The refusal of COVID-19 vaccination and its associated factors: a meta-analysis

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Fajar" }, { "@type": "Person", "name": "Gatot Soegiarto" }, { "@type": "Person", "name": "Laksmi Wulandari" }, { "@type": "Person", "name": "Andy P. Kusuma" }, { "@type": "Person", "name": "Erwin A. Pasaribu" }, { "@type": "Person", "name": "Reza P. Putra" }, { "@type": "Person", "name": "Muhammad Rizky" }, { "@type": "Person", "name": "Tajul Anshor" }, { "@type": "Person", "name": "Maya Novariza" }, { "@type": "Person", "name": "Surya Wijaya" }, { "@type": "Person", "name": "Guruh Prasetyo" }, { "@type": "Person", "name": "Adelia Pradita" }, { "@type": "Person", "name": "Qurrata Aini" }, { "@type": "Person", "name": "Mario V.P.H. Mete" }, { "@type": "Person", "name": "Rahmat Yusni" }, { "@type": "Person", "name": "Yama S. Putri" }, { "@type": "Person", "name": "Chiranjib Chakraborty" }, { "@type": "Person", "name": "Kuldeep Dhama" }, { "@type": "Person", "name": "Harapan Harapan" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background To date, more than 10% of the global population is unvaccinated against the coronavirus disease 2019 (COVID-19), and the reasons why this population is not vaccinated are not well identified. Objectives We investigated the prevalence of COVID-19 vaccine refusal and to assess the factors associated with COVID-19 vaccine refusal. Methods A meta-analysis was conducted from August to November 2022 (PROSPERO: CRD42022384562). We searched for articles investigating the refusal of COVID-19 vaccination and its potential associated factors in PubMed, Scopus, and the Web of Sciences. The quality of the articles was assessed using the Newcastle–Ottawa scale, and data were collected using a pilot form. The cumulative prevalence of the refusal to vaccinate against COVID-19 was identified through a single-arm meta-analysis. Factors associated with COVID-19 vaccine refusals were determined using the Mantel-Haenszel method. Results A total of 24 articles were included in the analysis. Our findings showed that the global prevalence of COVID-19 vaccine refusal was 12%, with the highest prevalence observed in the general population and the lowest prevalence in the healthcare worker subgroup. Furthermore, individuals with a high socioeconomic status, history of previous vaccination, and a medical background had a lower rate of COVID-19 vaccination refusal. Subsequently, the following factors were associated with an increased risk of COVID-19 vaccine refusal: being female, educational attainment lower than an undergraduate degree, and living in a rural area. Conclusion Our study identified the prevalence of and factors associated with COVID-19 vaccine refusal. This study may serve as an initial reference to achieve global coverage of COVID-19 vaccination by influencing the population of COVID-19 vaccine refusal. 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F1000Research 2024, 12 :54 ( https://doi.org/10.12688/f1000research.128912.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Systematic Review Revised The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 3 approved with reservations] Previously titled: The refusal of COVID-19 vaccination and its associated factors: a systematic review Fredo Tamara https://orcid.org/0000-0002-2534-9108 1 , Jonny K. Fajar https://orcid.org/0000-0002-0309-5813 1 , Gatot Soegiarto https://orcid.org/0000-0002-9197-3873 2 , [...] Laksmi Wulandari 3 , Andy P. Kusuma 4 , Erwin A. Pasaribu https://orcid.org/0000-0003-4675-0117 4 , Reza P. Putra https://orcid.org/0000-0003-1878-0060 5 , Muhammad Rizky https://orcid.org/0000-0003-1342-9028 6 , Tajul Anshor 6 , Maya Novariza 5 , Surya Wijaya 7 , Guruh Prasetyo 8 , Adelia Pradita 9 , Qurrata Aini 9 , Mario V.P.H. Mete 9 , Rahmat Yusni 10 , Yama S. Putri https://orcid.org/0000-0003-4967-8149 7 , Chiranjib Chakraborty 11 , Kuldeep Dhama https://orcid.org/0000-0001-7469-4752 12 , Harapan Harapan https://orcid.org/0000-0001-7630-8413 13-15 Fredo Tamara https://orcid.org/0000-0002-2534-9108 1 , Jonny K. Fajar https://orcid.org/0000-0002-0309-5813 1 , [...] Gatot Soegiarto https://orcid.org/0000-0002-9197-3873 2 , Laksmi Wulandari 3 , Andy P. Kusuma 4 , Erwin A. Pasaribu https://orcid.org/0000-0003-4675-0117 4 , Reza P. Putra https://orcid.org/0000-0003-1878-0060 5 , Muhammad Rizky https://orcid.org/0000-0003-1342-9028 6 , Tajul Anshor 6 , Maya Novariza 5 , Surya Wijaya 7 , Guruh Prasetyo 8 , Adelia Pradita 9 , Qurrata Aini 9 , Mario V.P.H. Mete 9 , Rahmat Yusni 10 , Yama S. Putri https://orcid.org/0000-0003-4967-8149 7 , Chiranjib Chakraborty 11 , Kuldeep Dhama https://orcid.org/0000-0001-7469-4752 12 , Harapan Harapan https://orcid.org/0000-0001-7630-8413 13-15 PUBLISHED 29 Jan 2024 Author details Author details 1 Brawijaya Internal Medicine Research Center, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia 2 Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60286, Indonesia 3 Department of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60286, Indonesia 4 Faculty of Medicine, Universitas Brawijaya, Malang, 65145, Indonesia 5 Department of Pediatric; Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia 6 Department of Surgery; Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia 7 Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia 8 Faculty of Animal Science, Universitas Brawijaya, Malang, 65145, Indonesia 9 Faculty of Nursing, Universitas Indonesia, Depok, 16424, Indonesia 10 Department of Biotechnology, Postgraduate School, Institut Pertanian Bogor, Bogor, 16680, Indonesia 11 Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, 700126, India 12 Division of Pathology, Indian Veterinary Research Institute, Uttar Pradesh, 243122, India 13 Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia 14 Tropical Diseases Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia 15 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia Fredo Tamara Roles: Conceptualization, Data Curation, Investigation, Methodology, Software, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Jonny K. Fajar Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Gatot Soegiarto Roles: Conceptualization, Data Curation, Investigation, Methodology, Supervision, Writing – Review & Editing Laksmi Wulandari Roles: Investigation, Methodology, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Andy P. Kusuma Roles: Data Curation, Formal Analysis, Investigation, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Erwin A. Pasaribu Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Reza P. Putra Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Muhammad Rizky Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Tajul Anshor Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Maya Novariza Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Surya Wijaya Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Guruh Prasetyo Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Adelia Pradita Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Qurrata Aini Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Mario V.P.H. Mete Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Rahmat Yusni Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Yama S. Putri Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Chiranjib Chakraborty Roles: Supervision, Validation, Visualization, Writing – Review & Editing Kuldeep Dhama Roles: Supervision, Validation, Visualization, Writing – Review & Editing Harapan Harapan Roles: Supervision, Validation, Visualization, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Emerging Diseases and Outbreaks gateway. This article is included in the Sociology of Health gateway. This article is included in the Sociology of Vaccines collection. Abstract Background To date, more than 10% of the global population is unvaccinated against the coronavirus disease 2019 (COVID-19), and the reasons why this population is not vaccinated are not well identified. Objectives We investigated the prevalence of COVID-19 vaccine refusal and to assess the factors associated with COVID-19 vaccine refusal. Methods A meta-analysis was conducted from August to November 2022 (PROSPERO: CRD42022384562). We searched for articles investigating the refusal of COVID-19 vaccination and its potential associated factors in PubMed, Scopus, and the Web of Sciences. The quality of the articles was assessed using the Newcastle–Ottawa scale, and data were collected using a pilot form. The cumulative prevalence of the refusal to vaccinate against COVID-19 was identified through a single-arm meta-analysis. Factors associated with COVID-19 vaccine refusals were determined using the Mantel-Haenszel method. Results A total of 24 articles were included in the analysis. Our findings showed that the global prevalence of COVID-19 vaccine refusal was 12%, with the highest prevalence observed in the general population and the lowest prevalence in the healthcare worker subgroup. Furthermore, individuals with a high socioeconomic status, history of previous vaccination, and a medical background had a lower rate of COVID-19 vaccination refusal. Subsequently, the following factors were associated with an increased risk of COVID-19 vaccine refusal: being female, educational attainment lower than an undergraduate degree, and living in a rural area. Conclusion Our study identified the prevalence of and factors associated with COVID-19 vaccine refusal. This study may serve as an initial reference to achieve global coverage of COVID-19 vaccination by influencing the population of COVID-19 vaccine refusal. READ ALL READ LESS Keywords COVID-19; vaccination; refusal; acceptance; risk factors. Corresponding Author(s) Fredo Tamara ( [email protected] ) Jonny K. Fajar ( [email protected] ) Gatot Soegiarto ( [email protected] ) Close Corresponding authors: Fredo Tamara, Jonny K. Fajar, Gatot Soegiarto Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2024 Tamara F et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Tamara F, Fajar JK, Soegiarto G et al. The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 3 approved with reservations] . F1000Research 2024, 12 :54 ( https://doi.org/10.12688/f1000research.128912.2 ) First published: 13 Jan 2023, 12 :54 ( https://doi.org/10.12688/f1000research.128912.1 ) Latest published: 29 Jan 2024, 12 :54 ( https://doi.org/10.12688/f1000research.128912.2 ) Revised Amendments from Version 1 In the revised version of the article, we made several modifications based on the reviewer's suggestions, but these changes did not alter the final findings of our study. The revisions to our article encompassed adjustments to the title, abstract, introduction, methods, results, and discussion. In the title, we replaced the term "a systematic review" with "a meta-analysis." In the abstract, we conducted a reproofreading to enhance sentence clarity as directed by the reviewer. In the introduction, we revised by adding explanations about the differences between vaccine refusal and vaccine hesitancy, specifically incorporated into the second paragraph of the introduction. Furthermore, we conducted reproofreading in the introduction to clarify sentence meanings. In the methods section, we added I-squared analysis, in addition to p heterogeneity, to assess heterogeneity across studies. Moreover, in the search strategy, we included the keyword "intention not to get vaccinated" as an additional term related to vaccine refusal. In the results section, we modified Table 1 by adding columns detailing final findings and sample size methods. Additionally, in the results section, we removed the design of included studies from the Table 1 and included it in the text under the subheading “selection of studies." In the discussion section, we introduced discussions on the 3Cs model in the fourth discussion paragraph, emphasized socioeconomic factors (SES) on vaccine refusal in the third paragraph of the discussion, and explained the contextual differences between vaccine refusal and hesitancy in the first discussion paragraph. Regarding study limitations, in the last paragraph of the discussion section, we removed the limitation about the study design of included studies and added country-specific factors and the WHO BeSD framework as additional study limitations. Finally, in the discussion section, we conducted proofreading to clarify the meaning of the discussions. In the revised version of the article, we made several modifications based on the reviewer's suggestions, but these changes did not alter the final findings of our study. The revisions to our article encompassed adjustments to the title, abstract, introduction, methods, results, and discussion. In the title, we replaced the term "a systematic review" with "a meta-analysis." In the abstract, we conducted a reproofreading to enhance sentence clarity as directed by the reviewer. In the introduction, we revised by adding explanations about the differences between vaccine refusal and vaccine hesitancy, specifically incorporated into the second paragraph of the introduction. Furthermore, we conducted reproofreading in the introduction to clarify sentence meanings. In the methods section, we added I-squared analysis, in addition to p heterogeneity, to assess heterogeneity across studies. Moreover, in the search strategy, we included the keyword "intention not to get vaccinated" as an additional term related to vaccine refusal. In the results section, we modified Table 1 by adding columns detailing final findings and sample size methods. Additionally, in the results section, we removed the design of included studies from the Table 1 and included it in the text under the subheading “selection of studies." In the discussion section, we introduced discussions on the 3Cs model in the fourth discussion paragraph, emphasized socioeconomic factors (SES) on vaccine refusal in the third paragraph of the discussion, and explained the contextual differences between vaccine refusal and hesitancy in the first discussion paragraph. Regarding study limitations, in the last paragraph of the discussion section, we removed the limitation about the study design of included studies and added country-specific factors and the WHO BeSD framework as additional study limitations. Finally, in the discussion section, we conducted proofreading to clarify the meaning of the discussions. See the authors' detailed response to the review by Amy Morrison See the authors' detailed response to the review by Angelo Capodici READ REVIEWER RESPONSES Introduction At the beginning of 2021, the coronavirus disease 2019 (COVID-19) vaccination program involving several designs including protein subunit, vector, inactivated, and mRNA, was started. 1 Currently, referring to data presented on Our World in Data, this vaccination program has included 84.6% of the global population, and the reason the rest of the population (15.4%) did not receive vaccination is still unknown. 2 The high number of vaccinated country populations is the result of the hard work of various parties, and this may be associated with factors such as the seriousness of governments in promoting vaccination programs, equitable distributions of vaccines, hard work of healthcare workers, good public awareness about the importance of vaccination, and effective promotion of vaccines to populations who have the power to hesitate about vaccines. 3 Contrarily, in the unvaccinated population, several factors may contribute to hesitation, including fear of harmful ingredients in vaccines, distrust of pharmaceutical companies, lack of knowledge about COVID-19, belief that a healthy lifestyle and a good diet are sufficient to fight against COVID-19, preference for natural immunity, lack of seriousness from the government in promoting vaccination programs, religious rules suggesting not to vaccinate, and limited information regarding the safety of vaccination. These factors have been reported to trigger hesitancy and refusal of the COVID-19 vaccination. 4 – 7 Moreover, there is a distinction between vaccine hesitancy and vaccine refusal. Vaccine hesitancy involves a delay in accepting or refusing vaccines despite the availability of vaccination services, and it is a complex and context-specific phenomenon that varies across time, place, and vaccines. Influencing factors include complacency, convenience, and confidence. 8 In contrast, vaccine refusal is characterized by a lack of vaccination and an explicit intention not to get vaccinated. 9 Consequently, it can be inferred that the context of vaccine hesitancy has a more extensive scope and includes the population of refusal. In our previous study, we had explored the global prevalence of COVID-19 vaccination hesitancy and its potential associated factors. 10 However, because the hesitancy population consists of both hesitancy and refusal populations, and the refusal population can influence individuals within their circle to become hesitant or refuse the COVID-19 vaccine, the prevalence of the COVID-19 vaccine refusal should also be investigated. It is widely known that new vaccines or vaccine candidates are commonly met with hesitation or rejection by the public. Before the COVID-19 pandemic, this phenomenon has been widely reported in several cases, such as: dengue, 11 malaria, 12 Ebola, 13 chikungunya, 14 and monkeypox. 15 This might be caused by poor public knowledge regarding the vaccine, including inadequate understanding of vaccine efficacy and side effects. In the case of COVID-19, this phenomenon might be affected by multiple factors, and theoretically, the factors had been contextualized into three major categories, including poor knowledge of vaccination programs, socioeconomic status, and social interaction. 16 Moreover, recently, influencers in their podcasts discussed the rejection of the COVID-19 vaccine, which is a dilemma that can influence people in society to reject COVID-19 vaccinations, thereby threatening the success of the COVID-19 vaccination program. 17 However, to date, there are no precise data on the prevalence of COVID-19 vaccination refusal and its potential associated factors. Several previous studies have investigated the refusal of COVID-19 vaccines; however, the results of these studies have been inconclusive. In the present study, we seek to explore the global prevalence of COVID-19 vaccination refusal and identify the associated factors using a meta-analysis approach. Methods Study design A meta-analysis following the guidelines of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol was carried out from August to November 2022 (PROSPERO: CRD42022384562). The PRISMA checklist in our study is provided in Figshare. 