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Implementation of quantitative microbial risk assessment (QMRA) for public drinking water supplies: Systematic review
Journal Pre-proof Implementation of quantitative microbial risk assessment (QMRA) for public drinking water supplies: Systematic review Christopher E.L. Owens, Mark L. Angles, Peter T. Cox, Paul M. Byleveld, Nicholas J. Osborne, Md Bayzid Rahman PII:
S0043-1354(20)30150-0
DOI:
https://doi.org/10.1016/j.watres.2020.115614
Reference:
WR 115614
To appear in:
Water Research
Received Date: 26 October 2019 Revised Date:
2 February 2020
Accepted Date: 10 February 2020
Please cite this article as: Owens, C.E.L., Angles, M.L., Cox, P.T., Byleveld, P.M., Osborne, N.J., Rahman, M.B., Implementation of quantitative microbial risk assessment (QMRA) for public drinking water supplies: Systematic review, Water Research (2020), doi: https://doi.org/10.1016/ j.watres.2020.115614. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.
Eligibility
Screening
Identification
Systematic literature review
Structured narrative synthesis
Records (N = 1264) identified from PubMed (n = 296), Scopus (n = 369),
Identification
Critical synthesis
Web of Science (n = 405), BASE (n = 194) Duplicates excluded (n = 680)
Primary objective: QMRA implementation approaches Secondary objective: Studies’ reported health risk results
Titles and abstracts screened (n = 584)
Records excluded by screening (n = 450)
Results
Recommendations
Approaches varied widely
Greater focus on reporting of assumptions
Study complexity did not indicate greater certainty
Optimisation of QMRA resourcing given application context
Use of location-specific information varied broadly
Broad aspects of uncertainty should be considered
Full-text assessed for eligibility (n = 134) Full-text version excluded (n = 95)
Included
Categorisation
Records included in review (n = 39) Journal articles (n = 35)
Academic theses (n = 4)
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Implementation of quantitative microbial risk assessment (QMRA) for
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public drinking water supplies: Systematic review
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Christopher E. L. Owens a, b, *, Mark L. Angles c, Peter T. Cox b, Paul M. Byleveld d, Nicholas J.
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Osborne a, e, f, Md Bayzid Rahman a
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Affiliations:
a
School of Public Health and Community Medicine, Faculty of Medicine,
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University of New South Wales, Kensington NSW 2052, Australia
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b
Sydney Water Corporation, Parramatta NSW 2124, Australia
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c
Water Angles Consulting, Vaucluse NSW 2030, Australia
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d
Water Unit, NSW Health, North Sydney NSW 2059, Australia
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e
School of Public Health, Faculty of Medicine, University of Queensland,
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Herston QLD 4006, Australia
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f
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Royal Cornwall Hospital, Truro TR1 3HD, United Kingdom
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European Centre for Environment and Human Health, University of Exeter,
* Corresponding author: [email protected]
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Implementation of quantitative microbial risk assessment (QMRA) for
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public drinking water supplies: Systematic review
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Abstract
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In the more than 15 years since its introduction, quantitative microbial risk assessment (QMRA)
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has become a widely used technique for assessing population health risk posed by waterborne
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pathogens. However, the variation in approaches taken for QMRA in relation to drinking water
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supply is not well understood. This systematic review identifies, categorises, and critically
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synthesises peer-reviewed and academic case studies of QMRA implementation for existing
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distributed public drinking water supplies. Thirty-nine English-language, peer-reviewed and
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academic studies published from 2003 to 2019 were identified. Key findings were synthesised
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in narrative form. The overall designs of the included studies varied widely, as did the
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assumptions used in risk calculation, especially in relation to pathogen dose. There was also
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substantial variation in the degree to which the use of location-specific data weighed with the
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use of assumptions when performing risk calculation. In general, the included studies’
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complexity did not appear to be associated with greater result certainty. Factors relating to
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pathogen dose were commonly influential on risk estimates whereas dose-response parameters
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tended to be of low relative influence. In two of the included studies, use of the ‘susceptible
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fraction’ factor was inconsistent with recognised guidance and potentially led to the
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underestimation of risk. While approaches and assumptions used in QMRA need not be
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standardised, improvement in the reporting of QMRA results and uncertainties would be
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beneficial. It is recommended that future authors consider the water supply QMRA reporting
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checklist developed for the current review. Consideration of the broad types of uncertainty
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relevant to QMRA is also recommended. Policy-makers should consider emergent discussion on
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acute microbial health-based targets when setting normative guidelines. The continued
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representation of QMRA case studies within peer-reviewed and academic literature would also 2
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enhance future implementation. Further research is needed on the optimisation of QMRA
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resourcing given the application context.
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Keywords
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Quantitative microbial risk assessment (QMRA)
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Systematic review
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Drinking water
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Distributed water supply
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1 Introduction
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Public drinking water supplies have a crucial role in protecting public health. Access to a safe
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and adequate drinking water supply prevents a massive disease burden that would otherwise
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occur (Bartram & Cairncross, 2010; Hunter et al., 2010). Yet, the microbial safety of drinking
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water supplies remains a matter for continued improvement globally. Distributed water supplies
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still represent a significant exposure route for faecal-derived human pathogens in less developed
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settings. Against common expectation, serious contamination in more developed settings also
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still occurs (Hrudey & Hrudey, 2004, 2007). For example, water treatment processes and
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management practices may not be commensurate with the increased risk associated with
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extreme weather conditions (Khan et al., 2015) and imperfect distribution system integrity can
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cause exposure of consumers to pathogens, regardless of water treatment (Säve-Söderbergh et
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al., 2017; Viñas et al., 2019).
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Water supply is understood to contribute to a baseline of sporadic, undetectable cases
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even under ideal operating conditions (Bylund et al., 2017; Colford et al., 2009; Westrell, 2004).
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Reduction of pathogens in drinking water is achieved through controlled physicochemical water
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treatment processes (Hijnen & Medema, 2010; Howe et al., 2012), with removal typically
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measured in terms of logarithmic reduction. Even with the most effective existing treatments,
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the total removal of pathogens can be approached but probabilistically never achieved. Further,
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insufficient analytical sensitivity means that the assurance of drinking water safety cannot
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confidently be based on the non-detection of pathogens from laboratory analyses of water
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samples (Signor & Ashbolt, 2006). This is a contributing reason for why routine monitoring of
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specific microbial pathogens in treated drinking water is not recommended (NHMRC &
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NRMMC, 2011).
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Clinical practice and public health surveillance systems are also insufficient to detect all
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but the highest levels of pathogens and water supply-related illness outbreaks (Sinclair et al.,
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2005). Because public drinking water supplies involve broad public exposure, the ability to
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observe all but the most extreme of associated disease outbreaks is highly likely to be masked
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by the inability to differentiate water-associated cases from cases arising from other causes
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(Haas et al., 1996; Westrell, 2004).
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Still, a high level of sensitivity is required to demonstrate adherence to commonly-
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adopted ‘health-based targets’ for the microbial safety of drinking water, including the 1 in
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10,000 annual risk of infection and the 10-6 disability-adjusted life years (DALY) person-1 year-1
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(Health Canada, 2019; USEPA, 2006; WHO, 2011). To this end, the quantitative microbial risk
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assessment (QMRA) technique enables the estimation of potential health risk consequential to
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any exposure severity. The approach reflects that for the assessment of health risk from
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carcinogens and toxins (NRC, 1983) and is comprised of four key stages, being problem
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formulation, exposure assessment, health effects assessment, and risk characterisation (Haas et
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al., 2014; WHO, 2016). These stages integrate scientific understanding of pathogens, their fate
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and transport through natural and engineered systems, and their routes to human exposure and
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consequential disease (WHO, 2016). Importantly, QMRA can account for the exposure of large
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populations to very low levels of pathogens, which typifies the risk profile of distributed water
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supplies. This is facilitated by the dose-response assessment (part of the health effects
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assessment) being centred on single hit theory, which assumes that the chance of each organism
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reaching the target organ and causing infection or disease is non-zero and independent of that of
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the other organisms present (Haas, 1983; Nilsen & Wyller, 2016).
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QMRA has been a widely recognised practice since at least 2004. It sits within a
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spectrum of risk assessment approaches available to be used as part of the ‘water safety plan’
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approach to drinking water quality management (Fitzgerald et al., 2018; Health Canada, 2018;
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Medema & Ashbolt, 2006; Murphy et al., 2014; WHO, 2011, 2016) and a consensus on the
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broad approach has been established (e.g. EPHC et al. (2006); Haas et al. (2014); Health Canada
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(2018); WHO (2011, 2016); WSAA (2015)). However, in the more than 15 years since its broad
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introduction, a range of QMRA approaches are applied by practitioners, which differ at the
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detailed assumption level. A systematic review of QMRA application for drinking water
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supplies is required to summarise the current state of the field, with the primary study objective
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to examine variation in the application of assumptions and resultant impact on QMRA
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outcomes. Three key related areas are examined: (i) the current range of approaches to QMRA
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implementation taken for drinking water supplies (as represented by peer-reviewed and
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academic literature); (ii) whether the QMRA study designs (particularly study complexity) has
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been commensurate with what is required to know whether the water is safe; and (iii) whether a
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consensus position has emerged on a preferable balance between the location-specific empirical
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data and default modelling assumptions used in QMRA. Systematic review is ideal to address
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these questions. It is a technique used to map the different localities, subjects, variables, and
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results that are available in literature (Pickering & Byrne, 2014).
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To identify outcomes of immediate relevance to practitioners and to reduce biases, the
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current review focuses on real-life case studies. The criterion of including only peer-reviewed
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and academic literature is deemed an acceptable benchmark to ensure study quality. As a
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secondary review objective, the current study design provides an opportunity to summarise the
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health risk estimates reported by the in-scope literature. As the quality of drinking water supply
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is a key element of societal development, tracking of these results against the defined human
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development index is also of interest. Based on the critical review outcomes, considerations
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relating to the approaches taken for QMRA, as implemented in real-world supply systems, are
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identified to guide future QMRA application.
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2 Method
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Peer-reviewed and academic literature describing case studies implementing QMRA for
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existing, distributed public drinking water supplies were identified, categorised, and critically
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synthesised. The review was guided by Pickering and Byrne (2014) and was in accordance with
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the Preferred Reporting for Systematic Reviews and Meta-analyses (PRISMA) statement
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(Moher et al., 2009) and checklist (Supplement A). Registration of the systematic review
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protocol was deemed unnecessary due to its relatively small scale.
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2.1 Study selection
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2.1.1 Inclusion criteria
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As the overarching study objective was to examine the variation in applications of QMRA
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relevant to water supply, three important selection criteria were imposed. These criteria were
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devised to identify the most robust and peer-accepted cases of QMRA implementation, whilst
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representing a breadth of implementation approaches immediately applicable to practitioners.
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First, the review focused exclusively on real-life scenarios of consumers being supplied
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by drinking water distribution systems. Only case studies demonstrating the application of
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QMRA to actual operational systems were included. This criterion is based on the notion that
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approaches taken in current water supply systems are likely to be of greatest relevance to
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practitioners.
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Second, because of the focus on real-life exposure scenarios, only those case studies
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deriving dose assumptions based on empirical microbial observations at the study site were
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included. This criterion was to focus inclusion on studies with a high level of robustness, given
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the importance of site-specific microbial data in QMRA implementation (Petterson & Ashbolt,
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2016; WHO, 2016). Studies were included regardless of whether they involved direct
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observation of reference pathogens or the observation of microbial indicators or surrogates that
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were later converted to assumed reference pathogen dose. Inclusion was also regardless of
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whether dose assumptions were based on the quality of water immediately following water
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treatment or whether dose also (or solely) considered risk of distribution.
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Third, only peer-reviewed journal articles and academic theses were included. The peer
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and academic review processes are expected to have focused the identification of studies on
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those of a high level of rigour. Because peer-reviewed journal articles and academic theses are
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inherently biased towards representing novel content, this criterion still allowed for a breadth of
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approaches applied to drinking water supplies to be included.
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Initial literature identification involved searches of scholarly databases PubMed,
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Scopus, and Web of Science. Title and abstract text were queried for the terms “*water*” and
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“quantitative microbial risk assessment” or “QMRA”. The query terms were deliberately broad
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to avoid potential issues raised by inconsistent or lack of detail in terminology and thus
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maximise the identification of relevant literature. Search conditions included original, English-
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language peer-reviewed journal articles and master’s and doctoral theses with a publication date
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from January 2003 to October 2019. The publication date lower bound was selected on the
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rationale of capturing the contemporary approach of preventive risk management and microbial
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health-based targets for drinking water safety, which were primarily included in the third edition
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of the WHO (2004) Guidelines for drinking-water quality. The initial identification of theses
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was performed using the Bielefeld Academic Search Engine (BASE) database using the same
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query conditions. As the option to search abstract was not available, search queries applied to
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the entire document.
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Screening involved the removal of duplicate records and inspection of the title and
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abstract, where available, (regardless of whether full-text was immediately available) such that
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only records potentially meeting the inclusion criteria were included. Studies were deemed
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eligible for inclusion through inspection of the full-text version.
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2.1.2 Exclusion criteria
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Records found to not meet the inclusion criteria were excluded. Thus, narrative reviews,
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hypothetical case studies, conceptual models, and analyses that were pilot-scale or desktop-
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based were excluded.
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Analysis of exposure routes where the water was no longer representative of the
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distributed supply were excluded, such as analyses of spa baths and splash parks. Analyses
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involving wastewater and water recycling schemes were not included unless the schemes
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articulated into a drinking water supply system. Analyses of decentralised schemes (e.g.
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involving roof-harvest rainwater or well water) and of point-of-use and home water treatment
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practices were excluded.
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Studies measuring the indirect effects of intermittent access to water supply were excluded as the current review focuses on quality of the supplied water during times of supply.
2.1.3 Minimisation of bias
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Risk of bias in the conduct of the systematic review was minimised as guided by Popay et al.
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(2006). This included measures to reduce bias in the selection of studies and within the review
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synthesis.
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Bias in the selection of studies was minimised by study eligibility being independently
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assessed by two reviewers (C. Owens and M. Angles). Each reviewer prepared a tentative list of
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included studies and synthesised discussion points without knowledge of the other’s results.
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Once completed, review outcomes were compared, and inconsistencies were discussed until a
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consensus position was achieved.
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The design of the selection process also focused on including studies with lower
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potential bias. The scope included only existing, in-situ water supply schemes where empirical
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observation of microbial quality at the study site was performed. This was intended to minimise
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the number of default literature values in the included QMRA case studies, the
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representativeness of which being a major potential source of error when comparing health risk
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estimates across studies (with reference to the secondary review objective). Studies judged by
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both reviewers to be of equal technical quality were given equal weight in the discussion.
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Residual biases were discussed in terms of an uncertainty typology for water supply
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(Supplement B), adapted for this study from Bouwknegt et al. (2014) and Knol et al. (2009).
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2.2 Data extraction
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Full-text manuscripts of eligible records were obtained. Data were extracted for the application
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locality, unit processes comprising the treatment train relevant to pathogen reduction, summary
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exposure route, reference organisms, reference levels of risk used, dose-response models and
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cited source, health effects assumptions, sensitivity analyses performed, and whether QMRA
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was used as an input or is informed by the output of other models. For this purpose, a checklist
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for reporting on water supply QMRA was developed (Supplement C). Key findings were
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summarised. Water treatment processes that were not directly for the reduction of pathogens
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were not extracted. Thus, processes for secondary disinfection, aesthetic and physical quality,
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fluoridation, and pre-requisites supporting the effectiveness of treatment, such as pH
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adjustment, were not extracted.
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2.3 Synthesis
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The included studies were subject to structured narrative synthesis through textual description,
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tabulation, and thematic analysis as guided by Popay et al. (2006). Thematic analysis focused on
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characterising the breadth of choices that can be made in QMRA implementation. The themes
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were the included studies’ consideration of: exposure routes, reference pathogens, indicator and
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surrogate organisms, dose-response models, methods for estimating health risk, reference levels
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of risk, incorporation of high-risk events, the interfacing of models with QMRA, the approach
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to sensitivity analysis, and approach to the recognition of potential biases.
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The secondary study outcomes, i.e. performance against health-based targets and tracking with
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development settings, were examined through the application of a common rubric. This took the
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form of forest plots grouped by exposure scenario and ranked by the human development index
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of the study locality according to UNDP (2018). The representation of different development
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settings was also compared to expected proportions based on world population using exact
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multinomial and pairwise exact binomial tests with Bonferroni correction using R 3.6.1 and the
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EMT 1.1 package. A significance level (α) of 0.05 was used for all statistics.
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Topics were identified for further critical discussion. First, the primary review objective and
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associated focus areas were discussed with reference to the included studies. Second, the
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influence of QMRA input factors were discussed based on the synthesis of the included studies’
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sensitivity analyses. Third, themes derived from the included studies’ overall designs were
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discussed. Finally, the secondary study outcomes were discussed.
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3 Results
3.1 Study selection
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Using the described search strategy, 1264 records were identified as of potential relevance. Of
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these, 584 (46%) were non-duplicates and 39 (3%) were included in this review. The most
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common reason for exclusion during the screening stage was due to studies not reporting on a
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public drinking water supply. During the eligibility stage the most common reason for exclusion
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was due to studies not using empirical, site-specific microbial data. The study inclusion results
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(Figure 1) were mutually agreed by the two independent reviewers. The two reviewers had no
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outstanding divergent views.
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3.2 Study characteristics
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Of the 39 included studies (categorised in Table 1), 35 (90%) were in the form of peer-reviewed
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journal articles and four (10%) were academic theses. The journal articles were published in 16
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different peer-reviewed journals.
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3.2.1 Publication date
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The number of publications by year (Figure 2) indicates that there were fewer than 10 peer-
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reviewed and academic studies applying QMRA to empirical data of drinking water supplies for
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any given year, since 2003.
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3.3 Results of primary review objective – QMRA implementation approaches
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Approaches taken for QMRA implementation were synthesised into the themes of: exposure
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routes (Section 3.3.1), reference pathogens (Section 3.3.2), indicator and surrogate organisms
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(Section 3.3.3), dose-response models (Section 3.3.4), estimation of public health risk (Section
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3.3.5), and reference levels of risk (Section 3.3.6). Aspects of broader study design were 11
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synthesised under the themes of the incorporation of high-risk events (Section 3.3.7), the
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interfacing of models with QMRA (Section 3.3.8), the approach to sensitivity analysis (Section
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3.3.9), and approaches to the recognition of potential biases (Section 3.3.10). The included
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studies’ approaches were tabulated under the themes of major exposure assumptions
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(Supplement D), dose-response models (Supplement E), and health effects assumptions
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(Supplement F). Other factors contributing to the application of QMRA, such as pathogen
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recovery, infectivity, and viability (Supplement D), and the use of pathogens and indicators and
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assumptions on treatment process efficiency (Supplement D) were also summarised.
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3.3.1 Exposure routes
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The majority of the included studies focused strongly on the conveyance of pathogens from
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source water through the water supply system to the consumer (Table 1). Only two studies
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considered other major exposure routes: van Lieverloo et al. (2007), who focused on risk arising
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from ingress to the distribution network (subsequent to water treatment); and Sharaby et al.
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(2019), who focused on Legionella pneumophila inhabiting the distribution system. All included
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studies considered the intentional ingestion of drinking-water leading to gastrointestinal illness,
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except Sharaby et al. (2019), who focused on inhalation of aerosols leading to Legionnaires’
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disease. Four studies (10% of those included) covered inadvertent forms of human exposure to
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drinking-water, including from bathing, toothbrushing, and inhalation (Table 1).
