key: cord-0770343-87e61p8v
authors: Bordino, Valerio; Marengo, Noemi; Garlasco, Jacopo; Cornio, Alessandro Roberto; Meddis, Davide; Ditommaso, Savina; Giacomuzzi, Monica; Memoli, Gabriele; Gianino, Maria Michela; Vicentini, Costanza; Zotti, Carla Maria
title: A cross‐sectional study of SARS‐CoV‐2 seropositivity among healthcare workers and residents of long‐term facilities in Italy, January 2021
date: 2022-03-06
journal: J Med Virol
DOI: 10.1002/jmv.27670
sha: 156b18cf98f1f9a2bfca118a4105e9ddf7f77855
doc_id: 770343
cord_uid: 87e61p8v

Long‐term care facilities (LTCFs) are high‐risk settings for SARS‐CoV‐2 infection. This study aimed to describe SARS‐CoV‐2 seropositivity among residents of LTCFs and health‐care workers (HCWs). Subjects were recruited in January 2021 among unvaccinated HCWs of LTCFs and hospitals and residents of LTCFs in Northern Italy. Information concerning previous SARS‐CoV‐2 infections and a sample of peripheral blood were collected. Anti‐S SARS‐CoV‐2 IgG antibodies were measured using the EUROIMMUN Anti‐SARS‐CoV‐2 QuantiVac ELISA kit (EUROIMMUN Medizinische Labordiagnostika AG). For subjects with previous COVID‐19 infection, gender, age, type of subject (HCW or resident), and time between last positive swab and blood draw were considered as possible determinants of two outcomes: the probability to obtain a positive serological result and antibody titer. Six hundred and fifty‐eight subjects were enrolled. 56.1% of all subjects and 65% of residents presented positive results (overall median antibody titer: 31.0 RU/ml). Multivariable models identified a statistically significant 4% decrease in the estimated antibody level for each 30‐day increase from the last positive swab. HCWs were associated with significant odds for seroreversion over time (OR: 0.926 for every 30 days, 95% CI: 0.860–0.998), contrary to residents (OR: 1.059, 95% CI: 0.919–1.22). Age and gender were not factors predicting seropositivity over time. Residents could have a higher probability of maintaining a seropositive status over time compared to HCWs.

The coronavirus disease 2019 (COVID- 19) was declared a Public Health Emergency of International Concern (PHEIC) on January 30, 2020. 1 As of June 18, 2021, there have been 177 million confirmed cases worldwide, including 3.8 million deaths. 2, 3 Italy was one of the first EU countries hit by the pandemic. The Italian Council of Ministers declared a state of emergency throughout the country on January 31, 2020. 4 Since then, Italy has faced three epidemic waves and in response, the government has implemented increasingly strict containment measures. [5] [6] [7] [8] Currently, Italy has reported 7 611 614 cases of COVID-19 and 138 651 deaths. 9 The gold standard for COVID-19 diagnosis is the detection of SARS-CoV-2 by real-time reverse-transcription polymerase chain reaction (real time-RT-PCR). 10, 11 However, it is possible that many asymptomatic or mildly symptomatic patients remain undetected, contributing to the spread of the virus. 12 In this context, serological investigations can be used to evaluate previous exposure to the virus, as well as the presence of an immune response. 11 Seroprevalence studies could represent a fundamental tool to generate a more realistic estimate of the cumulative incidence of disease, 13 especially in countries where PCR testing was insufficient in the initial stages of the epidemic due to a contingent allocation of resources. 14 It is not yet clear whether the antibody titer is a marker of protective immunity, nor whether there is a protective immunity threshold against the virus. 15 Further, the duration of adaptive immunity to SARS-CoV-2 after natural infection remains to be determined. [16] [17] [18] Long-term care facilities (LTCFs) are high-risk settings for SARS-CoV-2 infection, both for residents and personnel. The purpose of this multicentric study was to describe the antibody response to SARS-CoV-2 among individuals at high risk of exposure due to the environment in which they live or work: residents of LTCFs and health-care workers (HCWs) of acute-care hospitals and LTCFs, following the first two pandemic waves in Italy (January 2021). It is important to state that the vaccination campaign against SARS-CoV-2 in Italy began on December 27, 2020. All subjects analyzed in this study were therefore unvaccinated at the time of blood sample collection.