18 A systematic search was implemented in PubMed , Scopus , and Web of Science ; and the information was collected from each relevant article to determine the prevalence and associated factors of COVID-19 vaccines refusal. Eligibility criteria Pre-defined eligibility criteria were assigned prior to performing the search strategy. The inclusion criteria were: (1) assessment of the prevalence of COVID-19 vaccination refusal, and (2) investigation of the factors associated with COVID-19 vaccination refusal. Articles with double publications, letters to the editor, commentaries, and reviews were excluded. Search strategy and data extraction PubMed , Scopus , and Web of Science were searched up to November 5 th , 2022. Before conducting a search for the primary outcome, we identified the factors that might have an impact on the incidence of refusal of COVID-19 vaccines. The potential keywords adapted from medical subject headings were applied: “vaccine,” “vaccination,” or “immunization;” “COVID-19” or “coronavirus disease 2019;” “refusal” or “rejection” or “acceptance” or “intention not to get vaccinated.” The search strategy used only English words. In case of duplication, articles with a lower sample size used in the study were excluded. Moreover, to acquire additional references, a search on the reference list of related articles was also carried out. A pilot form was used to collect data from each study and consisted of the following items: (1) first author name, (2) time of publication, (3) design of study, (4) study period, (5) Newcastle–Ottawa scale (NOS), (6) the event rate of COVID-19 vaccination refusal or rejection or intention not to get vaccinated, and (7) factors associated with COVID-19 vaccination refusal. Data were collected by FT, JKF, APK, EAP, RPP, MR, TA, MN, SW, GP, AP, QA, MVPHM, RY, and YSP. Assessment of the methodological quality The NOS was used to assess the quality of potential articles. We included articles of moderate and high quality, and low quality articles were excluded. A score of 0–3, 4–6, and 7–9 indicated low, moderate, and high quality articles, respectively. The NOS assessment was performed by JKF, APK, and EAP using the NOS pilot form. Disagreements were resolved through discussion. Outcome measures The primary endpoints of our study were the global prevalence and factors associated with COVID-19 vaccination refusal. Potential factors associated with the refusal of COVID-19 vaccination were: age group, gender, marital status, educational attainment, employment status, healthcare-related job, socioeconomic status (SES), urbanity, presence of children and elderly people at home, individual with medical background, history of testing for COVID-19, family member/friend ever diagnosed with COVID-19, personal history of COVID-19 diagnosis, history of hospitalization due to COVID-19 among people in a social circle, and history of previous vaccination. Statistical analysis Data are presented as n (%). The statistical analysis consisted of the following parameters: publication bias among studies, heterogeneity among studies, event rate, and odds ratio with a 95% confidence interval (OR95%CI). Publication bias was assessed using Egger’s test. A p-value of less than 0.05 indicated that publication bias existed among studies. The heterogeneity in our study was determined using the Q test. Evidence of heterogeneity was considered if the p-value was less than 0.10 and I-squared was more than 50%. If we found heterogeneity among studies, we applied the random effects model, and in cases where no heterogeneity was found, we used a fixed effects model. The cumulative event rate of COVID-19 vaccine refusal was calculated using a single-arm meta-analysis with a dichotomous model, and the pooled OR95%CIs of factors associated with the refusal of COVID-19 vaccination were calculated using the Mantel-Haenszel method. The analysis was performed using the R package ( RStudio version 4.1.1, R Studio, California, MA, USA). Results Selection of studies A total of 3,422 papers and 4 papers were assessed from the databases and reference lists of related articles, respectively. In the initial evaluation, we excluded 33 papers due to duplication and 3,318 papers due to irrelevant topics. Subsequently, 75 articles were included in further review. We further excluded 17 articles as they were reviews and 34 articles due to insufficient data. Finally, the data retrieved from 24 articles were analyzed to estimate the cumulative prevalence and factors associated with COVID-19 vaccination refusal. 19 – 42 The flow diagram of article selection in our study is outlined in Figure 1 , and the characteristics of the articles included in our study are listed in Table 1 . The design employed for all articles in our study was cross-sectional. Figure 1. A flowchart of article selection in our study. Table 1. Baseline characteristics of articles included in our study. Author & year Country Sample selection method Sample size Study period Population NOS Study findings Al- Sanaf et al. 2021 Kuwait Convenience 1019 March 2021 HCW 5 Females, nurses, and private sector healthcare workers had higher vaccine refusal rates. Aurilio et al. 2021 Italy NA 531 December 2020 HCW 6 Female gender and belief in vaccine efficacy strongly predict vaccine intention. Baniak et al. 2021 USA Convenience 275 February 2021 HCW 6 Vaccine uptake was higher with confidence in safety and over 10 years of experience. Fakonti et al. 2021 Cyprus Stratified 435 December 2020 HCW 6 Cyprus nurses and midwives showed vaccine reluctance due to concerns. Fares et al. 2021 Egypt Probability 385 December 2020–January 2021 HCW 7 Vaccine acceptance depended on COVID-19 concerns and confidence in safety and effectiveness, while refusal was linked to limited trials and fear of side effects; accurate information was seen as crucial for increased acceptance. Fisher et al. 2020 USA Convenience 991 April 2020 GP 6 Vaccine refusal was associated with younger age, Black race, lower education, and not receiving the prior year's influenza vaccine, while refusal reasons included vaccine-specific concerns, a need for more information, anti-vaccine attitudes, and lack of trust. Grochowska et al. 2021 Poland Purposive 419 September–November 2020 HCW 5 For 86.3% of those hesitant and refusing COVID-19 vaccination, assurance of safety and efficacy would be convincing. Handam et al. 2021 Lebanon Probability 758 May–June 2021 Students 6 Vaccine refusal was linked to nationality, residency, and university rank, with less refusal among those confident in vaccine safety. Non-receipt of the flu vaccine, endorsement of conspiracies, and lower COVID-19 knowledge were associated with higher refusal. İkiışık et al. 2021 Turkey Probability 384 December 2020 GP 5 Vaccine acceptance was influenced by individual perceptions of risk and age. Janssen et al. 2021 France Convenience 4349 December 2020–March 2021 HCW 5 The primary concern among professionals who declined was the fear of adverse events. Kose et al. 2020 Turkey Convenience 1138 September 2020 HCW 7 Men, students, and those with a prior flu shot were willing to get the COVID-19 vaccine. Kozak et al. 2021 Germany Convenience and snowball 3368 March–April 2021 HCW 6 There was a high vaccination rate and strong willingness to receive the vaccine (over 80%) among all professional groups and fields of work Manning et al. 2021 USA Stratified and convenience 1205 August 2020–September 2020 Students 7 The primary factors for not receiving vaccination were concerns related to vaccine safety and potential side effects. Mena et al. 2021 Spain NA 865 December 2020–January 2021 HCW 7 Addressing doubts and fears about vaccination is crucial, especially among less inclined groups like females, younger individuals, and those without recent influenza vaccinations. Ousseine et al. 2021 France Non-probability 15426 February–April 2021 GP 6 Lower education level, low health literacy, financial hardship, being under 30 years old, and residing in a rural area were independently associated with uncertainty and unwillingness to get vaccinated. Paris et al. 2021 France Convenience 1965 February–February 2021 HCW 7 COVID-19 vaccine intention was independently associated with age, occupation, flu vaccine history, and concerns about AstraZeneca vaccine tolerability. Pataka et al. 2021 Greece Probability 656 December 2020 HCW 6 Most responders intending to accept vaccination were male physicians, older, married with children, and treated COVID-19 patients, with predictors for healthcare professionals' willingness being parenthood, physician status, and treating confirmed/suspected COVID-19 cases. Rodriguez-Blanco et al. 2021 Spain Convenience and probability 2494 November–December 2020 GP 7 Acceptance of the COVID-19 vaccine might be more likely among males, individuals aged over 60, married, retired, highly educated, or with a leftist political inclination. Saied et al. 2021 Egypt Convenience and probability 2133 January 2021 Students 7 The primary barriers to COVID-19 vaccination were insufficient data on vaccine side effects and a lack of information about the vaccine itself. Schwarzinger et al. 2021 France Stratified random 1942 July 2020 GP 6 Refusing vaccines and vaccine hesitancy were significantly associated with being female, age, lower education, poor compliance with past vaccinations, and the absence of specified chronic conditions or having only hypertension. Shaw et al. 2021 USA NA 5287 November–December 2020 HCW 6 Older, male, White, or Asian respondents showed higher vaccination likelihood, while predominant concerns among participants included vaccine safety, potential adverse events, efficacy, and speed of development. Spinewine et al. 2021 Belgium Convenience 1132 January 2021 HCW 6 A positive outlook on COVID-19 vaccination was associated with older age, physician status, seasonal flu vaccination, and various Health Belief Model factors. Vignier et al. 2021 French Guiana Convenience and snowball 579 January–March 2021 HCW 8 Older and concerned healthcare workers were more willing to get vaccinated, while nurses or those in non-medical professions, especially those born in French Guiana, were less likely due to fears of adverse effects or lack of trust in pharmaceutical companies and authorities' epidemic management. Wang et al . 2020 Hong Kong Stratified and convenience 806 February–March 2020 HCW 6 Individuals in the private sector, those with chronic conditions, those in contact with suspected or confirmed COVID-19 patients, and those who accepted influenza vaccination in 2019 were more inclined to accept COVID-19 vaccination. The cumulative prevalence of the refusal to COVID-19 vaccination Our analysis identified that the cumulative prevalence of the refusal to COVID-19 vaccination was 12% (event rate: 0.12; 95%CI: 0.10, 0.15; p Egger: 0.5290; p Heterogeneity<0.0001; p<0.0001) ( Figure 2A ). Subsequently, sub – group analysis found that the prevalence of the refusal to COVID-19 in general population was 20% ( Figure 2B ), healthcare workers 10% ( Figure 2C ), and students 11% ( Figure 2D ). Figure 2. The prevalence of COVID-19 vaccines refusal. A). All prevalence of COVID-19 refusal (Event rate: 0.12; 95%CI: 0.10, 0.15; p Egger: 0.5290; p Heterogeneity<0.0001; p<0.0001). B). The prevalence in general population subgroup (Event rate: 0.20; 95%CI: 0.15, 0.26; p Egger: 0.4100; p Heterogeneity<0.0001; p<0.0001). C). The prevalence in healthcare workers subgroup (Event rate: 0.10; 95%CI: 0.07, 0.14; p Egger: 0.6760; p Heterogeneity<0.0001; p<0.0001). D). The prevalence in student subgroup (Event rate: 0.11; 95%CI: 0.06, 0.20; p Egger: 0.5830; p Heterogeneity<0.0001; p<0.0001). Factors associated with COVID-19 vaccination refusal Table 2 and Figures 3 – 5 summarize the factors associated with the refusal of COVID-19 vaccination. Our calculation revealed that six of the 15 factors had a significant impact on COVID-19 vaccine refusal. We found that an increased risk of COVID-19 vaccine refusal was observed in the following covariates: female ( Figure 3A ), educational attainment lower than an undergraduate degree ( Figure 4A ) and living in rural areas ( Figure 5B ). Table 2. Factors associated with refusal to COVID-19 vaccination. Covariates Refusal/Total (n [%]) NS p Egger p Het OR 95% CI p Age group (years) <30 1827/9397 (19.44%) 9 0.2570 50 639/5344 (11.96%) 9 0.5240 <0.0001 0.91 0.60-1.36 0.6300 Sex Male 1194/8499 (14.05%) 22 0.2550 <0.0001 0.71 0.61-0.82 <0.0001 Female 3659/19842 (18.44%) 22 0.2550 <0.0001 1.42 1.22-1.65 <0.0001 Marital status Married 1702/9960 (17.09%) 6 <0.0001 <0.0001 0.77 0.71-0.82 <0.0001 Single 1954/9693 (20.16%) 6 <0.0001 0.9620 1.31 1.21-1.41 <0.0001 Educational attainment <BSc 2892/12199 (23.71%) 10 0.3620 <0.0001 1.74 1.34-2.26 <0.0001 ≥BSc 2397/16931 (14.16%) 10 0.3620 <0.0001 0.58 0.44-0.75 <0.0001 Employment Not Working 1751/8770 (19.97%) 7 0.1240 0.0550 1.11 0.96-1.28 0.1670 Working 2666/14218 (18.75%) 7 0.1450 0.0240 0.93 0.79-1.09 0.3850 Socioeconomic status Low 712/2947 (24.16%) 6 0.6470 <0.0001 1.05 0.59-1.84 0.8770 Medium 1947/8561 (22.74%) 6 1.0000 <0.0001 1.86 0.81-4.28 0.1450 High 1238/10286 (12.04%) 6 0.9900 <0.0001 0.42 0.18-0.99 0.0480 Having children at home 421/1775 (23.72%) 4 0.5940 <0.0001 0.65 0.35-1.21 0.1730 Hospitalization due to COVID-19 among people in the same social circle 16/668 (2.40%) 2 0.5890 0.0250 0.62 0.05-7.13 0.7030 Health literacy about COVID-19 vaccine 1795/11153 (16.09%) 4 0.5630 0.0010 0.60 0.31-1.17 0.1330 History of previous vaccination 2456/18868 (13.02%) 14 0.9710 <0.0001 0.28 0.17-0.48 <0.0001 History of chronic disease(s) 275/2317 (11.87%) 9 0.5130 <0.0001 0.94 0.64-1.38 0.7510 Personal history of COVID-19 diagnosis 494/3498 (14.12%) 8 0.1970 0.0280 0.94 0.77-1.15 0.5560 Family member/friend ever diagnosed with COVID-19 932/6739 (13.83%) 8 0.2800 <0.0001 0.94 0.72-1.21 0.6120 Ever tested for COVID-19 26/582 (4.47%) 2 <0.0001 0.6930 0.80 0.48-1.34 0.3950 Medical background 200/3642 (5.49%) 11 0.9170 <0.0001 0.32 0.17-0.58 <0.0001 Residential Urban 2629/16172 (16.26%) 4 0.3030 <0.0001 0.62 0.44-0.88 0.007 Rural 859/3324 (25.84%) 4 0.3030 <0.0001 1.61 1.14-2.28 0.007 Figure 3. Females had higher risk of refusal to COVID-19 vaccination than males (A), and individual history of previous vaccination had lower risk of refusal to COVID-19 vaccination (B). Figure 4. Individual with educational attainment <BSc had higher risk of refusal to COVID-19 vaccination than ≥BSc (A), and individual with medical background had lower risk of refusal to COVID-19 vaccination compared to general population (B). Figure 5. Individual with high SES had lower risk of refusal to COVID-19 vaccination than low SES (A), and rural residential living was associated with increased risk of refusal to COVID-19 vaccination than urban population (B). In contrast, the decreased risk of refusal of COVID-19 vaccination was affected by the following factors: high socioeconomic status ( Figure 5A ), history of previous vaccination ( Figure 3B ), and individuals with a medical background ( Figure 4B ). Source of heterogeneity and potential publication bias Our analysis using the Q test revealed that two variables (single marital status and history of testing for COVID-19) had no evidence of heterogeneity; thereafter, we applied a fixed-effects model. In contrast, a random-effects model was applied to the other covariates ( Table 2 ). Subsequently, our analysis using Egger’s test revealed that the marital status and ever tested for COVID-19 covariates exhibited a risk of publication bias ( Table 2 ). Discussion Our meta-analysis revealed that the prevalence of refusal to undergo the COVID-19 vaccination was 12%. Our findings were lower than those reported by Cenat et al. and Robinson et al. 43 , 44 In our study, we had a larger sample size than those reported by in these studies. Moreover, studies by Cenat et al. and Robinson et al. also involved articles that reported COVID-19 vaccination hesitancy. 43 , 44 It is well known that the terminologies of refusal and hesitancy to vaccinate are different, and not everyone is hesitant to vaccinate. Vaccine hesitancy is when individuals delay or decline vaccination despite vaccine availability. 8 On the other hand, vaccine refusal is the complete avoidance of vaccination with a clear intention not to get vaccinated. Vaccine hesitancy encompasses a wider range, including those who outright refuse vaccination. 9 Thus, it can be assumed that the context of previous studies has a gap in the definition of vaccine refusal. Therefore, our study may provide better data on the prevalence rate of COVID-19 vaccination refusal. Moreover, we also reported the prevalence of COVID-19 vaccination refusal in some subgroup populations: the general population, healthcare workers, and students. We found that healthcare workers had the lowest prevalence of COVID-19 vaccination refusal, followed by students, and the general population. Our current findings indicate that vaccination knowledge might affect our findings. We assumed that healthcare workers and students may have a better knowledge of vaccination programs than the general population. This assumption is supported by the results of previous studies, which found that healthcare workers and students had better knowledge of COVID-19 vaccination than the general population, 45 , 46 and this factor was also shown to contribute to the acceptance of vaccination programs. 47 Our study found that the increased risk of COVID-19 vaccination refusal was higher in females and individuals with educational levels below an undergraduate degree (BSc). In contrast, lower risk of COVID-19 vaccination refusal was found in individuals with a history of previous vaccination and a medical background. Our current findings suggest that the factors related to knowledge of COVID-19 vaccination had the potential to affect the refusal to vaccinate against COVID-19. As previously reported, a study revealed that females lacked literacy regarding COVID-19 vaccination than males. 48 This may be attributed to the fact that the majority of females are housewives, and therefore, may have less social interaction than males, as they are based at home rather than going out to work. 49 This possibility might contribute to the lack of knowledge on COVID-19 vaccination in the female population. Furthermore, one study found that the majority of the side effects of COVID-19 vaccination were reported among female individuals. 50 Taken together, those factors may affect the decision to accept or refuse the vaccines. Moreover, individuals with educational level below the undergraduate (BSc) degree might have an inadequate source of literature regarding COVID-19 vaccination compared to those with an educational level higher than an undergraduate (BSc) degree. In the context of vaccination knowledge, a study found that educational attainment was one of the predictors of vaccination knowledge, where lower educational attainment was associated with poorer knowledge of vaccination. 51 Therefore, the population with an educational level below the undergraduate (BSc) degree might have insufficient consideration for COVID-19 vaccination compared to those with an educational level higher than the undergraduate (BSc) degree. Further, individuals with a history of previous vaccination and a medical background may have adequate information regarding the importance of COVID-19 vaccination, therefore, may have sufficient awareness regarding COVID-19 vaccination. Previous studies found that individuals with a medical background had better knowledge of COVID-19 vaccination than the general population. 45 , 46 Likewise, another study revealed that individuals with a history of previous annual vaccination demonstrated good awareness and knowledge of the importance of vaccination programs. 52 Prior to the COVID-19 pandemic, studies have extensively reported that knowledge of disease prevention and the adoption of good health behavior practices had a significant impact on the acceptance rate of vaccination, as observed in cases such as Monkeypox, Ebola, and Dengue. 53 – 55 Thus, this might imply that this population (individuals with a history of previous vaccination and medical background) has a low rate of refusal to vaccinate against COVID-19, as reported in our meta-analysis. Our study also identified a higher risk of COVID-19 vaccination refusal in rural compared to urban populations, and a lower risk of COVID-19 vaccination refusal in individuals with high SES compared to those with low SES. Currently, providing precise explanations for the reasons underlying our findings might be challenging and could vary between different regions. However, we can propose the following reasons: social privileges, administrative requirements, and social circles. First, in the aspect of social privilege, individuals with high SES might take pride in being vaccinated, while this sense of pride might not be as prevalent in rural population. Studies found that COVID-19 vaccination was considered a socioeconomic privilege and political ideology, 56 while the rural population may not view the COVID-19 vaccine as a privilege and tended to have poorer perception toward vaccine safety. 57 The second reason is administrative requirements. Individuals with high SES might need COVID-19 vaccination for various activities, such as business, travelling, and career requirements, as the World Health Organization (WHO) has implemented a COVID-19 vaccine certificate as an administrative requirement for travel or business. 58 However, these administrative requirements were not necessary for rural individuals, as the majority of rural individual jobs are in private and traditional sectors, such as farmers, fishermen, and manual laborers. 59 , 60 The third factor is social circle. Individuals with high SES might have social circles that engage in high intellectual content, whereas in rural populations, their social circle might be limited to neighbors with similar intellectual contents. This factor might also indirectly contribute to the understanding of COVID-19 vaccinations, and consequently, affect their decision to accept or refuse the COVID-19 vaccine. This is supported by previous studies that revealed that SES was associated with the level of knowledge of vaccination programs and physical health status. 61 , 62 Additionally, earlier studies had shown that vaccine refusal in Italy, Ghana, and Pakistan was influenced by SES, highlighting its importance as a determining factor. 63 – 65 Moreover, our previous study on dengue also revealed that SES was one of the predictive indicators for the acceptance of vaccination. 55 Our meta-analysis is one of the first to report the prevalence of COVID-19 vaccination refusal and the potential factors associated with the refusal of COVID-19 vaccination. Our study also had a larger sample size compared to previous meta-analyses in a similar context. 