<p>Detecting geographical areas in a territory with excess mortality is a crucial step to understand health disparities and implement effective public health policies. In practice, this means identifying both individual areas and clusters of neighbouring areas where mortality is higher than in the rest of the territory. Mortality clusters are commonly detected using spatial scan statistics, which are tools that scan the territory with moving windows and test the presence of excess mortality. However, these techniques often detect spurious clusters or encompass areas not at risk into existing clusters, leading to unreliable epidemiological results. Here, we propose a data-driven initialisation of a generalised linear model scan statistic that improves its specificity and reduces its computational cost. Our strategy consists of identifying individual areas with a significant mortality excess through an improved version of the Besag–York–Mollié model, and using them to initialise the clustering procedure. We investigate the properties of our method with a series of simulation experiments, showing that our proposed initialisation increases clustering specificity relative to standard approaches and also prevents the erroneous inclusion of areas not at risk within clusters of elevated mortality. Finally, we demonstrate the usefulness of the proposed tool for healthcare authorities using a case study on mortality data from the Padua province in northeastern Italy.</p>