To the Editor:
As currently used by CDC, NIH and FDA the word is used to imply unreliability and therefore by implication invalid information. The legal profession uses this term in a similar manner to attack information it doesn’t want a court to consider in a legal case.
On the other hand, business managers are often highly reliant on anecdotal information to make extremely important, high-risk, rapid decisions. And those who are successful at this function receive promotions (or make millions and own lots of hotels). In the business world it is known that, if you wait to collect all the data you would like to have, the opportunity for success will have passed by you and you will lose. Decades ago I was taught that “ready, aim, fire” doesn’t work because it takes too long to collect enough data to be certain your aim is correct. Instead we were taught, “ready, fire, aim, fire, aim, fire, aim, etc.” constantly collecting data and refining our response.
We could be saving lives and keep many more people from experiencing serious infections solely by telling the FDA to stop playing their well known delay tactics caused by government law and regulations, and by legal tort case law, and approving hydroxychloroquine, plus zinc and an antibiotic for early treatment of the virus.
It is morally reprehensible that public employees and politicians are blocking and/or slowing the use of this drug combination while they wait for positive proof. In the mean time they are causing people to suffer unnecessarily and some of those people die, when they could have been saved!
I’d postulate that the current data set, being used to drive the most serious life and economic altering decisions our country has every faced, is not only insufficient for the intended task, it has been intentionally biased toward promotion of a single predetermined decision path.
Unfortunately, we have been preconditioned by political pollsters to accept this information as being credible. Routinely, after polling a biased sample of 1,000 to 2,000 people a pollster issues their report that x percent of people support xyz, thus by implication declaring that this reflects the thoughts of 330,000,000 people. The media picks up the poll and, for example, then “factually reports” that President Trump will loose the election seven months from now.
It is time to seriously question the data that is publicly available. (I’m using data available from the Florida Health Department’s web site as of April 11, 2020 at 5 p.m.)
Number of tests: 175,834 out of a population of approximately 22 million, plus unknown non-residents. This implies that we have tested .8 percent (.00799) of the population.
Testing distribution by age group, race, nutritional health, behavior (for example drug use), etc.: not available
Number of Positive Tests: 18,986 that is .086 percent (.00086) of the population.
Number of those with serious pre-existing conditions, or high-risk life behavior problems such as drug use: unknown.
Number of Deaths: 446 that is .002 percent (.0000203) of the population.
Number of those with life-threatening, pre-existing conditions, etc.: unknown.
Number of deaths reported during the same time period with causes other than the virus: unknown, however, potential zero because the CDC has manipulated the reporting rules.
Number hospitalized: this is a meaningless number because it is cumulative, not the number of people currently in a hospital.
Who is being counted? Everyone? Only those who test positive? Number currently hospitalized: unknown.
Number discharged from hospitals: not reported.
Number who have recovered from the virus: not reported.
We should ask questions about these data sets to determine whether or not this information is meaningful and valid for use in making informed decisions. This is highly important because State and Federal governments have taken drastic actions with long-term impact that effect the lives of 21,817,151 people who have not been tested.
The primary question should be whether or not the data is statistically reliable. Statistical reliability is dependent upon sample size and unbiased sampling. I’m certain in many situations it can be argued and probably proven that a 1 percent sample size is highly reliable.
However, in this instance the data totally fails to be statistically reliable because the CDC testing rules have established that the sample population is 100% biased towards those individuals who exhibit moderate to severe symptoms. That data is not valid for any purpose requiring extrapolation into a likely measurement of the general population. On this basis alone, no population wide decision should be made based on this data.
But, then of course the counter argument has been that there are a lot more cases we haven’t been able to test, plus there are all those unknown asymptomatic people who might cause someone else to become serious ill. These are perhaps true statements, but, also misleading and meaningless. Why? They are based on the assumption that, if 10 percent of the tests are positive for the virus, then some great percentage of the total population must have the virus. We start with a biased sample and extrapolate backwards into a false conclusion about the whole population. No efforts have been made to validate the assumptions through the collection of anecdotal information, or prove them by scientific methods.
