22 d’octubre 2020

Health impact of social isolation and loneliness

Social Isolation And Health

The lonely century

Social isolation and loneliness are both terms that denote a degree of social disconnection. Social isolation is an objective state marked by few or infrequent social contacts. Loneliness is the subjective and distressing feeling of social isolation, often defined as the discrepancy between actual and desired level of social connection.

Social connection and connectedness encompass a variety of terms used in the scientific literature (for example, social support, social integration, social cohesion) that document the ways that being physically or emotionally connected to others can influence health and well-being. 

In this HA Brief, the author explains that loneliness impact on health may be greater than we think. Rather than being alarming, she shows a description of the situation, the evidence and a proposal for policy agenda.

And if you are not convinced, than I would suggest this book:



21 d’octubre 2020

An unprecedented discovery



 

Mapping genetic regulation

The GTEx Consortium atlas of genetic regulatory effects across human tissues

A great step in research:
The GTEx v8 data release represents a deep survey of both intra- and interindividual transcriptome variation across a large number of tissues. With 838 donors and 15,201 samples—approximately twice the size of the v6 release used in the previous set of GTEx Consortium papers—we have created a comprehensive resource of genetic variants that influence gene expression and splicing in cis. This substantially expands and updates the GTEx catalog of sQTLs, doubles the number of eGenes per tissue, and saturates the discovery of eQTLs with greater than twofold effect sizes in ~40 tissues. The fine-mapping data of GTEx cis-eQTLs provide a set of thousands of likely causal functional variants. While trans-QTL discovery and the characterization of sex- and population-specific genetic effects are still limited by sample size, analyses of the v8 data provide important insights into each.

Fig. 1 Sample and data types in the GTEx v8 study.
(A) Illustration of the 54 tissue types examined (including 11 distinct brain regions and two cell lines), with sample numbers from genotyped donors in parentheses and color coding indicated in the adjacent circles. Tissues with 70 or more samples were included in QTL analyses. (B) Illustration of the core data types used throughout the study. Gene expression and splicing were quantified from bulk RNA-seq of heterogeneous tissue samples, and local and distal genetic effects (cis-QTLs and trans-QTLs, respectively) were quantified across individuals for each tissue.




20 d’octubre 2020

On being honest in politics

 Eliciting preferences for truth-telling in a survey of politicians

From PNAS: 

Voters who would like to accurately evaluate the performance of politicians in office often rely on incomplete information and are uncertain whether politicians’ words can be trusted. Honesty is highly valued in politics because politicians who are averse to lying should in principle provide more trustworthy information. Despite the importance of honesty in politics, there is no scientific evidence on politicians’ lying aversion. We measured preferences for truth-telling in a sample of 816 elected politicians and study observable characteristics associated with honesty. We find that in our sample, politicians who are averse to lying have lower reelection rates, suggesting that honesty may not pay off in politics.

Therefore, in this case, truth-telling is a dominant non-rational strategy in politics. We still do need more evidence. Maybe the next presidential election will provide us a confirmation of this article.

Fact-Checker from WP

Bill Brandt at KBR-Mapfre Barcelona


19 d’octubre 2020

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.




 

17 d’octubre 2020

The burden of disease around the world

Global Burden of Disease (GBD) 2019

The Lancet latest issue: 

The potential to improve health by risk reduction is well reported in GBD 2019. All risks quantified in GBD collectively account for 48% of global DALYs. Exposure to many risks highly correlated with SDI has been steadily decreasing as global SDI has increased, including household air pollution; child growth failure; and unsafe water, sanitation, and handwashing. Additionally, there have been notable decreases in exposure to smoking. Figure 4 shows the annualised rate of change in exposure from 2010 to 2019 for select risk factors ordered by global attributable DALYs. Among the 15 leading causes of attributable DALYs shown, high systolic blood pressure, high fasting plasma glucose, high body-mass index (BMI), ambient particulate matter pollution, alcohol use, and drug use stand out because rates of exposure are increasing by more than 0·5% per year.

 Success in reducing the disease burden from causes of communicable, maternal, neonatal, and nutritional  CMNN diseases by global collective action to fund key programmes should be celebrated. Catch-up social and economic development is fuelling more rapid health progress in the lower socio-demographic index (SDI) quintiles. But there is reason to believe that, although the past 70 years have largely been a story of sustained improvements in health, rising exposure to crucial risks, such as high BMI, high fasting plasma glucose, and ambient particulate matter pollution, as well as stagnant exposure to many other behavioural risks, including diet quality and physical activity, might attenuate progress. Most alarmingly, the mortality decreases in cardiovascular diseases of the past half a century have slowed substantially, or even reversed, in some nations with high SDI. 

Good news!


 

Check the resource center.

16 d’octubre 2020

The Bio Revolution has alredy started

The world is at a “special moment” as revolutionary advances in biology converge with progress in computing automation and Artificial Intelligence (Matthias Evers, Senior Partner at McKinsey & Company)


The Bio Revolution / Reshaping the EU’s industrial strategy, from competition to trade policy