The p53 nightmare

p53 and Me

This week you'll find a short piece in NEJM, a story written by a physician on how detecting a genetic p53 mutation changed her views. Key message:

Genetic knowledge is power only if both clinician and patient are equipped to move beyond a result and toward action, even if that merely means living well with what we know. I believe we need an expanded definition of genetic counseling; we require more data, yes, but also more sophisticated and sensitive ways of assimilating such data. And not just into databases we can mine to see what happens to people like me, but into programs for learning to live with uncertainty.

A must read

The spanish flu, a century later

Pale Rider: The Spanish Flu of 1918 and How it Changed the World

Laura Spinney has made a great job with her latest book "The Pale Rider". For those that are interested on the largest recent epidemy and public health crisis -the spanish flu of 1918,- this is the book to read. I enjoyed specially the details of what should be avoided, and nobody cared about it. The conflict between religion and medicine. You'll not get the precise number of deaths, but it was an enormous tragedy in social terms.
The book also explains how physicians were exposed to the disease without any tools and how it was arriving to the remote and less inhabited places of the world.
It is specially helpful to recognise how vulnerable are all of us, still now. Highly recommended.

PS. You'll find it also in spanish, "El jinete pálido"

Theranos: a massive fraud and epic failure

The Theranos deception on CBS News 60 Minutes

The weirdest health financing system of the world (2)

Tracking Universal Health Coverage: 2017 Global Monitoring Report

If I had to summarise the best outcome of health policy in the last century in western countries, I would say mandatory health insurance. No doubt. And the joint report by WHO and WB reminds us that there is still a long way to achieve such goal for the whole population in the world.  Mandatory insurance is the most efficient way to solve the failures of the health insurance market. We al know the details and difficulties that arise as a result of information asymmetries and opportunistic behaviour.
Therefore the recommendation is clear, for those that already have a mandatory system, keep on it. This is precisely what hasn't happened here. In 2012 the system changed from universal towards a social security based membership funded by taxes. The weirdest health financing system of the world.

Maya Fadeeva with Club des Belugas

Measuring morbidity in large populations

MODELIZACIÓN ECONÓMICA DEL AJUSTE POR RIESGO DEL GASTO SANITARIO PER CÁPITA SEGÚN MORBILIDAD EN LA COMUNIDAD VALENCIANA

There is a unique study on measuring morbidity in a large population. In Valencia (4.7 million inhabitants) the Clinical Risk Group classification system has been applied. And you may find the results comparing the whole population, and one Department (Denia). The study shows details about the utilization and costs related with morbidity. Interesting application that replicates former ones. Epidemiologists, clinicians, policy makers and managers should be interested in using these approaches for taking decisions.

Pablo Picasso, by Arnold Newman

Changing the production function of diagnostic tests

Next-generation diagnostics with CRISPR

Last week while reading Science I noticed a short and crucial article. Up to now CRISPR technology was focused on gene editing, now we can say that its usefulness is widening into diagnostics. It may change completely molecular diagnostics of "infectious diseases through detection of Zika virus (ZIKV), Dengue virus (DENV), and human papillomavirus (HPV) in human  samples, and noninfectious diseases, such as detection of gene mutations in circulating cell-free DNA from lung cancer patients." The production founction of lab testing would change completely.
Several articles explain details about it. The fight for patents is going to start again on CRISPR diagnostics. And this are unfortunately bad news.
Anyway, Science article reminds us:

These emerging diagnostic tools will by necessity be compared to standard diagnostics to ensure sensitivity and specificity and will need to be field-tested to guarantee performance in patient care settings, as environmental conditions and end-user application might affect performance. Proven assays, if affordable, promise to improve care in resource-limited settings where undifferentiated febrile illness is the norm and where gaps or delays in diagnosis, targeted care, and infection control contribute to infectious disease mortality and spread.

More details in The Verge.

