Les diferències en salut minven

 Assessing performance of the Healthcare Access and Quality Index, overall and by select age groups, for 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019

El missatge en positiu per a tots els que ens preocupen les diferències en salut és que hi ha evidència de la seva reducció en les darreres tres dècades. En un article al Lancet ho mesuren amb el Healthcare Access and Quality Index i diu:

Between 1990 and 2019, the HAQ Index increased overall (by 19·6 points, 95% uncertainty interval 17·9–21·3), as well as among the young (22·5, 19·9–24·7), working (17·2, 15·2–19·1), and post-working (15·1,13·2–17·0) age groups.

Evidentment hi ha moltes diferències entre països desenvolupats i els que no ho són, el que ells mesuren amb un indicador sociodemogràfic SDI. I també hi ha diferències entre grups d'edat.

Això els porta a concloure que cal millorar:

Although major gaps remain across levels of social and economic development, convergence in the young group is an encouraging sign of reduced disparities in health-care access and quality. However, divergence in the working and post-working groups indicates that health-care access and quality is lagging at lower levels of social and economic development. To meet the needs of ageing populations, health systems need to improve health-care access and quality for working-age adults and older populations while continuing to realise gains among the young.

Espanya es troba a 89,7 de l'índex, mentre que la mitjana d'Europa Occidental és a 87,2. Bé, em caldria més detall com ho fan amb altres àrees geogràfiques però no hi som.

L'esforç de l'Institut de Health Metrics and Evaluation és notable i el rigor és altíssim. Convé seguir-ho d'aprop amb dades més recents, després de la pandèmia.

Stanton

 

Quan l'excepcionalitat promou la discrecionalitat a l'avaluació econòmica

 The Innovative Medicines Fund: a universal model for faster and fairer access to new promising medicines or a Trojan horse for low-value creep?

Ja fa anys que es va iniciar un mecanisme per capgirar l'avaluació de tecnologies al NICE. El 2010 es va constituir el Cancer Drug Fund amb 50m £ que al 2015 s'havia multiplicat gairebé per 7 i segueix així en 340m. Molts vam veure aquella operació com una forma de crear l'excepcionalitat dins l'avaluació econòmica, el cost-efectivitat podia quedar tocat i enfonsat a partir d'aquell moment. I si es començava pel càncer doncs ja no hi hauria argument per altres malalties. I si això passava al NICE, doncs també podia passar a altres països. 

Tal dit tal fet, en plena pandèmia, el 2021 es va crear el Innovative Medicines Fund pel mateix import que el de càncer 340m. Ara l'excepcionalitat total és de 680m  £. Els criteris d'assignació estan descrits sota del gràfic:

Un article recent ho comenta i al final diu:

In conclusion, the IMF, like the CDF, should be an exceptional route to patient access while providing the requisite evidence (mainly from RCTs) for reducing uncertainty about a drug’s clinical and cost effectiveness. Potential inclusion in the IMF should be limited to those cases where the major uncertainties can be addressed within the defined time frame. The notion of opportunity cost must not be ignored as IMF funding could always be used for other health services and technologies with strong evidence on effectiveness and value for money, which could improve overall population health.

Doncs bé, ja ho tenim. L'excepcionalitat creix i tinc la impressió que no té aturador, font de discrecionalitat o arbitrarietat?. Mals temps per a criteris objectius d'assignació social de recursos, mals temps per a l'avaluació econòmica. Recordeu sempre que la discrecionalitat és una font de poder polític i econòmic fantàstica (i font de corrupteles). Qui prova la discrecionalitat no la deixa, és addictiva.

PS. Tot això de moment no passa aquí aprop, els polítics consideren que no cal l'avaluació de tecnologies. 

PS. La meva opinió breu sobre avaluació econòmica a p.24 d'aquest document

Les decisions basades en la ignorància en temps de pandèmia

 Divided We Survive? Multilevel Governance during the COVID-19 Pandemic in Italy and Spain

L'avaluació de polítiques públiques és l'assignatura que menys agrada als governs. Els agrada tan poc que no hi dediquen recursos i si algú treu conclusions, aleshores fan com si no passés res.

