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
Authors: Laetitia Marchand
Jean-Marc Sébaoun
Clément Gouraud
Nathalie Lydie
Xavier de Lamballerie
Jérôme Nicol
Celine Ribet
Xavier Thierry
Emmanuel Wiernik
Paola Mariela Saba Villarroel
Karen Milcent
Nathalie Druesne-Pecollo
Nazli Ayhan
Brigitte Ranque
Laetitia Ninove
Nathanael Lapidus
Nadège Senina
Sophie de Visme
Adriana Candea
Jean-Marie Gagliolo
Sofiane Kab
Cécile Zaros
Pascale Gerbouin-Rérolle
Laetitia Gressin
Santé, Pratiques, Relations et Inégalités Sociales en Population Générale Pendant la Crise COVID-19–Sérologie (SAPRIS-SERO) Study Group
Hélène Esperou
Toscane Fourié
Roselyn Gomes-Rima
Serge Hercberg
Hélène Blanché
Fanny Artaud
Younes Esseddik
Sandrine Couffin-Cadiergues
Julien Allegre
Céline Dorival
Elodie Lordmi
Cédric Lemogne
Thierry Simeon
Bertrand Geay
Valérie Morel
Clovis Lusivika-Nzinga
Morgan Seston
Emmanuelle Correia
Fabien Szabo de Edelenyi
Souand Mohamed Ali
Anass Ritmi
Lyan Hoang
Jean-François Deleuze
Stephane Le-Got
Delphine Rahib
Adeline Renuy
Mathilde Touvier
Valérie Benhammou
Camille Laplanche
Liza Belhadji
Stéphane Priet
Isabelle Goderel
Nicolas Hoertel
Gregory Pannetier
Cindy Lai
Ouissam Ouili
Marie-Noelle Dufourg
Marcel Goldberg
Pierre-Yves Ancel
Jean-Christophe Beaudoin
Joane Matta
Olivier Robineau
Gianluca Severi
Boris Pastorino
Marie Zins
Fabrice Carrat
Alpha Amadou Barry
Marie-Aline Charles
Abdenour Amroun
Mélody Enguix
Journal: JAMA Internal Medicine (2022)
Discipline: CLINICAL MEDICINE
Keywords: SARS-CoV-2; COVID-19; persistent physical symptoms; serology test results; beliefs
Affiliations: Université de Paris, “Population-based Cohorts Unit,” Institut National de la Santé et de la Recherche Médicale (INSERM), Paris Saclay University, Université de Versailles-Saint-Quentin-en-Yvelines, UMS 011, Paris, France, Université Lille, Centre Hospitalier de Tourcoing, ULR 2694-METRICS: Évaluation des technologies de santé et des pratiques médicales, Lille, France, Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, Département de Santé Publique, Hôpital Saint-Antoine, Assistance publique–Hôpitaux de Paris (AP-HP), Paris, France, Sorbonne Paris Nord University, INSERM U1153, Inrae U1125, Cnam, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center–University of Paris (CRESS), Bobigny, France, Université Paris-Saclay, UVSQ, INSERM, CESP U1018, Gustave Roussy, Villejuif, France, Department of Statistics, Computer Science, Applications “G. Parenti,” University of Florence, Florence, Italy, Unité des Virus Emergents, UVE: Aix Marseille Université, IRD 190, INSERM 1207, Institut Hospitalo-Universitaire Méditerranée Infection, Marseille, France, Centre d’Etude du Polymorphisme Humain, Fondation Jean Dausset, Paris, France, AP-HP, Hôpital Hôtel-Dieu, Département Médico-Universitaire Psychiatrie et Addictologie, Service de Psychiatrie de l’adulte, Paris, France, Université de Paris, AP-HP, Hôpital Corentin-Celton, DMU Psychiatrie et Addictologie, Service de Psychiatrie de l’adulte et du sujet âgé, INSERM, Institut de Psychiatrie et Neurosciences de Paris (IPNP), UMR_S1266, Paris, France, Université de Paris, AP-HP, Hôpital Européen Georges-Pompidou, DMU endocrinologie, ophtalmologie, médecine infectieuse, médecine interne & immunologie, médecine sociale, Service de Médecine interne, Paris, France, Université de Paris, AP-HP, Hôpital Hôtel-Dieu, DMU Psychiatrie et Addictologie, Service de Psychiatrie de l’adulte, INSERM, IPNP, UMR_S1266, Paris, France, for the Santé, Pratiques, Relations et Inégalités Sociales en Population Générale Pendant la Crise COVID-19–Sérologie (SAPRIS-SERO) Study Group

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." 

