I was surprised when our Governor decided to extend his Statewide “stay in place policy”, but I was even more surprised to read Betsy Russell’s “Eye on Boise” article about various state and local officials two that I know who own their own businesses, who supported the Governor’s decision. I will attempt to get my arms around the new strategy for the continuation of “stay in place” and for the support given to that policy by people in government. I am sure if Betsy went out and interviewed restaurant owners and small business people employing fewer than 50 people, her answers would be very different. This is the very group along with their families and employees and their families, that legislators say they represent!
In statistics and logic, there are described several types of bias. Among these are selection bias, information bias, and confounding bias. I was first introduced to selection bias in church school in the 7th grade when we read Mathew: 7. The log in your own eye compared to the sliver in your neighbor’s. Within the last year, there have been several articles written in the general scientific literature admonishing clinicians, public health specialists, bench researchers, about the misuse of data, and the failure to offer alternative hypothesis when describing outcomes. I will not discuss outcome bias today, except as it pertains to the issues at hand.
The process of acquiring data can itself produce information bias, and over time this bias can become a self-fulfilling prophecy. The process of acquiring information needs to be constantly scrutinized so that preexisting prejudices regarding outcomes aren’t preordained. This idea could also be applied to the press using the same sources over years when both the source and the reporter have a preexisting bias.
The best example of selection bias extrapolated to the media would be in Betsy Russell’s article mentioned above. She got the results she knowingly or unknowingly selected for I think she sees the log in her own eye and doesn’t care.
Confounding bias occurs when two variables acting independently effect an outcome that is obscured because the relationship of the variables is not understood. In statistics and graphic theory an almost analogous concept is described by three independent variables theory and in chemistry by “collision theory” of molecules. The point is when outcomes are being evaluated the impact of how the independent events impact each other needs to be taken into account. An example of confounding factors effect on epidemiological studies would be the impact of body weight and co-morbidities on clinical and statistical outcomes. Is everyone given a CPT diagnostic code of COVID-19 dying of their disease or with their disease?
Are their social factors not being taken into account like proximity of living conditions, use of public transportation, and air travel and immunologic demographic abnormalities considered in an analysis and the formulation of mitigation strategies? Should the same mitigation strategy be used in Boise Idaho as in New York City? What about Ada and Blaine counties? Should local officials be given more say in how these strategies would be applied?
I bet the local Mayors in the Russell article would provide a different strategy to the problem especially if they routinely share morning coffee in a local restaurant with their buddies who owned small businesses in their municipalities, The restaurant owners and their customers would advise the local public officials very differently than the local public officials are advising the Governor—probably because they don’t see the Governor during the course of their everyday activities and they are more likely to be courting his favor than he would be wanting to court their vote for office—an issue of agency further discussed below.
So there are three questions that have not been asked by scientists or media that prove my above hypothesis especially as it pertains to selection and confound bias:
1. What happened to 500,000 doses of hydroxychloroquine delivered to NY State? Don’t know, but every health care provider I have talked to both civilian and military including RN’s, physicians, and technicians currently serving in the tristate area is taking hydroxychloroquine for prophylaxis. I have also been told that every patient since three days ago in NY City that has been diagnosed and admitted to the hospital with a COVID-19 code has been offered hydroxyl and I bet knowing the creativity of the drug market in NY City it is being offered on the street. Is it just social distancing that is causing the drop in cases or are their other factors involved? Or both? Is the Black Market for hydroxychloroquine an example of private enterprise saving the day?
2. Why is the disease so different in different locals? Is it just because of confounding factors or could there be other causes? Three days ago in the New England Journal of Medicine, the “Icelandic Study” was reported on regarding their experience with COVID-19 in that country a country very similar to Idaho regarding demographics and access to medical care.
An answer to those questions was hinted at in the article when they discussed an extension to the PCR testing using haploid pair analysis. Maybe the virus coming into New York from Europe was different than the same virus minus or plus a couple of mutations that came in from the West into California and knowing this should mitigation be different?
3. Testing. In the Icelandic study focused and random testing of symptomatic and all comers demonstrated similar incidence rates—around 1%. Testing is important in certain situations like health care providers and food workers. But the public health strategies based on symptoms would not differ if an antigen or antibody test were positive or negative.
There are clinical and public health reasons for testing. The public health reason is not an emergency. And if hospitals and employers want to test their employees then they can pay for the test. That is what Treasure Valley Hospital is doing in our Valley and they also test family members. That is what the airlines will be offering passengers.
I know that there are people on the Governor’s panel who know these technicalities described above—that hopefully, this was one of the reasons they are advising him. The same is true about the people interviewed by Russell. In the end, there is one final reason for their opinions and Adam Smith described this as “the good fellow’s theory”. Despite what famous politicians have told us we cannot feel another person’s pain. Even losing a child does not make you able to feel the loss of your best friend’s child the same way.
The emotions of empathy and sympathy are qualitative, but placing yourself in anther’s shoes—a form of fiduciary is quantitative. Because of this an agency relationship is established whereby the despair of an upside failure is never approached by the joy of a downside gain. Advisor agents either because of money or reputation will always skew to mitigating the downside. This is not real or hopeful. If these same advisors were to put on an entrepreneurial hat when assessing risk, or if doctors were to never treat a cancer patient with less than a 50% chance of survival or when engineers were assessing the risk of a space shot, progress would never happen.
These advisors and our Governor when mitigating this risk are putting their own professional and political reputations ahead of their constituents who are small businessmen and women, employers, and employees. So is Betsy Russell by the way when she only interviews government workers and lobbyists.
In that regard, it is my opinion that intuitively President Trump understands risk in the same terms as Adam Smith. He may not be able to put it into the same words but he has been living—winning and losing with risk all his adult life. He is unique amongst the leaders in our country in this regard. I wish our Governor understood concepts of risk and opportunity cost the same way.