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INTRODUCTION
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2, a pathogen that primarily spreads through close contact from person to person and targets the human respiratory system [1]. On January 30th, 2020, the World Health Organization (WHO) declared COVID-19 a public health emergency of international concern. On March 11th, 2020, WHO characterized COVID-19 as a pandemic [2]. Up to July 29th, 2022, there were 572,239,451 confirmed cases and 6,390,401 confirmed deaths worldwide [3, 4].
The development of safe and effective COVID-19 vaccines was the first step toward a long-term solution to the pandemic. The first mass vaccination program started in December 2020. At the date of May 17th, 2022, Italy had one of the highest COVID-19 vaccination coverage in Europe, with only Portugal, Malta and Spain exceeding Italy in terms of percentage of population vaccinated with at least one dose [5, 6]. As of July 27th, 2022, 86.6% of Italian eligible subjects completed the primary vaccination cycle and 83.7% got the booster dose too, with slight differences among Italian regions [7].
Vaccination is recognized as one of the most cost-effective methods of avoiding diseases. The WHO estimated that it currently prevents 2-3 million deaths a year and a further 1.5 million could be avoided if global vaccination coverage improved [8]. A recent study confirmed that COVID-19 vaccination has changed the course of the pandemic, avoiding 14.4 million deaths in 185 countries between December 2020 and December 2021 [9]. However, vaccine hesitancy, defined as a “delay in acceptance or refusal of vaccination despite availability of vaccination services” [10, 11], is a phenomenon that has existed since the first vaccines were administered and has become much more difficult to face in the age of social media. Because it undermines the progress made in addressing vaccine-preventable diseases, vaccine hesitancy was recognized among the top 10 threats to global health by the WHO in 2019 [8].
COVID-19 vaccination campaign achieved overall high coverages in Italy; however, some pockets among population did not vaccinate at all or did not get the booster dose. This issue may be attributable to several reasons, including the dynamics of supply and service delivery in the Italian health system, but also people’s beliefs, attitudes, and behaviors. Among the barriers to the uptake of COVID-19 vaccination, vaccine hesitancy has been documented by a big body of evidence [12-22] as a key modifiable factor that places critical challenges to the successfully implementation of the COVID-19 vaccination campaign. Vaccine hesitancy is a complex and context-specific issue, varying across time, place, and vaccines [23-34]. According to the SAGE Working Group’s Vaccine Hesitancy Determinants Matrix, factors that can influence hesitancy could be grouped in three categories: contextual influences (due to historic, socio-cultural, environmental, health system/institutional, economic or political factors), individual and group influences (arising from personal perception of the vaccine or from the social/peer environment), and vaccine/vaccination-specific issues (directly related to vaccine or vaccination) [11, 23-30].
Uninterrupted efforts should be made to vaccinate everyone who is eligible in every country and an effective vaccination program cannot avoid considering the understanding of concerns and expectations of individuals and communities regarding vaccines and vaccination. In fact, this could help in reaching pockets of unvaccinated people and addressing hard-to-reach populations, through tailored interventions, even in contexts where vaccination coverage is high. The monitoring of vaccination coverage and of reasons for non-vaccination is a required activity to ensure population Essential Levels of Care (LEA) [31]. However, albeit also the Italian Society of Hygiene (Società Italiana di Igiene, Medicina Preventiva e Sanità Pubblica, SItI) underlined the need of monitoring these issues, a national monitoring system has not been implemented yet [35]. Furthermore, despite the increasing body of literature investigating COVID-19 vaccine hesitancy and its determinants in Italy, all available evidence has not been summarized to date. For this reason, the objective of this study was to carry out a systematic literature review of the Italian studies on the topic, in order to collect and summarize the evidence on factors associated with acceptance or hesitancy of COVID-19 vaccination in the Italian population. The synthesis of this evidence will be useful for better understanding the reasons for COVID-19 vaccine acceptance or hesitancy and, consequently, supporting evidence-informed interventions to increase COVID-19 vaccination coverage in Italy.
MATERIALS AND METHODS
A systematic review was conducted and reported according to the Preferred Reporting Items for Systematic Reviews (PRISMA) [36].
Search strategy
PubMed was searched to retrieve potential eligible articles published from the inception until May 3rd, 2022. The PubMed search was pursued with a search string developed on the PICO model (P, population/patient; I, intervention/indicator; C, comparator/control; and O, outcome) and reported below:
((vaccin*[tiab] OR immuniz*[tiab] OR immunis*[tiab]) AND (covid*[tiab] OR sars-cov-2[tiab] OR coronavirus[tiab] OR 2019ncov[tiab])) AND ((adherence[tiab] OR uptake[tiab] OR accept*[tiab] OR intent*[tiab] OR willingness[tiab] OR facilitator*[tiab] OR confiden*[tiab] OR trust[tiab] OR hesita*[tiab] OR refus*[tiab] OR reject*[tiab] OR unwillingness[tiab] OR opposition[tiab] OR barrier*[tiab] OR mistrust[tiab] OR distrust[tiab] OR anti-vaccin*[tiab] OR antivaccin*[tiab] OR exemption*[tiab] OR behaviour[tiab] OR attitude*[tiab] OR determinant*[tiab] OR predict*[tiab])) AND (Ital*).
The reference lists of included articles were hand-searched to look for additional eligible studies.
