1/ Opening schools in the midst of a COVID surge is a hard problem with unavoidable tradeoffs.
There are absolutist statements on either side of the debate, so I expect passionate rebuttals, but let me lay out a decision-making framework, from an epidemiologists' perspective
2/ First off, we have to make sure that the schools have the resources and space to implement the 5 key mitigation
strategies correctly and consistently.
Not a given.
*Masks
*Social distancing
*Contact tracing
*Hand hygiene
*Cleaning and disinfection
first 2 >> last 2
3/ Let's say we have some resources for testing, how does that contribute?
What we are trying to achieve?
Do you think the goal is Screening (identify asymptomatic infectious cases before they can expose others) or Surveillance (understand incidence to inform policy)?
4/ Either one is feasible, but we have to be really clear about what we are trying to accomplish.
If you really think you are using testing to ensure that asymptomatic cases are screened out before they can expose others, it needs to be FREQUENT (2x a week) and FAST (<24h TAT)
5/ This can work.
@testing4america is supporting the safe reopening of historically Black colleges and universities like @DelStateUniv
But is has to be fast, frequent, and "for real" - ie mandatory if you want to go in-person.
6/ I won't get into Antigen vs PCR issue here, but right now my perspective (and that of @testing4america) is to focus on PCR tests, and to reduce the cost through pooling, w testing all samples in positive pools ("Dorfman pooling")
If you have 1,200 faculty/students, pool size 12, will probably cost you ~$20 per student for pooled PCR at 1% positivity rate
That's minimum $50,000 a week-a cool $Million for the next 20 weeks Can't we test once a week? What if we stagger?
8/ In general, testing once a week means that most of the infections will occur before the infection is caught and isolated
theoretically, would work if you could figure out the logistics for splitting school into 2 cohort (aiding social distancing) 1/2 in person, 1/2 virtual ..
9/ Each cohort gets tested Wed morning, goes to school for 4 consecutive school days (Thu/Fri/Mon/Tues) every 2 weeks, then 9 days of remote learning
Assuming greatest risk is during school days, then nearly all infections should have time to become detectable by following Wed
10/ Those logistics are *hard*
-Got to get the kids into testing sites on non school day (buses?)
-Turn around time has to be <15 hours
-Mandatory testing may raise issues of health equity
-Teachers will have both in-person and remote students.
Is there another way?
11/ Let's look at surveillance testing
Goal is to detect a rise in cases and move away from in-person if a substantial increase in excess cases emerges
Operationally easier, less expensive, doesn’t require 100% participation–but isn't powered to prevent chains of transmission
12/ Surveillance testing will not significantly *create* a safe environment in the presence of community spread outside of the school.
It does require having a principled framework for deciding when school would be shifted back to remote learning based on test results
13/ While it is impossible to keep schools completely safe from COVID-19, our primary aim should be to ensure that in-person school does not cause a substantial increase in excess cases (and subsequent morbidity and mortality) in the larger school community- including families
14/ How many cases before schools shut down?
Many cases identified among faculty and students will reflect community infection, unrelated to school
Can we define “substantial increase”
Can we define “excess” cases (over community baseline)?
Can we operationalize thresholds?
15/ Here's a framework:
“Less than 5% chance that cases spread at school will result in a death in the school community”
Assuming IFR 0.5%/50% household transmission rate to adults
That's ~10 extra cases among school staff, or 20 extra cases among students (pop 1,000)
Is that
16/ Epidemiology gives us no special legitimacy in making value decisions about 1,000 kids in school vs _n_ lives saved.
You can place your own values, and this framework will relate that to thresholds for deciding when the risk is too high
But lets be honest about tradeoffs
17/ Can we define "excess cases"?
Here's an approach.
Get a pre-opening baseline by testing students/faculty, and compare it to the community infection estimate
18/ That's your school community's baseline rate of infection before school reopening. You can then use community rate to adjust your school's "baseline" as time goes on
eg. 1.2% community infection rate
0.6% school basline (6 cases per 1,000)
At 2% community - expect 10 cases
19/ This is a key point-
Just cause you have 14 cases in a week in the school testing doesn't mean that there were 14 cases "caused by" in-person school.
If community baseline correlates w 10 cases a week in school population, that's an excess of ~4 cases.
Make sense?
20/ I know, small numbers, lots of error in estimates, etc
but there's a whole body of science for "control charts" used to shut down processes (think assembly line) when cumulative deviations from expected rates exceed thresholds
*decide how many excess cases you're willing to tolerate over next 20 weeks (400~1 death)
*begin weekly pooled PCR testing
*monitor excess cases c/w expected baseline
*If trends statistically exceed your tolerance, close down or intensify testing
1/ It's indescribable seeing results from NYC EMS ambulance runs showing how cardiac arrests skyrocketed during COVID
(I started a program to monitor these symptoms in real time--among the very first application of syndromic surveillance in public health, 2 decades ago)
2/ Every day, crews from @FDNY are called to 20 to 30 patients who have collapsed, and attempt resuscitation. Can you imagine?
It's never like the movies. Most patients die, ribs cracked. 75% of the time you never get a heart rate back.
On April 6, there were 305.
305.
3/ In the dry language of medical research the researchers describe the horrible statistics.
During the peak, most patients had nonshockable presenting rhythms of asystole and pulseless electrical activity. 92.2% of the time they called off the resuscitation without a pulse.
1/ I've been feeling more and more disengaged from COVID work, disillusioned with the growing realization that all the smart research and policy doesn't make a damn bit of difference
Not for the 1st time, I've seen that what I thought was an information problem is something else
2/ I so admire those public health Cassandras who've been unrelenting, continuing to beat the drum of science and policy for the past 9 months
repeating over and over again what must be done, as the cases and deaths mount, with no strategy in sight
tweets, interviews, articles
3/ It's perhaps no accident that they (and I) are "formers"
People who ran the agencies, who know the pain of the experts and scientists working inside, and are free to speak
2/ To test hypothesis that health systems provide better care to patients w high needs, diff in quality b/w system‐affiliated & nonaffiliated physicians
ED visits were significantly *different* in system‐affiliated (117.5 per 100) & nonaffiliated POs (106.8 per 100, P < .0001).
3/ I love how delicately the RAND researchers approach this in their conclusion: “Health systems may not confer hypothesized quality advantages to patients with high needs.”
2/ When CMS first released their public use files, I ran some analyses looking for aberrations-
One thing that jumped right out was...Repetitive non-emergency ambulance runs- often for the same person going back and forth to dialysis 3 times a week.
"Medicare and law enforcement officials will need to create new processes for dealing with a potential flood of outlier reports from amateur sleuths like me."
2/ @AledadeACO is proud to be the largest, most successful nationwide enabler of physician-led ACOs, delivering better care at lower cost for >340,000 Medicare beneficiaries, saving Medicare and American taxpayers nearly $180 million in unnecessary health care spending last year!
3/ Here's the list of the physician-led ACOs we are supporting, and our performance data.
* It doesn't matter if you're urban, rural, suburban, or in which state
* It gets better. The longer you work, the more the chances of success