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Tancredietal.
International Journal for Equity in Health (2023) 22:51
https://doi.org/10.1186/s12939-023-01853-2
RESEARCH
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Open Access
International Journal for
Equity in Health
Changes insocioeconomic resources
andmental health afterthesecond COVID-19
wave (2020–2021): alongitudinal study
inSwitzerland
Stefano Tancredi1*†, Agnė Ulytė1,2†, Cornelia Wagner1, Dirk Keidel3,4, Melissa Witzig3,4, Medea Imboden3,4,
Nicole Probst‑Hensch3,4, Rebecca Amati5, Emiliano Albanese5, Sara Levati6, Luca Crivelli6, Philipp Kohler7,8,
Alexia Cusini7,8, Christian Kahlert7,8, Erika Harju9, Gisela Michel9, Chantal Lüdi9, Natalia Ortega1,10,
Stéphanie Baggio10,11, Patricia Chocano‑Bedoya1,10, Nicolas Rodondi10,12, Tala Ballouz13, Anja Frei13,
Marco Kaufmann13, Viktor Von Wyl13, Elsa Lorthe14, Hélène Baysson14,15, Silvia Stringhini14,15,
Valentine Schneider16, Laurent Kaufmann16, Frank Wieber17,18, Thomas Volken17, Annina Zysset17,
Julia Dratva4,17, Stéphane Cullati1,19 and the Corona Immunitas Research Group
Abstract
Background During the 2020/2021 winter, the labour market was under the impact of the COVID‑19 pandemic.
Changes in socioeconomic resources during this period could have influenced individual mental health. This associa‑
tion may have been mitigated or exacerbated by subjective risk perceptions, such as perceived risk of getting infected
with SARS‑CoV‑2 or perception of the national economic situation. Therefore, we aimed to determine if changes in
financial resources and employment situation during and after the second COVID‑19 wave were prospectively associ‑
ated with depression, anxiety and stress, and whether perceptions of the national economic situation and of the risk
of getting infected modified this association.
Methods One thousand seven hundred fifty nine participants from a nation‑wide population‑based eCohort in Swit‑
zerland were followed between November 2020 and September 2021. Financial resources and employment status
were assessed twice (Nov2020–Mar2021, May–Jul 2021). Mental health was assessed after the second measurement
of financial resources and employment status, using the Depression, Anxiety and Stress Scale (DASS‑21). We modelled
DASS‑21 scores with linear regression, adjusting for demographics, health status, social relationships and changes in
workload, and tested interactions with subjective risk perceptions.
Results We observed scores above thresholds for normal levels for 16% (95%CI = 15–18) of participants for depres‑
sion, 8% (95%CI = 7–10) for anxiety, and 10% (95%CI = 9–12) for stress. Compared to continuously comfortable or suf‑
ficient financial resources, continuously precarious or insufficient resources were associated with worse scores for all
outcomes. Increased financial resources were associated with higher anxiety. In the working‑age group, shifting from
†Stefano Tancredi and Agnė Ulytė contributed equally to this work.
*Correspondence:
Stefano Tancredi
stefano.tancredi@unifr.ch
Full list of author information is available at the end of the article
Page 2 of 15
Tancredietal. International Journal for Equity in Health (2023) 22:51
full to part‑time employment was associated with higher stress and anxiety. Perceiving the Swiss economic situation
as worrisome was associated with higher anxiety in participants who lost financial resources or had continuously
precarious or insufficient resources.
Conclusion This study confirms the association of economic stressors and mental health during the COVID‑19 pan‑
demic and highlights the exacerbating role of subjective risk perception on this association.
Keywords COVID‑19, Depressive symptoms, Anxiety, Stress, Socioeconomic condition, Financial resources
Introduction
e coronavirus disease 2019 (COVID-19) pandemic
prompted nationwide lockdowns and restrictive meas-
ures around the world, leading to profound effects on
the economy and on the labour market. e pandemic
slowed down economic activities and led to a rise in
the unemployment rate in multiple countries, with mil-
lions of people losing their jobs [1]. During the winter
of 2020–2021, Switzerland was experiencing the second
wave of the pandemic. During and after it, the country
implemented lighter mitigation strategies compared to
other European countries [2]. Nevertheless, Swiss labour
market suffered from the economic consequences caused
by COVID-19. Families’ private debts increased due to
the pandemic [3] and the unemployment rate in Swit-
zerland, as defined by the International Labour Organi-
sation, increased both in the last quarter of 2020 and at
the beginning of 2021 [4, 5]. Although the mean dispos-
able income of Swiss households remained stable in 2020
(compared to 2019) [6], 11.3% of the general population
experienced a loss of income due to the pandemic in
2021 [7].
e loss of socioeconomic resources may have
impacted mental health and mental well-being. Previ-
ous studies have shown an association between finan-
cial hardship and poorer mental health [8–15]. Losing
financial resources increases psychosocial stress and can
lead to a loss of flexible resources, such as power or pres-
tige, that can be used to minimize the consequences of a
stressful event [16, 17]. Moreover, the pandemic may have
exacerbated economic inequalities; existing evidence
suggests that the burden of the pandemic is not equally
distributed in the population, with a higher burden and
worse mental health in people with lower socioeconomic
status [18–20]. Further, the population’s perception of the
national economic situation and the individual percep-
tion of the risk of getting infected with COVID-19 could
have also influenced mental health. Previous studies have
shown a link between a higher risk perception of getting
infected and worse mental health [21–23], probably due
to the fear of falling ill, losing a loved one, and of possi-
ble social or economic consequences of isolation. Besides
their direct individual impact on mental health, these
factors can modify the association between changes in
socioeconomic resources and mental health, exacerbat-
ing the detrimental effects of the loss of socioeconomic
resources on mental health. To our knowledge, this has
not been reported yet in any study.
erefore, in this study, we aimed to determine [a] if
changes in financial resources and employment situ-
ation during and after the second COVID-19 wave in
2020–2021 were prospectively associated with self-
reported depression, anxiety and stress symptoms, and
[b] whether perceptions of the national economic situ-
ation and of the risk of getting infected modified this
association. Additionally, as the risk of financial loss was
higher among people in the working age, we also investi-
gated these associations in the sub-group of not retired
persons.
Methods
Study population anddesign
e Corona Immunitas digital follow-up (CI-DFU) eCo-
hort is a population-based digital longitudinal study [24].
e cohort is part of the Corona Immunitas research pro-
gramme [25], a nation-wide seroprevalence study coor-
dinated by the Swiss School of Public Health (SSPH +),
based on randomly selected adults living in Switzerland.
