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.

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.

Varicella accounts for substantial morbidities and remains a public health issue worldwide, especially in children. Little is known about the effect of meteorological variables on varicella infection risk for children. This study described the epidemiology of paediatric varicella notifications in Hong Kong from 2004 to 2010, and explored the association between paediatric varicella notifications in children aged <18 years and various meteorological factors using a time-stratified case-crossover model, with adjustment of potential confounding factors. The analysis found that daily mean temperature, atmospheric pressure and Southern Oscillation Index (SOI) were positively associated with paediatric varicella notifications. We found that an interquartile range (IQR) increase in temperature (8·38°C) at lag 1 day, a 9·50 hPa increase in atmospheric pressure for the current day, and a 21·91 unit increase in SOI for the current day may lead to an increase in daily cases of 5·19% [95% confidence interval (CI) 1·90–8·58], 5·77% (95% CI 3·01–8·61), and 4·32% (95% CI 2·98–5·68), respectively. An IQR increase in daily relative humidity (by 11·96%) was associated with a decrease in daily paediatric varicella (−2·79%, 95% CI −3·84 to −1·73). These findings suggest that meteorological factors might be important predictors of paediatric varicella infection in Hong Kong.

Haemorrhagic fever with renal syndrome (HFRS) is a type of vector-borne zoonosis sensitive to climate change. To explore the short-term effect of air temperature and amount of precipitation on HFRS incidence, a total of 13 722 clinically confirmed HFRS cases from January 1977 to December 2001 in Junan County, China were included in this study. According to symmetric bidirectional case-crossover design, the hazard period (the three calendar months preceding the month when the case was diagnosed) and the control period (the same calendar month of the year before and the year after the hazard period) matched and conditional logistic regression was used to examine the effect of monthly mean temperature and precipitation on the risk of HFRS. The results showed the facilitating climatic conditions for HFRS included: condition with moderate mean air temperature (10–25 °C) and abundant precipitation (>120 mm) 3 months before [odds ratio (OR) 1·346, 95% confidence interval (CI) 1·191–1·522] and 2 months before (OR 1·193, 95% CI 1·063–1·339); and condition with temperature >25 °C and abundant precipitation (>120 mm) 3 months before (OR 1·17, 95% CI 1·004–1·363). Temperature of 10–25 °C and moderate precipitation (10–120 mm) in the current month was the most favourable condition for HFRS incidence.

A case-control and a case-crossover study were performed to investigate a Campylobacter jejuni outbreak in Crete in 2009. Most cases originated from rural areas, served by a different water-supply system from that of the adjacent town. Thirty-seven cases and 79 controls were interviewed; cases were interviewed for two different time periods for the case-crossover study. Stool cultures, PFGE and MLST subtyping were run in human samples. Univariately, consumption of tap water was associated with C. jejuni infection. Stratified analysis revealed that water-supply system was an effect modifier of this association. In the multivariable analysis, the rural areas' water supplier and drinking tap water were risk factors. No risk factors were revealed in the case-crossover study. No Campylobacter were isolated in the tested water samples. There is strong epidemiological evidence that tap water was the vehicle of the outbreak.