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PLOS Medicine logoLink to PLOS Medicine
. 2022 Nov 7;19(11):e1004125. doi: 10.1371/journal.pmed.1004125

Risk and symptoms of COVID-19 in health professionals according to baseline immune status and booster vaccination during the Delta and Omicron waves in Switzerland—A multicentre cohort study

Baharak Babouee Flury 1, Sabine Güsewell 1, Thomas Egger 1, Onicio Leal 2,3, Angela Brucher 4, Eva Lemmenmeier 5, Dorette Meier Kleeb 6, J Carsten Möller 7, Philip Rieder 8, Markus Rütti 9, Hans-Ruedi Schmid 10, Reto Stocker 8, Danielle Vuichard-Gysin 11, Benedikt Wiggli 12, Ulrike Besold 13, Allison McGeer 14, Lorenz Risch 15,16,17, Andrée Friedl 12, Matthias Schlegel 1, Stefan P Kuster 1, Christian R Kahlert 1,18,*,#, Philipp Kohler 1,*,#; on behalf of the SURPRISE Study Group
Editor: James G Beeson19
PMCID: PMC9678290  PMID: 36342956

Abstract

Background

Knowledge about protection conferred by previous Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and/or vaccination against emerging viral variants allows clinicians, epidemiologists, and health authorities to predict and reduce the future Coronavirus Disease 2019 (COVID-19) burden. We investigated the risk and symptoms of SARS-CoV-2 (re)infection and vaccine breakthrough infection during the Delta and Omicron waves, depending on baseline immune status and subsequent vaccinations.

Methods and findings

In this prospective, multicentre cohort performed between August 2020 and March 2022, we recruited hospital employees from ten acute/nonacute healthcare networks in Eastern/Northern Switzerland. We determined immune status in September 2021 based on serology and previous SARS-CoV-2 infections/vaccinations: Group N (no immunity); Group V (twice vaccinated, uninfected); Group I (infected, unvaccinated); Group H (hybrid: infected and ≥1 vaccination). Date and symptoms of (re)infections and subsequent (booster) vaccinations were recorded until March 2022. We compared the time to positive SARS-CoV-2 swab and number of symptoms according to immune status, viral variant (i.e., Delta-dominant before December 27, 2021; Omicron-dominant on/after this date), and subsequent vaccinations, adjusting for exposure/behavior variables.

Among 2,595 participants (median follow-up 171 days), we observed 764 (29%) (re)infections, thereof 591 during the Omicron period. Compared to group N, the hazard ratio (HR) for (re)infection was 0.33 (95% confidence interval [CI] 0.22 to 0.50, p < 0.001) for V, 0.25 (95% CI 0.11 to 0.57, p = 0.001) for I, and 0.04 (95% CI 0.02 to 0.10, p < 0.001) for H in the Delta period. HRs substantially increased during the Omicron period for all groups; in multivariable analyses, only belonging to group H was associated with protection (adjusted HR [aHR] 0.52, 95% CI 0.35 to 0.77, p = 0.001); booster vaccination was associated with reduction of breakthrough infection risk in groups V (aHR 0.68, 95% CI 0.54 to 0.85, p = 0.001) and H (aHR 0.67, 95% CI 0.45 to 1.00, p = 0.048), largely observed in the early Omicron period. Group H (versus N, risk ratio (RR) 0.80, 95% CI 0.66 to 0.97, p = 0.021) and participants with booster vaccination (versus nonboosted, RR 0.79, 95% CI 0.71 to 0.88, p < 0.001) reported less symptoms during infection. Important limitations are that SARS-CoV-2 swab results were self-reported and that results on viral variants were inferred from the predominating strain circulating in the community at that time, rather than sequencing.

Conclusions

Our data suggest that hybrid immunity and booster vaccination are associated with a reduced risk and reduced symptom number of SARS-CoV-2 infection during Delta- and Omicron-dominant periods. For previously noninfected individuals, booster vaccination might reduce the risk of symptomatic Omicron infection, although this benefit seems to wane over time.


In this multi-centre cohort study from Switzerland, Dr Christian R. Kahlert and colleagues investigate the risk and symptoms of COVID-19 among healthcare professionals, according to baseline immune status and booster vaccination, during the Delta and Omicron waves.

Author summary

Why was this study done?

  • Preexisting immunity against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)—either from previous infection or vaccination—confers protection against severe Coronavirus Disease 2019 (COVID-19).

  • Few studies have prospectively determined the SARS-CoV-2 infection risk based on large-scale serologic testing to detect previous asymptomatic infections.

  • Real-world evaluations of infections during periods with distinct SARS-CoV-2 variants are valuable to assess protection by preexisting immunity and inform health policy guidelines.

What did the researchers do and find?

  • In September 2021, 2,554 healthcare workers were classified into four different groups based on previous SARS-CoV-2 serology results and infection/vaccination history.

  • Participants were followed until March 2022 to assess the association of immune status and additional vaccinations with self-reported COVID-19 and symptoms.

  • Hybrid immunity (i.e., previous infection and at least one vaccination) resulted in a reduced SARS-CoV-2 infection risk and less symptoms during the Delta or Omicron period.

  • Booster vaccination was associated with reduced infection risk and less symptoms during the first half of the Omicron period analysed in our study.

What do these findings mean?

  • Individuals who were previously infected and vaccinated seem to be best protected and exhibit less symptoms of SARS-CoV-2 infection than those with other immune status.

  • Booster vaccination might further reduce both the risk of Omicron breakthrough infection and the number of reported symptoms, although this benefit fades over time.

  • These findings might inform healthcare providers and public health authorities in estimating the risk of SARS-CoV-2 (re)infection in individuals or communities.

Background

Mitigation of Coronavirus Disease 2019 (COVID-19) relies on establishing an ideally long-lasting immune barrier against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Studies from the pre-Omicron era show that specific neutralizing antibodies as marker for humoral immunity reduce the risk of symptomatic (re)infection and thus disease recurrence upon reexposure to a homologous viral strain [1]. Although natural infection with SARS-CoV-2 elicits a broader humoral response than mRNA vaccine administration, levels of neutralizing antibodies are lower after natural infection [2]. Concerning (re)infection however, previous infection is associated with 85% protection over at least 9 months [3]; also, infected individuals might be even better protected than only vaccinated individuals [4]. A still lower reinfection risk has been reported for individuals with hybrid immunity, i.e., SARS-CoV-2 infection plus at least one vaccination [510].

