Associations between Depression and Self-Reported COVID-19 Symptoms among Adults: Results from Two Population-Based Seroprevalence Studies in Switzerland
by
, ,
Giovanni Piumatti
1,
Rebecca Amati
2,
Aude Richard
3
,
Hélène Baysson
4,5,
Marianna Purgato
6,7,
Idris Guessous
4,5,
Silvia Stringhini
4,5,
Emiliano Albanese
2,3,*
on behalf of the SEROCoV-POP,
Specchio-COVID19 Study Group
† and
the Corona Immunitas Ticino Working Group
† 1
Fondazione Agnelli, 10125 Turin, Italy
2
Institute of Public Health, Faculty of BioMedicine, Università della Svizzera Italiana, 6900 Lugano, Switzerland
3
Institute of Global Health, University of Geneva, 1202 Geneva, Switzerland
4
Division of Primary Care, Geneva University Hospitals, 1206 Geneva, Switzerland
5
Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, 1211 Geneva, Switzerland
6
Section of Psychiatry, WHO Collaborating Centre for Research and Training in Mental Health and Service Evaluation, Department of Neuroscience, Biomedicine, and Movement Sciences, University of Verona, 37134 Verona, Italy
7
Cochrane Global Mental Health, University of Verona, 37129 Verona, Italy
*
Author to whom correspondence should be addressed.
†
Specchio-COVID19 Study Group and the Corona Immunitas Ticino Working Group are listed in Acknowledgments.
Int. J. Environ. Res. Public Health 2022, 19(24), 16696; https://doi.org/10.3390/ijerph192416696
Submission received: 15 November 2022
/
Revised: 6 December 2022
/
Accepted: 7 December 2022
/
Published: 12 December 2022
(This article belongs to the Special Issue Mental Health in the Time of COVID-19)
Abstract
:(1) Mental health may modulate the perceived risk of SARS-CoV-2 infection. However, it is unclear how psychological symptoms may distort symptom perception of COVID-19 and SARS-CoV-2 infection. We assessed whether depressive symptoms predicted self-reported COVID-19 symptoms, independently of serologically confirmed SARS-CoV-2 infection. (2) Participants (aged 20–64) in the Geneva (N = 576) and Ticino (N = 581) Swiss regions completed the Patient Health Questionnaire before being tested for anti-SARS-CoV-2 IgG antibodies and recalled COVID-19-compatible symptoms on two occasions: April–July 2020 (baseline), and January–February 2021 (follow-up). We estimated prevalence ratios for COVID-19 symptoms by depression scores in interaction with serological status. (3) At baseline, in Geneva, higher depression predicted higher probability of reporting systemic, upper airways, and gastro-intestinal symptoms, and fever and/or cough; in Ticino, higher depression predicted systemic, upper airways, and gastro-intestinal symptoms, fever and/or cough, dyspnea, and headache. At follow-up, in Geneva, higher depression predicted higher probability of reporting systemic symptoms and dyspnea; in Ticino, higher depression predicted higher probability of reporting systemic and upper airways symptoms, dyspnea and headache (all p values < 0.05). (4) We found positive associations between depressive symptoms and COVID-19-compatible symptoms, independently of seropositivity. Mental wellbeing has relevant public health implications because it modulates self-reported infection symptoms that inform testing, self-medication, and containment measures, including quarantine and isolation.
1. Introduction
The COVID-19 pandemic has increased the risk of psychological distress and mental disorders in the general population [1,2]. The mental health of individuals may have disproportionally worsened during the pandemic in those with pre-existing, pre-pandemic mental disorders [3,4], and poor mental health may be associated with a higher perceived risk of infection at the population level [5,6,7].
Depression can cause hyperawareness or altered body perception and is associated with somatic symptoms and medical illness catastrophizing [8,9]. According to both the tenth revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) [10], and the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [11], hypochondriacal symptoms are considered secondary, accompanying symptoms of major depression. Depressive symptoms may thus complexify COVID-19 detection and diagnosis. However, no study has yet investigated whether and the extent to which depressive symptoms modify or distort perceptions of COVID-19 symptomatology in people with and without a serologic confirmed SARS-CoV-2 infection.
In the present study, we aimed to assess whether higher depressive symptoms increased the likelihood of reporting COVID-19-compatible symptoms during the first and the second pandemic waves in Switzerland, accounting for serological evidence of previous SARS-CoV-2 infection. We used data from two population-based seroprevalence studies with similar designs conducted in two Swiss regions (Geneva and Ticino) [12,13].
2. Materials and Methods
2.1. Study Design and Participants
An individual participant dataset with repeated observations on a sample of 1157 adult participants was used. The dataset pooled data from two studies with a similar design, namely the SEROCoV-POP study in Geneva, and the Corona Immunitas study in Ticino, Switzerland. Full details about sampling, recruitment and data collection procedures for both studies have been previously described [12,13]. Consistent with the two original study designs, we focus on adults aged 20 to 74. Briefly, the SEROCoV-POP study is a population-based observational seroprevalence study including former participants of the Bus Santé study [14], a repeated cross-sectional population-based health survey in the canton of Geneva. Every year, about 1000 Bus Santé participants are recruited as a representative sample of Geneva noninstitutionalized residents aged 20 to 74. Between April and June 2020, former Bus Santé participants were recruited within the SEROCoV-POP study, along with their family members. Starting in December 2020, SEROCoV-POP participants were further invited to create an account on the dedicated digital platform Specchio-COVID19 [15], designed to follow-up adult participants of several sero-surveys carried out in the Canton of Geneva during the COVID-19 pandemic. The Specchio-COVID19 digital platform longitudinally collects from the same participants, information on self-reported symptoms and SARS-CoV-2 testing, infection risk and perception of exposure to risk, data on physical and mental health, and changes in working conditions.
The Corona Immunitas study [16] is a population-based, prospective cohort study purposely designed and conducted to assess the spread of the COVID-19 pandemic and its associated impacts, including on mental health. Participants were recruited after the first wave of the pandemic (in June 2020) and are followed-up for repeated serological testing, self-reported symptoms and assessments, including mental and physical health, psychological wellbeing, and lifestyle changes over time. Table A1 in the summarizes longitudinal data collection design across study sites and assessments.
2.2. Laboratory Analysis
Anti-SARS-CoV-2 immunoglobulin G (IgG) serology status was assessed in Geneva with a commercially available enzyme-linked immunosorbent assay (ELISA) (Euroimmun; Lübeck, Germany #EI 2606-9601 G), targeting the S1 domain of the spike protein. ELISA IgG ratio < 0.5 was indicative of seronegativity, while all sera with an IgG ratio of ≥0.5 were also tested with an immunofluorescence assay (IFA) and classified as either seropositive or seronegative according to the results from this test. In Ticino, in accordance with the Corona Immunitas study protocol [13], anti-SARS-CoV-2 IgG serology status was assessed using SenASTrIS (Sensitive Anti-SARS-CoV-2 Spike Trimer Immunoglobulin Serological) assay developed by the Centre Hospitalier Universitaire Vaudois (CHUV), the Swiss Federal Institute of Technology in Lausanne (EPFL) and the Swiss Vaccine Center [17].
2.3. Measurements and Procedures
We collected self-assessments of depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9), a 4-point depression module of PHQ (score 0 to 27) [18,19]. In Geneva, PHQ-9 data were collected between 2013 and 2019 within the Bus Santé study using paper-and-pencil questionnaires, which were completed at home and posted back via ordinary mail free of charges. In the Ticino site, PHQ-9 data were collected a few weeks before the blood collection appointment for the serological testing as part of an inclusion questionnaire using secured online questionnaires implemented in the Research Electronic Data Capture software (REDCap) [20], hosted at the Università della Svizzera Italiana. We computed a standardised continuous score of depression (with higher scores indicating higher depressive symptomatology) using a graded response model from the item response theory framework [21]. Cronbach α was equal to 0.84 in Geneva and 0.85 in Ticino.
Before the blood collection appointment, participants at both study sites received a link to online questionnaires covering socio-demographic information, health status, exposure to SARS-CoV-2-infected individuals, and self-reported COVID-19-compatible symptoms. Main participant characteristics included: age (categorised into aged 20–49, and aged 50–74), gender (women, men), smoking status (non-smoker, current smoker) and obesity (defined as body mass index ≥ 30 kg/m2, from self-reported body height and weight). Socio-demographic and health status indicators were comparable across study sites, and coded at baseline as follows: education (categorised into up to higher secondary/apprenticeship, and higher tertiary, work status (unemployed/retired, employed/student), and existing chronic conditions (none, any among hypertension, diabetes, cardiovascular disease, cancer, immunological syndrome, or respiratory syndrome). Presence of COVID-19 cases among close contacts or family members was assessed in both sites as a dichotomous variable with the following question: “How many people who live in your own household or with whom you are regularly in close contact, have tested positive for COVID-19 after taking a laboratory test?”. Participants were also asked to self-report whether they experienced any COVID-19-compatible symptoms not related to a known chronic condition since January 2020. To harmonize the lists of symptoms between study sites, we defined the following categories: systemic symptoms (i.e., fatigue, myalgia/arthralgia or loss of appetite); upper airways symptoms (i.e., either sneezing/rhinorrhoea or sore throat); gastro-intestinal symptoms (i.e., abdominal pain, nausea/vomiting or diarrhoea); fever and/or cough; dyspnoea; headache; and anosmia/dysgeusia [22] (Table A2). Within 8 subsequent weekly online follow-up surveys (during January and February 2021), participants were again asked to answer whether they experienced the same typologies of COVID-19 symptoms in the previous week.
