Variation in time and space of non-outbreak Legionnaires' disease in Scotland

Analysis of Genetic Characterization and Clonality of Legionella pneumophila Isolated from Cooling Towers in Japan

We investigated the genetic characteristics of 161 Legionella pneumophila strains isolated over a period of 10 years from cooling towers in Japan. Minimum spanning tree analysis based on the sequence-based typing (SBT) of them identified three clonal complexes (CCs); CC1 (105/161, 65.2%), CC2 (22 /161, 13.7%), and CC3 (20/161, 12.4%). CC1 was formed by serogroup (SG) 1 and SG7, whereas CC2 was mainly formed by SG1. All of the CC3 isolates except two strains were SG13. The major sequence types (STs) in CC1 and CC2 were ST1 (88/105, 83.8%) and ST154 (15/22, 68.2%), respectively. These STs are known as typical types of L. pneumophila SG1 in Japanese cooling tower. Additionally, we identified 15 strains of ST2603 as the major type in CC3. This ST has not been reported in Japanese cooling tower. Whole genome sequencing (WGS) analysis of the representative strains in the three CCs, which were isolated from various cooling towers over the 10 years, elucidated high clonal population of L. pneumophila in Japanese cooling tower. Moreover, it revealed that the strains of CC2 are phylogenetically distant compared to those of CC1 and CC3, and belonged to L. pneumophila subsp. fraseri.

Environmental sources of community-acquired legionnaires' disease: A review

BACKGROUND

Most Legionnaires' disease in the US and abroad is community-acquired and believed to be sporadic, or non-outbreak associated. Most patients are exposed to numerous water sources, thus making it difficult to focus environmental investigations. Identifying known sources of sporadic community-acquired Legionnaires' disease will inform future sporadic Legionnaires' disease investigations as well as highlight directions for research. The objective is to summarize and rank sporadic Legionnaires' disease sources based on the level of linkage between the environmental source and cases.

METHODS

A PubMed search was conducted using the search terms legion* and (origins or source or transmission) and (sporadic or community-acquired). Studies of nosocomial and/or outbreak-associated disease were excluded from this review. Definite, probable, possible and suspect ranks were assigned to sources based on evidence of linkage to sporadic Legionnaires' disease.

RESULTS

The search yielded 196 articles and 47 articles were included in the final review after application of exclusion criteria. A total of 28 sources were identified. Of these, eight were assigned definite rank including residential potable water and car air-conditioner water leakage. Probable rank was assigned to five sources including solar-heated potable water and soil. Possible rank was assigned to nine sources including residential potable water and cooling towers. Suspect rank was assigned to 20 sources including large building water systems and cooling towers.

CONCLUSION

Residential potable water, large building water systems and car travel appear to contribute to a substantial proportion of sporadic Legionnaires' disease. Cooling towers are also a potentially significant source; however, definitive linkage to sporadic cases proves difficult. The sources of sporadic Legionnaires' disease cannot be definitively identified for most cases.

Rapid Identification of a Cooling Tower-Associated Legionnaires' Disease Outbreak Supported by Polymerase Chain Reaction Testing of Environmental Samples, New York City, 2014-2015

We investigated an outbreak of eight Legionnaires' disease cases among persons living in an urban residential community of 60,000 people. Possible environmental sources included two active cooling towers (air-conditioning units for large buildings) <1 km from patient residences, a market misting system, a community-wide water system used for heating and cooling, and potable water. To support a timely public health response, we used real-time polymerase chain reaction (PCR) to identify Legionella DNA in environmental samples within hours of specimen collection. We detected L. pneumophila serogroup 1 DNA only at a power plant cooling tower, supporting the decision to order remediation before culture results were available. An isolate from a power plant cooling tower sample was indistinguishable from a patient isolate by pulsed-field gel electrophoresis, suggesting the cooling tower was the outbreak source. PCR results were available <1 day after sample collection, and culture results were available as early as 5 days after plating. PCR is a valuable tool for identifying Legionella DNA in environmental samples in outbreak settings.

Analysis of population-level determinants of legionellosis: spatial and geovisual methods for enhancing classification of high-risk areas

Background

Although the incidence of legionellosis throughout North America and Europe continues to increase, public health investigations have not been able to identify a common exposure in most cases. Over 80% of cases are sporadic with no known source. To better understand the role of the macro-environment in legionellosis risk, a retrospective ecological study assessed associations between population-level measures of demographic, socioeconomic, and environmental factors and high-risk areas.

Methods

Geographic variability and clustering of legionellosis was explored in our study setting using the following methods: unadjusted and standardized incidence rate and SaTScan™ cluster detection methods using default scanning window of 1 and 50% as well as a reliability score methodology. Methods for classification of “high-risk” census tracts (areas roughly equivalent to a neighborhood with average population of 4000) for each of the spatial methods are presented. Univariate and multivariate logistic regression analyses were used to estimate associations with sociodemographic factors: population ≥ 65 years of age, non-white race, Hispanic ethnicity, poverty, less than or some high school education; housing factors: housing vacant, renter-occupied, and built pre-1950 and pre-1970; and whether drinking water is groundwater or surface water source.