Yet, weak or waning humoral response in tandem with the emergence of new viral variants, capable of potentially escaping immune response, lead to a continued risk of reinfection with heterologous SARS-CoV-2 strains [1,11,12]. This is particularly true for the Omicron variant [13], which is the predominant viral strain globally as of October 2022 [14], causing mostly milder infections compared to preceding strains [1517]. Available evidence from adult and pediatric populations points towards good effectiveness of prior mRNA vaccination against severe COVID-19 and hospitalisation due to Omicron variants, but less so against asymptomatic and symptomatic, mild breakthrough infections [11,1821]. Similar to the pre-Omicron era, some studies have shown that previous infection or hybrid immunity (compared to vaccination alone) might provide better protection against the Omicron variant. However, these studies were either small [22] or relied on population-based data [23], ignoring the fact that a substantial proportion of SARS-CoV-2 infections remain undetected with the risk of misclassification [24]. Also, behavior and exposure variables, which are likely to be different between previously vaccinated and unvaccinated individuals, were not considered in these studies. The influence of type and timing of SARS-CoV-2 vaccination on eventual risk of breakthrough infection has been described for previous variants, but not for Omicron [7,25].

Within a prospective, multicentre healthcare worker (HCW) cohort study, we aimed to determine the risk for and symptoms of COVID-19 by comparing Delta- and Omicron-dominant periods, depending on previous immune status based on infection/vaccination history and serology results. Furthermore, we examined the role of booster vaccination on these outcomes. For those with two vaccinations, we assessed the role of mRNA vaccine type and timing of vaccine doses on breakthrough infection risk.

Methods

Study design and population

This prospective cohort (SURPRISE) was initiated in summer 2020, after the first COVID-19 wave in Switzerland. The study was approved by the ethics committee of Eastern Switzerland (#2020–00502), and participants provided digital written informed consent. Health professionals (with and without patient contact) aged 16 years or older from ten healthcare networks located in Northern and Eastern Switzerland were included between June 2020 and March 2021. From their inclusion until September 2021, participants were prospectively followed through weekly questionnaires on SARS-CoV-2 infections/vaccinations, and periodic SARS-CoV-2 serology measurements in August 2020, January 2021, and August/September 2021 [26]. Participants without available serology from August/September 2021 were excluded. For the present work, no prespecified analysis plan was designed.

Baseline immune status was assessed as of September 20, 2021 (Fig 1), based on previous infection/vaccination history and all available serology results. During the local emergence of the Delta (October to December 2021) and Omicron B.1.1.529.1 (Nextstrain 21K; BA.1) variant (January to March 2022), the follow-up survey through questionnaires was continued at monthly intervals. Information collected included SARS-CoV-2 exposures, reports of nasopharyngeal swab (NPS) tests (positive and negative), symptoms associated with positive NPS, and receipt of subsequent (booster or first) vaccinations.

Fig 1. Study timeline depicting the longitudinal follow-up of participants in correlation with the virus variants circulating in Eastern Switzerland.

Fig 1

SARS-CoV-2 diagnostics

Participants were asked to get tested for SARS-CoV-2 in case of compatible symptoms, according to national recommendations. SARS-CoV-2 was detected by polymerase chain reaction (PCR) or rapid antigen diagnostic (RAD) test, depending on the participating institutions. Some facilities also switched from PCR to RAD in the course. No sequencing for determination of the viral variants was performed; the viral variant was inferred from the predominating strain circulating in the community at that time (see below). To verify the completeness and accuracy of self-reported NPS results (PCR or RAD), all positives and a random sample of negatives were validated for a subgroup of HCWs from the largest participating institution as described previously [27]. Anti-nucleocapsid (anti-N) and anti-spike (anti-S) antibodies were measured using the Roche Elecsys (Roche Diagnostics, Rotkreuz, Switzerland) electro-chemiluminescence immunoassay [28].

Definition of predictor variables

We defined four distinct immune status groups (i.e., main predictor) as of September 20, 2021, according to previous questionnaires and serology results: (i) Group N (reference): no reported infection and anti-N/anti-S negative and no previous SARS-CoV-2 vaccination; (ii) Group V (vaccinated): no reported infection and anti-N negative, but twice vaccinated (with any time interval between doses) with the second dose being at least 7 days ago; (iii) Group I (infected): infection reported or anti-N positive (at any time), but no vaccination; (iv) Group H (hybrid immunity): reported infection or anti-N positive (at any time) and vaccination (≥1 dose) at least 7 days ago. In addition, we collected information on vaccination after September 20, 2021, either booster vaccination (available from November 2021 on) for groups V and H or first vaccinations for groups N and I. Again, participants were considered as boosted 7 days after receipt of the vaccine. Immune status was treated as time-dependent variable, so that participants switched from group I to H after the first vaccination, and from group N to V after the second vaccination. Participants in group N with only one vaccination were not considered and were only included 7 days after receipt of their second dose. The different groups and outcomes are illustrated in S1 Fig. For other predictor variables, S1 Table shows definitions and time points of the corresponding questionnaire.

Outcomes

The main outcome was time to the first SARS-CoV-2-positive NPS reported after September 20, 2021. Outcomes occurring between September 20, 2021 and March 6, 2022 were included. The period before December 27, 2021 was defined as Delta-dominant (i.e., Delta period), the period on and after this date as Omicron-dominant (i.e., Omicron period), based on sequencing data from North-Eastern Switzerland [29]. We treated our outcome as a survival event, i.e., participants were no longer at risk after their first positive NPS. We also calculated the number of symptoms reported during SARS-CoV-2 infection.

Statistical analysis regarding infection risk

We computed Kaplan–Meier curves to compare the occurrence of SARS-CoV-2 (re)infection (first positive NPS) through time according to immune status; noninfected participants were censored at the time of the last available follow-up questionnaire. The risk of (re)infection was compared among groups (treated as time-dependent variable) using Cox regression; hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were calculated. This analysis was performed separately for the Delta and Omicron periods, with and without adjusting for receipt of booster vaccination. In addition, Kaplan–Meier curves were computed to visualize the association of booster vaccination on infection risk during the Omicron period, when most of these vaccinations had already been received.

Multivariable Cox-regression analysis was performed to correct for additional confounding variables, which were a priori selected based on previous analyses and expected importance [30]. Multiple imputation was used to substitute missing values (S1 Methods). Time-dependent variables were, besides immune status, receipt of booster vaccination, SARS-CoV-2 infection of a household contact within the same month, and documentation of ≥1 negative test in the previous month (to adjust for differences in testing behaviour). Time-independent variables included baseline anthropometric data and further variables reflecting SARS-CoV-2 risk exposures and behaviours (S1 Table). We performed a sensitivity analysis excluding infections occurring during the period of variant overlap between December 6, 2021 and January 3, 2022 to minimize contamination of infections occurring in the Delta and Omicron periods with the respective other viral variant.

Supplementary analyses

Because model diagnostics showed that the influence of booster vaccination was time dependent in the Omicron period, we further split this period into an early (before February 15, 2022) and late Omicron phase (after this date).

