Practicing Medicine Wisely: Routine Use of Urine Legionella in Suspicious Pneumonia - Are we Overdoing?

ABSTRACT: Legionella spp. is an underreported cause of Community Acquired pneumonia that affects significant population specially in urban areas and its prevalence is on an increasing trend. The routine practice of testing for urinary antigen of Legionella in all suspected cases of pneumonia is prevalent is resource rich western countries. Although several studies have shown no distinct advantage of performing routine urinary antigen testing, this practice continues to be preferred by clinicians. In this viewpoint, we have discussed the advantages and disadvantages of urinary antigen testing, its relevance in current practice and its impact on clinical outcomes.

Can genomics and meteorology predict outbreaks of legionellosis in urban settings?

Legionella pneumophila is ubiquitous and sporadically infects humans causing Legionnaire's disease (LD). Globally, reported cases of LD have risen fourfold from 2000 to 2014. In 2016, Sydney, Australia was the epicenter of an outbreak caused by L. pneumophila serogroup 1 (Lpsg1). Whole-genome sequencing was instrumental in identifying the causal clone which was found in multiple locations across the city. This study examined the epidemiology of Lpsg1 in an urban environment, assessed typing schemes to classify resident clones, and investigated the association between local climate variables and LD outbreaks. Of 223 local Lpsg1 isolates, we identified dominant clones with one clone isolated from patients in high frequency during outbreak investigations. The core genome multi-locus sequence typing scheme was the most reliable in identifying this Lpsg1 clone. While an increase in humidity and rainfall was found to coincide with a rise in LD cases, the incidence of the major L. pneumophila outbreak clone did not link to weather phenomena. These findings demonstrated the role of high-resolution typing and weather context assessment in determining source attribution for LD outbreaks in urban settings, particularly when clinical isolates remain scarce.IMPORTANCEWe investigated the genomic and meteorological influences of infections caused by Legionella pneumophila in Sydney, Australia. Our study contributes to a knowledge gap of factors that drive outbreaks of legionellosis compared to sporadic infections in urban settings. In such cases, clinical isolates can be rare, and thus, other data are needed to inform decision-making around control measures. The study revealed that core genome multi-locus sequence typing is a reliable and adaptable technique when investigating Lpsg1 outbreaks. In Sydney, the genomic profile of Lpsg1 was dominated by a single clone, which was linked to numerous community cases over a period of 40 years. Interestingly, the peak in legionellosis cases during Autumn was not associated with this prevalent outbreak clone. Incorporating meteorological data with Lpsg1 genomics can support risk assessment strategies for legionellosis in urban environments, and this approach may be relevant for other densely populated regions globally.

Increasing Detection of Legionnaires' Disease in a Large Italian Hospital in the Period 2016-2023.

The pandemic marked the beginning of an era of dynamic and rapid changes in the diagnosis of respiratory infections. Herein we describe Legionnaires' disease trend in the years 2016-2023 in a large Italian hospital showing how improvements in diagnostic algorithms impact on its detection.

Factors Associated with Legionella Detection in the Water Systems of National Lodging Organization Facilities with Water Management Programs in the United States.

A better understanding of risk factors and the predictive capability of water management program (WMP) data in detecting Legionella are needed to inform the efforts aimed at reducing Legionella growth and preventing outbreaks of Legionnaires' disease. Using WMPs and Legionella testing data from a national lodging organization in the United States, we aimed to (1) identify factors associated with Legionella detection and (2) assess the ability of WMP disinfectant and temperature metrics to predict Legionella detection. We conducted a logistic regression analysis to identify WMP metrics associated with Legionella serogroup 1 (SG1) detection. We also estimated the predictive values for each of the WMP metrics and SG1 detection. Of 5435 testing observations from 2018 to 2020, 411 (7.6%) had SG1 detection, and 1606 (29.5%) had either SG1 or non-SG1 detection. We found failures in commonly collected WMP metrics, particularly at the primary test point for total disinfectant levels in hot water, to be associated with SG1 detection. These findings highlight that establishing and regularly monitoring water quality parameters for WMPs may be important for preventing Legionella growth and subsequent disease. However, while unsuitable water quality parameter results are associated with Legionella detection, this study found that they had poor predictive value, due in part to the low prevalence of SG1 detection in this dataset. These findings suggest that Legionella testing provides critical information to validate if a WMP is working, which cannot be obtained through water quality parameter measurements alone.