2.4. Statistical Analysis
We modelled COVID-19 symptoms as binary dependent variables in separate Poisson regressions with robust standard errors [23,24] to estimate prevalence ratios (PR) and 95% confidence intervals (95% CI) by depression scores. At both baseline and follow-up assessments, analyses were run separately for each study site adjusting for age group, gender, education, work status, chronic diseases, obesity, smoking status, assuming invariance between follow-up assessments, and for presence of COVID-19 cases among close contacts or family members, and anti-SARS-CoV-2 IgG seropositivity. For Geneva, we further adjusted for the year of PHQ-9 completion (for data collected before the pandemic).
In further analyses, we added interaction terms between depression scores with serology status, gender and age group to test whether the association between depression and COVID-19 symptoms varied according to serological evidence for previous SARS-CoV-2 infection, gender or age, respectively. Likelihood ratio tests assessed significance and magnitude of interaction effects, comparing models with added interactions and models with no interactions [25]. For the rest of the analyses, we considered significance at p < 0.05 as standard. All analyses were performed using Stata 15 (StataCorp. 2015. Stata Statistical Software: Release 15. StataCorp LP, College Station, TX, USA).
3. Results
A total of 1157 adult participants from two large population-based observational studies (n = 576 Geneva Bus Santé and SEROCoV-POP studies; n = 581 Corona Immunitas Ticino study) were included in the analysis. Descriptive statistics of clinical and socio-demographic variables are reported in Table 1.
The flow-chart of recruited participants is reported in Figure 1. Between 2013 and 2019, we recruited 7785 participants in the Bus Santé study (mean participation rate was 50% over this period). Among these, 1618 (21%) took part to the psychological self-assessment returning their questionnaires via ordinary mail. Between April and June 2020, 716 among this smaller group of Bus Santé participants were recruited to SEROCoV-POP providing blood samples for laboratory analyses. To match samples across study sites in terms of age composition, we dropped SEROCoV-POP participants older than 64 years (n = 126) from the analytic sample. We also excluded participants with missing questionnaire data (n = 14), leaving a total of 576 (mean age 46 years; 58% females) for baseline analyses in Geneva. Among these, 365 (63% of the baseline sample; mean age 46 years; 61% females) also completed at least one weekly follow-up questionnaire with no missing information between January and February 2021, screening for COVID-19 symptoms in the previous week (Figure 1).
In July 2020, we recruited 1009 participants in the Corona Immunitas digital study in Ticino (participation rate was 27% accounting for all invitation letters sent to eligible subjects of the representative sample provided by the Federal Office of Statistics). Among these, 647 were randomly selected and provided blood samples for serological testing. We excluded 66 participants with missing data, obtaining an analytic sample of 581 (mean age 46 years; 57% females) in Ticino. Between January and February 2021, 536 (92% of the baseline sample; mean age 46 years; 58% females) completed at least one weekly follow-up questionnaire with no missing information, reporting whether they experienced any COVID-19 symptoms in the previous week (Figure 1).
In terms of frequencies of self-reported COVID-19-compatible symptoms across studies, we found that upper airways symptoms were the most reported at both baseline and follow-up in Geneva (baseline: n = 268, 47%; follow-up: n = 47, 13%) and Ticino (baseline: n = 162, 28%; follow-up: n = 105, 20%). The least frequently reported symptoms were anosmia/dysgeusia at both baseline and follow-up in Geneva (baseline: n = 39, 7%; follow-up: n = 6, 2%) and Ticino (baseline: n = 25, 4%; follow-up: n = 6, 1%) (Table A3).
Figure 2 shows the results of Poisson regressions estimating likelihoods of declaring COVID-19 symptoms at baseline based on levels of depressive symptoms, controlling for sociodemographic factors (i.e., age, gender, education, work status), health status and behaviours (i.e., obesity, smoking, chronic diseases), and COVID-19 past exposure (i.e., presence of COVID-19 cases among close contacts or family members, anti-SARS-CoV-2 IgG serology status).
In Geneva, participants with higher levels of depressive symptoms were significantly more likely to report systemic (Prevalence Ratio (PR) = 1.15, 95% Confidence Intervals (95% CI): 1.03, 1.30, p = 0.017), upper airways (PR = 1.13, 95% CI: 1.03, 1.24, p = 0.010), gastro-intestinal symptoms (PR = 1.43, 95% CI: 1.19, 1.71, p < 0.001), and fever and/or cough (PR = 1.15, 95% CI: 1.02, 1.30, p = 0.023) (Table A4).
In Ticino, participants with higher levels of depressive symptoms were significantly more likely to report systemic (PR = 1.33, 95% CI: 1.15, 1.53, p < 0.001), upper airways (PR = 1.16, 95% CI: 1.01, 1.33, p = 0.035), gastro-intestinal symptoms (PR = 1.41, 95% CI: 1.06, 1.84, p = 0.016), fever and/or cough (PR = 1.19, 95% CI: 1.02, 1.39, p = 0.026), dyspnoea (PR = 1.39, 95% CI: 1.06, 1.82, p = 0.016), and headache (PR = 1.29, 95% CI: 0.85, 1.91, p = 0.007) (Table A5).
We found no significant interaction effects between depression and serology status, age group or gender (p > 0.05) (Table A6 and Table A7). Accordingly, in seronegative participants (n = 529 in Geneva, and n = 534 in Ticino) higher levels of depressive symptoms were associated with higher likelihoods of declaring COVID-19-compatible symptoms.
Figure A1 shows the results of Poisson regressions estimating likelihoods of reporting COVID-19 symptoms at follow-up. In both Geneva and Ticino, participants with higher levels of depressive symptoms were significantly more likely to report systemic symptoms (Geneva: PR = 1.72, 95% CI: 1.18, 2.51, p = 0.005; Ticino: PR = 1.29, 95% CI: 1.09, 1.54, p = 0.004) and dyspnoea (Geneva: PR = 3.34, 95% CI: 1.46, 3.62, p = 0.004; Ticino: PR = 1.63, 95% CI: 1.16, 2.31, p = 0.005) at follow-up (Table A8 and Table A9).
Higher levels of depressive symptoms in Ticino were associated with upper airways symptoms (PR = 1.29, 95% CI: 1.10, 1.53, p = 0.002) and headache (PR = 1.59, 95% CI: 1.32, 1.92, p < 0.001) at follow-up (Table A9).
4. Discussion
The present study described the association between the severity of depressive symptoms and likelihoods of reporting COVID-19 symptoms in a large sample of adult participants based in Switzerland, a country that was severely hit by the COVID-19 pandemic. We found that higher self-reported levels of depressive symptoms were associated with higher likelihoods of reporting COVID-19 symptoms, including systemic symptoms, upper airways symptoms, gastro-intestinal symptoms, fever and/or cough, dyspnoea and headache. These associations did not vary by evidence of previous SARS-CoV-2 infection confirmed by serology.
Anosmia and dysgeusia were not associated with depressive symptomatology. This finding may indicate that the self-reported loss of smell and/or taste is indeed a specific, nearly pathognomonic COVID-19 symptom, which can be useful to identify people for further testing, although it occurs less frequently than other symptoms [26,27,28]. On the other hand, depressive symptoms were consistently associated with both long- and short-term recalls of dyspnoea and systemic symptoms (i.e., fatigue, myalgia/arthralgia or loss of appetite), that are listed among common manifestations of COVID-19 by the World Health Organization [29]. Depression is a frequent comorbidity in chronic respiratory and fatigue syndromes [30,31], and depression itself can be the cause of physical manifestations including fatigue, headaches, appetite loss, disseminated pain and dyspnoea, for example via comorbid anxiety [10,11]. Extensive exposure to alarmistic messages from authorities and the media during the pandemic months, including on the clinical manifestation and course of COVID-19, may have contributed to the association of depression with COVID-19 symptoms in our samples because this was independent of infection status.