Cost-effectiveness of genome sequencing (2)

Application of next-generation sequencing to improve cancer management: A review of the clinical effectiveness and cost-effectiveness

If you want to go deeper on the issue, have a look at this article. It is focused on one disease, cancer and tries to combine clinical effectiveness and cost effectiveness. Sounds good. At the end you'll see that the number of available studies is limited (6), but that's the situation and these are the conclusions:

We report the rate of successfully detecting mutations from the clinical studies. The incremental cost-effectiveness ratio and sensitivity analysis outcomes are reported for the cost-effectiveness articles. Fifty-six articles reported that sequencing patient samples using targeted gene panels, and 83% of the successfully sequenced patients harboured at least 1 mutation.

 In our evaluation of the effectiveness of NGS, we found that NGS is effective at identifying mutations in cancer patients, and we reported that 37% of the diagnosed patients proceeded to receive therapy matching their genetic profile. However, with only 6 articles available that assess the cost-effectiveness of NGS in various settings, it remains an area for future research to determine whether the technology is cost-effective in routine cancer management.

PS. Today this blog has surpassed its 200.000 visits. That's great! Thank you for your loyalty.

Sally Mann, On the Maury, 1992, gelatin silver print, Private collection.

Washington National Gallery, current exhibition

Cost-effectiveness of genome sequencing

Are whole-exome and whole-genome sequencing approaches cost-effective? A systematic review of the literature

It is quite difficult to talk about value in genetic tests without any reference to analytical validity, clinical validity and clinical utility. Once these three issues are appropriately solved, then we need to assess costs. Cost effectivenes makes sense once this three steps are covered successfully.
An analysis of cost-effectiveness of whole genome/exome sequencing it sounds too generic if there is no reference to specific baseline that allows to estimate incremental cost-effectiveness ratios.
That's the reason why a recent article trying to review existing studies fails to achieve any conclusion.

The current health economic evidence base to support the more widespread use of WES and WGS in clinical practice is very limited. Studies that carefully evaluate the costs,
effectiveness, and cost-effectiveness of these tests are urgently needed to support their translation into clinical practice.

 Let's start focusing on the assessment of three key perspectives before entering into a black hole.

Diagnosing diseases and editing genes

CRISPR: The gene-editing tool revolutionizing biomedical research
The WIRED Guide to Crispr
A New Startup Wants to Use Crispr to Diagnose Disease

Take your time and read Wired and watch CBSNews. It will provide an exciting perspective of CRISPR. And just to end, imagine that diagnostic tests could be based on CRISPR while you read the last piece of WIRED.
We are closer than you may think.

Mental health: the problem and what can be done

THRIVE: How Better Mental Health Care Transforms Lives and Saves Money

I have to recognise it. Mental health is a difficult issue, and all the efforts to decrease its impact on individual and social welfare are not enough by now. The book by Layard and Clark is a useful reference. I had to read it since long time. It says:

Mental illness is the great hidden problem in our societies, so most people are amazed when they hear the scale of it. In the Western world today one in six of all adults suffers from depression or a crippling anxiety disorder. Roughly a third of households currently include someone who is mentally ill.

I don't know the exact figure, but I agree with the statement.

Mental illness is not just a problem for those it affects directly. It also imposes huge costs on the rest of society. So the case for tackling the problem is not just humanitarian—it is also a matter of plain economics. Mental health problems diminish work, increase crime, and make additional demands on physical health care.

So, what is the cost? The answer is huge. Layard and Clark provide some figures. And in the second part of the book, they review the alternative approaches to the issue. A highly recommended book by one of the greatest economists of our time.

PS Great Tribute to Uwe Reinhardt in NYT by Paul Krugman.

Medicine as a data science (2)

The Evolution of Patient Diagnosis: From Art to Digital Data-Driven Science

Currently medical diagnosis is driven by a standard way to proceed. We could say that the pattern of the decision flow has not changed for years.

A physician takes a history, performs an examination, and matches each patient to the traditional taxonomy of medical conditions. Symptoms, signs, family history, and laboratory reports are interpreted in light of clinical experience and scholarly interpretation of the medical literature.

