Cloth Masks Are Comfort Blankets according to SAGE committee member

[BlueArmy]

Keir Starmer has come out in favour of masks. As he’s a barrister and well used to weighing up arguments for both sides, you’d like to think he’s done that in this instance, (and isn’t just saying it because he feels as the Opposition he’s got to oppose the Government stance!)

Covid: Lifting all restrictions at once is reckless, says Sir Keir Starmer

Boris Johnson has urged caution as most legal rules on social contact are lifted in England.

www.bbc.co.uk

I think it's just classic opposition politics - as in, say the opposite to the government. It's lame really - because he can't lose saying it. There is no way of telling if not wearing them has been a good thing or not really. Personally, I think the government has got the country to the position where it has offered every adult a jab, now it's a experiment to just let it rip and see what happens. Not a fan of this government, but I support this personally. Can't run and hide forever and its getting to the point where the cure is worse than the disease.

 

[Eddy Edson]

Anyway, FWIW this evidence review study gets cited a lot in epidemiologist discussions I've followed: https://www.pnas.org/content/118/4/e2014564118

 

[Eddy Edson]

Grist for the mill ... New study looking at the impact of a mandatory mask policy on Melbourne's outbreak last year: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0253510

(Recalling that Melbourne managed to get cases from a daily peak of ~700 down to a sustainable zero over the course of a couple of months.)

Conclusion: The study estimates the mask mandate, independently of other measures, reduced Reff from ~1.2 to ~0.9. In other words, to a level where the outbreak was diminishing.

Obviously, how convincing you find this should depend on yr assessment of the quality of the methods, but in fact will probably depend on yr priors 🙂

Background​

Whilst evidence of use of face masks in reducing COVID-19 cases is increasing, the impact of mandatory use across a large population has been difficult to assess. Introduction of mandatory mask use on July 22, 2020 during a resurgence of COVID-19 in Melbourne, Australia created a situation that facilitated an assessment of the impact of the policy on the epidemic growth rate as its introduction occurred in the absence of other changes to restrictions.

Methods and findings​

Exponential epidemic growth or decay rates in daily COVID-19 diagnoses were estimated using a non-weighted linear regression of the natural logarithm of the daily cases against time, using a linear spline model with one knot (lspline package in R v 3.6.3). The model’s two linear segments pivot around the hinge day, on which the mask policy began to take effect, 8 days following the introduction of the policy. We used two forms of data to assess change in mask usage: images of people wearing masks in public places obtained from a major media outlet and population-based survey data. Potential confounding factors (including daily COVID-19 tests, number of COVID-19 cases among population subsets affected differentially by the mask policy–e.g., healthcare workers) were examined for their impact on the results. Daily cases fitted an exponential growth in the first log-linear segment (k = +0.042, s.e. = 0.007), and fitted an exponential decay in the second (k = -0.023, s.e. = 0.017) log-linear segment. Over a range of reported serial intervals for SARS-CoV-2 infection, these growth rates correspond to a 22–33% reduction in an effective reproduction ratio before and after mandatory mask use. Analysis of images of people in public spaces showed mask usage rose from approximately 43% to 97%. Analysis of survey data found that on the third day before policy introduction, 44% of participants reported “often” or “always” wearing a mask; on the fourth day after, 100% reported “always” doing so. No potentially confounding factors were associated with the observed change in growth rates.

Conclusions​

The mandatory mask use policy substantially increased public use of masks and was associated with a significant decline in new COVID-19 cases after introduction of the policy. This study strongly supports the use of masks for controlling epidemics in the broader community.

​

 

[everydayupsanddowns]

That's my defence for using the basketball analogy. 🙂 It's not perfect, but show me a scientific model which is.

For me the analogy treads a tricky line between clear comedy exaggeration, and functional illustration.

I confess I found it so unlikely that I spent a minute or two with the “500,000 times bigger”, and working out the diameter of a basketball to quickly discover that it was nothing like as big as Belgium.

Plus the 500,000 relies on the size of the virus particle itself, and this (as we all agree) is not uniformly how the virus will emerge.

The virus particle (or multiple particles) will be attached to droplets of widely varying sizes.

And from a carefully cnstructed n=1 experiment I have demonstrated to my own satisfaction that some air (doubtless containing small particles) passes through the layers of the masks I wear, but that quite a lot of water vapour is captured.

And so, in just the same way as I recognise that my recycling efforts are not going to entirely solve the global warming planet crisis single handed, I am happy to make a small contribution by doing something rather than nothing.

