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Infection
https://doi.org/10.1007/s15010-023-02011-0
RESEARCH
Seroprevalence trends ofanti‑SARS‑CoV‑2 antibodies andassociated
risk factors: apopulation‑based study
StefanoTancredi1· ArnaudChiolero1,2,3· CorneliaWagner1· MoaLinaHaller3,4· PatriciaChocano‑Bedoya1,3·
NataliaOrtega1,3· NicolasRodondi3,4· LaurentKaufmann5· ElsaLorthe6· HélèneBaysson6,7· SilviaStringhini6,7,8·
GiselaMichel9· ChantalLüdi9· ErikaHarju9,10· IreneFrank10· MedeaImboden11,12· MelissaWitzig11,12·
DirkKeidel11,12· NicoleProbst‑Hensch11,12· RebeccaAmati13· EmilianoAlbanese13· LaurieCorna14· LucaCrivelli14·
JuliaVincentini15· SemiraGonsethNusslé15· MurielleBochud15· ValérieD’Acremont11,15· PhilippKohler16·
ChristianR.Kahlert16,17· AlexiaCusini18· AnjaFrei19· MiloA.Puhan19· MarcoGeigges19· MarcoKaufmann19·
JanFehr19· StéphaneCullati1,20on behalf of the Corona Immunitas Research Group
Received: 15 November 2022 / Accepted: 22 February 2023
© The Author(s) 2023
Abstract
Purpose We aimed to assess the seroprevalence trends of SARS-CoV-2 antibodies in several Swiss cantons between May
2020 and September 2021 and investigate risk factors for seropositivity and their changes over time.
Methods We conducted repeated population-based serological studies in different Swiss regions using a common methodol-
ogy. We defined three study periods: May–October 2020 (period 1, prior to vaccination), November 2020–mid-May 2021
(period 2, first months of the vaccination campaign), and mid-May–September 2021 (period 3, a large share of the popula-
tion vaccinated). We measured anti-spike IgG. Participants provided information on sociodemographic and socioeconomic
characteristics, health status, and adherence to preventive measures. We estimated seroprevalence with a Bayesian logistic
regression model and the association between risk factors and seropositivity with Poisson models.
Results We included 13,291 participants aged 20 and older from 11 Swiss cantons. Seroprevalence was 3.7% (95% CI
2.1–4.9) in period 1, 16.2% (95% CI 14.4–17.5) in period 2, and 72.0% (95% CI 70.3–73.8) in period 3, with regional
variations. In period 1, younger age (20–64) was the only factor associated with higher seropositivity. In period 3, being
aged ≥ 65years, with a high income, retired, overweight or obese or with other comorbidities, was associated with higher
seropositivity. These associations disappeared after adjusting for vaccination status. Seropositivity was lower in participants
with lower adherence to preventive measures, due to a lower vaccination uptake.
Conclusions Seroprevalence sharply increased over time, also thanks to vaccination, with some regional variations. After
the vaccination campaign, no differences between subgroups were observed.
Keywords COVID-19 pandemic· SARS-CoV-2· Seroprevalence· Epidemiology· Public health· Surveillance
Introduction
An accurate description of the severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2) spread dynamics
is key to informing and driving policymakers’ decisions.
Yet, surveillance based on PCR or antigen-reported cases
resulted in biased estimates of the virus spread [1] due to
a large share of a- or pauci-symptomatic infections [2, 3],
changes in care-seeking behaviours, and different screen-
ing and diagnostic strategies across regions and over time.
For instance, when SARS-CoV-2 first emerged, many Euro-
pean countries had limited testing capacities [4], and some,
including Switzerland, restricted testing to patients admit-
ted to hospitals. This led to a surveillance bias [5], with
an underestimation of the number of SARS-CoV-2 cases.
By contrast, serological studies account for all infections,
The members of the instutional author group “the Corona
Immunitas Research Group” was processed under
acknowledgements section.
* Stefano Tancredi
stefano.tancredi@unifr.ch
Extended author information available on the last page of the article
S.Tancredi et al.
1 3
providing a more representative, albeit less timely, picture of
the extent and dynamics of the COVID-19 pandemic.
So far, many SARS-CoV-2 seroprevalence studies have
been conducted, both in the general population and in spe-
cific subgroups, to monitor the pandemic and inform on pop-
ulation levels of immunity [6–8]. A recent literature review
[9] showed substantial worldwide geographical variability
in seroprevalence estimates, caused by differences in the
extent of infections and vaccination coverage. It also showed
evidence of considerable infection under-ascertainment,
highlighting the importance of seroprevalence estimates to
describe the true number of SARS-CoV-2 infections. How-
ever, variabilities in research designs, tests used, or stud-
ies quality and reporting, make it challenging to compare
estimates between countries or between regions within the
same country. In addition to their role in assessing immunity
levels and monitoring the virus’s spread, seroepidemiologi-
cal studies are also a strong tool to understand the drivers
of the spread and to identify groups at higher risk of infec-
tion. During the pandemic, many factors have been linked
to increased seropositivity, including socioeconomic, soci-
odemographic, or health characteristics. A higher exposure
to SARS-CoV-2 is possible in socioeconomically disadvan-
taged individuals [10] (e.g., with a lower income or lower
educational level), and differences in exposure have been
found in different age groups [11, 12] or according to job
type, health behaviours (e.g., smokers versus non-smokers)
or health characteristics (e.g., with respect to different BMI
levels or number of comorbidities) [11, 13, 14]. Addition-
ally, the evidence suggests that different levels of strin-
gency of mitigation policies [9] and adherence to preven-
tive measures were also associated with seropositivity [15].
However, countries experienced a wide range of different
epidemiological situations; governments recommendations
and individual behaviours changed, and vaccines have been
rolled out. It is therefore likely that factors associated with
seropositivity have also changed over time.