To estimate the influence of time since last immunization event, we included time from previous infection or vaccination (i.e., time of preimmunization) until September 20, 2021 for those individuals for which this information was available. The exact day of initial infection could not be assessed in those with only positive anti-N but no report of positive NPS; also, group N, which per definition did not have an immunization event, was excluded and group V was chosen as reference category instead.

Finally, to assess the impact of type and timing of vaccination within group V, we included the type of vaccine (i.e., mRNA-1273 or BNT162b2), the time of preimmunization, and time between first and second vaccination dose.

Frequency of SARS-CoV-2 symptoms

We used univariable and multivariable Poisson regression to assess the impact of baseline immune status on symptom number for infections that occurred before any booster or first vaccination. Covariables included the virus variants (Delta versus Omicron period), the month of (re)infection (to adjust for the fact that immune protection wanes over time), and a priori selected variables based on their importance in previous analyses [31].

To assess the impact of booster vaccination on number of symptoms, we performed a second analysis restricted to groups V and H, and to infections occurring during the Omicron period (because of the small number of infections preceded by booster in the Delta period). The study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 STROBE Checklist) [32]. R version 4.1.2 was used for statistical analyses.

Results

Study population

Of the 5,792 initial cohort participants, 2,595 (45%) underwent serology testing in August/September 2021 and completed at least one follow-up questionnaire. Thereof, 2,554 were classified into one of the immune status groups at baseline: 581 (22.7%) in group H, 162 (6.3%) in I, 1,643 (64.3%) in V, and 168 (6.6%) in N; additionally, 41 individuals were assigned to group V during follow-up (after their second vaccination) (S2 Fig). Median follow-up was 171 days (interquartile range 131 to 171). Baseline characteristics are summarized in Table 1.

Table 1. Baseline characteristics of study participants (n = 2,582) according to immune status at baseline.

Number (n) of individuals with missing values are also indicated.

Immune status at baseline N V I H Missing
n = 168 n = 1,643 n = 162 n = 581 (n)
Age at baseline (median, years) 38.3 45.2 35.2 41.7 4
Sex (% male vs. female) 8.4% 21.5% 8.7% 20.7% 22
Body mass index >30 kg/m2 at study inclusion (%) 10.1% 12.1% 12.3% 11.2% 0
Any comorbidity at study inclusion (%) 31.1% 40.1% 32.1% 40.4% 120
Any patient contact at baseline (%) 79.1% 76.5% 87.9% 86.3% 117
Always wearing respirator mask at baseline (%) 13.9% 22.0% 9.4% 18.8% 117
Anti-spike titer (median, in BAU/ml) <0.4 1085 103 3502 82
Any negative test during follow-up (%) 83.3% 73.4% 74.1% 71.6% 0
Any positive household member during follow-up (%) 36.9% 34.8% 28.4% 26.1% 0
Duration of follow-up (median, days) 171 171 171 171 0

None (N): no reported infection and anti-N/-S negative and no previous SARS-CoV-2 vaccination; V (vaccinated): no reported infection and anti-N negative, but twice vaccinated; I (infected): infection reported or anti-N positive (at any time), but no vaccination; H (hybrid immunity): reported infection or anti-N positive (at any time) and vaccination (≥1 dose).

SARS-CoV-2 (re)infections by immune status and time period

A total of 764 (29.4%) infections were reported in 2,595 participants, whereof 173 (22.6%) occurred during the Delta and 591 (77.4%) during the Omicron period. During Delta, the risk for COVID-19 was significantly reduced for groups V (HR 0.33, 95% CI 0.22 to 0.50, p < 0.001), I (HR 0.25, 95% CI 0.11 to 0.57, p = 0.001), and H (HR 0.04, 95% CI 0.02 to 0.10, p < 0.001) compared to group N. These associations were less pronounced during the Omicron period for all groups (Fig 2).

Fig 2.

Fig 2

Left: influence of baseline immune status on the time course of (re)infection events, shown separately for the two periods (Delta vs. Omicron) by resetting Kaplan–Meier curves to 0 on December 27, 2021. Note that group differences depicted in the graph include any impact of booster vaccination (in groups V and H). Right: HRs with 95% CIs from Cox regression regarding risk of SARS-CoV-2 (re)infection for each immune status compared with group N (no previous infection or vaccination), both without and with adjustment for booster vaccination. CI, confidence interval; HR, hazard ratio; SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2.

Booster vaccination and risk of infection

By March 2022, 80% of participants in groups V and H had received a booster (median time of booster was December 14, 2021) (S3 Fig). Adjusting the univariable model for booster vaccination, the adjusted HR (aHR) remained similar for the Delta period as in the unadjusted model. In the Omicron period, only group H showed a reduced risk (aHR 0.45, 0.30 to 0.67, p < 0.001) compared to group N, whereas no significant risk reduction was observed for groups V and I (Fig 2). Restricting the analysis to the Omicron period, receipt of booster vaccination was associated with reduced infection risk in groups V (aHR 0.68, 95% CI 0.54 to 0.85, p = 0.001) and H (aHR 0.67, 95% CI 0.45 to 1.00, p = 0.048) (Fig 3).

Fig 3. Influence of booster on the time course of (re)infection events during Omicron dominance, with HRs and 95% CIs, according to immune status.

Fig 3

Note that participants receiving their booster after December 27, 2021 were initially classified as “no” and subsequently switched to “yes,” so that numbers at risk for “yes” increase initially. CI, confidence interval; HR, hazard ratio.

Multivariable analysis and sensitivity analyses

When models included exposure and behaviour variables, results were similar, with group H being the only group with reduced infection risk (aHR 0.52, 95% CI 0.35 to 0.77, p = 0.001) in the Omicron period (Table 2). The main risk correlate for SARS-CoV-2 positivity was having a SARS-CoV-2-positive household (as reported by the participant), both in the Delta (aHR 9.66, 95% CI 7.15 to 13.05, p < 0.001) and the Omicron period (aHR 6.17, 95% CI 5.24 to 7.27, p < 0.001).

Table 2. HRs with 95% CIs from multivariable Cox regression regarding COVID-19 risk according to immune status and period.