Subjects were recruited on a voluntary basis in January 2021, among a convenience sample of HCWs (medical doctors, nurses, and ancillary staff) of LTCFs and one hospital (total n = 495; LTCFs = 372; hospital = 123) and residents of LTCFs (n = 163) in the region of Piedmont, in Northern Italy. Subjects were enrolled at six LTCFs and the main hospital of the city of Alessandria, two LTCFs of Cuneo, and five LCTFs of Turin. All the subjects were unvaccinated, as the samples were collected the day before vaccination was scheduled.

Demographic characteristics of enrolled subjects, as well as information concerning previous SARS-CoV-2 infections confirmed by RT-PCR testing, were collected from the Health Directorates of the involved facilities and checked on the regional database in which all official swabs are registered. Further, participants were asked whether they had previously been infected by SARS-CoV-2 and if so, when. After acquiring the written consent from all subjects, a sample of peripheral blood was collected.

The analysis was carried out at the Laboratory of Serology and Microbiology applied to Hygiene of the Department of Public Health and Paediatrics of the University of Turin. Blood samples were delivered to the laboratory and, after centrifugation, sera were extracted and stored at −20°C until analysis. SARS-CoV-2 IgG antibodies were measured using the EUROIMMUN Anti-SARS-CoV-2 QuantiVac ELISA kit (EUROIMMUN Medizinische Labordiagnostika AG). The kit allows the specific detection of IgG antibodies using the S1 domain of the spike protein including the immunologically relevant receptor-binding domain (RBD). Sera were analyzed in a 100-fold and 1000-fold dilution, and IgG results were expressed in relative units per milliliter (RU/ml) using a 6 point calibration curve. A peroxidasebased revelation system was used, and, after color development, optical density at 450 nm was determined. The IgG antibodies titers were determined using the calibration curve obtained from standards.

The result should be interpreted as negative if lower than 8 RU/ml, borderline if between 8 and 11 RU/ml, and positive if ≥11 RU/ml, according to the instructions of the kit manufacturer. A conversion factor of 3.2 has been identified by the manufacturer to convert relative units to binding antibody units/ml (BAU/ml); this measurement unit has been indicated by the WHO as a standard unit and conversion factors have been identified by the manufacturers. 19 

Descriptive characteristics were presented as medians and interquartile ranges or means and standard deviations following the results of corresponding statistics yielded by the Shapiro-Wilk normality test.

For subjects with a previous positive swab confirming infection, gender, age, type of subject (HCW or resident), and time between last positive swab and blood draw were considered as possible determinants of two outcomes: the probability to obtain a positive serological result and, for subjects who showed detectable antibodies, the actual antibody titer. Consequently, multivariable regression models (logistic and log-linear, respectively) were built to evaluate the impact of explanatory variables on each outcome, by allowing also for the presence of possible interactions between determinants. Nonlinear effects of continuous variables (age and time between swab and blood draw) were also investigated through restricted cubic splines regression.

The significance of interactions in the model was evaluated through likelihood ratio tests, to keep the simplest model with explanatory power. 20 Relevant diagnostics for final models were conducted, including the analysis of variance inflation factors (VIFs) to check for multicollinearity and the verification of residual normality assumption. Optimism due to overfitting was quantified through validation via bootstrap by resampling 1000 times. 21 

The significance level was set at α = 0.05 for all analyses, except for likelihood ratio tests, where a level of 0.1 was chosen for a more conservative approach towards interactions

The statistical software R (version 4.0.5) 22 was used for all computation and plotting: models and diagnostics were performed using the "rms" 23 and "lmtest" packages. 24 3 | RESULTS Descriptive characteristics are reported in further detail in Table 1 , and the distribution of antibody titers according to previous PCR positivity is presented in Figure 1 . Interestingly, all adjusted models suggest a similar value for the probability of antibody loss over time, with a decrease in antibody titer by 4.01%-4.61% every 30 days (Table 2B) .

