Right here, for 50th time
"Among the 335,382 participants who completed symptom sur- veys, 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%) com- pared 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)."
There's no 76% or 68% anywhere in that paragraph. There's a 0.76% and a 0.68%, but they have nothing to do with "the percentage of serum tests that are positive" (that number was 22%). And the 0.76% applies to the control group, but you used it for the intervention group.
Again, please explain where you got those numbers from.
Yeah duh. Of course that's my claim. Because that is what the study claims it did (see quote from above - now 51st time I've pointed it out to you)
Huh? Why would I say that? I didn't say that
Huh? You just said that. You said that they extrapolated the results from the blood tests. Well the only results from the blood tests were "22% of the blood tests were positive for COVID and 78% weren't. If you are going to claim that that result was extrapolated to the asymptomatic population, there would have to be a claim somewhere that all the people who weren't sick were 22% positive for COVID and 78% not positive for COVID. But nobody made that claim. And most certainly nobody made that claim in the paragraph you quoted.
The study either ends with the blood serum test or those results get extrapolated to a larger population or this study is a complete miss mash of disjointed concepts and objectives (I think the latter) that you are defending in troll like fashion.
The study ends with counting all the people in each group, including the group who were symptomatic and had a blood test positive for COVID. I can't even tell what you are trying to claim here, it's so incoherent and uninformed. Are you trying to claim that they don't count anybody but the number of people in one group, and one group only?
OK. So you say now that the study ends with the blood serum test treatment villagers = 5,006 and 4,971 control villagers.
That's it. Those results define the study. If that's the case, then the study is baloney. You cannot make statements about how the masks worked (or didn't) for hundreds of thousands of subjects from a small subset who got blood tested).
The study ends with collecting all the information you need to determine the outcome. In order to determine "symptomatic seroprevalence" (prevalence is a rate, so you need the numerator and denominator), you need the following information for each group:
1. Number of people completing a symptom survey
2. Number of people completing a symptom survey who had symptoms of COVID
3. Number of people completing a symptom survey who had symptoms of COVID and consented to a blood test
4. Number of people completing a symptom survey who had symptoms of COVID, consented to a blood test, whose blood test was positive for COVID
YOU MOST DEFINITELY DO NOT ONLY COLLECT THE LAST NUMBER. YOU NEED ALL OF THOSE NUMBERS TO CALCULATE THE OUTCOME - SYMPTOMATIC SEROPREVALENCE.
I agree with those numbers. But what do they mean? How do they use them in a calculation to show significance?
They are the numbers you use to calculate "symptomatic seroprevalence". Please note, the numbers in the list are for the whole population. You can easily figure out the numbers for each group using the same information (the results paragraph, Table A1, and post #86).
Numerator = number of people who were symptomatic and whose blood tested positive for COVID (line 4 from above).
Denominator = number of people completing a symptom survey (line 1) - number of symptomatic people who did not consent to a blood test (line 2 minus line 3).
As I mentioned, the numbers I gave earlier were for the whole population (not one set of numbers for Intervention and one set for Control). But I will show the calculation for illustration purposes using those numbers.
Line 1 = 335,382
Line 2 = 27,166
Line 3 = 10,952
0Line 4 = 2293
2293/(335382 - (27166-10952)) = 2293/319168 = 0.0072 or 0.72%.
If we use the numbers per group instead, we have:
1131/(174171 - (13273 - 5414)) = 1131/166312 = 0.0068 or 0.68% for the intervention group
1162/(161211 - (13893 - 5538)) = 1162/152856 = 0.0076 or 0.76% for the control group.
Again, are you now telling me they calculated from the subset of serum positive? Like I did in comment 155?
If not, show your calculation. Put up or shut up time for you. I don't think you got it, but prove me wrong.
Someone who claims to be an actuary does not need to be told how to calculate a prevalence. They also know that an ANOVA is not an appropriate method for comparing count data, and a Chi-square test is.
Just saying.
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