The main objective of this study is to determine the pooled hesitancy rate for COVID 19 vaccine uptake globally.
- What are proportions of people who are hesitant to take COVID 19 vaccine globally?
- How does race, religion, location, occupation, socioeconomic class, level of education and gender influence COVID 19 hesitancy globally?
- How does misinformation and lack of information influence COVID 19 hesitancy globally?
- How does social media influence COVID 19 vaccine hesitancy?
- How does safety concerns and adverse events influence COVID 19 vaccine hesitancy?
- With the pooled hesistancy rate globally, is it possible to achieve herd immunity by vaccination?
This is a protocol for systematic review and meta-analysis of obervational studies from 2020 to present time. It is designed to enable a robust, reliable and accurate data synthesis on COVID 19 hesitancy qualitatively and quantitatively.
- Observational studies: Cohort studies, case controls, cross-sectional studies, historic cohort studies.
- Studies must report the primary outcome: COVID 19 vaccine hesitancy.
- Study must be retrievable in the English language.
a) Reviews, editorials, interventional studies, commentaries, methodological articles, letters to editors, case reports
b) Duplicates/ replicates of studies.
c) Studies not retrievable in the English Language.
Populations: global population that are eligible for COVID 19 vaccination
Intervention: COVID 19 vaccine
Comparator: Willingness to take the COVID 19 vaccine
Outcomes: The primary outcome is proportion of people globally who are hesitant to take COVID 19 vaccine.
The secondary outcomes include categorical and quantitative variables that influence hesitancy to COVID 19 vaccination. These variables include race, occupation, gender, location, religion, socioeconomic class and level of education. It also includes variables such as genuine safety concerns, levels of misinformation and social media influence.
The search will employ sensitive topic-based strategies designed for each database. The search will be carried out in the following databases: PUBMED, EMBASE, CINAHL, RESEARCHGATE, AJOL, GOOGLE SCHOLAR, WEB OF SCIENCE, SCOPUS and COCHRANE LIBRARY. Only observational studies will be included, from 2020 to present time.
The search strategy includes MESH terms, text words and entry terms. Table 1 shows the search strategies as used in the Pubmed. The same search strategy will be used in other databases with slight modifications.
Data Extraction and Management
a. Data Extraction
Data will be managed in three main softwares: DistillerSR, CMA version 3 and Microsoft Excel.
Identified studies will be screened independently in pairs and blindly using the DistillerSR software at 6 different levels:
- Level 1 would involve screening of identified studies for the study design. Only observational studies would be accepted
- Level 2 will involve screening of identified studies in the titles and abstracts using entry terms, keywords and meSH terms
- Level 3 will involve further screening of the contents of articles by reading the full text articles using the same search strategy.
- Level 4 will involve snowballing of literature on references from included studies.
- Level 5: Studies will be screened at outcome levels to select those that reported the primary outcome with or without secondary outcomes
- Level 6 will involve grey literature that report primary outcome and or secondary outcomes.
Conflicts during screening will be resolved by a third independent reviewer who servies as a tie breaker.
b. Selection Process:
Screened studies will be selected based on study charateristics: study design, inclusion/exclusion criteria and agreement between two independent and blinded reviewers. Authors of included studies with missing data will be contacted via email and telephone. After selection, studies will be deduplicated. Data items wil be extracted from selected studies into predefined forms in the DistillerSR.
c. Data Collection: Data items to be extracted from selected studies include:
- Surname of first author and year of publication
- Prevalence of COVID 19 hesitancy.
- Race, Religion, Location, Occupation
- Socioeconomic class, Gender, Level of Education
- Vaccine safety and adverse events
- Level of misinformation, social media influence and information gap.
Data items will be exported into predefined format in Microsoft Excel, to be imported into the CMA software for quantitative analysis.
The effect size for the primary outcome is prevalence. The effect sizes for secondary outcomes are some categorical and others quantitative.
Extacted data items will be used for both narrative synthesis and quantitative analysis.
The following criteria will be applied for analysis:
- Studies that passed the methodological quality assessment using the NIH quality assessment tool will be crosschecked with the Cochrane Risk of Bias tool will be included. The results will be presented in tabular format, indicating all the extractable data items as listed under data collection.
- All studies with primary outcomes will be used for narrative synthesis.
- All studies with primary outcomes and secondary outcomes that pass heterogeneity tests will be used for quantitative synthesis.
- Further Analysis: Subgroup analysis will be performed using variables such as race, gender, socioenomic status, geographical location (country), level of education and occupation. Meta-regression will be performed on quantitative variables such as level of misinformation (%), level of safety concerns (%), level of influence by social media (%) and age.
- Where heterogeneity exists, sensitivity testing using include/exclude functions in the CMA software will be performed.
- The computational model for analysis is Random effect model since the several studies across the globe will be included.
Risk of bias
The risk of bias in the included studies will be assessed for the individual article using the National Institute of Health (NIH) Quality assessment tool for observational cohort and cross-sectional studies. The NIH Quality assessment tool has 14 questions, with scores about 8 indicating good quality study. This will be cross-checked with the Cochrane tool of risk of bias assessment for non-randomized study. Publication bias in the selection of studies will be visually assessed using the funnel plot (trim and fill method) and test for asymmetry. Other statistical tests such as Egger’s regression intercept, Begg and Mazumdar's rank correlation and Orwin’s fail-safe N will be used where appropriate. Studies with extreme bias will be subjected to sensitivity testing using the include/exclude function in the CMA Software.
Assessment of Meta-bias
Meta-bias will be assessed as follows:
- Method of testing/reporting of COVID 19 hesitancy at outcome level.
- Index of reporting outcomes in studies: Studies that were reported in different indices but similar in outcome and design will be converted to the primary effect size (prevalence) based on individual case evaluation.
- Heterogeneity will be assessed at the study level using the Q statistics, and its p-value, I², Ʈ² (Tau squared). As a rule of thumb, I² values of less than 40% will be considered low heterogeneity while values > 40 but < 75 % will be considered moderate and values > 75% are high.
Presentation and Reporting of Results
The study selection process will be summarised in a Prisma flow chart according to the PRISMA 2015 Statement and PRISMA-P Checklist. A table of the search strategy in various databases showing text words, MeSH terms, and entry terms will be included. A list of included studies with extracted data items will be summarized in a table. Quantitative data such as prevalence of COVID 19 hesitancy, 95 % CI, P values, and relative weights assigned to studies and heterogeneity tests will be included in the forest plots. A table of quality scores and risk of bias of each eligible study will be included. Forest and regression plots to show sub-group analysis and meta-regression respectively will be included.