Department of Public Health and Surveillance, Sciensano, Brussels, Belgium.
Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium.
Epidemiol Infect. 2020 Apr 29;148:e150. doi: 10.1017/S0950268820000886.
The number of reported cases with Legionnaires' disease (LD) is increasing in Belgium. Previous studies have investigated the associations between LD incidence and meteorological factors, but the Belgian data remained unexplored. We investigated data collected between 2011 and 2019. Daily exposure data on temperature, relative humidity, precipitation and wind speed was obtained from the Royal Meteorological Institute for 29 weather stations. Case data were collected from the national reference centre and through mandatory notification. Daily case and exposure data were aggregated by province. We conducted a time-stratified case-crossover study. The 'at risk' period was defined as 10 to 2 days prior to disease onset. The corresponding days in the other study years were selected as referents. We fitted separate conditional Poisson models for each day in the 'at risk' period and a distributed lag non-linear model (DLNM) which fitted all data in one model. LD incidence showed a yearly peak in August and September. A total of 614 cases were included. Given seasonality, a sequence of precipitation, followed by high relative humidity and low wind speed showed a statistically significant association with the number of cases 6 to 4 days later. We discussed the advantages of DLNM in this context.
在比利时，报告的军团病（LD）病例数量正在增加。先前的研究已经调查了 LD 发病率与气象因素之间的关系，但比利时的数据仍未得到探索。我们调查了 2011 年至 2019 年期间收集的数据。从皇家气象研究所获得了 29 个气象站的温度、相对湿度、降水和风速的每日暴露数据。病例数据是从国家参考中心和强制通报中收集的。按省份汇总了每日病例和暴露数据。我们进行了时间分层病例交叉研究。“风险期”定义为发病前 10 天至 2 天。其他研究年份的相应天数被选为参照。我们为“风险期”内的每一天拟合了单独的条件泊松模型，以及一个适合所有数据的分布式滞后非线性模型（DLNM）。LD 发病率在 8 月和 9 月呈现出每年的高峰。共纳入了 614 例病例。考虑到季节性，一系列降水，随后是高相对湿度和低风速，与 6 至 4 天后的病例数量呈统计学显著关联。我们在这方面讨论了 DLNM 的优势。