Allò que va passar durant la pandèmia sabem tots que va ser molt fort. Em refereixo a decisions que es podien haver pres, que sabíem quines podien ser i en quin moment calia prendre-les. Em refereixo a decisions sobre les que no hi havia competència per prendre-les. Totes aquestes decisions incertes van tenir conseqüències, algunes d'elles letals i que no seran quantificades ni ningú se'n farà responsable. A data d'avui el nostre cervell és molt ràpid en oblidar tot plegat.

Podeu consultar en un article recent quin va ser el resultat comparat de prendre decisions mitjançant coordinació descentralitzada o centralització jeràrquica. És a dir, aprofitant petites peces d'informació local en un context d'ignorància o no. El resultat va ser aquest:

We document that in Spain, the pre-crisis governance mechanisms that would prove crucial in a pandemic were effectively paralyzed with the implementation of a single command that effectively centralized healthcare decision-making, and to large extent, inhibited incentives for cooperation between different governments. That is, the logic of the state of alarm deterred information sharing and regional co-governance. 

In contrast, in Italy, intergovernmental tensions emerged only in the second wave, when it was clearer how to manage the virus. During the first wave of the pandemic, regions passively allowed an increasing coordination role led by the central state. However, given that such coordination was not hierarchically imposed, it did not reduce the incentives to share information on best practices, or to implement more restrictive policies at the regional level. Comparing the reactions to the pandemic in two countries (Italy and Spain) allows us to study whether hierarchical centralization in Spain fared  better than informal decentralized coordination implemented in Italy. Our findings suggest that decentralized governance gives rise to better health outcomes and outputs than hierarchical  centralization.

I la conclusió:

When the source of the pandemic is localized and policy uncertainty is high (as during the first wave of the pandemic), a decentralized coordination mechanism, even when passively adopted, such as in Italy, would be advantageous (better outcomes and outputs) because it combines enhanced coordination, particularly information sharing and the profiling of their policy restrictions to the regional needs and priorities, above and beyond those of the central government.

Estic convençut que si tornés una altra pandèmia tornarien a replicar-se els mateixos errors. I és que l'error bàsic és trobar-nos enmig d'aquest enigma de dominació jeràrquica del que no hi ha manera de sortir-ne.

PD. I de l'excés de mortalitat actual i d'aquest estiu passat, gairebé no se'n parla.

Pandemethics (3)

 The Ethics of Pandemics. An Introduction

Table of Contents

1. Introduction: Why Ethics of Pandemics?

2. The General Principle of Pandemic Response

3. Rationing of Scarce Health Care Resources

4. Pandemics in an Unequal World

5. Restricting Freedom

6. Inducing Voluntary Behavioral Change

7. Moral Mathematics under Uncertainty.

Option value of healthcare technologies

 Broadening the Concept of Value: A Scoping Review on the Option Value of Medical Technologies

Key messages, 

Traditionally, cost-effectiveness analyses have been conducted from the payer perspective, although the question of whether they should be expanded to take a broader perspective continues to animate a lively debate. Lately, the attention has focused on wider components of benefits, including the so-called  option value. Our scoping review provides a comprehensive synthesis of conceptual and empirical aspects related to this topic recently introduced in the value assessment framework debate.

From a conceptual standpoint, the coexistence of 3 distinct definitions of option value in the literature emerging from our scoping review urges us to advocate for greater clarity of language in future research. We recommend using “insurance value” when referring to the utility of knowing that one may have access to a healthcare service should one need it in the future, as in definition A. Definition B mainly relates to decision making under uncertainty and specifically to the value of deferring uncertain unrecoverable decisions to a later time. In the evaluation of healthcare technologies and programs, this dimension of value originates from the possibility of delaying a reimbursement/adoption decision, if there is an expectation that better information on a technology’s (cost-) effectiveness will become  available in the future—for example, because a new clinical trial reports its results. Because this definition is rooted in financial options theory and its application to capital investment decisions, we recommend using the term “real option value,” consistently with the terminology used outside the healthcare sector 

According to the third definition, the claimed value does not originate from the uncertainty around a decision and the flexibility of deferring it, as in definition B, but rather it stems from the consideration that the value of a life-extending technology should also include the benefits of future treatments that otherwise would be precluded to patients if they did not benefit from improved survival. This definition of value pertains to the broader discussion on whether future costs and benefits not directly linked to the intervention being assessed should be accounted for when evaluating a technology.Therefore, we recommend that research related to this definition adopt the term “option value of survival.”