43 , 44 The findings of our study might serve as the initial step to prevent the failure of COVID-19 vaccination programs. By identifying the potential factors associated with refusal to vaccinate against COVID-19, we expect that governments may provide advanced interventions to those populations. Furthermore, concerning vaccine refusal, it is important to take into account the 3C concept: confidence, complacency, and convenience. Confidence involves a lack of trust in either the vaccine or the provider. Complacency is the absence of recognition for the need or value of the vaccine. Convenience pertains to the unavailability of easy access to vaccination services. 66 As previously reported, the main concern in obtaining public trust regarding COVID-19 vaccines was the lack of adequate evidence from long-term and large-scale studies on the effectiveness and safety of COVID-19 vaccination. 67 However, several studies have suggested interventions for the refusal population, including providing reliable information regarding the COVID-19 pandemic and the COVID-19 vaccination. Effective, ethical, and evidence-based communication, preferably delivered by community leaders and healthcare practitioners, is also recommended. 68 – 70 Our meta-analysis has several limitations. First, several potential confounding factors, such as the level of knowledge about COVID-19 vaccination, attitude toward COVID-19 prevention, government regulation, types of vaccine, environmental factors, and the source of literature regarding COVID-19 vaccination, were not included in the analysis due to the lack of available data. Second, the sample size in our present study was limited; therefore, further studies involving larger sample sizes are needed. Third, our meta-analysis could not reflect the prevalence of the global numbers because the proportion of sample sizes in each region was unequal. Fourth, as the earlier investigation indicated the efficacy of the WHO BeSD framework in forecasting COVID-19 vaccination acceptance, and our present study faced constraints in gathering covariates associated with the WHO BeSD framework, additional study that encompasses all elements of the WHO BeSD framework might be warranted. 71 Fifth, it is highlighted that vaccine refusal is an intricate issue, and the extent to which populations reject vaccines may differ from one country to another. Unfortunately, due to data constraints, we could not analyze subgroups based on the country in which the study was conducted. Hence, it is important to recognize these limitations in future study. Conclusion In conclusion, we revealed that the cumulative prevalence of refusal to COVID-19 vaccination was 12%, with the highest prevalence observed in the general population and the lowest in the healthcare worker subgroup. The individuals with the following characteristics are at an increased risk of refusing COVID-19 vaccination: being female, having an educational attainment lower than an undergraduate degree, and living in a rural area. Conversely, reduced risk of refusing COVID-19 vaccination is associated with high socioeconomic status, a history of previous vaccination, and individuals with a medical background. Author contribution Conceptualization: FT, JKF, GS; Data Curation: FT, JKF, GS, APK, EAP, RPP, MR, TA, MN, SW, GP, AP, QA, MVPHM, RY, YSP; Formal Analysis: JKF, APK, EAP, RPP, MR, TA, MN, SW, GP, AP, QA, MVPHM, RY, YSP; Investigation: FT, JKF, GS, LW, APK, EAP, RPP, MR, TA, MN, SW, GP, AP, QA, MVPHM, RY, YSP; Project Administration: APK, EAP, RPP, MR, TA, MN, SW, GP, AP, QA, MVPHM, RY, YSP; Resources: APK, EAP, RPP, MR, TA, MN, SW, GP, AP, QA, MVPHM, RY, YSP; Methodology: FT, JKF, GS, LW, EAP, RPP, MR, TA, MN, SW, GP, AP, QA, MVPHM, RY, YSP; Software: FT, JKF, APK, EAP, RPP, MR, TA, MN, SW, GP, AP, QA, MVPHM, RY, YSP; Visualization: APK, EAP, RPP, MR, TA, MN, SW, GP, AP, QA, MVPHM, RY, YSP, CC, KD, HH; Supervision: FT, JKF, GS, LW, CC, KD, HH; Validation: FT, JKF, LW, CC, KD, HH; Writing – Original Draft Preparation: FT, JKF, LW, APK, EAP, RPP, MR, TA, MN, SW, GP, AP, QA, MVPHM, RY, YSP; Writing – Review & Editing: FT, JKF, GS, LW, CC, KD, HH. All authors have critically reviewed and approved the final draft and are responsible for the content and similarity index of the manuscript. Data availability Underlying data All data underlying the results are available as part of the article and no additional sources of data are required. Reporting guidelines Figshare: PRISMA checklist for ‘The refusal of COVID-19 vaccination and its associated factors: A meta-analysis’. https://doi.org/10.6084/m9.figshare.21617979.v1 . 18 Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Acknowledgements We thank Lembaga Pengelola Dana Pendidikan (LPDP) and Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi (Kemendikbudristek) Republic of Indonesia for supporting this project. References 1. Carneiro DC, Sousa JD, Monteiro-Cunha JP: The COVID-19 vaccine development: A pandemic paradigm. Virus Res. 2021; 301 : 198454. Epub 2021/05/21. PubMed Abstract | Publisher Full Text | Free Full Text 2. Coronavirus (COVID-19) Vaccinations: Our World in Data.2022 [cited 5 November 2022]. Reference Source 3. Elgendy MO, Abdelrahim MEA: Public awareness about coronavirus vaccine, vaccine acceptance, and hesitancy. J. Med. Virol. 2021; 93 (12): 6535–6543. Epub 2021/07/14. PubMed Abstract | Publisher Full Text | Free Full Text 4. Daniel CL, Williams J, Legg R, et al. : Factors associated with COVID-19 vaccination intentions among adults in the deep South. Vaccine. 2022; 40 (6): 841–853. PubMed Abstract | Publisher Full Text | Free Full Text 5. Hassan W, Kazmi SK, Tahir MJ, et al. : Global acceptance and hesitancy of COVID-19 vaccination: A narrative review. Narra. J. 2021; 1 (3): e57. Publisher Full Text 6. Rosiello D, Anwar S, Yufika A, et al. : Acceptance of COVID-19 vaccination at different hypothetical efficacy and safety levels in ten countries in Asia, Africa, and South America. Narra. J. 2021; 1 (3): e55. Publisher Full Text 7. Rayhan MA, Mudatsir M, Nurjannah N, et al. : Acceptance and willingness to purchase a hypothetical COVID-19 vaccine in a region under Shariah law: A cross-sectional study in Aceh, Indonesia. Narra. J. 2022; 2 (2). Publisher Full Text 8. Nuwarda RF, Ramzan I, Weekes L, et al. : Vaccine Hesitancy: Contemporary Issues and Historical Background. Vaccines (Basel). 2022; 10 (10): 1595. PubMed Abstract | Publisher Full Text | Free Full Text 9. Song I, Lee SH: COVID-19 vaccine refusal associated with health literacy: findings from a population-based survey in Korea. BMC Public Health. 2023; 23 (1): 255. PubMed Abstract | Publisher Full Text | Free Full Text 10. Fajar JK, Sallam M, Soegiarto G, et al. : Global Prevalence and Potential Influencing Factors of COVID-19 Vaccination Hesitancy: A Meta-Analysis. Vaccines (Basel). 2022; 10 (8). Epub 2022/08/27. PubMed Abstract | Publisher Full Text | Free Full Text 11. Fatima K, Syed NI: Dengvaxia controversy: impact on vaccine hesitancy. J. Glob. Health. 2018; 8 (2): 010312. Epub 2018/11/10. (available on request from the corresponding author) and declare no conflict of interest. PubMed Abstract | Publisher Full Text | Free Full Text Reference Source 12. Aide P, Bassat Q, Alonso PL: Towards an effective malaria vaccine. Arch. Dis. Child. 2007; 92 (6): 476–479. PubMed Abstract | Publisher Full Text | Free Full Text 13. Mesch GS, Schwirian KP: Vaccination hesitancy: fear, trust, and exposure expectancy of an Ebola outbreak. Heliyon. 2019; 5 (7): e02016. Epub 2019/08/02. PubMed Abstract | Publisher Full Text | Free Full Text 14. Olson D, Rick AM, Krager S, et al. : Vaccine Demand and Willingness-to-pay for Arbovirus Vaccines: A Cross-sectional Survey in Rural Guatemala. Pediatr. Infect. Dis. J. 2018; 37 (11): 1184–1189. PubMed Abstract | Publisher Full Text 15. Zucman D, Fourn E, Touche P, et al. : Monkeypox Vaccine Hesitancy in French Men Having Sex with Men with PrEP or Living with HIV in France. Vaccines (Basel). 2022; 10 (10). Epub 2022/10/28. PubMed Abstract | Publisher Full Text | Free Full Text 16. Zanobini P, Lorini C, Caini S, et al. : Health Literacy, Socioeconomic Status and Vaccination Uptake: A Study on Influenza Vaccination in a Population-Based Sample. Int. J. Environ. Res. Public Health. 2022; 19 (11). Epub 2022/06/11. PubMed Abstract | Publisher Full Text | Free Full Text 17. Germani F, Biller-Andorno N: The anti-vaccination infodemic on social media: A behavioral analysis. PLoS One. 2021; 16 (3): e0247642. Epub 2021/03/04. PubMed Abstract | Publisher Full Text | Free Full Text 18. Fajar JK: Supplementary files: The refusal of COVID-19 vaccination and its associated factors: A meta-analysis. Figshare. 2022; 1 (1): 1. Publisher Full Text 19. Al-Sanafi M, Sallam M: Psychological Determinants of COVID-19 Vaccine Acceptance among Healthcare Workers in Kuwait: A Cross-Sectional Study Using the 5C and Vaccine Conspiracy Beliefs Scales. Vaccines (Basel). 2021; 9 (7). Epub 2021/07/03. PubMed Abstract | Publisher Full Text | Free Full Text 20. Aurilio MT, Mennini FS, Gazzillo S, et al. : Intention to Be Vaccinated for COVID-19 among Italian Nurses during the Pandemic. Vaccines (Basel). 2021; 9 (5). Epub 2021/06/03. PubMed Abstract | Publisher Full Text | Free Full Text 21. Baniak LM, Luyster FS, Raible CA, et al. : COVID-19 Vaccine Hesitancy and Uptake among Nursing Staff during an Active Vaccine Rollout. Vaccines (Basel). 2021; 9 (8). Epub 2021/08/29. PubMed Abstract | Publisher Full Text | Free Full Text 22. Fakonti G, Kyprianidou M, Toumbis G, et al. : Attitudes and Acceptance of COVID-19 Vaccination Among Nurses and Midwives in Cyprus: A Cross-Sectional Survey. Front. Public Health. 2021; 9 : 656138. Epub 2021/07/06. PubMed Abstract | Publisher Full Text | Free Full Text 23. Fares S, Elmnyer MM, Mohamed SS, et al. : COVID-19 Vaccination Perception and Attitude among Healthcare Workers in Egypt. J. Prim. Care Community Health. 2021; 12 : 215013272110133. Epub 2021/04/30. PubMed Abstract | Publisher Full Text | Free Full Text 24. Fisher KA, Bloomstone SJ, Walder J, et al. : Attitudes Toward a Potential SARS-CoV-2 Vaccine: A Survey of U.S. Adults. Ann. Intern. Med. 2020; 173 (12): 964–973. PubMed Abstract | Publisher Full Text | Free Full Text 25. Grochowska M, Ratajczak A, Zdunek G, et al. : A Comparison of the Level of Acceptance and Hesitancy towards the Influenza Vaccine and the Forthcoming COVID-19 Vaccine in the Medical Community. Vaccines (Basel). 2021; 9 (5). Epub 2021/06/03. PubMed Abstract | Publisher Full Text | Free Full Text 26. Hamdan MB, Singh S, Polavarapu M, et al. : COVID-19 vaccine hesitancy among university students in Lebanon. Epidemiol. Infect. 2021; 149 : e242. Epub 2021/11/03. PubMed Abstract | Publisher Full Text | Free Full Text 27. Ikiisik H, Akif Sezerol M, Tasci Y, et al. : COVID-19 vaccine hesitancy: A community-based research in Turkey. Int. J. Clin. Pract. 2021; 75 (8): e14336. Epub 2021/05/12. PubMed Abstract | Publisher Full Text | Free Full Text 28. Janssen C, Maillard A, Bodelet C, et al. : Hesitancy towards COVID-19 Vaccination among Healthcare Workers: A Multi-Centric Survey in France. Vaccines (Basel). 2021; 9 (6). Epub 2021/06/03. PubMed Abstract | Publisher Full Text | Free Full Text 29. Kose S, Mandiracioglu A, Sahin S, et al. : Vaccine hesitancy of the COVID-19 by health care personnel. Int. J. Clin. Pract. 2021; 75 (5): e13917. Publisher Full Text 30. Kozak A, Nienhaus A: COVID-19 Vaccination: Status and Willingness to Be Vaccinated among Employees in Health and Welfare Care in Germany. Int. J. Environ. Res. Public Health. 2021; 18 (13). Epub 2021/07/03. PubMed Abstract | Publisher Full Text | Free Full Text 31. Manning ML, Gerolamo AM, Marino MA, et al. : COVID-19 vaccination readiness among nurse faculty and student nurses. Nurs. Outlook. 2021; 69 (4): 565–573. PubMed Abstract | Publisher Full Text | Free Full Text 32. Mena G, Blanco B, Casas I, et al. : Attitudes of Spanish hospital staff towards COVID-19 vaccination and vaccination rates. PLoS One. 2021; 16 (9): e0257002. Epub 2021/09/11. PubMed Abstract | Publisher Full Text | Free Full Text 33. Ousseine YM, Vaux S, Vandentorren S, et al. : Predictors of Uncertainty and Unwillingness to Receive the COVID-19 Vaccine in Men Who Have Sex with Men in France. Int. J. Environ. Res. Public Health. 2022; 19 (9). Epub 2022/05/15. PubMed Abstract | Publisher Full Text | Free Full Text 34. Paris C, Benezit F, Geslin M, et al. : COVID-19 vaccine hesitancy among healthcare workers. Infect. Dis. Now. 2021; 51 (5): 484–487. PubMed Abstract | Publisher Full Text | Free Full Text 35. Pataka A, Kotoulas S, Stefanidou E, et al. : Acceptability of Healthcare Professionals to Get Vaccinated against COVID-19 Two Weeks before Initiation of National Vaccination. Medicina (Kaunas). 2021; 57 (6). Epub 2021/07/03. PubMed Abstract | Publisher Full Text | Free Full Text 36. Rodriguez-Blanco N, Montero-Navarro S, Botella-Rico JM, et al. : Willingness to Be Vaccinated against COVID-19 in Spain before the Start of Vaccination: A Cross-Sectional Study. Int. J. Environ. Res. Public Health. 2021; 18 (10). Epub 2021/06/03. PubMed Abstract | Publisher Full Text | Free Full Text 37. Saied SM, Saied EM, Kabbash IA, et al. : Vaccine hesitancy: Beliefs and barriers associated with COVID-19 vaccination among Egyptian medical students. J. Med. Virol. 2021; 93 (7): 4280–4291. PubMed Abstract | Publisher Full Text | Free Full Text 38. Schwarzinger M, Watson V, Arwidson P, et al. : COVID-19 vaccine hesitancy in a representative working-age population in France: a survey experiment based on vaccine characteristics. Lancet Public Health. 2021; 6 (4): e210–e221. PubMed Abstract | Publisher Full Text | Free Full Text 39. Shaw J, Stewart T, Anderson KB, et al. : Assessment of US Healthcare Personnel Attitudes Towards Coronavirus Disease 2019 (COVID-19) Vaccination in a Large University Healthcare System. Clin. Infect. Dis. 2021; 73 (10): 1776–1783. PubMed Abstract | Publisher Full Text | Free Full Text 40. Spinewine A, Petein C, Evrard P, et al. : Attitudes towards COVID-19 Vaccination among Hospital Staff-Understanding What Matters to Hesitant People. Vaccines (Basel). 2021; 9 (5). Epub 2021/06/03. PubMed Abstract | Publisher Full Text | Free Full Text 41. Vignier N, Brureau K, Granier S, et al. : Attitudes towards the COVID-19 Vaccine and Willingness to Get Vaccinated among Healthcare Workers in French Guiana: The Influence of Geographical Origin. Vaccines (Basel). 2021; 9 (6). Epub 2021/07/03. PubMed Abstract | Publisher Full Text | Free Full Text 42. Wang K, Wong ELY, Ho KF, et al. : Intention of nurses to accept coronavirus disease 2019 vaccination and change of intention to accept seasonal influenza vaccination during the coronavirus disease 2019 pandemic: A cross-sectional survey. Vaccine. 2020; 38 (45): 7049–7056. PubMed Abstract | Publisher Full Text | Free Full Text 43. Robinson E, Jones A, Lesser I, et al. : International estimates of intended uptake and refusal of COVID-19 vaccines: A rapid systematic review and meta-analysis of large nationally representative samples. Vaccine. 2021; 39 (15): 2024–2034. PubMed Abstract | Publisher Full Text | Free Full Text 44. Cenat JM, Noorishad PG, Moshirian Farahi SMM, et al. : Prevalence and factors related to COVID-19 vaccine hesitancy and unwillingness in Canada: A systematic review and meta-analysis. J. Med. Virol. 2022. Epub 2022/09/17. PubMed Abstract | Publisher Full Text | Free Full Text 45. Ji W, Camara MA, Xia J, et al. : Knowledge, Attitude and Practice Toward COVID-19: A Cross-Sectional Study of Staff in China-Guinea Friendship Hospital. Guinea. Front Public Health. 2022; 10 : 889227. Epub 2022/06/17. PubMed Abstract | Publisher Full Text | Free Full Text 46. Harapan H, Wagner AL, Yufika A, et al. : Acceptance of a COVID-19 Vaccine in Southeast Asia: A Cross-Sectional Study in Indonesia. Front. Public Health. 2020; 8 : 381. Epub 2020/08/08. PubMed Abstract | Publisher Full Text | Free Full Text 47. Galle F, Sabella EA, Roma P, et al. : Knowledge and Acceptance of COVID-19 Vaccination among Undergraduate Students from Central and Southern Italy. Vaccines (Basel). 2021; 9 (6). Epub 2021/07/03. PubMed Abstract | Publisher Full Text | Free Full Text 48. Ishimaru T, Okawara M, Ando H, et al. : Gender differences in the determinants of willingness to get the COVID-19 vaccine among the working-age population in Japan. Hum. Vaccin. Immunother. 2021; 17 (11): 3975–3981. PubMed Abstract | Publisher Full Text | Free Full Text 49. Jeong S, Cho SI: Concordance in the Health Behaviors of Couples by Age: A Cross-sectional Study. J. Prev. Med. Public Health. 2018; 51 (1): 6–14. PubMed Abstract | Publisher Full Text | Free Full Text 50. Al-Qazaz HK, Al-Obaidy LM, Attash HM: COVID-19 vaccination, do women suffer from more side effects than men? A retrospective cross-sectional study. Pharm. Pract (Granada). 2022; 20 (2): 2610–2678. PubMed Abstract | Publisher Full Text | Free Full Text 51. Voo JYH, Lean QY, Ming LC, et al. : Vaccine Knowledge, Awareness and Hesitancy: A Cross Sectional Survey among Parents Residing at Sandakan District, Sabah. Vaccines (Basel). 2021; 9 (11). Epub 2021/11/28. PubMed Abstract | Publisher Full Text | Free Full Text 52. Barry MA, Aljammaz KI, Alrashed AA: Knowledge, Attitude, and Barriers Influencing Seasonal Influenza Vaccination Uptake. Can. J. Infect. Dis. Med. Microbiol. 2020; 2020 : 7653745. Epub 2020/10/31. PubMed Abstract | Publisher Full Text | Free Full Text 53. Harapan H, Wagner AL, Yufika A, et al. : Acceptance and willingness to pay for a hypothetical vaccine against monkeypox viral infection among frontline physicians: A cross-sectional study in Indonesia. Vaccine. 2020; 38 (43): 6800–6806. PubMed Abstract | Publisher Full Text | Free Full Text 54. Mudatsir M, Anwar S, Fajar JK, et al. : Willingness-to-pay for a hypothetical Ebola vaccine in Indonesia: A cross-sectional study in Aceh. F1000Res. 2019; 8 : 1441. Epub 2020/07/29. PubMed Abstract | Publisher Full Text | Free Full Text 55. Fajar JK, Harapan H: Socioeconomic and attitudinal variables associated with acceptance and willingness to pay towards dengue vaccine: a systematic review. Archives of. Clin. Infect. Dis. 2017; 12 (3). Publisher Full Text 56. Agarwal R, Dugas M, Ramaprasad J, et al. : Socioeconomic privilege and political ideology are associated with racial disparity in COVID-19 vaccination. Proc. Natl. Acad. Sci. U. S. A. 2021; 118 (33). Epub 2021/07/31. PubMed Abstract | Publisher Full Text | Free Full Text 57. Carter J, Rutherford S, Borkoles E: COVID-19 Vaccine Uptake among Younger Women in Rural Australia. Vaccines (Basel). 2021; 10 (1). Epub 2022/01/23. PubMed Abstract | Publisher Full Text | Free Full Text 58. Cascini F, Causio FA, Failla G, et al. : Emerging Issues From a Global Overview of Digital Covid-19 Certificate Initiatives. Front. Public Health. 2021; 9 : 744356. Epub 2021/12/07. PubMed Abstract | Publisher Full Text | Free Full Text 59. Pattayat SS, Parida JK, Awasthi IC: Reducing Rural Poverty Through Non-farm Job Creation in India. Indian J. Labour Econ. 2022; 65 (1): 137–160. PubMed Abstract | Publisher Full Text | Free Full Text 60. Kumar A, Kumar S, Singh DK: Rural employment diversification in India: trends, determinants and implications on poverty. Agric. Econ. Res. Rev. 2011; 24 (conf): 361–372. 61. Wang Y, Yang C, Zhang Y, et al. : Socioeconomic Status and Prosocial Behavior: The Mediating Roles of Community Identity and Perceived Control. Int. J. Environ. Res. Public Health. 2021; 18 (19). Epub 2021/10/14. PubMed Abstract | Publisher Full Text | Free Full Text 62. Wirawan GBS, Harjana NPA, Nugrahani NW, et al. : Health Beliefs and Socioeconomic Determinants of COVID-19 Booster Vaccine Acceptance: An Indonesian Cross-Sectional Study. Vaccines (Basel). 2022; 10 (5). Epub 2022/05/29. PubMed Abstract | Publisher Full Text | Free Full Text 63. Bertoncello C, Ferro A, Fonzo M, et al. : Socioeconomic Determinants in Vaccine Hesitancy and Vaccine Refusal in Italy. Vaccines (Basel). 2020; 8 (2): 276. PubMed Abstract | Publisher Full Text | Free Full Text 64. Ahmed W, Ali S, Waqas H, et al. : Identifying and Evaluating the Socioeconomic Factors of COVID Vaccine Hesitancy Through an Integrated Multicriteria Decision Analysis Approach. Glob. Soc. Welf. 2023:1–9. PubMed Abstract | Publisher Full Text | Free Full Text 65. Sampene AK, Li C, Oteng Agyeman F, et al. : Socioeconomic and demographic characteristics influencing the hesitancy and refusal of COVID-19 vaccine in Ghana. Ther. Adv. Vaccines Immunother. 2023; 11 : 25151355221149336. PubMed Abstract | Publisher Full Text | Free Full Text 66. Larson HJ, Jarrett C, Eckersberger E, et al. : Understanding vaccine hesitancy around vaccines and vaccination from a global perspective: a systematic review of published literature, 2007-2012. Vaccine. 