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In total, more than 360 water supply systems fell within scope (Table 1). Almost half of the
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included studies analysed more than one system (n = 16; 41% of the included studies). Thirty-
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one of the included studies (79%) described the involvement of surface water in the exposure
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route, three (8%) considered aquifer infiltration, four (10%) addressed the influence of treated
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wastewater, and seven (18%) did not describe the source water type (or it was not relevant).
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3.3.2 Reference pathogens
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A range of reference pathogens were used in the included studies (Table 1). Thirteen major
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reference pathogens were represented, and most studies analysed risk for multiple reference
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pathogens (n = 25; 64% of the included studies) (Table 1). The most frequently used was
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Cryptosporidium (used in 26 publications; 30% of total reference pathogen uses by publication)
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and the only other protozoal reference pathogen used was Giardia (used in 16 publications; 20%
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of reference pathogen uses by publication) (Figure 3). All studies assessing Giardia also
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assessed Cryptosporidium, except one (Rodriguez-Alvarez et al. (2015)) due to local regulations
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focusing on Giardia. Of the bacterial reference pathogens used, Escherichia coli was most
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common (used in 12 publications; 14% of reference pathogen uses by publication) (Figure 3).
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Its use was reported at the species level, as pathogenic E. coli, E. coli O157, E. coli O157:H7,
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and as the enterotoxigenic pathotype. Rotavirus was the most commonly used viral reference
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pathogen (used in seven publications; 8% of total reference pathogen uses by publication)
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(Figure 3). Detail on pathogen recovery, viability, and infectivity (and other microbiological
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factors relevant to the exposure assessment) were reported to varying extents (Supplement D).
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These and related terms were occasionally used in inconsistent senses across studies (refer to
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Discussion).
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3.3.3 Indicator and surrogate organisms
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The use of indicator organisms to represent the occurrence of pathogens was considered
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moderate, with 17 (44%) of the included studies making clear reference to their use at some
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point in the exposure route (Supplement D). The most commonly used indicators were faecal
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coliforms and E. coli.
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Use of surrogate organisms (which model the fate of pathogens (Sinclair et al., 2012)) for the
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estimation of treatment efficiency was described in six (15%) of the included studies
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(Supplement D). There was little commonality in the approaches taken (refer to Supplement D). 13
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3.3.4 Dose-response models
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Dose-response models and parameters, where stated, followed literature precedents
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(Supplement E). Except for nine instances, the probability of infection for a given dose was
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found using either the exponential or Beta-Poisson approximation models (Haas et al., 1996;
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Haas et al., 2014). For the nine other cases, the exact Beta-Poisson model (Nilsen & Wyller,
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2016; Schijven et al., 2015; Teunis & Havelaar, 2000) was used in six cases and the low-dose
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approximation method (WHO, 2016) was used in three cases.
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3.3.5 Estimation of population health risk
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Almost half (n = 18; 46%) of the included studies estimated population disease burden as the
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health risk outcome. The remaining studies (n = 21; 54% of the included studies) focused on the
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probability of infection. Population disease burden was found by accounting for the conditional
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events of disease aetiology following infection (main assumptions summarised in Supplement
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F). These events include the probability of illness given infection, the fraction of the population
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susceptible (used in some cases), and the disease burden per case of illness, all of which are
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multiplied with the result of the dose-response model.
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Notably, of the studies that incorporated population susceptibility (n = 8; 21% of the
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included studies), two distinct approaches were taken. The first usage type (occurring in six
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[15%] of the included studies) accounted for susceptibility in the exposed population based on
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factors such as life stage and immune status. The second usage (occurring in two [5%] of the
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included studies) accounted for a fraction of the population not exposed (either geographically,
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relative to the total population in a region, or temporally, accounting for extended periods of
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supply interruption).
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3.3.6 Reference levels of risk
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The included studies compared QMRA results to nine distinct reference levels of risk (Figure 4).
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All but four (10%) of the included studies used the annual probability of infection of less than
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10-4 or the annual disease burden of less than 10-6 disability-adjusted life years (DALY) person-1
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year-1 or both (Figure 4). Two studies expressed the 10-6 DALY person-1 year-1 target in terms of pathogen log-
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reduction values (Krkosek et al. (2016); Sokurenko (2014)) (Figure 4). Reported log-reduction
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values represented the required treatment efficiencies for Cryptosporidium, Giardia, and
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viruses, based on source water risk, for assurance that the health target was met.
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Daily reference levels of risk were used to account for acute, extreme events which may
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otherwise be offset in the annualisation of risk (Figure 4). They were used by Smeets et al.
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(2008), Taghipour et al. (2019), and Tolouei et al. (2019). The probability of infection of 2.74 ×
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10-7 per day and the disease burden of 2.74 × 10-9 per day were used, based on the 10-4 and 10-6
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annual targets, respectively, being divided into constituent days. Adherence to this daily level
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over a year will result in the annual target being met. The probability of infection of 365 × 10-4
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per day was also used by Smeets et al. (2008) to represent the annual target of 10-4 being
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breached within one day.
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Two studies used less stringent targets, modifying for local context (Figure 4). Bartak et
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al. (2015) chose levels that corresponded to the national and regional diarrhoeal burden, and
340
Machdar et al. (2013) used the targeted annual burden of disease of less than 10-4 DALY person-
341
1
342
(2011).
year-1, on the basis that this target may be more realistic for the setting, as discussed by WHO
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3.3.7 Incorporation of high-risk events
343
344
High-risk events were represented in many of the included studies. They often related to
345
contamination events in source water, perturbances to water treatment operations, or acute cases
346
of network ingress.
347
Rainfall runoff leading to acute source water contamination was examined by numerous
348
authors and was generally found to be highly influential on estimated health risk. For example,
349
Signor et al. (2007) found that such events (in this case occurring for 14 per cent of a year) were
350
attributable to most of the annual infection risk for the reference pathogens Cryptosporidium
351
and Giardia. The case study by Smeets et al. (2008) showed that annual infection risk was
352
dominated by the top 3 to 10 per cent of the highest monitoring results for Campylobacter,
353
thought to occur due to introduction of faecal contamination from local wildlife, especially after
354
rainfall.
355
The effect of wastewater discharge events upstream of drinking water abstraction was
356
also a topic of investigation. Tolouei et al. (2019) estimated that wastewater treatment bypasses
357
(as well as rainfall-induced contamination from unquantified faecal sources) led to a 1-log10
358
increase in health risk. Similarly, Taghipour et al. (2019) assessed the effects of acute overflows
359
of combined stormwater and sewer upstream of drinking water treatment abstraction points,
360
finding that the health risk of the plant closer to discharge points was more influenced by the
361
number of overflow events. Importantly, Åström et al. (2007) estimated a reduction in annual
362
health risk resulting from contemporaneous knowledge and active avoidance of such events.
363
The result was greater than could be achieved through relying on the microbial analysis of
364
intake water.
365
The ongoing stability of drinking water treatment processes was found to be an
366
important factor affecting health risk. In further analysis of data presented by Howard et al.
367
(2006), Hunter et al. (2009) found that the annual health benefits attributable to improved
16
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drinking water supply are almost entirely lost by a few days’ consumption of untreated water.
369
Xiao et al. (2012) found that performance of water treatment unit processes most affected health
370
risk, with the stability of the filtration process having the greatest impact. In a potable water
371
recycling case study by Ander and Forss (2011), typical water treatment failure modes and rates
372
(deemed realistic) were found to contribute to an increase in health risk. Similarly, treatment
373
failure scenarios analysed by Hamouda et al. (2016) led to estimated levels of risk above the
374
tolerable level.
375
van Lieverloo et al. (2007) performed the only analysis of acute network ingress events,
376
finding relatively high associated risk, though this was subject to substantial uncertainties. Other
377
studies accounted for network ingress in effect by including monitoring of the distribution
378
system (e.g. George et al. (2015)), though this may have accounted for baseline recontamination
379
rather than acute, high-risk disturbances in the distribution network.
380
3.3.8 Model interfaces
381
Six (15%) of the included studies described QMRA directly interfacing with other models
382
(Table 2). Ander and Forss (2011) used fault tree analysis to define water treatment failure
383
scenarios which then defined part of the problem formulation under QMRA. Health risk
384
estimates for each scenario were used to prioritise improvement across the fault causes.
385
Machdar et al. (2013) used QMRA output as an input to economic models, with health risk
386
estimates informing cost-effectiveness analyses. A range of engineering and programmatic
387
interventions were examined, using DALY gained as the numerator and a monetary
388
denominator. Sokolova et al. (2015), Taghipour et al. (2019), and Tolouei et al. (2019) each used
389
hydrodynamic models to estimate point-source inputs relevant to source water pathogen loading
390
for QMRA. Baseline conditions and loading events from rainfall and wastewater discharge
391
events upstream of the drinking water treatment plant were modelled. Swaffer et al. (2018)
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modelled associations between livestock stocking characteristics and pathogen load which
393
informed the assumptions used in the exposure assessment.
394
3.3.9 Approaches to sensitivity analysis
395
Nine (23%) of the included studies described the relative influence of some or all QMRA input
396
factors on health risk estimates through formal approaches to sensitivity analysis (Table 3).
397
Source water pathogen load and treatment efficiency were frequently reported as of highest
398
influence on health risk estimates. The test most prevalent (n = 6 uses; 67% of the studies
399
performing formal sensitivity analysis) was the ranked correlation coefficient. Other methods
400
used were differential analysis, factor sensitivity coefficient, and the pairwise comparison of
401
factors using surface plots (n = 1 for each approach respectively; 11% each respectively of the
402
studies performing formal sensitivity analysis).
403
Less-formal approaches for testing the relative impact of changes to assumptions were
404
also used in numerous included studies (summarised in Supplement D). However, there was
405
inconsistency in associated terminology (some were identified as ‘sensitivity analysis’ and some
406
were not) and there was a greater subjectivity in the rationales for performing these analyses
407
(each answering a distinct research question).
408
3.3.10 Risk of bias
409
The included studies did not commonly discuss the risk of bias. Nevertheless, biases were
410
introduced in the assumptions and measurements made, including in the selection of reference
411
pathogens and exposure routes (Table 1) and in the assumptions made for the: design of the
412
monitoring campaign, pathogen-indicator and -surrogate ratios, volume consumed, and other
413
exposure factors (Supplement D); dose-response (Supplement E); and health effects
414
(Supplement F). None of the included studies validated results against surveillance outbreak
415
data, though almost none represented declared outbreak scenarios.
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3.4 Results of secondary review objective – Studies’ reported results and
416
representation of development settings
417
3.4.1 Included studies’ reported population health risk
418
419
With respect to the secondary study objective, the included studies’ reported health risk
420
estimates varied in their adherence to the respective health-based target selected. Included
421
studies’ key results are summarised (Supplement G). Twenty-four (62%) of the included studies
422
provided results in clear numerical form, allowing for comparison of results across the studies.
423
Results of studies using the two most common reference levels of risk, 1 in 10,000
424
annual probability of infection and disease burden of 10-6 DALY person-1 year-1, were
425
benchmarked in Figure 5 and Figure 6, respectively. Because these reference levels are
426
approximately equivalent, exposure scenarios that very clearly adhere (or very clearly do not
427
adhere) to their respective reference level are likely to have a similar outcome when expressed
428
in the alternate metric (notwithstanding that the metrics are subject to important differences
429
arising from disease severity weightings and other factors relating to health effects).
430
Of the studies providing numerical results, a total of 101 scenarios involving a distinct
431
combination of exposure pathway and reference pathogen were identified (Figure 5 and Figure
432
6). Of these scenarios, Cryptosporidium was the reference pathogen that most often exceeded
433
selected reference levels of risk based on median result (n = 21 scenarios; 21% of the analysed
434
scenarios), though it also the most frequently used per study (section 3.3.2). E. coli and Giardia
435
were the next highest (n = 11 [11% of those analysed] and n = 10 [10% of those analysed]
436
scenarios, respectively). All other reference pathogens accounted for the remaining eight
437
scenarios (8% of the analysed scenarios) where median health risk exceeding selected reference
438
levels.
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3.4.2 Development settings and reported population health risk
439
440
The representation of study localities’ human development (categorised by UNDP (2018) as
441
‘very high’, ‘high’, ‘medium’, and ‘low’) was significantly different to what would be expected
442
if representation followed categories’ relative proportion of world population (exact multinomial
443
test, p < 0.001). Very-high human development settings were significantly over-represented
444
(expected: 0.19; observed: 0.59; p < 0.001) and medium human development settings were
445
significantly under-represented (expected: 0.37; observed: 0.15; p = 0.005).
446
In both Figure 5 and Figure 6, studies were shown in descending order of the human
447
development index of the study locality (according to UNDP (2018)). The studies incorporated
448
at the top of Figure 5, including those from Mohammed and Seidu (2019) to Sharaby et al.
449
(2019), were considered as being set in localities classified as of very high human development.
450
Of the studies that used annual disease burden (Figure 6), only the first listed study (Sokurenko
451
(2014)) was set in a locality classified by UNDP (2018) as of very high human development.
452
The remainder of the studies presented in Figure 5 and Figure 6 were set in locations of high to
453
low human development. A trend is broadly observable suggesting the included studies’ results
454
are consistent with an inverse relationship between development context and estimated health
455
risk.
456
4 Discussion
457
When considering variation in the application of assumptions and impact on QMRA concerning
458
the supply of drinking water, the available scientific and academic literature currently identifies
459
that: (i) the current range of approaches taken for QMRA implementation is broad; (ii) the
460
complexity of study design varies and does not appear to be related to what is required to judge
461
whether the water supply is safe; and (iii) while the approaches taken in the inclusion of
462
location-specific empirical data varies, norms have emerged in health effects assumptions,
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especially for the selection of dose-response models. Trends in the relative importance of
464
QMRA inputs emerged, as reflected in the included studies’ formal sensitivity analyses and in
465
study designs.
466
Though all included studies examined the microbial safety of respective drinking water
467
supplies, approaches taken for QMRA implementation were broad with respect to overall study
468
designs and the assumptions used in risk calculation. Variation in study design was especially
469
evident in the derivation of pathogen dose applicable to exposure. For instance, in studies that
470
considered it, source water pathogen concentration was derived using either pathogen
471
monitoring results or through estimation of the same with microbial indicator results. The latter
472
approach then involved either location-specific pathogen-surrogate ratios or assumed ratios
473
derived from literature. Similarly, in studies that considered it, the efficiency of pathogen
474
removal through water treatment processes was based on either observed surrogate reduction or
475
values assumed from literature. Many included studies also considered the impact of high-risk
476
events, with study designs focused accordingly. On a broader scale, variation in study design
477
was demonstrated through the QMRA model interfacing with a diverse range of other modelling
478
approaches, including taking inputs from external models in relation to pathogen dose and
479
QMRA outputs serving as an input to economic analysis (Section 3.3.8). Overall, the
480
demonstrated variation in approaches taken indicates that the QMRA method is highly flexible.
481
This is an especially beneficial quality for the assessment of drinking water supplies. It provides
482
opportunity for even scarce amounts of available data (when supplemented with default
483
literature values) to be used beneficially, albeit in recognition of potential error and biases that
484
may apply.
485
There was also substantial variation in the degree to which the use of location-specific
486
data weighed with the use of default assumptions (summarised in Supplements D through F).
487
With respect to the use of default literature values, strong norms have emerged in the selection
488
of dose-response models (and other factors in the health effects assessment stage). It seems
21
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likely that this is due to the limited availability of existing models and a lack of new primary
490
data to do otherwise. Other than the volume of exposure, there was little consistency across
491
studies in the use of location-specific or assumptions for other factors (e.g. treatment efficiency
492
and other major assumptions for dose). Though the current review was purposefully focused on
493
examining only studies with site-specific microbial monitoring data, QMRA studies can also be
494
performed using assumptions entirely sourced from literature. This can be considered as
495
‘screening-level’ QMRA. While this approach can appropriately serve the purpose of
496
delineating cases of clearly acceptable or clearly unacceptable risk, location-specific data are
497
likely the first major area for attention in cases where greater precision is needed.
498
In general, there was an observed mismatch between study complexity and the purpose
499
being served. It was not necessarily the case that increased study complexity increased the level
500
of certainty in outcomes. Some of the studies highly complex in statistical approach still
501
provided results with a high degree of uncertainty (as evidenced by the size of the error bars in
502
Figure 5 and Figure 6). Further, in some cases, the level of detail presented in studies did not
503
align with the level of expected risk outcome. This was evidenced in the level of detail provided
504
in studies’ QMRA assumptions (Supplement D) and in the sophistication of statistical
505
approaches taken. Some of the most complex study designs returned some of the lowest risk
506
estimates in systems that would be expected to perform well based on human development
507
status. Conversely, some of the simplest approaches were used on systems in lower human
508
development settings and were sufficient to identify poor risk outcomes. This could be due to
509
the relative ease of dealing with the fewer non-detect data in higher-risk systems. It is not clear
510
which approach is the most suitable, but it is reasonable to suggest that simpler approaches
511
would be more readily taken up by industry, with more complex approaches being warranted for
512
situations where risk outcomes are ‘borderline’ (e.g. where uncertainty bounds cross the
513
reference level of risk). Overall, there is a need for a consensus position to be established on
514
balancing complexity against accessibility and uncertainty.
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Based on the included case studies’ formal sensitivity analyses, factors relating to
516
pathogen dose were consistently of high importance to QMRA risk estimates. This finding
517
supports the notion that locally-appropriate information on pathogen risk is important (Petterson
518
et al., 2015b; WHO, 2016) and is a key reason for including this as an acceptance criterion in
519
the study. While water suppliers generally have water quality data at their disposal, their usage
520
in QMRA must be carefully considered. The reason for this was clearly shown, in quantitative
521
terms, by Åström et al. (2007), where a selective source water abstraction protocol was shown
522
to markedly alter the risk profile. Such an effect might easily be missed in the design of a water
523
quality monitoring program. Further, capturing such impacts may simply not be the intention of
524
past and present monitoring programs (e.g. a program may intend to characterise the source
525
generally rather than specifically characterise the untreated water as supplied to a treatment
526
plant). This strongly suggests that a water quality monitoring program addressing the specific
527
needs of QMRA, i.e. to appropriately characterise risk, is ideal.
528
Included studies’ formal sensitivity analyses also indicated that the impact of the dose-
529
response parameters tended to be relatively low. Despite this, it is possible that improvement in
530
these models would provide far-reaching benefit due to their largely consistent adoption in
531
QMRA implementation. QMRA fundamentally deals with the modelling of very low doses
532
across large populations. Because of this, existing dose-response models rely on the
533
extrapolation of dose-response relationships to levels far below what was empirically measured
534
in the original clinical studies (Haas, 1983; Teunis & Havelaar, 2000). While this limitation can
535
be overcome by validating QMRA model performance against public health surveillance data,
536
validation can only occur retrospectively and for substantial outbreaks for which data are
537
available. This approach is also limited by doses being poorly quantified and cases being
538
difficult to follow (WHO, 2016). Nonetheless, QMRA has been validated, at least in a general
539
sense, through retrospective analysis of epidemiological data (Burch, 2019; Haas et al., 2014;
540
Haas et al., 2000). These validation exercises have included outbreaks with mean doses of less
23
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541
than one pathogen per dose, thereby providing supporting evidence for the ‘single hit’ theory
542
underlying the ability to extrapolate the relationships to low dose levels.
543
As demonstrated through its exclusion from almost all study designs, direct testing of
544
pathogens in treated drinking water was not used to inform the estimation of health risk in many
545
circumstances. Though potentially counterintuitive, this is due to the sensitivity and physical
546
constraints inherent to microbiological analyses. Under WHO (2011) assumptions, a single
547
sample result equivalent to the targeted health outcome of 10-6 DALY person-1 year-1 is one
548
infective Cryptosporidium oocyst in ca. 7.9 × 104 L sample volume and is in the same order of
549
magnitude for other reference pathogens. This is well beyond current analytical capabilities.