To date, no consensus has been reached yet

Les escaliers de la rue Chappe  à Montmartre.

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

Risk-sharing agreements for drugs (3)

 Characterization of the Pharmaceutical Risk‑Sharing Arrangement Process in Catalonia

Table 1

Uncertainty type, scope, and considered variables for drug assessment

Uncertainty type	Uncertainty scope	Considered variables
Clinical	Efficacy, effectiveness, and safety	Time frame
		Clinical trial phase
		Patient characteristics
		Primary endpoint
		Surrogate endpoints
		Active comparator
		Sensitivity analysis
		Statistical analysis
		Patient subgroup analyses
		Time frames for treatment follow-up
Financial	BI and CE	Indication extension and concretion
		Treatment regimen
		Potentially replaceable treatments
		Net financial impact of treatment inclusion/replacement
		Potential use extensions
		Other modifications in use of resources linked to new treatment
		Availability of CE or CU studies

Adapted from [14]

Pandemic economic reasoning

 ECONOMICS IN ONE VIRUS. AN INTRODUCTION TO ECONOMIC REASONING THROUGH COVID-19

The outline of the book:

1. WHAT DOES IT MEAN TO BE ECONOMICALLY “WORSE OFF” DURING A PANDEMIC?

An introduction to economic welfare

2. SHOULD I BE FREE TO RISK INFECTING YOUR GRANDMA WITH A DEADLY VIRUS?

An introduction to externalities

3. DID WE CLOSE DOWN THE ECONOMY?

An introduction to public and private action

4. HOW MUCH WOULD YOU SPEND TO SAVE MY LIFE?

An introduction to the value of a statistical life

5. WHEN IS A LOCKDOWN CURE WORSE THAN THE DISEASE?

An introduction to cost-benefit analysis

6. WHY WAS I BANNED FROM GOING FISHING?

An introduction to thinking on the margin

7. WHAT GOOD IS A PANDEMIC PLAN WITH SO MANY UNKNOWNS?

An introduction to uncertainty and the knowledge problem

8. WHY DID PROTESTS AND MARCHES NOT LEAD TO OBVIOUS SPIKES IN COVID-19 CASES?

An introduction to endogeneity

9. WHY COULDN’T I GET A COVID-19 TEST BACK IN FEBRUARY AND MARCH 2020?

An introduction to regulatory tradeoffs

10. WHY WAS THERE NO HAND SANITIZER IN MY PHARMACY FOR MONTHS?

An introduction to the price mechanism

11. DOES THE PANDEMIC SHOW THAT WE NEED MORE U.S.-BASED MANUFACTURING?

An introduction to trade and specialization

12. WHY IS THAT GUY IN THE MASK GETTING SO CLOSE?

An introduction to moral hazard

13. WHY DID AIRLINES GET A SPECIAL BAILOUT BUT NOT MY INDUSTRY?

An introduction to public choice economics

14. WHY DIDN’T MY WORKERS WANT TO BE REHIRED?

An introduction to incentives

15. WHY WEREN’T WE WELL PREPARED FOR THE PANDEMIC?

An introduction to political incentives

16. CAN WE REALLY JUST TURN AN ECONOMY OFF AND BACK ON AGAIN?

An introduction to the nature of an economy

CONCLUSION: WHAT IS ECONOMICS GOOD FOR?

And a message on cost-benefit:

Cost-benefit analysis is a useful economic technique for considering whether a project improves societal welfare and to compare the societal net benefits of different projects. To do cost-benefit analysis well, we must account for all the direct and indirect impacts of the proposed policy on societal welfare, account for externalities, and ensure that we compare like-with-like in both timeframe and measurement. When it comes to COVID-19, cost-benefit analysis can, in theory, be used to examine the efficacy of lockdowns. However, there are huge uncertainties that make it hard to weigh up the precise costs and benefits of those policies. Even if the societal benefits do appear to exceed the costs on reasonable assumptions, that doesn’t mean the exact contours of the lockdown are “optimal policy.” In an ideal world, we’d find the policy mix that minimizes the overall societal costs of the pandemic.