Inclusion and exclusion criteria
The systematic review included observational analytical studies conducted on the Italian population that assessed acceptance or hesitancy towards COVID-19 vaccination as outcomes and any favorable or unfavorable factor associated to them.
We excluded systematic reviews, non-empirical studies, conference, editorials, commentaries, book reviews, and abstracts without a full text. In addition, studies whose full text could not be retrieved were excluded. International studies that did not analyze and report disaggregated data by countries were also excluded; if disaggregated data were reported, we extracted only separately reported Italian data.
Study selection
The study selection was conducted by one author and further cross-checked by another author for accuracy. Disagreements were iteratively discussed until agreement was reached. The selection of eligible articles was carried out by screening titles and abstracts first and then full texts. The study selection was performed from May 2022 to June 2022.
Data extraction and synthesis
The full text review and data extraction were conducted by one author and further cross-checked by another author for accuracy. Disagreements were iteratively discussed until agreement was reached. The data extraction was performed from June 2022 to July 2022.
A dedicated data extraction form developed on Excel was used to gather the following information for each eligible study:
- Study identification (first author, title, journal, and publication year);
- Study characteristics (region/city, period, design and study population);
- Study population characteristics (sample size, age, gender, and socio-cultural-economic characteristics, presence of any special health conditions, vaccination status);
- Study outcome(s) (outcomes of the study with relevant descriptive statistics, percentages; factors associated with the outcome).
Because of expected heterogeneity among studies, the synthesis of data was conducted only qualitatively and reported in summary tables.
Factors associated with acceptance or hesitancy towards COVID-19 vaccination were grouped according to the categories identified by the SAGE Working Group in the Vaccine Hesitancy Determinants Matrix [7], namely contextual, individual and group, and vaccine/vaccination-specific influences.
Quality assessment and risk of bias
The methodological quality and risk of bias of included articles were assessed through the Newcastle Ottawa Scale - NOS in its original version [37] and in a version adapted for the assessment of analytical cross-sectional studies [38]. The assessment was conducted by one author and further cross-checked by another one. Disagreements were resolved by discussion with a third researcher.
To summarize the results of the quality assessment and risk of bias, the articles were grouped into four categories: excellent (10-11 points), good (9-7 points), sufficient (6-5 points) and poor (4-0 points) quality. The risk of bias decreases as the quality increases.
RESULTS
Results of the search strategy
PubMed search returned 606 articles, of which, after the screening by title and abstract and by full text, 91 papers were retrieved for the assessment of final eligibility. Of these, 59 articles [39-97] met eligibility criteria and were included in the systematic review. The study selection process is reported in Figure 1.
Characteristics of the included studies
Among included articles, 27 studies (45.8%) addressed the whole Italian population [43, 44, 47, 48, 51, 59, 61, 63-66, 68, 70, 74, 76, 77, 79, 84, 86, 88-90, 93-97], whereas 12 studies (20.3%) [42, 45, 60, 62, 67, 71, 73, 75, 80-82, 92] were conducted in northern Italy, 5 (8.5%) [40, 41, 54, 83, 87] in central Italy and 12 (20.3%) [39, 46, 49, 50, 52, 53, 55, 56, 58, 69, 85, 91] in southern Italy.
The studies were conducted between February 2020 and January 2022; in particular, 18 [43, 47-50, 55, 59, 60, 63, 65, 73, 76, 78, 79, 88, 90, 95, 97] (30.5%) studies were conducted before the start of the vaccination campaign in Italy, 33 (55.9%) [39-42, 44-46, 51-54, 56-58, 61, 64, 69, 70, 74, 75, 77, 80-83, 85-87, 89, 92-94, 96] after the start of the vaccination campaign and 8 (13.6%) [62, 66-68, 71, 72, 84, 91] straddling the two periods. Twenty-five (41.7%) [41, 42, 47, 51, 56, 59-61, 63, 65, 66, 72, 74, 76, 79, 80-82, 84, 86, 88, 89, 95-97] studies investigated the attitudes of general adult population towards COVID-19 vaccination, and two [43, 58] (3.3%) the attitude of the elderly. Ten (17%) [44, 45, 52, 54, 64, 68, 69, 75, 87, 91], focused on potentially more fragile and/or at-risk population groups (patients with chronic diseases, persons previously tested positive for SARS-CoV-2, prisoners, migrants). Eight studies (13.3%) [46, 48, 50, 62, 70, 77, 90, 93] investigated the attitudes towards vaccination of healthcare workers. Eight (13.3%) [39, 40, 49, 57, 67, 73, 78, 85] involved students and/or university staff. Seven studies (11.7%) [53, 55, 69, 71, 83, 92, 94] investigated parents’ attitudes towards COVID-19 vaccination of their children, one [55] of which also assessed parents’ propensity to vaccinate for themselves.
In 25 studies (42.4%), the study population was balanced between females and males, 28 (47.4%) study populations were predominantly formed by females (>60% of the sample) while only two (3.4%) [52, 62] were predominantly formed by males; eventually four articles (6.8%) [46, 66, 76, 84] did not report gender distribution of the study population.
Two studies (3.4%) [39, 56] evaluated populations that had already undergone a full cycle of vaccination.
A full description of the characteristics of the included studies is given in Table 1.
Only 3 studies (5.1%) [41, 54, 82] assessed actual vaccine uptake as an outcome, while the others investigated attitudes towards vaccination considering the intention to vaccinate.
