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. 2023 Apr 7;34(5):603–615. doi: 10.1177/09567976231163875

No(cebo) Vax: COVID-19 Vaccine Beliefs Are Important Determinants of Both Occurrence and Perceived Severity of Common Vaccines’ Adverse Effects

Katia Mattarozzi 1, Arianna Bagnis 1,✉, Joanna Kłosowska 2, Przemysław Bąbel 2, Valeria Cremonini 3, Alessandra De Palma 4, Arianna Fabbri 3, Elisa Farinella 5, Lucrezia Puccini 6, Vittorio Sambri 1,6, Simona Semprini 6, Paolo Maria Russo 1
PMCID: PMC10083709  PMID: 37027171

Abstract

This study highlights the role of psychological influences in triggering and amplifying the adverse effects of the COVID-19 vaccine (i.e., nocebo effects). Fear, beliefs, and expectations about the COVID-19 vaccine, trust in health and scientific institutions, and stable personality traits were measured in 315 adult Italian citizens (145 men) during the 15-min waiting time after vaccination. The occurrence and severity of 10 potential adverse effects were assessed 24 hr later. Nonpharmacological variables predicted nearly 30% of the severity of the vaccine’s adverse effects. Expectations are important determinants of adverse effects from vaccines, and the results of the path analyses show that these expectations stem primarily from people’s vaccine beliefs and attitudes, which can be changed. Implications for increasing vaccine acceptability and limiting the nocebo effect are discussed.

Keywords: COVID-19 vaccine, nocebo

Vaccines against SARS-CoV-2 are the primary means for preventing moderate to severe COVID-19 and for reducing the viral circulation and the emergence of new viral variant (Levine-Tiefenbrun et al., 2021). Despite substantial data on the safety and efficacy of different types of SARS-CoV-2 vaccines provided by scientific institutions (Sadeghalvad et al., 2022), many people approach COVID-19 immune prophylaxis with concerns for adverse effects or even delay or refuse vaccination because of worry about their safety (Machingaidze & Wiysonge, 2021; Reno et al., 2021; Schwarzinger et al., 2021). Indeed, COVID-19 vaccine hesitancy is a major hurdle that needs to be overcome in order to cope with the COVID-19 pandemic.

A wealth of research indicates that expectation of adverse somatosensory events may induce the occurrence of such experiences, a phenomenon known as the nocebo effect (Colloca & Miller, 2011). The nocebo effect refers to a negative treatment outcome that is attributable to the psychosocial context surrounding treatment rather than to pharmacological or biological treatment properties (Bajcar et al., 2021; Bingel & Team, 2014; Blasini et al., 2017; Krauss, 2015). Studies point to the critical role of expectations about clinical outcomes, which are driven by learning through experience (i.e., associative or observational learning), explicit knowledge and beliefs (i.e., verbal information), and personality traits (e.g., pessimism or catastrophizing) that lead to nocebo effects (Atlas, 2021; Faasse & Petrie, 2013; Jensen et al., 2012).

Recent reviews based on SARS-CoV-2 vaccine trials indicated that a significant proportion of placebo recipients experienced adverse effects owing to nocebo responses (Amanzio et al., 2022; Haas et al., 2022). Emerging data, derived from a prospective longitudinal study in a large sample of COVID-19-vaccinated citizens, add importantly to the information on nocebo effects on COVID-19 vaccine reactogenicity (Geers et al., 2022). This study provides an estimate of the effect of prevaccine side-effects expectations, worries about COVID-19, and depressive symptoms on the occurrence of adverse effects after full vaccination. Taken together, these findings suggest the importance of deepening the relationship between nocebo-related factors and COVID-19 vaccine reactogenicity to design specific intervention and evidence-based information campaigns that build confidence and curb fear-reducing nocebo effects, thereby increasing vaccine uptake (Rief, 2021). To date, no research has explored how nocebo-related factors influence the variability of the reported adverse effects of COVID-19 vaccines and their contribution to amplifying perceptions of severity.

Statement of Relevance.

Despite robust data on the safety and efficacy of SARS-CoV-2 vaccines provided by rigorous scientific studies, many people approach COVID-19 vaccines with concerns about adverse effects. In this research, we investigated whether and to what extent fear, vaccine beliefs, and expectations would have an effect on the occurrence and severity of adverse effects of COVID-19 vaccines. We report evidence indicating that psychological factors amenable to change, such as COVID-19 vaccine beliefs and attitudes, significantly contribute to the occurrence and severity of adverse effects. Health-care professionals, scientific institutions, governments, and media share a collective responsibility for communicating the vaccines’ benefits and potential adverse effects with the aim of increasing vaccine acceptance and also preventing the nocebo effect.

