Vaccine nationalism (2)

 Designing Pull Funding For A COVID-19 Vaccine

If somebody wants to avoid vaccine nationalism, then there is a need for a global mechanism of allocation. You'll find a specific proposal in Health Affairs about this issue. Unfortunately, it seems that nobody cares about its application.

In baseline simulations, the optimal pull program spends an average of $50 per dose to obtain an average of 2.2 billion doses—$110.4 billion in total. The size of our pull program is driven by the enormous estimated benefit from COVID-19 vaccination, leading the optimal program to induce nearly all firms to participate (average of 9.8 out of 10), installing nearly all  available capacity, and allowing more people to be vaccinated with less delay. To secure this level of participation requires the award to cover all but the most exorbitant cost draws. On average, 2.9 of the 10 candidate firms develop a successful vaccine, generating a social benefit (net of program costs) of $2.8 trillion.

 Our mechanism offers two advantages over the free market. First, it dramatically lowers cost—by a factor of thirteen—by averting a bidding war. Given our program’s larger size compared with other policy proposals, it is ironic that its advantage would be to lower costs compared with the private market. Second, it allows for more efficient allocation, moving some vulnerable people in lowerincome countries up in the queue ahead of some from richer countries experiencing lower harm. A conjectured third benefit of our mechanism— enhancing investment in more candidates and more capacity—did not materialize in baseline simulations. Demand for a COVID-19 vaccine is so high that every firm in every simulation finds investing profitable under a free-market scenario. This third benefit does materialize in scenarios with substantially more per firm capacity than in the baseline.

 Eivissa, Francesc Català i Roca

 

Machine learning for clinical labs

 Machine Learning Takes Laboratory Automation to the Next Level

Good article on ML applications for microbiology lab.

There are two commercially available Food and Drug Administration (FDA)-approved microbiology laboratory automation platforms in the United States, namely, WASPLab (Copan Diagnostics Inc.) and Kiestra (Becton Dickinson) (6). Each system is highly customizable and consists of front-end processing, “smart” incubation according to laboratory protocol, and plate imaging. The processing unit performs medium selection, application of patient information and barcodes for tracking, medium inoculation, and plate streaking. Automation of these processes cuts down on and improves the consistency of repetitive tasks previously performed by technologists.

Image analysis software is not currently FDA approved, so the algorithm it deploys qualifies as a high-complexity laboratory-developed test when used to make definitive calls about microorganism presence/absence or culture significance. In this context, the end user need not understand the internal workings any more than they understand the inner workings of most computers. Additionally, as with most laboratory software, manufacturer assistance is provided in training the algorithm. Labs may, therefore, validate performance according to familiar sensitivity and specificity (for significant growth), precision and accuracy (for quantification), and procedural variation (coefficients of variation, Kappa statistics). As with any test, revalidation must be performed if components of the test change. The number of samples needed to train the algorithm (hundreds to thousands) will be algorithm dependent but easily available due to their common nature, facilitating both initial and revalidation using new plate images. Validation of machine learning image analysis for laboratory automation may, overall, be comparable to that performed for whole-slide imaging as used in histopathology, where the object of validation is a process as much as a machine (12) and where modest interobserver agreement may set a similarly modest benchmark for machine learning performance.

 Eivissa autèntica, Joaquim Gomis

Patient safety

 System governance towards improved patient safety: Key functions, approaches and pathways to implementation

A working paper by the OECD highlights the role of system governance in patient safety.

Safety in health is often considered as a dimension of quality of care and part of the overall performance of the health system. Similarities follow in the way safety and quality are governed. The OECD collects information on key health system characteristics every four years. The 2016 Health System Characteristics Survey provide the latest update of how OECD countries implement governance functions aiming to strengthen quality of health care services (Table A 3). OECD countries develop legislation and national and institutional regulations that define and ensure quality of care. Accreditation, inspections and audits are often used in monitoring compliance with national quality standards. 

The Health System Characteristics Survey created the basis for the development of the 2019 Patient Safety Governance Survey. The OECD distributed the survey to a network of country experts on safety governance and policies in the summer of 2019. With a response rate of 25 OECD countries, a set of semi-structured interviews were undertaken in the late 20192, creating a broad and robust knowledgebase of countries’ safety governance models.

Antonio Perrone

Tackling COVID-19

 Informe final del Grupo de Trabajo Mixto Covid-19

FEDEA has coordinated a group of 130 experts that have analysed current situation and options for the pandemic. I have participated in the health group. 

Health document

Abridged document.

Stop Covid with CRISPR Diagnostics (3)

 Detection of SARS-CoV-2 with SHERLOCK One-Pot Testing

Former posts have highlighted the potential of CRISPR for molecular  diagnostics, specially in case of Covid. Now NEJM provides details of Sherlock test.

