Optimising the case-crossover design for use in shared exposure settings.

With a case-crossover design, a case's exposure during a risk period is compared to the case's exposures at referent periods. The selection of referents for this self-controlled design is determined by the referent selection strategy (RSS). Previous research mainly focused on systematic bias associated with the RSS. We additionally focused on how RSS determines the number of referents per risk, sensitivity to overdispersion and time-varying confounding.We illustrated the consequences of different RSS using a simulation study informed by data on meteorological variables and Legionnaires' disease. By randomising the events and exposure time series, we explored statistical power associated with time-stratified and fixed bidirectional RSS and their susceptibility to systematic bias and confounding bias. In addition, we investigated how a high number of events on the same date (e.g. outbreaks) affected coefficient estimation. As illustrated by our work, referent selection alone can be insufficient to control for a time-varying confounding bias. In contrast to systematic bias, confounding bias can be hard to detect. We studied potential solutions: varying the model parameters and link-function, outlier-removal and aggregating the input-data over smaller areas. Our simulation study offers a framework for researchers looking to detect and to avoid bias in case-crossover studies.

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

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.

A large community outbreak of gastroenteritis associated with consumption of drinking water contaminated by river water, Belgium, 2010.

SUMMARY On 6 December 2010 a fire in Hemiksem, Belgium, was extinguished by the fire brigade with both river water and tap water. Local physicians were asked to report all cases of gastroenteritis. We conducted a retrospective cohort study among 1000 randomly selected households. We performed a statistical and geospatial analysis. Human stool samples, tap water and river water were tested for pathogens. Of the 1185 persons living in the 528 responding households, 222 (18·7%) reported symptoms of gastroenteritis during the time period 6-13 December. Drinking tap water was significantly associated with an increased risk for gastroenteritis (relative risk 3·67, 95% confidence interval 2·86-4·70) as was place of residence. Campylobacter sp. (2/56), norovirus GI and GII (11/56), rotavirus (1/56) and Giardia lamblia (3/56) were detected in stool samples. Tap water samples tested positive for faecal indicator bacteria and protozoa. The results support the hypothesis that a point-source contamination of the tap water with river water was the cause of the multi-pathogen waterborne outbreak.