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Date: 5/19/2020
Subject: A Selection of COVID-19 Readings #3
From: Beacon Hill Village



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May 19, 2020

A Selection of COVID-19 Readings - #3

Click here for Readings #1

Click here for Readings #2

 

Will We Have a SARS-CoV-2 Vaccine by the End of 2020?  How Does Global Collaboration Help?

 

https://www.nytimes.com/interactive/2020/04/30/opinion/coronavirus-covid-vaccine.html?referringSource=articleShare

 

https://www.nytimes.com/2020/05/13/magazine/can-team-science-yield-a-covid-19-treatment.html?referringSource=articleShare

 

There is increasing excitement and trepidation about the development of a COVID vaccine as we better understand of the pace of the disease and the likelihood of acquiring immunity.  The timeline of vaccine development is in flux—with hopeful promises made by the White House of vaccines being available by the end of the year (2020) and more practical promises from the experts that a vaccine will be available by 2021, if all goes well.


Today, it is difficult to predict the timeline of new vaccine development, even when comparing it to the steps taken in the past. COVID has forced us to reconsider our conventional approaches to vaccine development, creating new opportunities to standardize approaches (preclinical, regulatory, etc.), to share information, and to work collaboratively.  This could greatly speed up the timeline and also help us manage new pandemics in the future. 


Most notable is the impressive collaborative effort worldwide accompanied by competition and maneuvering among companies who are in a position to produce a vaccine quickly.  It is well recognized by countries that being first to deliver will reap national rewards—the first to restore their economy, and to wield considerable global influence.  Questions are being asked about equitable distribution and who will decide?


As of May 2020, there are 4-6 vaccine candidates that are in contention, some intimating that they can deliver a vaccine by the end of the year, and at least a hundred more vaccines in the pipeline. 


Leading the pack, Oxford’s Jenner Institute announced this week the results of a successful preclinical trial in Rhesus monkeys showing a single dose of their vaccine protected the monkeys from acquiring COVID-19.  Their trials in humans are well underway, having benefited from their previous safety trials with coronavirus vaccines showing the vaccine was safe in humans. By the end of June, they will test the safety and effectiveness of their vaccine in six thousand people.  If all goes well, the first million doses will be ready for emergency use by September.  

 

There is new technology, known as messenger RNA, being developed by both Moderna and Pfizer/BioNTech. This technology has allowed these companies to move quickly.  However, since no drug or vaccine based on mRNA has ever reached the market, there will be many questions about safety and efficacy and they will have to work extensively with the  FDA to assess the risks.


In China, three vaccines are being developed but there is a concern among Americans about their lack of transparency in developing vaccines that will lead to a vaccine that is not safe and not able to pass US safety requirements. 


This first article very nicely points out that the most likely scenario is that the COVID vaccine will take longer than is projected—setting a timeline of 2021. At this moment, it is possible that a vaccine will be developed and one million doses produced by the end of the year, but this will not be enough to serve the general public and open up a country.  The article also points out that NOT having a vaccine until then is not as bad as we might think. The newly developed therapeutic drugs, bridge vaccine, contact testing, and widely available tests for the virus and antibody will  help manage the viral infection—similar to what has been seen with AIDS. 


The second article is a wonderful example of the value of international collaborations and how they get started.



What do the data show about my risk of Covid-19?

 

During the recent BHV “Conversation With...” about immunology, questions were raised about the “relative risk” of acquiring Covid-19 for older people.  Since specific data on this are not available for Massachusetts, we decided to calculate it for you ourselves.

 

The tables below use data from the Massachusetts Department of Public Health for May 13, combined with Census population estimates for 2018 (any population figure reported anywhere that is not for 2010 is an estimate, since we only count our population every ten years – that said, the estimates are pretty good ones).

 

The first table shows the relative risk by age group for Massachusetts as of this month.  Since I think the modal age of BHV members is in the 70s, I will walk us through that row for an example. [Click here for the table]


This table shows the risk of getting Covid-19 for different age groups. For hospitalization and death it shows the risk in two ways: (1) the overall risk simply as a member of that age group; and (2) the risk of hospitalization or death after being diagnosed as positive for Covid-19. To work through a simple example, imagine that we had a perfectly representative group of 1000 Massachusetts residents between 70-79 years old.  The table tells us that we would expect 11 of those 1000 to come down with Covid-19 (1.1% of 1000 = 11).  Reading across, we see that we would expect 2 of those 1000 to be hospitalized for the disease (0.02% of 1000 = 2). 