Protocol here

Pandemethics

The Ethics of Pandemics

From this timely book I'm specially interested in Chapter 4: Scarce Resource Allocation. The whole book offers an overview of some of the most pressing issues of our time. Outline of chapter 4:

4.1 Ezekiel J. Emanuel et al., Fair Allocation of Scarce Medical Resources in the Time of COVID-19

4.2 Angela Ballantyne, ICU Triage: How Many Lives or Whose Lives?

4.3 Jackie Leach Scully, Disablism in a Time of Pandemic

4.4 Joseph J. Fins, Disabusing the Disability Critique of the New York State Task Force Report on Ventilator Allocation

4.5 Franklin G. Miller, Why I Support Age-Related Rationing of Ventilators for COVID-19 Patients

4.6 Shai Held, The Staggering, Heartless Cruelty toward the Elderly: A Global Pandemic Doesn’t Give Us Cause to Treat the Aged Callously

Case Study: Ventilator Shortages: Who Should Live?

How do people influence each other?

Conformity. The Power of Social Influences

In his book  Cass Sunstein focuses on two influences on individual belief and behavior: 

The first involves the information conveyed by the actions and statements of other people. If a number of people seem to believe that some proposition is true, there is reason to believe that that proposition is in fact true. Most of what we think—about facts, morality, and law—is a product not of firsthand knowledge but of what we learn from what others do and think

The second influence is the pervasive human desire to have and to retain the good opinion of others. If a number of people seem to believe something, there is reason not to disagree with them, at least not in public. The desire to maintain the good opinion of others breeds conformity and squelches dissent, especially but not only in groups that are connected by bonds of loyalty and affection, which can therefore prevent learning, entrench falsehoods, increase dogmatism, and impair group performance

The book is divided into four chapters. In chapter 1, I develop a central unifying theme, which is that in many contexts, individuals are suppressing their private signals—about what is true and what is right—and that this suppression can cause significant social harm. In chapter 2, I turn to social cascades, by which an idea or a practice spreads rapidly from one person to another, potentially leading to radical shifts. Focusing on group polarization, chapter 3 investigates how, why, and when groups of like-minded people go to extremes. Chapter 4 explores institutions.

 

Measuring and improving efficiency in health care

 TOOLS AND METHODOLOGIES TO ASSESS THE EFFICIENCY OF HEALTH CARE SERVICES IN EUROPE

An EU approach to health system performance assessment: Building trust and learning from each other

Inefficiency in a health care system can arise for two distinct, yet related reasons. Inefficiency materialises 1) when the  maximum possible improvement in outcome is not obtained from a fixed set of inputs (or, in other words, when the same – or even greater – outcome could be produced consuming less resources), and 2) when health resources are spent on a mix of services that fails to maximise societal health gains in aggregate. As explained in more detail below, these two types are conventionally referred to in the health economics literature as, respectively, technical and allocative efficiency.

How to set the drug market size in precision medicine

 EL SISTEMA NACIONAL DE SALUD ante la medicina de precisión

In this book you'll find my chapter with Carlos Campillo on "Los biomarcadores y la medicina de precisión", p.35

La medicina estratificada se caracteriza por la estrecha relación y dependencia entre el diagnóstico y la terapia farmacológica. La elección del punto de corte de un biomarcador (cut-off) determina la población sujeta a tratamiento y ello afecta a la rentabilidad del fármaco. La empresa farmacéutica anticipará la situación y decidirá si vale la pena situar en el mercado un medicamento estratificado o no. Además, según sea la perspectiva (pacientes o industria), las preferencias por un punto de corte diferirán y también según el tratamiento.

You'll find the details inside the chapter. 

 

Prioritizing population health or the economy

 Economics in the Age of COVID-19

Open access book by Joshua Gans. Must read. Controversial. Telling.

Forget false dilemmas, health vs economy.

The starting point is to understand that at any given point in time, there is only so much we can produce. Broadly speaking, if we want to have better public health outcomes, we need to take resources from elsewhere and so we can imagine that we get less of other stuff – which we would broadly call ‘the economy.’ What makes these trade-offs easy to grasp is that when we talk about producing some more public health, we can then think about how much less of the economy we get. Moreover, we are also confident that as we push for each extra bit of health, the more of the economy we have to give up each time. So, if our public health is poor, it is relatively ‘cheap’ (in terms of a reduction in the economy) to get more of it. When our public health is already prioritized, pushing the system further to gain even more health is relatively ”expensive” in terms of reductions to the economy. Thus, we do end up balancing and we don’t have the best imaginable public health outcomes because, frankly, we have decided not to pay the price. (In the technical interlude at the end of this chapter, I put all of this discussion in graphical terms that might be familiar to an Econ 101 student – the production possibilities frontier. You can delve into that or skip as you see fit.)

One reason a pandemic is awful is that it constrains even further what we can do with our scarce resources. We can neither sustain the level of the economy we had before without a decline in public health or vice versa. That in of itself would not pose an issue for our ability to fine-tune. Instead, there are two factors that fundamentally mean that we can no longer fine-tune and instead face a choice between prioritizing public health or the economy without the ability to balance those choices. Those two factors are (1) that a pandemic hollows out our ability to maintain the same balance between health and the economy and (2) that our choice of priority changes our options going forward; that is, they can drift.

