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.

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.

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.