4.1. Future Implications
Our findings nonetheless suggest that participants with higher depression levels may overreport certain categories of COVID-19-compatible symptoms. This can have important implications for screening and detection of possible or probable for SARS-CoV-2 infections, but also on the clinical management of depressive symptoms, particularly in relation to the implementation of indicated prevention strategies for lowering symptom levels and preventing their escalation to a formal psychiatric diagnosis [32,33].
Next, our results have important implications to inform public health strategies on testing, self-medication, quarantine, and self-isolation based on self-reported COVID-19 infection symptoms, which have also been used in observational studies as proxy of possible SARS-CoV-2 infections [28]. Self-reported symptomatology could be integrated with real-time tracking systems of self-reported potential COVID-19 symptoms such as smartphone-based apps, which may be better suited to collect predictive information for potential infection (including timing and frequency of symptoms occurrence) [34,35,36,37]. Nevertheless, accounting for responders’ mental health status could help refine algorithms triggering diagnostic work-ups, improving the appropriateness of testing, and contributing to averting the inflation of testing demand beyond public health infrastructure capacity.
Our findings are novel and are consistent, though somewhat difficult to compare with the results of previous studies. To the best of our knowledge, this is the first study specifically exploring the association between poor mental health and self-reporting of COVID-19 symptoms in a large sample of adults while further controlling for SARS-CoV-2 serology confirmed infection. Daniali and Flaten [38,39] observed how anxiety increased reports of COVID-like symptoms and beliefs of being infected by COVID-19. They interpreted these results as a nocebo effect, according to which negative expectations and health-related anxiety can lead to worsening of symptoms even in the absence of plausible biological mechanisms [40]. Other studies have reported an increase in general somatic symptoms, including fatigue, gastrointestinal symptoms, dyspnoea, headache, and nausea, following COVID-19-related anxiety [41,42,43,44]. Similarly, previous research tested whether anxiety and depression may confound chronic rhinosinusitis symptom reporting (including systemic symptoms, cough and fever), but did not find any significant association [45]. While evidence remains scarce [46], our results offer an insight into the potential confounding role of psychological symptoms for the detection of COVID-19 based on self-reported symptomatology.
4.2. Limitations and Strengths
This study was not without limitations. First, to keep data comparable across study sites, only a limited number of confounding factors could be included in the analyses. Residual confounding cannot be excluded. A more precise assessment of household income may have further contributed to explaining individual differences in reporting COVID-19 in association with depression. Moreover, a measure of health literacy could have contributed to disentangling the interaction between mental health perception and knowledge, psychological symptoms, and self-perception of SARS-CoV-2 infection, for example by acting as a moderator [47]. However, the effect of adjustments in our multivariate model was minimal. We tested prospective associations of past self-assessed depressive symptoms with long- and short-term recalls of more recent COVID-19 symptoms. We cannot exclude the possibility that associations between assessments taken at the same time vary. Moreover, mental health was assessed during and after the pandemic in Ticino and Geneva, respectively. Participation rates during the pandemic were higher than expected but participant selection in the study and the derivation of the analytic sample may have introduced some biases, though these were likely non-differential and may have contributed to shifting our estimates toward the null effect. For example, on the one hand, people experiencing or more prone to report COVID-19 symptoms may have been more likely to participate in the surveys, but on the other hand, depressive symptomatology, which includes lack of motivation and mild to severe psycho-motor retardation, may deter participation in research. Major strengths of our study include the internal replication of hypothesis testing in two large, representative, population-based samples, standardized assessments of mental health, robust and reliable data capturing, collection, and management tools in both sites, and the use of previously validated and highly reliable and valid serological testing for the detection of SARS-CoV-2 antibodies. The different timing of data collection between study sites further supports our finding that pre-pandemic depression levels were associated with COVID-19 symptom reporting in the medium-to-long term. Ongoing data collections at both sites within the Specchio-COVID19 and the Corona Immunitas Ticino study cohorts will extend the scope of the analyses presented here by including repeated assessments of serology status, self-reported COVID-19-compatible symptoms, as well as physical and mental health statuses.
5. Conclusions
This study is the first to show how depressive symptoms among adults are associated with a higher likelihood of recalling COVID-19-compatible symptoms, independently of serological evidence for previous SARS-CoV-2 infection. Individuals with higher degrees of depressive symptoms may benefit from public health advice to reduce the chances of misinterpreting symptoms comparable to COVID-19, for example, short-term preventative psychosocial interventions aimed at lowering psychological symptoms and improving coping strategies. The findings also have implications in the understanding of COVID-19 symptomatology based on self-reported information. In sum, mental health can have relevant public health implications for improving the use of self-reported COVID-19 symptoms to inform and prompt testing, self-medication, self-isolation and other measures aimed at mitigating and containing the spread of the COVID-19 pandemic.
Author Contributions
Conceptualization, G.P.; methodology, S.S.; formal analysis, G.P.; investigation, R.A., A.R., H.B., S.S. and E.A.; resources, R.A. and I.G.; writing—original draft preparation, G.P.; writing—review and editing, R.A., A.R., H.B. and M.P.; supervision, E.A.; project administration, R.A.; funding acquisition, I.G. and E.A. Recruitment of participants and data collection, members of the Specchio-COVID19 Study Group and the Corona Immunitas Ticino Working Group. All authors have read and agreed to the published version of the manuscript.
Funding
The SEROCoV-POP and the Specchio-COVID19 studies were funded by the Swiss Federal Office of Public Health, the General Directorate of Health of the Department of Safety, Employment and Health of the canton of Geneva, the Private Foundation of the Geneva University Hospitals, the Swiss School of Public Health (Corona Immunitas Research Program) and the Fondation des Grangettes. The Corona Immunitas Ticino study was funded by the Swiss School of Public Health through Corona Immunitas, which is a public-private partnership supported by the Federal Office of Public Health, various cantons, companies, and private individuals, and in part by Ceresio Foundation.
Institutional Review Board Statement
The ethics committees of the cantons of Ticino (BASEC Registration No 2020-01514), and Geneva (CER16-363) approved the study, and all participants provided written informed consent.
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The data presented in this study are available on request from the corresponding author.
Acknowledgments
We are grateful to all the participants, without whom this study would not have been possible. Specchio-COVID19 Study Group: Isabelle Arm-Vernez, Andrew S. Azman, Fatim Ba, Oumar Ba, Delphine Bachmann, Jean-François Balavoine, Michael Balavoine, Hélène Baysson, Lison Beigbeder, Julie Berthelot, Patrick Bleich, Gaëlle Bryand, François Chappuis, Prune Collombet, Delphine Courvoisier, Alain Cudet, Carlos de Mestral Vargas, Paola D’Ippolito, Richard Dubos, Roxane Dumont, Isabella Eckerle, Nacira El Merjani, Antoine Flahault, Natalie Francioli, Marion Frangville, Idris Guessous, Séverine Harnal, Samia Hurst, Laurent Kaiser, Omar Kherad, Julien Lamour, Pierre Lescuyer, François L’Huissier, Fanny-Blanche Lombard, Andrea Jutta Loizeau, Elsa Lorthe, Chantal Martinez, Lucie Ménard, Lakshmi Menon, Ludovic Metral-Boffod, Benjamin Meyer, Alexandre Moulin, Mayssam Nehme, Natacha Noël, Francesco Pennacchio, Javier Perez-Saez, Giovanni Piumatti, Didier Pittet, Jane Portier, Klara M. Posfay-Barbe, Géraldine Poulain, Caroline Pugin, Nick Pullen, Zo Francia Randrianandrasana, Aude Richard, Viviane Richard, Frederic Rinaldi, Jessica Rizzo, Khadija Samir, Claire Semaani, Silvia Stringhini, Stéphanie Testini, Didier Trono, Guillemette Violot, Nicolas Vuilleumier, Ania Wisniak, Sabine Yerly, María-Eugenia Zaballa. Corona Immunitas Ticino Working Group: Emiliano Albanese, Rebecca Amati, Antonio Amendola, Anna Maria Annoni, Granit Baqaj, Kleona Bezani, Peter Buttaroni, Anne Linda Camerini, AnnaPaola Caminada, Laurie Corna, Cristina Corti, Luca Crivelli, Daniela Dordoni, Marta Fadda, Luca Faillace, Ilaria Falvo, Maddalena Fiordelli, Carolina Foglia, Giovanni Franscella, Roberta Gandolfi, Sara Levati, Isabella Martinelli, Rosalba Morese, Anna Papis, Giovanni Piumatti, Greta Rizzi, Diana Sofia Da Costa Santos, Mauro Tonolla, Gladys Delai Venturelli.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A
Figure A1.
Prevalence ratios (95% confidence intervals) from Poisson regressions estimating likelihoods of reporting COVID-19 symptoms at follow-up across studies. Note: values are prevalence ratios with 95% confidence intervals from Poisson regressions with robust standard errors, adjusted for all listed variables. For Geneva, analyses were further adjusted for the year of PHQ-9 completion. a—excluded from the model when there were not enough positive responses to compute the statistics.
