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Article

SARS-CoV-2 Breakthrough Infections: Incidence and Risk Factors in a Large European Multicentric Cohort of Health Workers

by
Stefano Porru
1,2,*,
Maria Grazia Lourdes Monaco
2,
Gianluca Spiteri
2,
Angela Carta
1,2,
Maria Diletta Pezzani
3,
Giuseppe Lippi
4,
Davide Gibellini
5,
Evelina Tacconelli
3,6,
Ilaria Dalla Vecchia
6,
Emma Sala
7,
Emanuele Sansone
8,
Giuseppe De Palma
7,8,
Carlo Bonfanti
9,
Massimo Lombardo
10,
Luigina Terlenghi
9,
Enrico Pira
11,12,
Ihab Mansour
11,
Maurizio Coggiola
12,
Catalina Ciocan
11,12,
Alessandro Godono
11,
Adonina Tardon
13,
Marta-Maria Rodriguez-Suarez
13,
Guillermo Fernandez-Tardon
13,
Francisco-Jose Jimeno-Demuth
13,
Rafael-Vicente Castro-Delgado
13,
Tania Iglesias Cabo
13,
Maria Luisa Scapellato
14,15,
Filippo Liviero
14,15,
Angelo Moretto
14,15,
Paola Mason
14,15,
Sofia Pavanello
14,15,
Anna Volpin
15,
Luigi Vimercati
16,
Silvio Tafuri
16,
Luigi De Maria
16,
Stefania Sponselli
16,
Pasquale Stefanizzi
16,
Antonio Caputi
16,
Fabriziomaria Gobba
17,
Alberto Modenese
17,
Loretta Casolari
18,
Denise Garavini
18,
Cristiana D’Elia
18,
Stefania Mariani
18,
Francesca Larese Filon
19,
Luca Cegolon
19,
Corrado Negro
19,
Federico Ronchese
19,
Francesca Rui
19,
Paola De Michieli
19,
Nicola Murgia
20,
Marco Dell’Omo
20,
Giacomo Muzi
20,
Tiziana Fiordi
20,
Angela Gambelunghe
20,
Ilenia Folletti
20,
Dana Mates
21,
Violeta Claudia Calota
21,
Andra Neamtu
21,
Ovidiu Perseca
21,
Catalin Alexandru Staicu
21,
Angelica Voinoiu
21,
Eleonóra Fabiánová
22,
Jana Bérešová
23,
Zora Kľocová Adamčáková
24,
Roman Nedela
25,
Anna Lesňáková
26,
Jana Holčíková
27,
Paolo Boffetta
28,29,
Mahsa Abedini
28,
Giorgia Ditano
28,
Shuffield Seyram Asafo
28,
Giovanni Visci
28,
Francesco Saverio Violante
28,30,
Carlotta Zunarelli
28 and
Giuseppe Verlato
31add Show full author list remove Hide full author list
1
Section of Occupational Medicine, Department of Diagnostics and Public Health, University of Verona, 37134 Verona, Italy
2
Occupational Medicine Unit, University Hospital of Verona, 37134 Verona, Italy
3
Infectious Diseases Unit, University Hospital of Verona, 37134 Verona, Italy
4
Section of Clinical Biochemistry, Department of Diagnostics and Public Health, University of Verona, 37134 Verona, Italy
5
Section of Microbiology, Department of Diagnostics and Public Health, University of Verona, 37134 Verona, Italy
6
Section of Infectious Diseases, Department of Diagnostics and Public Health, University of Verona, 37134 Verona, Italy
7
Unit of Occupational Health, Hygiene, Toxicology and Prevention, University Hospital ASST Spedali Civili, 25121 Brescia, Italy
8
Unit of Occupational Health and Industrial Hygiene, Department of Medical Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25121 Brescia, Italy
9
Department of Molecular and Translational Medicine, Institute of Microbiology, University of Brescia-ASST Spedali Civili, 25121 Brescia, Italy
10
Chief Executive Office, ASST Spedali Civili di Brescia, 25121 Brescia, Italy
11
Department of Public Health and Pediatrics, University of Turin, 10126 Turin, Italy
12
Occupational Medicine Unit, University Hospital Città Della Salute e Della Scienza di Torino, 10126 Turin, Italy
13
Health Research Institute of Asturias (ISPA), CIBERESP and Public Health Department of the University of Oviedo, Campus del Cristo s/n, 33006 Oviedo, Spain
14
Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, 35128 Padova, Italy
15
University Hospital of Padova, 35128 Padova, Italy
16
Interdisciplinary Department of Medicine, University of Bari, 70124 Bari, Italy
17
Department of Biomedical, Metabolic and Neural Sciences, University of Modena & Reggio Emilia, 41125 Modena, Italy
18
Health Surveillance Service, University Hospital of Modena, 41125 Modena, Italy
19
Unit of Occupational Medicine, Department of Medical Science, University of Trieste, 34149 Trieste, Italy
20
Section of Occupational Medicine, Respiratory Diseases and Toxicology, Department of Medicine and Surgery, University of Perugia, 06123 Perugia, Italy
21
National Institute of Public Health, 050463 Bucharest, Romania
22
Occupational Health Department, Regional Authority of Public Health, 97556 Banská Bystrica, Slovakia
23
Epidemiology Health Department, Regional Authority of Public Health, 97556 Banská Bystrica, Slovakia
24
Health Promotion Department, Regional Authority of Public Health, 97556 Banská Bystrica, Slovakia
25
Health Informatics Department, Regional Authority of Public Health, 97556 Banská Bystrica, Slovakia
26
Infectology Clinic, Central Military Hospital, 03426 Ružomberok, Slovakia
27
Occupational Medicine Clinic, University Hospital, 83105 Bratislava, Slovakia
28
Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
29
Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY 11794, USA
30
IRCCS, Azienda Ospedaliero Universitaria di Bologna, 40138 Bologna, Italy
31
Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, 37134 Verona, Italy
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Author to whom correspondence should be addressed.
Vaccines 2022, 10(8), 1193; https://doi.org/10.3390/vaccines10081193
Submission received: 1 July 2022 / Revised: 22 July 2022 / Accepted: 24 July 2022 / Published: 27 July 2022
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Abstract

:
Background: The research aimed to investigate the incidence of SARS-CoV-2 breakthrough infections and their determinants in a large European cohort of more than 60,000 health workers. Methods: A multicentric retrospective cohort study, involving 12 European centers, was carried out within the ORCHESTRA project, collecting data up to 18 November 2021 on fully vaccinated health workers. The cumulative incidence of SARS-CoV-2 breakthrough infections was investigated with its association with occupational and social–demographic characteristics (age, sex, job title, previous SARS-CoV-2 infection, antibody titer levels, and time from the vaccination course completion). Results: Among 64,172 health workers from 12 European health centers, 797 breakthrough infections were observed (cumulative incidence of 1.2%). The primary analysis using individual data on 8 out of 12 centers showed that age and previous infection significantly modified breakthrough infection rates. In the meta-analysis of aggregated data from all centers, previous SARS-CoV-2 infection and the standardized antibody titer were inversely related to the risk of breakthrough infection (p = 0.008 and p = 0.007, respectively). Conclusion: The inverse correlation of antibody titer with the risk of breakthrough infection supports the evidence that vaccination plays a primary role in infection prevention, especially in health workers. Cellular immunity, previous clinical conditions, and vaccination timing should be further investigated.