In Switzerland, the Swiss School of Public Health
(SSPH +) launched in the early phases of the pandemic
the Corona Immunitas research program [16], implement-
ing repeated population-based seroprevalence studies, with
the aim of estimating the proportion of the population who
developed anti-SARS-CoV-2 antibodies over time. Conduct-
ing repeated studies using a common methodology, at regu-
lar intervals, and with shared coordination, offers unique
strengths to provide a clear picture of the population immu-
nological status over time and across regions, and allows
investigating trends in seroprevalence of SARS-CoV-2 anti-
bodies, making comparisons between regions, and investi-
gating differences in the virus’s exposure between differ-
ent populations’ groups. In light of the above, using data
from Corona Immunitas, we aimed to (1) assess the sero-
prevalence trends of SARS-CoV-2 antibodies in Switzerland
between May 2020 and September 2021, both at a quasi-
national and cantonal level (descriptive aim), and (2) inves-
tigate risk factors for seropositivity and their changes over
time (etiologic aim).
Methods
Study design
This study is part of Corona Immunitas [16]. Repeated pop-
ulation-based serological studies were conducted in different
regions of Switzerland. Testing periods could change for
each participating site. Invited participants were randomly
selected from the national residential registry by the Swiss
Federal Statistical Office for each new assessment wave;
65,500 participants were invited, the average participation
rate was around 21%, with regional differences (from 16 to
39%). For this study, we defined three study periods: period
1 from May 2020 to October 2020 (before the launch of
the vaccination campaign in Switzerland), period 2 from
November 2020 to mid-May 2021 (in the first months of the
vaccination campaign), and period 3 from mid-May 2021
to September 2021 (a significant share of the population
vaccinated). Each period corresponds to a time window
following each of the first three pandemic waves in Swit-
zerland (Fig.1). This choice was made because estimating
seroprevalence after each epidemic wave was deemed more
informative for descriptive purposes, and it is in line with
the World Health Organization (WHO) recommendations for
cross-sectional seroprevalence studies [17]. At each period,
participants provided a venous blood sample and filled out
a questionnaire on demographic and socioeconomic char-
acteristics, adherence to COVID-19 preventive measures,
health status and, once available, vaccination status. The
questionnaire could be completed either in person or online
(data were collected using REDCap, Research Electronic
Data Capture) [18].
Study population
We included 13,291 participants (in period 1 n = 3402, in
period 2 n = 5611, and in period 3 n = 4278) aged 20 and
older from 11 Swiss cantons (Additional file1: Fig. S1).
Around 5.9 million people live in these cantons, that is
roughly 68% of the entire Swiss population. Those aged
below 65-years of age and those above were sampled in a
ratio of 1:1, with few exceptions in some cantons where only
one age group has been recruited. For the second objective
of this study, we excluded participants who completed the
questionnaire more than 30days before or after having pro-
vided the blood sample for the serology test. The reason for
this exclusion was to avoid a possible mismatch between
Seroprevalence trends ofanti‑SARS‑CoV‑2 antibodies andassociated risk factors: a…
1 3
serology results and information reported in the question-
naires (preventive behaviours, health status and socioeco-
nomic status). For the same reason, we also excluded partici-
pants who provided information on vaccination status more
than 11days before or after providing the blood sample for
the serology test. Figure S2 (Additional file1: Fig. S2) shows
a flow diagram of the participants included in the study for
each study objective.
Testing procedure
We analysed venous blood samples using the SenASTrIS
assay, developed by the Vaud Central University Hospital
(CHUV), the Swiss Federal Institute of Technology in Laus-
anne (EPFL) and the Swiss Vaccine Center [19]. The assay
measures the amount of human immunoglobulins G (IgG)
that binds the trimeric SARS-CoV-2 spike protein, induced
either by infection or vaccination. The test was validated
on a sample of the general population and specificity and
sensitivity were 99.7% and 96.6% for the detection of IgG
antibodies. Borderline test results (i.e., a signal just below
the predefined cut-off) were categorized as seronegative
(n = 140, 1%). A detailed description of the test is available
elsewhere [19].
Potential risk factors
For the second objective of this study, we investigated the
following potential risk factors, selected based on findings
of previous studies, background expert knowledge and a
priori reasons for having an increased risk of being sero-
positive [10, 11, 13, 14, 20–22]: sex, age (20–64years old
vs 65years and older), educational level (primary, second-
ary, tertiary), body mass index (BMI; < 18.5; 18.5- 24.9;
25–29.9, ≥ 30kg/m2), household monthly income (≤ 3000
CHF, > 3000–6000 CHF, > 6000–9000 CHF, > 9000 CHF;
1 Euro equalled 1.046 to 1.112 CHF between 1st January
2020 and 25th November 2021), employment status (retired,
outside the labour force, self-employed, employed), number
of children in the household (none, one child, two or more
children), comorbidity score (0, 1, ≥ 2), smoking habit (cur-
rent smokers vs non-smokers; former smokers were included
in the non-smokers category), physical distancing during the
previous seven days (frequently, occasionally/rarely), staying
at home during the previous seven days (frequently, occa-
sionally/rarely), wearing a mask during the previous seven
days (frequently, occasionally/rarely), hygiene measures
during the previous seven days (frequently, occasionally/
rarely). BMI was categorized according to the World Health
Organization standard categories [23]. Educational level was
categorized according to the International Standard Classi-
fication of Education (ISCED). Physical distancing, staying
at home and hygiene measures’ variables were defined as
having implemented the measures recommended by public
health authorities (e.g.: keeping a distance of 1.5m, staying
at home whenever possible, avoiding unnecessary activi-
ties outside the home, no handshaking or hugging, washing
hands regularly, sneezing into the elbow, using tissues, etc.).
The comorbidity score goes from 0 to ≥ 2 and was calculated
using the following possible answers (one point for each
disease) to the question “Do you suffer from one or more of
the following diseases?”: cancer, immunological diseases,
cardiovascular diseases, diabetes, hypertension, respiratory
diseases and allergies.