2014; 32 (19): 2150–2159. PubMed Abstract | Publisher Full Text 67. Rodriguez-Morales AJ, Franco OH: Public trust, misinformation and COVID-19 vaccination willingness in Latin America and the Caribbean: today's key challenges. Lancet Reg. Health Am. 2021; 3 : 100073. Epub 2021/09/16. PubMed Abstract | Publisher Full Text | Free Full Text 68. Ratzan SC: “Vaccine Communication in a Pandemic: Improving Vaccine Literacy to Reduce Hesitancy, Restore Trust and Immunize Communities”: Editor’s Introduction. J. Health Commun. 2020; 25 (10): 745–746. Publisher Full Text 69. Hussain B, Latif A, Timmons S, et al. : Overcoming COVID-19 vaccine hesitancy among ethnic minorities: A systematic review of UK studies. Vaccine. 2022; 40 (25): 3413–3432. PubMed Abstract | Publisher Full Text | Free Full Text 70. Gendler Y, Ofri L: Investigating the Influence of Vaccine Literacy, Vaccine Perception and Vaccine Hesitancy on Israeli Parents' Acceptance of the COVID-19 Vaccine for Their Children: A Cross-Sectional Study. Vaccines (Basel). 2021; 9 (12). Epub 2021/12/29. PubMed Abstract | Publisher Full Text | Free Full Text 71. Alagarsamy S, Mehrolia S, Pushparaj U, et al. : Explaining the intention to uptake COVID-19 vaccination using the behavioral and social drivers of vaccination (BeSD) model. Vaccine X. 2022; 10 : 100140. PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 13 Jan 2023 ADD YOUR COMMENT Comment Author details Author details 1 Brawijaya Internal Medicine Research Center, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia 2 Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60286, Indonesia 3 Department of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60286, Indonesia 4 Faculty of Medicine, Universitas Brawijaya, Malang, 65145, Indonesia 5 Department of Pediatric; Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia 6 Department of Surgery; Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia 7 Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia 8 Faculty of Animal Science, Universitas Brawijaya, Malang, 65145, Indonesia 9 Faculty of Nursing, Universitas Indonesia, Depok, 16424, Indonesia 10 Department of Biotechnology, Postgraduate School, Institut Pertanian Bogor, Bogor, 16680, Indonesia 11 Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, 700126, India 12 Division of Pathology, Indian Veterinary Research Institute, Uttar Pradesh, 243122, India 13 Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia 14 Tropical Diseases Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia 15 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia Fredo Tamara Roles: Conceptualization, Data Curation, Investigation, Methodology, Software, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Jonny K. Fajar Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Gatot Soegiarto Roles: Conceptualization, Data Curation, Investigation, Methodology, Supervision, Writing – Review & Editing Laksmi Wulandari Roles: Investigation, Methodology, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Andy P. Kusuma Roles: Data Curation, Formal Analysis, Investigation, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Erwin A. Pasaribu Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Reza P. Putra Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Muhammad Rizky Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Tajul Anshor Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Maya Novariza Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Surya Wijaya Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Guruh Prasetyo Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Adelia Pradita Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Qurrata Aini Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Mario V.P.H. Mete Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Rahmat Yusni Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Yama S. Putri Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation Chiranjib Chakraborty Roles: Supervision, Validation, Visualization, Writing – Review & Editing Kuldeep Dhama Roles: Supervision, Validation, Visualization, Writing – Review & Editing Harapan Harapan Roles: Supervision, Validation, Visualization, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (2) version 2 Revised Published: 29 Jan 2024, 12:54 https://doi.org/10.12688/f1000research.128912.2 version 1 Published: 13 Jan 2023, 12:54 https://doi.org/10.12688/f1000research.128912.1 Copyright © 2024 Tamara F et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Tamara F, Fajar JK, Soegiarto G et al. The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 3 approved with reservations] . 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Reviewer Report For: The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 3 approved with reservations] . F1000Research 2024, 12 :54 ( https://doi.org/10.5256/f1000research.161802.r241859 ) The direct URL for this report is: https://f1000research.com/articles/12-54/v2#referee-response-241859 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 25 Oct 2024 Amy Morrison , Department of Pathology, Microbiology and Immunology, School of Veterinary Medicine, University of California, Davis (UC Davis), Davis, CA, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.161802.r241859 Review: The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 2 approved with reservations] Overall, the manuscript is significantly improved over the previous version and many of my concerns were addressed, ... Continue reading READ ALL Review: The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 2 approved with reservations] Overall, the manuscript is significantly improved over the previous version and many of my concerns were addressed, but not all. The authors provided information on the difference between vaccine hesitancy and vaccine refusal, which represented a significant improvement to the article. That being said, I did not see much of a contrast of the results from this current analysis with that of the previous publication “Fajar JK et.al.2022 (Ref 1) ”, and it remains unclear to me the criteria the authors used upon screening manuscripts to distinguish between hesitancy and refusal. Thank you for the inclusion on the PRISMA check and inclusion of search strings, but details under eligibility requirements were quite limited. While data collection was carried out by 15 people it is not clear if the same articles were accessed by more than one individuals and how disputes might be resolved. You excluded 3,318 manuscripts as irrelevant, but it would be helpful to have a few more details here. Since your search strategy did not include an “AND”, you were seeking a needle in a haystack and identification would have been tedious and I would hope that multiple individuals would have accessed those articles. In the process of determining what as irrelevant were all the articles read, titles screened, abstracts, how far did you go to identify articles that included your two inclusion criteria. We articles excluded that met only one of the two inclusion criteria? I’m not familiar with the BESD framework, but it sounds like an appropriate suggestion. My suggestion of including discussion of the 3 C model is the same. What is missing from this manuscript are the “reasons” for refusal. I do not feel that the authors addressed the following comment: “Context for your meta-analysis statistics: Although you report Egger and heterogeneity (Q) scores it is not clear readers unfamiliar with them what they are telling us”. A remaining concern for me is that you assess principally demographic risk factors, but there is no discussion of the reasons given for refusal, this feels like a major gap. The findings on gender are significant and important to understand. The forest plot especially from the random effects model was just above one with no CI. That seems weird to me but I think the conclusion would be that gender is not that important overall, but was important in some individual studies. This finding received a disproportional emphasis, considering the overall effect size. I found the comments about this being due to most women being housewives unconvincing. If there is clear evidence in the reviewed articles that the majority of respondents were indeed housewives, please provide data to show this. With most studies coming from high income countries (not all), I find it difficult to believe that this would be representative of the actual distribution of women in these countries, especially in the context that there is a high degree of working mothers as well as women who do not have children. The possibility of more adverse events seems more credible, but this gender difference is quite disturbing and deserved serious discussion. It appears that this statement came from two studies, one that clearly selected housewives as their population (cited in the discussion), but looking at Figure 3A, although the tendency is clear I only see 3 studies where the CI does not include one. I have some discomfort about including vaccines for Dengue, Monkeypox and Ebola in the same basket as SarCov2. Dengue vaccines have complicated safety and efficacy issues associated with them, and Monkeypox and Ebola are usually focused on high-risk populations. The statements are not wrong, but perhaps to general. The authors might suggest more specific study questions that would provide insights into understanding some of the risk factors for vaccine refusal. That is looking at subgroups of females for example. References 1. Fajar JK, Sallam M, Soegiarto G, Sugiri YJ, et al.: Global Prevalence and Potential Influencing Factors of COVID-19 Vaccination Hesitancy: A Meta-Analysis. Vaccines (Basel) . 2022; 10 (8). PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Dengue epidemiology; arbovirus epidemiology; systematic review I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Morrison A. Reviewer Report For: The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 3 approved with reservations] . F1000Research 2024, 12 :54 ( https://doi.org/10.5256/f1000research.161802.r241859 ) The direct URL for this report is: https://f1000research.com/articles/12-54/v2#referee-response-241859 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Al-kazzaz HH. Reviewer Report For: The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 3 approved with reservations] . F1000Research 2024, 12 :54 ( https://doi.org/10.5256/f1000research.161802.r255307 ) The direct URL for this report is: https://f1000research.com/articles/12-54/v2#referee-response-255307 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 05 Apr 2024 Hassan Hadi Al-kazzaz , Medical and Health Technology College, Al- Zahra University for Women, Karbala, Iraq Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.161802.r255307 Intention not to get vaccinated; its not clearly stated whether this refusal or hesitation, please explain. What is the accepted sample size according to your statement “articles with a lower sample size used in the study were excluded”? It's important ... Continue reading READ ALL Intention not to get vaccinated; its not clearly stated whether this refusal or hesitation, please explain. What is the accepted sample size according to your statement “articles with a lower sample size used in the study were excluded”? It's important to note that single-arm meta-analyses can have limitations, particularly in terms of drawing causal inferences or generalizing findings to broader populations. In the cumulative prevalence of the refusal to have COVID-19 vaccination, the p-value for heterogeneity is reported as less than 0.0001, indicating strong evidence of heterogeneity among the studies included in the analysis. This suggests that the variability in effect sizes across studies is unlikely to be due to chance alone. Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Yes Is the statistical analysis and its interpretation appropriate? Yes Are the conclusions drawn adequately supported by the results presented in the review? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: public health and family medicine I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Al-kazzaz HH. Reviewer Report For: The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 3 approved with reservations] . F1000Research 2024, 12 :54 ( https://doi.org/10.5256/f1000research.161802.r255307 ) The direct URL for this report is: https://f1000research.com/articles/12-54/v2#referee-response-255307 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 13 Jan 2023 Views 0 Cite How to cite this report: Capodici A. Reviewer Report For: The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 3 approved with reservations] . F1000Research 2024, 12 :54 ( https://doi.org/10.5256/f1000research.141551.r201174 ) The direct URL for this report is: https://f1000research.com/articles/12-54/v1#referee-response-201174 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 30 Aug 2023 Angelo Capodici , University of Bologna, Bologna, Italy Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.141551.r201174 Regarding the question of whether ample methodological and analytical details are furnished to facilitate replication by fellow researchers, my response leans toward the negative. The omission of the search string and the absence of a reported PRISMA checklist, despite claims ... Continue reading READ ALL Regarding the question of whether ample methodological and analytical details are furnished to facilitate replication by fellow researchers, my response leans toward the negative. The omission of the search string and the absence of a reported PRISMA checklist, despite claims of adhering to PRISMA guidelines, hinder the transparency and reproducibility of the study. It's worth noting that when delving into the assessment of vaccine hesitancy, a well-defined framework should ideally be employed. Notably, the widely recognized BESD framework by WHO stands out as a prominent choice. Disappointingly, the authors of the study failed to acknowledge this framework through proper citation, consequently weakening the robustness of their conclusions. Finally, since they cite studies about willingness regarding COVID-19 vaccination, I should mention that no studies updated to 2023 are cited, which is a shame. I would recommend improving this. Here's a few ideas: Gori et al. (2023) 1 ; Morgan et al. (2023) 2 ; Huang et al. (2023) 3 . Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? No Is the statistical analysis and its interpretation appropriate? Yes Are the conclusions drawn adequately supported by the results presented in the review? Yes References 1. Gori D, Capodici A, La Fauci G, Montalti M, et al.: COVID-19 Vaccine Refusal and Delay among Adults in Italy: Evidence from the OBVIOUS Project, a National Survey in Italy. Vaccines (Basel) . 2023; 11 (4). PubMed Abstract | Publisher Full Text 2. Morgan AK, Aziire MA, Cobbold J, Agbobada AA, et al.: Hesitant or not: A cross-sectional study of socio-demographics, conspiracy theories, trust in public health information, social capital and vaccine hesitancy among older adults in Ghana. Hum Vaccin Immunother . 2023; 19 (1): 2211495 PubMed Abstract | Publisher Full Text 3. Huang M, He R, Chen Q, Song J, et al.: COVID-19 vaccine booster dose hesitancy among key groups: A cross-sectional study. Hum Vaccin Immunother . 2023; 19 (1): 2166323 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Epidemiology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Capodici A. Reviewer Report For: The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 3 approved with reservations] . F1000Research 2024, 12 :54 ( https://doi.org/10.5256/f1000research.141551.r201174 ) The direct URL for this report is: https://f1000research.com/articles/12-54/v1#referee-response-201174 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 20 Mar 2024 Jonny Fajar , Brawijaya Internal Medicine Research Center, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, 65145, Indonesia 20 Mar 2024 Author Response Regarding the question of whether ample methodological and analytical details are furnished to facilitate replication by fellow researchers, my response leans toward the negative. The omission of the ... Continue reading Regarding the question of whether ample methodological and analytical details are furnished to facilitate replication by fellow researchers, my response leans toward the negative. The omission of the search string and the absence of a reported PRISMA checklist, despite claims of adhering to PRISMA guidelines, hinder the transparency and reproducibility of the study. It's worth noting that when delving into the assessment of vaccine hesitancy, a well-defined framework should ideally be employed. Notably, the widely recognized BESD framework by WHO stands out as a prominent choice. Disappointingly, the authors of the study failed to acknowledge this framework through proper citation, consequently weakening the robustness of their conclusions. Response: We appreciate your comments and suggestions. Regarding the PRISMA guidance, we have included the PRISMA checklist in the supplementary files. We assure that all points from the PRISMA checklist for meta-analysis studies are documented in the supplementary files. Additionally, concerning the WHO's BESD framework, initially, we aimed to incorporate all its variables. However, in the context of meta-analysis, our ability to analyze specific variables depends on the data available from supporting studies. Nevertheless, we have added a discussion on the limitations related to the BESD framework in our study. Finally, since they cite studies about willingness regarding COVID-19 vaccination, I should mention that no studies updated to 2023 are cited, which is a shame. I would recommend improving this. Here's a few ideas: Gori et al. (2023)1; Morgan et al. (2023)2; Huang et al. (2023)3. Response: We sincerely appreciate your advice regarding additional studies that could serve as supplementary data. However, we have previously registered our study protocol in PROSPERO, and the three additional studies were published after the timeframe specified in our protocol. Our study protocol has received approval from PROSPERO, and adding these additional studies would result in our departure from the registered study protocol in PROSPERO. Regarding the question of whether ample methodological and analytical details are furnished to facilitate replication by fellow researchers, my response leans toward the negative. The omission of the search string and the absence of a reported PRISMA checklist, despite claims of adhering to PRISMA guidelines, hinder the transparency and reproducibility of the study. It's worth noting that when delving into the assessment of vaccine hesitancy, a well-defined framework should ideally be employed. Notably, the widely recognized BESD framework by WHO stands out as a prominent choice. Disappointingly, the authors of the study failed to acknowledge this framework through proper citation, consequently weakening the robustness of their conclusions. Response: We appreciate your comments and suggestions. Regarding the PRISMA guidance, we have included the PRISMA checklist in the supplementary files. We assure that all points from the PRISMA checklist for meta-analysis studies are documented in the supplementary files. Additionally, concerning the WHO's BESD framework, initially, we aimed to incorporate all its variables. However, in the context of meta-analysis, our ability to analyze specific variables depends on the data available from supporting studies. Nevertheless, we have added a discussion on the limitations related to the BESD framework in our study. Finally, since they cite studies about willingness regarding COVID-19 vaccination, I should mention that no studies updated to 2023 are cited, which is a shame. I would recommend improving this. Here's a few ideas: Gori et al. (2023)1; Morgan et al. (2023)2; Huang et al. (2023)3. Response: We sincerely appreciate your advice regarding additional studies that could serve as supplementary data. However, we have previously registered our study protocol in PROSPERO, and the three additional studies were published after the timeframe specified in our protocol. Our study protocol has received approval from PROSPERO, and adding these additional studies would result in our departure from the registered study protocol in PROSPERO. Competing Interests: We have no competing interest. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 20 Mar 2024 Jonny Fajar , Brawijaya Internal Medicine Research Center, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, 65145, Indonesia 20 Mar 2024 Author Response Regarding the question of whether ample methodological and analytical details are furnished to facilitate replication by fellow researchers, my response leans toward the negative. The omission of the ... Continue reading Regarding the question of whether ample methodological and analytical details are furnished to facilitate replication by fellow researchers, my response leans toward the negative. The omission of the search string and the absence of a reported PRISMA checklist, despite claims of adhering to PRISMA guidelines, hinder the transparency and reproducibility of the study. It's worth noting that when delving into the assessment of vaccine hesitancy, a well-defined framework should ideally be employed. Notably, the widely recognized BESD framework by WHO stands out as a prominent choice. Disappointingly, the authors of the study failed to acknowledge this framework through proper citation, consequently weakening the robustness of their conclusions. Response: We appreciate your comments and suggestions. Regarding the PRISMA guidance, we have included the PRISMA checklist in the supplementary files. We assure that all points from the PRISMA checklist for meta-analysis studies are documented in the supplementary files. Additionally, concerning the WHO's BESD framework, initially, we aimed to incorporate all its variables. However, in the context of meta-analysis, our ability to analyze specific variables depends on the data available from supporting studies. Nevertheless, we have added a discussion on the limitations related to the BESD framework in our study. Finally, since they cite studies about willingness regarding COVID-19 vaccination, I should mention that no studies updated to 2023 are cited, which is a shame. I would recommend improving this. Here's a few ideas: Gori et al. (2023)1; Morgan et al. (2023)2; Huang et al. (2023)3. Response: We sincerely appreciate your advice regarding additional studies that could serve as supplementary data. However, we have previously registered our study protocol in PROSPERO, and the three additional studies were published after the timeframe specified in our protocol. Our study protocol has received approval from PROSPERO, and adding these additional studies would result in our departure from the registered study protocol in PROSPERO. Regarding the question of whether ample methodological and analytical details are furnished to facilitate replication by fellow researchers, my response leans toward the negative. The omission of the search string and the absence of a reported PRISMA checklist, despite claims of adhering to PRISMA guidelines, hinder the transparency and reproducibility of the study. It's worth noting that when delving into the assessment of vaccine hesitancy, a well-defined framework should ideally be employed. Notably, the widely recognized BESD framework by WHO stands out as a prominent choice. Disappointingly, the authors of the study failed to acknowledge this framework through proper citation, consequently weakening the robustness of their conclusions. Response: We appreciate your comments and suggestions. Regarding the PRISMA guidance, we have included the PRISMA checklist in the supplementary files. We assure that all points from the PRISMA checklist for meta-analysis studies are documented in the supplementary files. Additionally, concerning the WHO's BESD framework, initially, we aimed to incorporate all its variables. However, in the context of meta-analysis, our ability to analyze specific variables depends on the data available from supporting studies. Nevertheless, we have added a discussion on the limitations related to the BESD framework in our study. Finally, since they cite studies about willingness regarding COVID-19 vaccination, I should mention that no studies updated to 2023 are cited, which is a shame. I would recommend improving this. Here's a few ideas: Gori et al. (2023)1; Morgan et al. (2023)2; Huang et al. (2023)3. Response: We sincerely appreciate your advice regarding additional studies that could serve as supplementary data. However, we have previously registered our study protocol in PROSPERO, and the three additional studies were published after the timeframe specified in our protocol. Our study protocol has received approval from PROSPERO, and adding these additional studies would result in our departure from the registered study protocol in PROSPERO. Competing Interests: We have no competing interest. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Morrison A. Reviewer Report For: The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 3 approved with reservations] . F1000Research 2024, 12 :54 ( https://doi.org/10.5256/f1000research.141551.r190442 ) The direct URL for this report is: https://f1000research.com/articles/12-54/v1#referee-response-190442 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 09 Aug 2023 Amy Morrison , Department of Pathology, Microbiology and Immunology, School of Veterinary Medicine, University of California, Davis (UC Davis), Davis, CA, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.141551.r190442 This manuscript presents results from a systematic review and meta-analysis on the global prevalence of COVID-19 vaccine refusal factors that increase or decrease the risk of refusal. This manuscript follows the publication from the same group titled “Global Prevalence and ... Continue reading READ ALL This manuscript presents results from a systematic review and meta-analysis on the global prevalence of COVID-19 vaccine refusal factors that increase or decrease the risk of refusal. This manuscript follows the publication from the same group titled “Global Prevalence and Potential Influencing Factors of COVID-19 Vaccination Hesitance: A Meta-Analysis” which includes considerable overlap with the current article. The important distinction is summarized by the following difference in the search strategy: search term HESITANCY (original article) replaced by terms REFUSAL or REJECTION. Interestingly, this subtle distinction is important, and should be a key message and be the focus of the introduction, and the contrasts between this manuscript highlighted throughout the methods and discussion sections. For example, vaccine hesitancy represents a continuum from complete acceptance to refusal to be vaccinate under any circumstances. The authors need to clearly describe how they are making this distinction in their article assessment and how actual vaccination rates equate to refusal. That is, in the included studies was the survey methodology able to distinguish between individuals who will never allow vaccination versus those who might opt be able to overcome their hesitancy. I agree that there is a distinction between hesitancy and refusal, but how to clearly measure that distinction requires more explanation. This is clearly an important issue that merits publication, the analysis appears rigorous and appropriate, but could be better contextualized for readers. It is also important to contrast this study with the previous study by this group. Below I provide general and specific comment I hope the authors will consider. General Comments: As mentioned above the distinction between vaccination refusal and hesitancy needs to be clearly described, in the introduction of the manuscript. The authors do point out why the distinction is important, but they could elaborate more. But most importantly, how can we make this distinction within the evaluated studies. Your search strategy was slightly different and despite this search extending to November 2022 in comparison to your previously published study extending through May 2022, a total of 56 and 24 manuscripts were evaluated, respectively. I want to know if some of those manuscripts from the previous review, that were not included in the review would have been appropriate for the current analysis. Again, contrasting this information with your previous publication would be helpful. One co-variate which is mentioned but not included in your analysis is country. This seems like it could be an important factor. One objective for systematic reviews is the identification of clear knowledge gaps, which deserves a strong place in your discussion. The absence of studies in LMICs is concerning. I would also argue that the issue of refusal/hesitancy and how we really identify these hard-core refusers is important and the follow up to that is how many of these people are sources of misinformation, another potential gap. Context for your meta-analysis statistics: Although you report Egger and heterogeneity (Q) scores it is no clear readers unfamiliar with them what they are telling us. The manuscript needs further editorial review, there are numerous grammatical errors, missing words, etc. Overall, the article was understandable, but the errors did reduce the quality of the article. Consider providing a discussion of the range of prevalence values in addition to the model output, but there is considerable variability shown in the figures. I’m not an expert in Meta-Analysis, but I’m curious is a multivariate model is possible since many of the factors you are analyzing could be confounders. High socioeconomic status is significant, but marginally, that deserves a bit more emphasis. Again, more discussion on the continuum of hesitancy to refusal is needed, more context about the included studies, what the key questions were and how populations were selected. Specific Comments Title: consider including Meta-analysis is the title, you are doing both. Statistical analysis: How is the Egger and Q statistics interpreted beyond the p-value. Do they range from 0-1 with… Introduction: See my general comments. Another model which is not mentioned in your manuscript which I think could be helpful is Larson et al. 2014 1 that describes the 3 C’s model (Complacency, convenience, and confidence). Your article focuses on confidence rather than the other factors. Table 1. Consider including a column with a summary of the study findings. Also, abbreviations should be specified in the Table legend. You could eliminate the study design column as you state in the text that all the studies were cross-sectional. Discussion, last paragraph: “Fourth, all articles included in our analysis were cross-sectional studies, further meta-analyses involving only randomized controlled trials are required.” Please expand, having a hard time imagining this design for this question. What intervention would be randomized here. Sample selection is an important issue here. That is, how was a representative sample achieved with in individual studies, but beyond cross-sectional study we are give no additional information on sampling strategy, if the study was properly powered etc. This seems like an important issue, that may be reflected in the NOS score but not shared with the reader. Are the rationale for, and objectives of, the Systematic Review clearly stated? Partly Are sufficient details of the methods and analysis provided to allow replication by others? Partly Is the statistical analysis and its interpretation appropriate? Yes Are the conclusions drawn adequately supported by the results presented in the review? Partly References 1. Larson H, Jarrett C, Eckersberger E, Smith D, et al.: Understanding vaccine hesitancy around vaccines and vaccination from a global perspective: A systematic review of published literature, 2007–2012. Vaccine . 2014; 32 (19): 2150-2159 Publisher Full Text Competing Interests: Currently have funding to study vaccine hesitancy for dengue vaccines in Peru, where our research touched on hesitancy for COVID-19 vaccination as well. Reviewer Expertise: Dengue epidemiology; arbovirus epidemiology; systematic review I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Morrison A. Reviewer Report For: The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 3 approved with reservations] . F1000Research 2024, 12 :54 ( https://doi.org/10.5256/f1000research.141551.r190442 ) The direct URL for this report is: https://f1000research.com/articles/12-54/v1#referee-response-190442 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 20 Mar 2024 Jonny Fajar , Brawijaya Internal Medicine Research Center, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, 65145, Indonesia 20 Mar 2024 Author Response This manuscript presents results from a systematic review and meta-analysis on the global prevalence of COVID-19 vaccine refusal factors that increase or decrease the risk of refusal. This manuscript follows ... Continue reading This manuscript presents results from a systematic review and meta-analysis on the global prevalence of COVID-19 vaccine refusal factors that increase or decrease the risk of refusal. This manuscript follows the publication from the same group titled “Global Prevalence and Potential Influencing Factors of COVID-19 Vaccination Hesitance: A Meta-Analysis” which includes considerable overlap with the current article. The important distinction is summarized by the following difference in the search strategy: search term HESITANCY (original article) replaced by terms REFUSAL or REJECTION. Interestingly, this subtle distinction is important, and should be a key message and be the focus of the introduction, and the contrasts between this manuscript highlighted throughout the methods and discussion sections. For example, vaccine hesitancy represents a continuum from complete acceptance to refusal to be vaccinate under any circumstances. The authors need to clearly describe how they are making this distinction in their article assessment and how actual vaccination rates equate to refusal. That is, in the included studies was the survey methodology able to distinguish between individuals who will never allow vaccination versus those who might opt be able to overcome their hesitancy. I agree that there is a distinction between hesitancy and refusal, but how to clearly measure that distinction requires more explanation. This is clearly an important issue that merits publication, the analysis appears rigorous and appropriate, but could be better contextualized for readers. It is also important to contrast this study with the previous study by this group. Below I provide general and specific comment I hope the authors will consider. General Comments: As mentioned above the distinction between vaccination refusal and hesitancy needs to be clearly described, in the introduction of the manuscript. The authors do point out why the distinction is important, but they could elaborate more. But most importantly, how can we make this distinction within the evaluated studies. Response: We sincerely appreciate the insightful guidance given. The difference between vaccine hesitancy and refusal has been clarified in our introduction. Hesitancy signifies uncertainty about accepting the vaccine, whereas refusal pertains to outright rejection. As a meta-analysis, our study involves extracting data from prior publications within the same context, and details regarding vaccine refusal are sourced from studies explicitly stating results using terms such as refusal or rejection. However, we have also included additional explanations about the distinction between vaccine hesitancy and refusal in the introduction section. Your search strategy was slightly different and despite this search extending to November 2022 in comparison to your previously published study extending through May 2022, a total of 56 and 24 manuscripts were evaluated, respectively. I want to know if some of those manuscripts from the previous review, that were not included in the review would have been appropriate for the current analysis. Again, contrasting this information with your previous publication would be helpful. Response: We appreciate the invaluable advice. Indeed, our study is a continuation of our previous study on vaccine hesitancy. From the papers included in our earlier study, we have also incorporated several papers into our current study. These papers contain data specifically related to vaccine rejection or refusal. One co-variate which is mentioned but not included in your analysis is country. This seems like it could be an important factor. One objective for systematic reviews is the identification of clear knowledge gaps, which deserves a strong place in your discussion. The absence of studies in LMICs is concerning. I would also argue that the issue of refusal/hesitancy and how we really identify these hard-core refusers is important and the follow up to that is how many of these people are sources of misinformation, another potential gap. Response: We appreciate the highly valuable advice. We wholeheartedly agree with your suggestion. The country is a crucial variable in the context of vaccine refusal. Factors influencing vaccine rejection behavior may vary across different countries. Initially, we aimed to create sub-groups based on countries; however, this was not feasible due to data limitations. Forcing this could potentially introduce a high level of data bias. Nevertheless, we have included the country factor as one of the limitations in our study. Context for your meta-analysis statistics: Although you report Egger and heterogeneity (Q) scores it is no clear readers unfamiliar with them what they are telling us. Response: We sincerely appreciate your advice. The Egger test for assessing potential publication bias and the Q test for evaluating heterogeneity among studies are actually quite familiar in the context of meta-analysis. Most meta-analysis software includes these features (Egger and Q test). The manuscript needs further editorial review, there are numerous grammatical errors, missing words, etc. Overall, the article was understandable, but the errors did reduce the quality of the article. Response: We appreciate the very important advice. We have re-evaluated the grammatical aspects of our article. Consider providing a discussion of the range of prevalence values in addition to the model output, but there is considerable variability shown in the figures. Response: We are very grateful for the highly valuable advice. Indeed, the prevalence data for vaccine refusal exhibits variability across each study. However, here we report the cumulative prevalence of vaccine refusal, in accordance with our study method, which is meta-analysis. I’m not an expert in Meta-Analysis, but I’m curious is a multivariate model is possible since many of the factors you are analyzing could be confounders. Response: We fully agree with your suggestion. Due to the multitude of factors analyzed, the likelihood of confounding factors is also quite high. Unfortunately, in meta-analysis, we cannot eliminate confounding factors that we consider to have an impact if the data is not present in the studies we include. Nevertheless, we have outlined the potential confounding factors in the limitations section of our study. High socioeconomic status is significant, but marginally, that deserves a bit more emphasis. Response: Thank you for your highly valuable advice. We have added emphasis on socioeconomic status in the discussion. Again, more discussion on the continuum of hesitancy to refusal is needed, more context about the included studies, what the key questions were and how populations were selected. Response: We appreciate your very important advice. We have added a discussion on the contextual differences between vaccine refusal and hesitancy. Additionally, we have included key questions in the search strategy, and we have also added the study population in the data extraction. Specific Comments Title: consider including Meta-analysis is the title, you are doing both. Response: Thank you for your highly valuable advice. We have revised our title according to your suggestion. Statistical analysis: How is the Egger and Q statistics interpreted beyond the p-value. Do they range from 0-1 with… Response: Thank you for your question. The interpretation of the Egger test is based on the p-value, where a p-value below 0.05 is considered to indicate potential publication bias. Regarding the Q test for evaluating heterogeneity among studies, the interpretation relies on the p-value, with a value above 0.10 indicating no heterogeneity, while a value below 0.10 suggests the presence of heterogeneity. In addition to assessing heterogeneity, we have included I-squared. If the I-squared value is above 50%, it indicates heterogeneity, whereas if it's less than 50%, it suggests no heterogeneity. If there is heterogeneity, the analysis model uses a random-effects model, while if there is no heterogeneity, the analysis model employs a fixed-effects model. The explanation regarding the interpretation of the assessment of potential publication bias and heterogeneity has been elaborated in the sub-heading "statistical analysis." Introduction: See my general comments. Another model which is not mentioned in your manuscript which I think could be helpful is Larson et al. 20141 that describes the 3 C’s model (Complacency, convenience, and confidence). Your article focuses on confidence rather than the other factors. Response: Thank you for your suggestion. We have added an additional discussion regarding the 3Cs model. Table 1. Consider including a column with a summary of the study findings. Also, abbreviations