1. Introduction

Since its first identification [1], Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) infections have caused about 15 million deaths attributable to Coronavirus disease (COVID-19) worldwide [2]. The rapid development of multiple successful vaccines strongly impacted the clinical burden, preventing the evolution of the severe symptomatic disease and, consequently, mortality [3,4,5].
Vaccines also reduced the transmission rates of SARS-CoV-2, particularly in the first 4–6 months after the vaccination, due to a more rapid decline in viral load and decreased viability of the virus shed by vaccinated individuals and being less likely to be culture-positive [6].
However, the continuous emergence of new viral variants with different characteristics perpetuates viral transmission and threatens the vaccine’s efficacy. So far, five variants responsible for new resurgence waves have been isolated [7]. The Omicron variant is the most recent designated variant of concern (VOC) due to its remarkable ability to escape vaccine and infection-induced immunity and its resistance to therapeutic antibodies [8].
However, as early a few months after the SARS-CoV-2 outbreak, a critical issue emerged related to the fact that the virus continuously evolves, and changes in the genetic code (caused by genetic mutations or viral recombination) occur during genome replication, so the concept of SARS-CoV-2 variants was introduced [7,8].
Therefore, since late 2020, the appearance of variants that posed an increased risk to global public health prompted the characterization of specific Variants of Interest (VOIs) and Variants of Concern (VOCs) to prioritize global monitoring and research and ultimately inform the ongoing response to the COVID-19 pandemic [9].
To date, five variants responsible for new resurgence waves have been isolated: Alpha B.1.1.7; Beta B.1.351; Gamma P.1; Delta B.1.617.2; and the Omicron variant B.1.1.529, the most recent designated variant of concern (VOC) due to its remarkable ability to escape vaccine and infection-induced immunity and its resistance to therapeutic antibodies [10,11].
The incidence of breakthrough infections (BI), defined as infections occurring in fully vaccinated people, has been increasingly reported, even though it is associated with asymptomatic and mild diseases [12,13,14]. Reasons for the occurrence of BI include a combination of different factors, such as an ageing population, high-risk occupation, level of population immunity, partial viral escape, drop-in antibody activity over time, and uncertain duration of immune memory.
Early reports provided evidence of the decline in antibody titers a few months after completing the two-dose COVID-19 vaccine regimen [15,16,17]. Preliminary laboratory findings on the distribution of S-antibody levels at subsequent time points after vaccination showed a decrease for both BNT162b2 (Pfizer–BioNTech) and ChAdOx1 nCoV-19 (Oxford–AstraZeneca), a trend confirmed for different subgroup stratification [18]. Likewise, neutralizing antibody titers following SARS-CoV-2 infection drop over time [19]. A study involving all fully vaccinated Israeli adult population documented an increased infection rate with increasing time since vaccination, with a 1.6-to-2.1-fold higher rate of infection associated with a two-month increase in the time difference [20]. The growing evidence of the waning of vaccine-induced protection and natural immunity after 3–6 months has reinforced the need for a third dose, which has been shown to restore levels of neutralizing antibodies [21]. An observational study compared health workers (HWs) who received the two-dose regimen at least six months earlier with HWs who received a booster dose. A significantly lower BI rate in boosted individuals showed strong protection against SARS-CoV-2 infection conferred by the third dose [22]. However, the determinants of vaccine response are still unclear. So far, no striking variables (such as age, sex, or comorbidities) were identified as significantly associated with higher IgG titers [16].
Notably, the third dose increases neutralizing titers against VOCs, including the Omicron variant, even at a lower level [23,24,25].
Less is known about the B cell response in vaccinated individuals, but some studies have shown that vaccines induce the production of memory B cells, whose increase remains durable even at 6–9 months post-vaccination [26].
Evaluating the incidence of BI and the long-term efficacy of vaccines is crucial to drive public health decisions regarding timing of vaccination, screening, health surveillance, restriction measures, and access to the workplace [27].
In most European countries, HWs undergo constant surveillance due to their risk of exposure to SARS-CoV2 and relevance for Public Health; therefore, they represent an important population to evaluate current strategies’ effectiveness and delineate future interventions.
The ORCHESTRA project is a three-year international research project, building up different European cohorts by applying harmonized protocols for data collection, data sharing, sampling, and follow-up. The Work Package 5 of ORCHESTRA focuses on HWs to define the incidence and prevalence of SARS-CoV-2 infection, the duration of immunity induced by natural infection and vaccination by collecting biological samples at fixed time-points, and risk of reinfection and breakthrough infections.
To date, a number of single-center studies investigating the role of significant determinants on SARS-CoV-2 infections in HWs, from medium-sized samples, have inconsistent results on the possible role of age, sex, and job title [28].
This research aimed to investigate the incidence of SARS-CoV-2 breakthrough infections and their determinants in a European HWs cohort.

2. Materials and Methods

2.1. Design and Setting

A multicenter retrospective cohort study of HWs (including workers dedicated to clinical activities, as well as technicians and administrative workers) from 12 European centers involved in the ORCHESTRA project was performed. Data were collected from the following healthcare settings, mainly University Hospital places: Italy (Bari, Bologna, Brescia, Modena, Padova Perugia, Torino, Trieste, and Verona), Romania, Slovakia, and Spain (Oviedo). This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines [29].

2.2. Vaccination, Case Definition, and Inclusion Criteria

HWs enrolled in a continuous follow-up since the beginning of pandemic were included. The analysis was conducted on HWs that completed the vaccination course (fully vaccinated), according to the specified vaccine timing, since the 14th day after fully vaccination from February 2021 to November 2021. HWs not fully vaccinated, and those infected after the third dose, were excluded.
A total of 96.26% (N = 61,771) of enrolled HWs had received BNT162b2 (Pfizer–BioNTech), while 3.39% (N = 2176) recieved mRNA-1273 (Moderna), 0.28% (N = 182) ChAdOx1 nCoV-19 (Oxford–AstraZeneca), and 0.07% (N = 43) Ad26.COV2.S (Johnson).
Vaccine BI was defined as PCR-confirmed COVID-19 ≥14 days following the course completion of the four vaccine doses [30].

2.3. Outcome and Data Collection

The incidence of SARS-CoV-2 BI and their characteristics (sex, age, job-title, previous infections, SARS-CoV-2 antibody titer) were investigated.
Clinical and laboratory data were collected using a standardized data collection form.
SARS-CoV-2 infection was diagnosed by positive real-time reverse-transcriptase polymerase chain reaction (RT-PCR), performed using various commercially available assays in different clinical laboratories. Each center adopted a different and variable timing for screening programs through nasopharyngeal swabs, according to epidemiological contexts and local regulations.
S-specific IgG antibodies against SARS-CoV-2 titration were measured within 21 days and three months after the first vaccine dose. In order to compare the results provided by the laboratories of the included centers, a normalization approach was applied. Antibody levels were log-transformed to take into account the skewness of the distribution, and log-transformed results were normalized by dividing them by the center-specific standard error.

