Vaccine hesitancy for COVID-19 explored in a phenomic study of 259 socio-cognitive-behavioural measures in the UK-REACH study of 12,431 UK healthcare workers
Authors: McManus I Chris , Woolf Katherine , Martin Christopher A , Nellums Laura B , Guyatt Anna L , Melbourne Carl , Bryant Luke , Gupta Amit , John Catherine , Tobin Martin D , Carr Sue , Simpson Sandra , Gregary Bindu , Aujayeb Avinash , Zingwe Stephen , Reza Rubina , Gray Laura J , Khunti Kamlesh , Pareek Manish 
Published: December 2021

Background

Vaccination is key to successful prevention of COVID-19 particularly nosocomial acquired infection in health care workers (HCWs). ‘Vaccine hesitancy’ is common in the population and in HCWs, and like COVID-19 itself, hesitancy is more frequent in ethnic minority groups. UK-REACH (United Kingdom Research study into Ethnicity and COVID-19 outcomes) is a large-scale study of COVID-19 in UK HCWs from diverse ethnic backgrounds, which includes measures of vaccine hesitancy. The present study explores predictors of vaccine hesitancy using a ‘phenomic approach’, considering several hundred questionnaire-based measures.

Methods

UK-REACH includes a questionnaire study encompassing 12,431 HCWs who were recruited from December 2020 to March 2021 and completed a lengthy online questionnaire (785 raw items; 392 derived measures; 260 final measures). Ethnicity was classified using the Office for National Statistics’ five (ONS5) and eighteen (ONS18) categories. Missing data were handled by multiple imputation. Variable selection used theislassopackage inR, which provides standard errors so that results from imputations could be combined using Rubin’s rules. The data were modelled using path analysis, so that predictors, and predictors of predictors could be assessed. Significance testing used the Bayesian approach of Kass and Raftery, a ‘very strong’ Bayes Factor of 150, N=12,431, and a Bonferroni correction giving a criterion of p<4.02 × 10−8for the main regression, and p<3.11 × 10−10for variables in the path analysis.

Results

At the first step of the phenomic analysis, six variables were direct predictors of greater vaccine hesitancy: Lower pro-vaccination attitudes; no flu vaccination in 2019-20; pregnancy; higher COVID-19 conspiracy beliefs; younger age; and lower optimism the roll-out of population vaccination. Overall 44 lower variables in total were direct or indirect predictors of hesitancy, with the remaining 215 variables in the phenomic analysis not independently predicting vaccine hesitancy. Key variables for predicting hesitancy were belief in conspiracy theories of COVID-19 infection, and a low belief in vaccines in general. Conspiracy beliefs had two main sets of influences:

Higher Fatalism, which was influenced a) by high external and chance locus of control and higher need for closure, which in turn were associated with neuroticism, conscientiousness, extraversion and agreeableness; and b) by religion being important in everyday life, and being Muslim.

receiving information via social media, not having higher education, and perceiving greater risks to self, the latter being influenced by higher concerns about spreading COVID, greater exposure to COVID-19, and financial concerns.

There were indirect effects of ethnicity, mediated by religion. Religion was more important for Pakistani and African HCWs, and less important for White and Chinese groups. Lower age had a direct effect on hesitancy, and age and female sex also had several indirect effects on hesitancy.

Conclusions

The phenomic approach, coupled with a path analysis revealed a complex network of social, cognitive, and behavioural influences on SARS-Cov-2 vaccine hesitancy from 44 measures, 6 direct and 38 indirect, with the remaining 215 measures not having direct or indirect effects on hesitancy. It is likely that issues of trust underpin many associations with hesitancy. Understanding such a network of influences may help in tailoring interventions to address vaccine concerns and facilitate uptake in more hesistant groups.

Funding

UKMRI-MRC and NIHR