Retracted: No

PubPeer comments:
 Perry Wilson commented this paper and pointed some limitations : https: twitter.com fperrywilson status 1458095788811247621 https: www.youtube.com watch?v=1OAgD__BWjU This sensitivity bias among patients seems problematic for data interpretation to me, could authors address and discuss this issue ? Another methodology issue is about the questionnaire answering : it is unclear when each answer was given, and if the serological test result was clearly understood by patients : did they receive multiple questionnaires ? If so, did answers evolve before after test results ? How much time passed between serological test result (between may and december), and the questionnaire (january) ? If most serological tests were done in may, most infections posterior to serological testing might be missing and considered as  serological negative ! , 
 https: twitter.com Dr2NisreenAlwan status 1457817709127864331 , 
 Dr David Strain, Prof Kevin McConway, Dr Jeremy Rossman commented this study and pointed other limitations: https: www.sciencemediacentre.org expert-reaction-to-study-looking-at-the-association-of-self-reported-covid-19-infection-and-sars-cov -2-serology-test-results-with-persistent-physical-symptoms **D. Strain, Senior Clinical Lecturer, University of Exeter:** u201cIt 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 u201clong symptomsu201du201d. **K. McConway, Emeritus Professor of Applied Statistics, The Open University:** u201cThis looks as if itu2019s an interesting study u2013 and indeed it is interesting, but itu2019s potentially very misleading to take its results at face value.u201d  This could be taken to mean that the presence of these symptoms could be more affected by what people believe happened to them than by what previously happened to them as recorded by a test. That does remain a possibility in the light of these results, but itu2019s very far from being the only possibility. u201cOne basic issue is that this is an observational study, and itu2019s never possible to be certain about what causes what in observational studies. While thatu2019s always true in any observational study, there are some characteristics of this one that make it a particularly important issue here.u201d  Whatu2019s more, in this study, the participants already knew the result of their serology (antibody) test when they were asked the questions about whether they thought they had had Covid previously and about their symptoms.  So they are unlikely to be typical of people in the general French population who would, mostly, not have had an antibody test at all. This study therefore makes it difficult to draw general conclusions about how these things might work in the French population generally (let alone any other population), or to draw any clear conclusions about cause and effect.  Another issue about the general applicability of the findings is that these results are based on findings from a subset of a large population cohort study, that looks at volunteers aged 18-69 from the French population. I do not know all the details of this cohort study, the CONSTANCES study, but in general people who volunteer for a study like this, which is explicitly about health, is that they tend to be more interested in matters of health than is the population generally. Thatu2019s inevitable u2013 but it could be a particular issue when, as here, one is studying something that could possibly depend on peopleu2019s attitudes about their own health. Further, the participants in this new piece of research seem not to be particularly typical of the whole CONSTANCES cohort in some ways.  There are also issues about the serology test that was used.   they rightly acknowledge that the antibody test might, in some cases, not actually correspond to whether people had really been infected. Thatu2019s always the case for any diagnostic test of this sort, because no such test is perfect. The researchers provide arguments to support their belief that this is not in fact a big problem in this study u2013 but errors in diagnostic test results are often counter-intuitive, and I donu2019t entirely agree with the researchersu2019 conclusion.  What they donu2019t do is look at false positives u2013 with the assumptions they make, there would be rather a lot of false positives, in fact about 644 of them, which would be about four in every ten people who test positive for antibodies.   I think there could be some issues with the assumptions behind these calculations anyway.  Then, the researchers do not say where their figure of 4% prevalence of previous infection comes from.   In the people actually included in their statistical analysis, a little under 2% had a positive antibody test result. Thatu2019s a much lower figure than you would get with 4% prevalence and the stated performance rates.  this does indicate that the assumptions behind the performance of the test and about possible misclassification of who was previously infected are unlikely to be correct, and that can feed through into the interpretation of the results.  Iu2019m not trying to trash this research. These things are difficult to study and this new study does provide potentially useful information.  