Canine Leishmaniosis in Italy: Geospatial Trends and Incidence, a Shift in Epidemiological Approach.

introductionCanine 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.

methodsWe 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.

resultsIncidence 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.

conclusionsIntegrating 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.