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INTRODUCTION: Historically, leishmaniasis in Italy was constrained to areas with Mediterranean climate. In the last 20 years, sand fly vectors (Phlebotomus perniciosus), cases of canine leishmaniasis (CanL) and cases of human visceral leishmaniasis (VL) have been observed in Northern Italian regions, traditionally classified as cold areas unsuitable for sand fly survival. AIM: We aim to evaluate through a One-Health approach the risk of endemic transmission of Leishmania infantum in the Piedmont Region, Northern Italy. METHODS: We collected environmental, entomological, animal, and human data. We applied a geostatistical binomial model to map the probability of P. perniciosus presence in the study area, using selected environmental parameters as predictors. We evaluated the spatial relationship between the probability of P. perniciosus presence and the geographical distribution of CanL and VL cases observed between 1999 and 2013. RESULTS: Between 1999 and 2003, 142 sampling sets (17%) out of 839 resulted positive for P. perniciosus. Elevation, degree of slope, normalized difference vegetation index (NDVI) and summer temperatures were associated with positive sampling sets. During the study period, 164 (13.6%) of Piedmont municipalities reported at least one autochthonous case of CanL, while 89 VL cases were observed in 54 municipalities (4.5%). We observed an association between municipalities affected by autochthonous CanL cases and the estimated probability of P. perniciosus presence (Odds Ratio for 10% increase of probability: 2.66; 95% confidence intervals (CI): 2.16-3.37). We found that human VL incident cases were positively associated with the probability of the municipality of residence of being endemic for CanL (Incidence Rate Ratio for 10% increase of probability: 1.49; 95% CI 1.02-2.16). CONCLUSIONS: Using a One-Health approach, we quantified the spatial association between the distribution of P. perniciosus, municipalities endemic for CanL and incident cases of human VL, suggesting that the disease has become endemic in the Piedmont region.
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AIMS: We aimed to investigate the heterogeneity of seasonal suicide patterns among multiple geographically, demographically and socioeconomically diverse populations. METHODS: Weekly time-series data of suicide counts for 354 communities in 12 countries during 1986-2016 were analysed. Two-stage analysis was performed. In the first stage, a generalised linear model, including cyclic splines, was used to estimate seasonal patterns of suicide for each community. In the second stage, the community-specific seasonal patterns were combined for each country using meta-regression. In addition, the community-specific seasonal patterns were regressed onto community-level socioeconomic, demographic and environmental indicators using meta-regression. RESULTS: We observed seasonal patterns in suicide, with the counts peaking in spring and declining to a trough in winter in most of the countries. However, the shape of seasonal patterns varied among countries from bimodal to unimodal seasonality. The amplitude of seasonal patterns (i.e. the peak/trough relative risk) also varied from 1.47 (95% confidence interval [CI]: 1.33-1.62) to 1.05 (95% CI: 1.01-1.1) among 12 countries. The subgroup difference in the seasonal pattern also varied over countries. In some countries, larger amplitude was shown for females and for the elderly population (≥65 years of age) than for males and for younger people, respectively. The subperiod difference also varied; some countries showed increasing seasonality while others showed a decrease or little change. Finally, the amplitude was larger for communities with colder climates, higher proportions of elderly people and lower unemployment rates (p-values < 0.05). CONCLUSIONS: Despite the common features of a spring peak and a winter trough, seasonal suicide patterns were largely heterogeneous in shape, amplitude, subgroup differences and temporal changes among different populations, as influenced by climate, demographic and socioeconomic conditions. Our findings may help elucidate the underlying mechanisms of seasonal suicide patterns and aid in improving the design of population-specific suicide prevention programmes based on these patterns.