In the current study, we aimed to fill this gap and broaden the results obtained so far by extending the investigation to the plausibility of simultaneous comprehensive interrelationships (i.e., direct, indirect, and total effects) between psychological variables, some of which are amenable to change and others of which are fairly stable, that contribute to the nocebo mechanism in triggering and amplifying adverse effects of COVID-19 vaccines. Following several previous studies linking expectations and emotions to the nocebo effect (Atlas, 2021; Geers, 2022), we developed and tested, by means of mediation analyses, a theoretical model (see Fig. 1) based on the hypothesis that fear (i.e., the affective nocebo component) and expectations (i.e., the cognitive nocebo component) would have a direct effect on the exacerbation of adverse effects of the COVID-19 vaccine in terms of occurrence (Model 1) and of mean severity (Model 2). Additionally, we were interested in investigating where these expectations and fears come from. Therefore, consistent with studies suggesting that negative belief about medications (Smith et al., 2020, 2022) is associated with greater expectations of adverse effects, we hypothesized that fear and expectations of adverse effects were mediators associated with both (a) specific COVID-19 vaccination beliefs and attitudes and (b) general beliefs such as vaccine skepticism, trust in health and scientific institutions, and perceived susceptibility to (or perceived vulnerability to or risk of) disease. We also tested the paths between stable personality traits, such as pessimism and catastrophizing, and fears and expectations of adverse effects of vaccines.

Fig. 1.

Fig. 1.

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Hypothesized models. For simplicity, we show arrows from general beliefs and stable personality traits as a whole. Note that there should be three paths leading from perceived susceptibility to disease, optimism, and catastrophizing to expectations, attitudes toward the COVID-19 vaccine, and fear of developing adverse effects (AEs) and two paths leading from the utility of vaccines in general and trust in institutions to expectations and attitudes toward COVID-19 vaccine (but not to fear). Age and sex were included in the model as controls but are not displayed in the figure. The dependent variable (green box) refers to the number of AEs (Model 1) and the mean severity of AEs (Model 2). LOT-R = Life Orientation Test–Revised; PCS = Pain Catastrophizing Scale.

Several studies regarding immune response and safety of COVID-19 vaccines in healthy individuals indicated that higher reactogenicity is associated with more robust immune response, and the most common systemic adverse effects were fever, fatigue, headache, and myalgia, which followed a dose-dependent trend in which the highest incidence occurred after the second dose of the vaccine (Jackson et al., 2020; Sadeghalvad et al., 2022; Walsh et al., 2020). Therefore, in a subset of participants, we examined whether the systemic perceived severity of adverse effects was associated with a more robust immune response (i.e., anti-SARS-CoV-2 immunoglobulin G [IgG] antibody levels).

Open Practices Statement

The study reported in this article was not preregistered. Data from this study are available from the corresponding author on reasonable request.

Method

Participants

The target population included all citizens who came to a vaccination center between July 3 and 15, 2021, to receive their second shot of the COVID-19 vaccine (N = 350 citizens). The vaccination center was attended by healthy citizens at least 18 years old who worked for local companies that had made an agreement to vaccinate their employees within the time frame of the COVID-19 vaccination campaign issued by the local department of public health. The data collection coincided with the length of the vaccination agreement.

The flowchart in Figure 2 shows the procedure for sample recruitment. Of the 350 citizens invited to participate in the study, 315 (age: M = 29.87 years, SD = 9.76, range = 18–63; 145 men) completed both the first (Time 0) and the second (Time 1) part of the survey, yielding a 90% response rate (corresponding to ≈5 cases per each parameter estimated in path analyses testing the hypothesized model, which is a sample size adequate for running structural equation models according to Bentler & Chou, 1987). Twenty-one citizens did not consent to participate, eight completed only the Time 0 survey, and six citizens were excluded because they completed the Time 1 survey more than 30 hr later. No one declared that they had any chronic diseases, and 25 citizens reported a previous SARS-CoV-2 infection before the vaccination cycle. As substantiated by the vaccination center, all the participants were given the second dose of the BNT162b2 mRNA (Comirnaty; Pfizer [New York, NY] and BioNTech [Mainz, Germany]) COVID-19 vaccine. Ninety-four participants (age: M = 35.00 years, SD = 8.21, range 22–63; 59 men) were tested for anti-SARS-CoV-2 IgG antibody levels. See the Supplemental Material available online for more details.

Fig. 2.

Fig. 2.

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Flowchart showing sample recruitment and procedure overview. T0 = Time 0; T1 = Time 1; IgG = immunoglobulin G.

All participants gave informed written consent to take part in the study, which was approved by the institutional review board of the University of Bologna.

Procedure

At the vaccination center, the nursing staff gave citizens the following written information inviting them to participate in a self-reported online survey consisting of two parts:

To better understand the current pandemic and improve health services, we need you! Please answer some simple questions 15 minutes and 24 hours after the vaccine. Scan the QR code with the camera of your smartphone and fill out the survey. If you cannot scan the QR code, you can use this link. We thank you for your helpful collaboration!

After scanning a QR code or typing the Web address into an Internet browser, participants were asked to complete the first part of the survey during the 15-min waiting period after the vaccine dose (Time 0). Twenty-four hours later, a text message invited participants to complete the second part of the survey (Time 1). About 4 months later, from November 29 to December 9, 2021, participants who agreed to test for postvaccination IgG class-specific antibodies for SARS-CoV-2 (spike protein S1 subunit) were called back to the vaccination center for capillary blood sampling.

