Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique, Paris, France.
Santé publique France, Saint-Maurice, France.
Euro Surveill. 2023 Apr;28(14). doi: 10.2807/1560-7917.ES.2023.28.14.2200581.
BackgroundLyme borreliosis (LB) is the most widespread hard tick-borne zoonosis in the northern hemisphere. Existing studies in Europe have focused mainly on acarological risk assessment, with few investigations exploring human LB occurrence.AimWe explored the determinants of spatial and seasonal LB variations in France from 2016 to 2021 by integrating environmental, animal, meteorological and anthropogenic factors, and then mapped seasonal LB risk predictions.MethodsWe fitted 2016-19 LB national surveillance data to a two-part spatio-temporal statistical model. Spatial and temporal random effects were specified using a Besag-York-Mollie model and a seasonal model, respectively. Coefficients were estimated in a Bayesian framework using integrated nested Laplace approximation. Data from 2020-21 were used for model validation.ResultsA high vegetation index (≥ 0.6) was positively associated with seasonal LB presence, while the index of deer presence (> 60%), mild soil temperature (15-22 °C), moderate air saturation deficit (1.5-5 mmHg) and higher tick bite frequency were associated with increased incidence. Prediction maps show a higher risk of LB in spring and summer (April-September), with higher incidence in parts of eastern, midwestern and south-western France.ConclusionWe present a national level spatial assessment of seasonal LB occurrence in Europe, disentangling factors associated with the presence and increased incidence of LB. Our findings yield quantitative evidence for national public health agencies to plan targeted prevention campaigns to reduce LB burden, enhance surveillance and identify further data needs. This approach can be tested in other LB endemic areas.
莱姆病(LB)是北半球分布最广的硬蜱传播的动物源性传染病。欧洲现有的研究主要集中在节肢动物学风险评估上,很少有研究探讨人类 LB 的发生情况。
我们通过整合环境、动物、气象和人为因素,探讨了 2016 年至 2021 年法国 LB 的空间和季节变化的决定因素,并绘制了季节性 LB 风险预测图。
我们将 2016-19 年国家 LB 监测数据拟合到一个两部分时空统计模型中。空间和时间随机效应分别采用 Besag-York-Mollie 模型和季节性模型来指定。使用集成嵌套 Laplace 近似法在贝叶斯框架中估计系数。2020-21 年的数据用于模型验证。
高植被指数(≥0.6)与季节性 LB 的存在呈正相关,而鹿的存在指数(>60%)、土壤温度适中(15-22°C)、空气饱和差适中(1.5-5mmHg)和蜱虫叮咬频率较高与发病率增加有关。预测图显示,春季和夏季(4 月至 9 月)LB 风险较高,法国东部、中西部和西南部的部分地区发病率较高。
我们对欧洲的季节性 LB 发生情况进行了全国范围的空间评估,分解了与 LB 存在和发病率增加相关的因素。我们的研究结果为国家公共卫生机构提供了定量证据,以规划有针对性的预防活动,以减轻 LB 负担,加强监测,并确定进一步的数据需求。这种方法可以在其他 LB 流行地区进行测试。