Fig. 1 Blood samplings per
week and daily confirmed
COVID-19 cases reported in
Switzerland, May 2020–Sep-
tember 2021
Note: In green, daily new COVID-19 cases per million people in Switzerland; 7-day rolling average. In blue,
number of Corona Immunitas blood samplings per week
S.Tancredi et al.
1 3
Statistical analysis
To estimate seroprevalence (objective 1), we used a Bayesian
logistic regression model, adjusted for the antibody test sen-
sitivity and specificity performances [24]. Seroprevalence
estimates were weighted by the age and sex distribution of
the population of each canton. We investigated the associa-
tion between potential risk factors and seropositivity (objec-
tive 2) using Poisson regression models and expressed as
prevalence ratios (PR) and 95% confidence intervals. Robust
variance estimators were used to relax the assumption that
the outcome distribution followed a Poisson distribution.
Sex, age, educational level, BMI, income, employment sta-
tus, number of children in the household, comorbidity score
and smoking habit were included in the models (hereafter,
model 1). Results were stratified by study period. Models
for period 3 were adjusted for vaccination status (hereafter,
model 2; in Switzerland the vaccination campaign started at
the end of December 2020, during the second period of this
study). To investigate seropositivity risk factors and their
changes over time (objective 2), we used multiple imputa-
tion by chained equations to impute any missing data (30
imputations). Statistical analyses were conducted using Stata
17 software (Stata Corp, TX, 2021) and R Statistical Soft-
ware (version 4.1.2; R Foundation for Statistical Computing,
Vienna, Austria).
We also performed several sensitivity analyses: (1)
including participants who had completed the questionnaire
on demographic and socioeconomic characteristics, adher-
ence to COVID-19 preventive measures, and health status,
more than 60days before and after their blood sample; (2)
including a third age category (from 20 to 34years old;
based on the hypothesis that people in this category could
have had more social interactions and therefore an increased
risk of being infected) and (3) using a score computed from
the preventive behaviours variables (hereafter, preventive
behaviours score). The score goes from 0 to 4; one point for
every “occasionally/rarely” answer to the questions on pre-
ventive behaviours. The higher the score, the less frequent
the adherence to preventive behaviours.
Results
Characteristics ofthesample
We included 13,291 respondents (53% females), with a
mean age of 55.9years (SD = 16.9). Characteristics of
the participants are summarized in Table1. Participants’
characteristics across study periods and by cantons are
detailed in Tables S1 and S2 (Additional file1: Tables
S1 and S2). Some 61% of participants were aged between
20 and 64years and 39% were 65years and older. 46% of
Table 1 Characteristics of participants (n = 13,291), Corona Immuni-
tas study, Switzerland, May 2020—September 2021
Sociodemographic characteristics
Sex
Female 53%
Male 47%
Age group
≥ 65 39%
20–64 61%
Children in the household
No children 77%
One child 9%
More than one child 14%
Socioeconomic characteristics
Educational levela
Tertiary 46%
Secondary 48%
Primary 6%
Household income
> CHF 9000 34%
CHF > 6000–9000 28%
CHF > 3000–6000 28%
CHF ≤ 3000 10%
Employment status
Retired 38%
Outside the labour forceb10%
Self employed 10%
Employed 42%
Health status
Body Mass Index
< 18.5 3%
18.5–24.9 52%
25–29.9 33%
≥ 30 12%
Comorbiditiy scorec
0 53%
1 32%
≥ 2 15%
Smoking
Non-smoker 84%
Smoker 16%
Preventive behaviours
Physical distancing during previous 7days
Frequently 91%
Occasionally/rarely 9%
Staying at home during previous 7days
Frequently 69%
Occasionally/rarely 31%
Wearing mask during previous 7days
Frequently 83%
Occasionally/rarely 17%
Seroprevalence trends ofanti‑SARS‑CoV‑2 antibodies andassociated risk factors: a…
1 3
participants were highly educated, 42% were employed and
23% lived with children. 47% had one or more comorbidi-
ties. In our sample people aged over 65years were over-
represented by design, and smokers, employed participants,
households with one or more than one child, low-income
households and people with only primary education were
slightly underrepresented [25].
Seroprevalence estimates andtrends
During period 1, seroprevalence was 3.7% (95% CI 2.1–4.9).
It increased to 16.2% (95% CI 14.4–17.5) during period 2
and to 72.0% (95% CI 70.3–73.8) during period 3. Sero-
prevalence varied by age group, with higher estimates in
younger participants (20–64years) during period 1 and
in older participants (65years and older) during period 3
(Additional file1: TableS3). There were some regional vari-
ations between cantons (Table2 and Fig.2). During period
1, seroprevalence in cantons from the French and Italian
speaking regions of Switzerland ranged from 3.0 to 7.7%,
and in cantons from the German speaking regions from 2.1
to 5.0%. We found substantial differences between cantons
during periods 2 and 3.
