Association of Self-reported COVID-19 Infection and SARS-CoV-2 Serology Test Results With Persistent Physical Symptoms.., 2021, Matta et al

Full title: Association of Self-reported COVID-19 Infection and SARS-CoV-2 Serology Test Results With Persistent Physical Symptoms Among French Adults During the COVID-19 Pandemic

Key Points

Question Are the belief in having had COVID-19 infection and actually having had the infection as verified by SARS-CoV-2 serology testing associated with persistent physical symptoms during the COVID-19 pandemic?

Findings In this cross-sectional analysis of 26 823 adults from the population-based French CONSTANCES cohort during the COVID-19 pandemic, self-reported COVID-19 infection was associated with most persistent physical symptoms, whereas laboratory-confirmed COVID-19 infection was associated only with anosmia. Those associations were independent from self-rated health or depressive symptoms.

Meaning Findings suggest that persistent physical symptoms after COVID-19 infection should not be automatically ascribed to SARS-CoV-2; a complete medical evaluation may be needed to prevent erroneously attributing symptoms to the virus.


Abstract

Importance After an infection by SARS-CoV-2, many patients present with persistent physical symptoms that may impair their quality of life. Beliefs regarding the causes of these symptoms may influence their perception and promote maladaptive health behaviors.

Objective To examine the associations of self-reported COVID-19 infection and SARS-CoV-2 serology test results with persistent physical symptoms (eg, fatigue, breathlessness, or impaired attention) in the general population during the COVID-19 pandemic.

Design, Setting, and Participants Participants in this cross-sectional analysis were 26 823 individuals from the French population-based CONSTANCES cohort, included between 2012 and 2019, who took part in the nested SAPRIS and SAPRIS-SERO surveys. Between May and November 2020, an enzyme-linked immunosorbent assay was used to detect anti–SARS-CoV-2 antibodies. Between December 2020 and January 2021, the participants reported whether they believed they had experienced COVID-19 infection and had physical symptoms during the previous 4 weeks that had persisted for at least 8 weeks. Participants who reported having an initial COVID-19 infection only after completing the serology test were excluded.

Main Outcomes and Measures Logistic regressions for each persistent symptom as the outcome were computed in models including both self-reported COVID-19 infection and serology test results and adjusting for age, sex, income, and educational level.

Results Of 35 852 volunteers invited to participate in the study, 26 823 (74.8%) with complete data were included in the present study (mean [SD] age, 49.4 [12.9] years; 13 731 women [51.2%]). Self-reported infection was positively associated with persistent physical symptoms, with odds ratios ranging from 1.39 (95% CI, 1.03-1.86) to 16.37 (95% CI, 10.21-26.24) except for hearing impairment (odds ratio, 1.45; 95% CI, 0.82-2.55) and sleep problems (odds ratio, 1.14; 95% CI, 0.89-1.46). A serology test result positive for SARS-COV-2 was positively associated only with persistent anosmia (odds ratio, 2.72; 95% CI, 1.66-4.46), even when restricting the analyses to participants who attributed their symptoms to COVID-19 infection. Further adjusting for self-rated health or depressive symptoms yielded similar results. There was no significant interaction between belief and serology test results.

Conclusions and Relevance The findings of this cross-sectional analysis of a large, population-based French cohort suggest that persistent physical symptoms after COVID-19 infection may be associated more with the belief in having been infected with SARS-CoV-2 than with having laboratory-confirmed COVID-19 infection. Further research in this area should consider underlying mechanisms that may not be specific to the SARS-CoV-2 virus. A medical evaluation of these patients may be needed to prevent symptoms due to another disease being erroneously attributed to “long COVID.”

Open access, https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2785832

 

Science Media Centre reactions to this study, https://www.sciencemediacentre.org/...st-results-with-persistent-physical-symptoms/

Dr David Strain, Senior Clinical Lecturer, University of Exeter, said:
...

“It misses a very simple explanation. Whether the participants had COVID or not, there is no doubt that they were experiencing some illness that they attributed to COVID. There are multiple viral illness other than COVID that cause “long symptoms”. Historically these symptoms may have been labelled a post viral fatigue, or in more extreme cases Myalgic Encephamlomyelitis (ME). These diagnoses have been surrounded in stigma and therefore people may do not come forward with their symptoms or have them recognised.