Delta period (n = 2,593) Omicron period (n = 2,355)
aHR (95% CI) p-value aHR (95% CI) p-value
Group V (vs. N) 0.47 (0.31–0.74) 0.001 0.85 (0.58–1.23) 0.393
Group I (vs. N) 0.26 (0.11–0.59) 0.002 0.74 (0.45–1.19) 0.213
Group H (vs. N) 0.06 (0.02–0.14) <0.001 0.52 (0.35–0.77) 0.001
Age (per decade) 0.97 (0.84–1.13) 0.709 0.79 (0.73–0.86) <0.001
Male vs. female 1.16 (0.80–1.69) 0.439 0.87 (0.70–1.08) 0.193
Body mass index >30 kg/m2 0.84 (0.50–1.41) 0.518 1.02 (0.79–1.31) 0.890
Patient contact 0.77 (0.53–1.11) 0.167 0.89 (0.72–1.09) 0.253
Respirator mask use 0.99 (0.64–1.55) 0.980 1.09 (0.87–1.35) 0.461
Positive household in last month 9.66 (7.15–13.05) <0.001 6.17 (5.24–7.27) <0.001
Any negative test 1.26 (0.90–1.75) 0.181 1.11 (0.93–1.32) 0.236
Booster 0.42 (0.21–0.83) 0.014 0.81 (0.66–0.99) 0.043

N (no immunity): no reported infection and anti-N/-S negative and no previous SARS-CoV-2 vaccination; V (vaccinated): no reported infection and anti-N negative, but twice vaccinated; I (infected): infection reported or anti-N positive (at any time), but no vaccination; H (hybrid immunity): reported infection or anti-N positive (at any time) and vaccination (≥1 dose).

aHR, adjusted HR; CI, confidence interval; statistics obtained by pooling model coefficients and standard errors from ten imputations of missing covariate values; COVID-19, Coronavirus Disease 2019; HR, hazard ratio; SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2.

For results of univariable analysis, see S2 Table.

Excluding infections during the Delta-Omicron overlap period and performing further missing value imputations as well as a complete case analysis yielded similar HRs as the main analysis (S3 Table).

Supplementary analyses

Splitting the Omicron period into an early and late phase suggested a benefit of the booster vaccination for the early Omicron phase (aHR 0.60, 95% CI 0.47 to 0.76, p < 0.001), but not for the late Omicron phase (aHR 1.02, 95% CI 0.75 to 1.38, p = 0.90) (S4 Table).

Time since preimmunization was associated with infection risk during the Delta period (aHR 1.16 per additional months, 95% CI 1.06 to 1.28, p = 0.003), but not the Omicron period (aHR 1.00, 95% CI 0.96 to 1.04, p = 0.82). Hybrid immunity remained the immune status, which best protected against infection in both periods. Of note, group I (compared to group V) was better protected against infection in the Delta period (aHR 0.27, 95% CI 0.09 to 0.82, p = 0.023), but not in the Omicron period (aHR 0.84, 95% CI 0.49 to 1.44, p = 0.527) (S5 Table).

Within group V, neither the time interval between dose 1 and dose 2 (HR 0.96 per month, 95% CI 0.73 to 1.27 p = 0.788) nor receipt of the mRNA-1273 (versus BNT162b2) vaccine (HR 0.79, 95% CI 0.62 to 1.01, p = 0.060) were significantly associated with reduced infection risk (S6 Table).

SARS-CoV-2 symptoms according to immune status and time period

Participants reported a median of four symptoms during SARS-CoV-2 episodes in the Delta period, and three in the Omicron period (Fig 4). Group H reported fewer symptoms (median of three) than group N (median of four, adjusted rate ratio [aRR] 0.81, 95% CI 0.67 to 0.97, p = 0.026), even after adjusting for multiple covariables. Groups V and I did not report less symptoms compared to group N (S7 Table). Restricting the analysis to vaccinated participants, hybrid immunity (group H versus V, aRR 0.80, 95% CI 0.71 to 0.91, p < 0.001) and receipt of booster vaccination (aRR 0.79, 95% CI 0.71 to 0.88, p < 0.001), were associated with fewer symptoms, whereas having a comorbidity at baseline (aRR 1.17, 95% CI 1.06 to 1.28, p = 0.001) and being infected later during the Omicron period (aRR 1.22 per additional months, 95% CI 1.13 to 1.31, p < 0.001) were associated with more symptoms (S8 Table).

Fig 4.

Fig 4

Number of symptoms reported after (re)infection by baseline immune status, grouped by Delta and Omicron periods (panel A, left) and receipt of booster (panel B, right). N (no immunity): no reported infection and anti-N/-S negative and no previous SARS-CoV-2 vaccination; V (vaccinated): no reported infection and anti-N negative, but twice vaccinated; I (infected): infection reported or anti-N positive (at any time), but no vaccination; H (hybrid immunity): reported infection or anti-N positive (at any time) and vaccination (≥1 dose).

Discussion

In this prospective multicentre study, we observed that participants with hybrid immunity and those receiving booster vaccination reported the lowest risk for COVID-19 during the Delta and Omicron periods, and, when infected during the Omicron period, were less symptomatic than those only vaccinated or previously infected. However, this association with booster vaccination waned over time. Type and timing of baseline mRNA vaccines were not associated with the outcomes.

Hybrid immunity, defined as immunity acquired from previous infection plus at least one vaccination, was shown to be associated with reduced risk of SARS-CoV-2 reinfection compared to previous infection only, for up to 9 months [33]. We confirm findings of this Swedish study, which ended in October 2021 (i.e., before Delta was the predominant variant in Europe including Sweden) [34]. Also, our data are in line with a study from Israel performed mainly during the Delta period showing that persons with hybrid immunity were better protected against breakthrough infection compared to only vaccinated persons [10]. In addition, our findings suggest that hybrid immunity acquired from infections with previous variants not only provides protection against infections by the Delta, but also the Omicron variant, as described in a previous study from Qatar [23]. In contrast to this latter study, which relied on population-level data, we used baseline serology results allowing us to additionally capture previous asymptomatic infections and to adequately assign participants to the respective immune status groups. Furthermore, our study revealed that hybrid immunity was the only immune status associated with less symptoms compared to the group without any preexisting immunity, for both time periods. These findings are in line with results of a laboratory-based study, which showed that antibodies from sera of people with hybrid immunity were able to better neutralize the Omicron variant, compared to antibodies from only vaccinated or infected individuals [35].

When ignoring the booster, we did no longer observe any additional protection of two-dose vaccination in noninfected participants during the Omicron period. This finding adds to data from Qatar, where the effectiveness of two-dose BNT162b2 vaccination against symptomatic Omicron infection was found to be negligible [23]. For the Delta period, we observed a higher risk of (re)infection in twice vaccinated compared to previously infected, nonvaccinated participants. Similarly, in a large retrospective observational study in 124,500 participants, naturally acquired immunity to SARS-CoV-2 conferred stronger protection against infection and symptomatic disease caused by the Delta variant, compared to the BNT162b2 two-dose vaccine-induced immunity [36]. The benefit of previous infection could be explained by the broader immune response elicited by natural infection, with humoral and cellular immune responses not only targeting the spike protein but also other viral antigens. However, in our study, this association was no longer apparent during the Omicron period, where the infection risk of participants with only infection-induced immunity compared to those with vaccine-induced immunity was similar.