This ideal world doesn't exist. 

Emancipatory public health

NEW PANDEMICS, OLD POLITICS. Two Hundred Years of War on Disease and its Alternatives

Three selected paragraphs from the last chapter (the most interesting one):  

Experts in infectious diseases had been worried about the radical uncertainties of a new pathogen. It turned out that the science had so improved in the years since SARS that the uncertainties were well within the scope of the anticipated. The radical uncertainty was in the politics – something that none of the experts had thought to anticipate. SARS-CoV-2 is a politically sophisticated pathogen, whose impact lies more in what it does to the body politic than what it does to the human body. The politics of response to Covid-19 was a disorienting combination. The political right invited popular debate on public health expertise, in pursuit of its new-found agenda of disrupting institutions. In the name of free-thinking, agitators veered into pseudo-science and conspiracy theories. Liberals and the left valorized scientists and rushed to embrace a standardized set of suppression measures. Lockdowns were over-engineered and had momentous social and economic consequences; some critics detected authoritarian longings.

 Could Covid-19 become what Ulrich Beck called an ‘emancipatory catastrophe’?35 If so, what would be a new, emancipatory narrative for what we do about pandemic diseases, actual and threatened? I suggest that we begin with a return to a word introduced in chapter 1, and left waiting in the wings: ‘pandemy’. As our leading scientists insist, pandemic disease is too important to be left to the biomedical establishment. It’s a crisis in our way of life. In using the word ‘pandemy’, we can reclaim the concept of a holistic disruption, reaching backwards into the ecological, social, and health pathologies that have created virulent pathogens with pandemic potential, broadening to include other illnesses prevalent at the same time, and reaching forward into wider societal and political repercussions. In short, we can integrate the ‘One Health’ approach to where these diseases come from with the ‘people’s science’ practice of responding to them.

 Emancipatory public health begins with a conversation on this whole-of-society, whole-planet, ‘One Health’, democratic, and participatory agenda. The starting point is not the content of the policies but the process for getting to them. Those who are most vulnerable and most excluded will have some of the most important things to say. This means dismantling the ‘war on disease’ mindset and its politics, assembled over the last two centuries. If we do this, Covid-19 may yet be the emancipatory catastrophe we need.

 

Covid and social perspectives

 THE COVID-19 CRISIS. Social Perspectives

In Chapter 13

13 Post-pandemic Routes in the Context of Latin Countries: The Impact of COVID-19 in Italy and Spain by Anna Sendra, Jordi Farré, Alessandro Lovari and Linda Lombi

In terms of health and risk communication, the COVID crisis has emphasised the lack of specific training in crisis and emergency communication of many public sector organisations, including health institutions. This first social media pandemic has been a major challenge for health communicators; individuals often failed in effectively communicating data and numbers to counteract the infodemic and thus reduce the impact of false narratives. With the increasing diversification of social media platforms, ‘individuals’ health […] will be shaped by a multitude of social forces, each of which can mediate different kinds of health contagion processes’ (Zhang and Centola, 2019). Mitigating the spread of fake news seems to involve coordinated efforts between authorities, mass media and digital companies, but it also appears crucial to invest in education and digital literacy for developing a critical awareness of the use of digital technologies that could be useful for facing future health crises. In other words, the strengthening of comprehensive population-centred responses lies on finding answers concerning how the mechanisms of public concern will operate to engage in coherent protection rules or in what ways the forms of interaction will change

Outline of the book:

PART I: INTRODUCTION

1 COVID Society: Introduction to the Book

Deborah Lupton and Karen Willis

2. Contextualising COVID-19: Sociocultural Perspectives on Contagion

Deborah Lupton

PART II: SPACE, THE BODY AND MOBILITIES

3. Moving Target, Moving Parts: The Multiple Mobilities of the COVID-19 Pandemic

Nicola Burns, Luca Follis, Karolina Follis and Janine Morley

4. Physical Activity and Bodily Boundaries in Times of Pandemic

Holly Thorpe, Julie Brice and Marianne Clark

5. City Flows During Pandemics: Zooming in on Windows

Oimpia Mosteanu

6. The Politics of Touch-Based Help for Visually Impaired Persons During the COVID-19 Pandemic: An Autoethnographic Account

