INTRODUCTION: Canine leishmaniosis (CanL) has shown a marked geographic expansion in Italy, progressively affecting central and northern regions previously considered non-endemic. Laboratory-based prevalence estimates remain informative in well-designed surveys; however, prevalence derived from diagnostic submissions may be influenced by study design (e.g., convenience sampling and clinical bias), diagnostic performance and cut-offs, and incomplete demographic denominators, limiting representativeness and comparability. METHODS: We estimated a provincial incidence proxy for CanL during 2022-2023 by combining newly diagnosed cases from routine diagnostic submissions with canine population approximations derived from ISTAT demographic data, applying a baseline dog-to-human ratio of 1:5. Rates were expressed as cases per 10,000 dogs and provinces were classified into five predefined operational risk categories. Robustness was assessed through alternative denominator scenarios (1:4 and 1:6), a restricted registry-based regional analysis in SINAC-covered regions, and descriptive comparison with independent epidemiological, entomological, and ecological evidence. RESULTS: Incidence estimates revealed substantial heterogeneity across Italy, ranging from 0 to nearly 500 cases per 10,000 dogs. Overall, 49 provinces (45.4%) were classified as minimal risk, 30 (27.8%) as low risk, 21 (19.4%) as moderate risk, 4 (3.7%) as high risk, and 4 (3.7%) as very high risk. Most minimal and low-category provinces were located in northern and central Italy, whereas higher categories were predominantly observed in Southern Italy and Sicily; however, very high provinces were also identified in North-Western Italy (Liguria), notably Imperia and Savona. While absolute provincial values were sensitive to denominator assumptions, registry-based analyses in SINAC-covered regions preserved the regional ranking of incidence, supporting the framework mainly for broad spatial prioritisation rather than precise estimation of absolute burden. CONCLUSIONS: Integrating an incidence proxy alongside prevalence provides a pragmatic surveillance tool for spatial prioritisation and risk communication under real-world constraints. The framework is most informative for identifying higher-burden areas and supporting proportionate prevention and control strategies, especially when interpreted together with registry-based restricted analyses and independent external evidence. Within a One-Health perspective, incidence-based indicators in canine surveillance can improve comparability with routinely used human notification metrics and support coordinated planning across sectors, while remaining distinct in epidemiological meaning between hosts.
Bruno et al. (2026) studied this question.