Genotypic and phenotypic profiling of 127 Legionella pneumophila strains: Insights into regional spread.

Given the recent global surge in Legionnaires' disease cases, the monitoring of Legionella pneumophila becomes increasingly crucial. Epidemiological cases often stem from local outbreaks rather than widespread dissemination, emphasizing the need to study the characteristics of this pathogen at a local level. This study focuses on isolates of L. pneumophila in the Italian region of Friuli Venezia Giulia to assess specific genotype and phenotype distribution over time and space. To this end, a total of 127 L. pneumophila strains isolated between 2005 and 2017 within national surveillance programs were analysed. Rep-PCR, RAPD, and Sau-PCR were used for genotypic characterization, while phenotypic characterization was conducted through fatty acids analysis. RAPD and Sau-PCR effectively assessed genetic characteristics, identifying different profiles for the isolates and excluding the presence of clones. Although Sau-PCR is rarely used to analyse this pathogen, it emerged as the most discriminatory technique. Phenotypically, hierarchical cluster analysis categorized strains into three groups based on varying membrane fatty acid percentages. However, both phenotypic and genotypic analyses revealed a ubiquitous profile distribution at a regional level. These results suggest an absence of correlations between strain profiles, geographical location, and isolation time, indicating instead high variability and strain dissemination within this region.

Automated cooling tower detection through deep learning for Legionnaires' disease outbreak investigations: a model development and validation study.

BACKGROUND: Cooling towers containing Legionella spp are a high-risk source of Legionnaires' disease outbreaks. Manually locating cooling towers from aerial imagery during outbreak investigations requires expertise, is labour intensive, and can be prone to errors. We aimed to train a deep learning computer vision model to automatically detect cooling towers that are aerially visible. METHODS: Between Jan 1 and 31, 2021, we extracted satellite view images of Philadelphia (PN, USA) and New York state (NY, USA) from Google Maps and annotated cooling towers to create training datasets. We augmented training data with synthetic data and model-assisted labelling of additional cities. Using 2051 images containing 7292 cooling towers, we trained a two-stage model using YOLOv5, a model that detects objects in images, and EfficientNet-b5, a model that classifies images. We assessed the primary outcomes of sensitivity and positive predictive value (PPV) of the model against manual labelling on test datasets of 548 images, including from two cities not seen in training (Boston [MA, USA] and Athens [GA, USA]). We compared the search speed of the model with that of manual searching by four epidemiologists. FINDINGS: The model identified visible cooling towers with 95·1% sensitivity (95% CI 94·0-96·1) and a PPV of 90·1% (95% CI 90·0-90·2) in New York City and Philadelphia. In Boston, sensitivity was 91·6% (89·2-93·7) and PPV was 80·8% (80·5-81·2). In Athens, sensitivity was 86·9% (75·8-94·2) and PPV was 85·5% (84·2-86·7). For an area of New York City encompassing 45 blocks (0·26 square miles), the model searched more than 600 times faster (7·6 s; 351 potential cooling towers identified) than did human investigators (mean 83·75 min [SD 29·5]; mean 310·8 cooling towers [42·2]). INTERPRETATION: The model could be used to accelerate investigation and source control during outbreaks of Legionnaires' disease through the identification of cooling towers from aerial imagery, potentially preventing additional disease spread. The model has already been used by public health teams for outbreak investigations and to initialise cooling tower registries, which are considered best practice for preventing and responding to outbreaks of Legionnaires' disease. FUNDING: None.