The majority of the articles referred to COVID-19 vaccination in general, except for three studies (5.1%) which referred to Vaxzevria, [39], to mRNA [85] and viral vector [56] vaccine type; moreover, one study (1.7%) [56] specifically assessed the attitude towards the administration of the booster dose.
Among studies investigating COVID 19 acceptance and /or hesitancy, there is a considerable variability in definition of outcomes, in study population type and in periods assessed (Table 1). Vaccination hesitancy showed the highest values in a study conducted in November 2020 on a population of parents, who stated that they were not positively inclined to vaccinate themselves in 73.4% of cases or to vaccinate their children in 82.8% [55]. Regarding hesitancy about vaccinating children, a lower percentage (26.3%) was found among parents of children with chronic diseases between December 2021 and January 2022 [69]. The lowest percentage of vaccination hesitancy (2.4%) was recorded among healthcare professionals [70]. The vaccination acceptance ranged from 94.7% in a study conducted among students of the Catholic University of the Sacred Heart in July 2020 [78] to 36.2% in a study performed on the general population in October 2020 [84]; this study found an increase in acceptance rate up to 83.8% in May 2021 too [84].
Results of the quality assessment and risk of bias
The details of the quality assessment are shown in detail in the Supplementary Material available online whereas the overall scores are reported in Table 1. The quality scores ranged from 4 to 11 (median: 7; mean: 7.05). The quality was evaluated as “very good” for 6 studies (10.2%) [41, 43, 53, 56, 69, 82], “good” for 29 studies (49.2%) [39, 44-46, 49-52, 57-59, 62, 63, 66, 68, 74, 75, 77, 80, 81, 84, 86-89, 91, 92, 94, 96] and “sufficient” for 23 studies (39.0%) [40, 42, 47, 48, 54, 55, 60, 61, 64, 65, 67, 70-73, 76, 78, 79, 83, 85, 90, 95, 97], while for only one study (1.7%) [93] was evaluated as “low”. With regard to risk of bias, thirteen studies [40, 47, 48, 55, 61, 64, 70-73, 85, 93, 97] could be considered at high risk of selection bias as they were scored zero in three out of four items considered, namely representativeness of the sample, sample size and non-respondent. Three studies [54, 60, 65] have a zero score in the item of comparability, while no article has a zero score in the domain referred to outcome assessment. Special attention should be paid to the article of Di Valerio, 2021 [93], which totalized a NOS score of 4, so it is reasonable to assume that it is at high risk of bias. Nevertheless, the evidence on factors associated with acceptance or hesitancy of COVID-19 vaccination, that are hereafter summarized, came from many studies, thus minimizing the hazard of making conclusions based only on studies at high risk of bias.
Factors associated with COVID-19 vaccine acceptance or hesitancy
The complete matrix of factors associated with COVID-19 vaccination acceptance or hesitancy is reported in Table 2 and, hereafter, summarized according to the groups of influences.
Contextual influences
Among the contextual influences, socio-demographic and cultural factors have been the most investigated. Age was associated with adherence to vaccination, with a greater propensity to be vaccinated among older subjects than younger ones [41, 42, 52, 55, 56, 60, 64, 66, 71, 72, 74, 76, 79, 82, 84, 86, 88, 89, 91, 92, 96]. Similarly, a significant association was found between the higher age of children/adolescents and the propensity of parents to vaccinate them [53, 69, 71, 94]. Only few studies have come to opposite conclusions. In all except than two studies [40, 49] female gender was found to be associated with hesitancy [41-44, 47, 50, 55, 60, 61, 66, 71-73, 77, 80, 82, 84, 86, 88-90, 92, 96]. A medium/higher level of education was overall associated with a greater propensity to vaccination [40, 44, 55, 58, 61, 64-66, 69, 73, 84, 86, 88, 89, 96, 97], while a low educational level was associated with hesitancy [41-43, 71, 72, 74, 80, 81]. The evidence about health workers showed that they are more predisposed to accept vaccination [40, 48, 50, 61, 77, 96]. With regard to the source of information, there is a clear relation between the consultation of scientific/institutional information and the acceptance of vaccination [50, 55, 56, 81, 96], while the collection of information from mass media is associated to hesitancy [48, 58, 66, 71, 75, 81, 86, 96]. In the political sphere, both trust in government and institutions [47, 61, 74, 79, 86, 97] and support for health policies [66, 71, 96] are predictors of vaccination acceptance.
Individual and group influences
Beliefs, attitudes, and knowledge/awareness were the factors mostly addressed among individual and group influences. In particular, the attitude to preventive behaviours (such as use of masks, adherence to therapies, adherence to the flu vaccination campaign and cancer screening) was significantly associated with COVID-19 vaccination acceptance in half of the studies [40, 42, 43, 45-48, 51, 53, 57, 59, 60, 62, 64, 66, 67, 70, 72, 73, 76, 78, 82, 83, 86, 90, 91, 95, 97]. Twenty-five articles [39, 44-47, 57, 59, 61, 63, 66, 69, 71, 74, 76, 80, 81, 83, 84, 86, 88, 89, 92, 95-97] investigated the relationship between vaccination and confidence in science, medicine, health institutions and healthcare professionals, as well as confidence in vaccines in general; in contrast, propensity to alternative medicine [44] and previous experience of adverse events linked to vaccinations [67, 70, 93] were related to hesitancy. A positive association with acceptance was also found in relation to health literacy and health engagement [63, 68]. With regards to the perception of risk of disease, some studies showed a significant association between the perception of risks of COVID-19 and vaccination acceptance [40, 43, 44, 46, 49, 50, 52, 53, 61, 63, 66, 69, 77, 78, 80, 81, 83, 84, 86, 89, 95-97]. The perception of the safety [40, 47, 49, 50, 83, 85], efficacy [40, 83, 85, 90] and usefulness [46, 47, 53, 85] of the vaccine, as well as the experience of negative consequences of the disease among family members, friends and acquaintances [43, 56, 94, 96] were associated with vaccination acceptance. Vaccination hesitancy was associated with the perception of insufficient information about the vaccine [39, 56, 69]. Eventually, other factors associated with vaccination acceptance were the concern about emergency [40, 43, 78, 79] and economic situation [66, 96].
