A case-association cluster detection and visualisation tool with an application to Legionnaires' disease

Dispersion of Legionella bacteria in atmosphere: A practical source location estimation method

Legionnaires’ disease, a form of pneumonia which can be fatal, is transmitted via the inhalation of water droplets containing Legionella bacteria. These droplets can be dispersed in the atmosphere several kilometers from their source. The most common such sources are contaminated water within cooling towers and other air-conditioning systems but other sources such as ornamental fountains and spa pools have also caused outbreaks of the disease in the past. There is an obvious need to locate and eliminate any such sources as quickly as possible. Here a maximum likelihood model estimating the source of an outbreak from case location data has been developed and implemented. Unlike previous models, the average dose exposure sub-model is formulated using a atmospheric dispersion model. How the uncertainty in inferred parameters can be estimated is discussed. The model is applied to the 2012 Edinburgh Legionnaires’ disease outbreak.

Multistate analysis of prospective Legionnaires' disease cluster detection using SaTScan, 2011-2015

Detection of clusters of Legionnaires’ disease, a leading waterborne cause of pneumonia, is challenging. Clusters vary in size and scope, are associated with a diverse range of aerosol-producing devices, including exposures such as whirlpool spas and hotel water systems typically associated with travel, and can occur without an easily identified exposure source. Recently, jurisdictions have begun to use SaTScan spatio-temporal analysis software prospectively as part of routine cluster surveillance. We used data collected by the Active Bacterial Core surveillance platform to assess the ability of SaTScan to detect Legionnaires’ disease clusters. We found that SaTScan analysis using traditional surveillance data and geocoded residential addresses was unable to detect many common Legionnaires’ disease cluster types, such as those associated with travel or a prolonged time between cases. Additionally, signals from an analysis designed to simulate a real-time search for clusters did not align with clusters identified by traditional surveillance methods or a retrospective SaTScan analysis. A geospatial analysis platform better tailored to the unique characteristics of Legionnaires’ disease epidemiology would improve cluster detection and decrease time to public health action.

Simulation of Legionnaires' disease prospective spatiotemporal cluster detection, Allegheny County, Pennsylvania, USA

Legionnaires' disease (LD) incidence in the USA has quadrupled since 2000. Health departments must detect LD outbreaks quickly to identify and remediate sources. We tested the performance of a system to prospectively detect simulated LD outbreaks in Allegheny County, Pennsylvania, USA. We generated three simulated LD outbreaks based on published outbreaks. After verifying no significant clusters existed in surveillance data during 2014-2016, we embedded simulated outbreak-associated cases into 2016, assigning simulated residences and report dates. We mimicked daily analyses in 2016 using the prospective space-time permutation scan statistic to detect clusters of ⩽30 and ⩽180 days using 365-day and 730-day baseline periods, respectively. We used recurrence interval (RI) thresholds of ⩾20, ⩾100 and ⩾365 days to define significant signals. We calculated sensitivity, specificity and positive and negative predictive values for daily analyses, separately for each embedded outbreak. Two large, simulated cooling tower-associated outbreaks were detected. As the RI threshold was increased, sensitivity and negative predictive value decreased, while positive predictive value and specificity increased. A small, simulated potable water-associated outbreak was not detected. Use of a RI threshold of ⩾100 days minimised time-to-detection while maximizing positive predictive value. Health departments should consider using this system to detect community-acquired LD outbreaks.

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.

A review of back-calculation techniques and their potential to inform mitigation strategies with application to non-transmissible acute infectious diseases

Back-calculation is a process whereby generally unobservable features of an event leading to a disease outbreak can be inferred either in real-time or shortly after the end of the outbreak. These features might include the time when persons were exposed and the source of the outbreak. Such inferences are important as they can help to guide the targeting of mitigation strategies and to evaluate the potential effectiveness of such strategies. This article reviews the process of back-calculation with a particular emphasis on more recent applications concerning deliberate and naturally occurring aerosolized releases. The techniques can be broadly split into two themes: the simpler temporal models and the more sophisticated spatio-temporal models. The former require input data in the form of cases' symptom onset times, whereas the latter require additional spatial information such as the cases' home and work locations. A key aspect in the back-calculation process is the incubation period distribution, which forms the initial topic for consideration. Links between atmospheric dispersion modelling, within-host dynamics and back-calculation are outlined in detail. An example of how back-calculation can inform mitigation strategies completes the review by providing improved estimates of the duration of antibiotic prophylaxis that would be required in the response to an inhalational anthrax outbreak.