Hidi Lourens

PART III: INTIMACIES, SOCIALITIES AND CONNECTIONS

7. #DatingWhileDistancing: Dating Apps as Digital Health Technologies During the COVID-19 Pandemic

David Myles, Stefanie Duguay and Christopher Dietzel

8. ‘Unhome’ Sweet Home: The Construction of New Normalities in Italy During COVID-19

Veronica Moretti and Antonio Maturo

9. Queer and Crip Temporalities During COVID-19: Sexual Practices, Risk and Responsibility

Ryan Thorneycroft and Lucy Nicholas

10. Isol-AID, Art and Wellbeing: Posthuman Community Amid COVID-19

Marissa Willcox, Anna Hickey-Moody and Anne M. Harris

PART IV: HEALTHCARE PRACTICES AND SYSTEMS

11. Strange Times in Ireland: Death and the Meaning of Loss Under COVID-19

Jo Murphy-Lawless

12. Between an Ethics of Care and Scientific Uncertainty: Dilemmas of General Practitioners in Marseille

Romain Lutaud, Jeremy K. Ward, Gaëtan Gentile and Pierre Verger

13 Post-pandemic Routes in the Context of Latin Countries: The Impact of COVID-19 in Italy and Spain

Anna Sendra, Jordi Farré, Alessandro Lovari and Linda Lombi

14. Risky Work: Providing Healthcare in the Age of COVID-19

Karen Willis and Natasha Smallwood

PART V: MARGINALISATION AND DISCRIMINATION

15. The Plight of the Parent-Citizen? Examples of Resisting (Self-)Responsibilisation and Stigmatisation by Dutch Muslim Parents and Organisations During the COVID-19 Crisis

Alex Schenkels, Sakina Loukili and Paul Mutsaers

16. Anti-Asian Racism, Xenophobia and Asian American Health During COVID-19

Aggie J. Yellow Horse

17. Ageism and Risk During the Coronavirus Pandemic

Peta S. Cook, Cassie Curryer, Susan Banks, Barbara Barbosa Neves, Maho Omori, Annetta H. Mallon and Jack Lam

Distributional cost-effectiveness

 Distributional Cost-Effectiveness Analysis. Quantifying Health Equity Impacts and Trade-Offs

Distributional Cost-Effectiveness Analysis Comes of Age

Distributional cost-effectiveness analysis (DCEA) provides information about the equity impacts of health technologies and programs and the trade-offs that sometimes arise between equity and efficiency. This field has now come of age with a growing applied literature,1 new training resources,2 and a formal professional network: a special interest group on equity-informative economic evaluation within the International Health Economics Association

The outline of the book:

Part One: Preliminaries

1:Introduction, Richard Cookson, Susan Griffin, Ole F. Norheim, Anthony J. Culyer

2:Principles of health equity, Richard Cookson, Anthony Culyer, Ole F. Norheim

3:Designing a distributional cost-effectiveness analysis, Richard Cookson, Susan Griffin, Ole F. Norheim, Anthony J. Culyer

4:Describing equity impacts and trade-offs, Richard Cookson, Susan Griffin, Ole F. Norheim, Anthony J. Culyer

5:Introduction to the training exercises, Richard Cookson, James Love-Koh, Colin Angus, James Lomas

Part Two: Simulating Distributions

6:Health by disease categories, Kjell Arne Johansson, Matthew M. Coates, Jan-Magnus Økland, Aki Tsuchiya, Gene Bukhman, Ole F. Norheim, Øystein Haaland

7:Health by social variables, James Love-Koh and Andrew Mirelman

8:Costs and health effects, Colin Angus

9:Health opportunity costs, James Love-Koh

10:Financial protection, Andrew Mirelman and Richard Cookson

Part Three: Evaluating Distributions

11:Dominance analysis, Owen O'Donnell and Tom Van Ourti

12:Rank-dependent equity weights, Owen O'Donnell and Tom Van Ourti

13:Level-dependent equity weights, Ole F. Norheim, Miqdad Asaria, Kjell Arne Johansson, Trygve Ottersen and Aki Tsuchiya

14:Direct equity weights, Mike Paulden, James O'Mahony and Jeff Round

Part Four: Next Steps

15:Uncertainty about facts and heterogeneity of values, Susan Griffin

16:Future challenges, Richard Cookson, Alec Morton, Erik Schokkaert, Gabriela B. Gomez, Maria Merritt, Ole F. Norheim, Susan Griffin, and Anthony J. Culyer

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

Clinical philosophy

 Clinical Reasoning:Knowledge, Uncertainty,and Values in Health Care

The largest global public-health initiative

 The COVID-19 vaccines are here: What comes next?