Figure A1.
Prevalence ratios (95% confidence intervals) from Poisson regressions estimating likelihoods of reporting COVID-19 symptoms at follow-up across studies. Note: values are prevalence ratios with 95% confidence intervals from Poisson regressions with robust standard errors, adjusted for all listed variables. For Geneva, analyses were further adjusted for the year of PHQ-9 completion. a—excluded from the model when there were not enough positive responses to compute the statistics.
Table A1.
Longitudinal data collection design across study sites and assessments.
| Period | Assessments | ||
|---|---|---|---|
| Geneva | Ticino | ||
| 2013–2019 | Patient Health Questionnaire depression module (PHQ-9). | N/A | |
| 2020 | April | Socio-demographic information, health status, and exposure to SARS-CoV-2-infected individuals. Self-reported COVID-19-compatible symptoms. Serological testing. | N/A |
| May | |||
| June | |||
| July | N/A | Socio-demographic information, health status, and exposure to SARS-CoV-2-infected individuals. Patient Health Questionnaire depression module (PHQ-9). Self-reported COVID-19-compatible symptoms. Serological testing. | |
| 2021 | January | Self-reported COVID-19-compatible symptoms. | Self-reported COVID-19-compatible symptoms. |
| February | |||
Table A2.
List of available self-reported COVID-19-compatible symptoms across studies and assessments.
Table A2.
List of available self-reported COVID-19-compatible symptoms across studies and assessments.
| Symptom Categories | Geneva | Ticino | ||
|---|---|---|---|---|
| Baseline (April–June 2020) | Follow-Up (February–January 2021) | Baseline (July 2020) | Follow-Up (February–January 2021) | |
| Systemic symptoms | fatigue, muscular and/or articular pain, loss of appetite | fatigue, muscular and/or articular pain, loss of appetite | fatigue, muscular and/or articular pain, loss of appetite | fatigue, muscular and/or articular pain, loss of appetite |
| Upper airways symptoms | runny and/or itchy and/or stuffy nose, sore throat | runny nose, itchy nose, stuffy nose, sore throat | runny nose, itchy nose, stuffy nose, sore throat | runny nose, itchy nose, stuffy nose, sore throat |
| Gastro-intestinal symptoms | abdominal pain, nausea and/or vomiting, diarrhoea | stomach pain, nausea and/or vomiting, diarrhoea | stomach pain, nausea and/or vomiting, diarrhoea | stomach pain, nausea and/or vomiting, diarrhoea |
| Fever and/or cough | fever, cough | fever (>38 °C), dry cough, heavy cough | fever (>38 °C), dry cough, heavy cough | fever (>38 °C), dry cough, heavy cough |
| Dyspnoea | shortness of breath | shortness of breath | shortness of breath, respiratory distress | shortness of breath, respiratory distress |
| Headache | headache | headache | headache | headache |
| Anosmia/dysgeusia | loss of taste and/or smell | loss of taste and/or smell | loss of taste, loss of smell | loss of taste, loss of smell |
Table A3.
Frequencies (percentages) of self-reported COVID-19-compatible symptom categories across studies and assessments.
Table A3.
Frequencies (percentages) of self-reported COVID-19-compatible symptom categories across studies and assessments.
| Symptom Categories | Geneva | Ticino | ||
|---|---|---|---|---|
| Baseline, N = 576 (April–June 2020) | Follow-Up, N = 365 (February–January 2021) | Baseline, N = 581 (July 2020) | Follow-Up, N = 536 (February–January 2021) | |
| Systemic symptoms | 213 (39.9) | 30 (8.2) | 150 (25.8) | 101 (18.8) |
| Upper airways symptoms | 268 (46.5) | 47 (12.9) | 162 (27.9) | 105 (19.6) |
| Gastro-intestinal symptoms | 101 (17.5) | 22 (6.0) | 54 (9.3) | 56 (10.5) |
| Fever and/or cough | 199 (34.6) | 26 (7.1) | 140 (24.1) | 49 (9.1) |
| Dyspnoea | 50 (8.7) | 6 (1.6) | 44 (7.6) | 22 (4.1) |
| Headache | 193 (33.5) | 33 (9.0) | 99 (17.0) | 92 (17.2) |
| Anosmia/dysgeusia | 39 (6.8) | 6 (1.6) | 25 (4.3) | 6 (1.1) |
| Asymptomatic | 197 (34.2) | 292 (80.0) | 326 (56.1) | 328 (61.2) |
Table A4.
Prevalence ratios (95% confidence intervals) from Poisson regressions estimating likelihoods of reporting COVID-19 symptoms at baseline (April–June 2020), Geneva (N = 576).
Table A4.
Prevalence ratios (95% confidence intervals) from Poisson regressions estimating likelihoods of reporting COVID-19 symptoms at baseline (April–June 2020), Geneva (N = 576).
| Systemic Symptoms | Upper Airways Symptoms | Gastro-Intestinal Symptoms | Fever and/or Cough | Dyspnoea | Headache | Anosmia/ Dysgeusia | |
|---|---|---|---|---|---|---|---|
| Age group (ref: Aged 20–49) | |||||||
| Aged 50–64 | 0.98 (0.77, 1.24) | 0.92 (0.76, 1.11) | 0.77 (0.51, 1.17) | 0.98 (0.76, 1.25) | 1.20 (0.65, 2.22) | 0.89 (0.69, 1.14) | 0.99 (0.54, 1.84) |
| Gender (ref: Female) | |||||||
| Male | 0.86 (0.69, 1.07) | 0.84 (0.69, 1.01) | 0.62 (0.42, 0.91) * | 0.82 (0.64, 1.04) | 0.80 (0.45, 1.40) | 0.64 (0.49, 0.82) ** | 1.00 (0.58, 1.75) |
| Educational level (ref: Tertiary) | |||||||
| Up to higher secondary/ apprenticeship | 1.30 (1.03, 1.65) * | 1.05 (0.87, 1.26) | 1.51 (1.01, 2.26) * | 0.95 (0.76, 1.19) | 1.03 (0.58, 1.82) | 1.07 (0.84, 1.35) | 2.51 (1.12, 5.66) |
| Work status (ref: Unemployed/retired) | |||||||
| Employed/student | 0.93 (0.67, 1.27) | 0.97 (0.74, 1.27) | 0.88 (0.54, 1.45) | 0.84 (0.61, 1.16) | 1.02 (0.48, 2.16) | 1.05 (0.73, 1.51) | 0.60 (0.25, 1.47) |
| Obesity (ref: BMI < 30 kg/m2) | |||||||
| BMI ≥ 30 kg/m2 | 0.85 (0.57, 1.26) | 1.16 (0.89, 1.51) | 1.31 (0.78, 2.20) | 0.83 (0.54, 1.27) | 0.96 (0.41, 2.26) | 1.05 (0.72, 1.54) | 0.75 (0.28, 1.99) |
| Smoking (ref: No) | |||||||
| Yes | 1.10 (0.83, 1.45) | 1.05 (0.83, 1.33) | 1.46 (0.97, 2.20) | 0.97 (0.71, 1.34) | 1.66 (0.90, 3.07) | 1.01 (0.74, 1.39) | 0.68 (0.24, 1.94) |
| Chronic diseases (ref: No) | |||||||
| Yes | 1.28 (0.92, 1.76) | 1.03 (0.78, 1.35) | 1.05 (0.62, 1.77) | 1.13 (0.81, 1.58) | 2.52 (1.28, 4.98) ** | 1.08 (0.75, 1.54) | 1.12 (0.34, 3.74) |
| COVID-19 family cases (ref: No) | |||||||
| Yes | 1.11 (0.78, 1.59) | 1.06 (0.77, 1.44) | 0.65 (0.27, 1.55) | 0.98 (0.65, 1.48) | 0.95 (0.35, 2.58) | 1.33 (0.93, 1.90) | 0.90 (0.50, 1.62) |
| Anti-SARS-CoV-2 IgG (ref: Seronegative) | |||||||
| Seropositive | 2.00 (1.56, 2.57) *** | 1.48 (1.17, 1.87) ** | 1.55 (0.87, 2.77) | 2.17 (1.67, 2.80) *** | 2.92 (1.38, 6.19) ** | 1.64 (1.21, 2.21) ** | 18.07 (10.34, 31.60) *** |
| Depression | 1.15 (1.03, 1.30) * | 1.13 (1.03, 1.24) * | 1.43 (1.19, 1.71) *** | 1.15 (1.02, 1.30) * | 1.28 (0.96, 1.71) | 1.13 (0.99, 1.29) | 1.12 (0.84, 1.50) |
Note: values are prevalence ratios with 95% confidence intervals from Poisson regressions with robust standard errors, adjusted for all listed covariates. * p < 0.05; ** p < 0.01; *** p < 0.001.