Data availability, and specifically genetic data could change completely diagnostic process.

Initiatives to develop genetic reference data at the population level could be grouped into 3 categories.First are well-known databases of genotype-phenotype relationships
as observed and submitted by researchers (eg, Online Mendelian Inheritance in Man, ClinVar, and the National Human Genome Research Institute’s Genome-Wide Association Study [GWAS] Catalog). Second are databases, such as the Genome Aggregation Database (gnomAD), the next iteration of the ExomeAggregation Consortium (ExAC) database, and the 1000 Genomes Project, that aggregate sequences
collected from other studies for secondary use. Third, patients and other study participants are invited to donate data to registries like GenomeConnect or enroll
in cohorts like the National Institutes of Health All of Us initiative, which is recruiting 1 million patients to contribute biological samples and EHR data for research.

The reference to these databases is crucial to understand what's going on in US medicine, and how european medicine stands behind.
JAMA article develops the concept of Clinical Information Commons:

There should be a new compact between patients and the health system, such that captured data and biospecimen by- products of the care deliverysystem should be aggregated and linked to build a clinical information commons (CIC) to aid diagnosis

I agree. Saluscoop started as an alternative focused in this approach. As usual, the big question is: who is going to invest in a digital commons?. Unless governments take this initiative as a whole, the future of a data driven medicine is uncertain.

Adrian Piper: A Synthesis of Intuitions, 1965–2016

MoMA, New York, New York

Sat 31 Mar 2018 to Sun 22 Jul 2018

Equity and QALYs, terra ignota

Incorporating equity in economic evaluations: a multi-attribute equity state approach

Ptolemy used the term terra ignota for regions that have not been mapped or documented. QALYs were born for maximizing health, without any distributive considerations. All the efforts to introduce equity in QALYs have failed up to now. The cartography of QALYs has a pending dimension.
Maybe this dimension is not possible to be defined under a technical perspective, its a societal and policy issue. And at this level decisions are difficult to take.
Anyway, after reading this article you may reach a similar conclusion than mine, or otherwise you can be optimistic about it. It's up to you.

PS. Today I'll give the kenote speech at Col.legi d'Economistes de Catalunya: "La producció eficient i equitativa de salut".

Ai Weiwei

DROPPING A HAN DYNASTY URN

Man and machine, sharing the decision making effort

Big Data and Machine Learning in Health Care

From JAMA article

It is perhaps more useful to imagine an algorithm as existing along a continuum between fully human-guided vs fully machine-guided data analysis. To understand the degree to which a predictive or diagnostic algorithm can said to be an instance of machine learning requires understanding how much of its structure or parameters were predetermined by humans. The trade-off between human specification of a predictive algorithm’s properties vs learning those properties from data is what is known as the machine learning spectrum

 Higher placement on the machine learning spectrum does not imply superiority, because different tasks require different levels of human involvement. While algorithms high on the spectrum are often very flexible and can learn many tasks, they are often uninterpretable and function mostly as “black boxes.” In contrast, algorithms lower on the spectrum often produce outputs that are easier for humans to understand and interpret.

The meta-informational challenge of molecular data

The future of DNA sequencing

Where does DNA sequencing goes from here?. Nowadays, this is an appropriate question to pose.  The answer appears in an article in an interesting article in Nature.

Now, geneticists would like to have DNA sequences for everyone on Earth, and from every cell in every tissue at every developmental stage (including epigenetic modifications), in health and in disease. They would also like to get comprehensive gene-expression patterns by sequencing the complementary DNA copies of messenger RNA molecules.

In a mere 40 years, the central goal of putting molecular data about cells to practical use has changed from an informational challenge to a meta-informational one. Take clinical applications of genome-sequence data. It may soon be possible to use DNA sequencing routinely to analyse body fluids obtained for any clinical purpose. But only a vast amount of well-organized data about the multi-year medical histories of millions of people will provide the meta-information needed to establish when to ignore such data and when to act on them.