550
Thus, unless there is severe contamination present, treated water data generally lack the power
551
to meaningfully contribute to QMRA. They may also be insufficient for the consideration of
552
network ingress risks in otherwise satisfactory systems. Generally, data that are uninformative
553
or weakly informative should be avoided in experimental design (Schmidt et al., 2019) and thus
554
other methods, such as QMRA, are needed for estimating the effect of treated water events that
555
are difficult to detect yet of high impact. Similarly, the method used to infer microbial loadings
556
that are below the analytical limit of detection could impact on the risk estimate. In this regard,
557
Smeets et al. (2007) demonstrated a 0.6-log10 variation in risk estimate depending on method
558
selected (i.e. substituting non-detects with zero, log-linear extrapolation, and substituting non-
559
detects with the limit of detection). There are numerous academic papers addressing this issue
560
(e.g. Canales et al. (2018); Chik et al. (2018); Schmidt et al. (2013)) yet there remains a lack of
561
consensus on how results below the limit of detection should be treated.
562
The interpretation and statistical treatment of laboratory analytical results was focused
563
on in many of the included studies and is represented strongly in other QMRA literature. To
564
support the accuracy of risk estimation, measurement errors must be considered when dealing
565
with microbial enumeration methods that involve losses or counting errors (Schmidt & Emelko,
566
2011). Analytical considerations for the reference pathogen most seen in this review,
24
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Cryptosporidium, were reported with varying consistency. For example, Cryptosporidium
568
counts used in QMRA should ideally be adjusted for assay recovery (Petterson et al., 2007) and
569
represent viable, human-infective oocysts (Schmidt & Emelko, 2011; Swaffer et al., 2018;
570
WSAA, 2015). Recovery efficiency accounts for the unrecovered fraction of oocysts by
571
adjusting for the loss of known quantities of a positive control (e.g. Warnecke et al. (2003)),
572
with sample-specific recovery ideal (Schmidt & Emelko, 2011; WSAA, 2015). The identity
573
confirmation step rules out organisms that are morphologically similar to Cryptosporidium
574
oocysts. Recognised methods include enhanced characterisation of internal morphology through
575
vital stains and the use of differential interference contrast or phase contrast microscopy
576
(Grimason et al., 1994; Smith et al., 2002). Enumerating viability is an important further step to
577
distinguish between live and dead (or fatally injured) oocysts (Petterson et al., 2015b) and can
578
be estimated through cell culture infection assay (Johnson et al., 2012). Similar considerations
579
apply to other reference pathogens (e.g. as described by Dechesne and Soyeux (2007);
580
Lalancette et al. (2012); Lapen et al. (2016); Petterson et al. (2015a); Regli et al. (1991);
581
Schmidt et al. (2013); WHO (2016)). In total, these steps were not frequently described in the
582
included literature. If they were not performed, the appropriate estimation of risk is impacted
583
(Dechesne & Soyeux, 2007; Petterson et al., 2015b; WSAA, 2015).
584
Population characteristics and behaviours were found to affect the estimated severity of
585
health risk (in the studies which analysed these factors), highlighting the importance of centring
586
QMRA on the population exposed. This is especially relevant in less-developed settings, where
587
the exposure routes can be more complex and pathogen loadings potentially higher. It might be
588
less important in more developed settings, where nearly the entire population of a water supply
589
system service area can be expected to be exposed via direct consumption at relatively similar
590
rates. The difference in development context and its relevance on population characteristics and
591
behaviours manifested in the design of the exposure assessment in the included studies. Most
592
studies examining less-developed settings accounted for changes to the volume of water
593
consumed in the respective location (Supplement D), which varied for varying reasons. Distinct 25
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594
health behaviours that represented materially different risks were also important in defining risk
595
in some cases, such as the intentional consumption of tap water (in the absence of point-of-use
596
treatment) and the incidental exposure to untreated tap water through actions such as
597
toothbrushing, as modelled by Xiao et al. (2012) and Xiao et al. (2013). In both studies, these
598
exposure routes contributed significantly to overall health impact. Similarly, accounting for the
599
immunocompromised subpopulation was influential on overall risk (Xiao et al., 2012).
600
Inconsistencies in terminology hindered the comparability of the included studies and in
601
places may have led to the underestimation of risk. The most notably confused term was the
602
‘susceptible fraction’. Recognised QMRA guidance (e.g. WHO (2016)) uses this factor to
603
account for the proportion of susceptibility in exposed populations based on factors such as life
604
stage and immune status. Six of the eight included studies that included the susceptible fraction
605
did so in accordance with this recognised purpose (Section 3.3.5), though only three used a non-
606
unity assumption (Howard et al. (2006), Shamsollahi et al. (2019), and Xiao et al. (2012)).
607
However, two studies used this factor to account for fractions of the respective study
608
populations not exposed, either geographically or temporally. The adjustment for geographic
609
non-exposure accounted for consumers not being supplied by the water supply systems
610
examined. The issue would have been appropriately addressed by more clearly defining the
611
exposed population during the QMRA problem formulation stage (e.g. by performing individual
612
assessments for the separate water supply systems considered). The adjustment for temporal
613
non-exposure accounted for interruptions to access to the water supply. The desired effect would
614
have been achieved had the relevant exposure frequency be inputted during annualisation rather
615
than using the default value of 365. The effect of either issue involves potential underestimation
616
of annual population health risk.
617
The majority of studies included in the current review assessed the performance of
618
water supply schemes against benchmarks for either the annual risk of infection or annual
619
disease burden set in normative guidance (e.g. Health Canada (2019); NHMRC (2018); USEPA
26
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620
(2006); WHO (2011)) (Section 3.3.6). This raises a pressing policy matter of interpreting annual
621
microbial health-based targets when considering acute events. The studies included in the
622
current review demonstrated that the public health risk posed by water supply is highly sensitive
623
to relatively extreme (though typically acute and infrequent) events in source water, treatment
624
perturbances, and network ingress events rather than the low, endemic loading understood to
625
occur as a function of the logarithmic reduction of pathogens during normal water treatment.
626
The use of the annualisation process attenuates estimated risk levels where a substantial but
627
temporally acute event occurs. Critically, this could lead to intolerable scenarios being
628
unrecognised as such. It is possible, therefore, that annual metrics may not be the most suitable
629
approach. This consideration was addressed in several of the included studies through the use of
630
acute reference levels of risk adapted from recognised annual levels (Section 3.3.6). While
631
meeting one-365th of the annual level every day for a year has been considered a stricter goal
632
than simply meeting the corresponding annual target (Sokolova et al., 2015; Taghipour et al.,
633
2019), it is possible that very short-term events can dominate the yearly average risk (Smeets et
634
al., 2010). Accordingly, acute (or dose-based) reference levels of risk might be a suitable future
635
approach. It is suggested that policy-makers consider the discourse emerging on acute risk in the
636
future setting of normative guidelines. The need for water suppliers to plan for and promptly
637
respond to acute events is also strongly reinforced by the review presented.
638
There are few examples in current literature which strongly focus on risk as experienced
639
by the population exposed at the point of use. This is not unexpected, as normative guidance
640
(e.g. WHO (2016)) is relatively less developed with respect to the consideration of distribution
641
system risk in QMRA. Highlighting this, only one of the included studies set in developed
642
contexts assessed hazards introduced subsequent to water treatment, van Lieverloo et al. (2007),
643
where distribution system ingress was the central research question. Only two studies set in less-
644
developed contexts performed risk assessment for the distribution system (George et al. (2015);
645
Howard et al. (2006)) despite a higher occurrence of network integrity issues expected in these
646
settings. Further, though it was often not explicitly stated, many of the included studies 27
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647
performed retrospective analysis of monitoring data routinely collected under water safety plans
648
(verification monitoring results). This introduced substantial contextual limitations in many
649
cases. Ideally, a converse approach would be taken, where representativeness of data for the
650
purpose of QMRA is prioritised in the design of water quality monitoring programs. Where
651
resourcing permits, consideration of the entire exposure route (e.g. source water, treatment, and
652
distribution) should be made in the development of the QMRA problem formulation, with site-
653
specific monitoring to support this. Further research is likely required to understand how best
654
this monitoring should inform QMRA.
655
Biases undoubtedly affected all the included studies. As QMRA is an artificial
656
simplification of natural processes, sources of uncertainty are the clearest form of bias.
657
However, systematic, comprehensive QMRA uncertainty analysis is generally lacking. The
658
types of uncertainty inherent to the technique are numerous and can be considered as
659
comprising the dimensions of location (including contextual, model, and data uncertainty),
660
nature (including epistemic and ontic uncertainty), range (statistical and scenario uncertainty),
661
methodological unreliability, and value diversity amongst practitioners (Supplement B). While
662
many of the included studies addressed some aspects of this typology, usually implicitly, none
663
did so with a high degree of systematisation. Similarly, in literature, prevalent discussion on the
664
uncertainties arising from water supply QMRA focuses strongly on specific, limited aspects of
665
the typology, such as data uncertainty arising from analytical limitations of pathogen
666
enumeration methods. Other areas of uncertainty are described less frequently; for example,
667
system-specific epistemic and scenario uncertainties and value diversity amongst practitioners
668
have generally received little attention to date and may similarly bias results.
669
With reference to the secondary objective of reviewing included studies’ reported
670
adherence to health-based targets, there were mixed results. A number of included studies
671
returned health risk estimates that can be considered as being of ‘borderline’ adherence to the
672
relevant reference level of risk. As most assumptions used in QMRA are conservative, the
28
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question is raised of whether borderline intolerable results should result in the water supply
674
being deemed ‘unsafe’. Petterson and Ashbolt (2016) highlighted that policy and decision-
675
making by regulators and utilities depends on QMRA risk estimates being artificially drawn as a
676
thin line where in truth the result is ‘fuzzier’ due to the uncertainties involved. Accordingly,
677
improved reporting on QMRA generally, as well as the reduction, better assessment, and
678
reporting of uncertainties, are important goals if the technique is to be further appreciated as
679
valid and useful. To this end, future authors should consider reporting on QMRA assumptions in
680
accordance with Supplement C.
681
The reported health risks can, to an extent, be explained by the human development
682
status of the settings represented in the included studies. Those studies undertaken in settings
683
with higher human development indices tended to present a favourable estimated health risk
684
compared to those in less-developed settings. This occurred with one major exception, the direct
685
potable reuse scheme in Windhoek, Namibia. While classified as an area of low human
686
development status, its good system performance based on estimated annual risk of infection
687
likely reflects the high level of operational capability and increased international investment
688
placed in this direct potable water recycling scheme. Nevertheless, the results are consistent
689
with the estimates by WHO and UNICEF (2017) that showed in 2015 up to one quarter of the
690
world population did not have access to an improved water supply free from contamination,
691
with developing countries most affected. Of the reviewed literature, case studies set in very high
692
human development settings were found to be significantly overrepresented compared to the
693
proportion expected based on world population (Section 3.4.2). Further, the proportion of the
694
population serviced by public water supplies is likely to be smaller in settings that are
695
categorised other than very high human development, and thus these settings may have been
696
more likely to fall outside of the scope of this review.
697 698
The current review should be read in context of the limitations of the included studies and of the review method. The identification of issues for narrative synthesis is, at least in part,
29
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699
a subjective process (Popay et al., 2006). Identification of influential factors on risk estimation
700
was informed by included studies’ formal sensitivity analyses and though commonalities
701
emerged, generalisability is not known. Additionally, specific localities and key research groups
702
were strongly represented, which may have biased the representation of approaches taken to
703
QMRA implementation. Similarly, the study inclusion process is almost certainly impacted by
704
publication bias. By including only peer-reviewed literature and academic theses, it is accepted
705
that valuable analyses and perspectives are likely to have been missed (Rothstein et al., 2005).
706
This is especially relevant for a method intended for routine use in professional practice.
707
However, by its nature, peer-reviewed and scientific literature represents the breadth of current
708
scientific knowledge. As such, the review scope remained consistent with the main objective of
709
the study, to examine the current state of approaches to QMRA implementation.
710
The strength of the current review is that it followed recognised methods for systematic
711
review. The approach included a transparent and repeatable search strategy, and study inclusion
712
being performed by two independent reviewers. Nevertheless, the review was constrained by
713
substantial differences in approaches for QMRA implementation and reporting. Accordingly, a
714
quantitative meta-analysis was not able to be performed. Instead, individual study results were
715
transformed into a common rubric, as forest plots, which provided a sense for overarching
716
trends. Such an approach aids the reader in developing a meaningful summary of study results
717
even in the absence of quantitative meta-analysis (Popay et al., 2006). To date, review of the
718
implementation of the QMRA technique has mainly occurred through narrative discourse (e.g.
719
Haas (2002); Haas et al. (2014); Hamouda et al. (2018); Medema et al. (2003); Petterson and
720
Ashbolt (2016); Smeets (2019); Smeets et al. (2010)). This systematic review has offered a
721
further perspective.
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5 Conclusion
723
This review showed that QMRA has been used in a highly adaptive manner in order to suit
724
available data, a critical quality that allows for the customisation of risk assessment to local
725
conditions and available resources. However, this quality has also inadvertently led to difficulty
726
in interpretation of results and hindered the comparison of case studies.
727
Further work is needed to improve the accessibility and robustness of QMRA results.
728
Most salient in the current review is improvement in the way by which QMRA results and
729
uncertainties are reported. While the assumptions used in QMRA do not necessarily need to be
730
standardised (as study designs vary widely), authors and policy-makers could consider a degree
731
of standardisation in QMRA reporting. It is recommended that future authors consider the water
732
supply QMRA reporting checklist (Supplement C), developed for the current study, as a basis
733
for reporting case study detail. Consideration of broad aspects of uncertainty, such as those
734
contemplated in the described uncertainty typology (Supplement B), is also suggested. Policy-
735
makers should consider the application of health-based targets to circumstances involving acute
736
risk in recognition of emerging scholarly discourse.
737
Further research is needed on issues relevant to QMRA implementation. Specifically,
738
case studies exploring the optimisation of QMRA resourcing given the application context
739
would be beneficial. The continued representation of QMRA case studies within the peer-
740
reviewed and academic literature is also desired so as to furnish further novel approaches for
741
broader industry implementation.
742
Author contributions
743
C. Owens conceptualised the study, designed and performed all analyses, interpreted the results,
744
and drafted the manuscript. M. Angles was the second reviewer. All authors provided important
745
intellectual input and critically reviewed the manuscript. 31
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746
Acknowledgements
747
This work was supported by an Australian Government Research Training Program Scholarship.
748
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1107
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47
Table 1 Summary characteristics of the quantitative microbial risk assessment case studies for distributed public drinking water supplies published from 2003 to October 2019 No
Author
Locality
Pathogen treatment train
Summary exposure route
Reference organism
Reference level of risk
1
2
Ander and Forss (2011) Windhoek, Namibia
Åström et al. (2007)
Gothenburg, Sweden
Co, DAF, F, O3, BAC, GAC, UF,
Treated wastewater → surface water →
Cryptosporidium
Cl2
treatment → ingestion
Giardia
Selective abstraction, Flo, S, Cl2,
Surface water → selective abstraction →
Cryptosporidium
GAC, Cl2 + ClO2
treatment → ingestion
Giardia
inf,a
< 10-4
inf,a
< 10-4
Enterovirus Norovirus 3
Bartak et al. (2015)
Haridwar, India
RBF, Cl2
Surface water → treatment → ingestion
E. coli O157:H7
< 5.33 × 10-3 < 2.18 × 10-2
4
5
Bastos et al. (2013)
Bataiero et al. (2019)
Viçosa, Brazil
Brazil
Co, Flo, S, F, Cl2
Co, Flo, Flot, F, Cl2
Surface water → treatment → ingestion
Surface water → treatment → ingestion
Cryptosporidium
Cryptosporidium Giardia
inf,a
< 10-4
inf,a
< 10-3
inf,a
< 10-4
No
Author
Locality
Pathogen treatment train
Summary exposure route
Reference organism
Reference level of risk
6
Bichai et al. (2011)
Amsterdam, Netherlands
Two plants:
Surface water → treatment → ingestion
Campylobacter jejuni
inf,a
< 10-4
inf,a
< 10-4
(internalised in zooplankton)
Co, S, F, O3, GAC, F
E. coli (internalised in
F, O3, GAC, F
zooplankton) 7
8
Derx et al. (2016)
Elliott (2015) a
Danube River floodplain, RBF, Cl2
Surface water → AI → treatment →
Enterovirus
Austria
ingestion
Norovirus
Treatment → ingestion
Cryptosporidium
Canada
Ten plants: 4 × Co, Flo, S, F, Cl2
Giardia
Co, Flo, S, F, Cl2, UV
Campylobacter
F, Cl2, UV UF, Cl2 UF, Cl2, UV Co, Flo, S, F, O3, UV Cl2, O3, UV
E. coli O157 Rotavirus
< 10-6
No
Author
Locality
Pathogen treatment train
Summary exposure route
Reference organism
Reference level of risk
9
George et al. (2015)
Mysore, India
Two plants: 2 × Co, Flo, S, F, Cl2
Surface water → treatment → distribution
Campylobacter
→ ingestion
Pathogenic E. coli
< 10-6
Rotavirus
10
Hadi et al. (2019)
Tehran, Iran
Co, Flo, S, F, Cl2
Surface water → treatment → ingestion
Cryptosporidium
< 10-6 inf,a
11
Hamouda et al. (2016)
Brantford, Canada
Co, SBC, O3, F, UV, Cl2
Surface water → treatment → ingestion
Cryptosporidium
< 10-4
< 10-6
Giardia Campylobacter E. coli O157 Rotavirus 12
13
Howard et al. (2006)
Irda Sari et al. (2018) a
Kampala, Uganda
Bandung, Indonesia
Two plants:
Surface water → treatment → ingestion
C. parvum
F, Cl2
E. coli
Co, Flo, S, F, Cl2
Rotavirus
Not stated
Treatment → ingestion
Faecal coliforms
< 10-6
inf,a
< 10-4
inf,a
< 10-3
No
Author
Locality
Pathogen treatment train
Summary exposure route
Reference organism
Reference level of risk
14
Jaidi et al. (2009)
St Lawrence River, North Two plants: America
Surface water → treatment → ingestion
Cryptosporidium
inf,a
< 10-4
Giardia
F, Cl2 F, O3, Cl2
15
Katukiza et al. (2014) a
Kampala, Uganda
Not stated
Treatment → ingestion
E. coli O157:H7 Salmonella
< 10-6 inf,a
< 10-4
Adenovirus Rotavirus 16
Krkosek et al. (2016)
Nova Scotia, Canada
Seven plants:
Surface water → treatment → ingestion
Cryptosporidium
DAF, F, Cl2 b
Giardia
DAF, F, Cl2 b
E. coli
Co, Flo, S, UF, Cl2 UF, Cl2 Co, Flo, S, F, Cl2 UF, NF, UV, Cl2 b UF, NF, UV, Cl2 b
< 10-6
No
Author
Locality
Pathogen treatment train
Summary exposure route
Reference organism
Reference level of risk
17
Machdar et al. (2013) a
Nima, Accra, Ghana
Not stated
Distribution → ingestion
Campylobacter
< 10-4
E. coli O157:H7 Cryptosporidium Rotavirus 18
Mohammed and Seidu
Norway
(2019)
Ålesund: Cl2, F, UV
Surface water → treatment → ingestion
Campylobacter
Oset: Co, Flo, F, UV, Cl2
Cryptosporidium
Svartediket: Co, F, UV, Cl2
Giardia
inf,a
< 10-4
inf,a
< 10-4
Norovirus 19
Pintar et al. (2012) a
Ontario, Canada
Co, Flo, S, O3, F / GAC, UV, Cl2
Surface water → treatment → ingestion
Cryptosporidium
20
Razzolini et al. (2016)
São Paulo, Brazil
Co, Flo, S, F, Cl2
Surface water → treatment → ingestion
Cryptosporidium
< 10-6
Giardia 21
Rodriguez-Alvarez et
Vaqueros, Argentina
al. (2015) a
22
New plant: Co, Flo, S, F, Cl2
Surface water → treatment → ingestion
Old plant: Co, Flo, S, F, Cl2
Giardia
inf,a
< 10-4
inf,a
< 10-4
Pseudomonas aeruginosa
Bore: Cl2
Groundwater → treatment → ingestion
E. coli
Ryu and Abbaszadegan Phoenix, USA
Two plants, treatment process not
Surface water → treatment → ingestion
Cryptosporidium
(2008)
stated
Giardia
No
Author
Locality
Pathogen treatment train
Summary exposure route
Reference organism
Reference level of risk
23
Sato et al. (2013)
São Paulo, Brazil
Four plants, each involving Co,
Surface water → treatment → ingestion
Flo, S, F, Cl2
Cryptosporidium
inf,a
< 10-4
inf,a
< 10-4
Giardia
24
Schijven et al. (2019)
Rotterdam, Netherlands
Co, Flo, F, UV, ClO2
Surface water → treatment → ingestion
Adenovirus
25
Shamsollahi et al.
