Short-term associations between Legionnaires' disease incidence and meteorological variables in Belgium, 2011–2019

Abstract 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 p... Mehr ...

Verfasser: Braeye, T.
Echahidi, F.
Meghraoui, A.
Laisnez, V.
Hens, N.
Dokumenttyp: Artikel
Erscheinungsdatum: 2020
Reihe/Periodikum: Epidemiology and Infection ; volume 148 ; ISSN 0950-2688 1469-4409
Verlag/Hrsg.: Cambridge University Press (CUP)
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-28965014
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : http://dx.doi.org/10.1017/s0950268820000886

Abstract 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.