I will list the topics at the top of page and you can then scroll down to find the topic you’re interested in.
1. Do masks help?
2. How we measure risk:
Do masks help?
Cultural differences are always interesting, and I’m defining culture as ‘the way we do things around here’. One such difference that has leapt to the foreground during this pandemic is whether we wear masks or not. In many far eastern societies it is taken as normal that as soon as you have symptoms of a cold, flue, or something nastier you cover up with a mask when you go out. The reason is to stop infecting others – it’s your responsibility to protect other people. But in the UK and Europe generally wearing masks is more likely to provoke staring and smirking. A visit to the Co-op this week had exactly that effect – although it was a minority of those present I’m pleased to say. Yes, I wear a mask when enter shops. To me its a no-brainer and I have been puzzled by the advice we have been given on the subject. Am I wrong?
What is the evidence on masks and does it square with the official advice? That advice has been pretty clear up to now – don’t bother with masks, just wash your hands and stay 2 meters apart.
WHO “If you do not have any physical symptoms, such as fever, cough or runny nose, you do not need to wear a medical mask. Masks alone can give you a false feeling of protection and can even be a source of infection when not used properly.” Dr. April Baller WHO Health Emergencies Programme (Ref 1).
CDC: “If you are NOT sick: You do not need to wear a facemask unless you are caring for someone who is sick (and they are not able to wear a facemask). Facemasks may be in short supply and they should be saved for caregivers” (Ref 2).
US Surgeon General: “Seriously people – STOP BUYING MASKS! They are NOT effective in preventing general public from catching #Coronavirus, but if healthcare providers can’t get them to care for sick patients, it puts them and our communities at risk!” (Ref 3).
UK Deputy Chief Medical Officer (Dr Jenny Harries): “If a healthcare professional hasn’t advised you to wear a face mask, it’s usually quite a bad idea. People tend to leave them on, they contaminate the face mask and then wipe it over something. So it’s really not a good idea and doesn’t help” (Ref 4).
The arguments against masks are:
1. A mask gives a false sense of security
2. Transmits infection by not being used correctly
3. Depriving medical staff of scarce resources
One of the best sources of evidence based medical research is the Cochrane reviews. These follow strict protocols looking at all the data available. Two reviews on masks have been undertaken in 2011 and 2020. In the 2011 (Jefferson et al. “Physical interventions to interrupt or reduce the spread of respiratory viruses. 2011” )(Ref 5) The key data is presented as a table shown below:
The Effect size is given as an odds ratio for 9 interventions. The smaller the number the greater the positive effect. All the interventions had a statistically significant result. The effect size indicates how low was the chance of infection. So, N95 masks – the ones used in hospitals came out best (0.17) and wearing a mask was also well worth it.. And wearing a mask is more effective than just washing hands (11 times a day!). Clearly the best strategy is to COMBINE these interventions – Gloves, mask, handwashing and eye protection.
The 2020 review was a follow up on the first metaanalysis. (Burch & Bunt. Can physical interventions help reduce the spread of respiratory viruses? 2020) This paper concluded
The best evidence (moderate certainty) was for handwashing plus masks.
Why wouldn’t the results show that mask are effective when they are taken for granted in a medical setting? The evidence then supports the wearing of masks as they provide real protection. Of course its not perfect protection but lets not allow the perfect to be the enemy of the good.
The second point is that you have to handle masks carefully or they spread infection. So we should wash our hands after taking the masks off? Of course. Why is that a greater risk for members of the public compared to medical staff? Not clear. Take the mask off carefully lay it on a disposable paper towel. Use this to place it in the washing machine – Ill get to this in a moment – and wash your hands after putting the paper towel in an appropriate bin. Yes you can re-use masks if kept for personal use and washed after each use. Best washed at a high temperature.
Final question is about what mask to wear. This is irrelevant to the primary question of do masks work. If they do then we should use them. If you don’t wish to use medical masks because of the concerns over the supply chain then do as I do and make your own. Or just cover your face with a closely woven fabric – yes its that simple and CAN cut transmission by up to 50% – our way our of lockdown!
Here are some ideas:
My preferred option is made from a HEPA vacuum bag. The filtration is better than the N95 respirators (the ones used by the NHS):
However lots more options are available and a good place to start is #Masks4All (ref: 7) good luck and meet me (at 2m separation!) in the Co-op wearing your mask!
More to follow. Watch this space.
A recent update has begun to strengthen the case for wearing masks/face coverings in places where social distancing is difficult (public transport?) or places of work. A recent study from the University of Hong Kong has made a strong case for covering up to protect others and also ourselves. Have a look at this summary from Dr. John Cambell:
Ref 1: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public/when-and-how-to-use-masks
Ref 2: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html?
Ref 3: https://twitter.com/surgeon_general/status/1233725785283932160
Ref 4: Dr Jenny Harries with the Prime Minister, Boris Johnson, at 10 Downing Street, March 11th, 2020. https://twitter.com/10DowningStreet/status/1237760980450451456
Ref 5: Jefferson et al. Physical interventions to interrupt or reduce the spread of respiratory viruses. Cochrane Database Syst Rev. 2011. https://www.ncbi.nlm.nih.gov/pubmed/21735402
Ref 6: Burch & Bunt. Can physical interventions help reduce the spread of respiratory viruses? 2020 https://www.cochranelibrary.com/cca/doi/10.1002/cca.2965/full
Ref 7: https://masks4all.org/story
2. How we measure risk:
You will have seen or heard the latest ‘good news’ that the virus just got a lot more dangerous – 30% more dangerous. But what does that mean. Will 30 people out of every 100 over 60 who catch the disease die?