should be specified in the Table legend. You could eliminate the study design column as you state in the text that all the studies were cross-sectional. Response: We appreciate your crucial advice. We have revised Table 1 by adding study findings and eliminating study designs since all studies have a cross-sectional design. Instead, we have explained in the text that all studies have a cross-sectional design. Additionally, we have also added abbreviations explanations in the table legend. Discussion, last paragraph: “Fourth, all articles included in our analysis were cross-sectional studies, further meta-analyses involving only randomized controlled trials are required.” Please expand, having a hard time imagining this design for this question. What intervention would be randomized here. Sample selection is an important issue here. That is, how was a representative sample achieved with in individual studies, but beyond cross-sectional study we are give no additional information on sampling strategy, if the study was properly powered etc. This seems like an important issue, that may be reflected in the NOS score but not shared with the reader. Response: Thank you for your valuable advice. We wholeheartedly agree with your suggestion. The study limitations regarding the design of included studies have been revised. Furthermore, the sample selection method of included studies has also been added to Table 1. As for the NOS assessment results, we have included this information in the Table 1. This manuscript presents results from a systematic review and meta-analysis on the global prevalence of COVID-19 vaccine refusal factors that increase or decrease the risk of refusal. This manuscript follows the publication from the same group titled “Global Prevalence and Potential Influencing Factors of COVID-19 Vaccination Hesitance: A Meta-Analysis” which includes considerable overlap with the current article. The important distinction is summarized by the following difference in the search strategy: search term HESITANCY (original article) replaced by terms REFUSAL or REJECTION. Interestingly, this subtle distinction is important, and should be a key message and be the focus of the introduction, and the contrasts between this manuscript highlighted throughout the methods and discussion sections. For example, vaccine hesitancy represents a continuum from complete acceptance to refusal to be vaccinate under any circumstances. The authors need to clearly describe how they are making this distinction in their article assessment and how actual vaccination rates equate to refusal. That is, in the included studies was the survey methodology able to distinguish between individuals who will never allow vaccination versus those who might opt be able to overcome their hesitancy. I agree that there is a distinction between hesitancy and refusal, but how to clearly measure that distinction requires more explanation. This is clearly an important issue that merits publication, the analysis appears rigorous and appropriate, but could be better contextualized for readers. It is also important to contrast this study with the previous study by this group. Below I provide general and specific comment I hope the authors will consider. General Comments: As mentioned above the distinction between vaccination refusal and hesitancy needs to be clearly described, in the introduction of the manuscript. The authors do point out why the distinction is important, but they could elaborate more. But most importantly, how can we make this distinction within the evaluated studies. Response: We sincerely appreciate the insightful guidance given. The difference between vaccine hesitancy and refusal has been clarified in our introduction. Hesitancy signifies uncertainty about accepting the vaccine, whereas refusal pertains to outright rejection. As a meta-analysis, our study involves extracting data from prior publications within the same context, and details regarding vaccine refusal are sourced from studies explicitly stating results using terms such as refusal or rejection. However, we have also included additional explanations about the distinction between vaccine hesitancy and refusal in the introduction section. Your search strategy was slightly different and despite this search extending to November 2022 in comparison to your previously published study extending through May 2022, a total of 56 and 24 manuscripts were evaluated, respectively. I want to know if some of those manuscripts from the previous review, that were not included in the review would have been appropriate for the current analysis. Again, contrasting this information with your previous publication would be helpful. Response: We appreciate the invaluable advice. Indeed, our study is a continuation of our previous study on vaccine hesitancy. From the papers included in our earlier study, we have also incorporated several papers into our current study. These papers contain data specifically related to vaccine rejection or refusal. One co-variate which is mentioned but not included in your analysis is country. This seems like it could be an important factor. One objective for systematic reviews is the identification of clear knowledge gaps, which deserves a strong place in your discussion. The absence of studies in LMICs is concerning. I would also argue that the issue of refusal/hesitancy and how we really identify these hard-core refusers is important and the follow up to that is how many of these people are sources of misinformation, another potential gap. Response: We appreciate the highly valuable advice. We wholeheartedly agree with your suggestion. The country is a crucial variable in the context of vaccine refusal. Factors influencing vaccine rejection behavior may vary across different countries. Initially, we aimed to create sub-groups based on countries; however, this was not feasible due to data limitations. Forcing this could potentially introduce a high level of data bias. Nevertheless, we have included the country factor as one of the limitations in our study. Context for your meta-analysis statistics: Although you report Egger and heterogeneity (Q) scores it is no clear readers unfamiliar with them what they are telling us. Response: We sincerely appreciate your advice. The Egger test for assessing potential publication bias and the Q test for evaluating heterogeneity among studies are actually quite familiar in the context of meta-analysis. Most meta-analysis software includes these features (Egger and Q test). The manuscript needs further editorial review, there are numerous grammatical errors, missing words, etc. Overall, the article was understandable, but the errors did reduce the quality of the article. Response: We appreciate the very important advice. We have re-evaluated the grammatical aspects of our article. Consider providing a discussion of the range of prevalence values in addition to the model output, but there is considerable variability shown in the figures. Response: We are very grateful for the highly valuable advice. Indeed, the prevalence data for vaccine refusal exhibits variability across each study. However, here we report the cumulative prevalence of vaccine refusal, in accordance with our study method, which is meta-analysis. I’m not an expert in Meta-Analysis, but I’m curious is a multivariate model is possible since many of the factors you are analyzing could be confounders. Response: We fully agree with your suggestion. Due to the multitude of factors analyzed, the likelihood of confounding factors is also quite high. Unfortunately, in meta-analysis, we cannot eliminate confounding factors that we consider to have an impact if the data is not present in the studies we include. Nevertheless, we have outlined the potential confounding factors in the limitations section of our study. High socioeconomic status is significant, but marginally, that deserves a bit more emphasis. Response: Thank you for your highly valuable advice. We have added emphasis on socioeconomic status in the discussion. Again, more discussion on the continuum of hesitancy to refusal is needed, more context about the included studies, what the key questions were and how populations were selected. Response: We appreciate your very important advice. We have added a discussion on the contextual differences between vaccine refusal and hesitancy. Additionally, we have included key questions in the search strategy, and we have also added the study population in the data extraction. Specific Comments Title: consider including Meta-analysis is the title, you are doing both. Response: Thank you for your highly valuable advice. We have revised our title according to your suggestion. Statistical analysis: How is the Egger and Q statistics interpreted beyond the p-value. Do they range from 0-1 with… Response: Thank you for your question. The interpretation of the Egger test is based on the p-value, where a p-value below 0.05 is considered to indicate potential publication bias. Regarding the Q test for evaluating heterogeneity among studies, the interpretation relies on the p-value, with a value above 0.10 indicating no heterogeneity, while a value below 0.10 suggests the presence of heterogeneity. In addition to assessing heterogeneity, we have included I-squared. If the I-squared value is above 50%, it indicates heterogeneity, whereas if it's less than 50%, it suggests no heterogeneity. If there is heterogeneity, the analysis model uses a random-effects model, while if there is no heterogeneity, the analysis model employs a fixed-effects model. The explanation regarding the interpretation of the assessment of potential publication bias and heterogeneity has been elaborated in the sub-heading "statistical analysis." Introduction: See my general comments. Another model which is not mentioned in your manuscript which I think could be helpful is Larson et al. 20141 that describes the 3 C’s model (Complacency, convenience, and confidence). Your article focuses on confidence rather than the other factors. Response: Thank you for your suggestion. We have added an additional discussion regarding the 3Cs model. Table 1. Consider including a column with a summary of the study findings. Also, abbreviations should be specified in the Table legend. You could eliminate the study design column as you state in the text that all the studies were cross-sectional. Response: We appreciate your crucial advice. We have revised Table 1 by adding study findings and eliminating study designs since all studies have a cross-sectional design. Instead, we have explained in the text that all studies have a cross-sectional design. Additionally, we have also added abbreviations explanations in the table legend. Discussion, last paragraph: “Fourth, all articles included in our analysis were cross-sectional studies, further meta-analyses involving only randomized controlled trials are required.” Please expand, having a hard time imagining this design for this question. What intervention would be randomized here. Sample selection is an important issue here. That is, how was a representative sample achieved with in individual studies, but beyond cross-sectional study we are give no additional information on sampling strategy, if the study was properly powered etc. This seems like an important issue, that may be reflected in the NOS score but not shared with the reader. Response: Thank you for your valuable advice. We wholeheartedly agree with your suggestion. The study limitations regarding the design of included studies have been revised. Furthermore, the sample selection method of included studies has also been added to Table 1. As for the NOS assessment results, we have included this information in the Table 1. Competing Interests: We have no competing interest. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 20 Mar 2024 Jonny Fajar , Brawijaya Internal Medicine Research Center, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, 65145, Indonesia 20 Mar 2024 Author Response This manuscript presents results from a systematic review and meta-analysis on the global prevalence of COVID-19 vaccine refusal factors that increase or decrease the risk of refusal. This manuscript follows ... Continue reading This manuscript presents results from a systematic review and meta-analysis on the global prevalence of COVID-19 vaccine refusal factors that increase or decrease the risk of refusal. This manuscript follows the publication from the same group titled “Global Prevalence and Potential Influencing Factors of COVID-19 Vaccination Hesitance: A Meta-Analysis” which includes considerable overlap with the current article. The important distinction is summarized by the following difference in the search strategy: search term HESITANCY (original article) replaced by terms REFUSAL or REJECTION. Interestingly, this subtle distinction is important, and should be a key message and be the focus of the introduction, and the contrasts between this manuscript highlighted throughout the methods and discussion sections. For example, vaccine hesitancy represents a continuum from complete acceptance to refusal to be vaccinate under any circumstances. The authors need to clearly describe how they are making this distinction in their article assessment and how actual vaccination rates equate to refusal. That is, in the included studies was the survey methodology able to distinguish between individuals who will never allow vaccination versus those who might opt be able to overcome their hesitancy. I agree that there is a distinction between hesitancy and refusal, but how to clearly measure that distinction requires more explanation. This is clearly an important issue that merits publication, the analysis appears rigorous and appropriate, but could be better contextualized for readers. It is also important to contrast this study with the previous study by this group. Below I provide general and specific comment I hope the authors will consider. General Comments: As mentioned above the distinction between vaccination refusal and hesitancy needs to be clearly described, in the introduction of the manuscript. The authors do point out why the distinction is important, but they could elaborate more. But most importantly, how can we make this distinction within the evaluated studies. Response: We sincerely appreciate the insightful guidance given. The difference between vaccine hesitancy and refusal has been clarified in our introduction. Hesitancy signifies uncertainty about accepting the vaccine, whereas refusal pertains to outright rejection. As a meta-analysis, our study involves extracting data from prior publications within the same context, and details regarding vaccine refusal are sourced from studies explicitly stating results using terms such as refusal or rejection. However, we have also included additional explanations about the distinction between vaccine hesitancy and refusal in the introduction section. Your search strategy was slightly different and despite this search extending to November 2022 in comparison to your previously published study extending through May 2022, a total of 56 and 24 manuscripts were evaluated, respectively. I want to know if some of those manuscripts from the previous review, that were not included in the review would have been appropriate for the current analysis. Again, contrasting this information with your previous publication would be helpful. Response: We appreciate the invaluable advice. Indeed, our study is a continuation of our previous study on vaccine hesitancy. From the papers included in our earlier study, we have also incorporated several papers into our current study. These papers contain data specifically related to vaccine rejection or refusal. One co-variate which is mentioned but not included in your analysis is country. This seems like it could be an important factor. One objective for systematic reviews is the identification of clear knowledge gaps, which deserves a strong place in your discussion. The absence of studies in LMICs is concerning. I would also argue that the issue of refusal/hesitancy and how we really identify these hard-core refusers is important and the follow up to that is how many of these people are sources of misinformation, another potential gap. Response: We appreciate the highly valuable advice. We wholeheartedly agree with your suggestion. The country is a crucial variable in the context of vaccine refusal. Factors influencing vaccine rejection behavior may vary across different countries. Initially, we aimed to create sub-groups based on countries; however, this was not feasible due to data limitations. Forcing this could potentially introduce a high level of data bias. Nevertheless, we have included the country factor as one of the limitations in our study. Context for your meta-analysis statistics: Although you report Egger and heterogeneity (Q) scores it is no clear readers unfamiliar with them what they are telling us. Response: We sincerely appreciate your advice. The Egger test for assessing potential publication bias and the Q test for evaluating heterogeneity among studies are actually quite familiar in the context of meta-analysis. Most meta-analysis software includes these features (Egger and Q test). The manuscript needs further editorial review, there are numerous grammatical errors, missing words, etc. Overall, the article was understandable, but the errors did reduce the quality of the article. Response: We appreciate the very important advice. We have re-evaluated the grammatical aspects of our article. Consider providing a discussion of the range of prevalence values in addition to the model output, but there is considerable variability shown in the figures. Response: We are very grateful for the highly valuable advice. Indeed, the prevalence data for vaccine refusal exhibits variability across each study. However, here we report the cumulative prevalence of vaccine refusal, in accordance with our study method, which is meta-analysis. I’m not an expert in Meta-Analysis, but I’m curious is a multivariate model is possible since many of the factors you are analyzing could be confounders. Response: We fully agree with your suggestion. Due to the multitude of factors analyzed, the likelihood of confounding factors is also quite high. Unfortunately, in meta-analysis, we cannot eliminate confounding factors that we consider to have an impact if the data is not present in the studies we include. Nevertheless, we have outlined the potential confounding factors in the limitations section of our study. High socioeconomic status is significant, but marginally, that deserves a bit more emphasis. Response: Thank you for your highly valuable advice. We have added emphasis on socioeconomic status in the discussion. Again, more discussion on the continuum of hesitancy to refusal is needed, more context about the included studies, what the key questions were and how populations were selected. Response: We appreciate your very important advice. We have added a discussion on the contextual differences between vaccine refusal and hesitancy. Additionally, we have included key questions in the search strategy, and we have also added the study population in the data extraction. Specific Comments Title: consider including Meta-analysis is the title, you are doing both. Response: Thank you for your highly valuable advice. We have revised our title according to your suggestion. Statistical analysis: How is the Egger and Q statistics interpreted beyond the p-value. Do they range from 0-1 with… Response: Thank you for your question. The interpretation of the Egger test is based on the p-value, where a p-value below 0.05 is considered to indicate potential publication bias. Regarding the Q test for evaluating heterogeneity among studies, the interpretation relies on the p-value, with a value above 0.10 indicating no heterogeneity, while a value below 0.10 suggests the presence of heterogeneity. In addition to assessing heterogeneity, we have included I-squared. If the I-squared value is above 50%, it indicates heterogeneity, whereas if it's less than 50%, it suggests no heterogeneity. If there is heterogeneity, the analysis model uses a random-effects model, while if there is no heterogeneity, the analysis model employs a fixed-effects model. The explanation regarding the interpretation of the assessment of potential publication bias and heterogeneity has been elaborated in the sub-heading "statistical analysis." Introduction: See my general comments. Another model which is not mentioned in your manuscript which I think could be helpful is Larson et al. 20141 that describes the 3 C’s model (Complacency, convenience, and confidence). Your article focuses on confidence rather than the other factors. Response: Thank you for your suggestion. We have added an additional discussion regarding