 

[Docb]

Grist for the mill ... New study looking at the impact of a mandatory mask policy on Melbourne's outbreak last year: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0253510

(Recalling that Melbourne managed to get cases from a daily peak of ~700 down to a sustainable zero over the course of a couple of months.)

Conclusion: The study estimates the mask mandate, independently of other measures, reduced Reff from ~1.2 to ~0.9. In other words, to a level where the outbreak was diminishing.

Obviously, how convincing you find this should depend on yr assessment of the quality of the methods, but in fact will probably depend on yr priors 🙂

View attachment 18039

Background​

Whilst evidence of use of face masks in reducing COVID-19 cases is increasing, the impact of mandatory use across a large population has been difficult to assess. Introduction of mandatory mask use on July 22, 2020 during a resurgence of COVID-19 in Melbourne, Australia created a situation that facilitated an assessment of the impact of the policy on the epidemic growth rate as its introduction occurred in the absence of other changes to restrictions.

Methods and findings​

Exponential epidemic growth or decay rates in daily COVID-19 diagnoses were estimated using a non-weighted linear regression of the natural logarithm of the daily cases against time, using a linear spline model with one knot (lspline package in R v 3.6.3). The model’s two linear segments pivot around the hinge day, on which the mask policy began to take effect, 8 days following the introduction of the policy. We used two forms of data to assess change in mask usage: images of people wearing masks in public places obtained from a major media outlet and population-based survey data. Potential confounding factors (including daily COVID-19 tests, number of COVID-19 cases among population subsets affected differentially by the mask policy–e.g., healthcare workers) were examined for their impact on the results. Daily cases fitted an exponential growth in the first log-linear segment (k = +0.042, s.e. = 0.007), and fitted an exponential decay in the second (k = -0.023, s.e. = 0.017) log-linear segment. Over a range of reported serial intervals for SARS-CoV-2 infection, these growth rates correspond to a 22–33% reduction in an effective reproduction ratio before and after mandatory mask use. Analysis of images of people in public spaces showed mask usage rose from approximately 43% to 97%. Analysis of survey data found that on the third day before policy introduction, 44% of participants reported “often” or “always” wearing a mask; on the fourth day after, 100% reported “always” doing so. No potentially confounding factors were associated with the observed change in growth rates.

Conclusions​

The mandatory mask use policy substantially increased public use of masks and was associated with a significant decline in new COVID-19 cases after introduction of the policy. This study strongly supports the use of masks for controlling epidemics in the broader community.

​

I wonder how they came up with the "hinge" date. I also wonder what the effect of shifting it a few days either side of the 8 days might be. Also, never really did get my brain round the idea of linear regression of a logarithmic relationship.

 

[Bruce Stephens]

Also, never really did get my brain round the idea of linear regression of a logarithmic relationship.

That's simple enough, isn't it? Something that's exponential becomes linear when you take logs so a sensible thing to do in this case is to use linear regression on logs of your exponentially changing thing.

 

[Eddy Edson]

That's simple enough, isn't it? Something that's exponential becomes linear when you take logs so a sensible thing to do in this case is to use linear regression on logs of your exponentially changing thing.

Like Bruce, I don't see anything very suspect with that part of it. On the hinge day: the paper says the SI includes a sensitivity analysis (which I haven't delved into).

My immediate question on the methods was the use of archive photos from The Age newspaper to estimate mask compliance. Again, I haven't delved into it, but I'd want to know more about how robust that is.

 

[Docb]

I'm sure that my inabilities with respect to interpreting linear regression of logarithmic data has more to do with my brain than the method.

It would be nice to know how the hinge day was chosen and whether any sensitivity analysis was done. One thing for sure is that if I wanted to demonstrate an effect of masks, then I would have invented the idea and then put it exactly where it appears on the chart.

By the way, I am taking no side in the mask/no mask debate. Just trying to illustrate the point I keep trying to make, that things are rarely as clear cut as some would make out.

 

[Bruce Stephens]

It would be nice to know how the hinge day was chosen and whether any sensitivity analysis was done.

It's in the paper.

The hinge day was estimated to be 8 days following the introduction of masks, based on a previous report observing an 8 day delay from the introduction of Stage 3 restrictions in Melbourne and a change in the epidemic growth rate [28], biological plausibility (a mean generation interval estimated at 4–6 days [29, 30], combined with delays in test-seeking and reporting), and assessment of model robustness, with sensitivity analyses used to explore alternate assumptions (S1 File).​

 

[Docb]

Thanks Bruce, so as I thought it was an educated guess. As good a way as any of signalling the time you might expect to have an effect but not as clear cut as the analysis might suggest. Eyeballing the data might suggest that 6 August could be equally as good a guess - that seems to be where there is a break in slope - and if that were taken then the conclusions may well be quite different.