Tehran, Iran
Co, Flo, S, F, Cl2
Surface water → treatment → ingestion
Rotavirus
< 10-6
(2019) 26
Sharaby et al. (2019) a
Kiryat Tiv'on, Israel
Not stated
Reticulation → inhalation
Legionella pneumophila
27
Shea et al. (2016)
Western Victoria,
Seven plants:
Surface water → treatment → ingestion
Campylobacter
Australia
4 × DAFF, Cl2
Cryptosporidium
DAFF, NH2Cl
Virus
inf,a
< 10-4 c
< 10-6
S, F, Cl2 MF, NH2Cl 28
Signor et al. (2007)
Adelaide Hills, Australia
Co, Flo, F, Cl2
Surface water → treatment → ingestion
Campylobacter Cryptosporidium Giardia
inf,a
< 10-4
No
Author
Locality
Pathogen treatment train
Summary exposure route
Reference organism
Reference level of risk
29
Smeets et al. (2008)
Amsterdam, Netherlands
AI, F, O3
Surface water → AI → treatment →
Campylobacter
ingestion
30
Smeets et al. (2007)
United Kingdom
216 plants (treatment trains not
inf,a
< 10-4
inf,d
< 2.74 × 10-7
inf,d
< 365 × 10-4
inf,a
< 10-4
Treatment → ingestion
Cryptosporidium
Treated wastewater → surface water →
Norovirus
< 10-6
Cryptosporidium
< 10-6
stated) Eight plants: 5 × Co, S, F, GAC, Cl2 2 × Co, S, F, GAC, O3, Cl2 Co, S, DAF, F, GAC, Cl2 31
Sokolova et al. (2015) d Trollhättan, Sweden
Co, Flo, F, Cl2
treatment → ingestion 32
Sokurenko (2014)
Calgary, Canada
Bearspaw: Co, Flo, S, F, Cl2
Surface water → treatment → ingestion
Glenmore: Co, Flo, S, F, Cl2
Giardia
33
Swaffer et al. (2018)
Adelaide, Australia
Not stated
Surface water → treatment → ingestion
Cryptosporidium
< 10-6
34
Taghipour et al. (2019)
Quebec and Ontario,
Not stated
Combined sewer outfalls → surface water
Cryptosporidium
< 2.74 × 10-9
Canada
→ treatment → ingestion
No
Author
Locality
Pathogen treatment train
Summary exposure route
Reference organism
Reference level of risk
35
Thomas et al. (2015) a
Phnom Penh, Cambodia
Flo, S, F, Cl2
Surface water → treatment → ingestion
Enterotoxigenic E. coli
36
Tolouei et al. (2019)
Toronto, Canada
Co, Flo, S, F, Cl2
Wastewater outfalls → surface water →
Cryptosporidium
treatment → ingestion
E. coli O157:H7
< 10-6 inf,d
< 2.74 × 10-7
inf,a
< 10-4
inf,a
< 10-4
Giardia 37
van Lieverloo et al.
Netherlands
(2007)
Eight water utilities, treatment
Network ingress → distribution →
Campylobacter
trains not stated
ingestion
Cryptosporidium Giardia Enterovirus
38
39
a
Xiao et al. (2013) a
Xiao et al. (2012)
China, cities serviced by
Typical trains assessed: Co, S, F;
Surface water → treatment → ingestion and Cryptosporidium
Three Gorges Reservoir
Co, S, F, O3
incidental intake
Giardia
China, 33 cities
66 systems, including: Con; Con,
Treatment → ingestion and incidental
Cryptosporidium
O3; and Con, MF f
intake
< 10-6
Some exposure routes presented in the original paper fell outside of the scope of this review and were not included; b the modelled process train differed due to model limitations; c
were also incorrectly compared to a
inf,a
results
reference levels of risk; d this case study was also presented in a related thesis by Sokolova (2013); e order of magnitude for this figure was corrected from
the original manuscript to fit within the reported confidence interval; f treatment processes were not identified in greater detail.
Treatment process abbreviations: AI = aquifer infiltration; BAC = biological activated carbon; Cl2 = primary chlorination; ClO2 = chlorine dioxide; Co = coagulation; Con = conventional treatment; DAF = dissolved air flotation; DAFF = dissolved air flotation and filtration; Flo = flocculation; Flot = flotation; GAC = granular activated carbon; MF = microfiltration; NH2Cl = primary chloramination; NF = nanofiltration; O3 = ozonation; RBF = river bank filtration; S = sedimentation; SBC = sand-ballasted clarification; UF = ultrafiltration; UV = ultraviolet irradiation. Reference levels of risk abbreviations: annual probability of illness;
inf,a
= annual disease burden in disability-adjusted life years person-1 year-1;
= annual probability of infection;
inf,d
= daily probability of infection.
= daily disease burden in disability-adjusted life years person-1 year-1;
ill,a
=
Table 2 Summary of QMRA model interfaces No
Author
Model interfaces
1
Ander and Forss (2011)
Fault tree analysis (water treatment process failure modes) → QMRA
17
Machdar et al. (2013)
QMRA → economic model (cost-effectiveness analysis)
31
Sokolova et al. (2015)
Hydrodynamic model (pathogen transport) → QMRA
34
Taghipour et al. (2019)
Hydrodynamic model (pathogen fate and transport) → QMRA
36
Tolouei et al. (2019)
Hydrodynamic model (pathogen fate and transport) → QMRA
33
Swaffer et al. (2018)
Linear mixed-effects model (land-use, stocking, and pathogens) → QMRA
QMRA = quantitative microbial risk assessment.
Table 3 Relative influence of QMRA inputs on health risk estimates as identified through included studies’ formal sensitivity analyses No
Author
Technique
Factors tested in order of reported relative impact on health risk (most–least)
4
Bastos et al. (2013)
ρ
Consumption rate; source pathogen load; raw water turbidity; filtrate turbidity; dose-response parameter; oocyst recovery method; oocyst removal estimate
5
Bataiero et al. (2019) ρ
Source pathogen load; treatment removal; consumption rate; doseresponse parameter
8
Elliott (2015) a
Pairwise
All reference pathogens: filtrate turbidity; source pathogen load
comparison
All reference pathogens except rotavirus:
surface plots
filtrate turbidity; chlorination. UV dose; chlorination Cryptosporidium and Giardia: filtrate turbidity; chlorination Giardia: chlorination; source pathogen load Rotavirus: chlorination (low dose); filtrate turbidity chlorination (low dose); UV dose
18
Mohammed and
Factor
All reference pathogens: source water pathogen load; consumption
Seidu (2019)
sensitivity
rate; dose-response parameters
coefficient 19
Pintar et al. (2012)
ρ
Only the top factor was listed, for each seasonal condition and event scenario: Winter routine and suboptimal scenario: source water pathogen load Summer routine and suboptimal scenario: ozone dose yearly average conditions: water temperature
20
Razzolini et al. (2016)
ρ
Source pathogen load; consumption rate; dose-response parameter
No
Author
Technique
Factors tested in order of reported relative impact on health risk (most–least)
21
Rodriguez-Alvarez et Differential
Pseudomonas aeruginosa and Giardia: source pathogen load;
al. (2015)
consumption rate; dose-response parameter. Escherichia coli: source pathogen load; dose-response parameter; consumption rate
23
Sato et al. (2013) b
ρ
Source pathogen load; consumption rate; dose-response parameter
39
Xiao et al. (2012)
ρ
Filtration stability; source pathogen load; sedimentation; proportion of population who directly consume; coagulation stability; volume directly consumed; case fatality rate for ID; volume of incidental intake; probability of illness given infection for IC; case fatality for IC
a
Results interpolated by the reviewer; b results for ‘all regions’ followed this order, except for the children subpopulation
under alternative left-censoring approaches. Abbreviations: IC = immunocompetent subpopulation; ID = immunodeficient subpopulation; RSF = rapid sand filtration; UF = ultrafiltration; UV = ultraviolet treatment; ρ = rank correlation coefficient test.
Identification
Figure 1 Systematic review selection process
Records (N = 1264) identified from PubMed (n = 296), Scopus (n = 369), Web of Science (n = 405), BASE (n = 194) Duplicates excluded (n = 680)
Screening
Titles and abstracts screened (n = 584)
Records excluded by screening (n = 450)
Eligibility
Full-text assessed for eligibility (n = 134)
Full-text version excluded (n = 95)
Included
Records included in review (n = 39)
Journal articles (n = 35)
BASE = Bielefeld Academic Search Engine.
Academic theses (n = 4)
4 2
2008 1
2009 0
2
Year
2011
2
2012
4
2013 2
2014
6
2015
8
2019
2
2018
0
2017
5
2016
10
1
2007
2010
8
0
2006
6
0
2005
4
0
2004
2
0 2003
Figure 2 Number of included studies by publication year
Number of publications
Figure 3 Reference pathogen uses by publication
Pseudomonas (n = 1; 1%)
Salmonella (n = 1; 1%)
Legionella (n = 1; 1%) Faecal coliforms (n = 1; 1%)
Virus (n = 1; 1%)
Adenovirus (n = 2; 2%) Enterovirus (n = 3; 4%) Norovirus (n = 4; 5%) Cryptosporidium (n = 26; 30%) Rotavirus (n = 7; 8%)
Campylobacter (n = 10; 12%)
Giardia (n = 17; 20%) E. coli (n = 12; 14%)
N = 86.
Figure 4 Risk reference level uses by publication Ba < regional incidence (n = 1; 2%)
Bd < 2.74 × 10⁻ (n = 1; 2%)
Ba < national incidence (n = 1; 2%)
Pinf,d < 365 × 10⁻ (n= 1; 2%)
Ba < 10⁻ DALY person⁻¹ year⁻¹ (n = 1; 2%) Pinf,d < 2.74 × 10⁻ (n = 2; 4%) Pinf,a < 10⁻³ (n = 2; 5%) Pinf,a < 10⁻ (n = 22; 48%)
Ba < 10⁻ DALY person⁻¹ year⁻¹ (n = 15; 33%)
N = 46. Abbreviations: Ba = annual disease burden; Bd = daily disease burden; DALY = disability-adjusted life years; Pinf,a = annual probability of infection; Pinf,d = daily probability of infection.
Figure 5 Estimated infection risk presented in studies using 1 in 10,000 (10-4) probability of infection as reference level of risk, ranked by study locality human development index and grouped by scenario
Mohammed and Seidu (2019) - Ålesund - Campylobacter Mohammed and Seidu (2019) - Ålesund - Cryptosporidium Mohammed and Seidu (2019) - Ålesund - Giardia Mohammed and Seidu (2019) - Ålesund - norovirus Mohammed and Seidu (2019) - Oset - Campylobacter Mohammed and Seidu (2019) - Oset - Cryptosporidium Mohammed and Seidu (2019) - Oset - Giardia Mohammed and Seidu (2019) - Oset - norovirus Mohammed and Seidu (2019) - Svartediket - Campylobacter Mohammed and Seidu (2019) - Svartediket - Cryptosporidium Mohammed and Seidu (2019) - Svartediket - Giardia Mohammed and Seidu (2019) - Svartediket - norovirus Signor et al. (2007) - Campylobacter Signor et al. (2007) - Cryptosporidium Signor et al. (2007) - Giardia Bichai et al. (2011) - zooplankton-internalised C. jejuni Bichai et al. (2011) - zooplankton-internalised E. coli Jaidi et al. (2009) - regulatory credit method Pintar et al. (2012) - Cryptosporidium Ryu and Abbaszadegan (2008) - plant 1 - Giardia Ryu and Abbaszadegan (2008) - plant 2 - Cryptosporidium Ryu and Abbaszadegan (2008) - plant 2 - Giardia Derx et al. (2016) - dry year - enterovirus Derx et al. (2016) - dry year - norovirus Derx et al. (2016) - wet year - enterovirus Derx et al. (2016) - wet year - norovirus Sharaby et al. (2019) - showers - Legionella pneumophila Sharaby et al. (2019) - faucets - Legionella pneumophila Rodriguez-Alvarez et al. (2015) - new plant - E. coli Rodriguez-Alvarez et al. (2015) - new plant - Giardia Rodriguez-Alvarez et al. (2015) - new plant - P. aeruginosa Rodriguez-Alvarez et al. (2015) - old plant - E. coli Rodriguez-Alvarez et al. (2015) - old plant - Giardia Rodriguez-Alvarez et al. (2015) - old plant - P. aeruginosa Rodriguez-Alvarez et al. (2015) - bore - E. coli Rodriguez-Alvarez et al. (2015) - bore - Giardia Rodriguez-Alvarez et al. (2015) - bore - P. aeruginosa Hadi et al. (2019) - Age < 2 - Cryptosporidium Hadi et al. (2019) - Age 2–6 - Cryptosporidium Hadi et al. (2019) - Age 6–16 - Cryptosporidium Hadi et al. (2019) - Age > 16 - Cryptosporidium Bastos et al. (2013) - Cryptosporidium - empirical model Bataiero et al. (2019) - Children - Giardia Bataiero et al. (2019) - Children - Cryptosporidium Bataiero et al. (2019) - Adults - Giardia Bataiero et al. (2019) - Adults - Cryptosporidium Sato et al. (2013) - all regions - adults - Cryptosporidium Sato et al. (2013) - all regions - adults - Giardia Sato et al. (2013) - all regions - children - Cryptosporidium Sato et al. (2013) - all regions - children - Giardia Xiao et al. (2013) - Adv - drinking - Cryptosporidium Xiao et al. (2013) - Adv - drinking - Giardia Xiao et al. (2013) - Adv - incidental - Cryptosporidium Xiao et al. (2013) - Adv - incidental - Giardia Xiao et al. (2013) - Con - drinking - Cryptosporidium Xiao et al. (2013) - Con - drinking - Giardia Xiao et al. (2013) - Con - incidental - Cryptosporidium Xiao et al. (2013) - Con - incidental - Giardia Irda Sari et al. (2018) - faecal coliforms Ander and Forss (2011) - normal scenario - Cryptosporidium Ander and Forss (2011) - normal scenario - Giardia Ander and Forss (2011) - realistic scenario - Cryptosporidium Ander and Forss (2011) - realistic scenario - Giardia Katukiza et al. (2014) - E. coli
10 –16 10 –14 10 –12 10 –10 10 –8 10 –6 10 –4 Probability of infection
10 –2
10 0
Ranked by human development index of the study locality (UNDP, 2018) (top highest, bottom lowest). Error bars indicate 90th or 95th percentile range where reported. Only studies with stated numerical results shown. Where multiple operational configurations or case studies were presented, only those most representative of actual supply (as deemed by the reviewers) were included. Schijven et al. (2019) presented results graphically. Abbreviations: Adv = advanced treatment (not further defined); Con = conventional treatment.
Figure 6 Estimated disease burden in studies using 10-6 disability-adjusted life years (DALY) person-1 year-1 as reference level of risk, ranked by study locality human development index and grouped by scenario
Sokurenko (2014) - Bearspaw - 2009 - Giardia Sokurenko (2014) - Bearspaw - 2011 - Giardia Sokurenko (2014) - Bearspaw - 2011 - Cryptosporidium Sokurenko (2014) - Glenmore - 2003 - Cryptosporidium Sokurenko (2014) - Glenmore - 2011 - Cryptosporidium Hadi et al. (2019) - Age < 2 - Cryptosporidium Hadi et al. (2019) - Age 2–6 - Cryptosporidium Hadi et al. (2019) - Age 6–16 - Cryptosporidium Hadi et al. (2019) - Age > 16 - Cryptosporidium Shamsollahi et al. (2019) - low-dose formula - rotavirus Shamsollahi et al. (2019) - Beta-Poisson formula - rotavirus Razzolini et al. (2016) - Cryptosporidium Xiao et al. (2012) - Con - Cryptosporidium Xiao et al. (2012) - Con - immunocompetent - Cryptosporidium Xiao et al. (2012) - Con - immunodeficient - Cryptosporidium Xiao et al. (2012) - Con, O3 - Cryptosporidium Xiao et al. (2012) - Con, O3 - immunocompetent - Cryptosporidium Xiao et al. (2012) - Con, O3 - immunodeficient - Cryptosporidium Xiao et al. (2012) - MF - Cryptosporidium Xiao et al. (2012) - MF - immunocompetent - Cryptosporidium Xiao et al. (2012) - MF - immunodeficient - Cryptosporidium George et al. (2015) - Hongally WTP - Campylobacter George et al. (2015) - Hongally WTP - pathogenic E. coli George et al. (2015) - Hongally distrib. - pathogenic E. coli George et al. (2015) - Hongally WTP - rotavirus George et al. (2015) - Melapura WTP - Campylobacter George et al. (2015) - Melapura WTP - pathogenic E. coli George et al. (2015) - Melapura distrib. - pathogenic E. coli George et al. (2015) - Melapura WTP - rotavirus Thomas et al. (2015) - ETEC Howard et al. (2006) - plant 1 - 1999 - pathogenic E. coli Howard et al. (2006) - plant 1 - 2002 - pathogenic E. coli Howard et al. (2006) - plant 2 - 1999 - pathogenic E. coli Howard et al. (2006) - plant 2 - 2002 - pathogenic E. coli Howard et al. (2006) - distrib. - 1998 - pathogenic E. coli Howard et al. (2006) - distrib. - 1999 - pathogenic E. coli Katukiza et al. (2014) - E. coli 10 –10
10 –8
10 –6
10 –4
Disability-adjusted life years person
10 –2 -1
10 0
year -1
Ranked by human development index of the study locality (UNDP, 2018) (top highest, bottom lowest). Error bars indicate 90th or 95th percentile where reported. Only studies with stated numerical statistics of median (and optionally 5th and 95th percentile) health risk are shown. Elliott (2015), Hamouda et al. (2016), and Krkosek et al. (2016) presented results graphically. Sokurenko (2014) presented other years’ results graphically. Shea et al. (2016) and Sokolova et al. (2015) compared source water challenge and treatment effectiveness but did not provide numerical risk estimate for health risk. Swaffer et al. (2018) characterised source water risk only. Abbreviations: distrib. = distribution system; Con = conventional treatment; ETEC = enterotoxigenic Escherichia coli; MF = microfiltration; O3 = ozonation; WTP = water treatment plant.