Factors associated withseropositivity
Table3 shows the results of the multivariable models by
study period. Information on missing data are reported
in Tables S4 and S5 (Additional file1: Tables S4 and
S5). Before the start of the vaccination campaign (period
1), participants aged between 20 and 64years had a
higher prevalence of seropositivity (PR = 2.32, 95% CI
1.03–5.22) compared to older participants. After the start
of the vaccination campaign (period 3), participants aged
20–64years old (PR = 0.85, 95% CI 0.78–0.93), with a low
household income (PR = 0.75, 95% CI 0.68–0.82) or with
an employment status different from retired had a lower
prevalence of seropositivity compared to reference catego-
ries. Participants with a BMI of 25 or more (PR = 1.12,
95% CI 1.04–1.19) or with one or more comorbidities
(PR = 1.12, 95% CI 1.06–1.18) had a higher prevalence
a International Standard Classification of Education (ISCED)
b Outside the labour force includes participants in training/studying
and unemployed participants
c Comorbidity score goes from 0 to ≥ 2 and was calculated using the
following possible answers: cancer; immunological diseases; cardio-
vascular diseases or diabetes or hypertension; respiratory diseases;
allergies
Table 1 (continued)
Hygiene measures during previous 7days
Frequently 94%
Occasionally/rarely 6%
Table 2 Seroprevalence
estimatesa (IgG anti Sars-CoV-2
Spike) by study period and
canton, Corona Immunitas
study, Switzerland, May 2020–
September 2021
Samplings in Bern, Grisons, Lucerne and Saint Gallen started after period 1. Data for Ticino and Vaud
period 3 were not available for the analyses presented in this study
IgG immunoglobulin G, NA not available
a Seroprevalence was estimated using Bayesian regression adjusted for the antibody test sensitivity and
specificity performances and weighted by age and sex of the general population of each canton
b In cantons Grisons and Saint Gallen, only participants aged from 20 to 64years were tested
c In canton Ticino, during period 1, only participants aged from 20 to 64 were tested. During period 2 only
data from people aged 65years or more were available for these analyses
Time window Period 1, n = 3402 Period 2, n = 5611 Period 3, n = 4278
01/05/2020–31/10/2020 01/11/2020–15/05/2021 16/05/2021–31/09/2021
% (95%CI) % (95%CI) % (95%CI)
National level 3.7 (2.1–4.9) 16.2 (14.4–17.5) 72.0 (70.3–73.8)
Basel-Landschaft 2.9 (1.3–5.4) 16.5 (13.4–19.9) 82.8 (78.0–87.3)
Basel-Stadt 5.0 (2.6–7.8) 19.7 (16.2–23.2) 77.2 (72.7–81.6)
Bern NA 10.9 (7.4–14.7) 78.1 (74.1–82.0)
Fribourg 5.9 (3.2–9.1) 22.5 (18.7–26.6) 73.5 (68.4–78.4)
GrisonsbNA 15.7 (11.5–20.3) 43.2 (37.2–49.3)
Lucerne NA 15.5 (11.7–19.7) 58.9 (54.4–63.6)
Neuchâtel 3.0 (1.4–5.4) 19.2 (15.4–23.1) 79.2 (74.8–83.2)
Saint GallenbNA 11.7 (7.8–16.5) 62.2 (56.6–68.0)
Ticinoc7.7 (5.3–10.3) 6.8 (4.1–9.7) NA
Vaud 6.5 (3.8–9.8) 23.7 (20.3–27.1) NA
Zurich 2.1 (1.0–3.6) 9.7 (6.7–13.0) 78.5 (74.3–82.5)
S.Tancredi et al.
1 3
of seropositivity. However, all these differences disap-
peared upon adjustment for vaccination status in period
3 (percentages of vaccinated participants in period 3 are
reported in additional file1: TableS6).
None of the self-reported preventive behaviours
(Table4) were associated with seropositivity before the
start of the vaccination campaign (period 1). In period 3,
participants who reported to occasionally or rarely prac-
tice physical distancing (PR = 0.81, 95% CI 0.75–0.89),
stay at home (PR = 0.94, 95% CI 0.90–0.98), wear a mask
(PR = 0.76, 95% CI 0.70–0.84) and perform hygiene meas-
ures (PR = 0.79, 95% CI 0.72–0.87) had a lower prevalence
of seropositivity compared to participants who frequently
adhered to preventive behaviours. All these differences
disappeared after adjusting for vaccination status.
Sensitivity analyses gave similar results as the main
analyses and results are shown in supplementary material
(Additional file1: Tables S7–S10).
Discussion
Main findings
Seroprevalence in Switzerland rose sharply between May
2020 and September 2021, with some regional variations,
Fig. 2 Trends of seroprevalence
estimates (IgG anti SARS-
CoV-2 Spike) per canton and by
age group, Corona Immunitas
study, Switzerland, May 2020–
September 2021
Note: estimates are reported with 95% CIs
Abbreviations:
BL= Basel-Landschaft; BS= Basel-Stadt; BE= Bern; FR= Fribourg; GR= Grisons;
LU=
Lucerne; NE= Neuchâtel; SG= Saint Gallen; TI= Ticino; VD= Vaud; ZU= Zürich
Seroprevalence trends ofanti‑SARS‑CoV‑2 antibodies andassociated risk factors: a…
1 3
from 3.7% (95% CI 2.1–4.9) in May–October 2020, to 16.2%
(95% CI 14.4–17.5) between November 2020 and mid-
May 2021, and finally 72.0% (95% CI 70.3–73.8) between
mid-May and September 2021. Before the start of the vac-
cination campaign, seropositivity differed by age but not by
other factors. After the start of the vaccination campaign,
Table 3 Association of sociodemographic, socioeconomic characteristics and health status with SARS-CoV-2 seropositivity across study peri-
ods, Corona Immunitas study, Switzerland, May 2020–September 2021
a Model adjusted for: sex, age, educational level, body mass index, income, employment status, children in the household, comorbidity score and
smoking habit
b Model additionally adjusted for vaccination status
c Outside the labour force includes participants in training/studying and not employed participants
d Comorbidity score goes from 0 to ≥ 2 and was calculated using the following possible answers: cancer; immunological diseases; cardiovascular
