{"id":34995,"date":"2020-03-30T11:31:31","date_gmt":"2020-03-30T18:31:31","guid":{"rendered":"https:\/\/pocketwisdominsights.com\/pwicolab\/?p=34995"},"modified":"2020-06-25T12:03:24","modified_gmt":"2020-06-25T19:03:24","slug":"a-meaningless-metric","status":"publish","type":"post","link":"https:\/\/pocketwisdominsights.com\/pwicolab\/healthcare\/patient-doctor-health-co-lab\/third-party\/a-meaningless-metric\/","title":{"rendered":"A Meaningless Metric"},"content":{"rendered":"

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Opening Insights: The Numbers, By Themselves...<\/h2>\n

You are not entitled to your opinion.
You are entitled to your informed opinion.
No one is entitled to be ignorant.<\/em>
HARLAN ELLISON<\/p>\n

The United States faces an event of historical proportions with the outbreak of the coronavirus<\/em> which causes the COVID-19 disease<\/a>. Media sources and public officials alike have so much to say about the pandemic situation that threatens our health, security and economic stability, but do any of them really understand the MEANING of the numbers<\/em> they cite?<\/p>\n

\u201cThe numbers are almost meaningless,\u201d says Steve Goodman, a professor of epidemiology at Stanford University.  <\/p>\n

There is so much more than meets the eye when it comes to the numbers, percentages and comparisons being cited to describe a nation's relationship to the COVID-19 outbreak. Could it be that we still aren't asking the right questions?<\/p>\n

NOTE: coronavirus and COVID-19 are not interchangeable terms<\/strong>. Coronavirus describes a family of viruses that cause illnesses that range from sever diseases to the common cold<\/em>. COVID-19 is a specific disease that may occur as a result of contracting the coronavirus. This relationship is similar to how HIV (virus) causes the AIDs disease and how rubeola (virus) causes the measles disease.<\/p>\n

The proper name of COVID-19 is, \"severe acute respiratory coronavirus 2\" \u2013 or SARS-CoV-2. However, due to the alarm that would come from using the name \"SARS,\" WHO chose a less auspicious title (disambiguation provided by Health24.com<\/strong>)<\/a>.
https:\/\/www.health24.com\/Medical\/Infectious-diseases\/Coronavirus\/what-is-the-difference-between-covid-19-and-coronavirus-20200303<\/a><\/p>\n

Informational Insights: Numbers Don't Lie<\/h2>\n

The following article was published by Bloomberg, a \u201cglobal leader in business and financial data, news and insight[.]\u201d It was written by Faye Flam, a Bloomberg Opinion columnist. <\/p>\n

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It doesn\u2019t matter that the United States surpassed China this week in reported Covid-19 cases because those numbers (83,507 and 81,782 respectively as of March 26) don\u2019t tell us how many people actually became infected in either country. Nor do they tell us how fast the disease is spreading, since only a tiny portion of the population in the United States has been tested.<\/p>\n

\u201cThe numbers are almost meaningless,\u201d says Steve Goodman, a professor of epidemiology at Stanford University. There\u2019s a huge reservoir of people who have mild cases, and would not likely seek testing, he says. The rate of increase in positive results reflect a mixed-up combination of increased testing rates and spread of the virus.<\/p>\n

We will need more complete data, smarter data and more coordinated data to communicate something meaningful about the extent of Covid-19 in the United States, how many people are likely to die, which hospitals are likely to be swamped and whether drastic changes in the way Americans live will start to slow down the spread of the virus.<\/p>\n

With a population of 1.5 billion people, China\u2019s some 80,000 cases look like a rounding error, says Nigam Shah, an assistant professor of biomedical statistics at Stanford. And India\u2019s claim of some 754 cases probably reflects a severe lack of tests \u2014 not that the disease there is still so rare. The positive tests say little about how many people are dying or will die, since most cases are mild.<\/p>\n

What should we be watching instead? One possibility is hospitalizations. That idea was put forward by<\/a> statisticians Jacob Steinhardt, an assistant professor from UC Berkeley, and Steve Yadlowsky, a graduate student at Stanford who specializes in analyzing health care data. They argue that rate of increase in hospitalizations<\/a> could reflect the growth of the disease without being distorted by changes in the testing rate.<\/p>\n

Measuring death rates can eventually track the speed with which Covid-19 is spreading \u2014 as deaths represent a fraction of cases. But there\u2019s a lag of some three weeks between infection and death. Hospitalizations give an intermediate point, as Steinhardt and Yadlowsky explain: They estimate that it takes between 11 and 14 days for someone to get sick enough to show up at the hospital. Rates of increase in Covid-19 patients admitted to the ICU can provide additional useful data.<\/p>\n