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Highlights: First systematic review of QMRA implementation for public drinking water supplies Current approaches varied most for deriving dose and varied least for dose-response Factors for dose were commonly the most influential determinant of risk QMRA study complexity did not indicate greater certainty of risk estimates Greater consistency in reporting QMRA assumptions would be beneficial overall
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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
S0043-1354(20)30150-0
DOI:
https://doi.org/10.1016/j.watres.2020.115614
Reference:
WR 115614
To appear in:
Water Research
Received Date: 26 October 2019 Revised Date:
2 February 2020
Accepted Date: 10 February 2020
Please cite this article as: Owens, C.E.L., Angles, M.L., Cox, P.T., Byleveld, P.M., Osborne, N.J., Rahman, M.B., Implementation of quantitative microbial risk assessment (QMRA) for public drinking water supplies: Systematic review, Water Research (2020), doi: https://doi.org/10.1016/ j.watres.2020.115614. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.
Eligibility
Screening
Identification
Systematic literature review
Structured narrative synthesis
Records (N = 1264) identified from PubMed (n = 296), Scopus (n = 369),
Identification
Critical synthesis
Web of Science (n = 405), BASE (n = 194) Duplicates excluded (n = 680)
Primary objective: QMRA implementation approaches Secondary objective: Studies’ reported health risk results
Titles and abstracts screened (n = 584)
Records excluded by screening (n = 450)
Results
Recommendations
Approaches varied widely
Greater focus on reporting of assumptions
Study complexity did not indicate greater certainty
Optimisation of QMRA resourcing given application context
Use of location-specific information varied broadly
Broad aspects of uncertainty should be considered
Full-text assessed for eligibility (n = 134) Full-text version excluded (n = 95)
Included
Categorisation
Records included in review (n = 39) Journal articles (n = 35)
Academic theses (n = 4)
Owens et al. 2019
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Implementation of quantitative microbial risk assessment (QMRA) for
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public drinking water supplies: Systematic review
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Christopher E. L. Owens a, b, *, Mark L. Angles c, Peter T. Cox b, Paul M. Byleveld d, Nicholas J.
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Osborne a, e, f, Md Bayzid Rahman a
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Affiliations:
a
School of Public Health and Community Medicine, Faculty of Medicine,
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University of New South Wales, Kensington NSW 2052, Australia
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b
Sydney Water Corporation, Parramatta NSW 2124, Australia
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c
Water Angles Consulting, Vaucluse NSW 2030, Australia
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d
Water Unit, NSW Health, North Sydney NSW 2059, Australia
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e
School of Public Health, Faculty of Medicine, University of Queensland,
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Herston QLD 4006, Australia
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f
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Royal Cornwall Hospital, Truro TR1 3HD, United Kingdom
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European Centre for Environment and Human Health, University of Exeter,
* Corresponding author: [email protected]
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Implementation of quantitative microbial risk assessment (QMRA) for
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public drinking water supplies: Systematic review
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Abstract
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In the more than 15 years since its introduction, quantitative microbial risk assessment (QMRA)
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has become a widely used technique for assessing population health risk posed by waterborne
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pathogens. However, the variation in approaches taken for QMRA in relation to drinking water
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supply is not well understood. This systematic review identifies, categorises, and critically
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synthesises peer-reviewed and academic case studies of QMRA implementation for existing
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distributed public drinking water supplies. Thirty-nine English-language, peer-reviewed and
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academic studies published from 2003 to 2019 were identified. Key findings were synthesised
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in narrative form. The overall designs of the included studies varied widely, as did the
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assumptions used in risk calculation, especially in relation to pathogen dose. There was also
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substantial variation in the degree to which the use of location-specific data weighed with the
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use of assumptions when performing risk calculation. In general, the included studies’
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complexity did not appear to be associated with greater result certainty. Factors relating to
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pathogen dose were commonly influential on risk estimates whereas dose-response parameters
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tended to be of low relative influence. In two of the included studies, use of the ‘susceptible
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fraction’ factor was inconsistent with recognised guidance and potentially led to the
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underestimation of risk. While approaches and assumptions used in QMRA need not be
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standardised, improvement in the reporting of QMRA results and uncertainties would be
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beneficial. It is recommended that future authors consider the water supply QMRA reporting
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checklist developed for the current review. Consideration of the broad types of uncertainty
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relevant to QMRA is also recommended. Policy-makers should consider emergent discussion on
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acute microbial health-based targets when setting normative guidelines. The continued
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representation of QMRA case studies within peer-reviewed and academic literature would also 2
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enhance future implementation. Further research is needed on the optimisation of QMRA
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resourcing given the application context.
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Keywords
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Quantitative microbial risk assessment (QMRA)
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Systematic review
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Drinking water
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Distributed water supply
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1 Introduction
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Public drinking water supplies have a crucial role in protecting public health. Access to a safe
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and adequate drinking water supply prevents a massive disease burden that would otherwise
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occur (Bartram & Cairncross, 2010; Hunter et al., 2010). Yet, the microbial safety of drinking
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water supplies remains a matter for continued improvement globally. Distributed water supplies
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still represent a significant exposure route for faecal-derived human pathogens in less developed
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settings. Against common expectation, serious contamination in more developed settings also
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still occurs (Hrudey & Hrudey, 2004, 2007). For example, water treatment processes and
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management practices may not be commensurate with the increased risk associated with
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extreme weather conditions (Khan et al., 2015) and imperfect distribution system integrity can
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cause exposure of consumers to pathogens, regardless of water treatment (Säve-Söderbergh et
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al., 2017; Viñas et al., 2019).
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Water supply is understood to contribute to a baseline of sporadic, undetectable cases
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even under ideal operating conditions (Bylund et al., 2017; Colford et al., 2009; Westrell, 2004).
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Reduction of pathogens in drinking water is achieved through controlled physicochemical water
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treatment processes (Hijnen & Medema, 2010; Howe et al., 2012), with removal typically
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measured in terms of logarithmic reduction. Even with the most effective existing treatments,
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the total removal of pathogens can be approached but probabilistically never achieved. Further,
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insufficient analytical sensitivity means that the assurance of drinking water safety cannot
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confidently be based on the non-detection of pathogens from laboratory analyses of water
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samples (Signor & Ashbolt, 2006). This is a contributing reason for why routine monitoring of
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specific microbial pathogens in treated drinking water is not recommended (NHMRC &
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NRMMC, 2011).
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Clinical practice and public health surveillance systems are also insufficient to detect all
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but the highest levels of pathogens and water supply-related illness outbreaks (Sinclair et al.,
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2005). Because public drinking water supplies involve broad public exposure, the ability to
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observe all but the most extreme of associated disease outbreaks is highly likely to be masked
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by the inability to differentiate water-associated cases from cases arising from other causes
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(Haas et al., 1996; Westrell, 2004).
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Still, a high level of sensitivity is required to demonstrate adherence to commonly-
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adopted ‘health-based targets’ for the microbial safety of drinking water, including the 1 in
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10,000 annual risk of infection and the 10-6 disability-adjusted life years (DALY) person-1 year-1
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(Health Canada, 2019; USEPA, 2006; WHO, 2011). To this end, the quantitative microbial risk
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assessment (QMRA) technique enables the estimation of potential health risk consequential to
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any exposure severity. The approach reflects that for the assessment of health risk from
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carcinogens and toxins (NRC, 1983) and is comprised of four key stages, being problem
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formulation, exposure assessment, health effects assessment, and risk characterisation (Haas et
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al., 2014; WHO, 2016). These stages integrate scientific understanding of pathogens, their fate
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and transport through natural and engineered systems, and their routes to human exposure and
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consequential disease (WHO, 2016). Importantly, QMRA can account for the exposure of large
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populations to very low levels of pathogens, which typifies the risk profile of distributed water
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supplies. This is facilitated by the dose-response assessment (part of the health effects
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assessment) being centred on single hit theory, which assumes that the chance of each organism
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reaching the target organ and causing infection or disease is non-zero and independent of that of
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the other organisms present (Haas, 1983; Nilsen & Wyller, 2016).
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QMRA has been a widely recognised practice since at least 2004. It sits within a
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spectrum of risk assessment approaches available to be used as part of the ‘water safety plan’
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approach to drinking water quality management (Fitzgerald et al., 2018; Health Canada, 2018;
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Medema & Ashbolt, 2006; Murphy et al., 2014; WHO, 2011, 2016) and a consensus on the
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broad approach has been established (e.g. EPHC et al. (2006); Haas et al. (2014); Health Canada
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(2018); WHO (2011, 2016); WSAA (2015)). However, in the more than 15 years since its broad
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introduction, a range of QMRA approaches are applied by practitioners, which differ at the
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detailed assumption level. A systematic review of QMRA application for drinking water
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supplies is required to summarise the current state of the field, with the primary study objective
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to examine variation in the application of assumptions and resultant impact on QMRA
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outcomes. Three key related areas are examined: (i) the current range of approaches to QMRA
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implementation taken for drinking water supplies (as represented by peer-reviewed and
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academic literature); (ii) whether the QMRA study designs (particularly study complexity) has
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been commensurate with what is required to know whether the water is safe; and (iii) whether a
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consensus position has emerged on a preferable balance between the location-specific empirical
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data and default modelling assumptions used in QMRA. Systematic review is ideal to address
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these questions. It is a technique used to map the different localities, subjects, variables, and
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results that are available in literature (Pickering & Byrne, 2014).
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To identify outcomes of immediate relevance to practitioners and to reduce biases, the
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current review focuses on real-life case studies. The criterion of including only peer-reviewed
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and academic literature is deemed an acceptable benchmark to ensure study quality. As a
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secondary review objective, the current study design provides an opportunity to summarise the
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health risk estimates reported by the in-scope literature. As the quality of drinking water supply
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is a key element of societal development, tracking of these results against the defined human
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development index is also of interest. Based on the critical review outcomes, considerations
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relating to the approaches taken for QMRA, as implemented in real-world supply systems, are
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identified to guide future QMRA application.
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2 Method
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Peer-reviewed and academic literature describing case studies implementing QMRA for
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existing, distributed public drinking water supplies were identified, categorised, and critically
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synthesised. The review was guided by Pickering and Byrne (2014) and was in accordance with
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the Preferred Reporting for Systematic Reviews and Meta-analyses (PRISMA) statement
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(Moher et al., 2009) and checklist (Supplement A). Registration of the systematic review
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protocol was deemed unnecessary due to its relatively small scale.
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2.1 Study selection
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2.1.1 Inclusion criteria
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As the overarching study objective was to examine the variation in applications of QMRA
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relevant to water supply, three important selection criteria were imposed. These criteria were
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devised to identify the most robust and peer-accepted cases of QMRA implementation, whilst
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representing a breadth of implementation approaches immediately applicable to practitioners.
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First, the review focused exclusively on real-life scenarios of consumers being supplied
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by drinking water distribution systems. Only case studies demonstrating the application of
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QMRA to actual operational systems were included. This criterion is based on the notion that
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approaches taken in current water supply systems are likely to be of greatest relevance to
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practitioners.
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Second, because of the focus on real-life exposure scenarios, only those case studies
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deriving dose assumptions based on empirical microbial observations at the study site were
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included. This criterion was to focus inclusion on studies with a high level of robustness, given
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the importance of site-specific microbial data in QMRA implementation (Petterson & Ashbolt,
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2016; WHO, 2016). Studies were included regardless of whether they involved direct
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observation of reference pathogens or the observation of microbial indicators or surrogates that
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were later converted to assumed reference pathogen dose. Inclusion was also regardless of
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whether dose assumptions were based on the quality of water immediately following water
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treatment or whether dose also (or solely) considered risk of distribution.
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Third, only peer-reviewed journal articles and academic theses were included. The peer
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and academic review processes are expected to have focused the identification of studies on
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those of a high level of rigour. Because peer-reviewed journal articles and academic theses are
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inherently biased towards representing novel content, this criterion still allowed for a breadth of
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approaches applied to drinking water supplies to be included.
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Initial literature identification involved searches of scholarly databases PubMed,
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Scopus, and Web of Science. Title and abstract text were queried for the terms “*water*” and
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“quantitative microbial risk assessment” or “QMRA”. The query terms were deliberately broad
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to avoid potential issues raised by inconsistent or lack of detail in terminology and thus
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maximise the identification of relevant literature. Search conditions included original, English-
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language peer-reviewed journal articles and master’s and doctoral theses with a publication date
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from January 2003 to October 2019. The publication date lower bound was selected on the
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rationale of capturing the contemporary approach of preventive risk management and microbial
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health-based targets for drinking water safety, which were primarily included in the third edition
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of the WHO (2004) Guidelines for drinking-water quality. The initial identification of theses
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was performed using the Bielefeld Academic Search Engine (BASE) database using the same
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query conditions. As the option to search abstract was not available, search queries applied to
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the entire document.
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Screening involved the removal of duplicate records and inspection of the title and
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abstract, where available, (regardless of whether full-text was immediately available) such that
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only records potentially meeting the inclusion criteria were included. Studies were deemed
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eligible for inclusion through inspection of the full-text version.
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2.1.2 Exclusion criteria
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Records found to not meet the inclusion criteria were excluded. Thus, narrative reviews,
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hypothetical case studies, conceptual models, and analyses that were pilot-scale or desktop-
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based were excluded.
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Analysis of exposure routes where the water was no longer representative of the
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distributed supply were excluded, such as analyses of spa baths and splash parks. Analyses
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involving wastewater and water recycling schemes were not included unless the schemes
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articulated into a drinking water supply system. Analyses of decentralised schemes (e.g.
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involving roof-harvest rainwater or well water) and of point-of-use and home water treatment
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practices were excluded.
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Studies measuring the indirect effects of intermittent access to water supply were excluded as the current review focuses on quality of the supplied water during times of supply.
2.1.3 Minimisation of bias
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Risk of bias in the conduct of the systematic review was minimised as guided by Popay et al.
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(2006). This included measures to reduce bias in the selection of studies and within the review
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synthesis.
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Bias in the selection of studies was minimised by study eligibility being independently
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assessed by two reviewers (C. Owens and M. Angles). Each reviewer prepared a tentative list of
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included studies and synthesised discussion points without knowledge of the other’s results.
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Once completed, review outcomes were compared, and inconsistencies were discussed until a
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consensus position was achieved.
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The design of the selection process also focused on including studies with lower
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potential bias. The scope included only existing, in-situ water supply schemes where empirical
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observation of microbial quality at the study site was performed. This was intended to minimise
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the number of default literature values in the included QMRA case studies, the
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representativeness of which being a major potential source of error when comparing health risk
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estimates across studies (with reference to the secondary review objective). Studies judged by
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both reviewers to be of equal technical quality were given equal weight in the discussion.
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Residual biases were discussed in terms of an uncertainty typology for water supply
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(Supplement B), adapted for this study from Bouwknegt et al. (2014) and Knol et al. (2009).
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2.2 Data extraction
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Full-text manuscripts of eligible records were obtained. Data were extracted for the application
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locality, unit processes comprising the treatment train relevant to pathogen reduction, summary
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exposure route, reference organisms, reference levels of risk used, dose-response models and
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cited source, health effects assumptions, sensitivity analyses performed, and whether QMRA
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was used as an input or is informed by the output of other models. For this purpose, a checklist
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for reporting on water supply QMRA was developed (Supplement C). Key findings were
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summarised. Water treatment processes that were not directly for the reduction of pathogens
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were not extracted. Thus, processes for secondary disinfection, aesthetic and physical quality,
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fluoridation, and pre-requisites supporting the effectiveness of treatment, such as pH
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adjustment, were not extracted.
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2.3 Synthesis
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The included studies were subject to structured narrative synthesis through textual description,
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tabulation, and thematic analysis as guided by Popay et al. (2006). Thematic analysis focused on
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characterising the breadth of choices that can be made in QMRA implementation. The themes
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were the included studies’ consideration of: exposure routes, reference pathogens, indicator and
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surrogate organisms, dose-response models, methods for estimating health risk, reference levels
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of risk, incorporation of high-risk events, the interfacing of models with QMRA, the approach
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to sensitivity analysis, and approach to the recognition of potential biases.
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The secondary study outcomes, i.e. performance against health-based targets and tracking with
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development settings, were examined through the application of a common rubric. This took the
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form of forest plots grouped by exposure scenario and ranked by the human development index
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of the study locality according to UNDP (2018). The representation of different development
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settings was also compared to expected proportions based on world population using exact
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multinomial and pairwise exact binomial tests with Bonferroni correction using R 3.6.1 and the
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EMT 1.1 package. A significance level (α) of 0.05 was used for all statistics.
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Topics were identified for further critical discussion. First, the primary review objective and
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associated focus areas were discussed with reference to the included studies. Second, the
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influence of QMRA input factors were discussed based on the synthesis of the included studies’
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sensitivity analyses. Third, themes derived from the included studies’ overall designs were
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discussed. Finally, the secondary study outcomes were discussed.
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3 Results
3.1 Study selection
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Using the described search strategy, 1264 records were identified as of potential relevance. Of
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these, 584 (46%) were non-duplicates and 39 (3%) were included in this review. The most
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common reason for exclusion during the screening stage was due to studies not reporting on a
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public drinking water supply. During the eligibility stage the most common reason for exclusion
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was due to studies not using empirical, site-specific microbial data. The study inclusion results
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(Figure 1) were mutually agreed by the two independent reviewers. The two reviewers had no
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outstanding divergent views.
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3.2 Study characteristics
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Of the 39 included studies (categorised in Table 1), 35 (90%) were in the form of peer-reviewed
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journal articles and four (10%) were academic theses. The journal articles were published in 16
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different peer-reviewed journals.
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3.2.1 Publication date
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The number of publications by year (Figure 2) indicates that there were fewer than 10 peer-
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reviewed and academic studies applying QMRA to empirical data of drinking water supplies for
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any given year, since 2003.
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3.3 Results of primary review objective – QMRA implementation approaches
247
Approaches taken for QMRA implementation were synthesised into the themes of: exposure
248
routes (Section 3.3.1), reference pathogens (Section 3.3.2), indicator and surrogate organisms
249
(Section 3.3.3), dose-response models (Section 3.3.4), estimation of public health risk (Section
250
3.3.5), and reference levels of risk (Section 3.3.6). Aspects of broader study design were 11
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synthesised under the themes of the incorporation of high-risk events (Section 3.3.7), the
252
interfacing of models with QMRA (Section 3.3.8), the approach to sensitivity analysis (Section
253
3.3.9), and approaches to the recognition of potential biases (Section 3.3.10). The included
254
studies’ approaches were tabulated under the themes of major exposure assumptions
255
(Supplement D), dose-response models (Supplement E), and health effects assumptions
256
(Supplement F). Other factors contributing to the application of QMRA, such as pathogen
257
recovery, infectivity, and viability (Supplement D), and the use of pathogens and indicators and
258
assumptions on treatment process efficiency (Supplement D) were also summarised.
259
3.3.1 Exposure routes
260
The majority of the included studies focused strongly on the conveyance of pathogens from
261
source water through the water supply system to the consumer (Table 1). Only two studies
262
considered other major exposure routes: van Lieverloo et al. (2007), who focused on risk arising
263
from ingress to the distribution network (subsequent to water treatment); and Sharaby et al.
264
(2019), who focused on Legionella pneumophila inhabiting the distribution system. All included
265
studies considered the intentional ingestion of drinking-water leading to gastrointestinal illness,
266
except Sharaby et al. (2019), who focused on inhalation of aerosols leading to Legionnaires’
267
disease. Four studies (10% of those included) covered inadvertent forms of human exposure to
268
drinking-water, including from bathing, toothbrushing, and inhalation (Table 1).
269
In total, more than 360 water supply systems fell within scope (Table 1). Almost half of the
270
included studies analysed more than one system (n = 16; 41% of the included studies). Thirty-
271
one of the included studies (79%) described the involvement of surface water in the exposure
272
route, three (8%) considered aquifer infiltration, four (10%) addressed the influence of treated
273
wastewater, and seven (18%) did not describe the source water type (or it was not relevant).
