Key difference from other papers
-massive head sized cone
-high sampling rates
to try to capture ALL exhaled aerosol
Expired particles sampled
During respiratory ACTIVITIES
-breathing
-shouting
-exercise
-force expiration
-cough
With respiratory PROCEDURES
-nothing
-NIV or CPAP
-HFNO
-surgical mask
First key finding.
A modest increase in aerosols with respiratory THERAPIES during quiet breathing
BUT (big but) this disappeared & emissions fell with respiratory therapy during exertional respiratory activities
This fall in exhaled aerosols with NIV/CPAP/HFNO compared to without their use shows they are NOT AGPs
The fall likely due to
-PEEP reducing airway collapse and open/close cycling
-positive inward flow reducing egress
You may have picked it up already....
Yes coughing, exercise & forced expiration (each mimicking breathing patterns in illness) produce many-fold more aerosols than quiet breathing (log scale)
Respiratory ACTIVITY should be focus of concern
NOT respiratory PROCEDURES
Again - as per Gaeckle and others - there was a massive inter-individual variation
So what do we learn
1 NIV, CPAP, HFNO are not AGPs
2 Exertional activities (cough, fast deep breathing) generate up to 370-fold more aerosol than quiet breathing
3 This aerosol is respirable
4 It constitutes a significant volume of all expired volume
5 We need to rethink risk
We know some superspreader events create massive infection & can only be explained by aerosols
These likely combination of
-high viral load (disease time course)
-high risk person (high emitter/spreader)
-high risk activity (sing, shout etc)
-poor ventilation
-prolonged exposure
While droplets may be a high risk at short distance...
...the high production of respirable aerosols by exertional respiratory activities (common in illness) point to a prominent role of aerosol transmission at short distances
(& in some settings over long distances)
The other point I should’ve made. Even more important than an ultraclean setting..
....collaboration between aerosol/environment scientists & clinicians
Key to all the papers in the thread (& missing from many others)
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It studied
10 nurses
All white
9 female
9 elevated BMI, 5 BMI >30 kg/m2
2x 12 hour shifts
N95 +/- surgical mask over it
Unclear whether expiratory valve
Assessed
compliance comfort & physiology
2/15
The compliance and comfort evaluations showed
-lots of minor discomfort
-but rather well tolerated
-most removals at shift end or to drink
-compliance on day 2 better than day 1
The reasons can be illustrated by the median ages of patients in the 3 groups affected by COVID
-patients who died (median age 83) @ONS
-hospital admissions (age 73) @ISARIC1
-ICU admissions (age 61) @ICNARC
Impact of vaccination is much slower in the younger groups
2/n
There's been evidence vaccination is impacting deaths in the older groups for some time
Nice to see this published
Working with @john_actuary from @COVID19actuary we’ve modelled impact of vaccination on
-deaths
-hospital admissions
-ICU admissions
Vaccinating just by age would have this impact on the three measures
The lag in the last two is because the groups differ.
Median ages
-deaths 83
-hospitalised 73
-ICU 61
So the cohort who might get to ICU have to wait for vaccination
If the graphs are adjusted to account for
-gp2 health/social care workers
-gp4 extremely clin vulnerable
-gp6 high risk
They look like this with lag slightly reduced (and the health service staff protected)
Vaccinating 15% of popln
-huge impact on deaths
-modest impact on ICU