From McKinsey:

As vaccine availability nears, communities and consumers will want answers to many questions, including:

• Is the vaccine effective and safe?
• Who will get vaccinated first?
• Which vaccine will we receive, especially if multiple vaccines are available?
• Where and when can we get vaccinated?
• Will we have to pay?
• Above all, what do we need to worry about?

Although the scale of the task may seem daunting, countries benefit by starting end-to-end planning immediately. Our 6A framework lays out a structured approach to ensure vaccines are available, administrable, accessible, acceptable, affordable, and accountable while taking into account strategic considerations associated with uncertainty (for example, vaccine clinical and technical profile) and building system capabilities (Exhibit 2). We have developed, in granular detail, the individual activities and considerations behind each component of the framework. Through the collective initial effort of the pharma industry, the scientific community, global health institutions, and governments, most elements of the “available” segment of the 6A journey are being addressed

 

Paul Strand

 

Investing in pandemic preparedness

 The Cost Effectiveness of Stockpiling Drugs, Vaccines and Other Health Resources for Pandemic Preparedness

A short review on the topic, many questions, few answers. 

Health economics methods can be used to decide the optimal pandemic preparedness strategy based on cost effectiveness because different stockpiling of available measures can be implemented. The economic evaluation of pandemic preparedness strategies and pandemic preparedness measures is based on methods developed for health technology assessment. Nevertheless, this assessment differs from the traditional economic evaluations. The cost-effectiveness evaluation of a new drug compares healthcare costs and health effects for patients treated and not treated with the drug. The cost effectiveness of the drug will depend on the effectiveness of the drug in reducing clinical outcomes and healthcare costs. The drug will be used by the health system in patients with certainty. In contrast to this, the cost-effectiveness evaluation of pandemic preparedness measures and interventions is affected by several facts. First, pandemic preparedness measures are costly because they must be used to prevent and treat pandemic infections in a great number of persons. Second, investments in pandemic preparedness measures could be made many years before the emergence of the pandemic pathogen. Third, the health and economic benefits generated by pandemic preparedness measures will depend on the virulence and infectiousness of the pandemic pathogen. Fourth, pandemic preparedness measures can be associated with large costs and benefits outside the health system and great macroeconomics effects. There is a risk that an unknown pandemic agent will emerge and cause high morbidity and mortality, but we do not know when this will happen or how virulent and infectious a new pandemic agent will be. Although a new pandemic can be similar to previous pandemics, it can be also very different.

Ventilator support and intensive care for acute respiratory failure due to acute respiratory distress syndrome is a cost-effective intervention [8], but the cost effectiveness of stockpiling ventilators depends on the number of stockpiled ventilators and the severity of a future pandemic. The cost effectiveness of ventilator support and intensive care ranges from US$29,000 per QALY in low-risk patients (≥ 70% probability of surviving at least 2 months from the time of ventilator support) to US$110,000 per QALY in high-risk patients (prognostic estimate ≤ 50%) [7]. The question about the optimal number of stockpiling ventilators for pandemic preparedness depends on intervention costs and uncertainty about when the pandemic will happen and how virulent and infectious the pandemic pathogen will be.

Paul Strand at KBr

 

The time to stop recreational testing has come

 Direct-to-Consumer Genetic Testing: Value and Risk

Piecing together information from a variety of sources, one reporter concluded that by early 2019, more than 26 million people worldwide had been tested by the four leading companies, 23andMe, Ancestry, Gene By Gene, and MyHeritage (1). That volume was fueled by aggressive marketing, including discounts in the lead-up to major holidays to promote gifting of test kits. As of May 2020, the  undiscounted price of the basic test offered by the leading companies was $59–$99.

This is an example of what should not had happened. Recreative genomics doesn't add value and increases uncertainty and anxiety. 

