“Shared expectations lead to predictability.”
- Risk of dining out
Q: What are the risks of dining out??
A: The CDC has released a study showing that in a group of patients, twice as many who tested positive for Covid-19 had eaten at a restaurant during the previous two weeks. The study included 341 patients from 11 different states. Of this group, 160 tested negative, and 154 tested positive. 71% of those who tested positive declared they did wear cloth face coverings when out in public. The whole group was questioned by investigators about other activities including shopping, gym attendance, going to bars and salons, or working in offices. There was no statistical difference between those who tested negative and those who were positive. But in restaurants, the chance of getting a viral infection practically doubled. Among the possible reasons cited were that in restaurants, you cannot eat and drink and keep a mask on. In bars and coffee shops, one can remove masks for shorter periods of time than for eating a whole meal. Social distancing and poor ventilation were also cited. Further study is indicated to verify these findings with a larger group, differentiate between indoor and outdoor dining.
The CDC on September 6, identified four levels of risk for eating out:
- Lowest Risk – drive-through, delivery, take-out and curb-side service.
- More Risk – on-site dining with outdoor seating 6 feet or more apart.
- Even more Risk – Indoor seating with tables at least 6 feet apart.
- Highest Risk – Outdoor and indoor seating with tables closer than 6 feet apart.
- COVID-19 death toll projected to rise dramatically
Q: What does the future look like with increasing COVID-19 deaths?
A: The University of Washington’s Health Institute frequently revises its predictions on the spread of the COVID-19 infections, hospitalizations and deaths. “We expect the daily death rate because of seasonality and declining vigilance of the public to reach nearly 3,000 a day in December.” This means the total deaths in the US would reach 410,000 by January. This projection means the number of deaths to date – 7 months – will more than double in less than 4 more months.
118. Description of variables for those under age 21 who died from COVID-19
Q: What are the variables among children who die from COVID-19?
A: A recent CDC study reported in its Morbidity and Mortality Weekly Review showed that black and Hispanic children have a higher death rate than the rest of the US population. The study looked at data from 47 of the 50 states between February 12 through July 31. During this period, there were 121 deaths of children under the age of 21. Of this group, 73% were minorities, even though minorities in this age group represent only 41% of the population.
Data was presented by age group for all 121 children who died:
- Children under age 1 were 10%,
- Between ages 1 to 9 years 20% and
- Between 10 to 20 years was 70%
Of all deaths of children in the study, 63% were male. Those who had at least one prior medical condition accounted for 75% of the deaths. The remaining 25% were healthy.
The CDC recently reported that the number of children with COVID-19 is now steadily rising over time..
119. Trial launched for therapeutic drug fostamatinib (Trade name Tavalisse)
Q. What’s new on the research for medications to treat COVID-19?
A: Fostamatinib (Tavalisse) is a medication manufactured by Rigel Pharmaceuticals, Inc. in San Francisco. It is used to treat a rare blood autoimmune disease in which the body’s immune system destroys healthy platelets leading to easy bruising or bleeding. It is suspected that the same process in that disease for which Tavalisse is already approved may be present in severely ill COVID-19 patients. On September 17, the National Institutes of Health (NIH) approved a Phase 2 trial of Tavalisse on 60 critical COVID-19 patients. Half the group will be administered Tavalisse twice daily for 14 days, the other half will be given placebos or harmless pills. For the following 60 days, the results will be recorded leading to an evaluation of its safety and degree of effectiveness. The value of research by pharmaceutical companies is shown by the Rigel stock jumping 91% once the announcement of this phase 2 trial was announced.
- Science v. politics: Mixed messages – from testing/tracing to herd immunity back to promised vaccine availability and beyond.
Q: Why is everything so confusing about coronavirus responses?
A: Two factors appear to be at play here. For one, there is confusion by leaders and the public over the difference between public health and personal health. The other is the preponderance of mixed messages.
Public health is based on steps each person takes to protect the public – the entire population. Personal health is based on what one should do to protect their own health. For example, wearing a mask protects others in the population from becoming infected by anyone who is asymptomatic. Vaccinations are developed to reduce the number of people in the public from becoming infected. Some people today say they will not wear a mask or get vaccinated by saying “If I get sick, that’s a risk I will take.” While this is a responsible action for someone to decide on their own personal health, it is an irresponsible decision for the many other people in the public who would then become ill and might die.
Mixed messages cause confusion. Confusion results in conflicting expectations. Differing expectations can cause one group of people to disagree with others who don’t share the same view. Expectations not shared with others creates unpredictability. From the beginning, Trump’s directing each state to develop its own responses to COVID-19 has created confusion and competition. The president’s shifting to the goal of opening the economy resulted in different leaders demonstrating behavior that is against public health principles. Even in Connecticut, Governor Lamont has recently been confronted at public events by demonstrators loudly demanding that masks must not be required in public. Some legislators are demanding the state capitol be opened to the public for hearings and face-to-face meetings. Different and opposing expectations lead to confusion.
Recently, guidance came from the CDC stating that people who have been in contact with someone who tested positive do not need to be tested.[1] The origin of this apparently was encouraged by a new White House medical advisor, Scott Atlas[2]. It is now reported that Dr. Atlas was a commentator on Fox News espousing “herd immunity,”[3] and president Trump was impressed and asked him to come to the White House. The result is we are now implementing without any reporting a program of herd immunity (letting as many as 70% of the population get the disease to build immunity to stop its spread). Politicians at the US Department of Health and Human Services (DHHS) actually wrote the guidelines for no testing, and posted it on the CDC Website over the objections of the CDC scientists. Paul Alexander, a political appointee at DHHS stated that children have near total immunity from COVID and should attend schools.[4] Last week, news reports broke that this was clearly a move toward herd immunity. Scientists calculated this new effort would take several years, and 230 million people would become infected. It would result in up to 6 million deaths within the US. Paul Alexander was then terminated and the person who hired him is now on leave. It appeared that this attempt to subvert the science by placing political influence within the agency had been stopped. The NIH and CDC has continued to contradict the White House on estimates for the date for release of a vaccine.[5]
This past Sunday, the New York Times broke the news that the Secretary of DHHS, Alex M. Azar, has now prohibited the nation’s health agencies – including the FDA, NIH, and CDC – from “signing any new rules concerning the nation’s foods, medicines, medical devises and other products including vaccines.” Peter Laurie, former associate commissioner of the FDA called this “a power grab.” More mixed messages and unpredictability are now certain. All this validates the axiom, “Shared expectations lead to predictability.”
[1] See previous FAQ #102
[2] FAQ #110
[3] FAQ #84
[4] FAQ #115
[5] FAQ #43 #44 #109