The effectiveness of previous infection in preventing reinfection with the Alpha, Beta, and Delta variants of SARS-CoV-2 was around 90% and 60% for Omicron in another study from Qatar [37]. These estimates are higher than in our study, where previous infection was associated with a 75% risk reduction during the Delta, and 25% during the Omicron dominating period. In contrast to Altarawneh and colleagues, we included individuals with previous asymptomatic infection. As the humoral immune response elicited by asymptomatic is weaker compared to symptomatic infection [38], the benefit against reinfections might be lower, which could explain the observed discrepancy.

Previous data have shown that the effectiveness of mRNA-1273 might be superior to BNT162b2, likely because of the slower rise and faster decay of neutralizing antibody titers elicited by BNT162b2 [39,40]. Our study was not designed to assess true vaccine effectiveness. However, when adjusting for receipt of booster vaccine, the risk for breakthrough infection in participants receiving either vaccine was similar, as has also been reported from Qatar [23]; the time interval between first and second vaccine did not impact these results, as has shown previously for the ChAdOx1 vaccine [25].

Our data point towards a benefit of booster vaccination against infection in the Omicron period of approximately 30%, which is below the 47% booster effectiveness reported previously [6]. Also, in a study among US adults with COVID-like illness, the odds ratio for boosted versus nonboosted individuals was 0.16 for Delta and 0.34 for Omicron infections [19], which is in the range of a prospective cohort of frontline workers, where booster mRNA vaccine provided around 90% protection against Delta and 60% against Omicron infection [18]. In the latter study, participants were routinely tested for SARS-CoV-2, resulting in a relevant proportion of asymptomatic infections, which could have overestimated the benefit of the booster vaccination. Another explanation for the lower figures observed in our study is that we collected data over a longer period of Omicron activity (over 2 months), during which the booster effect might have waned [41]. Indeed, most participants in our cohort received their booster vaccination before onset of the Omicron period; at the same time, in a supplementary analysis of our data, the benefit associated with booster vaccination vanished in the late Omicron period.

Nevertheless, both with the Delta and the Omicron variants, mRNA booster lead to strong protection against COVID-19–related hospitalization and death [20,42]. Although these outcomes were not relevant in our context, we observed a reduction of symptoms among boosted versus nonboosted individuals during the Omicron period. However, we did not assess whether this reduction of symptoms led to less work absenteeism or visits to healthcare providers.

The most important strengths of this study are the availability of baseline serology data along with behavioural and exposure variables, the prospective nature, and the coverage of symptoms associated with COVID-19. Limitations of our study include that SARS-CoV-2 testing was not mandatory and that results were self-reported. However, we previously showed that self-reported NPS results were highly consistent with documented (for positive NPS) and nondocumented (negative NPS) infections [27]. In addition, we adjusted our analysis for the participants’ testing behaviour. Viral variants were categorized based on the community epidemiology only (predominating viral strain) and not on sequencing results. This potential imprecision could have led to underestimation of the differential impact of viral variant; yet, excluding infections occurring during the overlap period as sensitivity analysis did not significantly change the results. SARS-CoV-2-specific T-cell immunity is also likely relevant, for assessing the risk for (re)infection, particularly of severe infection. However, as we did not sample cells from the blood, we were not able to assess this part of the specific immune response. Our results are insofar not generalizable, as the cohort consisted of a well-defined group of young and healthy HCW with an increased exposure to SARS-CoV-2.

Conclusions

In this real-life study using large-scale serology data, we evaluated SARS-CoV-2 infections during the Delta and Omicron waves in Switzerland and observed that hybrid immunity in HCW—compared to other immune status—was associated with the lowest risk of (re)infection and less symptoms in case of infection. Booster vaccination was associated with a risk reduction and with fewer symptoms of SARS-CoV-2 breakthrough infection, although this benefit seemed to fade during the Omicron period. Thus, our findings might inform healthcare providers and public health authorities in prioritizing SARS-CoV-2 vaccinations.

Supporting information

S1 STROBE Checklist. STROBE checklist.

(PDF)

S1 Methods. “Missing value imputation for covariates in multivariable models” and “Verification of proportional-hazard assumption in Cox regression.”.

(PDF)

S1 Table. Covariable definitions, levels, and time points when variables were obtained.

(PDF)

S2 Table. Hazard ratios with 95% confidence intervals from separate univariable Cox regression models for each predictor involved in the multivariable models regarding COVID-19 risk by period (Table 1 in main text).

(PDF)

S3 Table. Sensitivity analyses: Comparison of hazard ratios obtained in Cox models with three independent missing value imputations, without missing value imputation (i.e., complete case analysis), and with exclusion of events occurring during the period of variant overlap between December 6, 2021 and January 3, 2022.

(PDF)

S4 Table. Model with time-dependent impact of booster in the Omicron-dominant period.

(PDF)

S5 Table. Adjusted hazard ratios (HR) with 95% confidence intervals (CI) from multivariable Cox regression regarding risk of SARS-CoV-2 (re)infection; group N (no immunity) excluded and group V (vaccinated) defined as reference group.

Model additionally includes time from preimmunization to serology (i.e., months since last infection or vaccination) compared to the main analysis.

(PDF)

S6 Table. Hazard ratios (HR) with 95% confidence intervals (CI) from multivariable Cox regression regarding risk of SARS-CoV-2 infection in the subgroup of those vaccinated but not infected (group V).

Model includes type of vaccine and timing of vaccinations between dose 1 and dose 2.

(PDF)

S7 Table. Rate ratio (RR) and 95% confidence intervals (CI) from multivariable Poisson regression regarding number of symptoms reported from SARS-CoV-2 infections during the Delta and Omicron period.

Model includes only infections not preceded by booster or first vaccination.

(PDF)

S8 Table. Rate ratio (RR) and 95% confidence intervals (CI) from multivariable Poisson regression regarding number of symptoms reported from SARS-CoV-2 infections during the Omicron period.

Model includes booster vaccine and is therefore restricted to groups V and H.

(PDF)

S1 Fig. Definition of four groups by immune status and outcomes.

Created with BioRender.com.

(TIFF)

S2 Fig. Flow sheet of participants in the SURPRISE study showing reasons (and respective number of participants) for exclusion from current analysis as well as participants within each immune status including number of subsequently vaccinated individuals, respectively.

(PDF)

S3 Fig. Time course of subsequent vaccinations (booster and new vaccinations).

N (no immunity): no reported infection and anti-N/-S negative and no previous SARS-CoV-2 vaccination; V (vaccinated): no reported infection and anti-N negative, but twice vaccinated; I (infected): infection reported or anti-N positive (at any time), but no vaccination; H (hybrid immunity): reported infection or anti-N positive (at any time) and vaccination (≥1 dose).