Outbreak of Legionnaires' disease in Rzeszów in 2023. / Ognisko choroby legionistów w Rzeszowie w 2023 roku.

BACKGROUND: Legionnaires' disease is a type of severe pneumonia caused by Legionella bacteria. The case fatality rate in this disease is 5-10%. People with various comorbidities, smokers and the elderly are at greater risk of developing the disease. OBJECTIVE: The aim of the work is to present the results of an epidemiological investigation into the outbreak of Legionnaires' disease that occurred in the city of Rzeszów and the surrounding area in August and September 2023 and to present the threat related to the presence of Legionella bacteria in water supply installations and networks. MATERIAL AND METHODS: The material for this publication was data from an epidemiological investigation conducted in the outbreak of Legionnaires disease in Rzeszów in 2023. RESULTS: Epidemiological investigation revealed 165 cases of Legionnaires' disease in the outbreak, including 152 confirmed cases and 13 probable cases. The case fatality rate in a legionellosis outbreak was 15%. Environmental tests were carried out in residential and public buildings and industrial installations during the investigation. As part of environmental tests, 187 water samples were collected, including 87 warm water samples. CONCLUSIONS: The outbreak of Legionnaires' disease in the city of Rzeszów draws attention to the potential threat from the Legionella bacteria to the health and life of especially elderly people suffering from chronic diseases. The environmental tests carried out confirmed the highest number of Legionella bacteria at medium and high levels in water samples taken in the private apartments of sick people. Despite the lack of strict legal regulations clearly specifying the obligations regarding periodic disinfection of internal hot water supply installations, cooperation with their owners should be undertaken to enforce plans and actions in this area.

An outbreak of Legionnaires' disease linked to a municipal and industrial wastewater treatment plant, The Netherlands, September-October 2022.

Wastewater treatment plants (WWTPs) are increasingly identified as Legionnaires' disease (LD) sources. An outbreak investigation was initiated following five LD cases reported in September 2022 in Houten, the Netherlands. Case identification was based on the European LD case definition, with symptom onset from 1 September 2022, residence in or within 5 km of Houten, or visit to Houten within the incubation period, without other likely sources. We sampled potential sources and genotyped environmental and clinical isolates. We identified 15 LD cases with onset between 13 September and 23 October 2022. A spatial source identification and wind direction model suggested an industrial (iWWTP) and a municipal WWTP (mWWTP) as potential sources, with the first discharging water into the latter. Both tested positive for Legionella pneumophila serogroups 1 and 6 with multiple sequence types (ST). We detected L. pneumophila sg1 ST42 in the mWWTP, matching with one of three available clinical isolates. Following control measures at the WWTPs, no further cases were observed. This outbreak underlines that municipal and industrial WWTPs can play an important role in community LD cases and outbreaks, especially those with favourable conditions for Legionella growth and dissemination, or even non-favourable conditions for growth but with the influx of contaminated water.

The increasing health burden of Legionella Pneumophila in NSW.

BACKGROUND: Legionella pneumophila can cause severe respiratory disease and is notifiable in NSW. An analysis of notifications linked to hospitalisation and death data over the period 2010-2022 was conducted to determine the burden of disease and any association with the introduction of NSW regulatory changes in 2018. METHODS: Cases were retrospectively identified from the Notifiable Conditions Records for Epidemiology and Surveillance (NCRES). Data on related morbidity and mortality were obtained from linked data within the NSW Communicable Disease Register (CDR). The impact of the regulatory change was evaluated by analysing monthly count data using an interrupted time series analysis. RESULTS: A total of 928 cases were notified with 84% admitted to hospital. Annual adjusted notification and admission rates increased over the period from 4.40 to 7.92 cases and 3.72 to 7.20 admissions, per 1,000,000 population, respectively. The mean length of hospital stay (LOS) was 14 days with a median of 8 days (range 1-262 days). Time series analysis identified an underlying increasing time trend in cases notified per month with an IRR of 1.069 (95% ci 0.751-1.523) post 2018 regulatory implementation. CONCLUSION: L. pneumophila is posing an increasing burden of disease with an underlying upward trend in notification incidence despite the introduction of regulatory changes in 2018. IMPLICATION FOR PUBLIC HEALTH PRACTICE: This study demonstrates how linking notification, hospitalisation and death data can measure the health burden of a notifiable condition. Furthermore, time-series analysis using these data is able to identify underlying temporal trends and evaluate policy changes.