Participants of Corona Immunitas were invited to join
the CI-DFU eCohort if they were at least 20 years old
and had a valid email address and internet access. Partici-
pants could answer the CI-DFU questionnaires by choos-
ing between four different languages: German, French,
Italian and English. e questionnaires were completed
online (data were collected using REDCap, Research
Electronic Data Capture). Weekly participation rates
ranged from 75% to 88.6% [24].
e present study included 1′759 participants from the
CI-DFU eCohort, living in several cantons in Switzerland
(Bern, Fribourg, Neuchatel, St-Gallen, Ticino, Winterthur
and Zurich). We administered a questionnaire to assess
the financial resources and employment situation of par-
ticipants twice: first, between November 2020 and March
2021 (hereafter: first questionnaire = Q1) and second,
between May and July 2021 (hereafter: second question-
naire = Q2). We assessed mental health using the Depres-
sion, Anxiety and Stress Scale (DASS-21) between May
and September 2021. Figure S1 (Additional file1: Figure
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Tancredietal. International Journal for Equity in Health (2023) 22:51
S1) shows a timeline of the questionnaires’ administra-
tion together with the COVID-19 pandemic context. We
included participants who [1] filled the questionnaire on
financial resources and employment situation both times,
with at least 60days between the first (Q1) and the sec-
ond (Q2) questionnaire’s response, and [2] completed
the mental health questionnaire at least 7days after Q2
(May – September 2021; median 28days after the second
questionnaire, range 7 to 116days). We excluded partici-
pants with missing data on the outcome variables or on
any other analyzed covariates (a comparison between the
characteristics of the included and excluded population
is reported in additional file1, tableS1 and S2; no dif-
ferences were found between the excluded and included
population according to working status). Figure S2 (Addi-
tional file1: Figure S2) shows a flow diagram of study
participants. e reporting of this study followed the
“Strengthening the reporting of observational studies in
epidemiology” (STROBE) statement [26].
Outcome measures
We measured mental health using the Depression,
Anxiety and Stress Scale (DASS-21) [27]. e DASS-
21 is a 21-item self-administered questionnaire devel-
oped to screen and assess mental health symptoms and
their severity, and comprises three 7-item subscales for
depression (Cronbach alpha 0.93 in our study popula-
tion), anxiety (Cronbach alpha 0.85) and stress symptoms
(Cronbach alpha 0.92). e principal component analysis
confirmed the one-dimensionality of each subscale in this
study population. DASS-21 is commonly used in health
research to assess symptoms of depression, anxiety and
stress in the general population and in clinical settings
[28]. We used the validated translations of the German
[29], French [30] and Italian [31] versions of the DASS-
21. Respondents reported the frequency of their depres-
sion, anxiety, and stress symptoms in the last seven days
on a four-point Likert scale (never; sometimes; often; and
almost always). Continuous scores for depression, anxi-
ety and stress-related symptoms were computed follow-
ing the usual standard [32], where each category score is
calculated by summing the subscale item scores and mul-
tiplying by two. e final scores range from 0 (no symp-
toms) to 42 (worst symptoms) for each subscale, and can
be categorized to severity levels according to the stand-
ard guidance (a score is considered “normal” if it is below
9 for depression, 7 for anxiety and 14 for stress) [32].
DASS-21 severity categories are described in supplemen-
tary tableS3 (Additional file1: TableS3).
Main predictors
We administered a structured questionnaire to assess
the financial resources and employment situation of
participants twice (between November 2020 and March
2021 (Q1) and between May and July 2021 (Q2)). Par-
ticipants were asked to assess their financial resources
with the prompt “In the past 6months, would you say
that financially…” followed by response options “You
are comfortable, money is not a concern and it is easy
to save money” (hereafter, comfortable), “Your income
allows you to cover your expenses and to compensate
for any minor contingencies” (hereafter, sufficient), “You
need to be careful with your expenses and an unforeseen
event could put you in financial difficulty” (hereafter, pre-
carious), “You are unable to cover your needs with your
income and need external support to function (debt,
credit, various financial aids)” (hereafter, insufficient).
We assessed the following combinations of changes in
financial resources: comfortable/sufficient resources at
Q1 and Q2 (used as reference category in our analyses);
comfortable/sufficient resources at Q1 and lower at Q2;
precarious/insufficient resources at Q1 and higher at Q2;
precarious/insufficient resources both at Q1 and Q2.
We categorised employment status according to the
participants’ situation during the month prior to the
completion of the questionnaire into the following
groups: employed full-time (minimum 37 h weekly),
employed part-time, self-employed, and other employ-
ment positions (unemployed, retired, student, at home
(domestic work, children care) or in another situa-
tion). All possible combinations between these groups
were assessed (i.e., Full-time or part-time at both Q1
and Q2; full-time at Q1 and part-time at Q2; part-time
at Q1 and full-time at Q2; full- or part-time at Q1 and
self-employed at Q2; self-employed at Q1 and full- or
part-time at Q2; Self-employed at Q1 and Q2; full- or
part-time at Q1 and other at Q2; other employment at
Q1 and Q2; other at Q1 and full- or part-time at Q2).
Employment was further grouped as a binary variable
of retired versus not retired (all other employment
options).
Potential moderators
Worries on the economic situation in Switzerland were
assessed on a Likert scale from 1 (not worried at all) to
5 (extremely worried) in response to the question “How
worried are you about the current coronavirus situation
in the following area: the general economic situation in
Switzerland”. e risk of getting infected in the last week
was reported as a response to the statement “In the last
7days, do you think the risk of being infected with coro-
navirus (SARS-CoV-2) is…” on a sliding scale from 0 (no
risk) to 100 (very high risk), divided by 10. We reported
median values assessed in weekly (risk of infection) and
monthly (economic situation) questionnaires between
Q1 and Q2. In a sensitivity analysis, we used the median
Page 4 of 15
Tancredietal. International Journal for Equity in Health (2023) 22:51
reported values for each month between January and
April 2021.
Control variables
We collected information on the demographic charac-
teristics, health status and social relationships of the par-
ticipants. For demographics, we assessed participants’
age (continuous), sex (women, men, other), and language
preference for filling the questionnaires (largely corre-
sponding to the linguistic regions in Switzerland). Lan-
guage preference was not recorded for some participants
from the cantons of Zurich and Ticino, and instead the
dominant language of the canton was recorded (Ger-
man and Italian, respectively). Health status included
the number of chronic conditions, having reported a
positive SARS-CoV-2 test (PCR or antigen test) by the
time of completing the Q2 questionnaire and vacci-
nation status by the time of completing the DASS-21
questionnaire. Chronic conditions were assessed with
the question “Do you suffer from one or more of the
following diseases? cancer, immunological diseases,
cardiovascular diseases, diabetes, hypertension and res-
piratory diseases”. Social relationships assessed whether
the participant was living alone in the household and
their reported loneliness (median of ree-Item Loneli-
ness Scale reported between Q1 and Q2, range from 3
(lowest) to 15 (highest) loneliness) [33]. Change in work-
load (no change, increase, decrease) in the past 6months
was also included as a control variable as measured in Q1
questionnaire.
Statistical analysis
We presented descriptive statistics of the cohort, both
for the total sample and stratified by being in the work-
ing-age population or retired. We modelled depression,
anxiety and stress outcome scores in univariable and
multivariable linear regressions. e main predictors in
the multivariable models were financial resources and
employment status at Q1, and their changes by Q2. e
models also adjusted for demographic characteristics
(age, sex, language), health status (chronic conditions,
positive COVID-19 test result, being vaccinated against
SARS-CoV-2), social relationships (loneliness, living
alone) and changes in workload. We tested the effect of
an interaction between changes in the socioeconomic
resources (financial resources and employment situation)
and potential moderators (perceived Swiss economic
situation and the perceived risk of getting infected with
SARS-CoV-2) by adding each potential moderator in a
separate multivariable linear regression model. We ran
multivariable models for the entire sample and separately
for the working-age population and retired persons. A
theoretical framework of the study is shown in supple-
mental figure S3 (Additional file1: Figure S3).