Vaccine- and vaccination-specific influences
among these influences short time needed to develop COVID-19 vaccines was reported as a cause of concern and therefore for vaccination hesitancy [85].
DISCUSSION
It has been estimated that in Italy, from January 2021 to January 2022, about 8 million cases, over 500,000 hospitalizations, over 55,000 hospitalizations in intensive care units and about 150,000 deaths were directly prevented by COVID-19 vaccination [98]. However, the phenomenon of vaccine hesitancy, both against COVID-19 vaccines and vaccination in general, skyrocketed since the beginning of the pandemic, with differences related to several aspects [99]. For this reason, every effort to understand the phenomenon is of great value to guide counteracting actions.
Our review addressed the determinants of COVID-19 vaccination acceptance and hesitancy in the Italian population, being the first one, to the best of our knowledge, to provide a broad and overall overview of the topic. The findings of our review showed that, as expected, the major reasons behind COVID-19 vaccination hesitancy were individual and group factors, such as perceived safety, efficacy and usefulness of the vaccine. In addition, the lack of awareness and information was often reported to negatively impact on vaccination attitudes too.
The reasons for COVID-19 vaccination acceptance or hesitancy have been investigated worldwide by a huge amount of literature, addressing not only the overall population but also specific groups, such as healthcare professionals and students [100-103], or subgroups with expected lower vaccine uptake, such as pregnant women [104, 105], ethnic minority [106-108], adolescents/young adults [109] and parents in respect to their children [110, 111]. Also, all this evidence highlighted that the main reasons for vaccine hesitancy belonged to individual and group influences, including lack of information or misinformation [100, 102, 104, 108], together with concerns about vaccine safety [100, 102-104, 106], efficacy [102-103, 106], and adverse events [100-102, 104]. Social and institution trust/mistrust was also identified as a relevant determinant [102, 103, 108, 109]. These factors were found to be significant determinants of COVID-19 vaccine acceptance or hesitancy in our review as well as in other reviews addressing the same topic at worldwide level [112-115] or in respect to other pandemics [116].
According to our review, contextual influences were the most studied factors after individual and group influences. In particular, socio-demographic factors, such as female gender, younger age, low income, and low educational level were found to be associated with COVID-19 vaccine hesitancy in Italy. These factors were found to be relevant determinants of COVID-19 vaccine hesitancy also by other reviews addressing the worldwide population [114, 115, 117, 118]. It is worthwhile to observe that influences of this kind are particularly relevant also in respect to children vaccination, according to our review as well as other ones [110]. Prevalent women’s role as children’s caregivers should particularly call for tailored programs addressing their concerns about vaccines to increase their compliance with vaccination for themselves and their children too.
Further studies should surely better disentangle the interrelationship between determinants of vaccine hesitancy and vaccination uptake and assess the effectiveness of context-specific interventions to counteract vaccine hesitancy. However, the available huge body of evidence on the topic suggests that interventions to counteract COVID-19 vaccine hesitancy should address information and health literacy to offer people the possibility of making evidence-based choices. Furthermore, these interventions should be primarily targeted to some population groups that are shown to be more hesitant, namely women, young people, and with low income.
As the Italian population mostly identifies the health scientific community as a reliable source of information [119], it is essential to seize the enormous opportunity offered by this position to counter vaccine hesitancy, both with structured continuous intervention programs and with targeted interventions aimed at specific population subgroups. On the other hand, especially to reach also those pockets among population that do not rely on science and on scientific community, innovative real effective communication strategies are needed to be applied; indeed, the point is not only giving more detailed information, but rather offering it in a more effective and reliable way. To reach this goal, healthcare professionals are especially called to face their main competitor as source of information, namely social media. Vaccine hesitancy seems to be strictly related to erosion of public trust on scientific and social institutions that is strongly amplified by misinformation widely spread and sustained on social media. In contrast to traditional media, social media are characterized by its potential to rapidly spread a huge amount of information in a disintermediate environment and easily produce infodemics.
The intersection between social media-supported infodemics and epidemics certainly represents one of the most critical areas for future studies and interventions. Indeed, as social media radically changed the mechanism of accessing information and forming opinions, we need to better understand how individuals do acquire or avoid information and how their decisions can affect their behaviour. Including the complexity of human behaviour in the management of an epidemic is of critical importance to address its many facets through a scientifically based approach, in order to support the design of effective communication strategies and develop tools to correctly manage both the infodemics and the epidemics. To achieve this goal and capture the overall dimensions of epidemic/infodemic management, health professionals cannot work alone relying on medical competences only, but a multidisciplinary approach is essentially needed [120]. As recognized and underlined also in the National Prevention Plan 2020-2025 [121], a such effort should not be limited to the pandemic context alone, but should be transformed into a structured and continuous program targeted to the population, and in particular to the new generations, to improve health literacy increase and provide people with the necessary tools to make conscious choices for their own health.