Survey

The online survey, implemented in Qualtrics software (https://www.qualtrics.com/core-xm/survey-software/), consisted of two parts. The first part (given at Time 0) lasted approximately 8 min and contained several sections in which participants provided informed consent and information about the following: We assessed participants’ health characteristics by asking them to report (a) the presence of chronic disease, by selecting from a list of chronic diseases identified by the World Health Organization (WHO), and (b) any history of SARS-CoV-2 infection. Second, stable personality traits (known in the literature to define high susceptibility to nocebo effects) were assessed with two measures. The first was the Pain Catastrophizing Scale (PCS; Sullivan et al., 1995), which measures catastrophic thoughts about pain; “catastrophizers” were described as individuals who tended to exaggerate the threat value or seriousness of pain sensations (a total PCS score of 30 represents a clinically relevant level of catastrophizing). The second measure was the Life Orientation Test–Revised (LOT-R; Carver et al., 1994), which measures individual dispositional levels of optimism (low optimism values range from 0 to 13, moderate optimism from 14 to 18, and high optimism from 19 to 24). Third, we assessed participants’ general belief about their perceived susceptibility to disease (Likert scale from 1, strongly disagree, to 10, strongly agree), utility of vaccines in general (scale from 1, not at all, to 10, extremely), and trust in health and scientific institutions (Likert scale from 1, strongly disagree, to 10, strongly agree). Fourth, we asked participants to rate their specific beliefs about the COVID-19 vaccine using a series of 10 adjectives: useless, beneficial, harmful, effective, dangerous, risky, safe, necessary, reassuring, and unreliable (scale from 1, not at all, to 10, extremely). They were also asked to rate their fear of developing adverse effects from the COVID-19 vaccine (scale from 1, not at all, to 10, extremely) and expectation of having adverse effects from the vaccine (scale from 1, not at all, to 10, extremely). Both of the latter scales were developed for the purposes of this study on the basis of established psychometric properties of a numerical-rating-scale format used in the literature.

The second part of the survey was administered at Time 1 (24 hr after vaccination) and lasting approximately 2 min. It consisted of a structured symptoms checklist that asked health-related questions about the presence and severity of local (i.e., pain, swelling, and redness at the site of injection) and systemic (i.e., fatigue, muscle soreness or pain, chills, diarrhea, joint pain, headache, skin rashes, fever) adverse effects (these effects were identified in COVID-19 vaccine randomized clinical trials and by the WHO). Presence of symptoms was indicated with a yes/no response, and severity of each adverse effect was rated on a scale from 1, not at all, to 10, extremely severe. The survey also included an open-ended response prompt in which participants could describe any experienced adverse effects.

Laboratory assay

Antibodies were measured 4 months after the vaccination using the GSP/DELFIA anti-SARS-CoV-2 automated IgG assay (PerkinElmer, Turku, Finland), a fully automated solid phase time-resolved fluoroimmunoassay. This test is based on an alternative collection method to the ordinary reference samples (plasma and serum), which is based onto the collection of dried blood spots (Morley et al., 2020; Turgeon et al., 2021). This procedure collects a capillary sample, and it is performed at the level of the digital pulp and blood drops taken from patient’s fingertip and collected onto filter paper.

To perform the assay, we punched sample disks into wells coated with recombinant SARS-CoV-2 spike protein S1 subunit produced in human cells, then the assay buffer elutes the analyte (human anti-SARS-CoV-2 IgG) from the paper matrix. The anti-SARS-CoV-2 IgG assay is based on the sandwich technique, using the SARS-CoV-2 spike protein subunit S1 as an antigen for the determination of antibodies’ development against SARS-CoV-2 and their quantification through fluorescence measurement, being proportional to the concentration of human anti-SARS-CoV-2 IgG in the sample.

The assay generates a numerical result (index) for each sample, calculated by dividing the sample counts (related to the sample signal) by calibrator counts. The determination of positive status for anti-SARS-CoV-2 IgG antibodies is based on a cutoff value, so any index value above the cutoff is considered positive. For this procedure, a cutoff index of 1.4 is recommended by the manufacturer.

Statistics

To test the hypothesized models explaining the occurrence (Model 1) and mean severity (Model 2) of adverse effects as well as direct and indirect effects, we conducted a path analysis in AMOS (Version 26). To establish mediation, we used the analytical procedure suggested by MacKinnon and Luecken (2008), which requires (a) a significant association between the independent and dependent variables, (b) a significant association between the mediator and dependent variable while controlling for the independent variable, and (c) a significant coefficient for the indirect path between the independent and dependent variable via the mediator. Bias-corrected bootstrapped 95% confidence intervals (CIs; N = 5,000) were used to determine whether the last criterion was met. Maximum likelihood estimation was implemented. Model fit was considered satisfactory if the following criteria were met: comparative fit index (CFI) > .95, Tucker-Lewis index (TLI) > .95, root-mean-square error of approximation (RMSEA) < .06, and standardized root-mean-square residual (SRMR) < .08 (Hu & Bentler, 1999). Age and sex (0 = male, 1 = female) were controlled for in the analysis.