Although many consumers of DTCgenetic testing express an intention to modify their lifestyle to address risk factors, studies typically show no changes at follow-up (15, 30). In the PGen Study, 59% of participants said that test results would influence their management of their health (31). However, an analysis of the 762 participants who had complete cancer-related data found that those who received elevated risk estimates were not significantly more likely to change lifestyle or engage in cancer screening than those who received average or below-average risk estimates (44). It may be relevant that no participants tested positive for pathogenic variants in highly penetrant cancer susceptibility genes. As for population health, the Centers for Disease Control and Prevention identify three conditions—hereditary breast and ovarian cancer syndrome,Lynch syndrome, and familial hypercholesterolemia—that are poorly ascertained despite the potential for early detection and intervention to significantly reduce morbidity and mortality (45). The hope is that DTC genetic testing could improve the situation (15). However,DTC genetic testing as currently carried out is likely to fill gaps in haphazard fashion, given the characteristics of purchasers, the scope of available products, and integration issues.

One message. Right now and until we don't know the implications of recreational genetic testing, direct to consumers testing should stop.

Banksy

 

Prioritising population health or the economy (2)

  The Pandemic Information Gap. The Brutal Economics of COVID-19

Joshua Gans has updated his former book on covid economics. And says:

Pandemics are an information problem. Solve the information problem and you can defeat the virus. There is a big difference between knowing someone you interact with is infectious and having to make a guess as to whether that person is infectious. In the former case, you can act and limit the interactions. In the latter case, you have to take a risk. And, in evaluating that risk, what we care about is not just whether you become infected but also whether you might pass that infection on to others.

 The difference between perfect knowledge and no knowledge is what causes an infectious disease to have an impact on social and economic interactions. With perfect knowledge, some people get sick, they are isolated, and life is (for most of us) essentially unchanged. With no knowledge at all and no interventions to prevent infections, then for COVID-19, at its peak, about 21 million people in the United States alone would likely be infectious at one time. With no restrictions on activity, the probability that you interact with one of the infectious people on a given day is 21 million divided by 327 million (the US population), or 6.4 percent.4 However, suppose you interact with only 10 people per week. In that situation, the probability that you are able to avoid any of those infected people is about 50-50. When going to public spaces, you may interact with over a hundred people per week. In that case, your probability of avoiding an infected person becomes close to zero. In other words, perfect knowledge allows you to avoid all infected people. No knowledge makes it near certain that you will encounter at least one infected person.

 Without knowledge of how many people are infected and whether particular people are carriers of thecoronavirus, we are forced to take drastic actions.

True. Pandemics are an information problem but information will never be perfect and complete. Uncertainty sorrounds us. And pandemics are more than an information problem. Because you may know who is infected, and not "act and limit interactions". Therefore, emotions, incentives and expectations count. We do have also a behavioral problem. And if it is behavioral, it has ethical implications. And finally, that's life, decisions with or without information and behavioral and ethical implications of such decisions.

Anyway, a useful introductory text.

Drug approval and geographic differences

 Approval of Cancer Drugs With Uncertain Therapeutic Value: A Comparison of Regulatory Decisions in Europe and the United States

We know that the regulation of medical devices is quite different between US and Europe, and with COVID tests we have experienced such divide. In drugs, one could expect a closer approach to approval. However, this is not the case. 

Regulatory agencies may have limited evidence on the clinical benefits and harms of new drugs when deciding whether new therapeutic agents are allowed to enter the market and under which conditions, including whether approval is granted under special regulatory pathways and obligations to address knowledge gaps through postmarketing studies are imposed.

In a matched comparison of marketing applications for cancer drugs of uncertain therapeutic value reviewed by both the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA), we found frequent discordance between the two agencies on regulatory outcomes and the use of special regulatory pathways. Both agencies often granted regular approval, even when the other agency judged there to be substantial uncertainty about drug benefits and risks that needed to be resolved through additional studies in the postmarketing period.

Postmarketing studies imposed by regulators under special approval pathways to address remaining questions of efficacy and safety may not be suited to deliver timely, confirmatory evidence due to shortcomings in study design and delays, raising questions over the suitability of the FDA’s Accelerated Approval and the EMA’s Conditional Marketing Authorization as tools for allowing early market access for cancer drugs while maintaining rigorous regulatory standards.