Results

Census tracts with high percentages of poverty, Hispanic population, and non-white population were more likely to be classified as high-risk for legionellosis versus a low-risk census tract. Vacant housing, renter-occupied housing, and homes built pre-1970 were also important positively associated risk factors. Drinking water source was not found to be associated with legionellosis incidence.

Discussion

Census tract level demographic, socioeconomic, and environmental characteristics are important risk factors of legionellosis and add to our understanding of the macro-environment for legionellosis occurrence. Our findings can be used by public health professionals to target disease prevention communication to vulnerable populations. Future studies are needed to explore the exact mechanisms by which these risk factors may influence legionellosis clustering.

Legionellae in engineered systems and use of quantitative microbial risk assessment to predict exposure

While it is well-established that Legionella are able to colonize engineered water systems, the number of interacting factors contributing to their occurrence, proliferation, and persistence are unclear. This review summarizes current methods used to detect and quantify legionellae as well as the current knowledge of engineered water system characteristics that both favour and promote legionellae growth. Furthermore, the use of quantitative microbial risk assessment (QMRA) models to predict potentially critical human exposures to legionellae are also discussed. Understanding the conditions favouring Legionella occurrence in engineered systems and their overall ecology (growth in these systems/biofilms, biotic interactions and release) will aid in developing new treatment technologies and/or systems that minimize or eliminate human exposure to potentially pathogenic legionellae.

Going with the flow: legionellosis risk in Toronto, Canada is strongly associated with local watershed hydrology

Legionella species are increasingly recognized as a cause of both healthcare- and community-acquired pneumonia (so-called "Legionnaire's disease"). These pathogens are ubiquitous in the environment, but environmental factors in the occurrence of sporadic legionellosis remain poorly understood. We analyzed all legionellosis cases identified in the Greater Toronto Area of Ontario from 1978 to 2006, and evaluated seasonal and environmental patterns in legionellosis case occurrence by using both negative binomial models and case-crossover analysis. A total of 837 cases were reported during the study period. After adjusting for seasonal effects, changes in the local watershed, rather than weather, were the strongest contributors to legionellosis risk. A 3.6-fold increase (95% confidence interval (CI), 2.4-5.3) in odds of disease was identified with decreasing watershed levels approximately 4 weeks before case-occurrence. We also found a 33% increase (95% CI, 8-64%) in odds of disease with decreasing lake temperature during the same period and a 34% increase (95% CI, 14-57%) with increasing humidity 5 weeks before case-occurrence. We conclude that local watershed ecology influences the risk of legionellosis, notwithstanding the availability of advanced water treatment capacity in Toronto. Enhancement of risk might occur through direct contamination of water sources or via introduction of micronutrients or commensal organisms into residential and hospital water supplies. These observations suggest testable hypotheses for future empiric studies.

Users’ perspectives on epidemiological, GIS and point pattern approaches to analysing environment and health data

Despite examples showing the usefulness of geographical information systems (GIS) and spatial point pattern analysis in health research, there remain barriers to their widespread use within health service settings. This paper explores potential users' views on the relative usefulness of such approaches for analysing spatially referenced environmental health data. Our findings indicate that researchers and practitioners do not always prefer the approach with which they are most familiar. In addition, there is a need for higher levels of understanding of, and confidence in, GIS and point pattern analysis techniques amongst health service professionals. The greatest need is for multi-disciplinary research which uses the most appropriate approach for each investigation, rather than that with which researchers are most familiar.

Pinpointing clusters of apparently sporadic cases of Legionnaires' disease

OBJECTIVES

To test the hypothesis that many non-outbreak cases of legionnaires' disease are not sporadic and to attempt to pinpoint cases clustering in space and time.

DESIGN

Descriptive study of a case series, 1978-86.

SETTING

15 health boards in Scotland.

PATIENTS

203 probable cases of non-outbreak, non-travel, community acquired legionnaires' disease in patients resident in Scotland.

MAIN MEASURES

Date of onset of disease and postcode and health board of residence of cases.

RESULTS

Space-time clustering was present and numerous groups of cases were identified, all but two being newly recognised. Nine cases occurred during three months within two postcodes in Edinburgh, and an outbreak was probably missed. In several places cases occurred in one area over a prolonged period--for example, nine cases in postcode districts G11.5 and G12.8 in Glasgow during five years (estimated mean annual incidence of community acquired, non-outbreak, non-travel legionnaires' disease of 146 per million residents v 4.8 per million for Scotland). Statistical analysis showed that the space time clustering of cases in the Glasgow and Edinburgh areas was unusual (p = 0.036, p = 0.068 respectively).

CONCLUSION

Future surveillance requires greater awareness that clusters can be overlooked; case searching whenever a case is identified; collection of complete information particularly of date of onset of the disease and address or postcode; ongoing analysis for space-time clustering; and an accurate yet workable definition of sporadic cases. Other researchers should re-examine their data on apparently sporadic infection.