But I donu2019t feel that this research can give a clear enough indication of how likely this is.  The conclusions to the research do suggest that the diagnosis and treatment of people who have this kind of symptom should at least consider other possibilities rather than always assuming everything was directly caused by the virus. **J. Rossman, Honorary Senior Lecturer in Virology, University of Kent:**  There are several concerns with this analysis.  First, a serological test for the presence of COVID-19 antibodies is an unreliable marker for previous infection, and some research in hospitalised patients suggests Long COVID patients can tend to have weaker antibody responses .  In addition, antibody levels diminish over time and the study did not report on the duration between reported infection and the serological test.  Furthermore, the authors report that only anosmia (or loss of smell) was associated with a positive serological test, but anosmia tends to be one of the shorter duration symptoms of Long COVID and thus, patients still experiencing anosmia may have been more recently infected and thus more likely to have detectable antibody levels.  u201cSecond, the authors state that having a confirmatory diagnostic test or diagnosis of COVID was only associated with anosmia and not any other Long COVID symptoms. However, in their analysis, the authors compare the likelihood of different Long COVID symptoms in people that believe they had been infected but have not had a diagnostic test with those that believe they were infected and had diagnostic confirmation. Thus, the 15 symptom categories the authors found to be associated with belief in having previously been infected are present in both patients with a positive diagnostic test and those without. If belief, specifically, was driving Long COVID symptoms, then having that belief confirmed would likely increase the odds of having those persistent symptoms. In this study, a confirmatory diagnostic test had no impact on the likelihood of having most Long COVID symptoms, suggesting that belief in having been infected is as accurate as having had a diagnostic test, a result that has been seen in other Long COVID studies.  [The] conclusion [of the study] reiterates a damaging narrative, implying that Long COVID is a psychological disease and that by taking steps to avoid symptom exacerbation patients are effectively making themselves sick. This is a narrative that Long COVID patients, advocates, physicians and scientist have been struggling to address in many countries around the world. There are multiple studies that demonstrate the presence of persistent physiological symptoms following SARS-CoV-2 infection that are not present in uninfected controls. , 
 This article investigates the association between self-reported COVID-19 infection and results of serology tests with persistent physical symptoms associated to  long Covid . It concludes 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. The study design suffers from multiple shortcomings, precluding any conclusion. **1. Participants had received their serology test results before answering the questionnaire** ( At the time they answered this question, the participants were aware of their serology test results ) - The authors do not test the consistency of the replies with this information, and do not have ways to assess whether the serology results have been properly received and understood by the participants. Answers will differ depending on whether the result was understood or not. The questionnaire should have included a question to test the participants understanding of their serology results. Understanding of the result should have been a covariate in the model. - In a proper design, the participants would have answered the questionnaire before learning their serology results. **2. The authors did not test the consistency of serology results and the results of participant-reported lab results** Participants who believe they have been infected, and only them, were asked whether they had received a lab or physician result. The association between this reported result and the serology is not given in the article, while it could help assess the serological test s sensibility. **3. The authors did not ask  non-believers about lab confirmation** Information about the existence of another lab test or physician diagnostic is not available for belief(-) participants **4. The association of Physician confirmation or lab result with symptoms was only tested for belief(+) participants** This is due to the questionnaire design (point 3), but as a result, the tests lack power. The test is conducted as if all belief(-) were NAs for test results, while some of them may have had lab confirmation that they had not been infected and could have been included in the model. **5. Serology is treated as a 100% true result** Since they have not tested the association with reported results, the authors are unable to assess the sensibility of their serological test, which is treated in the study as if it were perfect. --- To [#1 Alexander Samuel] , about their last comment: please note that  Participants who reported having an initial COVID-19 infection only after completing the serology test were excluded. --- , 