diseases or diabetes or hypertension; respiratory diseases; allergies
Factor Period 1, n = 3108
(01/05/2020–31/10/2020)
Period 2, n = 4969
(01/11/2020–15/05/2021)
Period 3, n = 2836
(16/05/2021–31/09/2021)
Model 1a, PR (95%) Model 1a, PR (95%) Model 1a, PR (95%) Model 2b, PR (95%)
Sex
Female 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
Male 1.15 (0.82–1.62) 1.05 (0.92–1.20) 0.93 (0.89–0.98) 0.97 (0.94–1.00)
Age groups
≥ 65 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
20–64 2.32 (1.03–5.22) 1.22 (0.96 -1.54) 0.85 (0.78–0.93) 0.94 (0.89–0.99)
Children in the household
No children 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
One child 0.71 (0.40–1.24) 1.20 (0.96–1.49) 0.90 (0.82–1.00) 0.97 (0.90–1.04)
More than one child 1.01 (0.64–1.60) 1.11 (0.90–1.35) 0.92 (0.85–0.99) 1.06 (0.99–1.12)
Educational level
Tertiary 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
Secondary 1.62 (1.12–2.35) 0.83 (0.72–0.96) 0.95 (0.91–0.99) 1.01 (0.98–1.04)
Primary 1.49 (0.59–3.79) 1.15 (0.89–1.50) 0.92 (0.82–1.03) 1.01 (0.93–1.10)
Household income
> CHF 9000 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
CHF > 6000–9000 0.72 (0.44–1.19) 1.07 (0.90–1.27) 0.88 (0.83–0.93) 0.99 (0.95–1.03)
CHF > 3000–6000 0.76 (0.46–1.27) 1.25 (1.04–1.51) 0.83 (0.78–0.88) 0.97 (0.94–1.01)
CHF ≤ 3000 0.79 (0.38–1.65) 0.94 (0.71–1.26) 0.75 (0.68–0.82) 0.94 (0.89–1.01)
Employment status
Retired 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
Outside the labour forceb1.09 (0.46–2.60) 0.69 (0.54–0.89) 0.84 (0.72–0.97) 1.00 (0.90–1.10)
Self employed 1.10 (0.50–2.41) 0.68 (0.49–0.95) 0.78 (0.69–0.88) 0.97 (0.90–1.06)
Employed 0.56 (0.25–1.26) 0.81 (0.63–1.04) 0.84 (0.77–0.93) 1.00 (0.95–1.06)
Body Mass Index
18.5–24.9 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
< 18.5 0.49 (0.12–1.96) 1.07 (0.70–1.63) 0.99(0.86–1.14) 1.04 (0.93–1.16)
25–29.9 0.72 (0.49–1.07) 1.12 (0.97–1.30) 1.08(1.03–1.13) 1.04 (1.00–1.07)
≥ 30 0.67 (0.38–1.17) 1.18 (0.97–1.44) 1.12(1.04–1.19) 1.04 (1.00–1.09)
Comorbiditiy scored
0 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
1 1.23 (0.82–1.79) 0.95 (0.82–1.10) 1.08(1.03–1.13) 1.01 (0.98–1.04)
≥ 2 1.26 (0.74–2.16) 1.24 (1.04–1.48) 1.12(1.06–1.18) 1.00 (0.97–1.03)
Smoking
Non-smoker 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
Smoker 0.80 (0.49–1.29) 0.81 (0.66–0.99) 0.93 (0.86–1.00) 0.97 (0.92–1.01)
S.Tancredi et al.
1 3
seropositivity was higher among participants over 65years,
with a high income, retired, overweight or obese, or with
other comorbidities, due to a higher vaccination uptake.
Seropositivity was lower in participants with lower adher-
ence to preventive measures, due to a reduced propensity for
vaccination uptake.
Comparison withother studies
This study’s findings describe the evolution of the SARS-
CoV-2 spread and of population immunological status in the
first phases of the COVID-19 pandemic in several cantons
of Switzerland, accounting for under-ascertainment and dif-
ferences in testing strategies across Swiss cantons. Euro-
pean seroprevalence estimates varied widely during the pan-
demic, depending on study populations, study periods and
methods used. However, our seroprevalence estimates were
roughly similar to estimates found by other seroprevalence
surveys in the same periods in other Swiss cantons [24, 26]
and to pooled estimates from other European high-income
countries [9]. We found some variations in seroprevalence
estimates between cantons during period 1, with estimates
ranging from 3.0 to 6.5% in the French speaking cantons,
being 7.7% in Ticino, and ranging from 2.1 to 5.0% in Ger-
man speaking cantons. These results are particularly inter-
esting in light of the fact that, especially at the beginning of
the pandemic, comparisons between studies were hindered
by differences in study designs. We also found substantial
differences during period 2 and 3. However, these results
are difficult to interpret, since seroprevalence estimates were
strongly influenced by vaccination rates during these peri-
ods, and differences in testing periods could have resulted
in different estimates.
During the first period of this study, i.e., before the start of
the vaccination campaign, we found a higher prevalence of
seropositivity in participants aged between 20 and 64years
compared to those aged 65years and older. Other studies
showed higher seroprevalence in younger adults [10–13]
compared to older population’s groups, and this could be due
to the fact that younger populations were considered at lower
risk of severe illness and therefore could have had more
social interactions. No other factor was associated with sero-
positivity during the first period of this study, despite several
studies showing differences in seropositivity according to
socioeconomic characteristics (e.g., higher seroprevalence in
people with lower income or lower educational level) [10],
health behaviours (e.g., higher seroprevalence in smokers vs
non- smokers) [11, 13] or other sociodemographic character-
istics (e.g., higher seroprevalence in households with more
than one child) [13]. The higher prevalence of seropositivity
found during the third period of this study among partici-
pants aged over 65years, overweight or obese, retired and
with other comorbidities, was due to a higher vaccination
rate in these subgroups. These results were expected, since
the vaccination campaign in Switzerland prioritised people
with a higher risk of severe illness and death (i.e., older
people and people with comorbidities or a high BMI) [27].
