From 2011 through 2018, there was a notable increase in sporadic Legionnaires' disease in the state of Minnesota. Sporadic cases are those not associated with a documented outbreak. Outbreak-related cases are typically associated with a common identified contaminated water system; sporadic cases typically do not have a common source that has been identified. Because of this, it is hypothesised that weather and environmental factors can be used as predictors of sporadic Legionnaires' disease. An ecological design was used with case report surveillance data from the state of Minnesota during 2011 through 2018. Over this 8-year period, there were 374 confirmed Legionnaires' disease cases included in the analysis. Precipitation, temperature and relative humidity (RH) data were collected from weather stations across the state. A Poisson regression analysis examined the risk of Legionnaires' disease associated with precipitation, temperature, RH, land-use and age. A lagged average 14-day precipitation had the strongest association with Legionnaires' disease (RR 2.5, CI 2.1–2.9), when accounting for temperature, RH, land-use and age. Temperature, RH and land-use also had statistically significant associations to Legionnaires' disease, but with smaller risk ratios. This study adds to the body of evidence that weather and environmental factors play an important role in the risk of sporadic Legionnaires' disease. This is an area that can be used to target additional research and prevention strategies.

According to the Centers for Disease Control and Prevention (CDC), from 2000 to 2014, reported cases of legionellosis per 100 000 population increased by 300% in the USA, although reports on disease seasonality are inconsistent. Using two national databases, we assessed seasonal patterns of legionellosis in the USA. We created a monthly time series from 1993 to 2015 of reported cases of legionellosis from the CDC, and from 1997 to 2006 of medical claims of legionellosis-related hospitalisation in older adults from the Centers for Medicaid and Medicare Services (CMS). We split the study time interval into two segments (before and after 2003), and applied a Poisson harmonic regression model to each dataset and each segment. The time series of monthly counts exhibited a significant shift of seasonal peaks from mid-September (9.676 ± 0.164 months) before 2003 to mid-August (8.452 ± 0.042 months) after 2003, along with an alarming increase in the amplitude of seasonal peaks in both CDC and CMS data. The lowest monthly reported cases of legionellosis in 2015 (281) exceed the maximum value reported before 2003 (206). We also observed a discrepancy between CDC and CMS data, suggesting that not all cases of legionellosis diagnosed by hospital-based laboratories were reported to the CDC. Improved reporting of legionellosis is required to better inform the public and organise disease prevention.

Legionella pneumophila is the main pathogen responsible for outbreaks of Legionnaires' disease, which can be related to contaminated water supplies such as cooling towers or water pipes. We combined conventional molecular methods and whole genome sequence (WGS) analysis to investigate an outbreak of L. pneumophila in a large Australian hospital. Typing of these isolates using sequence-based typing and virulence gene profiling, was unable to discriminate between outbreak and non-outbreak isolates. WGS analysis was performed on isolates during the outbreak, as well as on unlinked isolates from the Public Health Microbiology reference collection. The more powerful resolution provided by analysis of whole genome sequences allowed outbreak isolates to be distinguished from isolates that were temporally and spatially unassociated with the outbreak, demonstrating that this technology can be used in real-time to investigate L. pneumophila outbreaks.

We investigated a mixed outbreak of Legionnaires' disease (LD) and Pontiac fever (PF) at a military base to identify the outbreak's environmental source as well as known legionellosis risk factors. Base workers with possible legionellosis were interviewed and, if consenting, underwent testing for legionellosis. A retrospective cohort study collected information on occupants of the buildings closest to the outbreak source. We identified 29 confirmed and probable LD and 38 PF cases. All cases were exposed to airborne pathogens from a cooling tower. Occupants of the building closest to the cooling tower were 6·9 [95% confidence interval (CI) 2·2–22·0] and 5·5 (95% CI 2·1–14·5) times more likely to develop LD and PF, respectively, than occupants of the next closest building. Thorough preventive measures and aggressive responses to outbreaks, including searching for PF cases in mixed legionellosis outbreaks, are essential for legionellosis control.

Travel is a risk factor for Legionnaires' disease. In 2008, two cases were reported in condominium guests where we investigated a 2001 outbreak. We reinvestigated to identify additional cases and determine whether ongoing transmission resulted from persistent colonization of potable water. Exposures were assessed by matched case-control analyses (2001) and case-series interviews (2008). We sampled potable water and other water sources. Isolates were compared using sequence-based typing. From 2001 to 2008, 35 cases were identified. Confirmed cases reported after the cluster in 2001–2002 were initially considered sporadic, but retrospective case-finding identified five additional cases. Cases were more likely than controls to stay in tower 2 of the condominium [matched odds ratio (mOR) 6·1, 95% confidence interval (CI) 1·6–22·9]; transmission was associated with showering duration (mOR 23·0, 95% CI 1·4–384). We characterized a clinical isolate as sequence type 35 (ST35) and detected ST35 in samples of tower 2's potable water in 2001, 2002, and 2008. This prolonged outbreak illustrates the importance of striving for permanent Legionella eradication from potable water.

The aim was to analyse variation in incidence of sporadic Legionnaires' disease in a geographical information system in three time periods (1990–2005) by the application of a grid model and to assess the model's validity by analysing variation according to grid position. Coordinates of the addresses at time of disease of 606 confirmed cases with Legionnaires' disease were obtained. The incidence was calculated in cells of 10×10 km in 25 different grids superimposed on a map of Denmark. A 95% and 99% threshold was applied to identify cells with excess incidence representing potential clusters. Four cells had excess incidence in all three time periods. The analysis in 25 different grid positions indicated a low risk of overlooking cells with excess incidence in a random grid. The coefficient of variation ranged from 0·08 to 0·11 independent of the threshold. By application of a random grid model we demonstrated that it was possible to detect small areas with excess incidence that were not detected in the present surveillance system.