We also performed several sensitivity analyses: [1] we
ran logistic regression models for the full study popula-
tion with dichotomized mental health outcomes (normal
or mild vs moderate, severe or extremely severe catego-
ries). We re-ran [2] the linear models using a subset of
the full study population including monthly rather than
median values of risk perceptions, to account for poten-
tial influence of changing risk perceptions over time. We
re-ran [3] the linear multivariate models using multiple
imputation by chained equation to account for missing
information. Statistical analyses were performed using R
statistical software (version 4.0.2, R Foundation for Sta-
tistical Computing, Vienna, Austria; packages used for
statistics and tables: stats 4.0.2, gtsummary 1.4.1, mice
3.14.0; packages used for visualizations and plots: ggplot2
3.3.3, sjPlot 2.8.9).
Results
Respondents’ characteristics
We included 1′759 participants (79% of the included par-
ticipants filled the Q1 questionnaire between November
and December 2020; 99% filled the Q2 questionnaire in
May 2021; and 98% of participants filled the DASS-21
questionnaire between June and July 2021). e majority
of participants (79%) filled out the DASS questionnaire
28days or more after Q2. Descriptive characteristics of
the participants are presented in Table 1. e median
age was 53years (IQR = 40–64) and 52% of participants
were female. Mental health scores above thresholds for
normal levels were reported by 16% (95%CI = 15–18)
of participants for depression, 8% (95%CI = 7–10) for
anxiety, and 10% (95%CI = 9–12) for stress. e median
reported scores for depression, anxiety and stress were 0
(IQR = 0–6), 0 (IQR = 0–2) and 2 (IQR = 0–10), respe c-
tively; median scores for worries on the economic situ-
ation in Switzerland and risk of getting infected were 3
(out of 5, IQR = 2–4) and 4.5 (out of 10, IQR = 2.1–6.5),
respectively. Differences and similarities between retired
and not retired participants are reported in Table1. e
proportion of scores above the threshold for normal level
for the three mental health outcomes was lower among
retired participants compared to non-retired participants
(depression: 12% vs 18%, p = 0.002; anxiety: 6% vs 9%,
p = 0.017; stress: 5% vs 12%, p < 0.001).
e cross-sectional distribution of the reported
financial resources was similar in Q1 and Q2 among
the retired and not retired participants (Table2). Not
retired participants reported insufficient or precari-
ous financial resources more often than retired par-
ticipants both at Q1 (17% vs 9%) and Q2 (16% vs 9%).
Page 5 of 15
Tancredietal. International Journal for Equity in Health (2023) 22:51
Distributions of employment type were also similar
among the not retired participants in Q1 and Q2. Pro-
portion of changes in financial resources and employ-
ment situation are reported in table S4 (Additional
file1: TableS4).
Changes innancial resources
In univariable analyses, stable precarious or insufficient
financial resources (both at Q1 and Q2) were associ-
ated with worse (higher) anxiety, depression and stress
scores (Fig. 1). Participants who either improved or
Table 1 Descriptive characteristics of the studied population
a: Participants were grouped in retired or not retired categories according to their employment situation at Q1
b: Loneliness score ranged from 3 (smallest) to 15 (highest). Worries about Swiss economic situation ranged between 1 (smallest) to 5 (highest). Perceived risk to be
infected ranged between 0 (no risk) to 10 (very high)
c: Mental health outcomes were assessed between May and September 2021 using the DASS-21 score, with minimum 0 (best outcome) and maximum 42 (worst
outcome) score. See details on the instruments in Methods
Overall Not retiredaRetireda
N1759 1336 423
Age, median (IQR) 53 (40—64) 48 (36—56) 70 (67—74)
Sex
Female 909 (52%) 722 (54%) 187 (44%)
Male 846 (48%) 610 (46%) 236 (56%)
Other 4(0.1%) 4 (0.3%) 0 (0%)
One or more chronic health conditions 621 (35%) 356 (27%) 265 (63%)
SARS-CoV-2 positive test 154 (8.8%) 130 (9.7%) 24 (5.7%)
SARS-CoV-2 Vaccinated 397 (23%) 117 (8.8%) 280 (66%)
Language
German 886 (50%) 631(47%) 255 (60%)
Italian 505 (29%) 481 (36%) 24 (5.7%)
French 353 (20%) 211 (16%) 142 (34%)
English 15 (0.9%) 13 (1.0%) 2 (0.5%)
Living alone 278 (16%) 190 (14%) 88 (21%)
Loneliness score, median (IQR)b5 (4—7) 5 (3—7) 6 (4—8)
Worries about Swiss economy, median (IQR)b3 (2—4) 3 (2—4) 3 (3—4)
Perceived risk to be infected, median (IQR)b4.4 (2.1—6.5) 4.4 (2.1 – 6.4) 4.6 (1.9 – 6.9)
Change in workload
No change 1,046 (78%) 1,046 (78%) 0 (0%)
Increase 150 (11%) 150 (11%) 0 (0%)
Decrease 140 (10%) 140 (10%) 0 (0%)
Mental health outcomesc
Depression
Median (IQR) 0 (0—6) 0 (0—6) 0 (0—4)
Normal 1,472 (84%) 1,102 (82%) 370 (87%)
Mild to moderate 227 (13%) 183 (14%) 44 (10%)
Sever to extremely severe 60 (3.4%) 51 (3.8%) 9 (2.1%)
Anxiety
Median (IQR) 0 (0—2) 0 (0—2) 0 (0—2)
Normal 1,613 (92%) 1,214 (91%) 399 (94%)
Mild to moderate 107 (6.1%) 87 (6.6%) 20 (4.7%)
Sever to extremely severe 39 (2.2%) 35 (2.6%) 4 (0.9%)
Stress
Median (IQR) 2 (0—10) 2 (0—10) 0 (0—6)
Normal 1,581 (90%) 1,178 (88%) 403 (95%)
Mild to moderate 136 (7.7%) 120 (9.0%) 16 (3.8%)
Sever to extremely severe 42 (2.4%) 38 (2.8%) 4 (0.9%)
Page 6 of 15
Tancredietal. International Journal for Equity in Health (2023) 22:51
worsened their financial situation (precarious or insuffi-
cient resources at Q1 and higher at Q2, or comfortable or
sufficient resources at Q1 and lower at Q2) also reported
worse scores in all three outcomes. A higher perceived
risk to be infected and a worse perception of the Swiss
economic situation were associated with worse scores in
all outcomes (Fig.1).
In the multivariable model of the full study popula-
tion (Fig.2A), having precarious or insufficient resources
both at Q1 and Q2 was predictive of worse scores for all
outcomes (compared to respondents with comfortable
resources at both time points). Both improvement and
worsening of participants’ financial situation were associ-
ated with higher anxiety scores.
Higher perceived risk of getting infected was associ-
ated with higher stress scores and a worse perception of
the Swiss economic situation with slightly higher stress,
anxiety and depression scores. Similar associations were
observed in the non-retired subset of the study popula-
tion (Fig.2B). Coefficients and confidence intervals of the
multivariable model are reported in tableS5 (Additional
file1: TableS5).