(TIFF)

Acknowledgments

The members of the SURPRISE study team are (in alphabetical order): Ulrike Besold, Angela Brucher, Thomas Egger, Andrée Friedl, Fabian Grässli, Sabine Güsewell, Eva Lemmenmeier, Christian R. Kahlert, Joelle Keller, Dorette Meier Kleeb, Philipp Kohler, Stefan P. Kuster, Onicio Leal, Dorette Meier Kleeb, Allison McGeer, J. Carsten Möller, Maja F. Müller, Vaxhid Musa, Manuela Ortner, Philip Rieder, Lorenz Risch, Markus Ruetti, Matthias Schlegel, Hans-Ruedi Schmid, Reto Stocker, Pietro Vernazza, Matthias von Kietzell, Danielle Vuichard-Gysin, and Benedikt Wiggli.

We would like to thank the employees of the participating healthcare institutions who either took part in this study themselves or supported it. Furthermore, we thank the laboratory staff for shipment, handling, and analysis of the blood samples.

Abbreviations

aHR

adjusted HR

anti-N

anti-nucleocapsid

anti-S

anti-spike

aRR

adjusted rate ratio

CI

confidence interval

COVID-19

Coronavirus Disease 2019

HCW

healthcare worker

HR

hazard ratio

NPS

nasopharyngeal swab

PCR

polymerase chain reaction

RAD

rapid antigen diagnostic

RR

risk ratio

SARS-CoV-2

Severe Acute Respiratory Syndrome Coronavirus 2

Data Availability

Data are available at https://dx.doi.org/10.5281/zenodo.7149075.

Funding Statement

This work was supported by the Swiss National Sciences Foundation (grant number 31CA30_196544 to PK and CRK; grant number PZ00P3_179919 to PK), the Federal Office of Public Health (grant number 20.008218/421-28/1), and the Health Department of the Canton of St. Gallen. Funders played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Callam Davidson

8 Jun 2022

Dear Dr Kahlert,

Thank you for submitting your manuscript entitled "Risk and symptoms of COVID-19 during the Delta and Omicron waves according to baseline immune status and booster vaccination – a prospective multicentre cohort of health professionals in Switzerland" for consideration by PLOS Medicine.

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PLOS Medicine

Decision Letter 1

Callam Davidson

9 Aug 2022

Dear Dr. Kahlert,

Thank you very much for submitting your manuscript "Risk and symptoms of COVID-19 during the Delta and Omicron waves according to baseline immune status and booster vaccination – a prospective multicentre cohort of health professionals in Switzerland" (PMEDICINE-D-22-01834R1) for consideration at PLOS Medicine.

Your paper was evaluated by an associate editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

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Requests from the editors:

In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology."

Your study is observational and therefore causality cannot be inferred. Please remove language that implies causality, such as "Hybrid immunity and booster vaccination reduced the risk and symptom number of

SARS-CoV-2 infection during Delta- and Omicron-dominant periods" (Abstract Conclusions). Refer to associations instead. Other examples include use of the term 'impact' in the final paragraph of the Introduction.

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

As noted by Reviewer #3, your Introduction does not adequately describe prior work performed in this area. Please expand your Introduction to reflect the literature more accurately.

Given that there are now multiple omicron subvariants in circulation, it may be useful to specify which omicron subvariant was predominant during the study period.

Consider including an additional figure to clarify the groups included in your study, as the text description can at times be difficult to follow and a figure would help the reader visualise the design.

Please include the completed STROBE checklist as Supporting Information. Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)."

The STROBE guideline can be found here: http://www.equator-network.org/reporting-guidelines/strobe/

When completing the checklist, please use section and paragraph numbers, rather than page numbers.

Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

Table S2 ought to be moved to the main text as it is central to the interpretation of the findings.

When reporting adjusted hazard ratios, please label these as such (aHR).

When reporting significant findings, please quantify with both 95% CI and p-values where available.

Table 1: Definitions for abbreviations do not require an accompanying flag in the table itself.

For the adjusted analyses presented in Table 1, please also present the crude analyses (unadjusted HRs) in the Supporting Information.

Line 245: Please update 'not anymore' to 'no longer'.

Line 251 and line 308: Please delete 'marginally' and include the p-value.

Throughout, please ensure p-values/HRs/95% CIs are reported to the same number of decimal places between the main text and the Tables/Figures.

In all Tables and Figures presenting adjusted analyses, please ensure you include the variables adjusted for in the respective legends.

Line 280 and line 314: Please temper the use of causal language here, ('we show' could be updated to 'our observational findings suggest', or similar).

Line 289: Typo in 124,500.

Line 312: Your study was not designed to assess true vaccine effectiveness, please remove this.

The above noted concerns regarding causal language are also applicable to your Conclusions paragraph.

Please remove details of funding, author contributions, and conflicts of interest from the Acknowledgements. This information is captured via the submission form and, in the event of acceptance, will be published as metadata.

Comments from the reviewers:

Reviewer #1: Alex McConnachie, Statistical Review

This review looks at the use of statistics in the paper by Babouee Flury et al. The paper looks at COVID-19 infection/reinfection in relation to past infection/vaccination status, in a cohort of health workers, during the Delta and Omicron waves in Switzerland. Survival analysis methods (Kaplan-Meier, Cox regression) are used.

In general, the statistical aspects of the paper are very good. I have only a few observations.

Booster vaccination is included in the models as a time-varying covariate, but individuals are censored when they are vaccinated for the first time. Could vaccination status not also be seen as a time-varying covariate in these models? Similarly, if (re)infected, could that be seen as a change of status? This would provide additional follow-up time for these individuals. I am not suggesting this as the primary analysis, but such a model could be used as a sensitivity analysis, unless there is a good reason not to do it.

The adjusted regression models are based on individuals with complete baseline data only. Would multiple imputation of this missing data have been a better approach?

The analysis is repeated after exclusion of the N group. I am not sure what this achieves. Simply changing the reference group for the analysis to the V group would have done much the same thing. Looking at the coefficients in the different models, my guess is the results would be largely the same.

As ever, when fitting Cox models, there is always the question of the proportional hazards assumption. How was this checked? Was the assumption acceptable?

The tables report HRs to two decimal places, but the main text only uses one. I guess this is an editorial decision, but I prefer two decimal places.

I spotted a typo on line 260 - the point estimate of the HR for having a comorbidity at baseline is not within the confidence interval.

The authors fall into the trap of using causal language when describing an observational study (e.g. line 183 - "effect"; line 194 - "impact"). The associations may well be causal, but care needs to be taken to not overstate the results.