Table A5.
Prevalence ratios (95% confidence intervals) from Poisson regressions estimating likelihoods of reporting COVID-19 symptoms at baseline (July 2020), Ticino (N = 581).
Table A5.
Prevalence ratios (95% confidence intervals) from Poisson regressions estimating likelihoods of reporting COVID-19 symptoms at baseline (July 2020), Ticino (N = 581).
| Systemic Symptoms | Upper Airways Symptoms | Gastro-Intestinal Symptoms | Fever and/or Cough | Dyspnoea | Headache | Anosmia/ Dysgeusia | |
|---|---|---|---|---|---|---|---|
| Age group (ref: Aged 20–49) | |||||||
| Aged 50–64 | 0.72 (0.53, 0.97) * | 0.51 (0.38, 0.7) *** | 0.52 (0.28, 0.96) * | 0.67 (0.49, 0.93) * | 0.47 (0.24, 0.92) * | 0.55 (0.37, 0.83) ** | 0.3 (0.14, 0.65) ** |
| Gender (ref: Women) | |||||||
| Men | 0.93 (0.7, 1.23) | 0.85 (0.65, 1.11) | 0.69 (0.41, 1.16) | 1.05 (0.78, 1.4) | 0.93 (0.51, 1.66) | 0.9 (0.64, 1.29) | 0.99 (0.49, 2) |
| Educational level (ref: Tertiary) | |||||||
| Up to higher secondary/ apprenticeship | 0.95 (0.71, 1.28) | 0.9 (0.68, 1.18) | 1.38 (0.82, 2.34) | 0.85 (0.62, 1.17) | 0.91 (0.49, 1.68) | 0.8 (0.54, 1.19) | 0.45 (0.18, 1.16) |
| Work status (ref: Unemployed/retired) | |||||||
| Employed/student | 1.21 (0.78, 1.88) | 1.45 (0.91, 2.3) | 1.02 (0.46, 2.24) | 1.4 (0.87, 2.23) | 1.05 (0.46, 2.36) | 1.26 (0.72, 2.2) | 1.06 (0.44, 2.56) |
| Obesity (ref: BMI < 30 kg/m2) | |||||||
| BMI ≥ 30 kg/m2 | 0.92 (0.59, 1.44) | 1.2 (0.82, 1.75) | 1.09 (0.46, 2.57) | 1.05 (0.67, 1.65) | 2.24 (1.14, 4.41) * | 0.82 (0.45, 1.51) | 1.48 (0.56, 3.9) |
| Smoking (ref: No) | |||||||
| Yes | 0.88 (0.63, 1.24) | 0.96 (0.7, 1.31) | 0.76 (0.4, 1.45) | 0.94 (0.65, 1.35) | 1.36 (0.71, 2.58) | 0.77 (0.49, 1.23) | 0.99 (0.37, 2.68) |
| Chronic diseases (ref: No) | |||||||
| Yes | 1.18 (0.82, 1.71) | 1.04 (0.71, 1.53) | 0.78 (0.33, 1.86) | 1.19 (0.81, 1.74) | 1.37 (0.61, 3.05) | 1.1 (0.68, 1.78) | 1.91 (0.77, 4.7) |
| COVID-19 family cases (ref: No) | |||||||
| Yes | 1.49 (1.04, 2.12) * | 1.19 (0.83, 1.7) | 0.95 (0.4, 2.24) | 1.28 (0.88, 1.86) | 2.25 (1.22, 4.16) ** | 1.54 (0.96, 2.46) | 1.85 (0.83, 4.11) |
| Anti-SARS-CoV-2 IgG serology (ref: Seronegative) | |||||||
| Seropositive | 1.64 (1.14, 2.37) ** | 1.51 (1.07, 2.13) * | 0.92 (0.34, 2.54) | 2.16 (1.53, 3.03) *** | 2.42 (1.28, 4.57) ** | 1.89 (1.22, 2.95) ** | 9.55 (4.44, 20.53) *** |
| Depression | 1.33 (1.15, 1.53) *** | 1.16 (1.01, 1.33) * | 1.4 (1.06, 1.84) * | 1.19 (1.02, 1.39) * | 1.39 (1.06, 1.82) * | 1.29 (1.07, 1.56) ** | 1.28 (0.85, 1.91) |
Note: values are prevalence ratios with 95% confidence intervals from Poisson regressions with robust standard errors, adjusted for all listed covariates. * p < 0.05; ** p < 0.01; *** p < 0.001.
Table A6.
Interaction effects between depression scores with serology status, gender and age group at baseline (April–June 2020), Geneva (N = 576).
Table A6.
Interaction effects between depression scores with serology status, gender and age group at baseline (April–June 2020), Geneva (N = 576).
| Systemic Symptoms | Upper Airways Symptoms | Gastro-Intestinal Symptoms | Fever and/or Cough | Dyspnoea | Headache | Anosmia/ Dysgeusia | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| p-Value | χ2 | p-Value | χ2 | p-Value | χ2 | p-Value | χ2 | p-Value | χ2 | p-Value | χ2 | p-Value | χ2 | |
| Depression with anti-SARS-CoV-2 IgG serology | 0.803 | 0.06 | 0.376 | 0.79 | 0.066 | 3.35 | 0.412 | 0.68 | 0.387 | 0.75 | 0.163 | 1.96 | 0.231 | 1.42 |
| Depression with gender | 0.255 | 1.32 | 0.799 | 0.07 | 0.902 | 0.02 | 0.300 | 1.06 | 0.895 | 0.02 | 0.782 | 0.08 | 0.966 | 0.00 |
| Depression with age group | 0.315 | 1.02 | 0.918 | 0.01 | 0.882 | 0.02 | 0.364 | 0.82 | 0.216 | 1.53 | 0.635 | 0.22 | 0.998 | 0.00 |
Note: p-values are from Poisson regressions adjusted for education, work status, chronic diseases, obesity, smoking status, presence of COVID-19 cases among close contacts or family members, and year of mental health self-assessment completion. χ2: Chi-squared likelihood ratio test results (with 1 degree of freedom) comparing models with added interaction terms and models with no interaction terms.
Table A7.
Interaction effects between depression scores with serology status, gender and age group at baseline (July 2020), Ticino (N = 581).
Table A7.
Interaction effects between depression scores with serology status, gender and age group at baseline (July 2020), Ticino (N = 581).
| Systemic Symptoms | Upper Airways Symptoms | Gastro-Intestinal Symptoms | Fever and/or Cough | Dyspnoea | Headache | Anosmia/ Dysgeusia | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| p-Value | χ2 | p-Value | χ2 | p-Value | χ2 | p-Value | χ2 | p-Value | χ2 | p-Value | χ2 | p-Value | χ2 | |
| Depression with anti-SARS-CoV-2 IgG serology | 0.364 | 0.82 | 0.679 | 0.17 | 0.100 | 2.64 | 0.204 | 1.61 | 0.929 | 0.01 | 0.498 | 0.46 | 0.831 | 0.05 |
| Depression with gender | 0.149 | 2.06 | 0.448 | 0.57 | 0.796 | 0.07 | 0.096 | 2.74 | 0.109 | 2.54 | 0.970 | 0.00 | 0.117 | 2.49 |
| Depression with age group | 0.122 | 2.34 | 0.221 | 1.46 | 0.351 | 0.90 | 0.632 | 0.23 | 0.907 | 0.01 | 0.205 | 1.57 | 0.648 | 0.21 |
Note: p-values are from Poisson regressions adjusted for education, work status, chronic diseases, obesity, smoking status, presence of COVID-19 cases among close contacts or family members. χ2: Chi-squared likelihood ratio test results (with 1 degree of freedom) comparing models with added interaction terms and models with no interaction terms.
Table A8.
Prevalence ratios (95% confidence intervals) from Poisson regressions estimating likelihoods of reporting COVID-19 symptoms at follow-up (January–February 2021), Geneva (N = 365).
Table A8.
