“…elderly people and those with chronic ailments are extremely vulnerable to COVID-19. Furthermore, the disease is highly transmissible, which means it could spread like wildfire and overwhelm hospitals without extraordinary measures to contain it. This would greatly increase its death toll.
However, such precautionary measures often have economic and other impacts that can cost lives, and overreacting can ultimately kill more people than are saved.”
The facts show that:
The death rate for people who contract COVID-19 is uncertain but is probably closer to that of the seasonal flu than figures commonly reported by the press.
The average years of life lost from each COVID-19 death are significantly fewer than common causes of untimely death like accidents and suicides.
The virus that causes COVID-19 is “very vulnerable to antibody neutralization” and has limited ability to mutate, which means it is very unlikely to take lives year after year.
If 240,000 COVID-19 deaths ultimately occur in the United States, the virus will rob about 2.9 million years of life from all Americans who were alive at the outset of 2020, while accidents will rob them of about 409 million years—or about 140 times more than COVID-19.
The article below details the reasons why we need to get data right; and even more so with metadata. Because the combined effect of compiling (bad or inadequate) data does not make it more reliable; it likely compounds the error.
This is particularly problematic in scientific academy within the areas of medicine and nutrition research, as John Ioannidis has clearly exposed in his work.
Scientific data can go “absent without leave” for a number of different reasons:
Scientists don’t archive their data properly and they lose track of it, can’t make sense of it, or their hard-drive dies and they don’t have a back-up. This happens surprisingly (and embarrassingly) often.
Scientists begin a study but abandon it before it is completed due to lack of funds, unpromising preliminary results, or other priorities. The data might be useful in combination with data from other studies, but it’s not publishable on its own.
Scientists selectively publish data that supports a particular theory. Inconvenient data are quietly forgotten.
Scientists try and publish data but are unsuccessful because the results aren’t considered interesting enough by the scientific journals.
Knowing how difficult it will be to publish a null result, scientists prioritise writing up studies that gave them more publishable results.
The end result is what’s become known as the “file drawer problem”. The published scientific literature represents only a small and biased sample of the research that has actually been conducted. The rest is stuffed away at the back of the metaphorical filing cabinet.
There’s a lot of wasted effort here — data collected and then not used. But the bigger problem is the bias in what is published.
The drug is currently approved for malaria and also for rheumatoid arthritis and systemic lupus erythematosus, which is its main use in the U.S. It’s therefore available to be prescribed off-label, and some clinicians have already said they’re using it on COVID-19 patients. But neither Hahn nor other task force members addressed whether enough hydroxychloroquine is on hand to treat large numbers of coronavirus cases. Convalescent plasma is another treatment the FDA is considering for COVID-19, said FDA Commissioner Stephen Hahn, MD.
Convalescent plasma and the immune globulin that it contains is another possible treatment the agency is considering, Hahn added. “FDA’s been working for some time on this,” he said. “If you’ve been exposed to coronavirus and you’re better — you don’t have the virus in your blood — we could collect the blood, concentrate that and have the ability, once it’s pathogen-free, to give that to other patients, and the immune response could potentially provide a benefit to patients. That’s another thing we’re looking at; over the next couple of weeks, we’ll have information and we’re really pushing hard to try to accelerate that.” Such treatments have been effective in Ebola, for example.
The claim that medical errors are the third leading cause of death in the US has always rested on very shaky evidence; yet it’s become common wisdom that is cited as though everyone accepts it. But if estimates of 250,000 to 400,000 deaths due to medical error are way too high, what is the real number? A study published last month suggests that it’s almost certainly a lot lower and has been modestly decreasing since 1990.
However, as America’s founders attested so vehemently, rights are at the core of social interactions and government, violations of which can justify revolution. And unlike the physical sciences, where the goal of language is precision, in the social sciences, the language (and thus analysis) is often quite vague and inconsistent (e.g., current versions of “social justice” are inconsistent with the traditional meaning of “justice”), making clear communication, much less clear analysis, far harder.
What is the upshot of all this? Economics is not like physical sciences, and reasoning and analogies based on them are often misleading in economics. Further, they can be dangerous to society, particularly in the mouths of those who wish to subject others to their command and control. That is why Friedrich Hayek wrote,
“The curious task of economics is to demonstrate to men how little they really know about what they imagine they can design.”
In other words, economics is a science whose principles and logic tell us why we cannot know enough to control people, even if we do know enough to control rockets.
My teaching session with the medical student at the end of the day included a discussion about patient care decisions and recommendations that go beyond ticking quality boxes and following the latest guidelines. Initially, I felt as if I was rationalizing my delivery of suboptimal care and began to doubt myself.
However, the quality reports I receive each month do not capture the complexity of many patients’ lives.4 These reports fail to reflect the individualized and shared decisions made between a patient and her physician who have known each other for 15 years; the proprietary quality score calculation formulas do not adjust for the healing power of relationships.5 Amid the mounting evidence that primary care saves lives,6 our health care system does not (yet) have a population health analytics tool that captures and tracks the progress that she and I have made together in more than a decade. When will we create better systems with capabilities to measure the emergency department visits that were prevented, the stable housing that was obtained, the increased resiliency she has built into her life, her feelings of empowerment to be a better parent, the reduction in her self-destructive behaviors, and the trusting relationship we have built over time?
Adults who drank two to three cups of filtered coffee a day (the highest quartile of filtered coffee–metabolite score) had a 58% lower risk of developing type 2 diabetes within 10 years than those who drank fewer than one cup of filtered coffee a day (lowest quartile) after adjusting for multiple confounders (odds ratio, 0.42; 95% confidence interval, 0.23 – 0.75).
The protective effect of drinking this high amount on the risk of developing type 2 diabetes was not seen with boiled coffee.
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