 Based on statistical analysis of a cohort, the authors claim that physical symptoms typically associated with long Covid may result not from infection but from psychosomatic effects or misattribution of unrelated symptoms to Covid infection. Although they use the word u201csuggestu201d, their interpretation leaves no doubt: u201cTwo main mechanisms may account for our findings. First, having persistent physical symptoms may have led to the belief in having had COVID-19. [u2026] Second, the belief in having had COVID-19 infection may have increased the likelihood of symptoms.u201d This conclusion relies on an erroneous understanding of statistics. In statistics, u201cnon significantu201d does not mean that there is no effect: it means that the data are *compatible* with an absence of effect. But in this case, the confidence intervals make it clear that the data are *also* compatible with a substantial effect. To give an example, in table 3, the CI of the odds ratio for dizziness is 0.7-2.88 in model 3, serology. This means that an absence of relation between serology and dizziness (OR = 1) is possible, but so is an odds ratio of 2, which is very large. A non-significant result cannot rule out an absence of effect, but the analysis may rule out effects of a given size. For example, assuming the methodology is appropriate (see below), Table 3 says that positive serology is unlikely to increase the odds ratio for persistent concentration problems above 1.52. In the data, general prevalence is about 2.5%, so an OR of 1.52 means that prevalence is increased by 1.3%. Is this small? In April 2021, an estimate of 23% of the French population had been infected, or about 15 million people. Thus, for an OR of 1.52, compatible with this paperu2019s data, this would make 200 000 people with persistent concentration problems due to Covid infection. The remarks above address only the interpretation of the results, assuming the methodology is appropriate. But there are important issues with the methodology, which are not correctly taken into account in the discussion. The sensitivity of the serological test is reported to be 87%, but it is treated as if it were perfect. (As a side note, this figure corresponds to the probability that the test is positive given that the antibody level exceeds a certain threshold at the time of testing, and the figure was not obtained for self-administered tests, so the probability of a positive test given infection will be lower than 87%. But this is a minor problem compared to the issues discussed below.) Importantly, it must be noted that the respondents knew their serological results when responding to the questionnaire. Therefore, the two categories u201cbeliefu201d and u201cserologyu201d are not independent, which makes it virtually impossible to interpret the results. For example, suppose that the respondent is an ideal Bayesian observer that uses both serology and additional information (e.g. symptoms, PCR) to decide whether there was a previous infection. Since that decision already incorporates the knowledge of serology, a statistical analysis as done in this paper (model 3) would yield no association between serology and symptoms, since all information is already incorporated in the u201cbeliefu201d. This holds even if there is barely any additional information. This lack of association does not reflect psychosomatic effects, but on the contrary, it reflects the simple fact that a rational estimate based on serology plus additional information is more reliable than one based on serology alone. Consequently, the results shown in Table 3 are compatible with people simply being rational. In that case, the relevant odds ratio would be those in the column u201cBeliefu201d of model 3, which are very high. The conclusion would then be the exact opposite of the conclusion of this paper. What information did respondents base their belief on? We learn that about half of them actually had laboratory-confirmed infection (PCR or serology), which contradicts multiple statements in the paper that assign laboratory-confirmed infection only to positive serology obtained within the study. Another piece of information is obviously the symptoms they experienced, which in another 15% of cases, were confirmed by clinical examination. Since respondents had more detailed information in addition to the serological result, why couldnu2019t it be that their assessment is more reliable than the serological result alone? In the discussion, the authors discard this possibility by a quantitative argument based on the 87 % sensitivity of the serological test. This reveals another misunderstanding of probability. Sensitivity is the probability of a positive result given previous infection (more accurately, of a threshold antibody level). But obviously, probabilities change when conditioned to additional information (PCR, symptoms). To take an example, given the high specificity of PCR tests, it is obvious that the probability of infection given both a positive PCR test and a negative serology is much higher than the probability of infection given negative serology alone (which is about 0.5%). The same holds, although presumably to a smaller extent, for symptoms, unless it is considered that clinical examination is totally irrelevant to any medical diagnosis. Therefore, it cannot be ruled out that those who believed they had been infected despite knowing their negative serology were right to do so. In fact, it seems highly plausible at least for the large proportion who had a positive PCR. Given these serious shortcomings, it seems difficult to draw any conclusion from the data presented in this paper, even u201csuggestiveu201d. , 