Reviewer #2: The authors are addressing an important question on the role of immune status and risk of SARS-CoV-2 infection and related symptoms. I appreciate the authors approach of observing multiple groups of various immune status as it is very interesting and needed information.

The study design is large prospective cohort of health care professionals that were followed from September 2021 until the emergence of the Delta and Omicron variants, March 2022. This designed provides a valuable opportunity to assess infection as an outcome.

Title: Mentions severity of COVID-19 but unclear if severity of disease is reported. Please comment on any medically attended infection, hospitalization and deaths in this cohort. Please describe symptoms in the results section or supplement.

Below are a short list of data collection and definitions reported by the authors:

Weekly Questionnaires

Monthly follow up surveys

Self-reported exposures, NP swab results, symptoms associated with positive swabs, vaccinations

Periodic serology measurements

Group N = no infection, negative serology (anti N/S), no vaccination + first series data if applicable

Group V = no infection, negative serology (anti N), twice vaccinated + booster data if applicable

Group I = infection or anti N positive at any time, no vaccination + first series data if applicable

Group H = infection or anti N positive at any time, received greater than or equal to 1-dose + booster data if applicable

Exclusion criteria

Single vaccine dose + no previous infection

1st or 2nd dose between baseline serology and Sept 20th

Line-item questions:

Line 134: If no sequencing of variants were performed, how was a positive test attributed to a specific variant?

Line 134: Self-reported NPS results were verified by the authors. Was this the same case for rapid antigen test at institutions that used this type of test?

Line 142: Please clarify "twice vaccinated". Does this mean received 2-dose primary series within recommended timeframe? received 2 doses at anytime during the study period? etc.

Lines 148-149: Please clarify "single vaccine dose". It appears that Group H included at least those with 1-dose. Assuming single vaccine dose refers to vaccines that only have 1-dose but would like author's clarification.

Line 149: When was baseline serology collected? The authors mentioned periodic serology measurements earlier in the text. Please briefly include when the measurement times occurred. Due to several mentions of dates, it may be helpful to write out the full dates i.e. September 20th, 2021 instead of September 20th.

Lines 156-158: During variant overlap period, how were variant assignment decided for infections that occurred during circulations of both variants? I am concerned about that misclassification could impact the estimates. Please clarify how this was handled in the analysis. In Line 119, participants were recruited from Northern region as well. However, sequencing data was from Eastern region. Please include reasoning behind this method.

Lines 215-217: Any significant difference between groups V, I, and H?

Line 240: How were household infections data verified? Did all household contacts get tested if exposed or only if symptomatic? Please clarify in methods.

Figure 1: Great figure. Consider including Northern Switzerland since there were participants recruited from that region. Possibly describe dates for booster vaccination and follow-up in the description.

Figure 2: Great figure. Consider including adjustments made in the description.

Supplement: If applicable, please include description of testing practices, serology data collection, laboratory procedures, and questionnaires.

Reviewer #3: The goal of this paper is to estimate the protective effects of vaccination and prior infection against SARS-CoV-2 infection and symptoms, predominantly during the Omicron variant wave of infection. The authors state that "It remains currently unknown in how far natural and/or vaccine-induced immunity protect against symptomatic infection by the Omicron variant, and how this can be further mitigated by booster vaccinations" (Introduction line 101). However, in the discussion the authors go on to cite the following 5 papers, each of which report on exactly that:

1. Effects of Previous Infection and Vaccination on Symptomatic Omicron Infections - Altarawneh et al. NEMJ 2022

2. Duration of mRNA vaccine protection against SARS-CoV-2 Omicron BA.1 and BA.2 subvariants in Qatar Chemaitelly et al. Nature Communications 2022

3. Protection with a Third Dose of mRNA Vaccine against SARS-CoV-2 Variants in Frontline Workers - Yoon et al. NEMJ 2022

4. Association Between 3 Doses of mRNA COVID-19 Vaccine and Symptomatic Infection Caused by the SARS-CoV-2 Omicron and Delta Variants - Accorsi et al. JAMA 2022

5. Covid-19 Vaccine Effectiveness against the Omicron (B.1.1.529) Variant - Andrews et al. NEMJ 2022

There are also at least a further 4 papers that they don't cite, which add further evidence:

1. Observed protection against SARS-CoV-2 reinfection following a primary infection: A Danish cohort study among unvaccinated using two years of nationwide PCR-test data - Michlmayr et al. The Lancet Regional Health Europe 2022

2. SARS-CoV-2 Omicron Symptomatic Infections in Previously Infected or Vaccinated South African Healthcare Workers - Nunes et al. Vaccines 2022

3. Effectiveness of BNT162b2 Vaccine against Omicron in Children 5 to 11 Years of Age - Tan et al. NEMJ 2022

4. Effectiveness of a fourth dose of covid-19 mRNA vaccine against the omicron variant among long term care residents in Ontario, Canada: test negative design study - Grewal et al. BMJ 2022

Similarly, there are many papers assessing protection against Delta, which was dominant at the beginning of the study period in this paper.

Overall, there's a substantial body of existing evidence on the protection against SARS-CoV-2 Delta and Omicron variants associated with prior infection and vaccination. The authors need to acknowledge this and clearly situate their study within the context of the existing literature. As a relatively small occupational cohort, it isn't clear to me that this study makes a substantial contribution to the literature, so it doesn't seem like a good fit for PLoS Medicine.

Any attachments provided with reviews can be seen via the following link:

[LINK]

Decision Letter 2

Callam Davidson

3 Oct 2022

Dear Dr. Kahlert,

Thank you very much for re-submitting your manuscript "Risk and symptoms of COVID-19 during the Delta and Omicron waves according to baseline immune status and booster vaccination – a prospective multicentre cohort of health professionals in Switzerland" (PMEDICINE-D-22-01834R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by two reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

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If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.  

We look forward to receiving the revised manuscript by Oct 10 2022 11:59PM.   

Sincerely,

Callam Davidson,

Associate Editor 

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

Please ensure that the additional comments from Reviewer #1 are addressed.

Please update your title to “Risk and symptoms of COVID-19 in health professionals according to baseline immune status and booster vaccination during the Delta and Omicron waves in Switzerland: a multicentre cohort study”.

PLOS Medicine requires that the de-identified data underlying the specific results in a published article be made available, without restrictions on access, in a public repository or as Supporting Information at the time of article publication, provided it is legal and ethical to do so. Please see the policy at

http://journals.plos.org/plosmedicine/s/data-availability

and FAQs at

http://journals.plos.org/plosmedicine/s/data-availability#loc-faqs-for-data-policy

The Data Availability Statement (DAS) requires revision. For each data source used in your study:

If the data are freely available upon request, please note this and state the owner of the data set and contact information for data requests (web or email address). Note that a study author cannot be the contact person for the data.