These numbers might not accurately reflect the growth of the disease, however, if the hospitals or their ICUs become overwhelmed, start turning people away or raise the threshold for how sick you have to be to be admitted.<\/p>\n

But collecting this kind of data can help prevent that from happening, said Stanford\u2019s Shah.<\/p>\n

If we all behave responsibly, he says, then we can turn what would have been a hospital capacity problem into a logistics problem. Once you have a handle on the rate of new Covid-19 patients admitted to hospitals and ICUs, you can start to forecast how many more will arrive in coming days.<\/p>\n

Stanford\u2019s Goodman said that he\u2019s confident scientists will eventually collect the data we need to understand this pandemic and how it\u2019s playing out in the United States. \u201cRight now we are floundering in a sea of ignorance about who is infected and the fate of people who are infected,\u201d he says.<\/p>\n

Though death rate figures of around 1% have been tossed around, Goodman says he\u2019s skeptical that anyone knows the death rate of this disease since we don\u2019t know the true rates of infection.<\/p>\n

And we can\u2019t identify the most vulnerable groups. \u201cThere\u2019s this delusion being disseminated that it\u2019s all about age,\u201d he says. He thinks that since 95% of deaths to date in New York City were of people who had pre-existing conditions, this is the bigger risk factor. But since age is a risk factor for many of those conditions, the two are correlated.<\/p>\n

He could figure it out if he could get data on pre-existing conditions broken down by age, but says the New York health department won\u2019t release that data. It matters a lot, he says, since we\u2019re shaping policies around who is most vulnerable. We should find out who they are. They should know who they are.<\/p>\n

Some other useful data could easily be collected at testing sites. As doctors Farzad Mostashari and Ezekiel Emanuel pointed out last week in STATnews<\/a>, health departments should tally not just positives but total tests, and record demographic and symptom information on all the test takers. Much of that isn\u2019t collected or coordinated.<\/p>\n

Random sampling would help too, agree both Shah and Goodman, to estimate the number of mild or asymptomatic cases and get at the true total. And then there\u2019s the promise of widespread antibody testing<\/a>, which could reveal how many people in a given sample had been infected in the past.<\/p>\n

With attention to the right kinds of data, scientists can soon assess whether lockdowns and social distancing efforts are slowing the rate of spread in the United States. Any dent we\u2019ve made in new infections should start to show up in data on hospital admissions in a week or two.<\/p>\n

Trump promised Americans we could ease up on restrictions by Easter, while most scientists would like to wait until they can base such changes on evidence. Goodman says at this point, figuring out what to do next is like building an airplane in the air. In a later phase of the pandemic, we might be able to focus more on mass testing and quarantining people known to be sick or exposed. We probably can\u2019t responsibly stop lockdowns by Easter, but we may know enough by then to start to think about the timing and nature of an exit strategy.<\/p>\n

https:\/\/www.bloomberg.com\/opinion\/articles\/2020-03-28\/confirmed-coronavirus-cases-is-an-almost-meaningless-metric<\/a><\/cite><\/p><\/blockquote>\n

This article originally appeared on March 28, 2020 on BLOOMBERG<\/em>: Confirmed Coronavirus Cases Is an \u2018Almost Meaningless\u2019 Metric<\/a><\/p>\n

Possibilities for Consideration: Figures Never Lie, but Liars Figure<\/h2>\n

If I were to tell you that 36,560 died in the U.S. in 2018 as a result of motor vehicle deaths vs 1,922 in Canada, would that provide any insight into how safe the U.S. transportation infrastructure is?<\/p>\n

If I were to tell you that 29% of Americans identified as Democrat, 30% identified as Republican and 39% as Independent (Gallup<\/a>), would that provide any insight into President Trump's effectiveness as a Commander in Chief?<\/p>\n

Sure, we all want to have solid figures that we can trust <\/strong>so there is a rock to cling to<\/em> in these uncertain times, but it's foolish to trust something just because we want to<\/em>. It is every individual's responsibility to seek the truth and to question the elements that shape their lives.<\/p>\n

So, next time a media source or public official offers a statistic to tell their story, remember to do your part, question the data, question their intent and do your homework<\/em>.<\/p>\n

Question everything.<\/em>
SOCRATES<\/p>\n

(Explore a PWI blog, The Numbers Just Don't Add Up<\/a><\/strong>, to learn more about the research behind the numbers)<\/em> <\/p>\n

Add Your Insight<\/h2>\n

Take a moment and examine\u2026<\/p>\n