 

[Bruce Stephens]

Thanks Bruce, so as I thought it was an educated guess. As good a way as any of signalling the time you might expect to have an effect but not as clear cut as the analysis might suggest. Eyeballing the data might suggest that 6 August could be equally as good a guess

Well, 6 August would be 16 days after the rule change, which feels like it's not quite as plausible as 8 days. But maybe they could produce support for 16 days, too. If you went there you might also have to have an argument about the influence (or lack of it) of this curfew (from 3 August), especially since you'd probably want to include some later data (otherwise the graph stops just 4 days later).

Nevertheless, it looks to me like pretty good evidence (on its own) that the introduction of mask probably had a positive influence. Not a massive one, but significant. Which matches the Sage estimates from last year, I think.

 

[Bruce Stephens]

For me, the graph indicates that cases started falling and levelling off the day immediately after a the curfew was announced (30th July) which is the same date they used for their "hinge" day.

I think they'd argue that that's not plausible. You won't see the impact of a measure immediately: it'll take about a week, minimum.

 

[Eddy Edson]

Well, 6 August would be 16 days after the rule change, which feels like it's not quite as plausible as 8 days. But maybe they could produce support for 16 days, too. If you went there you might also have to have an argument about the influence (or lack of it) of this curfew (from 3 August), especially since you'd probably want to include some later data (otherwise the graph stops just 4 days later).

Nevertheless, it looks to me like pretty good evidence (on its own) that the introduction of mask probably had a positive influence. Not a massive one, but significant. Which matches the Sage estimates from last year, I think.

Below is the sensitivity analysis from the SI. The "Estimate" column is the model's estimated change in daily growth rate attributable to the mask mandate for different hinge days. Pulling the delay back to 6 days from 8 days means the change goes from -0.065 to -0.052, etc. Still pretty significant & enough to get Reff below 1.

FWIW, the guy who was deputy Victorian CHO at the time said the other day that the mask mandate was the big swing factor in getting things under control, reflecting what seems to be the general opinion amongst the public health people.

Table S1. Sensitivity analysis for hinge day in the linear spline model. Estimate for the change in slope parameter for models that assume different transition dates

Day mask impact observed	Estimate	Std. Error	t value	Pr(>|t|)	Adjusted R2 for overall model
29-Jul​	-0.052​	0.021​	-2.619​	0.014​	0.530​
30-Jul​	-0.060​	0.021​	-2.863​	0.008​	0.547​
31-Jul​	-0.065​	0.022​	-2.953​	0.006​	0.554​
1-Aug​	-0.070​	0.024​	-2.964​	0.007​	0.552​
2-Aug​	-0.080​	0.0264​	-2.744​	0.010​	0.558​

 

[everydayupsanddowns]

I think by now most of us probably have a pretty established view as to whether masks are acceptable to us personally, the level of discomfort they give us (mentally and physically), and whether they seem to be worthwhile as a precaution or not. And any time we see data or opinions about mask wearing and the effectiveness, or lack thereof, I think it’s inevitable that we will view those data through the lens of our existing opinion. Confirmation bias is a powerful thing.

For me, I think there is enough chance (not certainty) that they offer sufficient marginal benefit to overall transmission rates, that I am happy to accept the extremely minor inconvenience of a 2-4ply face covering when in a shop, and will continue to wear one while case rates are so high (they have never been higher where I live).

 

[Docb]

Below is the sensitivity analysis from the SI. The "Estimate" column is the model's estimated change in daily growth rate attributable to the mask mandate for different hinge days. Pulling the delay back to 6 days from 8 days means the change goes from -0.065 to -0.052, etc. Still pretty significant & enough to get Reff below 1.

FWIW, the guy who was deputy Victorian CHO at the time said the other day that the mask mandate was the big swing factor in getting things under control, reflecting what seems to be the general opinion amongst the public health people.

Table S1. Sensitivity analysis for hinge day in the linear spline model. Estimate for the change in slope parameter for models that assume different transition dates

Day mask impact observed	Estimate	Std. Error	t value	Pr(>|t|)	Adjusted R2 for overall model
29-Jul​	-0.052​	0.021​	-2.619​	0.014​	0.530​
30-Jul​	-0.060​	0.021​	-2.863​	0.008​	0.547​
31-Jul​	-0.065​	0.022​	-2.953​	0.006​	0.554​
1-Aug​	-0.070​	0.024​	-2.964​	0.007​	0.552​
2-Aug​	-0.080​	0.0264​	-2.744​	0.010​	0.558​

Can't help but think that if you were presented with the set of spots on the chart without all the lines and breaks and annotations, and told it was the log of a parameter vs time, you would be very brave if you suggested that something significant happened around 6 August.