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3.3.2 Reference pathogens
275
A range of reference pathogens were used in the included studies (Table 1). Thirteen major
276
reference pathogens were represented, and most studies analysed risk for multiple reference
277
pathogens (n = 25; 64% of the included studies) (Table 1). The most frequently used was
278
Cryptosporidium (used in 26 publications; 30% of total reference pathogen uses by publication)
279
and the only other protozoal reference pathogen used was Giardia (used in 16 publications; 20%
280
of reference pathogen uses by publication) (Figure 3). All studies assessing Giardia also
281
assessed Cryptosporidium, except one (Rodriguez-Alvarez et al. (2015)) due to local regulations
282
focusing on Giardia. Of the bacterial reference pathogens used, Escherichia coli was most
283
common (used in 12 publications; 14% of reference pathogen uses by publication) (Figure 3).
284
Its use was reported at the species level, as pathogenic E. coli, E. coli O157, E. coli O157:H7,
285
and as the enterotoxigenic pathotype. Rotavirus was the most commonly used viral reference
286
pathogen (used in seven publications; 8% of total reference pathogen uses by publication)
287
(Figure 3). Detail on pathogen recovery, viability, and infectivity (and other microbiological
288
factors relevant to the exposure assessment) were reported to varying extents (Supplement D).
289
These and related terms were occasionally used in inconsistent senses across studies (refer to
290
Discussion).
291
3.3.3 Indicator and surrogate organisms
292
The use of indicator organisms to represent the occurrence of pathogens was considered
293
moderate, with 17 (44%) of the included studies making clear reference to their use at some
294
point in the exposure route (Supplement D). The most commonly used indicators were faecal
295
coliforms and E. coli.
296
Use of surrogate organisms (which model the fate of pathogens (Sinclair et al., 2012)) for the
297
estimation of treatment efficiency was described in six (15%) of the included studies
298
(Supplement D). There was little commonality in the approaches taken (refer to Supplement D). 13
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3.3.4 Dose-response models
300
Dose-response models and parameters, where stated, followed literature precedents
301
(Supplement E). Except for nine instances, the probability of infection for a given dose was
302
found using either the exponential or Beta-Poisson approximation models (Haas et al., 1996;
303
Haas et al., 2014). For the nine other cases, the exact Beta-Poisson model (Nilsen & Wyller,
304
2016; Schijven et al., 2015; Teunis & Havelaar, 2000) was used in six cases and the low-dose
305
approximation method (WHO, 2016) was used in three cases.
306
3.3.5 Estimation of population health risk
307
Almost half (n = 18; 46%) of the included studies estimated population disease burden as the
308
health risk outcome. The remaining studies (n = 21; 54% of the included studies) focused on the
309
probability of infection. Population disease burden was found by accounting for the conditional
310
events of disease aetiology following infection (main assumptions summarised in Supplement
311
F). These events include the probability of illness given infection, the fraction of the population
312
susceptible (used in some cases), and the disease burden per case of illness, all of which are
313
multiplied with the result of the dose-response model.
314
Notably, of the studies that incorporated population susceptibility (n = 8; 21% of the
315
included studies), two distinct approaches were taken. The first usage type (occurring in six
316
[15%] of the included studies) accounted for susceptibility in the exposed population based on
317
factors such as life stage and immune status. The second usage (occurring in two [5%] of the
318
included studies) accounted for a fraction of the population not exposed (either geographically,
319
relative to the total population in a region, or temporally, accounting for extended periods of
320
supply interruption).
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3.3.6 Reference levels of risk
321
322
The included studies compared QMRA results to nine distinct reference levels of risk (Figure 4).
323
All but four (10%) of the included studies used the annual probability of infection of less than
324
10-4 or the annual disease burden of less than 10-6 disability-adjusted life years (DALY) person-1
325
year-1 or both (Figure 4). Two studies expressed the 10-6 DALY person-1 year-1 target in terms of pathogen log-
326 327
reduction values (Krkosek et al. (2016); Sokurenko (2014)) (Figure 4). Reported log-reduction
328
values represented the required treatment efficiencies for Cryptosporidium, Giardia, and
329
viruses, based on source water risk, for assurance that the health target was met.
330
Daily reference levels of risk were used to account for acute, extreme events which may
331
otherwise be offset in the annualisation of risk (Figure 4). They were used by Smeets et al.
332
(2008), Taghipour et al. (2019), and Tolouei et al. (2019). The probability of infection of 2.74 ×
333
10-7 per day and the disease burden of 2.74 × 10-9 per day were used, based on the 10-4 and 10-6
334
annual targets, respectively, being divided into constituent days. Adherence to this daily level
335
over a year will result in the annual target being met. The probability of infection of 365 × 10-4
336
per day was also used by Smeets et al. (2008) to represent the annual target of 10-4 being
337
breached within one day.
338
Two studies used less stringent targets, modifying for local context (Figure 4). Bartak et
339
al. (2015) chose levels that corresponded to the national and regional diarrhoeal burden, and
340
Machdar et al. (2013) used the targeted annual burden of disease of less than 10-4 DALY person-
341
1
342
(2011).
year-1, on the basis that this target may be more realistic for the setting, as discussed by WHO
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3.3.7 Incorporation of high-risk events
343
344
High-risk events were represented in many of the included studies. They often related to
345
contamination events in source water, perturbances to water treatment operations, or acute cases
346
of network ingress.
347
Rainfall runoff leading to acute source water contamination was examined by numerous
348
authors and was generally found to be highly influential on estimated health risk. For example,
349
Signor et al. (2007) found that such events (in this case occurring for 14 per cent of a year) were
350
attributable to most of the annual infection risk for the reference pathogens Cryptosporidium
351
and Giardia. The case study by Smeets et al. (2008) showed that annual infection risk was
352
dominated by the top 3 to 10 per cent of the highest monitoring results for Campylobacter,
353
thought to occur due to introduction of faecal contamination from local wildlife, especially after
354
rainfall.
355
The effect of wastewater discharge events upstream of drinking water abstraction was
356
also a topic of investigation. Tolouei et al. (2019) estimated that wastewater treatment bypasses
357
(as well as rainfall-induced contamination from unquantified faecal sources) led to a 1-log10
358
increase in health risk. Similarly, Taghipour et al. (2019) assessed the effects of acute overflows
359
of combined stormwater and sewer upstream of drinking water treatment abstraction points,
360
finding that the health risk of the plant closer to discharge points was more influenced by the
361
number of overflow events. Importantly, Åström et al. (2007) estimated a reduction in annual
362
health risk resulting from contemporaneous knowledge and active avoidance of such events.
363
The result was greater than could be achieved through relying on the microbial analysis of
364
intake water.
365
The ongoing stability of drinking water treatment processes was found to be an
366
important factor affecting health risk. In further analysis of data presented by Howard et al.
367
(2006), Hunter et al. (2009) found that the annual health benefits attributable to improved
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drinking water supply are almost entirely lost by a few days’ consumption of untreated water.
369
Xiao et al. (2012) found that performance of water treatment unit processes most affected health
370
risk, with the stability of the filtration process having the greatest impact. In a potable water
371
recycling case study by Ander and Forss (2011), typical water treatment failure modes and rates
372
(deemed realistic) were found to contribute to an increase in health risk. Similarly, treatment
373
failure scenarios analysed by Hamouda et al. (2016) led to estimated levels of risk above the
374
tolerable level.
375
van Lieverloo et al. (2007) performed the only analysis of acute network ingress events,
376
finding relatively high associated risk, though this was subject to substantial uncertainties. Other
377
studies accounted for network ingress in effect by including monitoring of the distribution
378
system (e.g. George et al. (2015)), though this may have accounted for baseline recontamination
379
rather than acute, high-risk disturbances in the distribution network.
380
3.3.8 Model interfaces
381
Six (15%) of the included studies described QMRA directly interfacing with other models
382
(Table 2). Ander and Forss (2011) used fault tree analysis to define water treatment failure
383
scenarios which then defined part of the problem formulation under QMRA. Health risk
384
estimates for each scenario were used to prioritise improvement across the fault causes.
385
Machdar et al. (2013) used QMRA output as an input to economic models, with health risk
386
estimates informing cost-effectiveness analyses. A range of engineering and programmatic
387
interventions were examined, using DALY gained as the numerator and a monetary
388
denominator. Sokolova et al. (2015), Taghipour et al. (2019), and Tolouei et al. (2019) each used
389
hydrodynamic models to estimate point-source inputs relevant to source water pathogen loading
390
for QMRA. Baseline conditions and loading events from rainfall and wastewater discharge
391
events upstream of the drinking water treatment plant were modelled. Swaffer et al. (2018)
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modelled associations between livestock stocking characteristics and pathogen load which
393
informed the assumptions used in the exposure assessment.
394
3.3.9 Approaches to sensitivity analysis
395
Nine (23%) of the included studies described the relative influence of some or all QMRA input
396
factors on health risk estimates through formal approaches to sensitivity analysis (Table 3).
397
Source water pathogen load and treatment efficiency were frequently reported as of highest
398
influence on health risk estimates. The test most prevalent (n = 6 uses; 67% of the studies
399
performing formal sensitivity analysis) was the ranked correlation coefficient. Other methods
400
used were differential analysis, factor sensitivity coefficient, and the pairwise comparison of
401
factors using surface plots (n = 1 for each approach respectively; 11% each respectively of the
402
studies performing formal sensitivity analysis).
403
Less-formal approaches for testing the relative impact of changes to assumptions were
404
also used in numerous included studies (summarised in Supplement D). However, there was
405
inconsistency in associated terminology (some were identified as ‘sensitivity analysis’ and some
406
were not) and there was a greater subjectivity in the rationales for performing these analyses
407
(each answering a distinct research question).
408
3.3.10 Risk of bias
409
The included studies did not commonly discuss the risk of bias. Nevertheless, biases were
410
introduced in the assumptions and measurements made, including in the selection of reference
411
pathogens and exposure routes (Table 1) and in the assumptions made for the: design of the
412
monitoring campaign, pathogen-indicator and -surrogate ratios, volume consumed, and other
413
exposure factors (Supplement D); dose-response (Supplement E); and health effects
414
(Supplement F). None of the included studies validated results against surveillance outbreak
415
data, though almost none represented declared outbreak scenarios.
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3.4 Results of secondary review objective – Studies’ reported results and
416
representation of development settings
417
3.4.1 Included studies’ reported population health risk
418
419
With respect to the secondary study objective, the included studies’ reported health risk
420
estimates varied in their adherence to the respective health-based target selected. Included
421
studies’ key results are summarised (Supplement G). Twenty-four (62%) of the included studies
422
provided results in clear numerical form, allowing for comparison of results across the studies.
423
Results of studies using the two most common reference levels of risk, 1 in 10,000
424
annual probability of infection and disease burden of 10-6 DALY person-1 year-1, were
425
benchmarked in Figure 5 and Figure 6, respectively. Because these reference levels are
426
approximately equivalent, exposure scenarios that very clearly adhere (or very clearly do not
427
adhere) to their respective reference level are likely to have a similar outcome when expressed
428
in the alternate metric (notwithstanding that the metrics are subject to important differences
429
arising from disease severity weightings and other factors relating to health effects).
430
Of the studies providing numerical results, a total of 101 scenarios involving a distinct
431
combination of exposure pathway and reference pathogen were identified (Figure 5 and Figure
432
6). Of these scenarios, Cryptosporidium was the reference pathogen that most often exceeded
433
selected reference levels of risk based on median result (n = 21 scenarios; 21% of the analysed
434
scenarios), though it also the most frequently used per study (section 3.3.2). E. coli and Giardia
435
were the next highest (n = 11 [11% of those analysed] and n = 10 [10% of those analysed]
436
scenarios, respectively). All other reference pathogens accounted for the remaining eight
437
scenarios (8% of the analysed scenarios) where median health risk exceeding selected reference
438
levels.
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3.4.2 Development settings and reported population health risk
439
440
The representation of study localities’ human development (categorised by UNDP (2018) as
441
‘very high’, ‘high’, ‘medium’, and ‘low’) was significantly different to what would be expected
442
if representation followed categories’ relative proportion of world population (exact multinomial
443
test, p < 0.001). Very-high human development settings were significantly over-represented
444
(expected: 0.19; observed: 0.59; p < 0.001) and medium human development settings were
445
significantly under-represented (expected: 0.37; observed: 0.15; p = 0.005).
446
In both Figure 5 and Figure 6, studies were shown in descending order of the human
447
development index of the study locality (according to UNDP (2018)). The studies incorporated
448
at the top of Figure 5, including those from Mohammed and Seidu (2019) to Sharaby et al.
449
(2019), were considered as being set in localities classified as of very high human development.
450
Of the studies that used annual disease burden (Figure 6), only the first listed study (Sokurenko
451
(2014)) was set in a locality classified by UNDP (2018) as of very high human development.
452
The remainder of the studies presented in Figure 5 and Figure 6 were set in locations of high to
453
low human development. A trend is broadly observable suggesting the included studies’ results
454
are consistent with an inverse relationship between development context and estimated health
455
risk.
456
4 Discussion
457
When considering variation in the application of assumptions and impact on QMRA concerning
458
the supply of drinking water, the available scientific and academic literature currently identifies
459
that: (i) the current range of approaches taken for QMRA implementation is broad; (ii) the
460
complexity of study design varies and does not appear to be related to what is required to judge
461
whether the water supply is safe; and (iii) while the approaches taken in the inclusion of
462
location-specific empirical data varies, norms have emerged in health effects assumptions,
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especially for the selection of dose-response models. Trends in the relative importance of
464
QMRA inputs emerged, as reflected in the included studies’ formal sensitivity analyses and in
465
study designs.
466
Though all included studies examined the microbial safety of respective drinking water
467
supplies, approaches taken for QMRA implementation were broad with respect to overall study
468
designs and the assumptions used in risk calculation. Variation in study design was especially
469
evident in the derivation of pathogen dose applicable to exposure. For instance, in studies that
470
considered it, source water pathogen concentration was derived using either pathogen
471
monitoring results or through estimation of the same with microbial indicator results. The latter
472
approach then involved either location-specific pathogen-surrogate ratios or assumed ratios
473
derived from literature. Similarly, in studies that considered it, the efficiency of pathogen
474
removal through water treatment processes was based on either observed surrogate reduction or
475
values assumed from literature. Many included studies also considered the impact of high-risk
476
events, with study designs focused accordingly. On a broader scale, variation in study design
477
was demonstrated through the QMRA model interfacing with a diverse range of other modelling
478
approaches, including taking inputs from external models in relation to pathogen dose and
479
QMRA outputs serving as an input to economic analysis (Section 3.3.8). Overall, the
480
demonstrated variation in approaches taken indicates that the QMRA method is highly flexible.
481
This is an especially beneficial quality for the assessment of drinking water supplies. It provides
482
opportunity for even scarce amounts of available data (when supplemented with default
483
literature values) to be used beneficially, albeit in recognition of potential error and biases that
484
may apply.
485
There was also substantial variation in the degree to which the use of location-specific
486
data weighed with the use of default assumptions (summarised in Supplements D through F).
487
With respect to the use of default literature values, strong norms have emerged in the selection
488
of dose-response models (and other factors in the health effects assessment stage). It seems
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likely that this is due to the limited availability of existing models and a lack of new primary
490
data to do otherwise. Other than the volume of exposure, there was little consistency across
491
studies in the use of location-specific or assumptions for other factors (e.g. treatment efficiency
492
and other major assumptions for dose). Though the current review was purposefully focused on
493
examining only studies with site-specific microbial monitoring data, QMRA studies can also be
494
performed using assumptions entirely sourced from literature. This can be considered as
495
‘screening-level’ QMRA. While this approach can appropriately serve the purpose of
496
delineating cases of clearly acceptable or clearly unacceptable risk, location-specific data are
497
likely the first major area for attention in cases where greater precision is needed.
498
In general, there was an observed mismatch between study complexity and the purpose
499
being served. It was not necessarily the case that increased study complexity increased the level
500
of certainty in outcomes. Some of the studies highly complex in statistical approach still
501
provided results with a high degree of uncertainty (as evidenced by the size of the error bars in
502
Figure 5 and Figure 6). Further, in some cases, the level of detail presented in studies did not
503
align with the level of expected risk outcome. This was evidenced in the level of detail provided
504
in studies’ QMRA assumptions (Supplement D) and in the sophistication of statistical
505
approaches taken. Some of the most complex study designs returned some of the lowest risk
506
estimates in systems that would be expected to perform well based on human development
507
status. Conversely, some of the simplest approaches were used on systems in lower human
508
development settings and were sufficient to identify poor risk outcomes. This could be due to
509
the relative ease of dealing with the fewer non-detect data in higher-risk systems. It is not clear
510
which approach is the most suitable, but it is reasonable to suggest that simpler approaches
511
would be more readily taken up by industry, with more complex approaches being warranted for
512
situations where risk outcomes are ‘borderline’ (e.g. where uncertainty bounds cross the
513
reference level of risk). Overall, there is a need for a consensus position to be established on
514
balancing complexity against accessibility and uncertainty.
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Based on the included case studies’ formal sensitivity analyses, factors relating to
516
pathogen dose were consistently of high importance to QMRA risk estimates. This finding
517
supports the notion that locally-appropriate information on pathogen risk is important (Petterson
518
et al., 2015b; WHO, 2016) and is a key reason for including this as an acceptance criterion in
519
the study. While water suppliers generally have water quality data at their disposal, their usage
520
in QMRA must be carefully considered. The reason for this was clearly shown, in quantitative
521
terms, by Åström et al. (2007), where a selective source water abstraction protocol was shown
522
to markedly alter the risk profile. Such an effect might easily be missed in the design of a water
523
quality monitoring program. Further, capturing such impacts may simply not be the intention of
524
past and present monitoring programs (e.g. a program may intend to characterise the source
525
generally rather than specifically characterise the untreated water as supplied to a treatment
526
plant). This strongly suggests that a water quality monitoring program addressing the specific
527
needs of QMRA, i.e. to appropriately characterise risk, is ideal.
528
Included studies’ formal sensitivity analyses also indicated that the impact of the dose-
529
response parameters tended to be relatively low. Despite this, it is possible that improvement in
530
these models would provide far-reaching benefit due to their largely consistent adoption in
531
QMRA implementation. QMRA fundamentally deals with the modelling of very low doses
532
across large populations. Because of this, existing dose-response models rely on the
533
extrapolation of dose-response relationships to levels far below what was empirically measured
534
in the original clinical studies (Haas, 1983; Teunis & Havelaar, 2000). While this limitation can
535
be overcome by validating QMRA model performance against public health surveillance data,
536
validation can only occur retrospectively and for substantial outbreaks for which data are
537
available. This approach is also limited by doses being poorly quantified and cases being
538
difficult to follow (WHO, 2016). Nonetheless, QMRA has been validated, at least in a general
539
sense, through retrospective analysis of epidemiological data (Burch, 2019; Haas et al., 2014;
540
Haas et al., 2000). These validation exercises have included outbreaks with mean doses of less
23
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than one pathogen per dose, thereby providing supporting evidence for the ‘single hit’ theory
542
underlying the ability to extrapolate the relationships to low dose levels.
543
As demonstrated through its exclusion from almost all study designs, direct testing of
544
pathogens in treated drinking water was not used to inform the estimation of health risk in many
545
circumstances. Though potentially counterintuitive, this is due to the sensitivity and physical
546
constraints inherent to microbiological analyses. Under WHO (2011) assumptions, a single
547
sample result equivalent to the targeted health outcome of 10-6 DALY person-1 year-1 is one
548
infective Cryptosporidium oocyst in ca. 7.9 × 104 L sample volume and is in the same order of
549
magnitude for other reference pathogens. This is well beyond current analytical capabilities.