Changes inemployment situation
Results of the univariate analyses among working-age
participants showed that changing from full to part-
time employment and being in other employment posi-
tions (unemployed, students, people at home, people in
another situation) than employed or self-employed both
at Q1 and Q2 were associated with higher scores in all
outcomes (Fig.3). Shifting from full or part-time employ-
ment to other employment positions was associated with
higher anxiety. Shifting from other employment positions
to full or part-time was associated with higher stress.
In multivariable analyses (Fig. 4), we found higher
scores in stress and anxiety for those shifting from full
to part-time employment. Being in other employment
positions both at Q1 and Q2 was associated with higher
scores in all outcomes. Coefficients and confidence inter-
vals of the multivariable model are reported in tableS6
(Additional file1: TableS6).
Interactions
Perceiving the Swiss economic situation as more wor-
risome was associated with higher anxiety scores in
persons who lost financial resources (comfortable or suf-
ficient at Q1 and became precarious or insufficient at
Q2) or had precarious or insufficient resources both at
Q1 and Q2. e predicted marginal effects of changes in
financial resources and perceived Swiss economic situa-
tion are shown in Fig.5. We also found that a higher per-
ceived risk of getting infected was associated with lower
anxiety scores in persons having consistently precarious
or insufficient results (Additional File 1: Figure S4). When
testing the interaction between changes in employment
status and perceived risk of getting infected or perceived
Swiss economic situation, we found no results with more
accurate predictions [Fig.5].
Sensitivity analyses
e results of the sensitivity analyses were consistent
with the main analyses and are shown in supplementary
material (Additional file1: Tables S7-10; Figure S5-S6).
When running the multivariable linear model using mul-
tiple imputation by chained equation, the association
between a worsened financial situation and higher anxi-
ety scores disappeared as well as the interaction effect
of perceived risk of getting infected on anxiety levels in
people with consistently precarious or insufficient results
(Additional file1: TableS7 and TableS10).
Table 2 Financial resources and employment situation of the
participants
Note:
Q1: rst questionnaire on nancial resources and employment situation;
November 2020 – March 2021
Q2: second questionnaire on nancial resources and employment situation;
May – July 2021
Not retired n is smaller than the n reported in Table2 because it refers to
participants who were not retired both at Q1 and Q2
a: P values were computed using Pearson’s Chi-squared test or Fisher’s exact test
b: unemployed, student, at home, or in another situation
Q1 Q2 P values a
Overall (n = 1759)
Financial resources 0.6
Comfortable 753 (43%) 783 (45%)
Sufficient 736 (42%) 724 (41%)
Precarious 241 (14%) 219 (12%)
Insufficient 29 (1.6%) 33 (1.9%)
Retired (n = 404)
Financial resources > 0.9
Comfortable 175 (43%) 176 (44%)
Sufficient 191 (47%) 190 (47%)
Precarious 37 (9.2%) 38 (9.4%)
Insufficient 1 (0.2%) 0 (0%)
Not retired (n = 1286)
Financial resources 0.6
Comfortable 552 (43%) 577 (45%)
Sufficient 512 (40%) 504 (39%)
Precarious 195 (15%) 176 (14%)
Insufficient 27 (2.1%) 29 (2.3%)
Employment situation > 0.9
Full‑time 641 (50%) 635 (49%)
Part‑time 349 (27%) 354 (28%)
Self employed 127 (9.9%) 131 (10%)
Other b169 (13%) 166 (13%)
Page 7 of 15
Tancredietal. International Journal for Equity in Health (2023) 22:51
Discussion
In this study, we investigated the impact of changes in
financial resources and employment situation on depres-
sion, anxiety and stress levels of the general population
during and after the second wave of the COVID-19
pandemic in Switzerland. We found that having consist-
ently precarious or insufficient financial resources was
associated with poorer mental health outcomes and
that an improvement in participants’ financial situation
was associated with higher anxiety scores. Moreover, in
the working-age population, we found higher scores for
stress and anxiety in participants shifting from full to
part-time employment. We also found that perceiving the
Swiss economic situation as worse was associated with
higher anxiety scores in participants with low or decreas-
ing financial resources.
Scores above the DASS-21 thresholds for normal lev-
els were reported by 16% of participants for depression,
8% for anxiety, and 10% for stress. Several systematic
reviews have assessed the prevalence of mental health
symptoms in the global general population during the
pandemic, although estimates vary depending on the
country and measurement instruments. In a meta-
review including 18 meta-analyses up to March 2021,
De Sousa etal. [34] found a prevalence of depression,
anxiety and stress in the general population of 27%, 28%
and 36%, respectively. In our sample, we found lower
estimates, in line with other Swiss studies conducted
during the same period [35]. ese results could be due
to the fact that Switzerland implemented lighter mitiga-
tion strategies [2] and had better economic conditions
(e.g., lower unemployment rates) compared to many
countries included in this review. Moreover, the major-
ity of the studies in the meta-review were performed in
Asian countries or included population subgroups that
usually have higher prevalence of depressive, stress and
anxiety symptoms (e.g., healthcare workers), making
a comparison with our data difficult. e proportion
of participants who reported depressive symptoms in
our study was similar to other assessments conducted
earlier (November 2020) in Switzerland that showed a
constantly increasing trend in self-reported depressive
symptoms during the early pandemic up to Novem-
ber 2020 [36]. Possibly, the psychological resilience
after the first phase of the COVID-19 pandemic [37]
or the secondary benefits of vaccination (starting at the
end of December 2020 in Switzerland), that has been
shown to be associated with a reduction in distress [38],
could contribute to overall improving mental health
outcomes.
Fig. 1 Changes in financial resources, risk and economic perception, and mental health outcomes: univariable regression. Note: a positive
coefficient (effect on score) means a higher DASS‑21 score (more symptoms) Q1: first questionnaire on financial resources and employment
situation; November 2020 – March 2021. Q2: second questionnaire on financial resources and employment situation; May – July 2021. Financial
resources categories are compared to the reference category of “comfortable or sufficient resources at both Q1 and Q2”
Page 8 of 15
Tancredietal. International Journal for Equity in Health (2023) 22:51
Fig. 2 Changes in financial resources, perceived risk of infection and economic perceptions, and mental health outcomes: multivariable regression.
Note: a positive coefficient (effect on score) means a higher DASS‑21 score (more symptoms) Q1: first questionnaire on financial resources and
employment situation; November 2020 – March 2021. Q2: second questionnaire on financial resources and employment situation; May – July
2021. Model estimates are adjusted for sex, age, number of chronic health conditions, positive COVID‑19 test before Q2, vaccination status, size of
household (living alone vs living with other persons), median loneliness score, employment status, changes in workload and language. Financial
resources categories are compared to the reference category of “comfortable or sufficient resources at both Q1 and Q2”
Page 9 of 15
Tancredietal. International Journal for Equity in Health (2023) 22:51
Our results are concordant with the evidence that finan-
cial insecurity is a risk factor for lower mental health
and wellbeing [8]. Other studies have reported the nega-
tive impact of these stressors on mental health during
the pandemic [39, 40], with possible detrimental con-
sequences (e.g., child maltreatment, domestic violence,
substance abuse), especially among vulnerable popula-
tion groups [41]. In our study we also found an associa-
tion between an increase of financial resources and higher
anxiety scores. While this finding may appear counterin-
tuitive, a possible explanation could be that people who
improved their financial situation during the pandemic
experienced changes in their work conditions that could
be associated with higher anxiety, such as increased job
demands and responsibilities. Moreover, an increase in
financial resources could also indicate a situation, such as
self-employment, which is usually associated with vary-
ing levels of income throughout the year and can be more
destabilizing than a stable income.