Again not saying that the interpretation is incorrect or that the CHO is mistaken, if he is good at his job then his judgement is probably the best indicator available. Just my usual thought that there is a lot of presenting data to fit a political position going on at the moment and one should be a bit wary of it.

I'm with @everydayupsanddowns. Wearing a mask is likely to have marginal benefit in some situations. I can make my own guesses about when it would be sensible to wear one but it would be good if people stopped trying to prove points and analysed the problem in an open manner. You may come to a conclusion that you cannot get definitive data and you might as well go on your gut reaction. I have no problem with that, some things cannot be resolved. The danger is in pretending that they can be.

 

[Eddy Edson]

Can't help but think that if you were presented with the set of spots on the chart without all the lines and breaks and annotations, and told it was the log of a parameter vs time, you would be very brave if you suggested that something significant happened around 6 August.

Again not saying that the interpretation is incorrect or that the CHO is mistaken, if he is good at his job then his judgement is probably the best indicator available. Just my usual thought that there is a lot of presenting data to fit a political position going on at the moment and one should be a bit wary of it.

I'm with @everydayupsanddowns. Wearing a mask is likely to have marginal benefit in some situations. I can make my own guesses about when it would be sensible to wear one but it would be good if people stopped trying to prove points and analysed the problem in an open manner. You may come to a conclusion that you cannot get definitive data and you might as well go on your gut reaction. I have no problem with that, some things cannot be resolved. The danger is in pretending that they can be.

Anyway, you asked what the effect in the model of changing the hinge date by a few days would be, and I provided the answer. Apols if that's not what you were actually looking for.

Of course, there's nothing particularly scientific about yr "marginal effect in some situations" judgement. As always, you can continue to hold on to unexamined priors, or you can engage with the science to try to improve them.

 

[Eddy Edson]

With such small overall cases numbers e.g around 400, a reduction in testing could quite easily be responsible for a drop in case numbers around the hinge date.

No idea where you're getting that from, but there was no particular reduction in testing, which was mainly widespread community testing, with hospital etc in-patients being a negligible proportion. Also, if you actually read the paper, you'll see that they address testing rates as a possible confounder:

Including daily tests and the mobility index as additional covariates in the multivariate linear regression made little difference to the estimated growth rates and neither the coefficient for mobility (-0.034, p = 0.28) nor tests (6 x 10−6, p = 0.36) were significantly different to zero (S1 File).

BTW, I don't really mean to mount a huge defence of the conclusions of this study. I really don't know enough about the statistical methods to do that & I'm waiting to see how it stands up to review and critique from experts. But it's a big piece of work and it deserves more than people just waving their priors at it.

 

[everydayupsanddowns]

But it's a big piece of work and it deserves more than people just waving their priors at it.

I don’t disagree at all. And I hope papers and research work like this can continue to clarify the situation, and improve our response to what happens in the future (it feels like we’re gonna need it).

Pragmatically, like many complex situations where there are differences of opinion among scientists, specialists and the great unwashed alike, this is one of those questions where I have (for my own personal sanity) decided to dodge the need to pour endless time and effort into balancing the emerging claim and counter claim. I don’t lose anything by wearing, and there is some evidence to suggest that my wearing might be helpful in reducing spread. So if that’s wrong, and they actially offer no benefit, I’ve not lost anything. But if there is benefit then society will have gained.

I hope that in the end someone will coalesce the available data into some uber meta analysis that settles the question, but I don’t need that to happen for me to continue for the time being.

 

[Docb]

Anyway, you asked what the effect in the model of changing the hinge date by a few days would be, and I provided the answer. Apols if that's not what you were actually looking for.

Of course, there's nothing particularly scientific about yr "marginal effect in some situations" judgement. As always, you can continue to hold on to unexamined priors, or you can engage with the science to try to improve them.

Yes I did and was too busy thinking about the graph to thank you for doing it! Still think that if you put your thumb over the last two data points, you would be hard pressed to say that the rest of the data did not come from the same population - unless you were trying to prove a point.

Totally agree that there is nothing particularly scientific about my judgement by I'm the only person who has to live with it. Dunno what I would say if others would have to rely on and work with what I said.