Identifying psychological antecedents and predictors of vaccine hesitancy through machine learning: A cross sectional study among chronic disease patients of deprived urban neighbourhood, India.

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.