This review has some limitations that should be considered when interpreting results. One of the major limitations is the PubMed search approach. However, our objective was to conduct a rapid synthesis of the evidence on factors associated to COVID-19 vaccine hesitancy in Italy and PubMed is a standalone, reliable platform to effectively retrieve most relevant publications. Evidence summarized from PubMed-based articles could indeed provide an initial but yet informative guidance for informing interventions to reach out hesitant people. Another limitation is that the protocol of this systematic review was not registered and that a potential bias in the selection of studies cannot be completely ruled out, even though selection was performed independently by two researchers. Eventually, the heterogeneity of studies’ methodology prevented us making a quantitative analysis and issuing more conclusive finding. In this respect, it should be said that the whole literature on vaccine hesitancy and its determinants is still undermined by the lack of standardization of definitions (i.e., confidence, acceptance and uptake are generally used interchangeably), data collection, and analysis. Nonetheless, to the best of our knowledge, this is the first systematic review giving an overview of determinants of COVID-19 vaccine hesitancy in the Italian population. Furthermore, as further strengths, most of the included studies were judged of moderate to good quality and the Vaccine Hesitancy Determinants Matrix was used to summarize the evidence.
CONCLUSION
Vaccine hesitancy represents a challenge for the successful implementation of COVID-19 vaccination in Italy. Our review demonstrated that various factors, particularly belonging to individual and group influences such as misinformation and perceived vaccine safety, efficacy, and usefulness, influence acceptance or hesitancy towards COVID-19 vaccination. Real effective interventions to increase vaccine uptake in Italy are needed and should rely on a multidisciplinary approach to address individuals’ concerns over vaccines, vaccine-related misinformation, social media-related infodemic dynamics and health literacy in order to support individuals in making conscious choices for individual and collective health.
Figures and tables
Figure 1. Preferred reporting items for systematic reviews (PRISMA) flowchart of the search strategy. From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:71. doi: 10.1136/bmj.n71. For more information, visit: http://www.prismastatement.org/.
| Author, year | Region/city | Period | Study population | Sample size(N) | Sex(female %) | Age | Study outcomes and results | Quality score | |||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Hesitancy | % | Acceptance | % | ||||||||
| Aliberti 2022 [39] | Salerno | May-August 2021 | University lecturers undergoing full cycle of Vaxzevria | 500 | 59.20 | range: 26-66 | Vaccine hesitancy (Vaxzevria) | 32.70 | 7 | ||
| Baccolini 2021 [40] | Roma | March-June 2021 | University students unvaccinated | 5,369 | 61.50 | mean (SD): 23.5 (4.5) | Vaccine hesitancy | 26.00 | 5 | ||
| Barello 2022 [51] | Italia | March 2021 | Adult population | 866 | 50.80 | range: 18-70 | Delay in vaccination while waiting for a ‘better’ vaccine | 46.00 | 9 | ||
| Belingheri 2021 [62] | Monza-Brianza | December 2020-January 2021 | Health workers | 421 | 28.50 | ≥25 | Intention to vaccinate | 82.20 | 7 | ||
| Belingheri 2021 [73] | Lombardia | December 2020 | Healthcare students | 422 | 82.90 | median (IQR): 21 (20-22) | Intention to vaccinate | 80.80 | 5 | ||
| Bucchi 2022 [84] | Italia | October 2020 | Adult population | 991 | NA | >15 | Intention to vaccinate (as soon as possible) | 36.02 | 7 | ||
| January 2021 | 987 | 59.90 | |||||||||
| May 2021 | 977 | 83.80 | |||||||||
| Buonsenso 2022 [94] | Italia | November 2021-January 2022 | Parents of children/adolescents with a previous diagnosis of COVID-19 | 121 | 81.20 | median (IQR): 42 (38-47) | Intention to vaccinate one’s children | 56.20 | 7 | ||
| Caserotti 2021 [95] | Italia | February-June 2020 | Adult population | 2,267 | 69.90 | mean (SD): 38.1 (14.0) | Intention to vaccinate | 40.10 | 6 | ||
| Caserotti 2022 [96] | Italia | January-February 2021 | Adult population | 5,006 | 50.00 | range: 18-70 | Intention to vaccinate | 88.00 | 9 | ||
| Caserotti 2022 [97] | Italia | May-June 2020 | Adult population | 448 | 70.80 | mean (SD): 33.8 (13.9) | Intention to vaccinate | NA | 5 | ||
