An application for relating Legionella shower water monitoring results to estimated health outcomes

Abstract

Exposure models are useful tools for relating environmental monitoring data to expected health outcomes. The objective of this study was to (1) compare two Legionella shower exposure models, and (2) develop a risk calculator tool for relating environmental monitoring data to estimated Legionella infection risks and Legionnaires' Disease (LD) illness risks. Legionella infection risks for a single shower event were compared using two shower Legionella exposure models. These models varied in their description of partitioning of Legionella in aerosols and aerosol deposition in the lung, where Model 1 had larger and fewer aerosol ranges than Model 2. Model 2 described conventional vs. water efficient showers separately, while Model 1 described exposure for an unspecified shower type (did not describe it as conventional or water efficient). A Monte Carlo approach was used to account for variability and uncertainty in these aerosolization and deposition parameters, Legionella concentrations, and the dose-response parameter. Methods for relating infection risks to illness risks accounting for demographic differences were used to inform the risk calculator web application ("app"). Model 2 consistently estimated higher infection risks than Model 1 for the same Legionella concentration in water and estimated deposited doses with less variability. For a 7.8-min shower with a Legionella concentration of 0.1 CFU/mL, the average infection risks estimated using Model 2 were 4.8 × 10-6 (SD=3.0 × 10-6) (conventional shower) and 2.3 × 10-6 (SD=1.7 × 10-6) (water efficient). Average infection risk estimated by Model 1 was 1.1 × 10-6 (SD=9.7 × 10-7). Model 2 was used for app development due to more conservative risk estimates and less variability in estimated dose. While multiple Legionella shower models are available for quantitative microbial risk assessments (QMRAs), they may yield notably different infection risks for the same environmental microbial concentration. Model comparisons will inform decisions regarding their integration with risk assessment tools. The development of risk calculator tools for relating environmental microbiology data to infection risks will increase the impact of exposure models for informing water treatment decisions and achieving risk targets.