Recurrence, Microevolution, and Spatiotemporal Dynamics of Legionella pneumophila Sequence Type 1905, Portugal, 2014-2022.

We investigated molecular evolution and spatiotemporal dynamics of atypical Legionella pneumophila serogroup 1 sequence type 1905 and determined its long-term persistence and linkage to human disease in dispersed locations, far beyond the large 2014 outbreak epicenter in Portugal. Our finding highlights the need for public health interventions to prevent further disease spread.

An Epidemiological Study on Legionnaires' Disease in Gyeonggi Province, Korea: Occurrence, Infection Routes, and Risk Factors for Mortality (2016-2022).

Background and Objectives: Legionnaires' disease (LD) is an acute respiratory disease with increasing annual numbers of reported domestic and global cases. This study aimed to establish foundational data for the prevention and control of LD by investigating the occurrence and infection routes of reported and suspected cases of LD in Gyeonggi Province, Korea, from January 2016 to December 2022, and by and analyzing the risk factors for death. Materials and Methods: A sex-and-age standardization was performed on LD patients and suspected cases reported in Gyeonggi Province. The monthly average number of confirmed cases was visualized using graphs, and a survival analysis was performed using Kaplan-Meier survival curves. The mortality risk ratio was estimated using the Cox proportional hazards model. Results: The incidence of LD in Gyeonggi Province mirrored the national trend, peaking in July with the highest number of confirmed and suspected cases. While there was no significant difference in survival rates by age, the survival rate was higher for suspected cases when analyzed separately. Comparing the death ratio by infection route, nosocomial infections showed the highest death ratio, and intensive care unit (ICU) admission and the presence of coinfections were significantly correlated with mortality. Factors such as nosocomial infection, admission within 1 to 3 days following diagnosis, and the development of complications were factors contributing to a higher risk of death. Conclusions: The general characteristics of patients with LD were similar to those suggested by previous studies. The proportion of community-acquired infections was lower than in previous studies, but the length of hospital stay was similar for survivors and the deceased, and the mortality rate within 30 days after diagnosis was higher for nosocomial infections. In conclusion, nosocomial infection, a period of up to 3 days from admission to diagnosis, and complications were significantly related to the mortality rate of LD.

Legionnaires' disease in transportation, construction and other occupations in 39 US jurisdictions, 2014-2016.

BACKGROUND: Certain workers are at increased risk for acquiring Legionnaires' disease compared with other workers. This study aims to identify occupations at increased risk for acquiring Legionnaires' disease. METHODS: Using data from the US Centers for Disease Control and Prevention's Supplemental Legionnaires' Disease Surveillance System, this study identified Legionnaires' disease confirmed patients ≥16 years of age in 39 states with reported symptom onset during 2014-2016. Age-adjusted and sex-adjusted incidence rate ratios (IRR) stratified by occupation group were calculated by comparing Legionnaires' disease patients in an occupation group (eg, transportation) to those in all other occupation groups (eg, non-transportation). RESULTS: A total of 2553 patients had a known occupation group. The two occupations with the highest burden were transportation (N=287; IRR=2.11) and construction (N=269; IRR=1.82). Truck drivers comprised the majority (69.7%) of the transportation occupation group and construction labourers comprised almost half (49%) of the construction occupation group. The healthcare support occupation had the highest IRR (N=75; IRR=2.16). CONCLUSION: Transportation and construction workers, who are generally not covered by guidance related to building water systems, have increased risk of Legionnaires' disease compared with other workers. One hypothesised risk factor for truck drivers is the use of non-genuine windshield cleaner in their vehicles. A simple intervention is to use genuine windshield cleaner with bactericidal properties (ie, includes isopropanol/methanol) which can reduce the risk of Legionella growth and transmission. To improve surveillance of Legionnaires' disease and identification of similar exposures, the authors encourage the collection of occupation and industry information for all patients with Legionnaires' disease.