2.4. Statistical Analysis

The primary statistical analysis utilized individual data from a subgroup of centers and consisted of survival analysis, where BI was the terminal event, and time elapsed since the second dose was the time variable. A univariable survival analysis was accomplished by Kaplan–Meier survival curves and a log-rank test and a multivariable survival analysis by centre-stratified Cox regression models, where sex, age, job-title, and infection pre-vaccination against SARS-CoV-2 were the explanatory variables. Proportional hazard assumptions of the Cox model were tested based on Schoenfeld residuals. In addition, the proportionality assumption was checked by graphic methods: it was verified whether -ln[-ln[survival]] curves for each category of risk factors were parallel when plotted vs ln [analysis time]. Moreover, a multinomial regression model compared the risk of SARS-CoV-2 infection before and after vaccination. The response variable was 0 = no infection/1 = infection before vaccination/2 = infection after vaccination; predictors were sex, age, and job title. Results were synthesized through the relative risk ratios (RRRs), adjusting standard errors for intra-center correlation. The significance of the association between timing of vaccination course completion and cumulative incidence of breakthrough infection in consecutive periods was evaluated by a chi-square test.
A secondary statistical analysis was performed on aggregate data from all of the 12 participating centers and was accomplished by meta-analyses. A test of heterogeneity was applied to evaluate variability among studies. The I-squared statistic was computed, which indicates the proportion of total variation among the effect estimates of different studies attributed to heterogeneity rather than sampling error. When the heterogeneity test was not significant (p > 0.050) and I-squared 2 was less than 30% [31,32], heterogeneity was ruled out. In this case, a fixed-effects model was adopted for the evaluation of the results, which were pooled using the method of Mantel and Haenszel. Otherwise, a random-effects model was used, and the pooling of results was done using the DerSimonian and Laird method [33]. The level of statistical significance was set at 5%, and confidence intervals (CI) were calculated at 95%. The results were displayed graphically using forest plots. Stata® software 15 (Stata Corp LP, College Station, TX, USA) was used for statistical analysis.

2.5. Ethical Approval

The research was performed following the 1964 Declaration of Helsinki standards and its later amendments. The study was approved (No.436 14 October 2021) by the Italian Medicine Agency (AIFA) and the Ethics Committee of the Italian National Institute of Infectious Diseases (INMI) Lazzaro Spallanzani. All the Cohorts collected loco-regional ethical approvals.

3. Results

During the study period, 64,172 HWs were enrolled, mainly provided by the Italian cohorts (75%). Most HWs were female (70.5 %), and almost half were over 40 years. Nurses (35.9%) and physicians (29.6%) were the most represented job title. A total of 797 BIs were detected, corresponding to an overall cumulative incidence of 1.2% (Table 1).

3.1. Primary Analysis on Individual Data

The primary analysis was conducted using data of 31,837 HWs belonging to eight centers.
The temporal trends of BI are shown in Supplementary Figure S1. The trend was similar in Italian centers, with a slightly higher cumulative incidence in Modena and a lower incidence in Bari. Cumulative incidence rose in the first 12 weeks after the full vaccination course, remained stable in the subsequent 12 weeks, and rose again thereafter. In Romania and Slovakia, the late increase was not observed.
Results of the main risk factors of BI from the Cox-regression analysis are displayed in Table 2. Age and previous infection were the only two significant determinants (p < 0.001). The risk of BI decreased by about 20% for an additional decade of age and was 4–5-times lower in people with pre-vaccination SARS-CoV-2 infection.
Figure 1 presents changes in the risk profile for SARS-CoV-2 infections before and after a full vaccination course. It can be appreciated that the association of age and job title with SARS-CoV-2 infection completely changed before and after vaccination. In detail, nurses and other HWs were at a higher risk of SARS-CoV-2 infection than administrative employees before the full vaccination course but not thereafter. Conversely, age was not associated with SARS-CoV-2 infection before vaccination, while the risk of SARS-CoV-2 infection was inversely related to age thereafter.
The BI cumulative incidence in three consecutive periods (May/June, July/August, September/October 2021) was calculated in 24,358 HWs from six Italian centers. The median time lag between second dose administration and BI was 136.5 days (p25–p75 = 60–201 days). Considering three different time frames, the observed cumulative incidence of BIs for HWs completing the vaccination course during January, February, and March ranged between 19 and 50, 12 and 31, and 12 and 63 per 10,000 HWs, respectively (p = 0.163) (Supplementary Figure S2).

3.2. Secondary Analyses on Aggregate Data

Table 3 shows the meta-analysis results on aggregate data from all 12 centers. Sex, age, and job title had no statistically significant effect, while previous SARS-CoV-2 infection and standardized antibody titer were inversely related to the risk of breakthrough infection (Table 3; Figure 2).