The next column, under the heading “Confirmed Covid-19”, requires us to consider a different population.  Imagine, in this case, that we had a different group of 1000 Massachusetts residents between the ages of 70-79, and all of this particular group had been tested and found to have Covid-19. Of that 1000 infected people, we would expect about 227 to be hospitalized (22.7% of 1000).


Under “Death”, the numbers are read the same way.  We would expect 2 deaths among our 70-79 year old 1000 residents (0.2% of 1000) – note: the 2 deaths are not necessarily from among the 2 hospitalized; those counts are separate (that is, those 2 could have left the hospital healthy and the 2 deaths have come from a long-term care facility or a home). Similarly, we would expect 159 deaths among our 1000 infected 70-79ers (15.9% of 1000) – again, all of those 159 not necessarily from among the 227 hospitalized.


So, these figures show that if you are 70-79 in Massachusetts, you have only a 0.2% chance of being hospitalized and a 0.2% chance of dying from Covid-19.  If you are in that age group and actually have Covid-19, your chance of needing hospitalization is 22.7%, and your chance of death from it is 15.9%.


But these numbers are highly inflated, for a number of reasons: 1) testing is not distributed evenly across the population – older people are far more likely to be tested, biasing the rates upward – even a somewhat-ill 80-year-old is more likely to be tested than a somewhat-ill 30-year-old; 2) the rates for the 70+ population are considerably biased upward by the fact that so many of those were in long-term-care facilities, not in hospitals; 3) anyone who is seriously ill is more likely to be tested at any age than those with mild symptoms, so in the official statistics we likely have a more-seriously-ill segment of the population (rather than a random segment) that have higher hospitalization and death rates than the population as a whole (i.e., all of our rates are biased upward).

 

The second table shows the raw numbers used in the calculations, which should be of greater comfort to you.  Again in the 70-79 row, we see that there were 702,066 people in Massachusetts in that age group; 1732 of them were hospitalized for Covid-19, and 1214 of them died of it.  Note especially the last row in that table. It shows the number of cases and number of deaths in long-term-care facilities from Covid-19.  Of the 80,259 cases in Massachusetts at that point, 17,706 of them were in LTC facilities – over 20%.  More striking, 3236 of the deaths from Covid-19 were in LTC.  Those 3000+ deaths represent 60% of all deaths in Massachusetts from the disease.  And consider that most of those 3,236 deaths in LTC will fall in the age group of 70+. There were a total of 4,532 deaths in Massachusetts in that age range.  The obvious conclusion: if you are in the 70+ age range but not in a long-term-care facility, your relative risk is considerably lower than that shown in the tables. [Click here for the table]



Note: I would have loved to calculate directly the relative risk for the non-long-term-care population only (e.g., what is the risk specifically for an 80-year-old who is not in a nursing home) , but cannot because of data issues (one big issue is that deaths in LTC are reported only for staff and residents combined – one can’t separate them, can’t even learn how many of each there are – and they are very different age groups).

 

The final chart, directly from MA DPH, shows why the relative risk is so high in Massachusetts. You may have seen data (most recently a chart from us from COVID-NET) showing fairly low rates of hospitalization per 100,000 by age groups. That particular chart showed a hospitalization rate of approximately 40 per 100,000 for older cohorts. Those are national data. Massachusetts has much higher rates. The chart shows that for those 70-79 in Massachusetts, e.g., the hospitalization rate is 7,614 per 100,000. [Click here for the table]


Conclusions:

1) Your relative risk in Massachusetts is higher than the national average, and considerably higher than in many other places in the country.

2) This doesn’t mean that your personal risk is enormously higher, assuming you exercise caution (remember that our high relative risk is very much influenced by our LTC population, not just by your individual characteristics). The high relative statistical risk is thus partially just because you live in a place that has many more vulnerable demographic segments of the population. The death rate among older people from Covid-19 is shockingly high, but the "does-not-die" rate is hugely larger, even in very hard-hit states like ours, especially when you factor out nursing home deaths.


Statistics are not destiny.


Finally, we offer an informative reading from the Economist on the problems of measuring deaths from Covid-19, and one from The Atlantic that illustrates how fraught our broader measurement problems really are.



 

https://www.economist.com/graphic-detail/2020/04/16/tracking-covid-19-excess-deaths-across-countries

 

https://www.theatlantic.com/health/archive/2020/05/cdc-publishing-covid-19-test-data/611764/