Having a high household income was also associated with
Table 4 Association of recommended preventive behaviours with SARS-CoV-2 seropositivity across study periods, Corona Immunitas study,
Switzerland, May 2020–September 2021
Data from Ticino and data from Vaud period 1 were not included because not harmonizable with data from other sites
a Model adjusted for sex, age, educational level, body mass index, income, employment status, children in the household, comorbidity score and
smoking habit
b Model additionally adjusted for vaccination status
Factor Period 1, n = 2151
(01/05/2020–31/10/2020)
Period 2, n = 4969
(01/11/2020–15/05/2021)
Period 3, n = 2836
(16/05/2021–31/09/2021)
Model 1a, PR (95%) Model 1a, PR (95%) Model 1a, PR (95%) Model 2b, PR (95%)
Physical distancing during previous 7days
Frequently 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
Occasionally/rarely 1.25 (0.61–2.59) 1.52 (1.18–1.96) 0.81 (0.75–0.89) 0.97 (0.92–1.02)
Staying at home during previous 7days
Frequently 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
Occasionally/rarely 1.43 (0.86–2.37) 1.13 (0.96–1.33) 0.94 (0.90–0.98) 1.02 (0.99–1.05)
Wearing mask during previous 7days
Frequently 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
Occasionally/rarely 0.68 (0.41–1.13) 1.06 (0.78–1.43) 0.76 (0.70–0.84) 0.96 (0.91–1.02)
Hygiene measures during previous 7days
Frequently 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
Occasionally/rarely 0.97 (0.31–3.02) 0.88 (0.62–1.25) 0.79 (0.72–0 .87) 1.01 (0.94–1.08)
Seroprevalence trends ofanti‑SARS‑CoV‑2 antibodies andassociated risk factors: a…
1 3
higher seropositivity due to a higher vaccination uptake.
This finding is consistent with other studies conducted in
Switzerland [28] and elsewhere [29, 30].
Regarding preventive behaviours, despite several personal
and social preventive measures associated with a reduction
in the incidence of COVID-19 [21, 31], we did not find
associations between adherence to preventive behaviours
and seropositivity before the start of the vaccination cam-
paign (period 1). This result could be due to selection bias,
as people who adhered less to preventive measures were
also less likely to participate in this study. Another hypo-
thetical explanation is that people who did not frequently
adhere to the recommended measures benefited from the
collective adherence to those same measures, or from the
low seroprevalence in period 1. During the last study period,
we found a lower prevalence of seropositivity in people with
lower adherence to recommended preventive behaviours,
especially in participants who less frequently wore masks.
This was explained by a lower vaccination uptake in these
groups. Other studies investigated the associations between
willingness to receive the COVID-19 vaccine and adher-
ence to preventive behaviours [32], showing that people
who are more prone to follow prevention recommendations
are also more likely to get vaccinated. Overall, the associa-
tions between risk factors and seroprevalence during period
2 were difficult to interpret because period 2 included blood
sample collected both before and after the vaccination cam-
paign and because, during the first months of the vaccina-
tion campaign, self-reported information on vaccination
status was less reliable, due to organizational difficulties in
promptly modifying the questionnaires to include questions
on vaccination status.
Strengths andlimitations
This study has some limitations. Overall, the participation
rate was moderate (21%). Moreover, despite random repre-
sentative samples of the population being invited, selection
bias is possible, with a higher participation rate of highly
educated participants compared to the Swiss general popula-
tion. Further, seroprevalence could be underestimated due to
waning immunity [33], people failing to produce antibodies
[34] and due to the fact that we only measured the amount
of anti-SARS-CoV-2 IgGs, without assessing other types
of antibodies. We were therefore not able to distinguish
between infection-related and vaccination-related antibod-
ies. Information bias is also possible, since the information
collected through the questionnaire was self-reported. The
key strengths of our study include the use of a large popu-
lation-based sample covering a significant proportion of the
country and with repeated samplings over time, the use of a
previously validated test with high sensitivity and specificity,
and post-stratification weights to account for differences in
sex and age.
Conclusions
Seroprevalence in Switzerland has increased sharply over
time, also thanks to the increasing vaccination coverage,
with some regional differences. After the vaccination cam-
paign, no differences between subgroups were observed.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s15010- 023- 02011-0.
Acknowledgements Corona Immunitas Research Group: The fol-
lowing 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 Eco-
nomics, Health and Social Care (DEASS), University of Applied Sci-
ences & Arts of Southern Switzerland (SUPSI), Switzerland; Alexia
Anagnostopoulos, MD MPH (Epidemiology, Biostatistics and Preven-
tion Institute, University of Zurich, Zurich, Switzerland); Daniela
Anker, PhD (Population Health Laboratory (#PopHealthLab), Univer-
sity of Fribourg, Switzerland; Institute of Primary Health Care
(BIHAM), University of Bern, Switzerland); Anna Maria Annoni, Msc
(Institute of Public Health (IPH), Uni-versità della Svizzera italiana,
Lugano, Switzerland); Hélène Aschmann, PhD (Epidemiology, Bio-
statistics and Prevention Institute, University of Zurich, Zurich, Swit-
zerland); Andrew Azman, PhD (Unit of Population Epidemiology,
Division of Primary Care Medicine, Geneva University Hospi-tals,
Geneva, Switzerland; Department of Epidemiology, Johns Hopkins
Bloomberg School of Pub-lic 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 (Epidemiology, Bio-
statistics and Prevention Institute, University of Zurich, Zurich, Swit-
zerland); 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 Epide-
miology, 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 Laus-
anne, Switzerland); Patrick Bo-denmann, MD, Msc (Center for Primary
Care and Public Health (Unisanté), University of Lausanne, Switzer-
land); 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, Swit-
zerland); Arnaud Chiolero, MD, PhD (Population Health Laboratory
(#PopHealthLab), University of Fribourg, Switzerland; Institute of
Primary Health Care (BIHAM), University of Bern, Switzerland;
S.Tancredi et al.
