Geographic distribution of smoking and high-risk alcohol consumption in Italy: A quintile-based approach using PASSI surveillance data

Geographical analysis is a powerful tool for public health surveillance because it allows the visualization of spatial patterns, territorial inequalities, and population groups at greater risk. Maps provide an immediate and intuitive representation of epidemiological indicators, supporting the identification of geographic clusters and helping policy makers prioritize prevention strategies and resource allocation. Within the Italian PASSI surveillance system, referred to as adult population aged 18–69 years, geographic visualization can enhance the interpretation of behavioral risk factors by highlighting regional differences that may not emerge from national averages alone. This talk presents a geographic analysis of two PASSI indicators, smoking status and high-risk alcohol consumption, using data from the 2024–2025 biennium. Regional estimates were calculated as weighted prevalences through the svy command in Stata, ensuring that the results account for the complex sampling design and are representative of the resident adult population. The indicators were then displayed through thematic maps at the regional level and local level realized through Stata maps visualization commands. To improve the interpretability of territorial differences, we classified prevalence estimates using quintiles rather than fixed thresholds or comparisons with the national average. Quintile-based classification offers several advantages: it distributes regions more evenly across categories, enhances visual contrast, facilitates the identification of relative geographic gradients, and reduces the risk of masking meaningful variability when indicator distributions are skewed. Unlike classifications centered on a national benchmark, quintiles emphasize the relative position of each region within the overall distribution, providing a clearer picture of territorial inequalities. The use of weighted prevalence estimates combined with quintile-based thematic mapping represents an effective approach for communicating PASSI surveillance data and supporting evidence-based public health planning. Furthermore, this methodology can be easily extended to finer geographic levels, such as local health authorities (LHAs) and municipalities, allowing the identification of local patterns and inequalities that may be hidden in regional-level analyses.