630
Depending on the type of decision and the local settings, other methods than presented in this
631
paper can be more suitable to combine in the decision model. For decisions aiming at
632
reaching a certain guideline or threshold value, a CEA may be preferred, instead of a CBA.
633
CBA represents a strict anthropocentric and utilitarian context, only accounting for benefits
634
attributed to human values (Hutton 2001). If decision makers want to include intrinsic values,
635
they need to apply methods that can consider such values as well, such as multi-criteria
636
decision analysis (see e.g. DCLG 2009). In such multi-criteria decision models, the decision
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31support rendered from the CBA and the QMRA can be used as input for appropriate criteria.
638
To give some examples, the NPV can provide information to the economic dimension,
639
distributional analysis and QALY assessment can provide input to the social dimension, and
640
water quality modelling can provide input to the environmental dimension in a sustainability
641
assessment, see e.g. Rosén et al. (2015).
642
The focus of this study was to describe the methodology of comparing microbial risk
643
mitigation measures using CBA in combination with QMRA to estimate risk levels and the
644
effect of possible mitigation measures. Benefits, in terms of the health risk reduction obtained
645
in each alternative were described in detail. Environmental benefits were included using a
646
more simplified approach. However, including the environmental benefits illustrates a key
647
element of the CBA, i.e. the possibility to include other benefits, apart from the target risk
648
reduction. These additional benefits may be of substantial importance and heavily affect the
649
final decision.
650
The decision model incorporates both aleatory and epistemic uncertainties in the input
651
probability distributions. To further develop the model and to provide additional decision
652
support, these uncertainties can be divided. This separation would also facilitate additional
653
decision analyses, e.g. value of information analysis.
654
6 Conclusions
655
Results from the case study showed that the alternative to connect the smallest proportion
656
(25 %) of on-site wastewater treatment systems to the wastewater treatment plant (A1) at
657
Lake Vomb was the most societally beneficial. However, the only alternative that would
658
reduce the annual probability of infection to meet the WHO guidelines with a high degree of
659
certainty (95th percentile) was installing UV-disinfection (A4). In relation to the development
660
of the risk-based decision model, the following conclusions were drawn:
MAN
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32• The developed decision model is flexible and can be tailored to different drinking
662
water systems and different types of decision problems.
663
• To implement the decision model, a multitude of uncertainties and variabilities needs
664
to be addressed. However, the model provides tools to include these variabilities and
665
uncertainties in a structured manner.
666
• Through the process of performing the cost-benefit analysis, aspects important for
667
decision making that may otherwise easily be overlooked or ignored are openly
668
displayed and assessed.
669
• The combination of quantitative microbial risk assessment and cost-benefit analysis
670
provides a novel decision model that creates transparent and holistic decision support
671
tool for microbial risk mitigation.
672
• For improvement of the decision model, we suggest to further develop the valuation
673
and monetisation of health effects and the propagation of variability and uncertainty
674
between the included methods.
675
Acknowledgement
676
This research has been performed as part of the project Risk-Based Decision Support for Safe
677
Drinking Water, financed by the Swedish Water and Wastewater Association (project 13-
678
102), and as part of DRICKS, a framework programme for drinking water research at the
679
Chalmers University of Technology. The information provided by the local water utility
680
(Southern Sweden Water Supply) and the municipality of Sjöbo was most appreciated. Input
681
regarding the sensitivity analysis, provided from Tommy Norberg was highly appreciated.
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subjects. Gut 19(2), 146-150.
MAN
USCR
IPT
ACCE
PTED
• A decision model for drinking water context, combining QMRA and CBA, was developed.
• This flexible model can be tailored to different systems and decision problems.
• The microbial risk mitigation measures were compared in a Swedish case-study.
• Microbial risk reduction was measured in QALYs and monetised.