| Cesaroni 2022 41] | Lazio | December 2021 | Adult population | 3,186,728 | 54.00 | mean (SD): 58.9 (14.3) | Non-vaccination | 10.30 | 11 | ||
| Cocchio 2022 [42] | Veneto | January 2021 | Adult population | 4,467 | 51.10 | mean (SD): 46.8 (16.0), median (IQR): 48 (34-59) | Vaccine hesitancy | 15.70 | 6 | ||
| Contoli 2021 [43] | Italia | August-December 2020 | Elderly population | 1,876 | 53.60 | ≥65 | Vaccine hesitancy | 45.00 | 10 | ||
| Vaccine refusal | 16.00 | ||||||||||
| Costantino 2021 [44] | Italia | February 2021 | Patients suffering from inflammatory bowel disease | 1,252 | 58.20 | median (IQR): 48 (37-58) | Vaccine hesitancy | 18.10 | 7 | ||
| Vaccine refusal | 2.70 | ||||||||||
| Costantino 2021 [45] | Milano | February 2021 | Patients suffering from coeliac disease | 103 | 78.60 | range: 18-77 | Vaccine hesitancy | 25.20 | 7 | ||
| Vaccine refusal | 4.80 | ||||||||||
| Costantino 2022 [46] | Palermo | January-March 2021, October 2021 | Health workers | 1,450; 1,391 | 64.70 | mean (SD): 46.3 (15.7); | Intention to vaccinate | 64.00 | 9 | ||
| Del Riccio 2021 [47] | Italia | December 2020 | Adult population | 7,605 | 65.50 | median (IQR): 47 (34-58) | Intention to vaccinate | 81.90 | 5 | ||
| Di Gennaro 2021 [48] | Italia | October 2020 | Health workers | 1,723 | 57.70 | mean (SD): 35.5 (11.8) | Vaccine hesitancy | 33.00 | 5 | ||
| Di Giuseppe 2021 [49] | Caserta-Napoli | September-November 2020 | University staff | 1,501 | 60.80 | mean (SD): 36 (14.2); range: 18-73 | Intention to vaccinate | 84.10 | 9 | ||
| Di Giuseppe 2021 [50] | Caserta-Napoli | September-November 2020 | Health workers | 738 | 42.30 | mean (SD): 40.4 (12.8); range: 19-70 | Intention to vaccinate | 80.70 | 9 | ||
| Di Giuseppe 2022 [52] | Campania | March-April 2021 | Prisoners | 865 | 0.00 | mean (SD): 42.4 (11.9); range: 18-78 | Intention to vaccinate | 63.90 | 8 | ||
| Di Giuseppe 2022 [53] | Napoli | April-May 2021 | Parents of children/adolescents | 607 | 82.40 | mean (SD): 42.3 (6.5); range: 22-63 | Intention to vaccinate one’s children | 68.50 | 10 | ||
| Di Noia 2021 [54] | Roma | March 2021 | Patients suffering from oncological diseases | 914 | 61.00 | range: 21-97 | Vaccinated | 88.80 | 6 | ||
| Di Valerio 2021 [93] | Italia | 1 January-16 February 2021 | Healthcare professional members of a Facebook private group | 10,898 | 77.90 | ≥18 | Vaccine hesitancy | 1.10 | 4 | ||
| Fedele 2021 [55] | Napoli | November 2020 | Parents of children/adolescents | 640 | 73.90 | NA | Vaccine hesitancy regarding the vaccination of one’s children | 82.80 | 5 | ||
| Vaccine refusal regarding the vaccination of one’s children | 34.50 | ||||||||||
| Vaccine hesitancy | 73.40 | ||||||||||
| Vaccine refusal | 23.40 | ||||||||||
| Folcarelli 2022 [56] | Napoli | November-December 2021 | Adult population vaccinated with full cycle | 615 | 57.40 | mean (SD): 32.1 (15.9); range: 19-76 | Vaccine hesitancy on booster dose administration | 24.70 | Intention to vaccinate (booster dose) | 85.70 | 10 |
| Gallè 2021 [57] | Bari, Napoli, Roma | February-April 2021 | University students | 3,226 | 56.00 | mean (SD): 23.3 (3.9); median (IQR): 22 (21-25); range: 18-45 | Vaccinated or Intention to vaccinate | 92.90 | 8 | ||
| Gallè 2021 [58] | Apulia | June-August 2021 | Elderly population | 1,041 | 58.30 | mean (SD): 76.6 (6.5) | Vaccinated or Intention to vaccinate | 92.70 | 8 | ||
| Genovese 2022 [59] | Italia | February-July 2020 | Adult population | 4,116 | 64.10 | mean (SD): 33(13) | Intention to vaccinate | 76.00 | 8 | ||
| Gerussi 2021 [60] | Udine | September-November 2020 | Adult population with a previous diagnosis of COVID-19 | 599 | 53.40 | mean (SD): 53 (15.8); range: 19-76 | Vaccine hesitancy | 59.10 | 5 | ||
| Vaccine refusal | 24.90 | ||||||||||
| Giuliani 2021 [61] | Italia | January-February 2021 | Adult population | 1,074 | 67.50 | range: 18-88 | Intention to vaccinate | 85.40 | 5 | ||
| Graffigna 2020 [63] | Italia | May 2020 | Adult population | 1,004 | 50.90 | mean (SD): 44(14); range: 18-70 | Intention to vaccinate | 58.60 | 8 | ||
| Guaraldi 2021 [64] | Italia | January 2021 | Patients sufferingfrom diabetes | 1,176 | 73.10 | >18 | Vaccine hesitancy | 15.70 | 5 | ||
| Heyerdahl 2022 [65] | Italia | December 2020 | Adult population | 1,000 | 50.40 | range: 18-65 | Vaccination acceptance | 66.00 | 5 | ||
| Lindholt 2021 [66] | Italia | September 2020-February 2021 | Adult population | 2,411 | NA | >18 | Vaccination acceptance | 60.00 | 9 | ||
| Lo Moro 2022 [67] | Torino | November 2020-February 2021 | Health students | 902 | 63.50 | median (IQR): 24 (23-26) | Vaccine hesitancy | 6.70 | 6 | ||
| Vaccine refusal | 0.50 | ||||||||||
| Magon 2021 [68] | Italia | June-August 2020, October 2020-March 2021 | Patients undergoing anticoagulant therapy | 288 | 50.50 | mean (SD): 58(20) | Vaccine hesitancy | 35.60 | 7 | ||
| Miraglia del Giudice 2022 [69] | Napoli | December 2021-January 2022 | Parents of children/adolescents with chronic diseases | 430 | 86.50 | mean (SD): 40.5 (6.1); range: 25-57 | Vaccine hesitancy regarding the vaccination of one’s children | 26.30 | Intention to vaccinate one’s children | 38.80 | 10 |
| Monami 2021 [70] | Italia | January 2021 | Health workers | 7,881 | 76.30 | NA | Vaccine hesitancy | 2.40 | 5 | ||
| Montalti 2021 [71] | Bologna | December 2020-January 2021 | Parents of children/adolescents | 4,993 | 76.60 | NA | Vaccine hesitancy regarding the vaccination of one’s children | 39.50 | 5 | ||
| Vaccine refusal regarding the vaccination of one’s children | 9.90 | ||||||||||
| Montalti 2021 [72] | Bologna, Palermo | December 2020-February 2021 | Adult population | 443 | 56.40 | >18 | Intention to vaccinate | 75.60 | 5 | ||
| Moscardino 2022 [74] | Italia | June 2021 | Adult population | 1,200 | 49.40 | mean (SD): 29.8 (6.5); range: 18-40 | Vaccine hesitancy | 25.10 | 9 | ||
| Vaccine refusal | 7.50 | ||||||||||
| Page 2022 [75] | Milano | February-May 2021 | Migrants | 126 | 67.20 | median (IQR): 41(20) | Vaccination request | 52.00 | 8 | ||
| Palamenghi 2020 [76] | Italia | May 2020 | Adult population | 1,004 | NA | NA | Intention to vaccinate | 59.00 | 6 | ||
| Papini 2021 [77] | Italia | February-April 2021 | Health workers | 2,137 | 71.70 | NA | Vaccine hesitancy | 6.70 | 7 | ||
| Pastorino 2021 [78] | Milano, Brescia, Piacenza, Cremona, Roma | June-July 2020 | University students | 436 | 70.40 | median (IQR): 23.1 (21.3-24.7) | Intention to vaccinate | 94.70 | 6 | ||
| Prati 2020 [79] | Italia | April 2020 | Adult population | 624 | 54.00 | mean (SD): 32.3 (12.7); range: 18-72 | Intention to vaccinate | 75.80 | 6 | ||
| Reno 2021 [80] | Emilia-Romagna | January 2021 | Adult population | 1,011 | 55.20 | mean (SD): 46.9 (11.5); range: 19-70 | Intention to vaccinate | 68.90 | 8 | ||
| Reno 2021 [81] | Emilia-Romagna | January 2021 | Adult population | 1,011 | 55.20 | mean (SD): 46.9 (11.5); range: 19-70 | Intention to vaccinate | 68.90 | 8 | ||
| Russo AG 2021 [82] | Milano-Lodi | September 2021 | Adult population | 2,981,997 | 52.10 | >18 | Vaccinated | 84.40 | 11 | ||
| Russo L 2021 [83] | Roma | July-August 2021 | Parents of children/adolescents | 1,696 | 81.60 | median (IQR): 42 (37-47) | Vaccinated or Intention to vaccinate one’s children | 32.20 | 6 | ||
| Salerno 2021 [85] | Palermo | May 2021 | University students unvaccinated | 2,667 | 68.10 | mean (SD): 22.74 (3.81) | Vaccine hesitancy (mRNA vaccine) | 8.20 | 5 | ||
| Vaccine refusal (mRNA vaccine) | 1.00 | ||||||||||
| Vaccine hesitancy (viral vector vaccine) | 42.60 | ||||||||||
| Vaccine refusal (viral vector vaccine) | 12.20 | ||||||||||
| Santirocchi 2022 [86] | Italia | March-May 2021 | Adult population | 971 | 57.60 | >18 | Intention to vaccinate | 78.50 | 7 | ||
| Scoccimarro 2021 [87] | Firenze | January-April 2021 | Patients suffering from diabetes | 502 | 60.20 | >18 | Vaccine hesitancy | 18.30 | 7 | ||
| Simione 2021 [88] | Italia | April 2020 | Adult population | 350 | 8.00 | mean (SD): 40.8 (10.8) | Intention to vaccinate | NA | 7 | ||
| Steinert 2022 [89] | Italia | June 2021 | Adult population | 1,087 | 51.20 | >18 | Vaccine hesitancy | 15.00 | 8 | ||
| Trabucco Aurilio 2021 [90] | Italia | December 2020 | Health workers | 531 | 73.40 | NA | Intention to vaccinate | 91.50 | 6 | ||
| Viola 2021 [91] | Messina | October 2020-June 2021 | Patients suffering from inflammatory bowel disease | 470 | 43.60 | mean (SD): 48(18) | Vaccination acceptance (vaccinated or vaccine booking) | 85.00 | 7 | ||
| Zona 2021 [92] | Modena | July-August 2021 | Parents of children/adolescents | 1,799 | 76.40 | mean (SD): 45 (5.8) | Intention to vaccinate one’s children | 26.50 | 7 | ||
| Vaccination hesitancy: refers to delay in acceptance or refusal of vaccination despite availability of vaccination services. | |||||||||||
| Vaccination acceptance: refers to vaccinated subject, subject who has already booked to vaccinate and intention to receive vaccination. | |||||||||||
