Health behaviors and the risk of COVID-19 incidence: A Bayesian hierarchical spatial analysis

OBJECTIVES: Effective infection control measures, based on a sound understanding of geographical community-specific health behavioral characteristics, should be implemented from the early stage of disease transmission. However, few studies have explored health behaviors as a possible contributor to COVID-19 infection in the spatial context. We investigated health behaviors as potential factors of COVID-19 incidence in the early phase of transmission in the spatial context. METHODS: We extracted COVID-19 cumulative case data as of February 25, 2021—one day prior to nationwide COVID-19 vaccination commencement—regarding health behaviors and covariates, including health condition and socio-economic factors, at the municipal level from publicly available datasets. The spatial autocorrelation of incidence was analyzed using Global Moran’s I statistics. The associations between health behaviors and COVID-19 incidence were examined using Besag–York–Mollie models to deal with spatial autocorrelation of residuals. RESULTS: The COVID-19 incidence had positive spatial autocorrelation across South Korea (I = 0.584, p = 0.001). The results suggest that individuals vaccinated against influenza in the preceding year had a negative association with COVID-19 incidence (relative risk=0.913, 95 % Credible Interval=0.838–0.997), even after adjusting for covariates. CONCLUSIONS: Our ecological study suggests an association between COVID-19 infection and health behaviors, especially influenza vaccination, in the early stage of COVID-19 transmission at the municipal level.