550
Thus, unless there is severe contamination present, treated water data generally lack the power
551
to meaningfully contribute to QMRA. They may also be insufficient for the consideration of
552
network ingress risks in otherwise satisfactory systems. Generally, data that are uninformative
553
or weakly informative should be avoided in experimental design (Schmidt et al., 2019) and thus
554
other methods, such as QMRA, are needed for estimating the effect of treated water events that
555
are difficult to detect yet of high impact. Similarly, the method used to infer microbial loadings
556
that are below the analytical limit of detection could impact on the risk estimate. In this regard,
557
Smeets et al. (2007) demonstrated a 0.6-log10 variation in risk estimate depending on method
558
selected (i.e. substituting non-detects with zero, log-linear extrapolation, and substituting non-
559
detects with the limit of detection). There are numerous academic papers addressing this issue
560
(e.g. Canales et al. (2018); Chik et al. (2018); Schmidt et al. (2013)) yet there remains a lack of
561
consensus on how results below the limit of detection should be treated.
562
The interpretation and statistical treatment of laboratory analytical results was focused
563
on in many of the included studies and is represented strongly in other QMRA literature. To
564
support the accuracy of risk estimation, measurement errors must be considered when dealing
565
with microbial enumeration methods that involve losses or counting errors (Schmidt & Emelko,
566
2011). Analytical considerations for the reference pathogen most seen in this review,
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Cryptosporidium, were reported with varying consistency. For example, Cryptosporidium
568
counts used in QMRA should ideally be adjusted for assay recovery (Petterson et al., 2007) and
569
represent viable, human-infective oocysts (Schmidt & Emelko, 2011; Swaffer et al., 2018;
570
WSAA, 2015). Recovery efficiency accounts for the unrecovered fraction of oocysts by
571
adjusting for the loss of known quantities of a positive control (e.g. Warnecke et al. (2003)),
572
with sample-specific recovery ideal (Schmidt & Emelko, 2011; WSAA, 2015). The identity
573
confirmation step rules out organisms that are morphologically similar to Cryptosporidium
574
oocysts. Recognised methods include enhanced characterisation of internal morphology through
575
vital stains and the use of differential interference contrast or phase contrast microscopy
576
(Grimason et al., 1994; Smith et al., 2002). Enumerating viability is an important further step to
577
distinguish between live and dead (or fatally injured) oocysts (Petterson et al., 2015b) and can
578
be estimated through cell culture infection assay (Johnson et al., 2012). Similar considerations
579
apply to other reference pathogens (e.g. as described by Dechesne and Soyeux (2007);
580
Lalancette et al. (2012); Lapen et al. (2016); Petterson et al. (2015a); Regli et al. (1991);
581
Schmidt et al. (2013); WHO (2016)). In total, these steps were not frequently described in the
582
included literature. If they were not performed, the appropriate estimation of risk is impacted
583
(Dechesne & Soyeux, 2007; Petterson et al., 2015b; WSAA, 2015).
584
Population characteristics and behaviours were found to affect the estimated severity of
585
health risk (in the studies which analysed these factors), highlighting the importance of centring
586
QMRA on the population exposed. This is especially relevant in less-developed settings, where
587
the exposure routes can be more complex and pathogen loadings potentially higher. It might be
588
less important in more developed settings, where nearly the entire population of a water supply
589
system service area can be expected to be exposed via direct consumption at relatively similar
590
rates. The difference in development context and its relevance on population characteristics and
591
behaviours manifested in the design of the exposure assessment in the included studies. Most
592
studies examining less-developed settings accounted for changes to the volume of water
593
consumed in the respective location (Supplement D), which varied for varying reasons. Distinct 25
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health behaviours that represented materially different risks were also important in defining risk
595
in some cases, such as the intentional consumption of tap water (in the absence of point-of-use
596
treatment) and the incidental exposure to untreated tap water through actions such as
597
toothbrushing, as modelled by Xiao et al. (2012) and Xiao et al. (2013). In both studies, these
598
exposure routes contributed significantly to overall health impact. Similarly, accounting for the
599
immunocompromised subpopulation was influential on overall risk (Xiao et al., 2012).
600
Inconsistencies in terminology hindered the comparability of the included studies and in
601
places may have led to the underestimation of risk. The most notably confused term was the
602
‘susceptible fraction’. Recognised QMRA guidance (e.g. WHO (2016)) uses this factor to
603
account for the proportion of susceptibility in exposed populations based on factors such as life
604
stage and immune status. Six of the eight included studies that included the susceptible fraction
605
did so in accordance with this recognised purpose (Section 3.3.5), though only three used a non-
606
unity assumption (Howard et al. (2006), Shamsollahi et al. (2019), and Xiao et al. (2012)).
607
However, two studies used this factor to account for fractions of the respective study
608
populations not exposed, either geographically or temporally. The adjustment for geographic
609
non-exposure accounted for consumers not being supplied by the water supply systems
610
examined. The issue would have been appropriately addressed by more clearly defining the
611
exposed population during the QMRA problem formulation stage (e.g. by performing individual
612
assessments for the separate water supply systems considered). The adjustment for temporal
613
non-exposure accounted for interruptions to access to the water supply. The desired effect would
614
have been achieved had the relevant exposure frequency be inputted during annualisation rather
615
than using the default value of 365. The effect of either issue involves potential underestimation
616
of annual population health risk.
617
The majority of studies included in the current review assessed the performance of
618
water supply schemes against benchmarks for either the annual risk of infection or annual
619
disease burden set in normative guidance (e.g. Health Canada (2019); NHMRC (2018); USEPA
26
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(2006); WHO (2011)) (Section 3.3.6). This raises a pressing policy matter of interpreting annual
621
microbial health-based targets when considering acute events. The studies included in the
622
current review demonstrated that the public health risk posed by water supply is highly sensitive
623
to relatively extreme (though typically acute and infrequent) events in source water, treatment
624
perturbances, and network ingress events rather than the low, endemic loading understood to
625
occur as a function of the logarithmic reduction of pathogens during normal water treatment.
626
The use of the annualisation process attenuates estimated risk levels where a substantial but
627
temporally acute event occurs. Critically, this could lead to intolerable scenarios being
628
unrecognised as such. It is possible, therefore, that annual metrics may not be the most suitable
629
approach. This consideration was addressed in several of the included studies through the use of
630
acute reference levels of risk adapted from recognised annual levels (Section 3.3.6). While
631
meeting one-365th of the annual level every day for a year has been considered a stricter goal
632
than simply meeting the corresponding annual target (Sokolova et al., 2015; Taghipour et al.,
633
2019), it is possible that very short-term events can dominate the yearly average risk (Smeets et
634
al., 2010). Accordingly, acute (or dose-based) reference levels of risk might be a suitable future
635
approach. It is suggested that policy-makers consider the discourse emerging on acute risk in the
636
future setting of normative guidelines. The need for water suppliers to plan for and promptly
637
respond to acute events is also strongly reinforced by the review presented.
638
There are few examples in current literature which strongly focus on risk as experienced
639
by the population exposed at the point of use. This is not unexpected, as normative guidance
640
(e.g. WHO (2016)) is relatively less developed with respect to the consideration of distribution
641
system risk in QMRA. Highlighting this, only one of the included studies set in developed
642
contexts assessed hazards introduced subsequent to water treatment, van Lieverloo et al. (2007),
643
where distribution system ingress was the central research question. Only two studies set in less-
644
developed contexts performed risk assessment for the distribution system (George et al. (2015);
645
Howard et al. (2006)) despite a higher occurrence of network integrity issues expected in these
646
settings. Further, though it was often not explicitly stated, many of the included studies 27
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647
performed retrospective analysis of monitoring data routinely collected under water safety plans
648
(verification monitoring results). This introduced substantial contextual limitations in many
649
cases. Ideally, a converse approach would be taken, where representativeness of data for the
650
purpose of QMRA is prioritised in the design of water quality monitoring programs. Where
651
resourcing permits, consideration of the entire exposure route (e.g. source water, treatment, and
652
distribution) should be made in the development of the QMRA problem formulation, with site-
653
specific monitoring to support this. Further research is likely required to understand how best
654
this monitoring should inform QMRA.
655
Biases undoubtedly affected all the included studies. As QMRA is an artificial
656
simplification of natural processes, sources of uncertainty are the clearest form of bias.
657
However, systematic, comprehensive QMRA uncertainty analysis is generally lacking. The
658
types of uncertainty inherent to the technique are numerous and can be considered as
659
comprising the dimensions of location (including contextual, model, and data uncertainty),
660
nature (including epistemic and ontic uncertainty), range (statistical and scenario uncertainty),
661
methodological unreliability, and value diversity amongst practitioners (Supplement B). While
662
many of the included studies addressed some aspects of this typology, usually implicitly, none
663
did so with a high degree of systematisation. Similarly, in literature, prevalent discussion on the
664
uncertainties arising from water supply QMRA focuses strongly on specific, limited aspects of
665
the typology, such as data uncertainty arising from analytical limitations of pathogen
666
enumeration methods. Other areas of uncertainty are described less frequently; for example,
667
system-specific epistemic and scenario uncertainties and value diversity amongst practitioners
668
have generally received little attention to date and may similarly bias results.
669
With reference to the secondary objective of reviewing included studies’ reported
670
adherence to health-based targets, there were mixed results. A number of included studies
671
returned health risk estimates that can be considered as being of ‘borderline’ adherence to the
672
relevant reference level of risk. As most assumptions used in QMRA are conservative, the
28
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673
question is raised of whether borderline intolerable results should result in the water supply
674
being deemed ‘unsafe’. Petterson and Ashbolt (2016) highlighted that policy and decision-
675
making by regulators and utilities depends on QMRA risk estimates being artificially drawn as a
676
thin line where in truth the result is ‘fuzzier’ due to the uncertainties involved. Accordingly,
677
improved reporting on QMRA generally, as well as the reduction, better assessment, and
678
reporting of uncertainties, are important goals if the technique is to be further appreciated as
679
valid and useful. To this end, future authors should consider reporting on QMRA assumptions in
680
accordance with Supplement C.
681
The reported health risks can, to an extent, be explained by the human development
682
status of the settings represented in the included studies. Those studies undertaken in settings
683
with higher human development indices tended to present a favourable estimated health risk
684
compared to those in less-developed settings. This occurred with one major exception, the direct
685
potable reuse scheme in Windhoek, Namibia. While classified as an area of low human
686
development status, its good system performance based on estimated annual risk of infection
687
likely reflects the high level of operational capability and increased international investment
688
placed in this direct potable water recycling scheme. Nevertheless, the results are consistent
689
with the estimates by WHO and UNICEF (2017) that showed in 2015 up to one quarter of the
690
world population did not have access to an improved water supply free from contamination,
691
with developing countries most affected. Of the reviewed literature, case studies set in very high
692
human development settings were found to be significantly overrepresented compared to the
693
proportion expected based on world population (Section 3.4.2). Further, the proportion of the
694
population serviced by public water supplies is likely to be smaller in settings that are
695
categorised other than very high human development, and thus these settings may have been
696
more likely to fall outside of the scope of this review.
697 698
The current review should be read in context of the limitations of the included studies and of the review method. The identification of issues for narrative synthesis is, at least in part,
29
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699
a subjective process (Popay et al., 2006). Identification of influential factors on risk estimation
700
was informed by included studies’ formal sensitivity analyses and though commonalities
701
emerged, generalisability is not known. Additionally, specific localities and key research groups
702
were strongly represented, which may have biased the representation of approaches taken to
703
QMRA implementation. Similarly, the study inclusion process is almost certainly impacted by
704
publication bias. By including only peer-reviewed literature and academic theses, it is accepted
705
that valuable analyses and perspectives are likely to have been missed (Rothstein et al., 2005).
706
This is especially relevant for a method intended for routine use in professional practice.
707
However, by its nature, peer-reviewed and scientific literature represents the breadth of current
708
scientific knowledge. As such, the review scope remained consistent with the main objective of
709
the study, to examine the current state of approaches to QMRA implementation.
710
The strength of the current review is that it followed recognised methods for systematic
711
review. The approach included a transparent and repeatable search strategy, and study inclusion
712
being performed by two independent reviewers. Nevertheless, the review was constrained by
713
substantial differences in approaches for QMRA implementation and reporting. Accordingly, a
714
quantitative meta-analysis was not able to be performed. Instead, individual study results were
715
transformed into a common rubric, as forest plots, which provided a sense for overarching
716
trends. Such an approach aids the reader in developing a meaningful summary of study results
717
even in the absence of quantitative meta-analysis (Popay et al., 2006). To date, review of the
718
implementation of the QMRA technique has mainly occurred through narrative discourse (e.g.
719
Haas (2002); Haas et al. (2014); Hamouda et al. (2018); Medema et al. (2003); Petterson and
720
Ashbolt (2016); Smeets (2019); Smeets et al. (2010)). This systematic review has offered a
721
further perspective.
30
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722
5 Conclusion
723
This review showed that QMRA has been used in a highly adaptive manner in order to suit
724
available data, a critical quality that allows for the customisation of risk assessment to local
725
conditions and available resources. However, this quality has also inadvertently led to difficulty
726
in interpretation of results and hindered the comparison of case studies.
727
Further work is needed to improve the accessibility and robustness of QMRA results.
728
Most salient in the current review is improvement in the way by which QMRA results and
729
uncertainties are reported. While the assumptions used in QMRA do not necessarily need to be
730
standardised (as study designs vary widely), authors and policy-makers could consider a degree
731
of standardisation in QMRA reporting. It is recommended that future authors consider the water
732
supply QMRA reporting checklist (Supplement C), developed for the current study, as a basis
733
for reporting case study detail. Consideration of broad aspects of uncertainty, such as those
734
contemplated in the described uncertainty typology (Supplement B), is also suggested. Policy-
735
makers should consider the application of health-based targets to circumstances involving acute
736
risk in recognition of emerging scholarly discourse.
737
Further research is needed on issues relevant to QMRA implementation. Specifically,
738
case studies exploring the optimisation of QMRA resourcing given the application context
739
would be beneficial. The continued representation of QMRA case studies within the peer-
740
reviewed and academic literature is also desired so as to furnish further novel approaches for
741
broader industry implementation.
742
Author contributions
743
C. Owens conceptualised the study, designed and performed all analyses, interpreted the results,
744
and drafted the manuscript. M. Angles was the second reviewer. All authors provided important
745
intellectual input and critically reviewed the manuscript. 31
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746
Acknowledgements
747
This work was supported by an Australian Government Research Training Program Scholarship.
748
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47
Table 1 Summary characteristics of the quantitative microbial risk assessment case studies for distributed public drinking water supplies published from 2003 to October 2019 No
Author
Locality
Pathogen treatment train
Summary exposure route
Reference organism
Reference level of risk
1
2
Ander and Forss (2011) Windhoek, Namibia
Åström et al. (2007)
Gothenburg, Sweden
Co, DAF, F, O3, BAC, GAC, UF,
Treated wastewater → surface water →
Cryptosporidium
Cl2
treatment → ingestion
Giardia
Selective abstraction, Flo, S, Cl2,
Surface water → selective abstraction →
Cryptosporidium
GAC, Cl2 + ClO2
treatment → ingestion
Giardia
inf,a
< 10-4
inf,a
< 10-4
Enterovirus Norovirus 3
Bartak et al. (2015)
Haridwar, India
RBF, Cl2
Surface water → treatment → ingestion
E. coli O157:H7
< 5.33 × 10-3 < 2.18 × 10-2
4
5
Bastos et al. (2013)
Bataiero et al. (2019)
Viçosa, Brazil
Brazil
Co, Flo, S, F, Cl2
Co, Flo, Flot, F, Cl2
Surface water → treatment → ingestion
Surface water → treatment → ingestion
Cryptosporidium
Cryptosporidium Giardia
inf,a
< 10-4
inf,a
< 10-3
inf,a
< 10-4
No
Author
Locality
Pathogen treatment train
Summary exposure route
Reference organism
Reference level of risk
6
Bichai et al. (2011)
Amsterdam, Netherlands
Two plants:
Surface water → treatment → ingestion
Campylobacter jejuni
inf,a
< 10-4
inf,a
< 10-4
(internalised in zooplankton)
Co, S, F, O3, GAC, F
E. coli (internalised in
F, O3, GAC, F
zooplankton) 7
8
Derx et al. (2016)
Elliott (2015) a
Danube River floodplain, RBF, Cl2
Surface water → AI → treatment →
Enterovirus
Austria
ingestion
Norovirus
Treatment → ingestion
Cryptosporidium
Canada
Ten plants: 4 × Co, Flo, S, F, Cl2
Giardia
Co, Flo, S, F, Cl2, UV
Campylobacter
F, Cl2, UV UF, Cl2 UF, Cl2, UV Co, Flo, S, F, O3, UV Cl2, O3, UV
E. coli O157 Rotavirus
< 10-6
No
Author
Locality
Pathogen treatment train
Summary exposure route
Reference organism
Reference level of risk
9
George et al. (2015)
Mysore, India
Two plants: 2 × Co, Flo, S, F, Cl2
Surface water → treatment → distribution
Campylobacter
→ ingestion
Pathogenic E. coli
< 10-6
Rotavirus
10
Hadi et al. (2019)
Tehran, Iran
Co, Flo, S, F, Cl2
Surface water → treatment → ingestion
Cryptosporidium
< 10-6 inf,a
11
Hamouda et al. (2016)
Brantford, Canada
Co, SBC, O3, F, UV, Cl2
Surface water → treatment → ingestion
Cryptosporidium
< 10-4
< 10-6
Giardia Campylobacter E. coli O157 Rotavirus 12
13
Howard et al. (2006)
Irda Sari et al. (2018) a
Kampala, Uganda
Bandung, Indonesia
Two plants:
Surface water → treatment → ingestion
C. parvum
F, Cl2
E. coli
Co, Flo, S, F, Cl2
Rotavirus
Not stated
Treatment → ingestion
Faecal coliforms
< 10-6
inf,a
< 10-4
inf,a
< 10-3
No
Author
Locality
Pathogen treatment train
Summary exposure route
Reference organism
Reference level of risk
14
Jaidi et al. (2009)
St Lawrence River, North Two plants: America
Surface water → treatment → ingestion
Cryptosporidium
inf,a
< 10-4
Giardia
F, Cl2 F, O3, Cl2
15
Katukiza et al. (2014) a
Kampala, Uganda
Not stated
Treatment → ingestion
E. coli O157:H7 Salmonella
< 10-6 inf,a
< 10-4
Adenovirus Rotavirus 16
Krkosek et al. (2016)
Nova Scotia, Canada
Seven plants:
Surface water → treatment → ingestion
Cryptosporidium
DAF, F, Cl2 b
Giardia
DAF, F, Cl2 b
E. coli
Co, Flo, S, UF, Cl2 UF, Cl2 Co, Flo, S, F, Cl2 UF, NF, UV, Cl2 b UF, NF, UV, Cl2 b
< 10-6
No
Author
Locality
Pathogen treatment train
Summary exposure route
Reference organism
Reference level of risk
17
Machdar et al. (2013) a
Nima, Accra, Ghana
Not stated
Distribution → ingestion
Campylobacter
< 10-4
E. coli O157:H7 Cryptosporidium Rotavirus 18
Mohammed and Seidu
Norway
(2019)
Ålesund: Cl2, F, UV
Surface water → treatment → ingestion
Campylobacter
Oset: Co, Flo, F, UV, Cl2
Cryptosporidium
Svartediket: Co, F, UV, Cl2
Giardia
inf,a
< 10-4
inf,a
< 10-4
Norovirus 19
Pintar et al. (2012) a
Ontario, Canada
Co, Flo, S, O3, F / GAC, UV, Cl2
Surface water → treatment → ingestion
Cryptosporidium
20
Razzolini et al. (2016)
São Paulo, Brazil
Co, Flo, S, F, Cl2
Surface water → treatment → ingestion
Cryptosporidium
< 10-6
Giardia 21
Rodriguez-Alvarez et
Vaqueros, Argentina
al. (2015) a
22
New plant: Co, Flo, S, F, Cl2
Surface water → treatment → ingestion
Old plant: Co, Flo, S, F, Cl2
Giardia
inf,a
< 10-4
inf,a
< 10-4
Pseudomonas aeruginosa
Bore: Cl2
Groundwater → treatment → ingestion
E. coli
Ryu and Abbaszadegan Phoenix, USA
Two plants, treatment process not
Surface water → treatment → ingestion
Cryptosporidium
(2008)
stated
Giardia
No
Author
Locality
Pathogen treatment train
Summary exposure route
Reference organism
Reference level of risk
23
Sato et al. (2013)
São Paulo, Brazil
Four plants, each involving Co,
Surface water → treatment → ingestion
Flo, S, F, Cl2
Cryptosporidium
inf,a
< 10-4
inf,a
< 10-4
Giardia
24
Schijven et al. (2019)
Rotterdam, Netherlands
Co, Flo, F, UV, ClO2
Surface water → treatment → ingestion
Adenovirus
25
Shamsollahi et al.