Regarding the effect of perceived economic and infection
risks, we found that participants who perceived a higher
risk of getting infected had higher stress scores and those
who perceived a worse economic situation had slightly
higher anxiety, stress and depression scores. Various studies
described the impact of the perceived risk of getting infected
with COVID-19 on mental health [23, 42]; for instance, Ter-
raneo etal. found a positive association between risk per-
ception and reported depression in six European countries.
However, despite the potential negative effects of a higher
perceived risk of getting infected on mental health, this could
likely be a key motivator for protective behaviours [43], and
the balance between the positive and negative effects is diffi-
cult to assess. Additionally, our findings showed that perceiv-
ing the economic situation as more worrisome modified the
association between the loss of financial resources and anxi-
ety scores, increasing anxiety symptoms in an already vul-
nerable population group. is result is in line with the social
amplification of risk framework [44].
Fig. 3 Changes in employment situation and mental health outcomes among working‑age participants: univariable regression. Note: a positive
coefficient (effect on score) means a higher DASS‑21 score (more symptoms) Q1: first questionnaire on financial resources and employment
situation; November 2020 – March 2021. Q2: second questionnaire on financial resources and employment situation; May – July 2021. Change in
employment situation is compared to a reference category of “full‑ or part‑time employed at both Q1 and Q2”
Page 10 of 15
Tancredietal. International Journal for Equity in Health (2023) 22:51
is study has some limitations. We had no informa-
tion regarding participants’ history of mental health
symptoms before the pandemic and, therefore, we
could not assess pre/post pandemic and further longi-
tudinal changes. We cannot infer causal effects, since
we could not rule out the role of unmeasured variables
with potential confounding (e.g., low social support
network). Further, although a random representative
sample was invited to the study, selection effects could
not be excluded, such as higher participation of per-
sons with better mental health status or a higher socio-
economic status. Additionally, Q1 and Q2 assessments
were completed approximately half a year apart for
most of the participants, with only a limited number
of participants changing their socioeconomic situa-
tion, and the questions on financial situation referred to
the previous 6months, therefore the periods reported
in Q1 and Q2 might overlap in some cases. Moreover,
although our sample was sufficiently large for the main
analyses, it limited some sensitivity analyses (e.g., with
dichotomous outcome or in the not retired subgroup
of participants). us, although sensitivity analyses
matched the results of the main analyses in terms of
consistent effect estimates, wider confidence inter-
vals meant that we could not reproduce the statistical
significance of all results. Finally, we lacked detailed
information on participants’ specific job sectors, which
would have improved interpretability of the results on
the changes in employment situation. Strengths of this
study include the assessment of mental health with a
previously validated tool, and the fact that changes in
financial resources and employment situation were lon-
gitudinally assessed before mental health assessments.
Moreover, the population-based design of this study
improves the generalisability of our results for overall
Swiss population.
Fig. 4 Changes in employment situation and mental health outcomes among working‑age participants: multivariable regression. Note: a positive
coefficient (effect on score) means a higher DASS‑21 score (more symptoms). Q1: first questionnaire on financial resources and employment
situation; November 2020 – March 2021. Q2: second questionnaire on financial resources and employment situation; May – July 2021. Model
estimates are adjusted for sex, age, number of chronic health conditions, positive COVID‑19 test before Q2, vaccination status, size of household
(living alone vs living with other persons), median loneliness score, changes in financial resources, changes in workload and language. Change in
employment situation is compared to a reference category of “full‑ or part‑time employed at both Q1 and Q2”
Page 11 of 15
Tancredietal. International Journal for Equity in Health (2023) 22:51
Conclusion
is study confirms the negative association between
economic constraints and mental health and adds to
a growing literature on social determinants of mental
health during the COVID-19 pandemic. Further, it offers
an insight into the relationship between risk percep-
tion or perception of the economic situation and mental
health. Public authorities and the media should be aware
of the role that people’s perceptions can play on public
mental health.
Abbreviations
COVID‑19 Coronavirus disease 2019
SARS‑CoV‑2 Severe acute respiratory syndrome coronavirus 2
CI‑DFU eCohort Corona Immunitas digital follow‑up eCohort
DASS‑21 Depression, Anxiety and Stress Scale
PCR Polymerase chain reaction
Q1 First administration of the questionnaire on financial
resources and employment situation
Q2 Second administration of the questionnaire on financial
resources and employment situation
IQR Interquartile range
CI Confidence interval
SSPH + Swiss School of Public Health
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s12939‑ 023‑ 01853‑2.
Additional le1. Supplementary figures and tables
Acknowledgements
We thank the participants of the Corona Immunitas Digital Follow‑up eCohort
for their participation. We thank the Swiss Federal Statistical Office for provid‑
ing the random samples of participants of our study. We thank the Swiss
School of Public Health (SSPH+) for their help with coordination of all study
sites.
The following are members of Corona Immunitas Research Group (including
the authors of the present article), listed in alphabetical order:
Emiliano Albanese, MD, PhD (Institute of Public Health (IPH), Università della
Svizzera italiana, Lugano, Switzerland); Rebecca Amati, PhD (Institute of Public
Health (IPH), Università della Svizzera italiana, Lugano, Switzerland) Antonio
Amendola, Msc (Department of Business Economics, Health and Social Care
(DEASS),, University of Applied Sciences & Arts of Southern Switzerland
(SUPSI), Switzerland; Alexia Anagnostopoulos, MD MPH (Epidemiology,
Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland);
Daniela Anker, PhD (Population Health Laboratory (#PopHealthLab), University
of Fribourg, Switzerland; Institute of Primary Health Care (BIHAM), University of
Bern, Switzerland); Anna Maria Annoni, Msc (Institute of Public Health (IPH),
Università della Svizzera italiana, Lugano, Switzerland); Hélène Aschmann, PhD
(Epidemiology, Biostatistics and Prevention Institute, University of Zurich,
Zurich, Switzerland); Andrew Azman, PhD (Unit of Population Epidemiology,
Fig. 5 Predicted DASS‑21 anxiety scores: marginal effect of financial resources and perceived Swiss economic situation. Note:
Higher‑Higher = participants had sufficient or comfortable resources at both measurements (Q1 and Q2); Lower‑Lower = participants had
precarious or insufficient resources at both measurements (Q1 and Q2); Higher‑Lower = participants had sufficient or comfortable resources at Q1
and precarious or insufficient at Q2; Lower‑Higher = participants had precarious or insufficient at Q1 and sufficient or comfortable at Q2
Page 12 of 15
Tancredietal. International Journal for Equity in Health (2023) 22:51
Division of Primary Care Medicine, Geneva University Hospitals, Geneva,
Switzerland; Department of Epidemiology, Johns Hopkins Bloomberg School
of Public Health, Baltimore, MD, USA; Institute of Global Health, Faculty of
Medicine, University of Geneva, Geneva, Switzerland); Antoine Bal, MSc (Unit
of Population Epidemiology, Division of Primary Care Medicine, Geneva
University Hospitals, Geneva, Switzerland); Tala Ballouz, MD MPH (Epidemiol‑
ogy, Biostatistics and Prevention Institute, University of Zurich, Zurich,
Switzerland); Hélène Baysson, PhD (Unit of Population Epidemiology, Division
of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland;
Department of Health and Community Medicine, Faculty of Medicine,
University of Geneva, Geneva, Switzerland); Kleona Bezani, Msc (Institute of
Public Health (IPH), Università della Svizzera italiana, Lugano, Switzerland);
Annette Blattmann (Cantonal Hospital St. Gallen, Clinic for Infectious Diseases
and Hospital Epidemiology, St. Gallen, Switzerland); Patrick Bleich (Unit of
Population Epidemiology, Division of Primary Care Medicine, Geneva
University Hospitals, Geneva, Switzerland); Murielle Bochud, MD, PhD (Center
for Primary Care and Public Health (Unisanté), University of Lausanne,
Switzerland); Patrick Bodenmann, MD, Msc (Center for Primary Care and Public
Health (Unisanté), University of Lausanne, Switzerland); Gaëlle Bryand Rumley,
MSc (Unit of Population Epidemiology, Division of Primary Care Medicine,
Geneva University Hospitals, Geneva, Switzerland); Peter Buttaroni (Institute of
Public Health (IPH), Università della Svizzera italiana, Lugano, Switzerland);
Audrey Butty, MD (Center for Primary Care and Public Health (Unisanté),
University of Lausanne, Switzerland); Anne Linda Camerini, PhD (Institute of
Public Health (IPH), Università della Svizzera italiana, Lugano, Switzerland);
Arnaud Chiolero, MD, PhD (Population Health Laboratory (#PopHealthLab),
University of Fribourg, Switzerland; Institute of Primary Health Care (BIHAM),
University of Bern, Switzerland; Department of Epidemiology, Biostatistics and
Occupational Health, McGill University, Montréal, Canada); Patricia Orializ
Chocano‑Bedoya, MD, PhD (Institute of Primary Health Care (BIHAM),
University of Bern; Population Health Laboratory (#PopHealthLab), University
of Fribourg, Switzerland); Prune Collombet (Unit of Population Epidemiology,
Division of Primary Care Medicine, Geneva University Hospitals, Geneva,
Switzerland; Department of Health and Community Medicine, Faculty of
Medicine, University of Geneva, Geneva, Switzerland); Laurie Corna, PhD
(Department of Business Economics, Health and Social Care (DEASS),,
University of Applied Sciences & Arts of Southern Switzerland (SUPSI),
Switzerland); Luca Crivelli, PhD (Department of Business Economics, Health
and Social Care (DEASS), University of Applied Sciences & Arts of Southern
Switzerland (SUPSI), Switzerland); Institute of Public Health (IPH), Università
della Svizzera italiana, Lugano, Switzerland); Stéphane Cullati, PhD (Population
Health Laboratory (#PopHealthLab), University of Fribourg, Switzerland;
Department of Readaptation and Geriatrics, University of Geneva, Switzer‑
land); Valérie D’Acremont, MD, PhD (Center for Primary Care and Public Health
(Unisanté), University of Lausanne, Switzerland; Swiss Tropical and Public
Health Institute, Basel, Switzerland); Diana Sofia Da Costa Santos (Institute of
Public Health (IPH), Università della Svizzera italiana, Lugano, Switzerland);
Agathe Deschamps (Cantonal Medical Service Neuchâtel); Paola D’Ippolito
(Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva
University Hospitals, Geneva, Switzerland); Anja Domenghino, Dr. med.
(Epidemiology, Biostatistics and Prevention Institute, University of Zurich,
Zurich, Switzerland); Richard Dubos, MSc (Unit of Population Epidemiology,
Division of Primary Care Medicine, Geneva University Hospitals, Geneva,
Switzerland); Roxane Dumont, MSc (Unit of Population Epidemiology, Division
of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland);
Olivier Duperrex, MD,MSc (Center for Primary Care and Public Health
(Unisanté), University of Lausanne, Switzerland); Julien Dupraz, MD, MAS
(Center for Primary Care and Public Health (Unisanté), University of Lausanne,
Switzerland); Malik Egger (Center for Primary Care and Public Health
(Unisanté), University of Lausanne, Switzerland); Emna El‑May, MSc (Population
Health Laboratory (#PopHealthLab), University of Fribourg, Switzerland); Nacira
El Merjani (Unit of Population Epidemiology, Division of Primary Care
Medicine, Geneva University Hospitals, Geneva, Switzerland); Nathalie Engler
(Cantonal Hospital St. Gallen, Clinic for Infectious Diseases and Hospital
Epidemiology, St. Gallen, Switzerland); Adina Mihaela Epure, MD (Population
Health Laboratory (#PopHealthLab), University of Fribourg, Switzerland); Lukas
Erksam (Institute of Primary Health Care (BIHAM), University of Bern,
Department of General Internal Medicine, Inselspital, Bern University Hospital,
University of Bern); Sandrine Estoppey (Center for Primary Care and Public
Health (Unisanté), University of Lausanne, Switzerland); Marta Fadda, PhD
(Institute of Public Health (IPH), Università della Svizzera italiana, Lugano,
Switzerland); Vincent Faivre (Center for Primary Care and Public Health
(Unisanté), University of Lausanne, Switzerland); Jan Fehr, MD (Epidemiology,
Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland);
Andrea Felappi (Center for Primary Care and Public Health (Unisanté),
University of Lausanne, Switzerland); Maddalena Fiordelli, PhD (Institute of
Public Health (IPH), Università della Svizzera italiana, Lugano, Switzerland);
Antoine Flahault, MD, PhD (Institute of Global Health, Faculty of Medicine,
University of Geneva, Geneva, Switzerland; Division of Tropical and Humanitar‑
ian Medicine, Geneva University Hospitals, Geneva, Switzerland; Department
of Health and Community Medicine, Faculty of Medicine, University of Geneva,
Geneva, Switzerland); Luc Fornerod, MAS (Observatoire valaisan de la santé
(OVS), Sion, Switzerland); Cristina Fragoso Corti, PhD (Department of
environment construction and design (DACD, University of Applied Sciences &
Arts of Southern Switzerland (SUPSI), Switzerland); Natalie Francioli (Unit of
Population Epidemiology, Division of Primary Care Medicine, Geneva
University Hospitals, Geneva, Switzerland); Marion Frangville, MSc (Unit of
Population Epidemiology, Division of Primary Care Medicine, Geneva
University Hospitals, Geneva, Switzerland); Irène Frank, PhD (Luzerner
Kantonsspital, Spitalstrasse, 6000 Luzern 16); Giovanni Franscella, Msc (Institute
of Public Health (IPH), Università della Svizzera italiana, Lugano, Switzerland);
Anja Frei, PhD (Epidemiology, Biostatistics and Prevention Institute, University
of Zurich, Zurich, Switzerland); Marco Geigges, PhD (Epidemiology,
Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland);
Semira Gonseth Nusslé, MD, MSc (Center for Primary Care and Public Health
(Unisanté), University of Lausanne, Switzerland); Clément Graindorge, MD
(Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva
University Hospitals, Geneva, Switzerland); Idris Guessous, MD, PhD (Unit of
Population Epidemiology, Division of Primary Care Medicine, Geneva
University Hospitals, Geneva, Switzerland; Department of Health and
Community Medicine, Faculty of Medicine, University of Geneva, Geneva,
Switzerland); Erika Harju, PhD (Department of Health Sciences and Medicine,
University of Lucerne, Frohburgstrasse 3, 6002 Lucerne); Séverine Harnal (Unit
of Population Epidemiology, Division of Primary Care Medicine, Geneva
University Hospitals, Geneva, Switzerland); Medea Imboden, PhD (Swiss
Tropical and Public Health Institute, Department of Epidemiology and Public
Health, Basel, Switzerland; University of Basel, Basel, Switzerland); Emilie Jendly
(Center for Primary Care and Public Health (Unisanté), University of Lausanne,
Switzerland); Ayoung Jeong, PhD (Swiss Tropical and Public Health Institute,
Department of Epidemiology and Public Health, Basel, Switzerland; University
of Basel, Basel, Switzerland); Christian R Kahlert, MD (Cantonal Hospital St.
