Improvements to the Success of Outbreak Investigations of Legionnaires’ Disease: 40 Years of Testing and Investigation in New York State

Schoonmaker-Bopp, Dianna; Nazarian, Elizabeth; Dziewulski, David M.; Clement, Ernest J.; Baker, Deborah J.; Dickinson, Michelle; Saylors, Amy; Codru, Neculai; Thompson, Lisa; Lapierre, Pascal; Dumas, Nellie B.; Limberger, Ronald J.; Musser, Kimberlee A.

doi:10.1128/aem.00580-21

Cited by 8 publications

(3 citation statements)

References 31 publications

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“…The following potential drivers of improved case capture were identified from the literature: i) more consistent reporting to the CDC; ii) an increase in clinician awareness; iii) more frequent diagnostic testing; and iv) increased access to urinary antigen tests [ 2 , 4 , 14 , 16 , 17 , 19 , 21 ].…”

Section: Resultsmentioning

confidence: 99%

Legionellosis on the rise: A scoping review of sporadic, community-acquired incidence in the United States

Moffa,

Rock,

Galiatsatos

et al. 2023

Epidemiol. Infect.

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Over the past two decades, the incidence of legionellosis has been steadily increasing in the United States though there is noclear explanation for the main factors driving the increase. While legionellosis is the leading cause of waterborne outbreaks in the US, most cases are sporadic and acquired in community settings where the environmental source is never identified. This scoping review aimed to summarise the drivers of infections in the USA and determine the magnitude of impact each potential driver may have. A total of 1,738 titles were screened, and 18 articles were identified that met the inclusion criteria. Strong evidence was found for precipitation as a major driver, and both temperature and relative humidity were found to be moderate drivers of incidence. Increased testing and improved diagnostic methods were classified as moderate drivers, and the ageing U.S. population was a minor driver of increasing incidence. Racial and socioeconomic inequities and water and housing infrastructure were found to be potential factors explaining the increasing incidence though they were largely understudied in the context of non-outbreak cases. Understanding the complex relationships between environmental, infrastructure, and population factors driving legionellosis incidence is important to optimise mitigation strategies and public policy.

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Section: Resultsmentioning

confidence: 99%

Legionellosis on the rise: A scoping review of sporadic, community-acquired incidence in the United States

Moffa,

Rock,

Galiatsatos

et al. 2023

Epidemiol. Infect.

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“…Legionella testing has rapidly evolved since initial cases of LD were described more than 45 yr ago. Improvements have included refined testing algorithms, culture techniques, real-time PCR, pulsed-field gel electrophoresis (PFGE), and more recently, whole genome sequencing (WGS), a type of next-generation sequencing (NGS) in ongoing investigations of LD ( 6 ). WGS has become an essential method to support LD outbreak investigations in recent years due to its high discriminatory power.…”

Section: Introductionmentioning

confidence: 99%

A novel hybridization capture method for direct whole genome sequencing of clinical specimens to inform Legionnaires’ disease investigations

Weeber,

Singh,

Lapierre

et al. 2024

J Clin Microbiol

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The unprecedented precision and resolution of whole genome sequencing (WGS) can provide definitive identification of infectious agents for epidemiological outbreak tracking. WGS approaches, however, are frequently impeded by low pathogen DNA recovery from available primary specimens or unculturable samples. A cost-effective hybrid capture assay for Legionella pneumophila WGS analysis directly on primary specimens was developed. DNA from a diverse range of sputum and autopsy specimens PCR-positive for L. pneumophila serogroup 1 (LPSG1) was enriched with this method, and WGS was performed. All tested specimens were determined to be enriched for Legionella reads (up to 209,000-fold), significantly improving the discriminatory power to compare relatedness when no clinical isolate was available. We found the WGS data from some enriched specimens to differ by less than five single-nucleotide polymorphisms (SNPs) when compared to the WGS data of a matched culture isolate. This testing and analysis retrospectively provided previously unconfirmed links to environmental sources for clinical specimens of sputum and autopsy lung tissue. The latter provided the additional information needed to identify the source of these culture-negative cases associated with the South Bronx 2015 Legionnaires’ disease (LD) investigation in New York City. This new method provides a proof of concept for future direct clinical specimen hybrid capture enrichment combined with WGS and bioinformatic analysis during outbreak investigations. IMPORTANCE Legionnaires’ disease (LD) is a severe and potentially fatal type of pneumonia primarily caused by inhalation of Legionella -contaminated aerosols from man-made water or cooling systems. LD remains extremely underdiagnosed as it is an uncommon form of pneumonia and relies on clinicians including it in the differential and requesting specialized testing. Additionally, it is challenging to obtain clinical lower respiratory specimens from cases with LD, and when available, culture requires specialized media and growth conditions, which are not available in all microbiology laboratories. In the current study, a method for Legionella pneumophila using hybrid capture by RNA baiting was developed, which allowed us to generate sufficient genome resolution from L. pneumophila serogroup 1 PCR-positive clinical specimens. This new approach offers an additional tool for surveillance of future LD outbreaks where isolation of Legionella is not possible and may help solve previously unanswered questions from past LD investigations.

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“…Whole genome sequencing is used increasingly routinely for public health surveillance and infectious disease outbreak investigations and recent efforts have utilised the power of genomics to confirm suspected bacterial pathogen environmental sources (Abrams & Trees, 2017; Goldberg, Sichtig, Geyer, Ledeboer, & Weinstock, 2015; Krøvel et al, 2022; Petzold, Prior, Moran-Gilad, Harmsen, & Lück, 2017; Ricci et al, 2022; Rousseau et al, 2022; Schoonmaker-Bopp et al, 2021; Wüthrich et al, 2019). In particular, genomic analyses that assess core-genome variation (sites present in all isolate genomes) such as phylogenomic trees and pairwise SNP distances, have been useful to investigate disease transmission (Gorrie et al, 2021; Ingle, Howden, & Duchene, 2021; Kwong et al, 2016; Sintchenko & Holmes, 2015).…”

Section: Introductionmentioning

confidence: 99%

High performanceLegionella pneumophilasource attribution using genomics-based machine learning classification

Buultjens

Vandelannoote

Mercoulia

et al. 2023

Preprint

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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 butL. pneumophilahas 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 534L. pneumophilagenome 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 environmentalL. pneumophilagenomes. 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 maximises use of genomic variation data and is thus well-suited for supporting Legionnaires′ disease outbreak investigations.

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Improvements to the Success of Outbreak Investigations of Legionnaires’ Disease: 40 Years of Testing and Investigation in New York State

Cited by 8 publications

References 31 publications

Legionellosis on the rise: A scoping review of sporadic, community-acquired incidence in the United States

Legionellosis on the rise: A scoping review of sporadic, community-acquired incidence in the United States

A novel hybridization capture method for direct whole genome sequencing of clinical specimens to inform Legionnaires’ disease investigations

High performanceLegionella pneumophilasource attribution using genomics-based machine learning classification

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