Preprint A Bayesian Survival Analysis on Long COVID and non Long COVID patients: A Cohort Study …, 2024, Jiang+

[SNT Gatchaman]

A Bayesian Survival Analysis on Long COVID and non Long COVID patients: A Cohort Study Using National COVID Cohort Collaborative N3C Data
Sihang Jiang; Johanna Loomba; Andrea Zhou; Suchetha Sharma; Saurav Sengupta; Jiebei Liu; Donald Brown; N3C consortium

Since the outbreak of COVID-19 pandemic in 2020, numerous researches and studies have focused on the long-term effects of COVID infection. The Centers for Disease Control (CDC) implemented an additional code into the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for reporting 'Post COVID-19 condition, unspecified (U09.9)' effective on October 1st 2021, representing that Long COVID is a real illness with potential chronic conditions.

The National COVID Cohort Collaborative (N3C) provides researchers with abundant electronic health records (EHR) data by aggregating and harmonizing EHR data across different clinical organizations in the United States, making it convenient to build up a survival analysis on Long COVID patients and non Long COVID patients among large amounts of COVID positive patients.


Link | PDF (Preprint: MedRxiv)

 

[SNT Gatchaman]

Two main categories of biases in cohort studies include selection bias and information bias. In a cohort study of COVID patients, two elements of selection bias, sampling bias and confounding by indication, might lead to an unrepresentative sample of the population; and among information bias, observer bias and lead-time bias on Long COVID patients might affect the accuracy of survival analysis.

Competing risk in survival analysis is common. A competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. In studies on cardiovascular disease and depression respectively, failure to account correctly for competing events can result in unexpected consequences, including overestimation of the probability of the event and mis-estimation of the magnitude of relative effects of covariates on the outcome.

A[t] the completion of the study, there are 7,376,162 COVID positive patients in N3C as in the COVID summary table, and among them there are only 74,240 patients with the U09.9 diagnosis code.

After the matching process, the final cohort has 119,466 patients in total with 59,733 Long COVID (U09.9) patients and 59,733 COVID positive control patients.

According to the posterior estimation of parameters, patients with Long COVID (U09.9) indicator are more likely to have shorter survival length, patients with mild COVID symptoms are more likely to have longer survival length, and the obesity indicator is not significant with respect to the survival length.

 

[SNT Gatchaman]

7,376,162 COVID positive patients in N3C [...] only 74,240 [LC patients]

74,000 / 7.4m is 1%.

 

[rvallee]

The only thing worse than garbage data is corrupted data. And it's the people entering the data that corrupt it. On purpose. Not with a purpose to corrupt the data, their purpose just happens to completely corrupt it, as a choice. It's every bit as bad and corrupt as election poll workers fixing ballots, but without any verification mechanism and no recourse.

All official data on chronic illness is corrupted. All of it. None of it can be relied on, it's basically fiction. But it can't be fixed because the first step to solving a problem is acknowledging it, and medicine is incapable of that, prefers to sacrifice millions of lives than admit they got anything wrong. They don't even realize they're doing that, and simply deny and corrupt any data about it.

It's been 4.5 years and we can't even get reliable data. You can't do science without reliable data. The magnitude of this failure is so extreme it basically can't be discussed rationally, because there cannot be acknowledgement without major consequences, and, well, they don't want that, don't even understand that there's a problem at all and will refuse any step towards fixing it.

So what do you do when the people who are technically responsible for solving a problem are themselves the problem?