Gallen, Clinic for Infectious Diseases and Hospital Epidemiology, St. Gallen,
Switzerland; Children’s Hospital of Eastern Switzerland, Infectious Diseases and
Hospital Epidemiology, St. Gallen, Switzerland); Laurent Kaiser, MD, PhD
(Geneva Center for Emerging Viral Diseases and Laboratory of Virology,
Geneva University Hospitals, Geneva, Switzerland; Division of Infectious
Diseases, Geneva University Hospitals, Geneva, Switzerland; Department of
Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland);
Laurent Kaufmann (Service de La Santé Publique, Canton de Neuchâtel,
Neuchâtel, Switzerland); Marco Kaufmann PhD (Epidemiology, Biostatistics
and Prevention Institute, University of Zurich, Zurich, Switzerland); Dirk Keidel,
MSc (Swiss Tropical and Public Health Institute, Department of Epidemiology
and Public Health, Basel, Switzerland; University of Basel, Basel, Switzerland);
Simone Kessler (Cantonal Hospital St. Gallen, Clinic for Infectious Diseases and
Hospital Epidemiology, St. Gallen, Switzerland); Philipp Kohler, MD, MPH
(Cantonal Hospital St. Gallen, Clinic for Infectious Diseases and Hospital
Epidemiology, St. Gallen, Switzerland); Christine Krähenbühl (Luzerner
Kantonsspital, Spitalstrasse, 6000 Luzern 16); Susi Kriemler, MD (Epidemiology,
Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland);
Julien Lamour (Unit of Population Epidemiology, Division of Primary Care
Medicine, Geneva University Hospitals, Geneva, Switzerland); Sara Levati, PhD
(Department of Business Economics, Health and Social Care (DEASS),
University of Applied Sciences & Arts of Southern Switzerland (SUPSI),
Switzerland); Pierre Lescuyer, PhD (Division of Laboratory Medicine, Geneva
University Hospitals, Geneva, Switzerland); Andrea Loizeau, PhD (Unit of
Population Epidemiology, Division of Primary Care Medicine, Geneva
University Hospitals, Geneva, Switzerland); Elsa Lorthe, RM, PhD (Unit of
Population Epidemiology, Division of Primary Care Medicine, Geneva
University Hospitals, Geneva, Switzerland); Chantal Luedi (Department Health
Sciences and Medicine, University of Lucerne, Frohburgstrasse 3, 6002
Lucerne); Jean‑Luc Magnin, PhD (Laboratory, HFR‑Fribourg, Fribourg,
Switzerland); Chantal Martinez (Unit of Population Epidemiology, Division of
Page 13 of 15
Tancredietal. International Journal for Equity in Health (2023) 22:51
Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland); Eric
Masserey (Cantonal Medical Office, General Health Department, Canton of
Vaud, Switzerland); Dominik Menges, MD MPH (Epidemiology, Biostatistics and
Prevention Institute, University of Zurich, Zurich, Switzerland); Gisela Michel,
PhD (Department of Health Sciences and Medicine, University of Lucerne,
Frohburgstrasse 3, 6002 Lucerne); Rosalba Morese, PhD (Faculty of Communi‑
cation, Culture and Society, Università della Svizzera italiana, Lugano,
Switzerland; Faculty of Biomedical Sciences, Università della Svizzera italiana,
Lugano, Switzerland); Nicolai Mösli (Swiss TPH, Basel, Switzerland; University of
Basel, Basel, Swtizerland); Natacha Noël (Unit of Population Epidemiology,
Division of Primary Care Medicine, Geneva University Hospitals, Geneva,
Switzerland); Daniel Henry Paris, MD PhD (Swiss TPH, Basel, Switzerland;
University of Basel, Basel, Swtizerland); Jérôme Pasquier, PhD (Center for
Primary Care and Public Health (Unisanté), University of Lausanne, Switzer‑
land); Francesco Pennacchio, PhD (Unit of Population Epidemiology, Division
of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland);
Stefan Pfister, PhD (Laboratory, HFR‑Fribourg, Fribourg, Switzerland); Giovanni
Piumatti, PhD (Fondazione Agnelli, Turin, Italy); Géraldine Poulain (Division of
Laboratory Medicine, Geneva University Hospitals, Geneva, Switzerland);
Nicole Probst‑Hensch, Dr. phil.II, PhD, MPH (Swiss Tropical and Public Health
Institute, Department of Epidemiology and Public Health, Basel, Switzerland;
University of Basel, Basel, Swtizerland); Caroline Pugin (Unit of Population
Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals,
Geneva, Switzerland); Milo Puhan, MD, PhD (Epidemiology, Biostatistics and
Prevention Institute, University of Zurich, Zurich, Switzerland); Nick Pullen, PhD
(Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva
University Hospitals, Geneva, Switzerland); Thomas Radtke, PhD (Epidemiol‑
ogy, Biostatistics and Prevention Institute, University of Zurich, Zurich,
Switzerland); Manuela Rasi, MScN (Epidemiology, Biostatistics and Prevention
Institute, University of Zurich, Zurich, Switzerland); Aude Richard (Unit of
Population Epidemiology, Division of Primary Care Medicine, Geneva
University Hospitals, Geneva, Switzerland; Institute of Global Health, University
of Geneva, Switzerland); Viviane Richard, MSc (Unit of Population Epidemiol‑
ogy, Division of Primary Care Medicine, Geneva University Hospitals, Geneva,
Switzerland); Claude‑François Robert (Cantonal Medical Service Neuchâtel);
Pierre‑Yves Rodondi, MD (Institute of Family Medicine, University of Fribourg,
Fribourg, Switzerland); Nicolas Rodondi, MD, MAS (Institute of Primary Health
Care (BIHAM), University of Bern; Department of General Internal Medicine,
Inselspital, Bern University Hospital, University of Bern); Serena Sabatini, PhD
(Institute of Public Health (IPH), Università della Svizzera italiana, Lugano,
Switzerland); Khadija Samir (Unit of Population Epidemiology, Division of
Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland);
Javier Sanchis Zozaya, MD (Center for Primary Care and Public Health
(Unisanté), University of Lausanne, Switzerland); Virginie Schlüter, MAS (Center
for Primary Care and Public Health (Unisanté), University of Lausanne,
Switzerland); Alexia Schmid, MSc (Institute of Family Medicine, University of
Fribourg, Fribourg, Switzerland); Valentine Schneider (Cantonal Medical
Service Neuchâtel); Maria Schüpbach (Institute of Primary Health Care
(BIHAM), University of Bern, Department of General Internal Medicine,
Inselspital, Bern University Hospital, University of Bern); Nathalie Schwab
(Institute of Primary Health Care (BIHAM), University of Bern, Department of
General Internal Medicine, Inselspital, Bern University Hospital, University of
Bern); ); Claire Semaani (Unit of Population Epidemiology, Division of Primary
Care Medicine, Geneva University Hospitals, Geneva, Switzerland); Alexandre
Speierer (Institute of Primary Health Care (BIHAM), University of Bern;
Department of General Internal Medicine, Inselspital, Bern University Hospital,