We computed Spearman’s ρ correlation coefficients to evaluate the relationship between (a) anti-SARS-CoV-2 S1-specific IgG antibody levels after vaccination with the BNT162b2 mRNA COVID-19 vaccine and (b) the reported number and severity of systemic adverse effects. The significance threshold was set at .05. Correlational and descriptive analyses were conducted with SPSS (Version 27).

Results

Descriptive statistics

No participants reported severe adverse effects from the vaccine that required medical attention. The mean severities of experienced total adverse effects, local adverse effects, and systemic adverse effects were 3.34 (SD = 1.42, range = 1–8.22), 3.95 (SD = 1.64, range = 0.5–10), and 2.93 (SD = 1.53, range = 0.71–8), respectively. Fever was reported by 104 of 315 participants (33%; M = 37.9 °C, SD = 0.52, range = 37–39). Figure 3 summarizes the prevalence and severity of the reported adverse effects. Twenty-one participants reported other symptoms besides those listed, such as swollen lymph nodes, changes in the menstrual cycle, restlessness, insomnia, tachycardia, axillary pain, chest pain, and abdominal pain.

Fig. 3.

Fig. 3.

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Prevalence (N, %) and mean severity (bars) of each adverse effect. Shades of orange and red represent local adverse effects, and shades of green and blue represent systemic adverse effects. Error bars indicate standard errors.

Nocebo on adverse effects of the COVID-19 vaccine

Descriptive statistics for predictors are reported in Table 1. The internal reliability of the PCS and LOT-R scales in our sample (as measured by α coefficients) were .93 and .77, respectively. The total score of the scale of attitudes toward the COVID-19 vaccine was calculated by summing the scores for all adjectives except “useless” (range = 9–81). “Useless” was eliminated because it significantly reduced the Cronbach’s α of the scale (.79 vs. .88). Higher scores indicate more positive attitudes toward the COVID-19 vaccine.

Table 1.

Baseline (Time 0) Assessment of the Psychological Variables Used as Predictors of Adverse Effects of the COVID-19 Vaccine

Predictor M SD
Personality traits
 Catastrophizing (PCS) 17.52 9.53
 Optimism (LOT-R) 21.00 4.12
General beliefs
 Subjective perception of susceptibility to disease 4.41 2.13
 Utility of vaccines in general 9.06 1.31
 Trust in health and scientific institutions 7.24 1.56
Specific beliefs, fears, and expectations
 Attitude toward COVID-19 vaccine 74.27 11.74
 Fear of developing adverse effects from vaccine 3.59 2.47
 Expectation of having adverse effects from vaccine 4.23 2.45
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Note: Total N = 315. PCS = Pain Catastrophizing Scale; LOT-R: Life Orientation Test–Revised.

A principal components analysis (PCA) was used to test whether variance across items was explained by a common underlying factor. The results showed that the items were strongly related to each other (range = .51–.69), and the PCA showed a clear monofactorial structure (Cronbach’s α = .88) with high factor loadings (range = .61–.83) of all items to the first component, which explained 51.62% of the common variance between items.

Hypothesized model

The full models showed good fit to the data (both: CFI = 1.00, TLI = .96, RMSEA = .04, SRMR = .01), explaining 21% of variance in occurrence of adverse effects (Model 1; Fig. 4) and 28% of variance of mean severity of adverse effects (Model 2; Fig. 5).

Fig. 4.

Fig. 4.

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Model 1: influence of general beliefs and stable personality traits on the number of adverse effects (AEs) through specific beliefs, fears, and expectations. For simplicity, we show only significant paths and correlations. Single-headed arrows show paths, and double-headed arrows show partial correlations. Standardized path coefficients and correlation coefficients indicate the effect size of the relationships. The width of the arrows is proportional to the strength of the coefficients. Squared multiple correlations are presented to the upper right of the dependent variables. Indirect effect of perceived susceptibility to disease on number of adverse effects via expectations about adverse effects: b = 0.03, 95% confidence interval (CI) = [0.01, 0.06], β = 0.04, p < .01 (total effect of perceived susceptibility: b = 0.15, SE = 0.05, β = 0.17, p < .01); indirect effect of utility of vaccines in general via expectations about adverse effects: b = 0.05, 95% CI = [0.01, 0.11], β = 0.04, p < .05; indirect effect of utility of vaccines in general via attitudes toward COVID-19 vaccine: b = −0.19, 95% CI = [−0.29, −0.10], β = −0.13, p < .001; indirect effect of utility of vaccines in general via expectations about adverse effects via attitudes toward COVID-19 vaccine (serial mediation): b = −0.08, 95% CI = [−0.13, −0.05], β = −0.21, p < .001 (total effect of utility of vaccines in general: b = −0.24, SE = 0.09, β = −0.17, p < .01); indirect effect of attitudes toward COVID-19 vaccine via expectations about adverse effects: b = −0.02, 95% CI = [−0.03, −0.01], β = −0.12, p < .001 (total effect of attitudes toward COVID-19 vaccine: b = −0.06, SE = 0.01, β = −0.37, p < .001). LOT-R = Life Orientation Test–Revised; PCS= Pain Catastrophizing Scale.