Let’s begin with hollowing out. Recall that our ability to obtain our current balance of health and the economy is that we recognize that having a little more health or a little more economy is not worth the price in terms of what we give up for each. Absent other innovations – say a vaccine or, as I will discuss later, testing – the way to achieve our previous level of public health in the face of a pandemic is to socially distance. That means that we cannot physically interact with one another and, therefore, to a very large extent, we can no longer produce the economic outcomes we once could.

The problem is that the pandemic now changes the price of obtaining a little improvement in the economy. In order to do that, we must now give up a large degree of health. Being able to have slightly larger groups of people interact or have a few workplaces open poses a potentially high risk to public health because of the way a coronavirus might spread. Put simply, the option of sacrificing a little public health for having a little more economy is no longer open to us.

This also works on the flip side. One option with dealing with a pandemic is simply to ignore it and let life go on as usual. The hope from that plan would be to maintain the economy at its previous level, see the virus spread through much of the population, hope not too many people die and have a one to two-year large decline in public health. This was sometimes referred to as allowing the virus to ‘burn through’ the population. Even here the ability to fine-tune is compromised. You might want to achieve a slightly smaller loss of life from the pandemic but find now that the price of doing that, as even that would require a large amount of social distancing, has become very high.

Hollowing out means that you no longer want to maintain the same balance of the economy and health as you did previously. Instead, the ‘best’ choices are to prioritize one or the other. To be sure, there is a trade-off but no longer can you dial up a little bit more of this and a little bit less of that, you either prioritize the economy or you prioritize public health. You don’t want to try and do both.

  PS: update and subscribe alerts to the new twitter account @econsalut

The bioethics horizon

 What's on the horizon for bioethics?

The Nuffield Council of Bioethics released a short overview of current critical issues. It is really helpful.

Against Labor Tax funding for health

The Case Against Labor-Tax-Financed Social Health Insurance For Low- And Low-Middle-Income
Countries

Adam Wagstaff laid out a strong case against labor-tax financing for health insurance,

based on analyses of the potential revenue generation, the distributional implications, the impact on the labor market, and the potential for universality in service coverage

A key problem with labor-tax social health insurance is that it can actually redistribute resources toward the wealthy, not the poor. This occurs when general revenues subsidize labor-tax social health insurance institutions that predominantly serve upper-income groups instead of having those subsidies be used to extend coverage to the rest of the population. When expenditures on health care for the eligible workers in the formal sector—already higher than expenditures for the general population—exceed their contributions, the resulting subsidy is financed through taxes levied on the entire population (for example,value-added taxes), which is a form of upward redistribution.

 

Improving risk adjustment with reinsurance

 Very high and low residual spenders in private health insurance markets: Germany, The Netherlands and the U.S. Marketplaces

The high degree of persistence in membership in the extremes of the residual spending distribution in all three countries raises concerns that insurers might take steps to deter those who tend to be underpaid and attract those who tend to be overpaid. Attracting the healthy/deterring the sick among subsets of the populations with the disease indicators (such as diabetes) prevalent on both extremes of the residual spending distribution could be a highly profitable strategy, and potentially lead to distortions in the efficient care for these groups. In response to these findings, we proposed a form of reinsurance, based on residuals, and targeted to members of a “risk pool” defined on past-year very high undercompensation. Careful targeting (along with re-estimating the beta weights in risk adjustment to take into account the reinsurance payments) leads to very substantial improvements in overall fit of payments to spending, with especially large effects for the most extremely under- and overcompensated. The share of people affected by this form of risk sharing is very small, less than 3 in 1000 in all three countries. While our proposed policy seems effective in better tying payments to spending, there are alternative approaches to the same issue. One example would be to find ways to split groups like those with diabetes and other illnesses prevalent among the undercompensated into those likely to be on one or the other side of the residual spending distribution. Calling attention to the powerful effects members of the tails of the residual distribution have on the overall fit of the models is the first step in directing policy attention to these important groups.

 

Share of spending on drugs by residual spending groups

Compression of morbidity

Revisiting Compression of Morbidity and Health Disparities in the 21st Century

Age, Socioeconomic Status, and Health

Compression of morbidity:

an ideal population health dynamic in which people live long, healthy lives with declines in physical and cognitive health associated with senescence “compressed” into a short time period at the end of life.

 There are many ongoing debates in the compression of morbidity literature, including which measures of morbidity should be prioritized and the best methodological approaches for life course analyses that adequately address the thorny challenge of isolating age, period, and cohort effects. Even so, the extant research literature regarding compression of morbidity in the United States suggests that it is not happening at the population level.[3] However, much of the published research does not stratify analyses by socioeconomic position, as House and colleagues had recommended in 1990. House, Lantz, and Herd’s 2005 longitudinal analysis of ACL data affirmed that those in the highest income and education groups were significantly more likely to postpone physical functional impairments until much later in life