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.