Estimating the burden of illness caused by domestic waterborne Legionnaires' disease in Canada: 2015-2019.

Legionellosis is a disease caused by the bacterium Legionella that most commonly presents as Legionnaires' disease (LD), a severe form of pneumonia. From 2015 to 2019, an average of 438 LD cases per year were reported in Canada. However, it is believed that the actual number of cases is much higher, since LD may be underdiagnosed and underreported. The purpose of this study was to develop an estimate of the true incidence of illnesses, hospitalizations, and deaths associated with LD in Canada. Values were derived using a stochastic model, based on Canadian surveillance data from 2015 to 2019, which were scaled up to account for underdiagnosis and underreporting. Overall, there were an estimated 1,113 (90% CrI: 737-1,730) illnesses, 1,008 (90% CrI: 271-2,244) hospitalizations, and 34 (90% CrI: 4-86) deaths due to domestically acquired waterborne LD annually in Canada from 2015 to 2019. It was further estimated that only 36% of illnesses and 39% of hospitalizations and deaths were captured in surveillance, and that 22% of illnesses were caused by Legionella serogroups and species other than Legionella pneumophila serogroup 1 (non-Lp1). This study highlights the true burden and areas for improvement in Canada's surveillance and detection of LD.

High performance Legionella pneumophila source attribution using genomics-based machine learning classification.

Fundamental to effective Legionnaires' disease outbreak control is the ability to rapidly identify the environmental source(s) of the causative agent, Legionella pneumophila. Genomics has revolutionized pathogen surveillance, but L. pneumophila has a complex ecology and population structure that can limit source inference based on standard core genome phylogenetics. Here, we present a powerful machine learning approach that assigns the geographical source of Legionnaires' disease outbreaks more accurately than current core genome comparisons. Models were developed upon 534 L. pneumophila genome sequences, including 149 genomes linked to 20 previously reported Legionnaires' disease outbreaks through detailed case investigations. Our classification models were developed in a cross-validation framework using only environmental L. pneumophila genomes. Assignments of clinical isolate geographic origins demonstrated high predictive sensitivity and specificity of the models, with no false positives or false negatives for 13 out of 20 outbreak groups, despite the presence of within-outbreak polyclonal population structure. Analysis of the same 534-genome panel with a conventional phylogenomic tree and a core genome multi-locus sequence type allelic distance-based classification approach revealed that our machine learning method had the highest overall classification performance-agreement with epidemiological information. Our multivariate statistical learning approach maximizes the use of genomic variation data and is thus well-suited for supporting Legionnaires' disease outbreak investigations.IMPORTANCEIdentifying the sources of Legionnaires' disease outbreaks is crucial for effective control. Current genomic methods, while useful, often fall short due to the complex ecology and population structure of Legionella pneumophila, the causative agent. Our study introduces a high-performing machine learning approach for more accurate geographical source attribution of Legionnaires' disease outbreaks. Developed using cross-validation on environmental L. pneumophila genomes, our models demonstrate excellent predictive sensitivity and specificity. Importantly, this new approach outperforms traditional methods like phylogenomic trees and core genome multi-locus sequence typing, proving more efficient at leveraging genomic variation data to infer outbreak sources. Our machine learning algorithms, harnessing both core and accessory genomic variation, offer significant promise in public health settings. By enabling rapid and precise source identification in Legionnaires' disease outbreaks, such approaches have the potential to expedite intervention efforts and curtail disease transmission.

Legionnaires Disease in Solid Organ Transplant Recipients: A Decade-Long Nationwide Study in France.