the 3Cs model. Table 1. Consider including a column with a summary of the study findings. Also, abbreviations should be specified in the Table legend. You could eliminate the study design column as you state in the text that all the studies were cross-sectional. Response: We appreciate your crucial advice. We have revised Table 1 by adding study findings and eliminating study designs since all studies have a cross-sectional design. Instead, we have explained in the text that all studies have a cross-sectional design. Additionally, we have also added abbreviations explanations in the table legend. Discussion, last paragraph: “Fourth, all articles included in our analysis were cross-sectional studies, further meta-analyses involving only randomized controlled trials are required.” Please expand, having a hard time imagining this design for this question. What intervention would be randomized here. Sample selection is an important issue here. That is, how was a representative sample achieved with in individual studies, but beyond cross-sectional study we are give no additional information on sampling strategy, if the study was properly powered etc. This seems like an important issue, that may be reflected in the NOS score but not shared with the reader. Response: Thank you for your valuable advice. We wholeheartedly agree with your suggestion. The study limitations regarding the design of included studies have been revised. Furthermore, the sample selection method of included studies has also been added to Table 1. As for the NOS assessment results, we have included this information in the Table 1. This manuscript presents results from a systematic review and meta-analysis on the global prevalence of COVID-19 vaccine refusal factors that increase or decrease the risk of refusal. This manuscript follows the publication from the same group titled “Global Prevalence and Potential Influencing Factors of COVID-19 Vaccination Hesitance: A Meta-Analysis” which includes considerable overlap with the current article. The important distinction is summarized by the following difference in the search strategy: search term HESITANCY (original article) replaced by terms REFUSAL or REJECTION. Interestingly, this subtle distinction is important, and should be a key message and be the focus of the introduction, and the contrasts between this manuscript highlighted throughout the methods and discussion sections. For example, vaccine hesitancy represents a continuum from complete acceptance to refusal to be vaccinate under any circumstances. The authors need to clearly describe how they are making this distinction in their article assessment and how actual vaccination rates equate to refusal. That is, in the included studies was the survey methodology able to distinguish between individuals who will never allow vaccination versus those who might opt be able to overcome their hesitancy. I agree that there is a distinction between hesitancy and refusal, but how to clearly measure that distinction requires more explanation. This is clearly an important issue that merits publication, the analysis appears rigorous and appropriate, but could be better contextualized for readers. It is also important to contrast this study with the previous study by this group. Below I provide general and specific comment I hope the authors will consider. General Comments: As mentioned above the distinction between vaccination refusal and hesitancy needs to be clearly described, in the introduction of the manuscript. The authors do point out why the distinction is important, but they could elaborate more. But most importantly, how can we make this distinction within the evaluated studies. Response: We sincerely appreciate the insightful guidance given. The difference between vaccine hesitancy and refusal has been clarified in our introduction. Hesitancy signifies uncertainty about accepting the vaccine, whereas refusal pertains to outright rejection. As a meta-analysis, our study involves extracting data from prior publications within the same context, and details regarding vaccine refusal are sourced from studies explicitly stating results using terms such as refusal or rejection. However, we have also included additional explanations about the distinction between vaccine hesitancy and refusal in the introduction section. Your search strategy was slightly different and despite this search extending to November 2022 in comparison to your previously published study extending through May 2022, a total of 56 and 24 manuscripts were evaluated, respectively. I want to know if some of those manuscripts from the previous review, that were not included in the review would have been appropriate for the current analysis. Again, contrasting this information with your previous publication would be helpful. Response: We appreciate the invaluable advice. Indeed, our study is a continuation of our previous study on vaccine hesitancy. From the papers included in our earlier study, we have also incorporated several papers into our current study. These papers contain data specifically related to vaccine rejection or refusal. One co-variate which is mentioned but not included in your analysis is country. This seems like it could be an important factor. One objective for systematic reviews is the identification of clear knowledge gaps, which deserves a strong place in your discussion. The absence of studies in LMICs is concerning. I would also argue that the issue of refusal/hesitancy and how we really identify these hard-core refusers is important and the follow up to that is how many of these people are sources of misinformation, another potential gap. Response: We appreciate the highly valuable advice. We wholeheartedly agree with your suggestion. The country is a crucial variable in the context of vaccine refusal. Factors influencing vaccine rejection behavior may vary across different countries. Initially, we aimed to create sub-groups based on countries; however, this was not feasible due to data limitations. Forcing this could potentially introduce a high level of data bias. Nevertheless, we have included the country factor as one of the limitations in our study. Context for your meta-analysis statistics: Although you report Egger and heterogeneity (Q) scores it is no clear readers unfamiliar with them what they are telling us. Response: We sincerely appreciate your advice. The Egger test for assessing potential publication bias and the Q test for evaluating heterogeneity among studies are actually quite familiar in the context of meta-analysis. Most meta-analysis software includes these features (Egger and Q test). The manuscript needs further editorial review, there are numerous grammatical errors, missing words, etc. Overall, the article was understandable, but the errors did reduce the quality of the article. Response: We appreciate the very important advice. We have re-evaluated the grammatical aspects of our article. Consider providing a discussion of the range of prevalence values in addition to the model output, but there is considerable variability shown in the figures. Response: We are very grateful for the highly valuable advice. Indeed, the prevalence data for vaccine refusal exhibits variability across each study. However, here we report the cumulative prevalence of vaccine refusal, in accordance with our study method, which is meta-analysis. I’m not an expert in Meta-Analysis, but I’m curious is a multivariate model is possible since many of the factors you are analyzing could be confounders. Response: We fully agree with your suggestion. Due to the multitude of factors analyzed, the likelihood of confounding factors is also quite high. Unfortunately, in meta-analysis, we cannot eliminate confounding factors that we consider to have an impact if the data is not present in the studies we include. Nevertheless, we have outlined the potential confounding factors in the limitations section of our study. High socioeconomic status is significant, but marginally, that deserves a bit more emphasis. Response: Thank you for your highly valuable advice. We have added emphasis on socioeconomic status in the discussion. Again, more discussion on the continuum of hesitancy to refusal is needed, more context about the included studies, what the key questions were and how populations were selected. Response: We appreciate your very important advice. We have added a discussion on the contextual differences between vaccine refusal and hesitancy. Additionally, we have included key questions in the search strategy, and we have also added the study population in the data extraction. Specific Comments Title: consider including Meta-analysis is the title, you are doing both. Response: Thank you for your highly valuable advice. We have revised our title according to your suggestion. Statistical analysis: How is the Egger and Q statistics interpreted beyond the p-value. Do they range from 0-1 with… Response: Thank you for your question. The interpretation of the Egger test is based on the p-value, where a p-value below 0.05 is considered to indicate potential publication bias. Regarding the Q test for evaluating heterogeneity among studies, the interpretation relies on the p-value, with a value above 0.10 indicating no heterogeneity, while a value below 0.10 suggests the presence of heterogeneity. In addition to assessing heterogeneity, we have included I-squared. If the I-squared value is above 50%, it indicates heterogeneity, whereas if it's less than 50%, it suggests no heterogeneity. If there is heterogeneity, the analysis model uses a random-effects model, while if there is no heterogeneity, the analysis model employs a fixed-effects model. The explanation regarding the interpretation of the assessment of potential publication bias and heterogeneity has been elaborated in the sub-heading "statistical analysis." Introduction: See my general comments. Another model which is not mentioned in your manuscript which I think could be helpful is Larson et al. 20141 that describes the 3 C’s model (Complacency, convenience, and confidence). Your article focuses on confidence rather than the other factors. Response: Thank you for your suggestion. We have added an additional discussion regarding the 3Cs model. Table 1. Consider including a column with a summary of the study findings. Also, abbreviations should be specified in the Table legend. You could eliminate the study design column as you state in the text that all the studies were cross-sectional. Response: We appreciate your crucial advice. We have revised Table 1 by adding study findings and eliminating study designs since all studies have a cross-sectional design. Instead, we have explained in the text that all studies have a cross-sectional design. Additionally, we have also added abbreviations explanations in the table legend. Discussion, last paragraph: “Fourth, all articles included in our analysis were cross-sectional studies, further meta-analyses involving only randomized controlled trials are required.” Please expand, having a hard time imagining this design for this question. What intervention would be randomized here. Sample selection is an important issue here. That is, how was a representative sample achieved with in individual studies, but beyond cross-sectional study we are give no additional information on sampling strategy, if the study was properly powered etc. This seems like an important issue, that may be reflected in the NOS score but not shared with the reader. Response: Thank you for your valuable advice. We wholeheartedly agree with your suggestion. The study limitations regarding the design of included studies have been revised. Furthermore, the sample selection method of included studies has also been added to Table 1. As for the NOS assessment results, we have included this information in the Table 1. Competing Interests: We have no competing interest. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 13 Jan 2023 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 2 (revision) 29 Jan 24 read read Version 1 13 Jan 23 read read Amy Morrison , University of California, Davis (UC Davis), Davis, USA Angelo Capodici , University of Bologna, Bologna, Italy Hassan Hadi Al-kazzaz , Al- Zahra University for Women, Karbala, Iraq Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Morrison A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Oct 2024 | for Version 2 Amy Morrison , Department of Pathology, Microbiology and Immunology, School of Veterinary Medicine, University of California, Davis (UC Davis), Davis, CA, USA 0 Views copyright © 2024 Morrison A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Review: The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 2 approved with reservations] Overall, the manuscript is significantly improved over the previous version and many of my concerns were addressed, but not all. The authors provided information on the difference between vaccine hesitancy and vaccine refusal, which represented a significant improvement to the article. That being said, I did not see much of a contrast of the results from this current analysis with that of the previous publication “Fajar JK et.al.2022 (Ref 1) ”, and it remains unclear to me the criteria the authors used upon screening manuscripts to distinguish between hesitancy and refusal. Thank you for the inclusion on the PRISMA check and inclusion of search strings, but details under eligibility requirements were quite limited. While data collection was carried out by 15 people it is not clear if the same articles were accessed by more than one individuals and how disputes might be resolved. You excluded 3,318 manuscripts as irrelevant, but it would be helpful to have a few more details here. Since your search strategy did not include an “AND”, you were seeking a needle in a haystack and identification would have been tedious and I would hope that multiple individuals would have accessed those articles. In the process of determining what as irrelevant were all the articles read, titles screened, abstracts, how far did you go to identify articles that included your two inclusion criteria. We articles excluded that met only one of the two inclusion criteria? I’m not familiar with the BESD framework, but it sounds like an appropriate suggestion. My suggestion of including discussion of the 3 C model is the same. What is missing from this manuscript are the “reasons” for refusal. I do not feel that the authors addressed the following comment: “Context for your meta-analysis statistics: Although you report Egger and heterogeneity (Q) scores it is not clear readers unfamiliar with them what they are telling us”. A remaining concern for me is that you assess principally demographic risk factors, but there is no discussion of the reasons given for refusal, this feels like a major gap. The findings on gender are significant and important to understand. The forest plot especially from the random effects model was just above one with no CI. That seems weird to me but I think the conclusion would be that gender is not that important overall, but was important in some individual studies. This finding received a disproportional emphasis, considering the overall effect size. I found the comments about this being due to most women being housewives unconvincing. If there is clear evidence in the reviewed articles that the majority of respondents were indeed housewives, please provide data to show this. With most studies coming from high income countries (not all), I find it difficult to believe that this would be representative of the actual distribution of women in these countries, especially in the context that there is a high degree of working mothers as well as women who do not have children. The possibility of more adverse events seems more credible, but this gender difference is quite disturbing and deserved serious discussion. It appears that this statement came from two studies, one that clearly selected housewives as their population (cited in the discussion), but looking at Figure 3A, although the tendency is clear I only see 3 studies where the CI does not include one. I have some discomfort about including vaccines for Dengue, Monkeypox and Ebola in the same basket as SarCov2. Dengue vaccines have complicated safety and efficacy issues associated with them, and Monkeypox and Ebola are usually focused on high-risk populations. The statements are not wrong, but perhaps to general. The authors might suggest more specific study questions that would provide insights into understanding some of the risk factors for vaccine refusal. That is looking at subgroups of females for example. References 1. Fajar JK, Sallam M, Soegiarto G, Sugiri YJ, et al.: Global Prevalence and Potential Influencing Factors of COVID-19 Vaccination Hesitancy: A Meta-Analysis. Vaccines (Basel) . 2022; 10 (8). PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Dengue epidemiology; arbovirus epidemiology; systematic review I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Morrison A. Peer Review Report For: The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 3 approved with reservations] . F1000Research 2024, 12 :54 ( https://doi.org/10.5256/f1000research.161802.r241859) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/12-54/v2#referee-response-241859 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Al-kazzaz H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 05 Apr 2024 | for Version 2 Hassan Hadi Al-kazzaz , Medical and Health Technology College, Al- Zahra University for Women, Karbala, Iraq 0 Views copyright © 2024 Al-kazzaz H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Intention not to get vaccinated; its not clearly stated whether this refusal or hesitation, please explain. What is the accepted sample size according to your statement “articles with a lower sample size used in the study were excluded”? It's important to note that single-arm meta-analyses can have limitations, particularly in terms of drawing causal inferences or generalizing findings to broader populations. In the cumulative prevalence of the refusal to have COVID-19 vaccination, the p-value for heterogeneity is reported as less than 0.0001, indicating strong evidence of heterogeneity among the studies included in the analysis. This suggests that the variability in effect sizes across studies is unlikely to be due to chance alone. Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Yes Is the statistical analysis and its interpretation appropriate? Yes Are the conclusions drawn adequately supported by the results presented in the review? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise public health and family medicine I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Al-kazzaz HH. Peer Review Report For: The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 3 approved with reservations] . F1000Research 2024, 12 :54 ( https://doi.org/10.5256/f1000research.161802.r255307) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/12-54/v2#referee-response-255307 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2023 Capodici A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 30 Aug 2023 | for Version 1 Angelo Capodici , University of Bologna, Bologna, Italy 0 Views copyright © 2023 Capodici A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Regarding the question of whether ample methodological and analytical details are furnished to facilitate replication by fellow researchers, my response leans toward the negative. The omission of the search string and the absence of a reported PRISMA checklist, despite claims of adhering to PRISMA guidelines, hinder the transparency and reproducibility of the study. It's worth noting that when delving into the assessment of vaccine hesitancy, a well-defined framework should ideally be employed. Notably, the widely recognized BESD framework by WHO stands out as a prominent choice. Disappointingly, the authors of the study failed to acknowledge this framework through proper citation, consequently weakening the robustness of their conclusions. Finally, since they cite studies about willingness regarding COVID-19 vaccination, I should mention that no studies updated to 2023 are cited, which is a shame. I would recommend improving this. Here's a few ideas: Gori et al. (2023) 1 ; Morgan et al. (2023) 2 ; Huang et al. (2023) 3 . Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? No Is the statistical analysis and its interpretation appropriate? Yes Are the conclusions drawn adequately supported by the results presented in the review? Yes References 1. Gori D, Capodici A, La Fauci G, Montalti M, et al.: COVID-19 Vaccine Refusal and Delay among Adults in Italy: Evidence from the OBVIOUS Project, a National Survey in Italy. Vaccines (Basel) . 