| SD: standard deviation; IQR: interquartile range. | |||||||||||
| Macroareas of factors | Factors associated with: | Hesitancy [references] | Acceptance [references] |
|---|---|---|---|
| Contextual influences | Socio-demographic factors, religion, culture, gender | ||
| Age | |||
| Young | [71, 72, 79, 80] | [48, 59] | |
| Intermediate | [42, 76, 81, 95] | [93] | |
| Advanced | [74, 82, 88, 89] | [41, 52, 55, 56, 60, 64, 66, 84, 86, 91, 92, 96] | |
| Higher in children | [53, 69, 71, 94] | ||
| Gender (female) | [41-44, 47, 50, 55, 60, 61, 66, 71-73, 77, 80, 82, 84, 86, 88-90, 92, 96] | [40, 49] | |
| Citizenship/birth abroad | [41, 82] | ||
| Marital status (married) | [86] | [49] | |
| High household size | [53] | ||
| Educational level | |||
| Low | [41-43, 71, 72, 74, 80, 81] | ||
| Medium-high | [51] | [73] | |
| High | [40, 44, 55, 58, 61, 64-66, 69, 84, 86, 88, 89, 96, 97] | ||
| Low income | [74, 80, 81, 89] | ||
| Occupation | |||
| Unemployed | [65, 69, 74] | [47, 92] | |
| In contact with the public | [42] | [60] | |
| Entrepreneurs | [97] | ||
| Administrative staff | [49] | ||
| Health workers and in particular doctors compared to other health professionals | [40, 48, 50, 61, 77, 96] | ||
| Deprivation (high) | [41, 82] | ||
| Residence | |||
| North | [70, 74] | ||
| Central | [43] | ||
| South | |||
| High population density areas | [43] | ||
| Religious affiliation | [88] | ||
| Information | |||
| Media | [48, 71, 75, 81, 96] | [58] | |
| Institutional and scientific information sources | [50, 55, 56, 81, 96] | ||
| Belief in misinformation | [66, 86] | ||
| Policy | |||
| Political ideology | [40, 66] | ||
| Trust in government and institutions | [47, 61, 74, 79, 86, 97] | ||
| Support for public health policies (e.g., compulsory vaccination) | [66, 71, 96] | ||
| Lockdown phase, during the emergency | [95] | ||
| Individual and group influences | Knowledge, beliefs, attitudes, experiences about health and prevention | ||
| Confidence in science, medicine, health institutions, health professionals | [39, 61, 66, 69, 71, 76, 84, 86, 88, 96, 97] | ||
| Positive attitude to alternative medicine | [44] | ||
| Attitude towards for preventive behaviour (e.g., use of masks, flu vaccination, screening, adherence to possible therapies) | [40, 42, 43, 45-48, 51, 53, 57, 59, 60, 62, 64, 66, 67, 70, 72, 73, 76, 78, 82, 83, 86, 90, 91, 95, 97] | ||
| Confidence vaccines (in general) | [44-47, 57, 59, 63, 74, 80, 81, 83, 89, 92, 95-97] | ||
| Health literacy (highlevel) | [68] | ||
| Health engagement | [63, 68] | ||
| Underlying chronic diseases | [39, 70, 87] | [41, 43, 54, 80-82, 96] | |
| Perceived health status (good) | [39] | [55, 61] | |
| Living with fragile subjects | [70, 85] (viral vector vaccines) | [48, 85] (mRNA vaccines) | |
| Previous reactions after vaccination | [67, 70, 93] | ||
| Vaccine and disease perception | |||
| Vaccine perception | |||
| Safety | [40, 47, 49, 50, 83, 85] | ||
| Efficacy | [40, 83, 85, 90] | ||
| Usefulness/Utility | [46, 47, 53, 85] | ||
| Insufficient information | [39, 56, 69] | ||
| Desire to choose the type of vaccine | [85] | ||
| Disease perception | |||
| Risks related to COVID-19 (due to severity of illness, high exposure, susceptibility to infection) | [51] | [40, 43, 44, 46, 49, 50, 52, 53, 61, 63, 66, 69, 77, 78, 80, 81, 83, 84, 86, 89, 95-97] | |
| Previous infection (confirmed or presumed) | [50, 62, 69, 70, 74] | [82, 85] | |
| Experience of the disease and its consequences (e.g., hospitalisation, death) among relatives/friends/acquaintances | [43, 56, 94, 96] | ||
| Human-psychological factors | |||
| Negative affective state | [96] | ||
| External health locus of control | [52, 61] | ||
| Conspiracy mentality | [51, 66, 74, 79, 85, 88, 96] | ||
| Calculation | [51] | ||
| Low perception of social support from family and friends | [74] | ||
| Desire to protect | [48, 67, 96] | ||
| Desire to return to normality | [78] | ||
| Economic concerns | [66, 96] | ||
| Concern about the emergency situation | [40, 43, 78, 79] | ||
| Attachment to the home country | [74] | ||
| Social life (extremely active or very inactive) | [42] | ||
| Relatives/friends opposed to the vaccine | [67] | ||
| Vaccine and vaccination-specific influences | New vaccines | ||
| Speed of new vaccine development | [85] | ||
| Role of health professionals | |||
| Recommendation by the doctor | [69, 71] |
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