I have been annoyed by the scare mongering going on at the moment. Apparently the new COVID mutation B1.1.7 is 30% more ‘deadly’;. Ummm. Even the New Scientist reports the news that the virus is ‘30% more deadly’;. What annoys about this statement is the ready recourse to the most dramatic form of presentation that the data can allow. What the New Scientist and many other newspapers are doing is quoting the Relative increase in risk from the virus. Not the increased absolute risk that each of us is exposed to if we meet the specific criteria of the situation. Numbers without context become misleading. I do wish people would use absolute risk measures rather than this over inflated ‘relative risk’ measure. let me explain by going back to a classic example of this ‘hyperinflation’ technique.
Back in 1997 a new drug was released that it was claimed would help reduce the risk of dieing from heart attack and stroke. That drug was the statin, Lipitor. It would make Pfizer billions and become the biggest selling drug of all time.
The manufacturer claimed that it reduced the risk of heart attack and stroke by 36%. Wow! – wouldn’t we all want that! Except the small print of blue text on a pale blue background (I wonder why?) says something different. This near invisible statement says that patients on Lipitor had a 1% reduction in risk. What is going on?
This claim was based on a Random Control Trial (RCT) taking place in the UK and Scandinavia; the ASCOT trial. The RCT is considered the gold standard of health research. This type of study involves two groups (at least) the Treatment group, those getting the drug, and the Control group who aren’t. Both groups should be as identical as possible. This is usually achieved through the random allocations of subjects to the two haves of the study.
The research was focussed on blood pressure (hypertension) lowering drugs and a statin (Lipitor). The subjects were people with raised blood pressure who had NOT had a heart attack but may have had a stroke or transient event. Of these, a number (over 10k) were selected who had a blood cholesterol level below 6.5 mmol/L They also had to have 3 other risk factors for heart disease. This group were white, male and over 63 old. The trial duration was planned to be 5 years long but ended up just 3.3 years duration. The Treatment group were given Lipitor (10mg) and those in the Control group were given a placebo.
In the trial the measured outcomes were the occurrence of fatal, non-fatal or silent heart attacks. It can be argued that ‘hard’ endpoints are the best measure as these are clear cut and of importance to the patient. This would mean only the fatal heart attacks should be counted. Adding in other ‘softer’ endpoints can lead to some confusion on what the real effect is that matters.
In the part of the study focussed on Lipitor it was found that total cholesterol and Low-density cholesterol (LDL fell by 1 mmol/L relative to placebo. So somebody entering the study with a Total Cholesterol (TC) of 6 mmol/L would end up after 3.3 years with a level of 5mmol/L Pretty small effect but it might be clinically significant and that was what the trial was trying to establish.
The endpoints of the trial showed a small difference between the two groups. There were 100 ‘events’ – mostly non-fatal Heart attacks in the treatment group of 5000 and 154 events in the placebo arm, also about 5000 subjects. Expressed as percentages that was 1.9% of people taking Lipitor experienced an ‘event’ while 3% in the placebo group did the same.
However if you look just at fatal heart attacks the difference between the two groups shrinks to just 0.12% absolute risk advantage of taking Lipitor. The number to treat now expands to 800 i.e. you need to treat 800 people with a high risk of heart attack to benefit 1 individual. And of course with few women in the trial the data did not show any significant differences.
So ‘astoundingly good’ were these results that the trial was stopped early. Instead of 5 years it was ended after 3.3 years. Really?
In medical and research literature generally there are accepted ways of presenting data.
1. Relative Risk/reduction:
The Placebo group suffered 3% events while the Lipitor group had 1.9% rate. A reduction of 1.1%. Thus the Relative reduction can be represented as 1.1/3 x 100 = 36%
You can see how more advantageous that figure is to the presentation of results than next method.
2. Absolute risk/reduction:
This is the recommended method in that its the way most serious journals have said data should be presented.
Here the reduction is presented as 1.1% There is no percentage inflation of small differences as is apparent in the relative risk method. But the problem for drug companies is that trying to sell their products on the basis of a 1.1% reduction of risk of heart attacks is much more difficult compared claiming a 36% reduction.
3. Number needed to treat:
This method was introduced to clarify the real risk to people. It is the measure of how many people would need to be treated (with Lipitor in this case) for 1 person to benefit. If we round the data to make the situation clear, a 1% absolute risk reduction would require 100 people to be treated over 3 years for 1 person to benefit. Put the other way 99 people will have the same outcome as if they had NOT taken Lipitor.
It is also worth pointing out that little data is provided on the side effects of the drug.
Which methods gives the clearest picture of our actual risk? I’m happy to say that the relative risk method gives the most distorted view from the individuals perspective. Take Lipitor and your chances of having a heart attack of any variety is reduced by 1.1% not 36%. Perhaps we should all be better statisticians but the reality is that the relative risk calculation clearly serves the interests of the marketing people at Pfizer.
Of course the advert was a great success and made Pfizer a lot of money. Oddly, the people on Lipitor didn’t live any longer lives, they died just as often as those not taking the pill. All cause mortality, as it is known was the same.
So don’t become alarmed by the ‘30%’ rise in the killer strength of the virus, as your risk has in fact only risen by 0.3%. Instead of 10 people out of 1000 over 60 who have caught the disease dying, now it will be 13. Worse, yes but our risk is better seen through the clarity of absolute risk calculations than the over dramatised presentation of relative risk.