1 3
Department of Epidemiology, Biostatistics and Occupational Health,
McGill University, Montréal, Canada); Patricia Orializ Chocano-
Bedoya, MD, PhD (Institute of Primary Health Care (BIHAM), Uni-
versity of Bern; Population Health Laboratory (#PopHealthLab), Uni-
versity 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, Swit-
zerland); 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 Swit-
zerland (SUPSI), Switzerland); Institute of Public Health (IPH), Uni-
versità della Svizzera italiana, Lugano, Switzerland); Stéphane Cullati,
PhD (Population Health Laboratory (#PopHealthLab), University of
Fribourg, Switzerland; Department of Readaptation and Geriatrics,
University of Geneva, Switzerland); 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), Uni-versità della Svizzera italiana, Lugano, Switzerland);
Agathe Deschamps (Cantonal Medical Service Neuchâtel); Paola
D’Ippolito (Unit of Population Epidemiology, Division of Primary
Care Medi-cine, Geneva University Hospitals, Geneva, Switzerland);
Anja Domenghino, Dr. med. (Epidemiology, Biostatistics and Preven-
tion 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, Swit-
zerland); 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 Laus-
anne, 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); Nath-
alie Engler (Cantonal Hospital St. Gallen, Clinic for Infectious Diseases
and Hospital Epidemiology, St. Gallen, Switzerland); Adina Mihaela
Epure, MD (Population Health Laboratory (#PopHealthLab), Univer-
sity 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, Uni-
versity 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 Humanitarian 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, Swit-
zerland); 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 Frang-
ville, MSc (Unit of Population Epidemiology, Division of Primary Care
Medicine, Geneva University Hospitals, Geneva, Switzerland); Irène
Frank, PhD (Luzerner Kantonsspital, Spitalstras-se, 6000 Luzern 16);
Giovanni Franscella, Msc (Institute of Public Health (IPH), Università
della Svizzera italiana, Lugano, Switzerland); Anja Frei, PhD (Epide-
miology, Biostatistics and Prevention Institute, University of Zurich,
Zurich, Switzerland); Marco Geigges, PhD (Epidemiology, Biostatis-
tics and Prevention Institute, University of Zurich, Zurich, Switzer-
land); Semira Gonseth Nusslé, MD, MSc (Center for Primary Care and
Public Health (Unisanté), University of Lausanne, Switzerland); Clé-
ment Graindorge, MD (Unit of Population Epidemiology, Division of
Primary Care Medicine, Geneva University Hospitals, Geneva, Swit-
zerland); 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, Uni-
versity 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 Epi-
demiology 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 Hos-
pital St. Gallen, Clinic for Infectious Diseases and Hospital Epidemiol-
ogy, St. Gallen, Switzerland; Children's Hospital of Eastern Switzer-
land, Infectious Diseases and Hospital Epidemiology, St. Gallen,
Switzerland); Laurent Kaiser, MD, PhD (Geneva Center for Emerging
Viral Diseases and Laboratory of Virology, Geneva University Hospi-
tals, 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 Neuchâ-
tel, Neuchâtel, Switzerland); Marco Kaufmann PhD (Epide-miology,
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, Swit-
zerland; University of Basel, Basel, Switzerland); Simone Kessler
(Cantonal Hospital St. Gallen, Clinic for Infectious Diseases and Hos-
pital 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 Kriem-
ler, MD (Epidemiology, Biostatistics and Prevention In-stitute, Univer-
sity of Zurich, Zurich, Switzerland); Julien Lamour (Unit of Population
Epidemiolo-gy, 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), Switzer-
land); 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 Uni-versity 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 Primary Care Medicine,
Geneva University Hospitals, Geneva, Switzerland); Eric Masserey
(Cantonal Medical Office, General Health Depart-ment, Canton of
Vaud, Switzerland); Dominik Menges, MD MPH (Epidemiology,
Seroprevalence trends ofanti‑SARS‑CoV‑2 antibodies andassociated risk factors: a…
1 3
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 Communication, Culture and Society,
Università della Svizzera italiana, Lugano, Switzerland; Faculty of
Biomedical Sciences, Università della Svizzera italiana, Lugano, Swit-
zerland); Nicolai Mösli (Swiss TPH, Basel, Switzerland; University of
Basel, Basel, Swtizerland); Natacha Noël (Unit of Population Epide-
miology, Division of Primary Care Medicine, Ge-neva University Hos-
pitals, 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, Switzerland); Francesco Pennac-
chio, PhD (Unit of Population Epidemiology, Division of Primary Care
Medicine, Geneva University Hospitals, Geneva, Switzerland); Stefan
Pfister, PhD (Laboratory, HFR-Fribourg, Fribourg, Swit-zerland); Gio-
vanni Piumatti, PhD (Fondazione Agnelli, Turin, Italy); Géraldine
Poulain (Division of Laboratory Medicine, Geneva University Hospi-
tals, Geneva, Switzerland); Nicole Probst-Hensch, Dr. phil.II, PhD,
MPH (Swiss Tropical and Public Health Institute, Department of Epi-
demiology 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, Biosta-
tistics and Prevention In-stitute, University of Zurich, Zurich, Switzer-
land); Nick Pullen, PhD (Unit of Population Epidemi-ology, Division
of Primary Care Medicine, Geneva University Hospitals, Geneva,
Switzerland); Thomas Radtke, PhD (Epidemiology, 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, Divi-sion of Primary Care Medi-
cine, Geneva University Hospitals, Geneva, Switzerland; Institute of
Global Health, University of Geneva, Switzerland); Viviane Richard,
MSc (Unit of Population Epidemiology, 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 Pri-
mary Health Care (BIHAM), University of Bern; Department of Gen-
eral Internal Medicine, Inselspital, Bern University Hospital, Univer-
sity 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 Hospi-tals, 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 Laus-
anne, 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), Univer-sity of Bern, Department of
General Internal Medicine, Inselspital, Bern University Hospital, Uni-
versity of Bern); Nathalie Schwab (Institute of Primary Health Care
(BIHAM), University of Bern, Department of General Internal Medi-
cine, Inselspital, Bern University Hospital, University of Bern); Claire
Semaani (Unit of Population Epidemiology, Division of Primary Care
Medicine, Ge-neva University Hospitals, Geneva, Switzerland); Alex-
andre 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
(#PopHealth-Lab), 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), Uni-
versity of Lausanne, Switzer-land); Mauro Tonolla, PD PhD (Depart-
ment of environment construction and design (DACD, University of
Applied Sciences & Arts of Southern Switzerland (SUPSI), Switzer-
land); Nicolas Troillet, MD, MSc (Office du médecin cantonal, Sion,
Switzerland); Agne Ulyte, MD (Epidemiology, Biostatistics and Pre-
vention Institute, University of Zurich, Zurich, Switzerland); Sophie
Vassaux (Center for Primary Care and Public Health (Unisanté), Uni-
versity of Lausanne, Switzerland); Thomas Vermes, MSc (Swiss Tropi-
cal and Public Health Institute, Department of Epidemiology and Pub-
lic Health, Basel, Switzerland; University of Basel, Basel, Swtizerland);
Jennifer Villers, PhD (Unit of Population Epidemiology, Division of
Primary Care Medicine, Geneva University Hospitals, Geneva, Swit-
zerland); 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, Switzer-
land); 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,
Ge-neva University Hospitals, Geneva, Switzerland); Kyra Zens, PhD,
MPH (Epidemiology, Biostatis-tics and Prevention Institute, University
of Zurich, Zurich, Switzerland); Claire Zuppinger (Center for Primary
Care and Public Health (Unisanté), University of Lausanne,
Switzerland).