BACKGROUND: Legionnaires disease (LD) is a rare, life-threatening opportunistic bacterial infection that poses a significant risk to patients with impaired cell-mediated immunity such as solid organ transplant recipients. However, the epidemiologic features, clinical presentation, and outcomes of LD in this population are poorly described. RESEARCH QUESTION: What are the clinical manifestations, radiologic presentation, risk factors for severity, treatment, and outcome of LD in solid organ transplant recipients? STUDY DESIGN AND METHODS: In this 10-year multicenter retrospective cohort study in France, where LD notification is mandatory, patients were identified by hospital discharge databases. Diagnosis of LD relied on positive culture findings from any respiratory sample, positive urinary antigen test (UAT) results, positive specific serologic findings, or a combination thereof. Severe LD was defined as admission to the ICU. RESULTS: One hundred one patients from 51 transplantation centers were eligible; 64 patients (63.4%) were kidney transplant recipients. Median time between transplantation and LD was 5.6 years (interquartile range, 1.5-12 years). UAT results were positive in 92% of patients (89/97). Among 31 patients with positive culture findings in respiratory samples, Legionella pneumophila serogroup 1 was identified in 90%. Chest CT imaging showed alveolar consolidation in 98% of patients (54 of 57), ground-glass opacity in 63% of patients (36 of 57), macronodules in 21% of patients (12 of 57), and cavitation in 8.8% of patients (5 of 57). Fifty-seven patients (56%) were hospitalized in the ICU. In multivariate analysis, severe LD was associated with negative UAT findings at presentation (P = .047), lymphopenia (P = .014), respiratory symptoms (P = .010), and pleural effusion (P = .039). The 30-day and 12-month mortality rates were 8% (8 of 101) and 20% (19 of 97), respectively. In multivariate analysis, diabetes mellitus was the only factor associated with 12-month mortality (hazard ratio, 3.2; 95% OR, 1.19-8.64; P = .022). INTERPRETATION: LD is a late and severe complication occurring in solid organ transplant recipients that may present as pulmonary nodules on which diabetes impacts its long-term prognosis.

Legionnaires' Disease Outbreak Associated With a Hot Tub Display at the North Carolina Mountain State Fair, September 2019.

OBJECTIVES: On September 23, 2019, the North Carolina Division of Public Health identified a legionellosis increase in western North Carolina; most patients had recently attended the North Carolina Mountain State Fair. We conducted a source investigation. METHODS: Cases were fair attendees with laboratory-confirmed legionellosis and symptom onset within 2 to 14 days (Legionnaires' disease) or ≤3 days (Pontiac fever). We conducted a case-control study matching cases to non-ill fair attendees as control participants and an environmental investigation, and we performed laboratory testing (Legionella bacteria culture and polymerase chain reaction) of 27 environmental samples from fairgrounds and hot tubs and 14 specimens from case patients. We used multivariable unconditional logistic regression models to calculate adjusted odds ratios for potential Legionella exposure sources and risk factors. RESULTS: Of 136 people identified with fair-associated legionellosis, 98 (72%) were hospitalized and 4 (3%) died. Case patients were more likely than control participants to report walking by hot tub displays (adjusted odds ratio = 10.0; 95% CI, 4.2-24.1). Complete hot tub water treatment records were not kept, precluding evaluation of water maintenance conducted on display hot tubs. Legionella pneumophila sequence types (STs) were consistent among 10 typed clinical specimens (ST224) but distinct from the only positive environmental sample from the fair (ST7 and ST8). CONCLUSIONS: Hot tub displays were identified as the most likely outbreak source, making this the largest hot tub-associated Legionnaires' disease outbreak worldwide. Following the investigation, the North Carolina Division of Public Health and the Centers for Disease Control and Prevention released guidance on mitigating risk of Legionella exposure from hot tub displays. Results highlight the importance of properly maintaining equipment that aerosolizes water, including hot tubs intended for display purposes only.

Association between the Timing of Urinary Antigen Testing and Outcomes in Legionella Pneumonia Patients: A Nationwide Database Study.

