenditure. In 2020 we spent 1786€ per capita. The total budget for 2021 was 13.162m€, 1700€ per capita. Now the budget for 2022 says that we are going to spend 1.446€. This is an absolute nonsense. The budget approval has no relationship with former incurred expenditures...

Somebody should explain it clearly to the population and take measures. Forget this figure, it has no relationship with reality.

Bundled payments update (2)

 Year 1 of the Bundled Payments for Care Improvement–Advanced Model

A NEJM article shows a negligible effect of bundled payments. Unfortunately, I haven't seen any comment about the flaws in the design. A design mistake for not taking into account a holistic view.

If you reduce 78$ out of 27,315 $ per episode, this is an absolute FAILURE! (it is not a small reduction!!!)

However, the conclusion is:

In this study, we found that the BPCI-A program was associated with small reductions in Medicare payments among participating hospitals. Longer-term evaluation is needed to determine the full effect of the program.

Jean Pierre Capron

 

 

Integrated care and population health (2)

 Handbook Integrated Care

Opioid epidemic as a crime (3)

 Unsettled. How the Purdue Pharma Bankruptcy Failed the Victims of the American Overdose Crisis

Medtech regulatory affairs

 Medical Regulatory Affairs. An International Handbook for Medical Devices and Healthcare Products

Platforms, a business model (7)

 Platform Business Models. Frameworks, Concepts and Design

Platforms, a business model (6)

 The Platform Paradox. How Digital Businesses Succeed in an Ever-Changing Global Marketplace

The knowledge economy

 The Knowledge Economy

Adam Smith and Karl Marx recognized that the best way to understand the economy is to study the most advanced practice of production. Today that practice is no longer conventional manufacturing: it is the radically innovative vanguard known as the knowledge economy. In every part of the production system it remains a fringe excluding the vast majority of workers and businesses. This book explores the hidden nature of the knowledge economy and its possible futures.

The long and bumpy road to CRISPR (3)

 Cut-and-Paste Genetics. A CRISPR Revolution

Behind-the-scenes look at the U.S. Coronavirus crisis

 Preventable. The Inside Story of How Leadership Failures, Politics, and Selfishness Doomed the U.S. Coronavirus Response

The foundation of Silicon Valley

 The Big Score:  The Billion-Dollar Story of Silicon Valley

A reedition of 1985 classic. Just to understand our world, right now.

Incentives for innovation

 Inventing Ideas. Patents, Prizes, and the Knowledge Economy

How do knowledge and ideas influence the competitiveness of firms and nations?

You'll find the answer inside this book:

The twentieth century has been characterized as “the American century,” but at this critical juncture, new global competitors are adopting economic policies and institutions that have the potential to outpace U.S. achievements. Whether the twenty-first century will remain the American century will largely depend on the extent to which the lessons of the past are kept to the forefront. American technological and industrial progress owed to democratic open-access markets in ideas where entrepreneurial innovators succeeded, not by decree of administrators, but because their creations satisfied the ultimate judges—consumers in the marketplace. The evolution of administered innovation systems over the past three centuries largely serves as a cautionary tale rather than as a success story. The economic history of innovations instead suggests that the best incentive for necessary changes is failure in the marketplace; while the best prize for creative contributions to the knowledge economy is success in the marketplace.

Comparing health systems

 Comparative Health Systems.A New Framework

AI everywhere (10)

 Atlas of AI. Power, Politics, and the Planetary Costs of Artificial Intelligence

Artificial intelligence is not an objective, universal, or neutral computational technique that makes determinations without human direction. Its systems are embedded in social, political, cultural, and economic worlds, shaped by humans, institutions, and imperatives that determine what they do and how they do it. They are designed to discriminate, to amplify hierarchies, and to encode narrow classifications. When applied in social contexts such as policing, the court system, health care, and education, they can reproduce, optimize, and amplify existing structural inequalities. This is no accident: AI systems are built to see and intervene in the world in ways that primarily benefit the states, institutions, and corporations that they serve. In this sense, AI systems are expressions of power that emerge from wider economic and political forces, created to increase profits and centralize control for those who wield them. But this is not how the story of artificial intelligence is typically told.

The standard accounts of AI often center on a kind of algorithmic exceptionalism—the idea that because AI systems can perform uncanny feats of computation, they must be smarter and more objective than their flawed human creators.

 

Framing

Framers: Human Advantage in an Age of Technology and Turmoil 

Frames are mental models of the world that we use to understand problems, and to come up with new or refined solutions. As a tool, framing has always been with us. But as long as we were focused on traits like memory and reasoning that were more obviously essential to human cognition, framing didn’t get much attention. Now that computers have become better at some of those cognitive tasks, framing stands out as a critical function—one that matters more than ever because it can’t be handed off to the machines.

