7 hours later…
08:42
The question asks whether surgical masks are _"effective"_ for source control.
This answer fails to address that question. At all.
This answer fails to address that question. At all.
We see two references: one is a lab study, from which we simply cannot infer to effectiveness, as it remains on a theoretical level, and makes unrealistic assumptions to support a mechanistic plausibility. Nothing more.
The other is large garbage trial that mainly claims to look at the personal protective equipment level. And fails to do that adequately. The first version of this answer had one of the most obvious flaws of the findings in the data presented exposed as a nice illustrative graph, that showed: 'surgical masks were of no use for people under 50 and a small effect for those above that age'.
Starting conditions/base-line are not sufficiently investigated, randomised, controlled.
What is quite conspicuously missing: what was the previous disease burden in each case locally? If a village has been hit hard in the past, then its inhabitants have now built up excellent immunity and tend not to benefit from a mask effect, which is only theoretical anyway: a large proportion of them will not show any symptoms. With such a tiny effect, precision is needed, such a systematic bias is unforgivable.
What is quite conspicuously missing: what was the previous disease burden in each case locally? If a village has been hit hard in the past, then its inhabitants have now built up excellent immunity and tend not to benefit from a mask effect, which is only theoretical anyway: a large proportion of them will not show any symptoms. With such a tiny effect, precision is needed, such a systematic bias is unforgivable.
The PCR testing of madness that has already become common. When forming groups, only test results were sought, but not at least the test rate and thus the positive rate was determined. Actually, one should have looked at the actual prevalence. But this is so 2019...
If you first take the broken 'case' definition and later work with seroprevalence, then of course you don't keep anything constant in the measurement methods. ("designed to pair unions that were similar in terms of (limited) COVID-19 case data", p7)
It is presumptuous to even assume a sample quality with an extremely low overall test rate in this kind of world area, 'balanced' only by the enormous number of people in the sample, but with highly fluctuating test strategies and consistently uncontrolled test quality.
The bottom line is that no reasonable cohorting/grouping/pairing was done at all. Valid control data on seroprevalence in the villages was previously assumed, but then sorted by 'cases' to measure seroprevalence again. Unclean.
So what was done overall? Only mask mandate introduced? Or a gigantic bundle of interventions? Signage, incentives, nudges, mask colours, phone and observation checks, text-message reminders, physical distancing, etc.
Most important: the seroprevalence after the intervention was not systematically and representatively surveyed at all!
> "Not all symptomatic seroprevalence is necessarily a result of infections occurring during our intervention; individuals may have pre-existing infections and then become symptomatic (perhaps caused by an infection other than SARS-CoV-2)." (p23)
> "Symptomatic Seroprevalence Among the 335,382 participants w*ho completed symptom surveys, *27,166 (8.1%) reported experiencing COVID-like illnesses during the study period."
> "More participants in the control villages reported incident COVID-like illnesses (n=13,893, 8.6%) compared with participants in the intervention villages (n=13,273, 7.6%). Over one-third (40.3%) of symptomatic participants agreed to blood collection. Omitting symptomatic participants who did not consent to blood collection, symptomatic seroprevalence was 0.76% in control villages and 0.68% in the intervention villages.
> Because these numbers omit non-consenters, it is likely that the true rates of symptomatic seroprevalence are substantially higher (perhaps by 2.5 times, if non-consenters have similar seroprevalence to consenters)." (p22)
> "Table A1 summarizes sample selection for our analysis. We began with 342,126 individuals at baseline. We were able to collect follow-up symptom data (whether symptomatic or not) from 335,382 (98%). Of these, 27,166 (7.9%) reported COVID-like symptoms during the 8-weeks intervention in their village. We attempted to collect blood samples from all symptomatic individuals.
> Of these, 10,952 (40.3%) consented to have blood collected, including 40.8% in the treatment group and 39.9% in the control group (the difference in consent rates is not statistically significant, p = 0.24). We show in Table A2 that consent rates are about 40% across all demographic groups in both treatment and control villages."
> "As such, the sample for which we have symptom data is much larger than the sample for whom we have serology data. We tested 9,977 (91.1%) of the collected blood samples to determine seroprevalence for SARS-CoV-2 IgG antibodies. Untested blood either lacked sufficient quantity for our test or could not be matched to individuals from our sample because of a barcode scanning error.
> In our primary outcome analysis, we drop individuals for whom we are missing symptom data or who did not consent to blood spot collection. For the analyses where symptomatic status is the outcome, we report results using both this smaller sample, as well as the larger sample of all individuals for whom we collected symptom data."
We have above all other short comings of this unclean study a clear social desirability effect on suppressing symptom reporting, self-selection bias on blood sampling etc.
Only those reporting symptoms get called for testing, and of those quite some refused. Only 40% decided to get pricked? How do they differ/compare to those who did not consent?
And that's really a final straw on the camel's back: can you give any better explanation for the otherwise nonsensical result that this 'intervention package' (which by sheer coincidence also includes some masking shenanigans) was shown ineffective for people younger than 50? And that "my mask protects you" is also invalidated by this, as old people are deemed protected, by younger were not?
@JonathanReez What I am asking you is why you deleted your own comment highlighting this fatal flaw exposing the 'results' of this study as flawed, edited out the obvious graph, and awarded the bounty to an answer that failed to answer the question adequately, using unsuitable references and terrible studies. What the bounty system says & enforces cannot be all the answer to it? (Isn't it: Auto to new answers, auto at half the rate etc?))
3 hours later…
11 hours later…
23:00
Granted. But an explanation as to how your very own observation towards the inadequacy of the presentend material relates towards your very convenient edit to remove the most obviously damnining & even invalidating damling result remains: xcuseme_ funny?
But you should need to explain: how CAN YOU award a bounty on an answer, that you yourself found so much lackign, that in within your own 'conrfirmation bias seeking obsession', you yourself, had to edit out the most obvious data from the original study to get to your point0
@JonathanReez You have plenty of material to (at leats finally now, eventually) reconsider your erroneous judgement: perhaps comment below an answer, saying very explicitly, that you yourself, firmly& personally convinced, do not believe the BS told by governemnts, or Skeptics:SE, as signified by arbitrary bounties… So go 'onn forget it…
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