Tehran, Iran
Co, Flo, S, F, Cl2
Surface water → treatment → ingestion
Rotavirus
< 10-6
(2019) 26
Sharaby et al. (2019) a
Kiryat Tiv'on, Israel
Not stated
Reticulation → inhalation
Legionella pneumophila
27
Shea et al. (2016)
Western Victoria,
Seven plants:
Surface water → treatment → ingestion
Campylobacter
Australia
4 × DAFF, Cl2
Cryptosporidium
DAFF, NH2Cl
Virus
inf,a
< 10-4 c
< 10-6
S, F, Cl2 MF, NH2Cl 28
Signor et al. (2007)
Adelaide Hills, Australia
Co, Flo, F, Cl2
Surface water → treatment → ingestion
Campylobacter Cryptosporidium Giardia
inf,a
< 10-4
No
Author
Locality
Pathogen treatment train
Summary exposure route
Reference organism
Reference level of risk
29
Smeets et al. (2008)
Amsterdam, Netherlands
AI, F, O3
Surface water → AI → treatment →
Campylobacter
ingestion
30
Smeets et al. (2007)
United Kingdom
216 plants (treatment trains not
inf,a
< 10-4
inf,d
< 2.74 × 10-7
inf,d
< 365 × 10-4
inf,a
< 10-4
Treatment → ingestion
Cryptosporidium
Treated wastewater → surface water →
Norovirus
< 10-6
Cryptosporidium
< 10-6
stated) Eight plants: 5 × Co, S, F, GAC, Cl2 2 × Co, S, F, GAC, O3, Cl2 Co, S, DAF, F, GAC, Cl2 31
Sokolova et al. (2015) d Trollhättan, Sweden
Co, Flo, F, Cl2
treatment → ingestion 32
Sokurenko (2014)
Calgary, Canada
Bearspaw: Co, Flo, S, F, Cl2
Surface water → treatment → ingestion
Glenmore: Co, Flo, S, F, Cl2
Giardia
33
Swaffer et al. (2018)
Adelaide, Australia
Not stated
Surface water → treatment → ingestion
Cryptosporidium
< 10-6
34
Taghipour et al. (2019)
Quebec and Ontario,
Not stated
Combined sewer outfalls → surface water
Cryptosporidium
< 2.74 × 10-9
Canada
→ treatment → ingestion
No
Author
Locality
Pathogen treatment train
Summary exposure route
Reference organism
Reference level of risk
35
Thomas et al. (2015) a
Phnom Penh, Cambodia
Flo, S, F, Cl2
Surface water → treatment → ingestion
Enterotoxigenic E. coli
36
Tolouei et al. (2019)
Toronto, Canada
Co, Flo, S, F, Cl2
Wastewater outfalls → surface water →
Cryptosporidium
treatment → ingestion
E. coli O157:H7
< 10-6 inf,d
< 2.74 × 10-7
inf,a
< 10-4
inf,a
< 10-4
Giardia 37
van Lieverloo et al.
Netherlands
(2007)
Eight water utilities, treatment
Network ingress → distribution →
Campylobacter
trains not stated
ingestion
Cryptosporidium Giardia Enterovirus
38
39
a
Xiao et al. (2013) a
Xiao et al. (2012)
China, cities serviced by
Typical trains assessed: Co, S, F;
Surface water → treatment → ingestion and Cryptosporidium
Three Gorges Reservoir
Co, S, F, O3
incidental intake
Giardia
China, 33 cities
66 systems, including: Con; Con,
Treatment → ingestion and incidental
Cryptosporidium
O3; and Con, MF f
intake
< 10-6
Some exposure routes presented in the original paper fell outside of the scope of this review and were not included; b the modelled process train differed due to model limitations; c
were also incorrectly compared to a
inf,a
results
reference levels of risk; d this case study was also presented in a related thesis by Sokolova (2013); e order of magnitude for this figure was corrected from
the original manuscript to fit within the reported confidence interval; f treatment processes were not identified in greater detail.
Treatment process abbreviations: AI = aquifer infiltration; BAC = biological activated carbon; Cl2 = primary chlorination; ClO2 = chlorine dioxide; Co = coagulation; Con = conventional treatment; DAF = dissolved air flotation; DAFF = dissolved air flotation and filtration; Flo = flocculation; Flot = flotation; GAC = granular activated carbon; MF = microfiltration; NH2Cl = primary chloramination; NF = nanofiltration; O3 = ozonation; RBF = river bank filtration; S = sedimentation; SBC = sand-ballasted clarification; UF = ultrafiltration; UV = ultraviolet irradiation. Reference levels of risk abbreviations: annual probability of illness;
inf,a
= annual disease burden in disability-adjusted life years person-1 year-1;
= annual probability of infection;
inf,d
= daily probability of infection.
= daily disease burden in disability-adjusted life years person-1 year-1;
ill,a
=
Table 2 Summary of QMRA model interfaces No
Author
Model interfaces
1
Ander and Forss (2011)
Fault tree analysis (water treatment process failure modes) → QMRA
17
Machdar et al. (2013)
QMRA → economic model (cost-effectiveness analysis)
31
Sokolova et al. (2015)
Hydrodynamic model (pathogen transport) → QMRA
34
Taghipour et al. (2019)
Hydrodynamic model (pathogen fate and transport) → QMRA
36
Tolouei et al. (2019)
Hydrodynamic model (pathogen fate and transport) → QMRA
33
Swaffer et al. (2018)
Linear mixed-effects model (land-use, stocking, and pathogens) → QMRA
QMRA = quantitative microbial risk assessment.
Table 3 Relative influence of QMRA inputs on health risk estimates as identified through included studies’ formal sensitivity analyses No
Author
Technique
Factors tested in order of reported relative impact on health risk (most–least)
4
Bastos et al. (2013)
ρ
Consumption rate; source pathogen load; raw water turbidity; filtrate turbidity; dose-response parameter; oocyst recovery method; oocyst removal estimate
5
Bataiero et al. (2019) ρ
Source pathogen load; treatment removal; consumption rate; doseresponse parameter
8
Elliott (2015) a
Pairwise
All reference pathogens: filtrate turbidity; source pathogen load
comparison
All reference pathogens except rotavirus:
surface plots
filtrate turbidity; chlorination. UV dose; chlorination Cryptosporidium and Giardia: filtrate turbidity; chlorination Giardia: chlorination; source pathogen load Rotavirus: chlorination (low dose); filtrate turbidity chlorination (low dose); UV dose
18
Mohammed and
Factor
All reference pathogens: source water pathogen load; consumption
Seidu (2019)
sensitivity
rate; dose-response parameters
coefficient 19
Pintar et al. (2012)
ρ
Only the top factor was listed, for each seasonal condition and event scenario: Winter routine and suboptimal scenario: source water pathogen load Summer routine and suboptimal scenario: ozone dose yearly average conditions: water temperature
20
Razzolini et al. (2016)
ρ
Source pathogen load; consumption rate; dose-response parameter
No
Author
Technique
Factors tested in order of reported relative impact on health risk (most–least)
21
Rodriguez-Alvarez et Differential
Pseudomonas aeruginosa and Giardia: source pathogen load;
al. (2015)
consumption rate; dose-response parameter. Escherichia coli: source pathogen load; dose-response parameter; consumption rate
23
Sato et al. (2013) b
ρ
Source pathogen load; consumption rate; dose-response parameter
39
Xiao et al. (2012)
ρ
Filtration stability; source pathogen load; sedimentation; proportion of population who directly consume; coagulation stability; volume directly consumed; case fatality rate for ID; volume of incidental intake; probability of illness given infection for IC; case fatality for IC
a
Results interpolated by the reviewer; b results for ‘all regions’ followed this order, except for the children subpopulation
under alternative left-censoring approaches. Abbreviations: IC = immunocompetent subpopulation; ID = immunodeficient subpopulation; RSF = rapid sand filtration; UF = ultrafiltration; UV = ultraviolet treatment; ρ = rank correlation coefficient test.
Identification
Figure 1 Systematic review selection process
Records (N = 1264) identified from PubMed (n = 296), Scopus (n = 369), Web of Science (n = 405), BASE (n = 194) Duplicates excluded (n = 680)
Screening
Titles and abstracts screened (n = 584)
Records excluded by screening (n = 450)
Eligibility
Full-text assessed for eligibility (n = 134)
Full-text version excluded (n = 95)
Included
Records included in review (n = 39)
Journal articles (n = 35)
BASE = Bielefeld Academic Search Engine.
Academic theses (n = 4)
4 2
2008 1
2009 0
2
Year
2011
2
2012
4
2013 2
2014
6
2015
8
2019
2
2018
0
2017
5
2016
10
1
2007
2010
8
0
2006
6
0
2005
4
0
2004
2
0 2003
Figure 2 Number of included studies by publication year
Number of publications
Figure 3 Reference pathogen uses by publication
Pseudomonas (n = 1; 1%)
Salmonella (n = 1; 1%)
Legionella (n = 1; 1%) Faecal coliforms (n = 1; 1%)
Virus (n = 1; 1%)
Adenovirus (n = 2; 2%) Enterovirus (n = 3; 4%) Norovirus (n = 4; 5%) Cryptosporidium (n = 26; 30%) Rotavirus (n = 7; 8%)
Campylobacter (n = 10; 12%)
Giardia (n = 17; 20%) E. coli (n = 12; 14%)
N = 86.
Figure 4 Risk reference level uses by publication Ba < regional incidence (n = 1; 2%)
Bd < 2.74 × 10⁻ (n = 1; 2%)
Ba < national incidence (n = 1; 2%)
Pinf,d < 365 × 10⁻ (n= 1; 2%)
Ba < 10⁻ DALY person⁻¹ year⁻¹ (n = 1; 2%) Pinf,d < 2.74 × 10⁻ (n = 2; 4%) Pinf,a < 10⁻³ (n = 2; 5%) Pinf,a < 10⁻ (n = 22; 48%)
Ba < 10⁻ DALY person⁻¹ year⁻¹ (n = 15; 33%)
N = 46. Abbreviations: Ba = annual disease burden; Bd = daily disease burden; DALY = disability-adjusted life years; Pinf,a = annual probability of infection; Pinf,d = daily probability of infection.
Figure 5 Estimated infection risk presented in studies using 1 in 10,000 (10-4) probability of infection as reference level of risk, ranked by study locality human development index and grouped by scenario
Mohammed and Seidu (2019) - Ålesund - Campylobacter Mohammed and Seidu (2019) - Ålesund - Cryptosporidium Mohammed and Seidu (2019) - Ålesund - Giardia Mohammed and Seidu (2019) - Ålesund - norovirus Mohammed and Seidu (2019) - Oset - Campylobacter Mohammed and Seidu (2019) - Oset - Cryptosporidium Mohammed and Seidu (2019) - Oset - Giardia Mohammed and Seidu (2019) - Oset - norovirus Mohammed and Seidu (2019) - Svartediket - Campylobacter Mohammed and Seidu (2019) - Svartediket - Cryptosporidium Mohammed and Seidu (2019) - Svartediket - Giardia Mohammed and Seidu (2019) - Svartediket - norovirus Signor et al. (2007) - Campylobacter Signor et al. (2007) - Cryptosporidium Signor et al. (2007) - Giardia Bichai et al. (2011) - zooplankton-internalised C. jejuni Bichai et al. (2011) - zooplankton-internalised E. coli Jaidi et al. (2009) - regulatory credit method Pintar et al. (2012) - Cryptosporidium Ryu and Abbaszadegan (2008) - plant 1 - Giardia Ryu and Abbaszadegan (2008) - plant 2 - Cryptosporidium Ryu and Abbaszadegan (2008) - plant 2 - Giardia Derx et al. (2016) - dry year - enterovirus Derx et al. (2016) - dry year - norovirus Derx et al. (2016) - wet year - enterovirus Derx et al. (2016) - wet year - norovirus Sharaby et al. (2019) - showers - Legionella pneumophila Sharaby et al. (2019) - faucets - Legionella pneumophila Rodriguez-Alvarez et al. (2015) - new plant - E. coli Rodriguez-Alvarez et al. (2015) - new plant - Giardia Rodriguez-Alvarez et al. (2015) - new plant - P. aeruginosa Rodriguez-Alvarez et al. (2015) - old plant - E. coli Rodriguez-Alvarez et al. (2015) - old plant - Giardia Rodriguez-Alvarez et al. (2015) - old plant - P. aeruginosa Rodriguez-Alvarez et al. (2015) - bore - E. coli Rodriguez-Alvarez et al. (2015) - bore - Giardia Rodriguez-Alvarez et al. (2015) - bore - P. aeruginosa Hadi et al. (2019) - Age < 2 - Cryptosporidium Hadi et al. (2019) - Age 2–6 - Cryptosporidium Hadi et al. (2019) - Age 6–16 - Cryptosporidium Hadi et al. (2019) - Age > 16 - Cryptosporidium Bastos et al. (2013) - Cryptosporidium - empirical model Bataiero et al. (2019) - Children - Giardia Bataiero et al. (2019) - Children - Cryptosporidium Bataiero et al. (2019) - Adults - Giardia Bataiero et al. (2019) - Adults - Cryptosporidium Sato et al. (2013) - all regions - adults - Cryptosporidium Sato et al. (2013) - all regions - adults - Giardia Sato et al. (2013) - all regions - children - Cryptosporidium Sato et al. (2013) - all regions - children - Giardia Xiao et al. (2013) - Adv - drinking - Cryptosporidium Xiao et al. (2013) - Adv - drinking - Giardia Xiao et al. (2013) - Adv - incidental - Cryptosporidium Xiao et al. (2013) - Adv - incidental - Giardia Xiao et al. (2013) - Con - drinking - Cryptosporidium Xiao et al. (2013) - Con - drinking - Giardia Xiao et al. (2013) - Con - incidental - Cryptosporidium Xiao et al. (2013) - Con - incidental - Giardia Irda Sari et al. (2018) - faecal coliforms Ander and Forss (2011) - normal scenario - Cryptosporidium Ander and Forss (2011) - normal scenario - Giardia Ander and Forss (2011) - realistic scenario - Cryptosporidium Ander and Forss (2011) - realistic scenario - Giardia Katukiza et al. (2014) - E. coli
10 –16 10 –14 10 –12 10 –10 10 –8 10 –6 10 –4 Probability of infection
10 –2
10 0
Ranked by human development index of the study locality (UNDP, 2018) (top highest, bottom lowest). Error bars indicate 90th or 95th percentile range where reported. Only studies with stated numerical results shown. Where multiple operational configurations or case studies were presented, only those most representative of actual supply (as deemed by the reviewers) were included. Schijven et al. (2019) presented results graphically. Abbreviations: Adv = advanced treatment (not further defined); Con = conventional treatment.
Figure 6 Estimated disease burden in studies using 10-6 disability-adjusted life years (DALY) person-1 year-1 as reference level of risk, ranked by study locality human development index and grouped by scenario
Sokurenko (2014) - Bearspaw - 2009 - Giardia Sokurenko (2014) - Bearspaw - 2011 - Giardia Sokurenko (2014) - Bearspaw - 2011 - Cryptosporidium Sokurenko (2014) - Glenmore - 2003 - Cryptosporidium Sokurenko (2014) - Glenmore - 2011 - Cryptosporidium Hadi et al. (2019) - Age < 2 - Cryptosporidium Hadi et al. (2019) - Age 2–6 - Cryptosporidium Hadi et al. (2019) - Age 6–16 - Cryptosporidium Hadi et al. (2019) - Age > 16 - Cryptosporidium Shamsollahi et al. (2019) - low-dose formula - rotavirus Shamsollahi et al. (2019) - Beta-Poisson formula - rotavirus Razzolini et al. (2016) - Cryptosporidium Xiao et al. (2012) - Con - Cryptosporidium Xiao et al. (2012) - Con - immunocompetent - Cryptosporidium Xiao et al. (2012) - Con - immunodeficient - Cryptosporidium Xiao et al. (2012) - Con, O3 - Cryptosporidium Xiao et al. (2012) - Con, O3 - immunocompetent - Cryptosporidium Xiao et al. (2012) - Con, O3 - immunodeficient - Cryptosporidium Xiao et al. (2012) - MF - Cryptosporidium Xiao et al. (2012) - MF - immunocompetent - Cryptosporidium Xiao et al. (2012) - MF - immunodeficient - Cryptosporidium George et al. (2015) - Hongally WTP - Campylobacter George et al. (2015) - Hongally WTP - pathogenic E. coli George et al. (2015) - Hongally distrib. - pathogenic E. coli George et al. (2015) - Hongally WTP - rotavirus George et al. (2015) - Melapura WTP - Campylobacter George et al. (2015) - Melapura WTP - pathogenic E. coli George et al. (2015) - Melapura distrib. - pathogenic E. coli George et al. (2015) - Melapura WTP - rotavirus Thomas et al. (2015) - ETEC Howard et al. (2006) - plant 1 - 1999 - pathogenic E. coli Howard et al. (2006) - plant 1 - 2002 - pathogenic E. coli Howard et al. (2006) - plant 2 - 1999 - pathogenic E. coli Howard et al. (2006) - plant 2 - 2002 - pathogenic E. coli Howard et al. (2006) - distrib. - 1998 - pathogenic E. coli Howard et al. (2006) - distrib. - 1999 - pathogenic E. coli Katukiza et al. (2014) - E. coli 10 –10
10 –8
10 –6
10 –4
Disability-adjusted life years person
10 –2 -1
10 0
year -1
Ranked by human development index of the study locality (UNDP, 2018) (top highest, bottom lowest). Error bars indicate 90th or 95th percentile where reported. Only studies with stated numerical statistics of median (and optionally 5th and 95th percentile) health risk are shown. Elliott (2015), Hamouda et al. (2016), and Krkosek et al. (2016) presented results graphically. Sokurenko (2014) presented other years’ results graphically. Shea et al. (2016) and Sokolova et al. (2015) compared source water challenge and treatment effectiveness but did not provide numerical risk estimate for health risk. Swaffer et al. (2018) characterised source water risk only. Abbreviations: distrib. = distribution system; Con = conventional treatment; ETEC = enterotoxigenic Escherichia coli; MF = microfiltration; O3 = ozonation; WTP = water treatment plant.
Owens et al. 2009
Submission to Water Research
Highlights: First systematic review of QMRA implementation for public drinking water supplies Current approaches varied most for deriving dose and varied least for dose-response Factors for dose were commonly the most influential determinant of risk QMRA study complexity did not indicate greater certainty of risk estimates Greater consistency in reporting QMRA assumptions would be beneficial overall
1
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: