Identifying psychological antecedents and predictors of vaccine hesitancy through machine learning: A cross sectional study among chronic disease patients of deprived urban neighbourhood, India.
Monaldi Arch Chest Dis
; 92(4)2022 Mar 16.
Article
em En
| MEDLINE
| ID: mdl-35315260
ABSTRACT
COVID-19 vaccine hesitancy among chronic disease patients can severely impact individual health with the potential to impede mass vaccination essential for containing the pandemic. The present study was done to assess the COVID-19 vaccine antecedents and its predictors among chronic disease patients. This cross-sectional study was conducted among chronic disease patients availing care from a primary health facility in urban Jodhpur, Rajasthan. Factor and reliability analysis was done for the vaccine hesitancy scale to validate the 5 C scale. Predictors assessed for vaccine hesitancy were modelled with help of machine learning (ML). Out of 520 patients, the majority of participants were female (54.81%). Exploratory factor analysis revealed four psychological antecedents' "calculation"; "confidence"; "constraint" and "collective responsibility" determining 72.9% of the cumulative variance of vaccine hesitancy scale. The trained ML algorithm yielded an R2 of 0.33. Higher scores for COVID-19 health literacy and preventive behaviour, along with family support, monthly income, past COVID-19 screening, adherence to medications and age were associated with lower vaccine hesitancy. Behaviour changes communication strategies targeting COVID-19 health literacy and preventive behaviour especially among population sub-groups with poor family support, low income, higher age groups and low adherence to medicines may prove instrumental in this regard.
Texto completo:
1
Coleções:
01-internacional
Contexto em Saúde:
1_ASSA2030
/
2_ODS3
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4_TD
Base de dados:
MEDLINE
Assunto principal:
Vacinação
/
COVID-19
Tipo de estudo:
Observational_studies
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Prevalence_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Female
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Humans
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Male
País/Região como assunto:
Asia
Idioma:
En
Revista:
Monaldi Arch Chest Dis
Ano de publicação:
2022
Tipo de documento:
Article