A Bayesian spatio-temporal model for cluster detection: identifying HPV suboptimal vaccine coverage

Abstract

Human papillomavirus (HPV) is a common sexually transmitted virus responsible for several types of cancer. HPV vaccines have been included in the systematic vaccination of the Valencia Region since 2008. Despite clinical agreement on the safety and effectiveness of the vaccines, vaccination coverage remains suboptimal in many areas. In order to facilitate the implementation of targeted strategies to enhance vaccination coverage, we develop here an easy-to-implement spatio-temporal clustering model that identifies groups of health districts that share similar behaviour. Namely, health districts are clustered twice. First, they are grouped into spatial clusters based on their underlying vaccination coverage that remains constant over time. Second, they are clustered according to their evolution of annual vaccination rates. A feature of our model is that it does not impose that geographically neighboring areas are assigned to the same spatial cluster or the same temporal trend. This flexibility allows us to explore different spatial and temporal structures. Suboptimal HPV vaccination coverage was found in some health districts. The results reveal the presence of three spatial clusters, with estimated coverage ranging between 82 and 97%, for 14-year-old girls and five clusters for 12-year-old girls, with coverage ranging between 61 and 96%. However, most health districts in this age group show an increasing vaccination trend. The need for future efforts to identify factors causing suboptimal vaccination coverage so that they can be acted upon specifically is highlighted.