So here is an anecdotal conclusion, which perhaps could be validated by contacting the major grocery store chains in Florida. If this backward extrapolation were true then we should be seeing lots of reports of grocery stores closing, either short-term for major decontamination or long-term, because 10 percent of their employees have become moderately or seriously ill with the virus. I haven’t read a single report of problems in grocery stores, or pharmacies, or big box stores like Wal-Mart. I believe this is anecdotal evidence that the assumptions are false and the shutdowns are not necessary.
Significant missing data:
As identified above there is a lot of missing data, which inhibits any ability to micro- analyze the population of infected people and reach any meaningful conclusions.
Instead of searching for answers we keep jumping to the most adverse negative assumptions. Why? Is this because of the misleading reporting methodology and the lack of proper data?
Perhaps one of the serious breaches of public trust in this area are the CDC guidelines essentially instructing hospitals to report almost all deaths as being virus related.
We do not know how many people have tested positive. That is a true statement. Everyone is assuming and making decisions based on the number of reported tests. This is a false assumption. Read the footnote which states: “People tested on multiple days will be included for each day a new result was received.” All that is being reported each day is the number of tests and their results. Individuals are not tracked from one day to the next.
I don’t know the current test accuracy. I’d assume it something better than the original CDC 40 percent accuracy, but it is not 100 percent accurate.
There are less people, perhaps many less people, seriously sick from the virus. We’ve tested a lot less people than assumed by looking at the reported data. And there are a lot less people seriously ill from the data than what is assumed by looking at the reports.
Imagine a sick person with symptoms who calls their doctor. On how many days are they repeatedly tested without showing a positive? Then they finally show a positive test and stay at home in self-isolation. Then they become worse and go to a hospital where they are tested again. Then later after appearing to be getting better they’re tested again, but are still positive. Then even later they’re tested again and appear to no longer have the virus. How many times does that single individual add to the number count of negative and positive test results? Somewhere between one and perhaps five, six or more times.
It is conceivable that Florida doesn’t have 19,000 confirmed cases of the virus, but perhaps only 9,000 or 6,000.
Again we see that the problem is being grossly overstated.
The primary reporting and media focus has been on cumulative number of tests, positive tests, and hospitalization, rather than the number of people who are seriously ill with the virus, and their percentage of the population. This is highly bias, misleading and inflammatory.
I have yet to see much useful timeline data. However, looking at what data is available from Florida it is not apparent that shutting down the state’s economy accomplished anything beneficial.
Our governments are making life-altering decisions based on biased data, missing data, false assumptions and probable false conclusions. The economy is within hours of being destroyed for the next 10 years due to this fear-mongering.
It is too late to wait for the data we should have already had. It is very obvious the economy must be reopened now.
The latest media hot item is to discuss the reports that more black people have moderate or severe cases of the virus, and then using this to make all types of unsubstantiated claims.
However, important information is lacking that might support any conclusions. We don’t know important information including: The number of black people tested; the number with pre-existing conditions; their nutritional health; the number who are using and/or abusing drugs or alcohol; their geographic distribution, and many more unknown variables. And, why aren’t we tracking Asians separately in great detail. Before the CCP began censoring scientific reports from their country, Chinese researchers reported that 80 percent of Asians had the specific DNA string that the virus attacks, while the percentage in some other races was only 50 percent.
I’d speculate there might be societal factors, which may be the primary cause of more cases appearing among big-city black populations. For decades many black people have been told, “don’t trust the Man,” meaning white people, white politicians and white dominated media. Perhaps the real cause is that, where there are large concentrations of blacks, they have not been receiving or believing the reports or following the social distancing recommendations.
We might be able to begin analyzing the assumptions and various conclusions if the data enabled the comparison of black people living in inner cities with black people living in mixed-race suburbs.
Regardless there are way too many unknowns to be reaching any conclusions regarding the impact of the virus based on race.