Abstract：
The number of reported cases of Legionnaires\' disease (LD) has risen markedly in Switzerland (6.5/100,000 inhabitants in 2021) and abroad over the last decade. Legionella, the causative agent of LD, are ubiquitous in the environment. Therefore, environmental changes can affect the incidence of LD, for example by increasing bacterial concentrations in the environment or by facilitating transmission.
The aim of this study is to understand the environmental determinants, in particular weather conditions, for the regional and seasonal distribution of LD in Switzerland.
We conducted a series of analyses based on the Swiss LD notification data from 2017 to 2021. First, we used a descriptive and hotspot analysis to map LD cases and identify regional clusters. Second, we applied an ecological model to identify environmental determinants on case frequency at the district level. Third, we applied a case-crossover design using distributed lag non-linear models to identify short-term associations between seven weather variables and LD occurrence. Lastly, we performed a sensitivity analysis for the case-crossover design including NO2 levels available for the year 2019.
Canton Ticino in southern Switzerland was identified as a hotspot in the cluster analysis, with a standardised notification rate of 14.3 cases/100,000 inhabitants (CI: 12.6, 16.0). The strongest association with LD frequency in the ecological model was found for large-scale factors such as weather and air pollution. The case-crossover study confirmed the strong association of elevated daily mean temperature (OR 2.83; CI: 1.70, 4.70) and mean daily vapour pressure (OR: 1.52, CI: 1.15, 2.01) 6-14 days before LD occurrence.
Our analyses showed an influence of weather with a specific temporal pattern before the onset of LD, which may provide insights into the effect mechanism. The relationship between air pollution and LD and the interplay with weather should be further investigated.
The aim of this study is to understand the environmental determinants, in particular weather conditions, for the regional and seasonal distribution of LD in Switzerland.
We conducted a series of analyses based on the Swiss LD notification data from 2017 to 2021. First, we used a descriptive and hotspot analysis to map LD cases and identify regional clusters. Second, we applied an ecological model to identify environmental determinants on case frequency at the district level. Third, we applied a case-crossover design using distributed lag non-linear models to identify short-term associations between seven weather variables and LD occurrence. Lastly, we performed a sensitivity analysis for the case-crossover design including NO2 levels available for the year 2019.
Canton Ticino in southern Switzerland was identified as a hotspot in the cluster analysis, with a standardised notification rate of 14.3 cases/100,000 inhabitants (CI: 12.6, 16.0). The strongest association with LD frequency in the ecological model was found for large-scale factors such as weather and air pollution. The case-crossover study confirmed the strong association of elevated daily mean temperature (OR 2.83; CI: 1.70, 4.70) and mean daily vapour pressure (OR: 1.52, CI: 1.15, 2.01) 6-14 days before LD occurrence.
Our analyses showed an influence of weather with a specific temporal pattern before the onset of LD, which may provide insights into the effect mechanism. The relationship between air pollution and LD and the interplay with weather should be further investigated.
摘要：
背景：在过去十年中，瑞士（2021年为6.5/100,000居民）和国外的退伍军人病（LD）报告病例数量显着增加。军团菌,LD的病原体，在环境中无处不在。因此,环境变化会影响LD的发病率，例如通过增加环境中的细菌浓度或促进传播。
目的：本研究的目的是了解环境决定因素，特别是在天气条件下，瑞士LD的区域和季节性分布。
方法：我们根据2017年至2021年的瑞士LD通知数据进行了一系列分析。首先,我们使用描述性和热点分析来绘制LD病例图并确定区域集群。第二,我们应用了一个生态模型来确定地区一级病例频率的环境决定因素。第三，我们使用分布式滞后非线性模型进行案例交叉设计,以确定7个天气变量与LD发生之间的短期关联.最后,我们对病例交叉设计进行了敏感性分析,包括2019年可用的NO2水平.
结果：瑞士南部提契诺州在聚类分析中被确定为热点，标准化通知率为14.3例/100,000居民（CI：12.6，16.0）。对于天气和空气污染等大规模因素，发现与生态模型中LD频率的最强关联。病例交叉研究证实，在LD发生前6-14天，每日平均温度升高（OR2.83；CI：1.70，4.70）和平均每日蒸气压（OR：1.52，CI：1.15，2.01）之间存在很强的关联。
结论：我们的分析表明，在LD发作之前，天气对特定的时间模式有影响，这可以提供对效应机制的见解。应进一步研究空气污染与LD之间的关系以及与天气的相互作用。
目的：本研究的目的是了解环境决定因素，特别是在天气条件下，瑞士LD的区域和季节性分布。
方法：我们根据2017年至2021年的瑞士LD通知数据进行了一系列分析。首先,我们使用描述性和热点分析来绘制LD病例图并确定区域集群。第二,我们应用了一个生态模型来确定地区一级病例频率的环境决定因素。第三，我们使用分布式滞后非线性模型进行案例交叉设计,以确定7个天气变量与LD发生之间的短期关联.最后,我们对病例交叉设计进行了敏感性分析,包括2019年可用的NO2水平.
结果：瑞士南部提契诺州在聚类分析中被确定为热点，标准化通知率为14.3例/100,000居民（CI：12.6，16.0）。对于天气和空气污染等大规模因素，发现与生态模型中LD频率的最强关联。病例交叉研究证实，在LD发生前6-14天，每日平均温度升高（OR2.83；CI：1.70，4.70）和平均每日蒸气压（OR：1.52，CI：1.15，2.01）之间存在很强的关联。
结论：我们的分析表明，在LD发作之前，天气对特定的时间模式有影响，这可以提供对效应机制的见解。应进一步研究空气污染与LD之间的关系以及与天气的相互作用。