As noted in your response to Reviewer #2, comment 3 (R2.3), please include the additional information in your Methods (around lines 181-182).

It was noted that author OL is affiliated with Epitrack – please confirm the involvement of Epitrack and provide comment on whether you feel this affiliation ought to be declared as a competing interest.

Please shorten the bullet point at lines 90-94 (single sentence bullets are preferred in the Author Summary).

Please include the number of participants in your Author Summary (this can be added to the bullet at lines 99-101).

Line 150: ‘…we aimed to determine...’

Line 161: Please include details of how informed consent was sought from participants and specify whether this was verbal or written.

Thank you for creating Figure S1 in response to my previous comment. As the figure was produced using BioRender, please confirm that the usage rights for this figure are compatible with PLOS Medicine’s Creative Commons Attribution (CC BY) license (which is applied to all figures we publish). The following may be useful: https://help.biorender.com/en/articles/5898558-where-can-i-use-my-illustrations

Lines 225 and 259: To ensure correct hyperlinking, please ensure references to Supporting Information are labelled as described here: https://journals.plos.org/plosmedicine/s/supporting-information (i.e., S1 Methods, S1 Checklist, etc.).

Please define the groups in the legend of Figure S2.

Table 2: Please move the reference to Table S2 from the Table 2 title to the legend (likewise for the corresponding reference in Table S2).

Lines 329 and 420: Given the observational design, please refer to associations rather than effects.

Please relocate the discussion of strengths at lines 330-333 to later in the Discussion (where strengths and limitations are discussed). As far as is possible, the Discussion should be organised as follows: a short, clear summary of the article's findings; what the study adds to existing research and where and why the results may differ from previous research; strengths and limitations of the study; implications and next steps for research, clinical practice, and/or public policy; one-paragraph conclusion.

Comments from Reviewers:

Reviewer #1: Alex McConnachie, Statistical Review

I thank the authors for their consideration of my original points, and I am generally happy with their responses.

However, whilst Table S3 shows that the model results from three imputed datasets are similar (as would be expected), they are not identical (or nearly identical, as the authors suggest). This table shows that there is some variation between imputations. By using a single imputed dataset, the analysis fails to take account of the added uncertainty in the results caused by the fact that some data are missing. By using multiple imputation, this uncertainty can be represented through the confidence intervals around the model parameters.

Three is a small number of imputed datasets. Personally I use ten, though others would argue for more. As far as I know, there is no penalty to doing extra imputations. The important thing is to pool the results from the models fitted to each dataset. This is built in to the mice package in R, so should be straightforward.

Reviewer #3: The authors have now addressed all my concerns from the first version of the manuscript. The revised version situates the study much more clearly in the existing literature - it is now clear what evidence already exists, and what this study adds.

Any attachments provided with reviews can be seen via the following link:

[LINK]

Decision Letter 3

Callam Davidson

14 Oct 2022

Dear Dr Kahlert, 

On behalf of my colleagues and the Academic Editor, Professor James Beeson, I am pleased to inform you that we have agreed to publish your manuscript "Risk and symptoms of COVID-19 in health professionals according to baseline immune status and booster vaccination during the Delta and Omicron waves in Switzerland – a multicentre cohort study" (PMEDICINE-D-22-01834R3) in PLOS Medicine.

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To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper. 

Sincerely, 

Philippa Dodd MBBS MRCP PhD

Associate Editor

PLOS Medicine

On behalf of - Callam Davidson 

Associate Editor 

PLOS Medicine

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 STROBE Checklist. STROBE checklist.

    (PDF)

    S1 Methods. “Missing value imputation for covariates in multivariable models” and “Verification of proportional-hazard assumption in Cox regression.”.

    (PDF)

    S1 Table. Covariable definitions, levels, and time points when variables were obtained.

    (PDF)

    S2 Table. Hazard ratios with 95% confidence intervals from separate univariable Cox regression models for each predictor involved in the multivariable models regarding COVID-19 risk by period (Table 1 in main text).

    (PDF)

    S3 Table. Sensitivity analyses: Comparison of hazard ratios obtained in Cox models with three independent missing value imputations, without missing value imputation (i.e., complete case analysis), and with exclusion of events occurring during the period of variant overlap between December 6, 2021 and January 3, 2022.

    (PDF)

    S4 Table. Model with time-dependent impact of booster in the Omicron-dominant period.

    (PDF)

    S5 Table. Adjusted hazard ratios (HR) with 95% confidence intervals (CI) from multivariable Cox regression regarding risk of SARS-CoV-2 (re)infection; group N (no immunity) excluded and group V (vaccinated) defined as reference group.

    Model additionally includes time from preimmunization to serology (i.e., months since last infection or vaccination) compared to the main analysis.

    (PDF)

    S6 Table. Hazard ratios (HR) with 95% confidence intervals (CI) from multivariable Cox regression regarding risk of SARS-CoV-2 infection in the subgroup of those vaccinated but not infected (group V).

    Model includes type of vaccine and timing of vaccinations between dose 1 and dose 2.

    (PDF)

    S7 Table. Rate ratio (RR) and 95% confidence intervals (CI) from multivariable Poisson regression regarding number of symptoms reported from SARS-CoV-2 infections during the Delta and Omicron period.

    Model includes only infections not preceded by booster or first vaccination.

    (PDF)

    S8 Table. Rate ratio (RR) and 95% confidence intervals (CI) from multivariable Poisson regression regarding number of symptoms reported from SARS-CoV-2 infections during the Omicron period.

    Model includes booster vaccine and is therefore restricted to groups V and H.

    (PDF)

    S1 Fig. Definition of four groups by immune status and outcomes.

    Created with BioRender.com.

    (TIFF)

    S2 Fig. Flow sheet of participants in the SURPRISE study showing reasons (and respective number of participants) for exclusion from current analysis as well as participants within each immune status including number of subsequently vaccinated individuals, respectively.

    (PDF)

    S3 Fig. Time course of subsequent vaccinations (booster and new vaccinations).

    N (no immunity): no reported infection and anti-N/-S negative and no previous SARS-CoV-2 vaccination; V (vaccinated): no reported infection and anti-N negative, but twice vaccinated; I (infected): infection reported or anti-N positive (at any time), but no vaccination; H (hybrid immunity): reported infection or anti-N positive (at any time) and vaccination (≥1 dose).

    (TIFF)

    Attachment

    Submitted filename: PMEDICINE-D-22-01834R2_RevisionNotes.pdf

    Attachment

    Submitted filename: PMEDICINE-D-22-01834R3_RevisionNotes.pdf

    Data Availability Statement

    Data are available at https://dx.doi.org/10.5281/zenodo.7149075.


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