Fig. 5.

Fig. 5.

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Model 2: influence of general beliefs and stable personality traits on the mean severity of adverse effects (AEs) through specific beliefs, fears, and expectations. For simplicity, we show only significant paths and correlations. Single-headed arrows show paths, and double-headed arrows show partial correlations. Standardized path coefficients and correlation coefficients indicate the effect size of the relationships. The width of the arrows is proportional to the strength of the coefficients. Squared multiple correlations are presented to the upper right of the dependent variables. Indirect effect of perceived susceptibility to disease on mean severity of adverse effects via expectations about adverse effects: b = 0.03, 95% confidence interval (CI) = [0.01, 0.05], β = 0.04, p < .01 (total effect of perceived susceptibility: b = 0.10, SE = 0.04, β = 0.16, p < .01); indirect effect of attitudes toward COVID-19 vaccine on adverse effects via expectations about adverse effects: b = −0.02, 95% CI = [−0.02, −0.01], β = −0.13, p < .01 (total effect of attitudes toward COVID-19 vaccine: b = −0.05, SE = 0.01, β = −0.41, p < .001). LOT-R = Life Orientation Test–Revised; PCS = Pain Catastrophizing Scale.

Firstly, the direct effects of expectations of having adverse effects from the vaccine and fear of developing adverse effects on the number of adverse effects and the mean severity of adverse effects were tested. We found that only expectations of having adverse effects from the vaccine was a significant predictor of both number of adverse effects (b = 0.20, SE = 0.05, β = 0.26, p < .001) and mean severity of adverse effects (b = 0.16, SE = 0.03, β = 0.29, p < .001).

Next, we tested whether specific believes (i.e., attitudes toward the COVID-19 vaccine) would predict the number of adverse effects and mean severity of adverse effects, indirectly (i.e., mediated by expectations of having adverse effects and fear of developing adverse effects) and/or directly. We found that expectation of having adverse effects from the vaccine mediated both the association between attitudes toward the vaccine and the number of adverse effects (indirect effect: b = −0.02, 95% CI = [−0.03, −0.01], β = −0.12, p < .001) and the association between attitudes toward the vaccine and the severity of adverse effects (indirect effect: b = −0.02, 95% CI = [−0.02, −0.01], β = −0.13, p < .001). More negative attitudes toward the vaccine were associated with higher expectations of the occurrence of adverse effects (b = −0.09, SE = 0.01, β = −0.44, p < .001), which in turn were related to the occurrence and perceived severity of adverse effects. The direct effect of attitudes toward the vaccine on the number of adverse effects was significant (b = −0.04, SE = 0.001, β = −0.27, p < .001) but weaker than the total effect (b = −0.06, SE = 0.01, β = −0.37, p < .001). Similarly, the direct effect of attitudes toward the vaccine on the mean severity of adverse effects was significant (b = −0.03, SE = 0.01, β = −0.26, p < .001) and weaker than the total effect (b = −0.05, SE = 0.01, β = −0.41, p < .001), indicating partial mediations.

The total effect of belief about the utility of vaccines in general on the number of adverse effects (b = −0.25, SE = 0.09, β = −0.17, p < .01) was significant. After inclusion of mediators in the model, it became nonsignificant (full mediation). The indirect effects of belief about the utility of vaccines via expectations on the number of adverse effects (b = 0.05, 95% CI = [0.01, 0.11], β = 0.04, p < .05), as well as the indirect effect of the utility of vaccines through attitudes toward the vaccine on the number of adverse effects (b = −0.19, 95% CI = [−0.29, −0.10], β = −0.13 p < .001), were significant. The indirect effect via expectation and then attitudes (serial mediating effect) was also significant (b = −0.08, 95% CI = [−0.13, −0.05], β = −0.21, p < .001). The total effect of belief about the utility of vaccines on the severity of adverse effects was not significant (b = −0.12, SE = 0.06, β = −0.11, p = .06); therefore, indirect effects were not analyzed.