Prevalence ratios (95% confidence intervals) from Poisson regressions estimating likelihoods of reporting COVID-19 symptoms at follow-up (January–February 2021), Geneva (N = 365).
| Systemic Symptoms | Upper Airways Symptoms | Gastro-Intestinal Symptoms | Fever and/or Cough | Dyspnoea | Headache | Anosmia/ Dysgeusia | |
|---|---|---|---|---|---|---|---|
| Age group (ref: Aged 20–49) | |||||||
| Aged 50–64 | 1.27 (0.61, 2.67) | 0.67 (0.36, 1.27) | 0.79 (0.28, 2.23) | 0.74 (0.33, 1.67) | 0.96 (0.17, 5.53) | 0.7 (0.33, 1.49) | 1.48 (0.24, 9.23) |
| Gender (ref: Women) | |||||||
| Men | 0.94 (0.46, 1.94) | 0.82 (0.46, 1.46) | 0.8 (0.33, 1.93) | 1.39 (0.66, 2.9) | 1.77 (0.31, 10.19) | 0.82 (0.41, 1.64) | 0.77 (0.14, 4.41) |
| Educational level (ref: Tertiary) | |||||||
| Up to higher secondary/ apprenticeship | 0.61 (0.28, 1.33) | 0.72 (0.39, 1.31) | 0.34 (0.11, 1.1) | 0.77 (0.35, 1.69) | 1.66 (0.28, 9.84) | 0.63 (0.29, 1.36) | 1.03 (0.21, 5.10) |
| Work status a (ref: Unemployed/retired) | |||||||
| Employed/student | 1.42 (0.44, 4.65) | 1.27 (0.45, 3.54) | 0.59 (0.18, 1.88) | 0.67 (0.21, 2.12) | - | 1.15 (0.36, 3.68) | - |
| Obesity a (ref: BMI < 30 kg/m2) | |||||||
| BMI ≥ 30 kg/m2 | 0.42 (0.07, 2.37) | 0.88 (0.31, 2.52) | 0.68 (0.09, 5) | 0.51 (0.08, 3.34) | 2.82 (0.35, 22.88) | 0.43 (0.06, 3.02) | - |
| Smoking a (ref: No) | |||||||
| Yes | 1.41 (0.53, 3.76) | 0.99 (0.44, 2.25) | 0.72 (0.16, 3.24) | 1.57 (0.61, 4.01) | 2.42 (0.33, 17.47) | 0.49 (0.12, 2.02) | - |
| Chronic diseases a (ref: No) | |||||||
| Yes | 0.66 (0.17, 2.48) | 0.64 (0.21, 1.96) | 0.97 (0.26, 3.69) | 0.71 (0.16, 3.09) | 1.04 (0.12, 9.29) | 0.3 (0.04, 2.12) | - |
| Depression | 1.72 (1.18, 2.51) ** | 1.22 (0.86, 1.73) | 1.11 (0.61, 2.01) | 1.34 (0.88, 2.05) | 3.34 (1.46, 7.62) ** | 1.15 (0.71, 1.85) | 1.07 (0.44, 2.55) |
Note: values are prevalence ratios with 95% confidence intervals from Poisson regressions with robust standard errors, adjusted for all listed covariates. Analyses were further adjusted for the year of mental health self-assessment completion. a—excluded from the model when there were not enough positive responses to compute the statistics. ** p < 0.01.
Table A9.
Prevalence ratios (95% confidence intervals) from Poisson regressions estimating likelihoods of reporting COVID-19 symptoms at follow-up (January–February 2021), Ticino (N = 536).
Table A9.
Prevalence ratios (95% confidence intervals) from Poisson regressions estimating likelihoods of reporting COVID-19 symptoms at follow-up (January–February 2021), Ticino (N = 536).
| Systemic Symptoms | Upper Airways Symptoms | Gastro-Intestinal Symptoms | Fever and/or Cough | Dyspnoea | Headache | Anosmia/ Dysgeusia | |
|---|---|---|---|---|---|---|---|
| Age group (ref: Aged 20–49) | |||||||
| Aged 50–64 | 0.82 (0.57, 1.2) | 0.72 (0.49, 1.07) | 0.67 (0.39, 1.16) | 0.78 (0.43, 1.39) | 1.12 (0.43, 2.88) | 0.71 (0.47, 1.06) | 2.75 (0.49, 15.41) |
| Gender (ref: Women) | |||||||
| Men | 0.68 (0.47, 0.97) * | 1.34 (0.95, 1.9) | 0.52 (0.3, 0.89) * | 1.15 (0.67, 1.96) | 0.63 (0.27, 1.5) | 0.68 (0.45, 1.02) | 0.27 (0.03, 2.84) |
| Educational level a (ref: Tertiary) | |||||||
| Up to higher secondary/ apprenticeship | 1.3 (0.86, 1.95) | 0.94 (0.63, 1.39) | 0.87 (0.5, 1.5) | 1.15 (0.61, 2.16) | 1.56 (0.53, 4.63) | 1.27 (0.84, 1.9) | - |
| Work status a (ref: Unemployed/retired) | |||||||
| Employed/student | 1.37 (0.81, 2.31) | 0.89 (0.54, 1.46) | 1.53 (0.67, 3.48) | 0.91 (0.42, 1.96) | 0.76 (0.27, 2.15) | 1.36 (0.77, 2.4) | - |
| Obesity a (ref: BMI < 30 kg/m2) | |||||||
| BMI ≥ 30 kg/m2 | 0.61 (0.31, 1.2) | 0.86 (0.48, 1.54) | 0.31 (0.08, 1.25) | 0.73 (0.27, 2.02) | 1.21 (0.36, 4.08) | 0.6 (0.27, 1.3) | - |
| Smoking (ref: No) | |||||||
| Yes | 1.65 (1.14, 2.38) ** | 1.28 (0.87, 1.89) | 1.27 (0.72, 2.25) | 1.73 (0.98, 3.03) | 1.46 (0.61, 3.47) | 0.89 (0.57, 1.39) | 1.84 (0.34, 10.01) |
| Chronic diseases (ref: No) | |||||||
| Yes | 2.21 (1.49, 3.28) *** | 1.05 (0.63, 1.73) | 2.2 (1.15, 4.2) * | 1.34 (0.63, 2.82) | 2.18 (0.84, 5.7) | 0.75 (0.43, 1.31) | 0.84 (0.08, 8.81) |
| Depression | 1.29 (1.09, 1.54) ** | 1.29 (1.10, 1.53) ** | 1.22 (0.95, 1.55) | 1.26 (0.98, 1.64) | 1.63 (1.16, 2.31) ** | 1.59 (1.32, 1.92) *** | 1.95 (0.88, 4.31) |
Note: values are prevalence ratios with 95% confidence intervals from Poisson regressions with robust standard errors, adjusted for all listed covariates. a—excluded from the model when there were not enough positive responses to compute the statistics. * p < 0.05; ** p < 0.01; *** p < 0.001.
References
- Cénat, J.M.; Blais-Rochette, C.; Kokou-Kpolou, C.K.; Noorishad, P.-G.; Mukunzi, J.N.; McIntee, S.-E.; Dalexis, R.D.; Goulet, M.-A.; Labelle, P.R. Prevalence of symptoms of depression, anxiety, insomnia, posttraumatic stress disorder, and psychological distress among populations affected by the COVID-19 pandemic: A systematic review and meta-analysis. Psychiatry Res. 2021, 295, 113599. [Google Scholar] [CrossRef] [PubMed]
- Phiri, P.; Ramakrishnan, R.; Rathod, S.; Elliot, K.; Thayanandan, T.; Sandle, N.; Haque, N.; Chau, S.W.; Wong, O.W.; Chan, S.S.; et al. An evaluation of the mental health impact of SARS-CoV-2 on patients, general public and healthcare professionals: A systematic review and meta-analysis. EClinicalMedicine 2021, 34, 100806. [Google Scholar] [CrossRef] [PubMed]
- Lopes, B.C.d.S.; Jaspal, R. Understanding the mental health burden of COVID-19 in the United Kingdom. Psychol. Trauma Theory Res. Pract. Policy 2020, 12, 465–467. [Google Scholar] [CrossRef] [PubMed]
- Newby, J.M.; O’Moore, K.; Tang, S.; Christensen, H.; Faasse, K. Acute mental health responses during the COVID-19 pandemic in Australia. PLoS ONE 2020, 15, e0236562. [Google Scholar] [CrossRef]
- Bendau, A.; Petzold, M.B.; Pyrkosch, L.; Mascarell Maricic, L.; Betzler, F.; Rogoll, J.; Große, J.; Ströhle, A.; Plag, J. Associations between COVID-19 related media consumption and symptoms of anxiety, depression and COVID-19 related fear in the general population in Germany. Eur. Arch. Psychiatry Clin. Neurosci. 2021, 271, 283–291. [Google Scholar] [CrossRef]
- Schweda, A.; Weismüller, B.; Bäuerle, A.; Dörrie, N.; Musche, V.; Fink, M.; Kohler, H.; Teufel, M.; Skoda, E.-M. Phenotyping mental health: Age, community size, and depression differently modulate COVID-19-related fear and generalized anxiety. Compr. Psychiatry 2020, 104, 152218. [Google Scholar] [CrossRef]
- Yan, L.; Gan, Y.; Ding, X.; Wu, J.; Duan, H. The relationship between perceived stress and emotional distress during the COVID-19 outbreak: Effects of boredom proneness and coping style. J. Anxiety Disord. 2020, 77, 102328. [Google Scholar] [CrossRef]
- Haug, T.T.; Mykletun, A.; Dahl, A.A. The Association Between Anxiety, Depression, and Somatic Symptoms in a Large Population: The HUNT-II Study. Psychosom. Med. 2004, 66, 845–851. [Google Scholar] [CrossRef]
- Vaccarino, A.L.; Sills, T.L.; Evans, K.R.; Kalali, A.H. Prevalence and association of somatic symptoms in patients with Major Depressive Disorder. J. Affect. Disord. 2008, 110, 270–276. [Google Scholar] [CrossRef]
- World Health Organization. The ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines; World Health Organization: Geneva, Switzerland, 1992; Available online: https://apps.who.int/iris/handle/10665/37958 (accessed on 9 November 2022).