4. Discussion

In the HWs ORCHESTRA cohorts, the cumulative incidence of BIs, from February 2021 to November 2021, was 1.2%, with differences between centers that could be explained by different epidemiological infection patterns in distinct regions. In addition, each center had its timing for nasopharyngeal screening, based on epidemiological contexts and local regulations. Consequently, it was likely that centers adopting a screening approach with frequent testing and fast turnaround detected a higher number of asymptomatic infections, in line with what has been reported in the literature [34].
We aimed to investigate the relationship between the occurrence of BIs among HWs and its association with occupational and socio-demographic characteristics and to provide a comparison with their role in pre-vaccine infections.
In the pre-vaccination phase, the risk of SARS-CoV-2 infection was not affected by either sex or age, whereas risk differences were detected within job categories. In detail, using administrative health personnel as a reference, nurses and other HWs were at greater risk, with statistically significant differences, as were doctors, although without statistical significance. Our results agree with the study of Al Youha S et al. [35], conducted during the second wave in the pre-vaccine era, showing that among HWs, the odds of contracting SARS-CoV-2 infection were the highest among nurses (adjusted OR 1.77, 95% CI 1.15−2.71) with doctors as a reference. No significant differences were found within sex. As reported in the meta-analysis by Gomez-Ochoa SA et al. [36] the high number of positive nurses for SARS-CoV-2 could be explained by their major involvement in patient care.
The comparison with the literature must also consider the pandemic trend and the different waves that occurred worldwide. Regarding SARS-CoV-2 incidence during the first wave, in a previous Italian monocentric study, partially overlapped with the present population, Porru et al. [37] reported that the risk of SARS-CoV-2 detection was not significantly affected by sex, age, or type of job while, from an analysis in the same period in an HWs cohort from six Italian centers, partially overlapping with the present population, Boffetta et al. [38] reported no differences in the risk of infection according to job title and age, while females were at a lower risk of infection than males. According to this evidence, in a rapid review updating previous papers, Chou R et al. [39] reported no association between sex or job title and risk for SARS-CoV-2 infection.
In the present study, the determinants of SARS-CoV-2 infection in the post-vaccinal phase were analyzed on both individual (eight centers) and aggregated (12 centers) data.
The analysis of individual data showed that age (per 10-year increase) and previous infection were inversely associated with the incidence of BI. In contrast, in an aggregated data analysis, age had no significant statistical effect. Previous SARS-CoV-2 infection and standardized antibody titer were inversely related to the risk of breakthrough infection.
The currently available literature provides conflicting views on the role of occupational and socio-demographic characteristics. Some studies have shown an association between age and increased risk, while others have not found any association [28]. It should be noted that the age effect could be appraised through other determinants, including comorbidities and the infection severity. Increased social contacts and assignment might influence increased risk in younger people to higher risk wards [40]. In addition, a healthy worker effect could also occur in a population of HWs.
Regarding sex and job title, our study is in line with the body of the available literature. There is no association between risk for SARS-CoV-2 infection in HWs and sex or job-title. Concerning the Job-title, the difference between pre-vaccination and breakthrough risk of infection may be due to the change in the type of exposure during the different waves of the pandemic, with a preponderance of household or private setting exposure compared to occupational exposure [28]. Moreover, during the first pandemic wave, global shortages of masks, respirators, face shields, and gowns, caused by surging demand and supply chain disruptions, led to efforts to conserve personal protective equipment through extended use or reuse, with a subsequent increased risk of COVID-19 [41]. The later implementation of safety procedures and the increased availability of protective equipment may have influenced the evolution of the risk scenario.
In this research, SARS-CoV-2 titration was a protective factor for breakthrough infection.
According to available literature, there is a persistence of protection lasting at least six months after the second dose, despite a waning of antibody titer from the vaccine second dose, for both BNT162b2 and ChAdOx1 nCoV-19 [15,20].
Furthermore, in our sample, with a follow-up 7–9 months after the full vaccination, we did not observe a significant variation in the incidence of breakthroughs for HWs vaccinated at different times, while Mizrahi et al. [18] did.
A SARS-CoV-2 previous infection elicits a protective antibody response that is reinforced by vaccination, in line with other research available. Wei et al. found that antibody levels among previously infected individuals were higher than those in non-previously infected individuals. The boosting effect of the second dose was less as titers were very high already after the first dose. Hall et al. [42] also showed that two doses of the BNT162b2 vaccine were associated with increased short-term protection against SARS-CoV-2 infection; this protection waned considerably after six months, and infection-acquired immunity boosted with vaccination remained high more than one year after infection.

Strengths and Limitations

Major strengths of the paper are the sample size, the largest (to the best of our knowledge) in the available published literature on HWs; the multicentric recruitment; and the significant contribution from countries of the Mediterranean area, Italy and Spain, and Romania and Slovakia, strongly affected by the pandemics.
These aspects lend power to the study and provide good stability of results and generalizability. This is coupled with a solid research methodology, also supported by the laboratory data from certified laboratory centers. In addition, the normalization made it possible to make the data comparable despite the different titration techniques.
In addition, the time frame of observation was wide and enabled the evaluation of the entire time available before the third dose.
It is also an ongoing study that will be followed by the papers on the BI after the third dose.
The study also has some limitations to be disclosed. The analysis of individual data was only conducted on about half of the population (30,000 HWs), albeit a large one, for which individual data were available. Furthermore, due to the unavailability of data, it was impossible so far to assess the BIs’ clinical characteristics, to distinguish between symptomatic and asymptomatic cases and the source case, and to distinguish between occupational and household exposure. Information on clinical determinants, such as previous clinical conditions, of HWs was also missing. These will be addressed in the ongoing follow-up via ad hoc online questionnaires.

5. Conclusions

This study confirmed that SARS-CoV-2 infections occur even after a full vaccination course. The risk of infection is not influenced by age, sex, and job title, while a higher SARS-CoV-2 antibody titer and a previous positive molecular test result in certain protections. Interestingly, regarding job title, its effect is variable between pre- and post-vaccine administration, with the effect rising in post. This finding will be further investigated together with the trend of BI after the third dose.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/vaccines10081193/s1. Figure S1: Cumulative incidence of breakthrough infection in the eight centers providing individual data, estimated by the Kaplan–Meier method; Figure S2: Cumulative incidence of breakthrough infections in three different consecutive periods (May/June, July/August, September/October 2021).

Author Contributions

Conceptualization, S.P. (Stefano Porru), M.G.L.M., G.S., P.B. and G.V. (Giuseppe Verlato); Data curation, S.P. (Stefano Porru), M.G.L.M., G.S., A.C. (Angela Carta), E.S. (Emma Sala), E.S. (Emanuele Sansone), E.P., I.M., A.T., M.-M.R.-S., M.L.S., F.L., L.V., S.T., F.G., A.M. (Alberto Modenese), F.L.F., L.C. (Luca Cegolon), N.M., M.D., D.M., V.C.C., E.F., J.B., P.B., M.A., G.D., S.S.A., G.V. (Giovanni Visci) and G.V. (Giuseppe Verlato); Formal analysis, G.S., M.A., G.D., S.S.A., G.V. (Giovanni Visci) and G.V. (Giuseppe Verlato); Funding acquisition, S.P. (Stefano Porru) and P.B.; Investigation, S.P. (Stefano Porru), M.G.L.M., G.S., A.C. (Angela Carta), M.D.P., G.L., D.G. (Davide Gibellini), E.T., I.D.V., E.S. (Emma Sala), E.S. (Emanuele Sansone), G.D.P., C.B., M.L., L.T., E.P., I.M., M.C., C.C., A.G. (Alessandro Godono), A.T., M.-M.R.-S., G.F.-T., F.-J.J.-D., R.-V.C.-D., T.I.C., M.L.S., F.L., A.M. (Angelo Moretto), P.M., S.P. (Sofia Pavanello), A.V. (Anna Volpin), L.V., S.T., L.D.M., S.S., P.S., A.C. (Antonio Caputi), F.G., A.M. (Alberto Modenese), L.C. (Loretta Casolari), D.G. (Denise Garavini), C.D., S.M., F.L.F., L.C. (Luca Cegolon), C.N., F.R. (Federico Ronchese), F.R. (Francesca Rui), P.D.M., N.M., M.D., G.M., T.F., A.G. (Angela Gambelunghe), I.F., D.M., V.C.C., A.N., O.P., C.A.S., A.V. (Angelica Voinoiu), E.F., J.B., Z.K.A., R.N., A.L., J.H., P.B., M.A., G.D., S.S.A., G.V. (Giovanni Visci), F.S.V. and C.Z.; Methodology, S.P. (Stefano Porru), M.G.L.M. and G.S.; Project administration, S.P. (Stefano Porru) and P.B.; Supervision, S.P. (Stefano Porru) and P.B.; Writing—original draft, M.G.L.M., G.S., M.D.P., I.D.V., and G.V. (Giuseppe Verlato); Writing—review & editing, S.P. (Stefano Porru), M.G.L.M., G.S., A.C. (Angela Carta), M.D.P., G.L., D.G. (Davide Gibellini), E.T., I.D.V., E.S. (Emma Sala), E.S. (Emanuele Sansone), G.D.P., C.B., M.L., L.T., E.P., I.M., M.C., C.C., A.G. (Alessandro Godono), A.T., M.-M.R.-S., G.F.-T., F.-J.J.-D., R.-V.C.-D., T.I.C., M.L.S., F.L., A.M. (Angelo Moretto), P.M., S.P. (Sofia Pavanello), A.V. (Anna Volpin), L.V., S.T., L.D.M., S.S., P.S., A.C. (Antonio Caputi), F.G., A.M. (Alberto Modenese), L.C. (Loretta Casolari), D.G. (Denise Garavini), C.D., S.M., F.L.F., L.C. (Luca Cegolon), C.N., F.R. (Federico Ronchese), F.R. (Francesca Rui), P.D.M., N.M., M.D., G.M., T.F., A.G. (Angela Gambelunghe), I.F., D.M., V.C.C., A.N., O.P., C.A.S., A.V. (Angelica Voinoiu), E.F., J.B., Z.K.A., R.N., A.L., J.H., P.B., M.A., G.D., S.S.A., G.V. (Giovanni Visci), F.S.V., C.Z. and G.V. (Giuseppe Verlato). All authors have read and agreed to the published version of the manuscript.