University of Bern); Amélie Steiner‑Dubuis (Center for Primary Care and Public
Health (Unisanté), University of Lausanne, Switzerland); Silvia Stringhini, PhD
(Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva
University Hospitals, Geneva, Switzerland; Department of Health and
Community Medicine, Faculty of Medicine, University of Geneva, Geneva,
Switzerland); Stefano Tancredi, MD (Population Health Laboratory (#Pop‑
HealthLab), University of Fribourg, Switzerland); Stéphanie Testini (Unit of
Population Epidemiology, Division of Primary Care Medicine, Geneva
University Hospitals, Geneva, Switzerland); Julien Thabard (Center for Primary
Care and Public Health (Unisanté), University of Lausanne, Switzerland); Mauro
Tonolla, PD PhD (Department of environment construction and design (DACD,
University of Applied Sciences & Arts of Southern Switzerland (SUPSI),
Switzerland); Nicolas Troillet, MD, MSc (Office du médecin cantonal, Sion,
Switzerland); Agne Ulyte, MD (Epidemiology, Biostatistics and Prevention
Institute, University of Zurich, Zurich, Switzerland); Sophie Vassaux (Center for
Primary Care and Public Health (Unisanté), University of Lausanne, Switzer‑
land); Thomas Vermes, MSc (Swiss Tropical and Public Health Institute,
Department of Epidemiology and Public Health, Basel, Switzerland; University
of Basel, Basel, Swtizerland); Jennifer Villers, PhD (Unit of Population
Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals,
Geneva, Switzerland); Viktor von Wyl (Epidemiology, Biostatistics and
Prevention Institute, University of Zurich, Zurich, Switzerland); Cornelia
Wagner, MSc (Population Health Laboratory (#PopHealthLab), University of
Fribourg, Switzerland); Rylana Wenger (Institute of Primary Health Care
(BIHAM), University of Bern, Department of General Internal Medicine,
Inselspital, Bern University Hospital, University of Bern); Erin West, PhD
(Epidemiology, Biostatistics and Prevention Institute, University of Zurich,
Zurich, Switzerland); Ania Wisniak, MD (Unit of Population Epidemiology,
Division of Primary Care Medicine, Geneva University Hospitals, Geneva,
Switzerland; Institute of Global Health, Faculty of Medicine, University of
Geneva, Geneva, Switzerland); Melissa Witzig, Msc (Swiss Tropical and Public
Health Institute, Department of Epidemiology and Public Health, Basel,
Switzerland; University of Basel, Basel, Swtizerland); María‑Eugenia Zaballa, PhD
(Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva
University Hospitals, Geneva, Switzerland); Kyra Zens, PhD, MPH (Epidemiol‑
ogy, Biostatistics and Prevention Institute, University of Zurich, Zurich,
Switzerland); Claire Zuppinger (Center for Primary Care and Public Health
(Unisanté), University of Lausanne, Switzerland).
Authors’ contributions
All authors designed the study. AU and SC analysed the data. ST and AU
drafted the manuscript with contributions of SC. All co‑authors contrib‑
uted to the data acquisition, interpretation and revised the first draft of the
manuscript. All authors approved the final version of the manuscript before
submission.
Funding
The Directorate of SSPH + is responsible for the coordination, communication,
fundraising, and legal aspects of the population‑based studies and the central
program of Corona Immunitas. This study was funded by several sources
that includes, but is not limited to, SSPH + and the Swiss Federal Office of
Public Health. Funders had no influence on the design, conduct, analyses and
publications.
Availability of data and materials
Deidentified individual participant data underlying the findings of this study
will be available for researchers submitting a methodologically sound pro‑
posal to achieve the aims of the proposal after the publication of this article.
Access to data requires contacting Corona Immunitas.
Declarations
Ethics approvals and consent to partecipate
This study has been approved by the responsible ethics committees (Cantons
of Zurich, St. Gallen, Grisons, Fribourg, Lucerne, Berne, Neuchatel: BASEC
2020–01247, Canton of Vaud: BASEC 2020–00887, Canton of Basle‑City and
Basle‑Country: BASEC 2020–00927, Canton of Ticino: BASEC 2020–01514). The
participants of the study provided informed consent prior to their participa‑
tion in the study. This study was conducted in accordance with the Declara‑
tion of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1 Population Health Laboratory (#PopHealthLab), University of Fribourg, Fri‑
bourg, Switzerland. 2 Biostatistics and Prevention Institute, University of Zurich,
Zurich, Switzerland. 3 Swiss Tropical and Public Health Institute, Allschwil,
Switzerland. 4 University of Basel, Basel, Switzerland. 5 Institute of Public Health,
Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano,
Switzerland. 6 Department of Business Economics, Health and Social Care
at the University of Applied Sciences and Arts of Southern Switzerland,
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Tancredietal. International Journal for Equity in Health (2023) 22:51
Manno, Switzerland. 7 Division of Infectious Diseases and Hospital Epidemiol‑
ogy, Kantonsspital St Gallen, St‑Gallen, Switzerland. 8 Department of Infectious
Diseases and Hospital Epidemiology, Children’s Hospital of Eastern Switzer‑
land, St Gallen, Switzerland. 9 Department Health Sciences and Medicine,
University of Lucerne, Lucerne, Switzerland. 10 Institute of Primary Health Care
(BIHAM), University of Bern, Bern, Switzerland. 11 Division of Prison Health,
Geneva University Hospitals & University of Geneva, Geneva, Switzerland.
12 Department of General Internal Medicine, Inselspital, Bern University Hospi‑
tal, University of Bern, Bern, Switzerland. 13 Epidemiology, Biostatistics and Pre‑
vention Institute, University of Zurich, Zurich, Switzerland. 14 Unit of Population
Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals,
Geneva, Switzerland. 15 Department of Health and Community Medicine,
Faculty of Medicine, University of Geneva, Geneva, Switzerland. 16 Cantonal
Public Health Service of the Canton of Neuchâtel, Neuchâtel, Switzerland.
17 Zurich University of Applied Sciences, Institute of Public Health, Winterthur,
Switzerland. 18 Department of Psychology, University of Konstanz, Konstanz,
Germany. 19 Department of Readaptation and Geriatrics, University of Geneva,
Geneva, Switzerland.
Received: 28 November 2022 Accepted: 25 February 2023
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