We also tested whether demographic characteristics (i.e., gender and age), stable personality traits (i.e., pain catastrophizing and optimism), and general beliefs (i.e., perceived susceptibility to disease, utility of vaccines in general, and trust in scientific institutions) had direct effects on specific beliefs (i.e., attitudes toward the COVID-19 vaccine), fears, and expectations of developing adverse effects from the vaccine. We also tested whether their indirect and direct effects on numbers of adverse effects (Model 1) and mean severity of adverse effects (Model 2) were statistically significant. Analyses showed that attitudes toward the vaccine were predicted (R2 = 39%) by belief in the utility of vaccines in general (b = 4.32, SE = 0.43, β = 0.48, p < .001), trust in scientific institutions (b = 1.11, SE = 0.35, β = 0.15, p < .01), optimism (b = 0.31, SE = 0.14, β = 0.11, p < .05), and age (b = −0.22, SE = 0.06, β = −0.19, p < .001). Along with attitudes toward the vaccine (described above), perceived susceptibility to disease (b = 0.17, SE = 0.06, β = 0.15, p < .01), belief in the utility of vaccines in general (b = 0.26, SE = 0.12, β = 0.14, p < .05), and trust in health and scientific institutions (b = 0.18, SE = 0.09, β = 0.12, p < .05) were also significantly and positively associated (R2 = 19%) with expectations of adverse effects. Fear of developing adverse effects was predicted (R2 = 39%) by higher tendency to pain catastrophizing (b = 0.03, SE = 0.01, β = 0.13, p < .01), more negative attitudes toward the vaccine (b = −0.06, SE = 0.01, β = −0.29, p < .001), higher expectations of adverse effects (b = 0.40, SE = 0.05, β = 0.40, p < .001), and gender (b = 0.53, SE = 0.24, β = 0.11, p < .05), with women reporting higher fear of adverse effects than men.

Finally, we found that expectation of adverse effects from the vaccine mediated the association between perceived susceptibility and both the number of adverse effects (indirect effect: b = 0.03, 95% CI = [0.01, 0.06], β = 0.04, p < .01) and the mean severity of adverse effects (indirect effect: b = 0.03, 95% CI = [0.01, 0.05], β = 0.04, p < .01). A higher subjective perception of susceptibility to disease was associated with higher expectations of adverse effects from the vaccine, which in turn were related to having more adverse effects and more severe adverse effects. The direct effect of perceived susceptibility to disease on the number of adverse effects was significant (b = 0.09, SE = 0.05, β = 0.11, p = .04; total effect: b = 0.15, SE = 0.05, β = 0.17, p < .01), indicating partial mediation, whereas the direct effect of perceived susceptibility to disease on the severity of adverse effects was not statistically significant (b = 0.05, SE = 0.03, β = 0.08, p =.11; total effect: b = 0.10, SE = 0.04, β = 0.16, p < .01), indicating full mediation. See the Supplemental Material for path coefficients of Model 1 (Table S1) and Model 2 (Table S2) as well as all estimates for total (Table S3) and indirect (Table S4) effects.

Anti-SARS-CoV-2 S1-specific IgG antibody levels and adverse effects

The average level of anti-SARS-CoV-2 S1-specific IgG was an index of 10.78 (SD = 6.46, range = 1.70–31.09). Anti-SARS-CoV-2 S1-specific IgG antibody levels were positively correlated with the number and severity of systemic adverse effects (Spearman’s ρ = .259, 95% CI = [.054, .444], p = .012; Spearman’s ρ = .286, 95% CI = [.082, .466], p = .005, respectively). Moreover, IgG amount was not significantly correlated with age (Spearman’s ρ = −.186, 95% CI = [−.380, .023], p = .072). No significant difference in average level of IgG was found between men (M = 10.55, SD = 6.60) and women (M = 11.16, SD = 6.29), t(92) = −0.442, p = .66, Cohen’s d = 0.094, 95% CI = [−0.512, 0.324].

Discussion

To contribute to interventions designed to build confidence and curb fear of and negative beliefs about the safety of the COVID-19 vaccine, we devised and tested a model that shed light on the role of psychological influences on the adverse effects of the COVID-19 vaccine (i.e., nocebo effects), both in terms of symptoms occurrence and perceived severity.

Consistent with data on the adverse effects of the COVID-19 vaccine, published by both independent (Riad et al., 2021) and manufacturer-funded (Klugar et al., 2021) studies, our results showed that 24 hr after receiving the COVID-19 vaccine, people reported at least one adverse effect, the most common of which were pain at the injection site, fatigue, headache, and muscle pain. The severity of symptoms varied from mild to moderate, and none required medical attention. Moreover, the experienced severity of adverse effects corresponded to a more robust immune response, similar to results found in recent studies (Jackson et al., 2020; Sadeghalvad et al., 2022; Walsh et al., 2020).

Remarkably, experienced symptoms were not explained only by the pharmacological or biological properties of the COVID-19 vaccine, emphasizing the contribution of nocebo mechanisms. These results confirm the previously reported association between nocebo-related factors and adverse effects of the COVID-19 vaccine, which were derived both from placebo recipients in vaccine trials (Amanzio et al., 2022; Haas et al., 2022) and from vaccinated citizens (Geers et al., 2022). Adding to these findings, the present study shows that nocebo-related factors contributed overall to a high incidence of experienced adverse effects and explained nearly the 30% of their perceived severity 24 hr after vaccination.