Funding

The ORCHESTRA project is funded by the European Commission, Horizon 2020 Program, Grant Agreement No. 101016167. The Verona and Oviedo cohorts were also supported by the Regional Health Authority (Azienda Zero), Veneto Region, Italy, and by the Health Research Institute of Asturias (ISPA), Spain, respectively. The funding sources had no role in the writing of the manuscript or the decision to submit it for publication. No author has been paid to write this article by a pharmaceutical company or other agency.

Institutional Review Board Statement

The study was conducted following the Declaration of Helsinki and approved (No.436 14 October 2021) by the Italian Medicine Agency (AIFA) and the Ethics Committee of the Italian National Institute of Infectious Diseases (INMI) Lazzaro Spallanzani. All the Cohorts collected loco-regional ethical approvals.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets generated during the current study are not publicly available, because they contain sensitive data to be treated under data protection laws and regulations. Appropriate forms of data sharing can be arranged after a reasonable request to the first author.

Acknowledgments

This project received funding from the EU Horizon 2020 research and innovation program under the ORCHESTRA project, Grant Agreement No. 101016167. The cohort from Verona is funded by the Regional Health Authority (Azienda Zero), Veneto Region, Italy. The Verona group thank the General Management, Medical Management, and all personnel of the Units of Occupational Health, Laboratory Medicine and Microbiology and of University Hospital of Verona and all personnel of the Unit of Epidemiology and Medical Statistics, University of Verona, for their constant support and generous contributions. The Oviedo Cohort is funded by Health Research Institute of Asturias (ISPA), the Epidemiology and Public Health Ciber (CIBERESP), and the University of Oviedo, Asturias, Spain. Acknowledgements for the contribution to the work on the Project Orchestra in Slovakia go to Alena Koščálová, Infectology Clinic, University Hospital, Bratislava, 83105, Slovak Republic; Oto Osina, Occupational Medicine Clinic, University Hospital, Martin, 03659, Slovakia; Zuzana Sirotná, Laboratory Dpt., Public Health Authority of the Slovak Republic, Bratislava, 826 45, Slovakia; Jarmila Beláková and co-workers, Occupational health Department, Regional Authority of Public Health, Banská Bystrica, 97556, Slovak Republic; Mariana Mrázová, Public health Institute, St. University of Health and Social Work, 81106 Bratislava, Slovakia; and Alexandra Bražinová, Institute of Epidemiology, Faculty of Medicine Comenius University, Bratislava, 81372, Slovakia.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhu, N.; Zhang, D.; Wang, W.; Li, X.; Yang, B.; Song, J.; Zhao, X.; Huang, B.; Shi, W.; Lu, R.; et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N. Engl. J. Med. 2020, 382, 727–733. [Google Scholar] [CrossRef]
  2. Global Excess Deaths Associated with COVID-19 (Modelled Estimates). Available online: https://www.who.int/data/sets/global-excess-deaths-associated-with-COVID-19-modelled-estimates (accessed on 9 May 2022).
  3. Baden, L.R.; El Sahly, H.M.; Essink, B.; Kotloff, K.; Frey, S.; Novak, R.; Diemert, D.; Spector, S.A.; Rouphael, N.; Creech, C.B.; et al. Efficacy and Safety of the MRNA-1273 SARS-CoV-2 Vaccine. N. Engl. J. Med. 2021, 384, 403–416. [Google Scholar] [CrossRef]
  4. Sadoff, J.; Gray, G.; Vandebosch, A.; Cárdenas, V.; Shukarev, G.; Grinsztejn, B.; Goepfert, P.A.A.; Truyers, C.; Fennema, H.; Spiessens, B.; et al. Safety and Efficacy of Single-Dose Ad26.COV2.S Vaccine against COVID-19. N. Engl. J. Med. 2021, 384, 2187–2201. [Google Scholar] [CrossRef]
  5. Angel, Y.; Spitzer, A.; Henig, O.; Saiag, E.; Sprecher, E.; Padova, H.; Ben-Ami, R. Association Between Vaccination with BNT162b2 and Incidence of Symptomatic and Asymptomatic SARS-CoV-2 Infections Among Health Care Workers. JAMA 2021, 325, 2457. [Google Scholar] [CrossRef] [PubMed]
  6. Klompas, M. Understanding Breakthrough Infections Following MRNA SARS-CoV-2 Vaccination. JAMA 2021, 326, 2018. [Google Scholar] [CrossRef]
  7. CDC. Coronavirus Disease 2019 (COVID-19) [Internet]. Centers for Disease Control and Prevention. 2020. Available online: https://www.cdc.gov/coronavirus/2019-ncov/variants/variant-classifications.html (accessed on 20 July 2022).
  8. Lippi, G.; Mattiuzzi, C.; Henry, B.M. Updated Picture of SARS-CoV-2 Variants and Mutations. Diagnosis 2021, 9, 11–17, Tracking SARS-CoV-2 Variants [Internet]. Available online: https://www.who.int/activities/tracking-SARS-CoV-2-variants) (accessed on 20 July 2022). [CrossRef] [PubMed]
  9. WHO Tracking SARS-CoV-2 Variants. Available online: https://www.who.int/health-topics/typhoid/tracking-SARS-CoV-2-variants (accessed on 19 April 2022).
  10. Planas, D.; Saunders, N.; Maes, P.; Guivel-Benhassine, F.; Planchais, C.; Buchrieser, J.; Bolland, W.-H.; Porrot, F.; Staropoli, I.; Lemoine, F.; et al. Considerable Escape of SARS-CoV-2 Omicron to Antibody Neutralization. Nature 2022, 602, 671–675. [Google Scholar] [CrossRef]