“Alternatively, the preconception by many, that COVID is the only viral illness that causes these ongoing problems, may have led many to assume their initial illness was COVID based on the presence of their long symptoms. The acceptance of long-COVID as a post viral syndrome has allowed many people to have their conditions recognised, and hopefully will lead to wider research opportunities in related conditions such as ME.”

 

Missed at first but one of the authors is Brigitte Ranque, who as best as I can tell is pretty much French Trudie Chalder. She also appears to be advising the French government along with some of her like-minded colleagues.

One of the things that isn't spoken out loud is that driving governments' denial of Long Covid are medical advisers assuring them there is nothing medically relevant here. None of this is random, there are medical professionals recommending to overlook it, it's not a passive thing.

 

Opinion piece about this paper in Medscape, https://www.medscape.com/viewarticle/962479

The author is on Twitter,
https://twitter.com/user/status/1458095790472171531

 

Only had a brief look at the paper but one issue might be that they divided groups based on the belief they had. I would be more interested in knowing whether seropositivity was associated with more symptoms or not in general, regardless of beliefs.

For example, based on the data reported in table I would think that more patients were reporting symptoms in the serology positive group (62 +22)/(638 + 453) = 7.7%, compared to the serology negative group (625 + 57)/(25271 + 461) = 2.6%.

EDIT: only just noted that the authors conducted a logistic regression with seropositivity but without belief (it's called model 2 in the paper). In this model, "a positive serology test result was associated with 10 categories of persistent symptoms". It's only when the regression adds beliefs that this association disappears.

Adding things to the regression, however, may not always be a good idea. Often it helps to clarify things but sometimes it can also obscure relationships. I think this is called "collider bias" see: https://catalogofbias.org/biases/collider-bias/ This paper may be a good illustration of this.

How did model 3 become the conclusion of the paper and not model 2?

 

Some of the push-back on Twitter:
https://twitter.com/user/status/1458070959613792260

 

It's quite possible that people are better at judging whether they've had covid than serology.
https://twitter.com/user/status/1458095817169031179


Also, in this study 36% of patients with a positive RT-PCR test did not seroconvert. https://wwwnc.cdc.gov/eid/article/27/9/21-1042_article

 

He makes some good points.

People have asked questions on r/covidlonghaulers on reddit similar to "I had a virus earlier this year. The virus wasn't covid (my PCR tests were negative for covid) but I now have long haul symptoms. Can I be a long hauler?"

I've even seen a few posts along the lines of: "I got EBV four years ago and have been ill and unable to work with long haul symptoms ever since. Do I have a form of long covid?"

That they (and presumably their doctors) have never heard of PVS or ME/CFS shows how deeply this illness has been buried.

 

The problem seems to come down to the potential false negative rate for the serology test. They are assuming accuracy of serology testing and concluding the patients have abnormal beliefs. I think this is going to prove to be an incredible error of inference.

Non-seroconversion or early seroreconversion following previous positive PCR seems unusually high for this virus. 36-37% seems to be the commonly quoted figures. I've also seen comment about positive nucleocapsid but negative spike serology down the line from infection also - I can't find a reference to support this though, so if anyone has come across this already, please post.

 

It's far worse than that. It assumes that:

1. Everyone who is infected will get tested, even those not showing any symptoms (false)
2. Everyone who wants a test will get it (false)
3. All tests are 100% reliable at any time (also false)

It's basically compounding fractions and reporting that it still adds up to 100% but not being smart enough to notice that multiplying by fractions cannot add up. It's dumb dumb dumb and that medical journals publish this rubbish without exercising the tiniest effort at common sense and scientific literacy is evidence of a massive crisis of basic competence in medical research.

 

The authors write:

"we cannot exclude the possibility of misclassification regarding serology test results. On the basis of the present results, we estimate the prevalence of previous SARS-CoV-2 infection to be about 4%, and with a sensitivity of 87%, we would expect 139 participants to have false-negative results, which is less than 1% of those with negative serology test results. False-negative results were thus unlikely to have much influence on the associations between persistent symptoms and serology."​

But it's quite likely that many of those ca. 140 false negatives were in the group with negative serology that believed they had COVID. There were only 461 people in that group so that could have certainly messed up their analysis.