Framing means dreaming with constraints, letting your mind wander in a methodical and structured way, or wondering how old tools could be applied to new problems. FRAMERS shows how framing will not just be a way to improve how we make decisions in the era of algorithms, but will be a matter of survival for humanity in the coming age of machine prosperity.

 

A guide to working with frames

I. Harness mental models

• Framing happens all the time but can deliberately be used to improve decisions.
• Identify and inspect the assumptions in your mental models.
• Ask “why” and “how” questions: Why did you reach this conclusion? How must the world work if you anticipate this happening?
• Imagine how a wise friend, a historical hero, or a rival might frame a certain challenge.
• Ask yourself what would need to change for you to want to frame a situation differently.
• When your views clash with another’s, try to characterize the underlying way they see the world.

II. Dream with constraints

• Applying a frame is about swiftly and efficiently identifying appropriate options.
• Focus on those elements that are most easily changeable.
• Start by making minimal changes to your constraints, and gradually contemplate more elaborate modifications.
• Be careful to remain consistent by weighing whether the change contradicts any underlying assumptions or beliefs.
• Embed constraints in a physical model if it is too difficult to keep all of them in mind at once.

III. Reframe wisely

• Switching to an alternative frame lets you see the world differently, but it is risky.
• See if you already have a frame in your repertoire that will work.
• Try repurposing a frame that you can apply from a different domain.
• Invent a new frame as a last resort, since it’s the hardest option.
• Keep in mind the trade-offs between tight frames (fast but limited) and broad ones (comprehensive but time-consuming).
• Don’t reframe repeatedly, since it leads to disorientation.

IV. Conditions matter

• We can improve our framing through cognitive diversity.
• Develop a curiosity for the unfamiliar to continually challenge your worldview.
• Be willing to accept tensions among frames: they are less an indication of faulty reasoning than of the complexity of reality.
• Speak the truth, even when it is not comforting for individuals or organizations. The courage is respected by those who matter.
• Seek dissent rather than confirmation.
• When deciding in teams, have each person frame the problem independently before sharing views and deciding as a group.

V. Think beyond yourself

• The role of society is to ensure frame pluralism to produce optimal responses in times of change.
• Strive to see the colorful; don’t be color-blind. Speak openly but respectfully about differences.
• Regard societal friction as an advantage, not a drawback.
• Use education to instill respect for others’ frames.
• Promote a commingling of cultures as a way to foster imagination, innovation, and dynamism in a society.
• Reject anything that presents itself as a single frame to encompass all of reality.

AI everywhere (9)

The Feeling Economy. How Artificial Intelligence Is Creating the Era of Empathy

We have seen that artificial intelligence (AI) is in the process of ushering in a new era that will have profound implications for how humans work and live. The emerging “Feeling Economy” is one in which AI assumes many of the mechanical and thinking tasks, leaving humans to emphasize feeling. Just as many people’s lives were transformed in the 1900s by the industrial revolution and automation, people’s lives are now again being transformed.

The transformation in the last century was from physical and mechanical tasks to thinking tasks. In the twenty-first century, the transformation is from thinking tasks to feeling tasks. Artificial intelligence is developing in the order of (a) mechanical, to (b) thinking, to (c) feeling. Mechanical AI is easiest, and is mostly accomplished already. Thinking intelligence is next easiest, and is an area of strong current innovation. Feeling intelligence is the hardest for AI, and competence in that is probably decades away.

The main thesis of this book is: As AI assumes more thinking tasks, humans will emphasize feeling. Our research, both theoretical and empirical, provides initial support for this thesis.

 

AI everywhere (8)

Doing AI: A Business-Centric Examination of AI Culture, Goals, and Values

A common external goal for artificial intelligence is cognitive plausibility. That is, in order to qualify as “real,” a solution must solve intelligence in much the way humans are intelligent. When it is discovered that a solution is not anthropomorphic enough, many dismiss the accomplishment. In other words, how insiders solve puzzles is as important as how they define puzzles.

 Is AI About Simulating the Brain?The answer is sometimes, but not always. However, many insiders believe that if a solution looks like the brain, then it might actually act like the brain. When a solution doesn’t act like the brain, insiders conclude that the solution teaches them nothing about the brain or intelligence. Simulating the brain effectively requires insiders to reverse engineer it. The so-called inverse problem is the process of calculating from a set of observations the causal factors that produced them. In other words: starting with the answer and working backward to the question. However, reverse engineering the brain and studying intelligence will always be an exercise that is more complex, with much longer payoffs, than identifying and solving real-world problems.