  11. Andrews, N.; Stowe, J.; Kirsebom, F.; Toffa, S.; Rickeard, T.; Gallagher, E.; Gower, C.; Kall, M.; Groves, N.; O’Connell, A.M.; et al. COVID-19 Vaccine Effectiveness against the Omicron (B.1.1.529) Variant. N. Engl. J. Med. 2022, 386, 1532–1546. [Google Scholar] [CrossRef] [PubMed]
  12. Bergwerk, M.; Gonen, T.; Lustig, Y.; Amit, S.; Lipsitch, M.; Cohen, C.; Mandelboim, M.; Levin, E.G.; Rubin, C.; Indenbaum, V.; et al. COVID-19 Breakthrough Infections in Vaccinated Health Care Workers. N. Engl. J. Med. 2021, 385, 1474–1484. [Google Scholar] [CrossRef] [PubMed]
  13. Menni, C.; Klaser, K.; May, A.; Polidori, L.; Capdevila, J.; Louca, P.; Sudre, C.H.; Nguyen, L.H.; Drew, D.A.; Merino, J.; et al. Vaccine Side-Effects and SARS-CoV-2 Infection after Vaccination in Users of the COVID Symptom Study App in the UK: A Prospective Observational Study. Lancet Infect. Dis. 2021, 21, 939–949. [Google Scholar] [CrossRef]
  14. Yamamoto, S.; Maeda, K.; Matsuda, K.; Tanaka, A.; Horii, K.; Okudera, K.; Takeuchi, J.S.; Mizoue, T.; Konishi, M.; Ozeki, M.; et al. COVID-19 Breakthrough Infection and Post-Vaccination Neutralizing Antibody among Healthcare Workers in a Referral Hospital in Tokyo: A Case-Control Matching Study. Clin. Infect. Dis. Off. Publ. Infect. Dis. Soc. Am. 2021. [Google Scholar] [CrossRef]
  15. Thomas, S.J.; Moreira, E.D.; Kitchin, N.; Absalon, J.; Gurtman, A.; Lockhart, S.; Perez, J.L.; Pérez Marc, G.; Polack, F.P.; Zerbini, C.; et al. Safety and Efficacy of the BNT162b2 MRNA COVID-19 Vaccine through 6 Months. N. Engl. J. Med. 2021, 385, 1761–1773. [Google Scholar] [CrossRef]
  16. Shrotri, M.; Navaratnam, A.M.D.; Nguyen, V.; Byrne, T.; Geismar, C.; Fragaszy, E.; Beale, S.; Fong, W.L.E.; Patel, P.; Kovar, J.; et al. Spike-Antibody Waning after Second Dose of BNT162b2 or ChAdOx1. Lancet 2021, 398, 385–387. [Google Scholar] [CrossRef]
  17. Gaebler, C.; Wang, Z.; Lorenzi, J.C.C.; Muecksch, F.; Finkin, S.; Tokuyama, M.; Cho, A.; Jankovic, M.; Schaefer-Babajew, D.; Oliveira, T.Y.; et al. Evolution of Antibody Immunity to SARS-CoV-2. Nature 2021, 591, 639–644. [Google Scholar] [CrossRef]
  18. Mizrahi, B.; Lotan, R.; Kalkstein, N.; Peretz, A.; Perez, G.; Ben-Tov, A.; Chodick, G.; Gazit, S.; Patalon, T. Correlation of SARS-CoV-2-Breakthrough Infections to Time-from-Vaccine. Nat. Commun. 2021, 12, 6379. [Google Scholar] [CrossRef] [PubMed]
  19. Seow, J.; Graham, C.; Merrick, B.; Acors, S.; Pickering, S.; Steel, K.J.A.; Hemmings, O.; O’Byrne, A.; Kouphou, N.; Galao, R.P.; et al. Longitudinal Observation and Decline of Neutralizing Antibody Responses in the Three Months Following SARS-CoV-2 Infection in Humans. Nat. Microbiol. 2020, 5, 1598–1607. [Google Scholar] [CrossRef] [PubMed]
  20. Goldberg, Y.; Mandel, M.; Bar-On, Y.M.; Bodenheimer, O.; Freedman, L.; Haas, E.J.; Milo, R.; Alroy-Preis, S.; Ash, N.; Huppert, A. Waning Immunity after the BNT162b2 Vaccine in Israel. N. Engl. J. Med. 2021, 385, e85. [Google Scholar] [CrossRef]
  21. Edelstein, M.; Beiruti, K.W.; Ben-Amram, H.; Bar-Zeev, N.; Sussan, C.; Asulin, H.; Strauss, D.; Bathish, Y.; Zarka, S.; Abu Jabal, K. Antibody-Mediated Immunogenicity against SARS-CoV-2 Following Priming, Boosting and Hybrid Immunity: Insights from 11 Months of Follow-up of a Healthcare Worker Cohort in Israel, December 2020–October 2021. Clin. Infect. Dis. Off. Publ. Infect. Dis. Soc. Am. 2022. [Google Scholar] [CrossRef]
  22. Oster, Y.; Benenson, S.; Nir-Paz, R.; Buda, I.; Cohen, M.J. The Effect of a Third BNT162b2 Vaccine on Breakthrough Infections in Health Care Workers: A Cohort Analysis. Clin. Microbiol. Infect. 2022, S1198743X2200043X. [Google Scholar] [CrossRef]
  23. Falsey, A.R.; Frenck, R.W.; Walsh, E.E.; Kitchin, N.; Absalon, J.; Gurtman, A.; Lockhart, S.; Bailey, R.; Swanson, K.A.; Xu, X.; et al. SARS-CoV-2 Neutralisation with BNT162b2 Vaccine Dose 3. N. Engl. J. Med. 2021, 385, 1627–1629. [Google Scholar] [CrossRef]
  24. Eliakim-Raz, N.; Leibovici-Weisman, Y.; Stemmer, A.; Ness, A.; Awwad, M.; Ghantous, N.; Stemmer, S.M. Antibody Titers Before and After a Third Dose of the SARS-CoV-2 BNT162b2 Vaccine in Adults Aged ≥ 60 Years. JAMA 2021, 326, 2203. [Google Scholar] [CrossRef]
  25. Garcia-Beltran, W.F.; St. Denis, K.J.; Hoelzemer, A.; Lam, E.C.; Nitido, A.D.; Sheehan, M.L.; Berrios, C.; Ofoman, O.; Chang, C.C.; Hauser, B.M.; et al. MRNA-Based COVID-19 Vaccine Boosters Induce Neutralising Immunity against SARS-CoV-2 Omicron Variant. Cell 2022, 185, 457–466.e4. [Google Scholar] [CrossRef]
  26. Terreri, S.; Piano Mortari, E.; Vinci, M.R.; Russo, C.; Alteri, C.; Albano, C.; Colavita, F.; Gramigna, G.; Agrati, C.; Linardos, G.; et al. Persistent B Cell Memory after SARS-CoV-2 Vaccination Is Functional during Breakthrough Infections. Cell Host Microbe 2022, S1931312822000397. [Google Scholar] [CrossRef]
  27. Lipsitch, M.; Krammer, F.; Regev-Yochay, G.; Lustig, Y.; Balicer, R.D. SARS-CoV-2 Breakthrough Infections in Vaccinated Individuals: Measurement, Causes and Impact. Nat. Rev. Immunol. 2022, 22, 57–65. [Google Scholar] [CrossRef]
  28. Chou, R.; Dana, T.; Buckley, D.I.; Selph, S.; Fu, R.; Totten, A.M. Update Alert 10: Epidemiology of and Risk Factors for Coronavirus Infection in Health Care Workers. Ann. Intern. Med. 2022, 175, W8–W9. [Google Scholar] [CrossRef]
  29. von Elm, E.; Altman, D.G.; Egger, M.; Pocock, S.J.; Gøtzsche, P.C.; Vandenbroucke, J.P. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies. Lancet Lond. Engl. 2007, 370, 1453–1457. [Google Scholar] [CrossRef]