It must be noted that, in log-linear models, the interaction between age (or subject type) and time since the last positive swab could not be evaluated because of collinearity, which conversely was not an issue in any of the logistic regression models (VIFs ≤ 2.5). Optimism evaluated by bootstrapping was lower than 0.25, thus confirming the validity of the results obtained. No violation of residual normality assumption was detected in the log-linear regression models.

Understanding the profile of serum antibody responses to SARS- These infections were presumably asymptomatic or mildly symptomatic as they did not lead to PCR testing and were associated with significantly lower titers, consistent with previous findings of a positive correlation between clinical severity and antibody levels. 15, 30, 31 On the other hand, 4% of subjects in our study with a documented previous positive swab did not present any detectable antibodies, and a further 14% did not reach the threshold of 8 RU/ml after a median swab. However, the potential for waning immunity remains a debated issue and further investigation is required to assess the timing and extent of this phenomenon. 13 According to the results of the multivariable analysis we performed, being an HCW was associated with significant odds for seroreversion over time whereas being a resident of an LTCF was not, after adjustment for age and gender. This finding suggests residents could have a higher probability of maintaining a seropositive status over time. LTCFs represent high-risk congregate settings with long-standing infection control challenges, 28, 37 and LTCF residents are exposed to a significantly higher risk of SARS-CoV-2 infection compared to LTCF personnel. 29, 38 Higher peak viral loads and prolonged viral shedding among residents represent additional risk factors for continued inter-facility transmission of SARS-CoV-2. 25, 28 The natural boosting of antibodies due to repeated exposure could explain the more durable antibody response over time found in residents in this study. Moreover, LTCF residents represent a frail and older population at increased risk for severe disease, which has been associated with more robust and durable antibody responses. 39 This study has several limitations. First, participation was voluntary among a convenience sample, which may have affected representativeness therefore a selection bias cannot completely be excluded, also due to the fact that in Italy, especially in the first waves, Piedmont and Lombardy were the epicenters of the epidemic.

The high proportion of previously positive HCWs and LTCF residents found in this study supports this concern. The cross-sectional design of this study has inherent limitations, particularly in light of the potential for waning immunity. Furthermore, it was not possible to T A B L E 2 Output of the multivariable regression models, related to participants with at least one previous positive swab for SARS-CoV-2 collect information regarding the timing or clinical characteristics of previous infections. Our results could also have been affected by the sensitivity and specificity of the assays we employed, as previously discussed. It must also be noted that seropositivity may not reflect immunity to reinfection, as other components of the immune response may contribute to protective immunity (e.g., T cell immunity), 15, 40 although results of previous studies suggest a strong correlation exists between anti-S antibody levels and neutralization activity. 31 In our study, being an LTCF resident was more important than the effect of age, as no association with seropositivity was found with age, inconsistent with previous reports. 31 

Antibody tests are an essential tool in the long-term management of infectious diseases. Seroprevalence studies have the potential of identifying previously infected subjects and could allow generating more accurate estimates of the cumulative incidence of infection compared to the number of infections reported through public health surveillance systems, although an adjustment for waning antibody kinetics is required. 13, 41 A reliable estimate of the cumulative incidence of SARS-CoV-2 infection is essential to assess disease severity, monitor immunity levels in a population, inform public health policies and predict the impact of vaccination strategies. 13 This study allowed to obtain a more comprehensive evaluation of previous exposure to SARS-CoV-2 and to assess the level of natural immunity in specific high-risk populations, providing context for assessing the success of past infection control policies and interventions.

Results of this study reinforce the concern that antibody levels following infection wane over time and highlight the importance of improving infection control practices in LTCFs in our region.

This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Open Access Funding provided by Universita degli Studi di Torino within the CRUI-CARE Agreement. Raso ("Se.R.E.M.I" Regional Epidemiology Service for Infectious Diseases, Local Health Authority "ASL AL," Alessandria, Italy), Pierfederico Torchio (SISP sede Cuneo-Local health unit ASL CN1, Italy).

The authors declare no conflicts of interest.

The research protocol was in accordance with the Declaration of 

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