Objective Recommendations on the timing of Legionella urinary antigen tests for community-acquired pneumonia patients differ among guidelines in Japan, the United States, and European nations. We therefore evaluated the association between the timing of urinary antigen tests and in-hospital mortality in patients with Legionella pneumonia. Methods We conducted a retrospective cohort study using the Diagnosis Procedure Combination database, a nationwide database of acute care inpatients in Japan. Patients who underwent Legionella urinary antigen tests on the day of admission formed the tested group. Patients who were tested on day 2 of admission or later or were unexamined formed the control group. We performed a propensity score matching analysis to compare in-hospital mortality, length of hospital stay and duration of antibiotics use between the two groups. Results Of the 9,254 eligible patients, 6,933 were included in the tested group. One-to-one propensity score matching generated 1,945 pairs. The tested group had a significantly lower 30-day in-hospital mortality than the control group (5.7 vs. 7.7%; odds ratio, 0.72; 95% confidence intervals, 0.55-0.95; p=0.020). The tested group also showed a significantly shorter length of stay and duration of antibiotics use than the control group. Conclusion Urine antigen testing upon admission was associated with better outcomes in patients with Legionella pneumonia. Urine antigen tests upon admission may be recommended for all patients with severe community-acquired pneumonia.

Epidemiological Analysis of Legionella Pneumonia in Japan: A National Inpatient Database Study.

BACKGROUND: Legionella pneumonia, a severe form of pneumonia, is caused by Legionella bacteria. The epidemiology of Legionnaires' disease in Japan, including seasonal trends, risk factors for severe disease, and fatality rates, is unclear. This study examined the epidemiology of Legionella pneumonia in Japan. METHODS: This retrospective cohort study included data of adult patients hospitalized for Legionella pneumonia (identified using the International Classification of Diseases, 10th revision code, A481) in the Japanese Diagnosis Procedure Combination inpatient database, from April 2011 to March 2021. We performed multivariable logistic regression analysis to explore the prognostic factors of in-hospital mortality. RESULTS: Of 7,370 enrolled hospitalized patients from 1,140 hospitals (male, 84.4%; aged >50 years, 87.9%), 469 (6.4%) died during hospitalization. The number of hospitalized patients increased yearly, from 658 in 2016 to 975 in 2020. Multivariable logistic regression analysis revealed that higher in-hospital mortality was associated with older age, male sex, lower body mass index, worsened level of consciousness, comorbidities (congestive heart failure, chronic renal diseases, and metastasis), hospitalization from November to May, and ambulance use. However, lower in-hospital mortality was associated with comorbidity (liver diseases), hospitalization after 2013, and hospitalization in hospitals with higher case volume. CONCLUSION: The characterized epidemiology of Legionella pneumonia in Japan revealed a high mortality rate of 6.4%. To the best of our knowledge, this is the first study to demonstrate a higher mortality rate in winter and in patients with congestive heart failure and metastasis. Further research is needed to understand the complex interplay between the prognostic factors of Legionella pneumonia.

Incidence of Legionnaires' Disease among Travelers Visiting Hotels in Germany, 2015-2019.

We determined whether the incidence rates of travel-associated Legionnaires' disease (TALD) in hotels in Germany increased after a previous occurrence and whether control measures required by the European Legionnaires' Disease Surveillance Network after a cluster (>2 cases within 2 years) restored the rate to baseline. We analyzed TALD surveillance data from Germany during 2015-2019; a total of 307 TALD cases (163 domestic, 144 nondomestic) in hotels were reported. The incidence rate ratio was 5.5 (95% CI 3.6-7.9) for a second case and 25 (95% CI 11-50) for a third case after a cluster had occurred, suggesting that control measures initiated after the occurrence of TALD clusters might be inadequate to restore the incidence rate to baseline. Our findings indicate that substantial LD preventive measures should be explored by hotels or other accommodations after the first TALD case occurs to reduce the risk for future infections.