Author contributions All authors designed the study. ST and SC ana-
lysed the data. ST drafted the manuscript with contributions of AC, JF
and SC. All co-authors contributed to the data acquisition, interpreta-
tion and revised the first draft of the manuscript. All authors approved
the final version of the manuscript before submission.
Funding Open access funding provided by University of Fribourg. The
Directorate of SSPH + is responsible for the coordination, communi-
cation, 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.
Data availability Deidentified individual participant data underlying
the findings of this study will be available for researchers submitting a
methodologically sound proposal to achieve the aims of the proposal
after the publication of this article. Access to data requires contacting
Corona Immunitas.
Declarations
Conflict of interest The authors declare no competing interests.
Ethical approval and consent to participate The Ethics Committees
of the various cantons approved this study (Cantons of Zurich, St.
Gallen, Grisons, Fribourg, Lucerne, Bern, Neuchâtel: BASEC 2020-
S.Tancredi et al.
1 3
01247, Canton of Vaud: BASEC 2020-00887, Canton of Basel-City
and Basel-Country: BASEC 2020-00927, Canton of Ticino: BASEC
2020-01514). The subjects of the study provided written informed con-
sent (included in submission) prior to their participation in the study.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
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Authors and Aliations
StefanoTancredi1· ArnaudChiolero1,2,3· CorneliaWagner1· MoaLinaHaller3,4· PatriciaChocano‑Bedoya1,3·
NataliaOrtega1,3· NicolasRodondi3,4· LaurentKaufmann5· ElsaLorthe6· HélèneBaysson6,7· SilviaStringhini6,7,8·
GiselaMichel9· ChantalLüdi9· ErikaHarju9,10· IreneFrank10· MedeaImboden11,12· MelissaWitzig11,12·
DirkKeidel11,12· NicoleProbst‑Hensch11,12· RebeccaAmati13· EmilianoAlbanese13· LaurieCorna14· LucaCrivelli14·
JuliaVincentini15· SemiraGonsethNusslé15· MurielleBochud15· ValérieD’Acremont11,15· PhilippKohler16·
ChristianR.Kahlert16,17· AlexiaCusini18· AnjaFrei19· MiloA.Puhan19· MarcoGeigges19· MarcoKaufmann19·
JanFehr19· StéphaneCullati1,20on behalf of the Corona Immunitas Research Group
1 Population Health Laboratory (#PopHealthLab), University
ofFribourg, Route Des Arsenaux 41, 1700Fribourg,
Switzerland
2 School ofPopulation andGlobal Health, McGill University,
Montreal, Canada
3 Institute ofPrimary Health Care (BIHAM), University
ofBern, Bern, Switzerland
4 Department ofGeneral Internal Medicine, Inselspital, Bern
University Hospital, University ofBern, Bern, Switzerland
5 Cantonal Public Health Service oftheCanton ofNeuchâtel,
Neuchâtel, Switzerland
6 Unit ofPopulation Epidemiology, Division ofPrimary Care
Medicine, Geneva University Hospitals, Geneva, Switzerland
7 Department ofHealth andCommunity Medicine, Faculty
ofMedicine, University ofGeneva, Geneva, Switzerland
8 University Center forGeneral Medicine andPublic Health,
University ofLausanne, Lausanne, Switzerland
9 Department Health Sciences andMedicine, University
ofLucerne, Lucerne, Switzerland
10 Clinical Trial Unit, Lucerne Cantonal Hospital, Lucerne,
Switzerland
11 Swiss Tropical andPublic Health Institute, Allschwil,
Switzerland
12 University ofBasel, Basel, Switzerland
13 Institute ofPublic Health, Faculty ofBiomedical Sciences,
Università Della Svizzera Italiana, Lugano, Switzerland
14 Department ofBusiness Economics, Health andSocial
Care, University ofApplied Sciences andArts ofSouthern
Switzerland, Manno, Switzerland
15 Center forPrimary Care andPublic Health (Unisanté),
University ofLausanne, Lausanne, Switzerland
16 Division ofInfectious Diseases andHospital Epidemiology,
Cantonal Hospital St Gallen, StGallen, Switzerland
17 Department ofInfectious Diseases andHospital
Epidemiology, Children’s Hospital ofEastern Switzerland,
St.Gallen, Switzerland
18 Division ofInfectious Diseases, Cantonal Hospital
ofGrisons, Chur, Switzerland
19 Epidemiology, Biostatistics andPrevention Institute,
University ofZurich, Zurich, Switzerland
20 Department ofReadaptation andGeriatrics, University
ofGeneva, Geneva, Switzerland
