BC HEALTH
Saved by @CodyyyGardner

THREAD
I created a simple table to illustrate the individual impact of the "flexible second dose timing" now recommended in the UK.

Coincidentally, @bob_wachter & @ashishkjha just tackled the US policy question in this important piece. 1/
https://t.co/n5bHkdIo0c

 I based this on recent statements from the UK chief medical officers, JCVI, and what we know from prior vaccine development. 2/

JCVI: https://t.co/6FQ25d6MFE

UK Chief Medical Officer (CMO) statement: https://t.co/RTpAIqgE1i

CMO letter to the profession: https://t.co/4WeexrVYWX
This table and thread focuses on the AZ vaccine, where more data on a delayed second dose is available than with the Pfizer vaccine. It is not intended to address questions about single-dose regimens or mix & match approaches. 3/
In the table, persons “A” and “B” both receive their first dose in January. “A” receives their second dose in February (4 weeks later), and “B” receives their second dose in April (12 weeks later). “C” receives their first dose in April and second dose in May (4 weeks later). 4/
I made a qualitative comparison the potential efficacy during the two months between “A” and “B’s” second dose, as well as the potential longer-term efficacy after “B” receives their second dose. 5/
For the two-month period between the timing of “A” and “B’s” second dose, the level of protection for “A” may be greater than “B,” as “A” has received their second dose, although JCVI suggests that most of this early protection is provided by the first dose. 6/
JCVI says first-dose protection against hospitalization is high, so for that two month interval, I’ve assumed “B” is expected to be protected against the more severe forms of COVID-19 (“+”). 7/
After “B’s” second dose in April, we expect a more robust, higher quality and longer-lasting immune response than “A,” as is often seen with longer intervals in a primary vaccination series and supported by AZ’s data immunogenicity data. 8/
https://t.co/cqKIpomUHg
The bottom line is that “A” may see some protective benefit over “B” for two months (although “B” is expected to be protected against severe COVID during that time), but “B” is expected to have better long-term protection after they receive their second dose at 12 weeks. 9/
In other words, for the two months between “A” and “B’s” second dose: A>B>C

For the longer term, after “B” receives their second dose: B>A=C

10/
At a population level, this strategy could double the number of people immunized immediately while production ramps up. Many in category “C” could shift to “B.”

This assumes immunization programs are keeping up with the number of doses available. 11/
A few important caveats: (1) the assumption of sustained efficacy after the first dose is based on an exploratory analysis in the age 18-55 population. This needs to be confirmed in the older adult population. Exploratory analyses have limitations, esp if not pre-specified. 12/
(2) the assumption that a longer schedule (“B”) will result in a higher-quality, more durable immune response is based on immunogenicity data and experience with other vaccines. The efficacy impact will have to be confirmed. 13/
(3) we need to confirm that vaccine protection against the new strain is comparable after the first and second doses, given the affinity maturation expected with a second dose. 14/
Some virologists have raised the theoretical concern that a change in schedule could promote emergence of vaccine escape mutants. This is an important consideration that has implications for single-dose vaccine development and WHO’s minimum vaccine efficacy threshold of 50%. 15/
Again, this illustration is based on statements from UK authorities, AZ’s published data, and what is known from prior vaccine development. It is not a prediction of the future or what will happen to any individual, and is subject to updates as new data becomes available. 16/
I hope this helps people understand the practical ramifications of the UK recommendations and the pros/cons at individual and population levels. The urgency of the question is driven by the emergence of the new variant in the UK as well as the limited supply of vaccine. 17/
Thanks to our vaccine CMO, Dr. Gary Dubin for his input. More thoughts and links to others' views in the thread below.

I went through this exercise to clarify my own views and thought others might find it useful. Please take it in that spirit! 18/ https://t.co/2vzJ9Sdfc4

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A week ago I voluntarily admitted myself to the hospital to get help bc I am losing weight as a result of increased food intolerances, secondary to my very severe #MECFS. A psychiatrist here decided that im mentally ill, 1/

purely on the basis that the medical workup was unable to explain my food intolerance. My psych eval only found mild anxiety and depression. 2/

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APL Apollo conducted their earnings con-call today at 2:00 pm

"Vision of capacity of 4 Million Tone till 2025"

@hiddengemsindia @gvkreddi

Here are the key highlights 😀👇

Business Updates:

• Net Working Capital Remains under 10 days which is best in the building material
• Continue expanding rural region.
• Added 15 new distributors.
• Value added products contributed 50-60% of the topline.

Revenue breakup


• EBIDTA Margin Improvement: This was mainly because of cost structarize and better operational

• Working Capital Cycle as per mgmt should remain sustainable

• Blue Ocean: Company owns 100% subsidiary which contributes nothing but own property where corporate office is there

Improvement in Net Selling Realization:
• With expanding capacities at 2.6Million Tones and utlization of 75%, scalability has been affected.
• While the prices of steel has gone by by around 35%, due to shortage of steel.

• Apl Apollo being the dominant player past connection with steel companies worked well.
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• Hence the discounting factor while selling the products has went away.
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You gotta think about this one carefully!

Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)

The test is 99% effective in detecting both sick and healthy people.

Your test comes back positive.

Are you really sick? Explain below 👇

The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through


You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.

👇


Here is what we know:

- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative

Assuming 1 million people take the test (including you):

- 100 of them are sick
- 999,900 of them are healthy

👇

Let's now test both groups, starting with the 100 people sick:

▫️ 99 of them will be diagnosed (correctly) as sick (99%)

▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)

👇
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