  30. CDC COVID-19 Vaccination. Available online: https://www.cdc.gov/coronavirus/2019-ncov/vaccines/effectiveness/why-measure-effectiveness/breakthrough-cases.html (accessed on 10 April 2022).
  31. Higgins, J.P.T.; Thompson, S.G. Quantifying Heterogeneity in a Meta-Analysis. Stat. Med. 2002, 21, 1539–1558. [Google Scholar] [CrossRef] [PubMed]
  32. Higgins, J.P.T.; Thompson, S.G.; Deeks, J.J.; Altman, D.G. Measuring Inconsistency in Meta-Analyses. BMJ 2003, 327, 557–560. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. DerSimonian, R.; Laird, N. Meta-Analysis in Clinical Trials. Control. Clin. Trials 1986, 7, 177–188. [Google Scholar] [CrossRef]
  34. Hellewell, J.; Russell, T.W.; Beale, R.; Kelly, G.; Houlihan, C.; Nastouli, E.; Kucharski, A.J. Estimating the Effectiveness of Routine Asymptomatic PCR Testing at Different Frequencies for the Detection of SARS-CoV-2 Infections. BMC Med. 2021, 19, 106. [Google Scholar] [CrossRef]
  35. Al Youha, S.; Alowaish, O.; Ibrahim, I.K.; Alghounaim, M.; Abu-Sheasha, G.A.; Fakhra, Z.; Al Hendi, S.; AlQabandi, Y.; Almazeedi, S.; Al Asoomi, F.; et al. Factors Associated with SARS-CoV-2 Infection amongst Healthcare Workers in a COVID-19 Designated Hospital. J. Infect. Public Health 2021, 14, 1226–1232. [Google Scholar] [CrossRef] [PubMed]
  36. Gómez-Ochoa, S.A.; Franco, O.H.; Rojas, L.Z.; Raguindin, P.F.; Roa-Díaz, Z.M.; Wyssmann, B.M.; Guevara, S.L.R.; Echeverría, L.E.; Glisic, M.; Muka, T. COVID-19 in Health-Care Workers: A Living Systematic Review and Meta-Analysis of Prevalence, Risk Factors, Clinical Characteristics, and Outcomes. Am. J. Epidemiol. 2021, 190, 161–175. [Google Scholar] [CrossRef] [PubMed]
  37. Porru, S.; Carta, A.; Monaco, M.G.L.; Verlato, G.; Battaggia, A.; Parpaiola, M.; Lo Cascio, G.; Pegoraro, M.; Militello, V.; Moretti, F.; et al. Health Surveillance and Response to SARS-CoV-2 Mass Testing in Health Workers of a Large Italian Hospital in Verona, Veneto. Int. J. Environ. Res. Public. Health 2020, 17, 5104. [Google Scholar] [CrossRef]
  38. Boffetta, P.; Violante, F.; Durando, P.; De Palma, G.; Pira, E.; Vimercati, L.; Cristaudo, A.; Icardi, G.; Sala, E.; Coggiola, M.; et al. Determinants of SARS-CoV-2 Infection in Italian Healthcare Workers: A Multicenter Study. Sci. Rep. 2021, 11, 5788. [Google Scholar] [CrossRef] [PubMed]
  39. Chou, R.; Dana, T.; Selph, S.; Totten, A.M.; Buckley, D.I.; Fu, R. Update Alert 6: Epidemiology of and Risk Factors for Coronavirus Infection in Health Care Workers. Ann. Intern. Med. 2021, 174, W18–W19. [Google Scholar] [CrossRef]
  40. Basso, P.; Negro, C.; Cegolon, L.; Larese Filon, F. Risk of Vaccine Breakthrough SARS-CoV-2 Infection and Associated Factors in Healthcare Workers of Trieste Teaching Hospitals (North-Eastern Italy). Viruses 2022, 14, 336. [Google Scholar] [CrossRef]
  41. Nguyen, L.H.; Drew, D.A.; Graham, M.S.; Joshi, A.D.; Guo, C.-G.; Ma, W.; Mehta, R.S.; Warner, E.T.; Sikavi, D.R.; Lo, C.-H.; et al. Risk of COVID-19 among Front-Line Health-Care Workers and the General Community: A Prospective Cohort Study. Lancet Public Health 2020, 5, e475–e483. [Google Scholar] [CrossRef]
  42. Hall, V.; Foulkes, S.; Insalata, F.; Kirwan, P.; Saei, A.; Atti, A.; Wellington, E.; Khawam, J.; Munro, K.; Cole, M.; et al. Protection against SARS-CoV-2 after COVID-19 Vaccination and Previous Infection. N. Engl. J. Med. 2022, 386, 1207–1220. [Google Scholar] [CrossRef]
Vaccines 10 01193 g001 550
Figure 1. Impact of socio-demographic characteristics on the risk of either pre-vaccine infection (blue columns) or breakthrough infection (orange columns) in the eight centers (31,837 HWs) providing individual data. Columns are Relative Risk Ratios (RRR), and bars are 95% confidence intervals. RRRs were estimated by multinomial logistic regression, adjusting standard errors for intra-center correlation.
Figure 1. Impact of socio-demographic characteristics on the risk of either pre-vaccine infection (blue columns) or breakthrough infection (orange columns) in the eight centers (31,837 HWs) providing individual data. Columns are Relative Risk Ratios (RRR), and bars are 95% confidence intervals. RRRs were estimated by multinomial logistic regression, adjusting standard errors for intra-center correlation.
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Vaccines 10 01193 g002 550
Figure 2. Forest plots evaluating the effect of SARS-CoV-2 infection before vaccination (upper panel) and antibody titers against SARS-CoV-2 after vaccination (lower panel) on the risk of breakthrough infection. A random-effect model was used for the former and a fixed-effects model for the latter. Odds ratio (OR) estimates for single centers are shown in boxes, and the pooled estimate is shown as a diamond. Error bars and values in parentheses indicate 95% confidence intervals.
Figure 2. Forest plots evaluating the effect of SARS-CoV-2 infection before vaccination (upper panel) and antibody titers against SARS-CoV-2 after vaccination (lower panel) on the risk of breakthrough infection. A random-effect model was used for the former and a fixed-effects model for the latter. Odds ratio (OR) estimates for single centers are shown in boxes, and the pooled estimate is shown as a diamond. Error bars and values in parentheses indicate 95% confidence intervals.