 https: thesciencebit.net 2021 11 11 no-that-antibody-study-does-not-show-that-long-covid-is-caused-by-beliefs , 
  Post COVID-19 in children, adolescents, and adults: results of a matched cohort study including more than 150,000 individuals with COVID-19 https: www.medrxiv.org content 10.1101 2021.10.21.21265133v1 , 
 There appear to be some numerical inconsistencies in this article and the supplement. First, in Table 2, over half of the percentages are too high by between 0.1 and 0.3. Furthermore, most of the *p* values above 0.001 for the chi-square tests are off by one or two places in the last digit. This might seem minor, but it could make one wonder whether some of these results were calculated more than once, perhaps with different overall sample sizes due to some change in exclusion criteria. ![file] Second, the sum of the Belief+ totals in Table 2 (461 + 453), together with the second half of the first paragraph of the Results section (starting two lines before the end of page E3), suggest that the number of people reporting a belief that they had been infected was 914. But in eTable 5, which reports the results of the follow-up question ( Has this [infection that you just mentioned] been confirmed? ), the numbers of participants sum to 1001, and all of the percentages for each response appear to be consistent with a denominator of 1001. Again, the curious reader might wonder about the origin of this discrepancy. Third, in eTable 7, two models for each reported symptom are presented on each line, with one sample size ( N ) for each symptom. It is unclear what this sample size represents. For example, for  Sleep problems the N is reported as 54, whereas in Table 2 we see that there were 2580 people S-B-, 49 S-B+, 55 S+B-, and 45 S+B+. It is not clear how much information a single N might mean here, since there are two models and each implies a (presumably different) set of + - numbers. , 
 Code to reproduce my analysis of Table 2 is available at https: gist.github.com sTeamTraen 617360320606b3b2c484f9f9a38298c5 , 
 It is not clear how the authors arrived at their estimation (in the u201cStrengths and Limitationsu201d section) of 4% u201c*[o]n the basis of the present results*u201d (emphasis added; that is, this figure is apparently not based on, for example, an estimation by the French public health authorities of the number of people infected during the relevant period) for the prevalence of SARS-CoV-2 infection. The most obvious possibility is that this represents the ratio of 1,091 seropositive tests to 26,823 total participants. However, as comments #1 and #3 note, the specificity of 97.5% means that over half of those 1,091 positive tests would be false positives, and the true prevalence rate, including false negatives, would be around 2.2%.Between the reported sensitivity and specificity of the test, the observed numbers of each type of test result, and the estimated prevalence rate, something has to give. If the authorsu2019 claim of a prevalence rate of 4% is indeed based on a simple ratio of 1,091 positive tests to 26,823 participants, omitting the false positives, that would appear to be a rather unfortunate oversight. , 
 We thank Dr Lemogne for his comments on the study webpage. We agree that serology may be a good enough tool to clarify the association between persistent symptoms and previous infection, provided that the cohort is large enough. Indeed, as table 2 in the publication shows, the authors did identify associations between positive serology and 10 categories of persistent symptoms. However, in the other models in support of the conclusion, associations with serology are analysed after individuals are (self-)classified as previously infected. In these models, almost 70% of the  belief-positive participants had implicit knowledge of a past SARS-CoV-2 infection because they had received a physician diagnosis or a positive lab result (eTable5). In contrast, only 40% of those in this group with a positive serology result were true positives (i.e. participants correctly identified by the serological test, see calculation below). Therefore, even if all of the 30% without an objective reason to assume a past infection were actually not infected, u201cbeliefu201d would still be a better classifier than serology. As a result, in the separate analysis of the participants by u201cbeliefu201d, the rate of previous infection is high within the  belief-positive and low within the  belief-negative group. Obviously, in these populations, serology dramatically lacks accuracy to identify subgroups with significantly different infection rates. In his reply to my comment, Dr Lemogne bases his calculations on an assumed prevalence of