Digging Around

Science is often not what it's cracked up to be.

I have been considering all the holes in this preprint study about Covid reinfection outcomes.

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The premise of the study is to find out the risks of Covid reinfection on all cause health issues. From the abstract:

“We use the national health care databases of the US Department of Veterans Affairs to build a cohort of people with first infection (n = 257,427), reinfection (2 or more infections, n = 38,926), and a non-infected control group (n = 5,396,855) to estimate risks and 6-month burdens of all-cause mortality, hospitalization, and a set of pre-specified incident outcomes. We show that compared to people with first infection, reinfection contributes additional risks of all-cause mortality, hospitalization, and adverse health outcomes in the pulmonary and several extrapulmonary organ systems (cardiovascular disorders, coagulation and hematologic disorders, diabetes, fatigue, gastrointestinal disorders, kidney disorders, mental health disorders, musculoskeletal disorders, and neurologic disorders); the risks were evident in those who were unvaccinated, had 1 shot, or 2 or more shots prior to the second infection; the risks were most pronounced in the acute phase, but persisted in the post-acute phase of reinfection, and most were still evident at 6 months after reinfection. Compared to non-infected controls, assessment of the cumulative risks of repeated infection showed that the risk and burden increased in a graded fashion according to the number of infections. The constellation of findings show that reinfection adds non-trivial risks of all-cause mortality, hospitalization, and adverse health outcomes in the acute and post-acute phase of the reinfection.”

I follow “Dr. Been” a medical research page on YouTube. He has great stuff, but in bringing up what this study missed, I’d say there’s quite a lot more fallibility herein than he even addresses.

The study looks at outcomes for people with confirmed cases of Covid, and their all cause issues post reinfection.

It compares those who were in the VA system, 90% of which are men. They bring up this gender bias and other potential limitations in the study. It accounts for groups of the unvaccinated, single vaccinated or multiple vaccinated, but it leaves off the uninfected vaccinated as a control or third group. Are these all cause issues from reinfection or possibly from vaccination? It’s a fair question. This is something “Dr. Been” brings up. The study also mentions as a limitation the issue of confounding symptoms. They don’t know if the all cause symptoms after reinfection may be lingering symptoms of the first infection. This potentially throws off their findings. They also brought up that data is still emerging and could change quickly as more data is revealed. That seems to be the standard disclaimer with any Covid research.

The biggest issue I had was only addressed in part by their first addressed limitation as described by the study. They didn’t know how many people who were positive went untested. That’s a big problem. Because, after vaccination, a lot of doctors for months assumed their patients had a 95% efficacy vaccine and stopped testing the vaccinated regardless of symptomology. Hospitals had protocols still in place for months (maybe even in some today) wherein all unvaccinated were required a test for admission but the vaccinated only required tests if they fit two or more criteria of acute symptomology. This throws off the findings. Imagine if there were reinfected people not picked up in the study because their doctors stopped testing them after vaccination. Surely this happened to a significant number of people early on.

Another issue I noted which was not mentioned by “Dr. Been” or the study is how many confirmed cases initially were asymptomatic and of those, how many were false positives. The first year of this disease, we had no vaccine. During that time PCR tests were run willy nilly with ultra high cycle counts churning out so called “confirmed” positive case numbers. After scrutiny, even Fauci was saying that above 40 cycles, the PCR was meaningless.

This is the main problem we have now in hindsight. It was quite coincidental and convenient that the WHO released guidelines for the PCR to be turned down to a meaningful cycle number in order to prevent false positives, and the FDA finally echoed that sentiment and adopted it in labs across the country immediately after the release of the vaccine. In so doing, case numbers would have dropped precipitously across the country from the removal of rampant false positives generated by the machine spitting out useless data for an entire year before correction, giving the appearance the vaccine had been a success. But when you stop having a machine produce consistent false positives, you have to go back and question whether any of the precorrection data of positives was significant. Add this to the sudden cancellation of weekly testing required of symptomatic and asymptomatic people alike in workplaces across the country as a prerequisite for employment- unless they abstained from vaccination, and you have a cluster of untested people and only testing a particular data set. This has to make a difference. Last, you stop testing mildly symptomatic people who were vaccinated as a prerequisite for hospitalization and the vaccinated stop having themselves tested at all absent acute symptoms, and that’s when the whole ordeal starts to look untrustworthy.

False positives could mean that the majority of first infections were bogus. It could mean that we aren’t looking at “reinfections” at all (the premise of the study), but rather people’s initial infections being misreported as reinfections.

I have to wonder if this constant assumption that data sets are indicative of what one imagines they are when attempting a study of such magnitude based on all kinds of manufactured circumstances and labels which do not necessarily or even likely apply is a new phenomena or whether scientific research has always been this corrupt and contaminated.

My issue is this. I’m new to the field of investigative research and scientific review. I am a complete amateur. What I am finding in every single study I read is dubious to me. I am confronted daily with something I, as a complete nobody, can see is a glaring problem. I am seeing holes and assumptions in every single piece of research I inspect, peer reviewed or not regardless of my sense of the likelihood of the outcome. Why are researchers not addressing these issues more… um, scientifically?

While this isn’t science, there’s a pretty substantial likelihood that the funding of most scientific research is shaping the outcomes. I can’t imagine this wouldn’t have some effect across every field of research. While there are fields in which I am more well versed, I seem to be finding the holes everywhere I look across the board with even modest, amateur scrutiny.

What bothers me, too, is that those who just accept whatever researchers in the fields of science and medicine hand them without any question will often tell me I’m unqualified to make such claims. They would rather I, too, sit back without question or thought and accept the findings without looking at the methods and data sets. They doubt my ability to see potential holes in these because I don’t have the proper certification. But what I’m finding doesn’t require a degree in astrophysics to surmise. It just requires the most elementary critical thought.

I don’t believe that I have to have a degree in nuclear physics to ask questions nor do I feel that suggesting funding shapes outcomes is an outlandish accusation. I just hate that we trust people to specialize in certain areas of scientific investigation to give us a fair review or heads up about the directions of research, and that I am finding more and more that multiple fields are being blatantly corrupted by financial influences for one motive or another across the board.

I guess this means we all have to start thinking for ourselves. I’m not saying that like it’s a bad thing. I just know so many will perceive it to be.