This also matches the percentages given - 48,511 is 31.09% of 156,011, not 156,468. Not a major issue, but should probably be corrected
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As to residual confounding - there are numerous potential differences between people who got packs and those who didn't. For one thing, I don't see any discussion of SES in the paper, which is a pretty big potential confounder
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Replying to @GidMK @EdoajoEric
Again, you ground your mistrust in tiny impactless items. You have an enormous improvement in hospitalization (76%) and your arguments absolutely can't explain even 10% of the impact. Do you think your points would turn the results insignificant?
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Replying to @jjchamie @EdoajoEric
Of course - residual confounding can entirely reverse the effects of an analysis. That's a very well-demonstrated epidemiological fact https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704-ep713_confounding-em/BS704-EP713_Confounding-EM4.html …
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Replying to @GidMK @EdoajoEric
They controlled in the analysis by age and comorbidities, and looked at more than 230,000 people. The results was 76% less hospitalizations in the group with ivermectin. After the study they rolled our IVM in the country and every COVID metric improved. CFR dropped 80%
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Replying to @jjchamie @EdoajoEric
So firstly, same issue as in your analysis - it's almost certain people in Mexico were taking the drug well before these packs were handed out. Age and comorbidities were controlled for in a fairly crude way, and there's a large potential for residual confounding
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Replying to @GidMK @EdoajoEric
Agreed, it's almost certain people in Mexico were taking the drug. This factor potentially reduces the study positive result because some in the control group are treated too Your recurrent card is "potential for residual confounding" This unknown can't explain big differences
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Replying to @jjchamie @EdoajoEric
Of course it can! This is a pretty fundamental part of epidemiology
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Replying to @GidMK @EdoajoEric
A 76% reduction in hospitalization in the ivermectin group. Same region, same season, same variant, controlled for age and comorbidities based on 230,000 people. Your theory that it's something else is an isoteric-sounding explanation. You aren't defending the truth.
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Replying to @jjchamie @EdoajoEric
Same income? Same HbA1c? Same access to hospitals? Same index of homelessness? Etc etc etc. As the authors say, you can only control for the factors you measure, and even with comorbidities they only had data on a small number it seems
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The simple fact is that large numbers DO NOT CONTROL for underlying issues such as confounding - they just make your confidence intervals narrower
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