Quantification of Legionella pneumophila in building potable water systems: A meta-analysis comparing qPCR and culture-based detection methods

Quantitative polymerase chain reaction (qPCR) offers a rapid, automated, and potentially on-site method for quantifying L. pneumophila in building potable water systems, complementing and potentially replacing traditional culture-based techniques. However, its application in assessing human health risks is complicated by a tendency to overestimate risks due to the detection of genomic copies unassociated with viable, infectious bacteria. This study examines the relationship between L. pneumophila measurements via qPCR and culture-based methods, aiming to establish qPCR-to-culture concentration ratios needed to inform associated health risks. Eligible studies collected quantitative data on L. pneumophila concentrations using molecular and culture-based methods within paired water samples. We developed a Poisson lognormal ratio model and a random-effects meta-analysis model to analyze variations in qPCR-to-culture ratios within and across sites. Of the 17 studies in the systematic review, seven, including 23 site-specific data sets, were used for meta-analysis. Our findings indicate these ratios typically vary from 1:1 to 100:1, with ratios close to 1:1 predicted at all sites. Consequently, adopting a default 1:1 conversion factor appears necessary as a cautious approach to convert qPCR concentrations to culturable concentrations for use in health risk models, such as quantitative microbial risk assessment (QMRA). Where this approach may be too conservative, viability-qPCR could improve the accuracy of qPCR-based QMRA. Standardizing qPCR and culture-based methods and reporting site-specific environmental factors affecting L. pneumophila culturability would improve understanding of the relationship between the two methods. The ratio model introduced here advances beyond simple correlation analyses, facilitating investigations of temporal and spatial heterogeneities in the relationship. This analysis is a step forward in the integration of QMRA and molecular biology, and the framework demonstrated for L. pneumophila is applicable to other pathogens monitored in the environment.