- American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders; American Psychiatric Association: Washington, DC, USA, 2013. [Google Scholar]
- Stringhini, S.; Wisniak, A.; Piumatti, G.; Azman, A.S.; Lauer, S.A.; Baysson, H.; de Ridder, D.; Petrovic, D.; Schrempft, S.; Marcus, K.; et al. Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): A population-based study. Lancet 2020, 396, 313–319. [Google Scholar] [CrossRef]
- West, E.A.; Anker, D.; Amati, R.; Richard, A.; Wisniak, A.; Butty, A.; Albanese, E.; Bochud, M.; Chiolero, A.; Crivelli, L.; et al. Corona Immunitas: Study protocol of a nationwide program of SARS-CoV-2 seroprevalence and seroepidemiologic studies in Switzerland. Int. J. Public Health 2020, 65, 1529–1548. [Google Scholar] [CrossRef] [PubMed]
- Guessous, I.; Bochud, M.; Theler, J.M.; Gaspoz, J.M.; Pechère-Bertschi, A. 1999–2009 Trends in Prevalence, Unawareness, Treatment and Control of Hypertension in Geneva, Switzerland. PLoS ONE 2012, 7, e39877. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Digital Platform Specchio-COVID19. Available online: https://www.specchio-covid19.ch/ (accessed on 4 July 2022).
- The Corona Immunitas Study. Available online: https://www.corona-immunitas.ch/en/ (accessed on 4 July 2022).
- Fenwick, C.; Croxatto, A.; Coste, A.T.; Pojer, F.; André, C.; Pellaton, C.; Farina, A.; Campos, J.; Hacker, D.; Lau, K.; et al. Changes in SARS-CoV-2 Spike versus Nucleoprotein Antibody Responses Impact the Estimates of Infections in Population-Based Seroprevalence Studies. J. Virol. 2021, 95, e01828-20. [Google Scholar] [CrossRef] [PubMed]
- Kroenke, K.; Spitzer, R.L.; Williams, J.B.W. The PHQ-9: Validity of a brief depression severity measure. J. Gen. Intern. Med. 2001, 16, 606–613. [Google Scholar] [CrossRef]
- Löwe, B.; Kroenke, K.; Herzog, W.; Gräfe, K. Measuring depression outcome with a brief self-report instrument: Sensitivity to change of the Patient Health Questionnaire (PHQ-9). J. Affect. Disord. 2004, 81, 61–66. [Google Scholar] [CrossRef]
- Harris, P.A.; Taylor, R.; Thielke, R.; Payne, J.; Gonzalez, N.; Conde, J.G. Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inform. 2009, 42, 377–381. [Google Scholar] [CrossRef] [Green Version]
- Samejima, F. Graded Response Model. In Handbook of Modern Item Response Theory; van der Linden, W.J., Hambleton, R.K., Eds.; Springer: New York, NY, USA, 1997; pp. 85–100. Available online: https://doi.org/10.1007/978-1-4757-2691-6_5 (accessed on 9 November 2022).
- Richard, A.; Wisniak, A.; Perez-Saez, J.; Garrison-Desany, H.; Petrovic, D.; Piumatti, G.; Baysson, H.; Picazio, A.; Pennacchio, F.; De Ridder, D.; et al. Seroprevalence of anti-SARS-CoV-2 IgG antibodies, risk factors for infection and associated symptoms in Geneva, Switzerland: A population-based study. Scand. J. Public Health 2021, 50, 124–135. [Google Scholar] [CrossRef]
- Barros, A.J.; Hirakata, V.N. Alternatives for logistic regression in cross-sectional studies: An empirical comparison of models that directly estimate the prevalence ratio. BMC Med. Res. Methodol. 2003, 3, 21. [Google Scholar] [CrossRef] [Green Version]
- Coutinho, L.M.S.; Scazufca, M.; Menezes, P.R. Methods for estimating prevalence ratios in cross-sectional studies. Rev. Saúde Pública 2008, 42, 992–998. [Google Scholar] [CrossRef] [Green Version]
- Aiken, L.S.; West, S.G.; Reno, R.R. Multiple Regression: Testing and Interpreting Interactions; SAGE: Thousand Oaks, CA, USA, 1991; 228p. [Google Scholar]
- Giorli, A.; Ferretti, F.; Biagini, C.; Salerni, L.; Bindi, I.; Dasgupta, S.; Pozza, A.; Gualtieri, G.; Gusinu, R.; Coluccia, A.; et al. A Literature Systematic Review with Meta-Analysis of Symptoms Prevalence in COVID-19: The Relevance of Olfactory Symptoms in Infection Not Requiring Hospitalization. Curr. Treat. Options Neurol. 2020, 22, 36. [Google Scholar] [CrossRef]
- Karni, N.; Klein, H.; Asseo, K.; Benjamini, Y.; Israel, S.; Nammary, M.; Olshtain-Pops, K.; Nir-Paz, R.; Hershko, A.; Muszkat, M.; et al. Self-Rated Smell Ability Enables Highly Specific Predictors of COVID-19 Status: A Case–Control Study in Israel. Open Forum Infect. Dis. 2020, 8, ofaa589. [Google Scholar] [CrossRef] [PubMed]
- Struyf, T.; Deeks, J.J.; Dinnes, J.; Takwoingi, Y.; Davenport, C.; Leeflang, M.M.; Spijker, R.; Hooft, T.; Emperador, D.; Domen, J.; et al. Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19 disease. Cochrane Database Syst. Rev. 2022. [Google Scholar] [CrossRef]
- World Health Organization (WHO). Coronavirus Disease (COVID-19). 2021. Available online: https://www.who.int/health-topics/coronavirus#tab=tab_1 (accessed on 9 June 2021).
- Larkin, D.; Martin, C.R. The interface between chronic fatigue syndrome and depression: A psychobiological and neurophysiological conundrum. Neurophysiol. Clin./Clin. Neurophysiol. 2017, 47, 123–129. [Google Scholar] [CrossRef] [PubMed]
- Matte, D.L.; Pizzichini, M.M.M.; Hoepers, A.T.C.; Diaz, A.P.; Karloh, M.; Dias, M.; Pizzichini, E. Prevalence of depression in COPD: A systematic review and meta-analysis of controlled studies. Respir. Med. 2016, 117, 154–161. [Google Scholar] [CrossRef] [PubMed]
- Cuijpers, P.; Ciharova, M.; Quero, S.; Miguel, C.; Driessen, E.; Harrer, M.; Purgato, M.; Ebert, D.; Karyotaki, E. The Contribution of “Individual Participant Data” Meta-Analyses of Psychotherapies for Depression to the Development of Personalized Treatments: A Systematic Review. J. Pers. Med. 2022, 12, 93. [Google Scholar] [CrossRef]
- Cuijpers, P.; Reynolds, C.F., III. Increasing the Impact of Prevention of Depression—New Opportunities. JAMA Psychiatry 2022, 79, 11–12. [Google Scholar] [CrossRef]
- Antonelli, M.; Capdevila, J.; Chaudhari, A.; Granerod, J.; Canas, L.; Graham, M.; Klaser, K.; Modat, M.; Molteni, E.; Murray, B.; et al. Optimal symptom combinations to aid COVID-19 case identification: Analysis from a community-based, prospective, observational cohort. J. Infect. 2021, 82, 384–390. [Google Scholar] [CrossRef]
- Menni, C.; Sudre, C.H.; Steves, C.J.; Ourselin, S.; Spector, T.D. Quantifying additional COVID-19 symptoms will save lives. Lancet 2020, 395, e107–e108. [Google Scholar] [CrossRef]
- Menni, C.; Valdes, A.M.; Freidin, M.B.; Sudre, C.H.; Nguyen, L.H.; Drew, D.A.; Ganesh, S.; Varsavsky, T.; Cardoso, M.J.; El-Sayed Moustafa, J.S.; et al. Real-time tracking of self-reported symptoms to predict potential COVID-19. Nat. Med. 2020, 26, 1037–1040. [Google Scholar] [CrossRef]
- Sudre, C.H.; Lee, K.A.; Ni Lochlainn, M.; Varsavsky, T.; Murray, B.; Graham, M.S.; Menni, C.; Modat, M.; Bowyer, R.C.E.; Nguyen, L.H.; et al. Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app. Sci. Adv. 2021, 7, eabd4177. [Google Scholar] [CrossRef]
- Daniali, H.; Flaten, M.A. What Psychological Factors Make Individuals Believe They Are Infected by Coronavirus 2019? Frontiers in Psychology. 2021. Available online: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.667722 (accessed on 9 November 2022).
