Dr. Ron Brown Discusses Outcome Reporting Bias in COVID-19 mRNA Clinical Trials | Podcast

CORRECTION: The following graphic replaces the graphic in the video at 5:44.

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Dr. Ron Brown of Waterloo University talks about his Cambridge and Medicina Paper. We discuss relative risk reduction and absolute risk reduction measures in the evaluation of clinical trial data.

Dr. Ron Brown discusses the danger of outcome reporting bias.

Articles Referenced:

Medicina | Free Full-Text | Outcome Reporting Bias in COVID-19 mRNA Vaccine Clinical Trials (mdpi.com)


Health Lessons Learned From Biases in Coronavirus Mortality Overestimation (cambridge.org)



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  1. Excellent report, Dr. Brown and thank you, TSN, for having him on. So very refreshing to hear Dr. Brown untangle the spidery web of obfuscation the American press has engaged in! When the “95% efficacy” numbers came out in December, my husband and I looked at each other, laughed and said in unison, “I smell a rat!” As a matter of fact, that rat turned out to be a very, very large whopper! Get the traps ready. Thanks again.

  2. The mixing of the case fatility rate and the infection fatality rate has been a key problem (strategy?) in the media since the very beginning.

    We still don’t know what the IFR is. But it is likely to vary very significantly between populations and demographs, in my belief this is contingent on the prevalence and pathogenicity of latent protozoan parasitic infection and the corresponding immunomodulatory effects when infected with covid.

    This in turn is contingent on a large number of determinents including immunological status, age, environmental exposures [air, water, food], nutritional status [sugar and simple carb intake], vitamin-d status [seasonal and skin tone], physical fitness, social stress / trauma, recent immunological insults…

    Somewhat esoteric paper about it here: https://www.sciencedirect.com/science/article/abs/pii/S175094670900097X One would need to read the whole review to understand why it’s relevant to my theory. But this is why antiparasitics are effective at reducing covid mortality.

    Antibody tests turn out to not be very useful in identifying even recent cases. In the UK, random population level antibody sampling was used to infer the national case rate and thence the IFR which varied between high income countries (~1.1%) and low income countries (0.23%). However the IgM signal depends on timing and severity…there are many false negatives. So case rates are understated and the IFR is lower, by who knows how much.

    The real tell is T-Cell testing, e.g. the adaptive biotechnologies test. This will pick up a t-cell signal from at least 9 months prior to infection [I suspect for your entire lifetime] regardless of symptom sevirity. Population sampling t-tell testing would give the total case rate and thence the IFR, per study population. It would also prove empirically national and demographic variations in IFR’s. The comparable numbers would be interesting and raise some difficult questions about Western public health policy.

    Protozoan parasitic infection elicits pro-inflammatory cytokine production: https://www.sciencedirect.com/science/article/abs/pii/S0014489416300959
    An inflammatory cytokine signature is key predictor of covid severity: https://www.nature.com/articles/s41591-020-1051-9
    Pathogenicity of protozoan parasites increases from environmental stressors: https://www.sciencedirect.com/science/article/abs/pii/S175094670900097X [social stress too incidentally, see immunopsychiatry]