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Table
Table 1. Breakthrough infections, demographic, and occupational characteristics of 64,172 Health Workers from 12 European centers.
Table 1. Breakthrough infections, demographic, and occupational characteristics of 64,172 Health Workers from 12 European centers.
Positive Sex (%)Job Title (%)Age (10 Years %)
CentreNCases (%)MaleFemaleAdministr.TechnicianNursePhysicianOther HW<3030–3940–49≥50
Individual data available
Verona640497 (1.5)1979 (30.9)4425 (69.1)514 (8.0)579 (9.0)2176 (34.0)2167 (33.8)968 (15.1)1068 (16.7)1635 (25.5)1241 (19.4)2460 (38.4)
Padua620892 (1.5)1901 (30.6)4307 (69.4)542 (8.8)428 (7.0)2370 (38.5)1844 (30.0)973 (15.8)850 (13.7)1214 (19.6)1400 (22.6)2744 (44.2)
Trieste 355959 (1.7)1013 (31.8)2169 (68.2)166 (5.2)144 (4.5)1313 (41.2)527 (16.5)1038 (32.6)220 (6.9)554 (17.4)854 (26.8)1554 (48.8)
Modena525090 (1.7)1550 (29.5)3699 (70.5)268 (5.2)179 (3.5)1846 (36.0)1603 (31.2)1239 (24.1)946 (18.0)1409 (26.8)1140 (21.7)1755 (33.4)
Perugia236430 (1.3)789 (33.4)1575 (66.6)170 (7.2)327 (13.8)1002 (42.4)514 (21.7)351 (14.9)31 (1.3)468 (19.8)543 (23.0)1322 (55.9)
Bari592338 (0.6)2330 (39.3)3593 (60.1)379 (6.4)200 (3.4)1612 (27.2)2884 (48.7)848 (14.3)901 (15.2)1356 (22.9)1081 (18.3)2585 (43.6)
Slovakia6719 (1.3)106 (15.8)565 (84.2)74 (11.1)36 (5.4)227 (34.0)83 (12.4)247 (37.0)75 (11.2)96 (14.3)220 (32.8)280 (41.7)
Romania145811 (0.8)276 (18.9)1182 (81.1)69 (4.7)13 (0.9)172 (11.8)1080 (74.1)124 (8.5)89 (6.1)179 (12.3)440 (30.2)750 (51.4)
Subtotal31,837426 (1.3)9944 (31.6)21,515 (68.4)2182 (7.0)1906 (6.1)10,718 (34.3)10,702 (34.2)5788 (18.5)4180 (13.3)6911 (22.0)6919 (22.0)13,450 (42.8)
Aggregated data only
Turin878775 (0.9)2427 (27.6)6360 (72.4)1225 (13.9)1169 (13.3)3219 (36.6)1841 (20.9)1333 (15.2)1063 (12.1)1197 (13.6)2033 (23.1)4491 (51.1)
Brescia8903134 (1.5)2446 (27.5)6457 (72.5)985 (11.1)702 (7.9)2855 (32.1)2642 (29.7)1719 (19.3)1386 (15.6)1968 (22.1)2015 (22.6)3534 (39.7)
Bologna722995 (1.3)2417 (33.4)4812 (66.6)274 (3.9)705 (10.0)2474 (34.9)1998 (28.2)1631 (23.0)1395 (19.3)1934 (26.7)1474 (20.4)2426 (33.6)
Oviedo741667 (0.9)1569 (21.2)5847 (78.8)691 (9.3)413 (5.6)3494 (47.1)1615 (21.8)1203 (16.2)582 (7.8)1290 (17.4)1760 (23.7)3784 (51.1)
Total64,172797 (1.2)18,803 (29.5)44,991 (70.5)5357 (8.4)4895 (7.7)22,760 (35.9)18,798 (29.6)11674 (18.4)8606 (13.5)13,300 (20.8)14,201 (22.3)27,685 (43.4)
Table
Table 2. Main risk factors of breakthrough infection in the eight centers (31,837 HWs). Hazard ratios and p-values were obtained by a Cox regression model, including sex, age, job title, and pre-vaccine SARS-CoV-2 infection, stratifying by center.
Table 2. Main risk factors of breakthrough infection in the eight centers (31,837 HWs). Hazard ratios and p-values were obtained by a Cox regression model, including sex, age, job title, and pre-vaccine SARS-CoV-2 infection, stratifying by center.
Hazard Ratio (95% CI)p-Value
Sex (Women vs. men)0.98 (0.79–1.23)0.899
Age (increase by 10 years)0.81 (0.74–0.88)<0.001
Job title
Administrative1 (reference)
Physician0.82 (0.53–1.27)0.374
Nurse0.98 (0.64–1.49)0.920
Technician0.75 (0.41–1.36)0.341
Other HW1.03 (0.66–1.61)0.910
Previous infection (Yes vs. No)0.23 (0.12–0.45)<0.001
Significant results are highlighted in bold. HW = Health Worker.
Table
Table 3. Summary of meta-analyses performed on all 12 centers with available aggregate data.
Table 3. Summary of meta-analyses performed on all 12 centers with available aggregate data.
CentresPooled OR (95% CI)p-ValueI-Squared
Gender: women vs. men110.91 (0.70–1.19)0.48851.6% (p = 0.024)
Age (increased by 10 years)120.91 (0.80–1.05)0.18845.5% (p = 0.043)
Job title (ref. administration)
Physician111.05 (0.62–1.79)0.85851.6% (p = 0.024)
Nurse121.29 (0.93–1.80)0.1300.0% (p = 0.562)
Technician101.37 (0.86–2.18)0.1910.0% (p = 0.777)
Other HW111.20 (0.82–1.74)0.3530.0% (p = 0.516)
Previous COVID-1960.425 (0.225–0.80)0.00840.1% (p = 0.138)
Ln (Antibody titre) (+1 sd)60.86 (0.775–0.96)0.0070% (p = 0.751)
Significant results are highlighted in bold.
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Porru, S.; Monaco, M.G.L.; Spiteri, G.; Carta, A.; Pezzani, M.D.; Lippi, G.; Gibellini, D.; Tacconelli, E.; Dalla Vecchia, I.; Sala, E.; et al. SARS-CoV-2 Breakthrough Infections: Incidence and Risk Factors in a Large European Multicentric Cohort of Health Workers. Vaccines 2022, 10, 1193. https://doi.org/10.3390/vaccines10081193

AMA Style

Porru S, Monaco MGL, Spiteri G, Carta A, Pezzani MD, Lippi G, Gibellini D, Tacconelli E, Dalla Vecchia I, Sala E, et al. SARS-CoV-2 Breakthrough Infections: Incidence and Risk Factors in a Large European Multicentric Cohort of Health Workers. Vaccines. 2022; 10(8):1193. https://doi.org/10.3390/vaccines10081193

Chicago/Turabian Style

Porru, Stefano, Maria Grazia Lourdes Monaco, Gianluca Spiteri, Angela Carta, Maria Diletta Pezzani, Giuseppe Lippi, Davide Gibellini, Evelina Tacconelli, Ilaria Dalla Vecchia, Emma Sala, and et al. 2022. "SARS-CoV-2 Breakthrough Infections: Incidence and Risk Factors in a Large European Multicentric Cohort of Health Workers" Vaccines 10, no. 8: 1193. https://doi.org/10.3390/vaccines10081193

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