 Hockney

Pricing panic, massageing data

The Price of Panic. How the Tyranny of Experts Turned a Pandemic into a Catastrophe

So, what caused the viral panic? The panic and lurching government overreach were inspired not so much by deaths people knew about firsthand, and not so much by the virus’s murky origins in China. They were sparked by a few forecasts that had the smell of science. The World Health Organization (WHO) favored a single, untested, apocalyptic model from Imperial College London. The United States government took its cues from the Institute for Health Metrics & Evaluation (IHME) at the University of Washington. We now know these models were so wrong they were like shots in the dark. After a few months, even the press admitted as much. But by then vast damage had been done.

But what of those experts? They treated predictive models—which are at best complex conjectures about future events—as if they were data. And then, when the models flopped, they began to massage the data. To get past this catastrophe we will need to forgive, but we should never forget. We should do whatever we can to dismantle such experts’ unchecked power over public policy.

These experts, however, could never have done so much damage without a gullible, self-righteous, and weaponized media that spread their projections far and wide. The press carpet-bombed the world with stories about impending shortages of hospital beds, ventilators, and emergency room capacity. They served up apocalyptic clickbait by the hour and the ton.

History shows that you will rarely lose your job making predictions if you’re wrong in the right direction. On the other hand, you may well lose it if you’re right in the wrong direction. Neither rulers nor subjects welcome the bearer of bad, but true, news. (Especially if it’s bad news for power-grabbing elites.)

Being wrong in the right direction, though, often reaps reward. Early pandemic models indicated that only prompt and massive state action could save us. The models were wrong—way off—but they were wrong in the right direction. They gave politicians justification for taking over almost every aspect of citizens’ lives. They gave the press clickbait galore. We’re not assuming malice here. We assume that many of these folks were moved by concern and even love for others. The issue is one of incentives and human nature, not bad intentions.

Our imagined pandemic model has made a huge mistake. How to explain that whopping error? In a perfect world, the experts who created the model, publicized, and used it to create public policy would reassess the assumptions they fed into the model—A, B, and C—find the mistake, and try new ones, which may better match the “observables.”

But we don’t live in a perfect world with perfect experts. What if experts are loath to admit that they were wrong? (We know that’s a real stretch, but stick with us here.) What if they have been feted by the press and promoted to positions of authority and power? They have other options besides the humiliating one of going back to the drawing board. For starters, such experts can stop using the word “predictions” to describe the forecasts that the model has been spitting out. Now they’re just “scenarios” or “guidelines” or “projections.” But these are just word games. There’s little daylight between a forecast, a prediction, a guess, a scenario, and a projection. All those words describe what the model is doing when it says, If A, B, and C are true, then something like X will happen, give or take a margin of error. If nothing like X happens, then something’s wrong with at least one of the model’s conceptual inputs, that is, with one of the propositions that that model assumed to be true. At least one of A, B, or C must be wrong.

It's only one view. Uncertainty is everywhere, and you may find snake-oil sellers in every corner.

PS. Tomorrow online presentation.

PS. Great post on pandemic models.

 

Dying from COVID

 Magnitude, demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries

The total mortality effect of the COVID-19 pandemic is the difference between the observed number of deaths from all causes and the number of deaths had the pandemic not occurred, which is not directly measurable. The most common approach to calculating the number of deaths had the pandemic not occurred has been to use the average number of deaths over previous years—for example, the most recent 5 years—for the corresponding week or month when the comparison is made. This approach, however, does not take into account changes in population size and age structure, nor long- and short-term trends in mortality, which are particularly pronounced for some age groups52,53. Nor does this approach account for time-varying factors, such as temperature, that are largely external to the pandemic but also affect death rates.

We developed an ensemble of 16 Bayesian mortality projection models that each make an estimate of weekly death rates that would have been expected if the COVID-19 pandemic had not occurred. We used multiple models because there is inherent uncertainty in the choice of model that best predicts death rates in the absence of pandemic. 

I suggest you to find where is Spain... 

Comparison of percent increase in deaths from any cause as a result of the COVID-19 pandemic between men and women, for all ages and by age group.