previous SARS-CoV-2 infection of 4% u2013 corresponding to the 4% of positive serology results in their cohort. However, with 1,091 seropositives out of 26,823 participants identified by a test with a sensitivity of 87% and a specificity of 97.5%, the correct estimate of the prevalence of previous infection is in fact only 1.86%. Thus, in the study of Matta et al., only 40% of serology tests actually represent truly infected participants (corresponding to 658 false positive test results). With respect to the analysis results, it has to be assumed that the high number of false positives is mainly to be found within the u201cbelief-negativeu201d group (similar to the actual negative serologies). Conversely, the nonconverters (up to one-third of infected individuals, as suggested in my previous comment) are expected to fall mainly into the u201cpositive beliefu201d group (together with the true positives). All this implies that the belief would indeed be a better marker of actual infection history than the serology results. It is therefore expected that persistent symptoms associated with serology are no longer statistically significant in models mutually adjusted with u201cbeliefu201d. Here, only anosmia stands out with a limited odds ratio of 2.72. With its very high unadjusted odds ratio of 15.69, this most specific symptom of Covid-19 basically  survives the admixture of misclassified cases. For the same reason, anosmia is the only symptom associated with positive serology when restricting the analysis to the  positive belief group. We conclude that reporting a past Covid-19 infection would be more reliable than serology to correctly ascertain cases, indeed, reporting a past infection shows significant association not only for anosmia but for most of the other self-reported symptoms. , 
 The study uses serology results to determine if any prolonged symptoms are associated with infection. Despite the known limitations of serology tests, they can be useful when comparing positive and negative serologies within a sufficiently large population. And in fact, model 2 shows u201ca positive serology test result was associated with 10 categories of persistent symptomsu201d. But the validity of this identification becomes questionable when separating analysis of the participants by u201cbeliefu201d. It seems that misclassification bias was not ruled out for the other serology models (model 3 and following), which only demonstrate an association with anosmia. False positives should be treated as negative serologies, and may be assumed to have the same u201cbeliefu201d distribution, meaning most of them would fall into the u201cBelief-u201d group. We might also expect that false negatives (individuals who were in fact previously infected) would fall into the u201cBelief+u201d group, especially since 65% of them have confirmation in the form of a clinical diagnosis or a test. Whatever the serological result, the rate of actual infection should be very high for individuals who report having had an infection, and very low for those who do not. These distributions dramatically reduce the usefulness of serology to identify association with actual Covid-19 infection. Are prolonged symptoms really expected to be statistically significant in the mutually adjusted models? We assume that anosmia is the only symptom to stand out because it is the symptom most specific to Covid-19 but close review of the data shows its odds ratio collapses from 15.7 (model 2) to 2.73 (model 3), which is further evidence of misclassification. This in itself represents sufficient bias to question the conclusions. , 
 The paper relies on correlations between a belief that one was infected by SARS-CoV-2 before, reported COVID-19-like symptoms, and infection confirmed by the Euroimmun serological test. But this test is prone to seroreversion. Examples of this seroreversion: * At the individual level, 26% (91 354) of the EI-positive cohort was seronegative with the EI test at follow-up (i.e. seroreverted, Table 2). * https: www.clinicalmicrobiologyandinfection.com article S1198-743X(21)00371-2 fulltext * However, while 86 88 (98%) of participants with initially detectable antibodies in the Diasorin assay continued to have detectable antibodies in this assay, numbers were significantly lower for the Euroimmun (73 90 (81%) p 0.0004) [...]. * https: www.nature.com articles s41598-021-94453-5 * Eight months after their infections, we detected [...] anti-S1 IgG in 40 (69.0%) (p 0.01) (Table 2). * https: wwwnc.cdc.gov eid article 27 3 20-4543_article For other studies, this seroreversion manifests as people with a previous SARS-CoV-2 infection diagnosis (whether by PCR, medical diagnosis, and or antigen test), who test negative on the Euroimmun assay more than 10 days later. The proportion that later test seropositive decreases as time extends further from large waves of infection because people have more time to serorevert. For instance: * Furthermore, study-specific sensitivity of 0.616 (95% CI 0.475 - 0.740) that takes into account antibody decay over time was estimated based on 133 participants with a self-reported positive SARS-CoV-2 test at least 11 days prior to DBS sampling. * https: www.medrxiv.org