In keeping with the nocebo literature (Blasini et al., 2017; Geers et al., 2022) and with more specific studies indicating that expectations are important determinants of unwanted effects of drugs (Bingel & Team, 2014; Webster et al., 2016), we found that adverse effects of the COVID-19 vaccine were largely driven by expectations. Conversely, the nocebo affective component (i.e., fear of developing adverse effects) did not have a direct association with adverse effects of the vaccine. Expectations of developing adverse effects were the strongest predictors directly associated with adverse effects: On average, a 1-standard-deviation increase in expectations was associated with an increase of 0.26 and 0.29 standard deviations for the number and mean severity of adverse effects, respectively. The studied model allowed us to better understand some of the factors contributing to these expectations and revealed that attitude toward the COVID-19 vaccine was a pivotal factor, confirming previous evidence on the role of beliefs and attitudes about medications to adverse effects of medications (Smith et al., 2020) and, more recently, on expectations of adverse effects from vaccines (Smith et al., 2022). Intriguingly, we found here that attitudes toward the COVID-19 vaccine had both an indirect effect (i.e., mediated-by-expectations) and a direct effect on experienced adverse effects. Negative attitudes to the vaccine led individuals to have higher expectations of adverse effects and also contributed independently on the number and perceived severity of adverse effects. Because expectations of adverse effects and vaccine attitude measure specific beliefs about COVID-19 vaccination, these findings confirm and emphasize the value of building a solid culture of trust in COVID-19 vaccination (Pennycook et al., 2020). Attention should be paid to different age groups, and the present results suggest that older people show a slight tendency to have a more negative attitude toward COVID-19 vaccination.

Remarkably, the model designed here provides crucial points by showing the interrelated role of unchangeable and changeable nocebo-related factors. The role of stable personality traits, such as catastrophizing and pessimism, was limited to an effect on fear of developing adverse effects and attitude toward the COVID-19 vaccine. This relation suggests that catastrophizing and dispositional pessimism may orient, although weakly, specific vaccine beliefs and fear of adverse effects. On the basis of previous research investigating how nocebo-related factors are related to one other (Devlin et al., 2021) and the current model, unsurprisingly, expectations and perceived susceptibility to disease shared some variance in predicting adverse effects. However, only perceived susceptibility to disease captured individual’s previous history. In addition, although it needs to be investigated further, it is interesting that the association between risk perception and adverse effects was only partially mediated by expectations of the occurrence of adverse effects and fully mediated by expectations of their perceived severity. This result seems to suggest the contribution of a general and not just a specific disease risk perception on adverse effects of the COVID-19 vaccine.

Importantly, we identified that factors amenable to change, such as beliefs about the utility of vaccination in general and trust in health and scientific institutions, predicted a substantial proportion of specific beliefs on COVID-19-vaccine-related expectations and attitudes. In keeping with several studies that examined the link between vaccine hesitancy and antiscience attitudes (Goldenberg, 2016), we found a strong correlation between vaccination skepticism and trust in health and scientific institutions. Not surprisingly, vaccination skepticism and low trust in health and scientific institutions led to a low favorability toward the COVID-19 vaccine. Moreover, the negative attitudes toward COVID-19 vaccines and expectations of adverse effects fully mediated the relationship between vaccination skepticism and the occurrence of adverse effects.

Taken together, the present findings indicated that specific and general vaccine-negative attitudes and trust in science can affect not only vaccine acceptance but also the likelihood of developing adverse effects from vaccines. Fostering the dissemination of clear and evidence-based information to increase positive attitudes about the safety of COVID-19 vaccines is a priority for increasing COVID-19 vaccination confidence and decreasing nocebo effects. However, when it comes to converting vaccine skeptics, it is well known that the explications approach has its limitations (Hornsey et al., 2018). No experts’ risk perception of an event relies primarily on irrational and emotional components rather than evidence-based information (Zinn, 2008). The role of trust in health and scientific institutions found here cautions against addressing COVID-19 vaccine reluctance and nocebo effects with evidence-based information campaigns; doing so might not be effective or may even have the opposite result (Betsch & Sachse, 2013; Horne et al., 2015; Larson et al., 2014). According to the attitude-roots model concerning general vaccine hesitancy (Hornsey et al., 2018), there is a need to tailor interventions addressing COVID-19 vaccine concerns. Health professionals, such as general practitioners, as the most trusted advisors and influencers of vaccination decisions (Paterson et al., 2016), can play a key role in motivating reluctant people. Therefore, health professionals should be familiar with strategies (Bingel & Team, 2014) to minimize COVID-19 vaccine nocebo effects, being aware of their responsibilities in optimizing COVID-19 vaccine attitudes and expectations. Supporting evidence-based information campaigns for the general population together with taking actions to increase the knowledge and skills of health professionals about nocebo effects may increase COVID-19 vaccination confidence and uptake.