2023; 11 (4). PubMed Abstract | Publisher Full Text 2. Morgan AK, Aziire MA, Cobbold J, Agbobada AA, et al.: Hesitant or not: A cross-sectional study of socio-demographics, conspiracy theories, trust in public health information, social capital and vaccine hesitancy among older adults in Ghana. Hum Vaccin Immunother . 2023; 19 (1): 2211495 PubMed Abstract | Publisher Full Text 3. Huang M, He R, Chen Q, Song J, et al.: COVID-19 vaccine booster dose hesitancy among key groups: A cross-sectional study. Hum Vaccin Immunother . 2023; 19 (1): 2166323 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Epidemiology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 20 Mar 2024 Jonny Fajar, Brawijaya Internal Medicine Research Center, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, 65145, Indonesia Regarding the question of whether ample methodological and analytical details are furnished to facilitate replication by fellow researchers, my response leans toward the negative. The omission of the search string and the absence of a reported PRISMA checklist, despite claims of adhering to PRISMA guidelines, hinder the transparency and reproducibility of the study. It's worth noting that when delving into the assessment of vaccine hesitancy, a well-defined framework should ideally be employed. Notably, the widely recognized BESD framework by WHO stands out as a prominent choice. Disappointingly, the authors of the study failed to acknowledge this framework through proper citation, consequently weakening the robustness of their conclusions. Response: We appreciate your comments and suggestions. Regarding the PRISMA guidance, we have included the PRISMA checklist in the supplementary files. We assure that all points from the PRISMA checklist for meta-analysis studies are documented in the supplementary files. Additionally, concerning the WHO's BESD framework, initially, we aimed to incorporate all its variables. However, in the context of meta-analysis, our ability to analyze specific variables depends on the data available from supporting studies. Nevertheless, we have added a discussion on the limitations related to the BESD framework in our study. Finally, since they cite studies about willingness regarding COVID-19 vaccination, I should mention that no studies updated to 2023 are cited, which is a shame. I would recommend improving this. Here's a few ideas: Gori et al. (2023)1; Morgan et al. (2023)2; Huang et al. (2023)3. Response: We sincerely appreciate your advice regarding additional studies that could serve as supplementary data. However, we have previously registered our study protocol in PROSPERO, and the three additional studies were published after the timeframe specified in our protocol. Our study protocol has received approval from PROSPERO, and adding these additional studies would result in our departure from the registered study protocol in PROSPERO. View more View less Competing Interests We have no competing interest. reply Respond Report a concern Capodici A. Peer Review Report For: The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 3 approved with reservations] . F1000Research 2024, 12 :54 ( https://doi.org/10.5256/f1000research.141551.r201174) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/12-54/v1#referee-response-201174 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2023 Morrison A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 09 Aug 2023 | for Version 1 Amy Morrison , Department of Pathology, Microbiology and Immunology, School of Veterinary Medicine, University of California, Davis (UC Davis), Davis, CA, USA 0 Views copyright © 2023 Morrison A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This manuscript presents results from a systematic review and meta-analysis on the global prevalence of COVID-19 vaccine refusal factors that increase or decrease the risk of refusal. This manuscript follows the publication from the same group titled “Global Prevalence and Potential Influencing Factors of COVID-19 Vaccination Hesitance: A Meta-Analysis” which includes considerable overlap with the current article. The important distinction is summarized by the following difference in the search strategy: search term HESITANCY (original article) replaced by terms REFUSAL or REJECTION. Interestingly, this subtle distinction is important, and should be a key message and be the focus of the introduction, and the contrasts between this manuscript highlighted throughout the methods and discussion sections. For example, vaccine hesitancy represents a continuum from complete acceptance to refusal to be vaccinate under any circumstances. The authors need to clearly describe how they are making this distinction in their article assessment and how actual vaccination rates equate to refusal. That is, in the included studies was the survey methodology able to distinguish between individuals who will never allow vaccination versus those who might opt be able to overcome their hesitancy. I agree that there is a distinction between hesitancy and refusal, but how to clearly measure that distinction requires more explanation. This is clearly an important issue that merits publication, the analysis appears rigorous and appropriate, but could be better contextualized for readers. It is also important to contrast this study with the previous study by this group. Below I provide general and specific comment I hope the authors will consider. General Comments: As mentioned above the distinction between vaccination refusal and hesitancy needs to be clearly described, in the introduction of the manuscript. The authors do point out why the distinction is important, but they could elaborate more. But most importantly, how can we make this distinction within the evaluated studies. Your search strategy was slightly different and despite this search extending to November 2022 in comparison to your previously published study extending through May 2022, a total of 56 and 24 manuscripts were evaluated, respectively. I want to know if some of those manuscripts from the previous review, that were not included in the review would have been appropriate for the current analysis. Again, contrasting this information with your previous publication would be helpful. One co-variate which is mentioned but not included in your analysis is country. This seems like it could be an important factor. One objective for systematic reviews is the identification of clear knowledge gaps, which deserves a strong place in your discussion. The absence of studies in LMICs is concerning. I would also argue that the issue of refusal/hesitancy and how we really identify these hard-core refusers is important and the follow up to that is how many of these people are sources of misinformation, another potential gap. Context for your meta-analysis statistics: Although you report Egger and heterogeneity (Q) scores it is no clear readers unfamiliar with them what they are telling us. The manuscript needs further editorial review, there are numerous grammatical errors, missing words, etc. Overall, the article was understandable, but the errors did reduce the quality of the article. Consider providing a discussion of the range of prevalence values in addition to the model output, but there is considerable variability shown in the figures. I’m not an expert in Meta-Analysis, but I’m curious is a multivariate model is possible since many of the factors you are analyzing could be confounders. High socioeconomic status is significant, but marginally, that deserves a bit more emphasis. Again, more discussion on the continuum of hesitancy to refusal is needed, more context about the included studies, what the key questions were and how populations were selected. Specific Comments Title: consider including Meta-analysis is the title, you are doing both. Statistical analysis: How is the Egger and Q statistics interpreted beyond the p-value. Do they range from 0-1 with… Introduction: See my general comments. Another model which is not mentioned in your manuscript which I think could be helpful is Larson et al. 2014 1 that describes the 3 C’s model (Complacency, convenience, and confidence). Your article focuses on confidence rather than the other factors. Table 1. Consider including a column with a summary of the study findings. Also, abbreviations should be specified in the Table legend. You could eliminate the study design column as you state in the text that all the studies were cross-sectional. Discussion, last paragraph: “Fourth, all articles included in our analysis were cross-sectional studies, further meta-analyses involving only randomized controlled trials are required.” Please expand, having a hard time imagining this design for this question. What intervention would be randomized here. Sample selection is an important issue here. That is, how was a representative sample achieved with in individual studies, but beyond cross-sectional study we are give no additional information on sampling strategy, if the study was properly powered etc. This seems like an important issue, that may be reflected in the NOS score but not shared with the reader. Are the rationale for, and objectives of, the Systematic Review clearly stated? Partly Are sufficient details of the methods and analysis provided to allow replication by others? Partly Is the statistical analysis and its interpretation appropriate? Yes Are the conclusions drawn adequately supported by the results presented in the review? Partly References 1. Larson H, Jarrett C, Eckersberger E, Smith D, et al.: Understanding vaccine hesitancy around vaccines and vaccination from a global perspective: A systematic review of published literature, 2007–2012. Vaccine . 2014; 32 (19): 2150-2159 Publisher Full Text Competing Interests Currently have funding to study vaccine hesitancy for dengue vaccines in Peru, where our research touched on hesitancy for COVID-19 vaccination as well. Reviewer Expertise Dengue epidemiology; arbovirus epidemiology; systematic review I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 20 Mar 2024 Jonny Fajar, Brawijaya Internal Medicine Research Center, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, 65145, Indonesia This manuscript presents results from a systematic review and meta-analysis on the global prevalence of COVID-19 vaccine refusal factors that increase or decrease the risk of refusal. This manuscript follows the publication from the same group titled “Global Prevalence and Potential Influencing Factors of COVID-19 Vaccination Hesitance: A Meta-Analysis” which includes considerable overlap with the current article. The important distinction is summarized by the following difference in the search strategy: search term HESITANCY (original article) replaced by terms REFUSAL or REJECTION. Interestingly, this subtle distinction is important, and should be a key message and be the focus of the introduction, and the contrasts between this manuscript highlighted throughout the methods and discussion sections. For example, vaccine hesitancy represents a continuum from complete acceptance to refusal to be vaccinate under any circumstances. The authors need to clearly describe how they are making this distinction in their article assessment and how actual vaccination rates equate to refusal. That is, in the included studies was the survey methodology able to distinguish between individuals who will never allow vaccination versus those who might opt be able to overcome their hesitancy. I agree that there is a distinction between hesitancy and refusal, but how to clearly measure that distinction requires more explanation. This is clearly an important issue that merits publication, the analysis appears rigorous and appropriate, but could be better contextualized for readers. It is also important to contrast this study with the previous study by this group. Below I provide general and specific comment I hope the authors will consider. General Comments: As mentioned above the distinction between vaccination refusal and hesitancy needs to be clearly described, in the introduction of the manuscript. The authors do point out why the distinction is important, but they could elaborate more. But most importantly, how can we make this distinction within the evaluated studies. Response: We sincerely appreciate the insightful guidance given. The difference between vaccine hesitancy and refusal has been clarified in our introduction. Hesitancy signifies uncertainty about accepting the vaccine, whereas refusal pertains to outright rejection. As a meta-analysis, our study involves extracting data from prior publications within the same context, and details regarding vaccine refusal are sourced from studies explicitly stating results using terms such as refusal or rejection. However, we have also included additional explanations about the distinction between vaccine hesitancy and refusal in the introduction section. Your search strategy was slightly different and despite this search extending to November 2022 in comparison to your previously published study extending through May 2022, a total of 56 and 24 manuscripts were evaluated, respectively. I want to know if some of those manuscripts from the previous review, that were not included in the review would have been appropriate for the current analysis. Again, contrasting this information with your previous publication would be helpful. Response: We appreciate the invaluable advice. Indeed, our study is a continuation of our previous study on vaccine hesitancy. From the papers included in our earlier study, we have also incorporated several papers into our current study. These papers contain data specifically related to vaccine rejection or refusal. One co-variate which is mentioned but not included in your analysis is country. This seems like it could be an important factor. One objective for systematic reviews is the identification of clear knowledge gaps, which deserves a strong place in your discussion. The absence of studies in LMICs is concerning. I would also argue that the issue of refusal/hesitancy and how we really identify these hard-core refusers is important and the follow up to that is how many of these people are sources of misinformation, another potential gap. Response: We appreciate the highly valuable advice. We wholeheartedly agree with your suggestion. The country is a crucial variable in the context of vaccine refusal. Factors influencing vaccine rejection behavior may vary across different countries. Initially, we aimed to create sub-groups based on countries; however, this was not feasible due to data limitations. Forcing this could potentially introduce a high level of data bias. Nevertheless, we have included the country factor as one of the limitations in our study. Context for your meta-analysis statistics: Although you report Egger and heterogeneity (Q) scores it is no clear readers unfamiliar with them what they are telling us. Response: We sincerely appreciate your advice. The Egger test for assessing potential publication bias and the Q test for evaluating heterogeneity among studies are actually quite familiar in the context of meta-analysis. Most meta-analysis software includes these features (Egger and Q test). The manuscript needs further editorial review, there are numerous grammatical errors, missing words, etc. Overall, the article was understandable, but the errors did reduce the quality of the article. Response: We appreciate the very important advice. We have re-evaluated the grammatical aspects of our article. Consider providing a discussion of the range of prevalence values in addition to the model output, but there is considerable variability shown in the figures. Response: We are very grateful for the highly valuable advice. Indeed, the prevalence data for vaccine refusal exhibits variability across each study. However, here we report the cumulative prevalence of vaccine refusal, in accordance with our study method, which is meta-analysis. I’m not an expert in Meta-Analysis, but I’m curious is a multivariate model is possible since many of the factors you are analyzing could be confounders. Response: We fully agree with your suggestion. Due to the multitude of factors analyzed, the likelihood of confounding factors is also quite high. Unfortunately, in meta-analysis, we cannot eliminate confounding factors that we consider to have an impact if the data is not present in the studies we include. Nevertheless, we have outlined the potential confounding factors in the limitations section of our study. High socioeconomic status is significant, but marginally, that deserves a bit more emphasis. Response: Thank you for your highly valuable advice. We have added emphasis on socioeconomic status in the discussion. Again, more discussion on the continuum of hesitancy to refusal is needed, more context about the included studies, what the key questions were and how populations were selected. Response: We appreciate your very important advice. We have added a discussion on the contextual differences between vaccine refusal and hesitancy. Additionally, we have included key questions in the search strategy, and we have also added the study population in the data extraction. Specific Comments Title: consider including Meta-analysis is the title, you are doing both. Response: Thank you for your highly valuable advice. We have revised our title according to your suggestion. Statistical analysis: How is the Egger and Q statistics interpreted beyond the p-value. Do they range from 0-1 with… Response: Thank you for your question. The interpretation of the Egger test is based on the p-value, where a p-value below 0.05 is considered to indicate potential publication bias. Regarding the Q test for evaluating heterogeneity among studies, the interpretation relies on the p-value, with a value above 0.10 indicating no heterogeneity, while a value below 0.10 suggests the presence of heterogeneity. In addition to assessing heterogeneity, we have included I-squared. If the I-squared value is above 50%, it indicates heterogeneity, whereas if it's less than 50%, it suggests no heterogeneity. If there is heterogeneity, the analysis model uses a random-effects model, while if there is no heterogeneity, the analysis model employs a fixed-effects model. The explanation regarding the interpretation of the assessment of potential publication bias and heterogeneity has been elaborated in the sub-heading "statistical analysis." Introduction: See my general comments. Another model which is not mentioned in your manuscript which I think could be helpful is Larson et al. 20141 that describes the 3 C’s model (Complacency, convenience, and confidence). Your article focuses on confidence rather than the other factors. Response: Thank you for your suggestion. We have added an additional discussion regarding the 3Cs model. Table 1. Consider including a column with a summary of the study findings. Also, abbreviations should be specified in the Table legend. You could eliminate the study design column as you state in the text that all the studies were cross-sectional. Response: We appreciate your crucial advice. We have revised Table 1 by adding study findings and eliminating study designs since all studies have a cross-sectional design. Instead, we have explained in the text that all studies have a cross-sectional design. Additionally, we have also added abbreviations explanations in the table legend. Discussion, last paragraph: “Fourth, all articles included in our analysis were cross-sectional studies, further meta-analyses involving only randomized controlled trials are required.” Please expand, having a hard time imagining this design for this question. What intervention would be randomized here. Sample selection is an important issue here. That is, how was a representative sample achieved with in individual studies, but beyond cross-sectional study we are give no additional information on sampling strategy, if the study was properly powered etc. This seems like an important issue, that may be reflected in the NOS score but not shared with the reader. Response: Thank you for your valuable advice. We wholeheartedly agree with your suggestion. The study limitations regarding the design of included studies have been revised. Furthermore, the sample selection method of included studies has also been added to Table 1. As for the NOS assessment results, we have included this information in the Table 1. View more View less Competing Interests We have no competing interest. reply Respond Report a concern Morrison A. Peer Review Report For: The refusal of COVID-19 vaccination and its associated factors: a meta-analysis [version 2; peer review: 3 approved with reservations] . F1000Research 2024, 12 :54 ( https://doi.org/10.5256/f1000research.141551.r190442) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/12-54/v1#referee-response-190442 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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