content 10.1101 2021.11.22.21266711v1.full * For stage 1 sixty-two percent (68 110) of those reporting ever having tested positive with a SARS-CoV-2 PCR had a positive antibody test in our sample. [...] For stage 2 80% (90 112) of those reporting ever having tested positive in a SARS-CoV-2 PCR had a positive antibody test. * https: papers.ssrn.com sol3 papers.cfm?abstract_id=3834300 Moreover, if rates of seroreversion vary based on symptom history and with demographic factors like age, then the proportion of the sample that seroreverts can change due to changing non-response bias among particular demographic groups and greater prior testing of asymptomatic individuals: * The maintained seropositivity of 62% after a median time of 70 days (interquartile range 33-193 days) in our study is lower which may be due to the population sample with more asymptomatic cases. * https: www.medrxiv.org content 10.1101 2021.11.22.21266711v1.full non-response selection bias: https: academic.oup.com jid article 224 2 188 6292145 seroreversion varying by hospitalization status: https: www.science.org doi 10.1126 sciadv.abh3409 (figure 5B) So seroreversion and changes in sampling likely biased the results of this study. This is seen even more clearly when comparing earlier data from this study to later data. A prior analysis of this sample included people from May 4 - June 23, 2020, with 983 out of 14,628 testing positive on Euroimmun. So 6.7% seropositive: https: academic.oup.com ije article 50 5 1458 6323645 Yet the current analysis from May - November 2020 has 1091 seropositive out of 26823, i.e. 4.1% seropositive. The inclusion of samples after June 2020 allowed from time for seroreversion since France s prior COVID-19 peak in April 2020 ( https: covid19.who.int region euro country fr ), decreasing the overall seropositive rate. **So the seropositive rate decreased as people were included later in the study, which points to seroreversion and or sampling bias, consistent with prior published work on sampling bias and seroreversion with the Euroimmun assay used in the study. So some of the study s belief+ seronegative people were probably infected, such that prior SARS-CoV-2 infection was a bigger predictor of reported long COVID-19 than study concluded.** Atomsk s Sanakan https: twitter.com AtomsksSanakan status 1460403528615313418 , 
 **Evidence that belief is a more reliable indicator of previous infection than serology** We believe that the study data and in particular the distribution into subgroups lead to an erroneous conclusion. Even more, the data may lead us to the opposite conclusion. First, inclusion of those testing positive via serology but not reporting infection is of questionable legitimacy. Prolonged symptoms appear primarily after symptomatic viral episodes but seldom after asymptomatic infections (1). Second, the number of false negative serologies is estimated using a prevalence of 4%, corresponding to positive serology results. Prevalence should be directly calculated in accordance with the testu2019s 87% sensitivity and 97.5% specificity to make it consistent with the number of positive tests (1,091). This gives a plausible prevalence rate of 1.86%, 658 false positive serologies and 433 true positives. Thus, only 39.7% of those with positive serology tests would have actually been infected. However, sensitivity may actually be much lower than stated: Levels of SARS-CoV-2 antibodies often undergo rapid decline after 6 to 9 months, which might lower the sensitivity to 61.2% (2,3), and up to one-third of people with SARS-CoV-2 infection do not seroconvert at all (4). This means that sensitivity could be as low as 39%, and there could be as many as 698 false negatives. This results in higher prevalence due to false negatives, although the number of true and false positives remains almost unchanged, respectively 449 and 642. Turning to distribution within the cohort u201cbelievingu201d (two-thirds of whom, as noted elsewhere, had either a positive test or a medical opinion to support this u201cbeliefu201d) that they had Covid-19: False positives are by definition in fact seronegatives, so the expectation is that they will have a distribution similar to negatives serologies, of which over 98% are u201cBelief-u201d. All the 638 seropositive participants who did not report any infection (u201cBeliefu2212u201d) would potentially be false positives, meaning they have never been infected. Conversely, true positives would all fall into the  Belief+ category, as well as the false negatives that should be grouped with them. Thus, it is possible that the 461 individuals who report an infection despite negative serology were all actually infected. In summary, the possibility that almost 100% of the  Belief+ group were in fact infected cannot be ruled out. Similarly, up to 100% of the  Beliefu2212 may not have been infected. Given the uncertainty, serology tests do not add to our knowledge over and beyond what the u201cbeliefu201d in having had Covid-19 provides (which, again, was backed up with objective, direct evidence in at least two-thirds of cases). Indeed the  self-reported infection parameter would thus appear (at least in these cohorts) as potentially a better mar