Some limitations of this study must be recognized. First, although the model suggests a cause-and-effect relationship, we acknowledge that this study is correlational, so we cannot claim that any of the nocebo-related factors are causally related to each other or to adverse effects of the COVID-19 vaccine. Another limitation is strictly related to the sample studied, which consisted of healthy working people belonging to a relatively narrow age range and who were invited to be vaccinated by their employers, limiting the generalizability of the present findings. Other limits concern using single-item measures of expectations of adverse effects and fear; however, to the best of our knowledge, there is no validated measure of these constructs, and we based our methodology on established psychometric properties of a numerical rating scale format used in the nocebo literature. More importantly, we are not able to consider the impact of learning such as the conditioning coming from the first vaccine dose, as we did not measure whether beliefs about the vaccine and adverse effects changed when participants experienced the symptoms (e.g., participants might perceive them as a beneficial immune system response). It is also important to keep in mind that the adverse effects of COVID-19 vaccination were predominantly demonstrated by subjective measures and that this study did not break down the functional and neurobiological mechanisms underlying the effect of the psychological variables on symptom perception. However, as substantiated by changes in physiological functions, expectations shape affective and attentional processes involved in sensory information processing (Atlas, 2021; Colloca & Barsky, 2020; Kirsch, 1985). In future studies, it will be interesting to investigate at which level of perceptual processing this modulation intervenes (e.g., at sensory threshold or higher level, such as semantic attribution/interpretation, or awareness).

Conclusion

Bearing in mind the limitations of the present study, the results highlight that the nocebo phenomenon in COVID-19 vaccination is substantial. Importantly, the present study helps to identify that people’s negative expectations and general and COVID-19-specific vaccine beliefs are important determinants of both occurrence and perceived severity of adverse effects of the COVID-19 vaccine. Consistent with studies suggesting that adverse effects play a pivotal role in drug-treatment discontinuation in clinical practice (Rief et al., 2006), these data shed light on the importance of people’s beliefs and subjective experience of adverse effects, which could substantially reduce vaccine adherence and could play a role in people’s withdrawal from necessary repeated vaccination.

Supplemental Material

sj-docx-1-pss-10.1177_09567976231163875 – Supplemental material for No(cebo) Vax: COVID-19 Vaccine Beliefs Are Important Determinants of Both Occurrence and Perceived Severity of Common Vaccines’ Adverse Effects
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Supplemental material, sj-docx-1-pss-10.1177_09567976231163875 for No(cebo) Vax: COVID-19 Vaccine Beliefs Are Important Determinants of Both Occurrence and Perceived Severity of Common Vaccines’ Adverse Effects by Katia Mattarozzi, Arianna Bagnis, Joanna Kłosowska, Przemysław Bąbel, Valeria Cremonini, Alessandra De Palma, Arianna Fabbri, Elisa Farinella, Lucrezia Puccini, Vittorio Sambri, Simona Semprini and Paolo Maria Russo in Psychological Science

Acknowledgments

The authors would like to thank healthcare professionals working at the Ravenna Medical Centre for their valuable contribution in data collection. We would also like to thank ConfIndustria Ravenna, Lega delle Cooperative e Confcooperative Ravenna to have facilitated the study, and any respondents for their time.

Footnotes

Supplemental Material: Additional supporting information can be found at http://journals.sagepub.com/doi/suppl/10.1177/09567976231163875

Transparency

Action Editor: Paul Jose

Editor: Patricia J. Bauer

Author Contributions

Katia Mattarozzi: Conceptualization; Funding acquisition; Methodology; Supervision; Writing – original draft.

Arianna Bagnis: Conceptualization; Data curation; Methodology; Visualization; Writing – original draft; Writing – review & editing.

Joanna Kłosowska: Formal analysis; Writing – review & editing.

Przemysław Bąbel: Conceptualization; Writing – review & editing.

Valeria Cremonini: Investigation; Writing – review & editing.

Alessandra De Palma: Investigation; Writing – review & editing.

Arianna Fabbri: Investigation; Writing – review & editing.

Elisa Farinella: Funding acquisition; Investigation; Resources; Writing – review & editing.

Lucrezia Puccini: Formal analysis; Writing – review & editing.

Vittorio Sambri: Data curation; Funding acquisition; Investigation; Resources; Writing – review & editing.

Simona Semprini: Formal analysis; Writing – review & editing.

Paolo Maria Russo: Conceptualization; Supervision; Writing – review & editing.

The author(s) declared that there were no conflicts of interest with respect to the authorship or the publication of this article.

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Supplementary Materials

sj-docx-1-pss-10.1177_09567976231163875 – Supplemental material for No(cebo) Vax: COVID-19 Vaccine Beliefs Are Important Determinants of Both Occurrence and Perceived Severity of Common Vaccines’ Adverse Effects
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Supplemental material, sj-docx-1-pss-10.1177_09567976231163875 for No(cebo) Vax: COVID-19 Vaccine Beliefs Are Important Determinants of Both Occurrence and Perceived Severity of Common Vaccines’ Adverse Effects by Katia Mattarozzi, Arianna Bagnis, Joanna Kłosowska, Przemysław Bąbel, Valeria Cremonini, Alessandra De Palma, Arianna Fabbri, Elisa Farinella, Lucrezia Puccini, Vittorio Sambri, Simona Semprini and Paolo Maria Russo in Psychological Science

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