 

I'm not sure because when they did a logistic regression adjusting for reasonable confounders like age, sex, educational level, and income, positive serology was significantly associated with 10 different symptoms. So the analysis actually proved that having had COVID, whether you believe you had it or not, results in higher symptom prevalence.

It's only when they added belief in having COVID-19 that this relationship between positive serology and symptoms disappeared.

I think that's because very few patients with negative serology thought they had COVID (only 1.8%) compared to 41% in the group with positive serology. So belief is the better predictor of long-term symptoms but that doesn't mean that serology doesn't predict it as well. Belief may account for the variation in symptoms that serology also explains while it may, in addition, correlate with additional predictors (for example those with lingering symptoms may be more inclined to say they believe they had COVID) in a way that serology doesn't.

It looks like a mess-up in the statistical analysis. Would be interesting in hearing other's view on this, for example @Lucibee @Adrian

 

If I understand correctly everyone in this cohort was sent an antibody test kit and all 26 823 included participants had such test results. So I don't think this is the problem with the study.

 

Both can be true. If negative serology is related to more symptoms (as some studies have suggested, even in those who were PCR-positive), then it means "belief in having had COVID-19" is a better predictor of having had COVID (well, duh!), so the other variables will drop out. (I think - as far as I remember.)

False negatives can happen for a reason. They are not just statistical anomalies. The test is genuinely detecting no antibodies - because there aren't any, despite prior infection.

I saw some blog about collider bias - but this almost seems to be operating in the opposite direction. The problem is that I suspect you can turn it round to explain whatever explanation you want it to be. And of course they will.

 

No, that antibody study does *not* show that Long COVID is caused by “beliefs”

"The pandemic of Long COVID psychobabble continues. This week saw yet another terrible study claiming that long-haul COVID is just an illusion of human cognition, an illness-like experience rooted in psychological processes.

The widely reported “finding” was published in the medical journal JAMA Internal Medicine by a group of French researchers. Having crunched some numbers from a large scale population-based cohort study, they claimed to have found a meaningful statistical association between long-term COVID symptoms and “the belief in having been infected with SARS-CoV-2.” They even speculated that such “beliefs” might cause people to develop “maladaptive health behaviours,” invoking a classic psychiatric trope that blames sick people for making themselves ill through faulty reasoning."

https://thesciencebit.net/2021/11/1...ot-show-that-long-covid-is-caused-by-beliefs/

 

Also: if I understand correctly, the belief that is mentioned and analyzed in the paper is whether participants thought they were infected by the coronavirus (not whether they thought their symptoms were the result of that infection).

Also remarkable is that "at the time they answered this question, the participants were aware of their serology test results". So the people who said they thought they were infected but had negative serology, already knew they had negative antibody testing but still said they thought they had COVID? They probably had some reason to think this, so it's quite likely that most of the false negatives are in this group.

Two-thirds of the group that believed they had COVID said it was confirmed by some other medical test or physician other than the antibody test the authors used in their study. For example, 165 people said it was confirmed by virological or PCR test. That might be more reliable than the antibody test the authors used. So a bit audacious for them to claim that patients falsely attributed their symptoms to long COVID.

 

Brian writes:

"what they fail to acknowledge is that there was also a significant rate of “false alarms” (or “false positives”). According to the parameters they described themselves, some 40% of all positive antibody results would probably have been wrong."
​

But assuming, as the authors did, that 4% of the cohort got infected and that the test has a specificity of 97.5%. Then we would get:

26823 (total cohort) x 0.96 (number that did not get infected) * 0.025 (that was negative but got a positive result) = 644 false positives.

In the study, Only 1091 participants had a serology test result positive for SARS-CoV-2. So that would mean that 60% (instead of 40%) of these could have been false positives.

 

Excellent, yes, thank you that's right. I think I saw this exact 60% computation worked out on twitter also (although I can't find it now). I will think about whether a tweak is appropriate for my blog post (maybe "more than 40%"?) but I don't like to make too many changes unless what I've written is misleading. I could add a footnote... Let me think about it!

This paper just gets worse the more people look at it...

 

Don't think your blog needs to be changed a lot because all your arguments remain valid (they actually become even more so). A footnote will do I think or simply strikethrough 40 and write 60.

Thanks very much for keep on writing about flawed papers on ME/CFS and long covid.