- Daniali, H.; Flaten, M.A. Experiencing COVID-19 symptoms without the disease: The role of nocebo in reporting of symptoms. Scand. J. Public Health 2022, 50, 61–69. [Google Scholar] [CrossRef] [PubMed]
- Mitsikostas, D.D.; Blease, C.; Carlino, E.; Colloca, L.; Geers, A.L.; Howick, J.; Evers, A.W.M.; Flaten, M.A.; Kelley, J.M.; Kirsch, I.; et al. European Headache Federation recommendations for placebo and nocebo terminology. J. Headache Pain 2020, 21, 117. [Google Scholar] [CrossRef] [PubMed]
- Colizzi, M.; Bortoletto, R.; Silvestri, M.; Mondini, F.; Puttini, E.; Cainelli, C.; Gaudino, R.; Ruggeri, M.; Zoccante, L. Medically unexplained symptoms in the times of COVID-19 pandemic: A case-report. Brain Behav. Immun.-Health 2020, 5, 100073. [Google Scholar] [CrossRef] [PubMed]
- Shevlin, M.; Nolan, E.; Owczarek, M.; McBride, O.; Murphy, J.; Gibson Miller, J.; Hartman, T.K.; Levita, L.; Mason, L.; Martinez, A.P.; et al. COVID-19-related anxiety predicts somatic symptoms in the UK population. Br. J. Health Psychol. 2020, 25, 875–882. [Google Scholar] [CrossRef]
- Willis, C.; Chalder, T. Concern for COVID-19 cough, fever and impact on mental health. What about risk of Somatic Symptom Disorder? J. Ment. Health 2021, 30, 551–555. [Google Scholar] [CrossRef]
- Yifan, T.; Ying, L.; Chunhong, G.; Jing, S.; Rong, W.; Zhenyu, L.; Zejuan, G.; Peihung, L. Symptom Cluster of ICU Nurses Treating COVID-19 Pneumonia Patients in Wuhan, China. J. Pain Symptom Manag. 2020, 60, e48–e53. [Google Scholar] [CrossRef]
- Bhattacharyya, N.; Wasan, A. Do Anxiety and Depression Confound Symptom Reporting and Diagnostic Accuracy in Chronic Rhinosinusitis? Ann. Otol. Rhinol. Laryngol. 2008, 117, 18–23. [Google Scholar] [CrossRef]
- Chan, A.S.W.; Ho, J.M.C.; Li, J.S.F.; Tam, H.L.; Tang, P.M.K. Impacts of COVID-19 Pandemic on Psychological Well-Being of Older Chronic Kidney Disease Patients. Front. Med. 2021, 8, 666973. [Google Scholar] [CrossRef]
- Do, B.N.; Tran, T.V.; Phan, D.T.; Nguyen, H.C.; Nguyen, T.T.P.; Nguyen, H.C.; Ha, T.H.; Dao, H.K.; Trinh, M.V.; Do, T.V.; et al. Health Literacy, eHealth Literacy, Adherence to Infection Prevention and Control Procedures, Lifestyle Changes, and Suspected COVID-19 Symptoms Among Health Care Workers During Lockdown: Online Survey. J. Med. Internet Res. 2020, 22, e22894. [Google Scholar] [CrossRef]
Figure 1.
Flow chart for the selection of participants in Geneva and Ticino, Switzerland.
Figure 2.
Results of Poisson regressions estimating likelihoods of reporting COVID-19 symptoms at baseline across studies. Note: values are prevalence ratios with 95% confidence intervals from Poisson regressions with robust standard errors, adjusted for all listed variables. For Geneva, analyses were further adjusted for the year of PHQ-9 completion.
Figure 2.
Results of Poisson regressions estimating likelihoods of reporting COVID-19 symptoms at baseline across studies. Note: values are prevalence ratios with 95% confidence intervals from Poisson regressions with robust standard errors, adjusted for all listed variables. For Geneva, analyses were further adjusted for the year of PHQ-9 completion.
Table 1.
Description of samples by study sites and assessments.
| Geneva | Ticino | |||
|---|---|---|---|---|
| Baseline (April–June 2020) | Follow-Up (February–January 2021) | Baseline (July 2020) | Follow-Up (February–January 2021) | |
| N | 576 | 365 | 581 | 536 |
| Age, years, mean (SD) | 46 (11) | 46 (11) | 46 (11) | 46 (11) |
| Age group (years) | ||||
| 20–49 | 333 (57.8) | 212 (58.1) | 327 (56.3) | 293 (54.7) |
| 50–64 | 243 (42.2) | 153 (41.9) | 254 (43.7) | 243 (45.3) |
| Gender | ||||
| Female | 333 (57.8) | 222 (60.8) | 328 (56.5) | 310 (57.8) |
| Male | 243 (42.2) | 143 (39.2) | 253 (43.5) | 226 (42.2) |
| Educational level | ||||
| Up to higher secondary/apprenticeship | 195 (33.9) | 122 (33.4) | 374 (64.4) | 345 (64.4) |
| Tertiary | 381 (66.2) | 243 (66.6) | 207 (35.6) | 191 (35.6) |
| Work status | ||||
| Unemployed/retired | 70 (12.2) | 38 (10.4) | 93 (16.0) | 87 (16.2) |
| Employed/student | 506 (87.8) | 327 (89.6) | 488 (84.0) | 449 (83.8) |
| Obese (BMI ≥ 30 kg/m2) | 47 (8.2) | 24 (6.6) | 62 (10.7) | 57 (10.6) |
| Smoking | ||||
| No | 487 (84.5) | 320 (87.7) | 453 (78.0) | 421 (78.5) |
| Yes | 89 (15.5) | 45 (12.3) | 128 (22.0) | 115 (21.5) |
| Chronic diseases | ||||
| No | 506 (87.9) | 324 (88.8) | 486 (83.7) | 454 (84.7) |
| Yes | 70 (12.1) | 41 (11.2) | 95 (16.3) | 82 (15.3) |
| COVID-19 family cases a | ||||
| No | 535 (92.9) | - | 525 (90.4) | - |
| Yes | 41 (7.1) | - | 56 (9.6) | - |
| Anti-SARS-CoV-2 IgG seropositive a | 47 (8.2) | - | 47 (8.1) | - |
Note: values are frequencies (percentages) unless stated otherwise. SD: standard deviation. BMI: Body mass index. Chronic diseases include hypertension, diabetes, cardiovascular disease, cancer, immunological syndrome, or respiratory syndrome. a—not assessed at follow-up.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Piumatti, G.; Amati, R.; Richard, A.; Baysson, H.; Purgato, M.; Guessous, I.; Stringhini, S.; Albanese, E., on behalf of the SEROCoV-POP; Specchio-COVID19 Study Group; the Corona Immunitas Ticino Working Group. Associations between Depression and Self-Reported COVID-19 Symptoms among Adults: Results from Two Population-Based Seroprevalence Studies in Switzerland. Int. J. Environ. Res. Public Health 2022, 19, 16696. https://doi.org/10.3390/ijerph192416696
AMA Style
Piumatti G, Amati R, Richard A, Baysson H, Purgato M, Guessous I, Stringhini S, Albanese E on behalf of the SEROCoV-POP, Specchio-COVID19 Study Group, the Corona Immunitas Ticino Working Group. Associations between Depression and Self-Reported COVID-19 Symptoms among Adults: Results from Two Population-Based Seroprevalence Studies in Switzerland. International Journal of Environmental Research and Public Health. 2022; 19(24):16696. https://doi.org/10.3390/ijerph192416696
Chicago/Turabian StylePiumatti, Giovanni, Rebecca Amati, Aude Richard, Hélène Baysson, Marianna Purgato, Idris Guessous, Silvia Stringhini, Emiliano Albanese on behalf of the SEROCoV-POP, Specchio-COVID19 Study Group, and the Corona Immunitas Ticino Working Group. 2022. "Associations between Depression and Self-Reported COVID-19 Symptoms among Adults: Results from Two Population-Based Seroprevalence Studies in Switzerland" International Journal of Environmental Research and Public Health 19, no. 24: 16696. https://doi.org/10.3390/ijerph192416696
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
