Climate change and food and water-borne diseases

Transcription

1 ECDC TECHNICAL DOCUMENT Climate and food and water-borne diseases A tool for quantitative microbial risk assessment

2 This technical report was commissioned and coordinated by Professor Jan Semeza, Dr. Bertrand Sudre and Jonathan Suk (Health Impact Section, Office of the Chief Scientist, ECDC). The report is based on work carried out under the tender OJ/2008/04/11 - PROC/2008/005, RIVM project number V/330344/01/FO in conjunction with a team from Rinjksinstituut voor Volkgezondheit en Milieu [National Institute for Public Health and the Environment] (RIVM) in the Netherlands composed of M. Bouwknegt, J.F. Schijven, S.A. Rutjes and A.M. de Roda Husman. Suggested citation: European Centre for Disease Prevention and Control. Climate and food and waterborne diseases: A tool for quantitative microbial risk assessment. Stockholm: ECDC; Stockholm, October 2011 European Centre for Disease Prevention and Control, 2011 Reproduction is authorised, provided the source is acknowledged.

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4 TECHNICAL REPORT Climate and food and water-borne diseases A tool for QMRA Contents Abbreviations... iv Executive summary... 1 Background... 1 Objectives... 3 Methodology... 3 Results... 4 Conclusion... 4 Quantitative Microbial Risk Assessment Tool... 5 Introduction... 5 Risk assessment framework... 5 Annex 1: Climate modules for Quantitative Microbial Risk Assessment tool Module F: Consumption raw/undercooked chicken fillet Module COY: Consumption of oysters Module CSO: Combined sewer overflow Module DR: Dose-response Modules GBW and GSW: temperature dependent growth in bathing/surface water Module GE: growth of Salmonella Enteritidis in eggs Module GOY: Temperature dependent bacterial growth in oysters Modules IBW and ISW: temperature dependent inactivation or die-off in bathing/surface water Module IOY: Temperature dependent inactivation or die-off in oysters Module PPF: Prevalence in poultry flocks Module RO: Surface run-off of water from land into surface water Module TDW: Treatment of drinking water Module VDW: Volume of unboiled drinking water consumption per person per day Module VBW: Volume of swallowed bathing water Annex 2: Climate modules pathway QMRA drinking water QMRA bathing water QMRA oysters QMRA eggs QMRA chicken fillet References iii

5 Climate and food-and water borne diseases A tool for QMRA TECHNICAL REPORT Abbreviations DALY ECDC EEA EFSA FAO FWD IHPH IP QMRA Disability-Adjusted Life Year European Centre for Disease Prevention and Control European Environment Agency European Food Safety Authority United Nations Food and Agriculture Organization Food- and Waterborne Diseases Institute for Hygiene and Public Health, Germany Intergovernmental Panel on Climate Change Quantitative Microbial Risk Assessment iv

6 TECHNICAL REPORT Title here Executive summary The transmission of food and waterborne diseases (FWD) is likely to be affected by climate, but the extent of the impact is uncertain. This report describes a decision support tool which has been developed to estimate the risk of infection from FWD through quantitative risk assessment for different climate scenarios. It has been designed for the diverse climatic regions of Europe, but is not limited to these settings. The decision support tool computes the relative infection risk associated with climate resulting from exposure to a number of human pathogens: norovirus, Campylobacter, Cryptosporidium, Vibrio (non cholera) and Salmonella Enteritidis. These pathogens can be combined with specific exposure pathways such as drinking water, bathing water, oysters, egg/egg products or chicken fillets. Current and projected climatic conditions for specific regions can also be set. The decision support tool can help prioritise significant pathogen pathways under certain climatic conditions and support the implementation of targeted and effective intervention strategies. Background Climate manifests itself not only through increases in land and sea surface temperatures, but also through increases in the intensity, duration and frequency of heat waves, droughts, storm surges, precipitation, floods and other extreme weather events. These patterns are projected to continue and even intensify according to current climate models proposed by the Intergovernmental Panel on Climate Change (IP)(1). The bio-geographic regions of Europe differ in their susceptibility to changing environmental conditions depending on their location. The northernmost part of Europe includes a number of large bodies of water, the Eurasian continental landmass and the Arctic, all of which have unique climatic vulnerabilities. While the IP climate models project annual mean temperature increases of o C (for compared to the average for ), regional variations far surpass these estimates (2). The Mediterranean region is expected to warm by 6 o C during the summer months and will be affected by droughts, with a fall in annual precipitation of up to 30% against current levels. Higher temperatures and lower precipitation will bring about not only water scarcity and droughts, but also heat waves, forest fires, lower crop yields, biodiversity loss, soil and ecosystem degradation, and eventually desertification. In Atlantic Europe, extreme weather events such as severe storm surges and flooding are projected to become more frequent, particularly during the winter as a result of warmer temperatures and increased moisture retention. The run-off may then result in increased river flow and higher risk of flooding in coastal areas. In central and eastern Europe an annual mean temperature rise in the order of 3 4 C is projected. However, in the more continental regions of Central Europe and around the Black Sea temperatures could climb by as much as C. Projections also foresee greater extremes in heat. Annual mean precipitation could increase by up to 10% in most regions. There would be more rain during winter months, but less in summer in several areas. Consequently, an increase in river flooding is projected for the winter and more forest fires during the summer. In northern Europe (boreal region) climatic conditions will be comparable to western Europe but with more variance in temperature and precipitation: reduced lake and river ice with less snow cover and increased river flow. As a result of this warmer climate the Baltic Sea could be increasingly affected by eutrophication (algal bloom) and pollution. Climatic conditions are key determinants of FWD transmission (3). Transport, dispersion, fate and the environmental exposure pathways of these pathogens are all intricately linked to local climate and weather conditions (4). The behaviour, viability, and reproduction rates of pathogenic food and waterborne microbes depend on environmental conditions, evidence of which is reflected in their seasonality. During the winter, concentrations of enteric water-borne pathogens in river water are typically higher than in the summer season, when elevated temperatures lead to higher die-off rates (5). In contrast, summer s elevated temperatures are favourable for certain FWD. Similarly, the intensity and frequency of precipitation events are related to concentrations of water-borne pathogens in surface water and flooding can result in the dispersion of pathogenic microbes, while drought conditions can concentrate pathogens in surface water and determine their fate and viability. Thus, the environmental exposure routes of food and waterborne pathogens are susceptible to the changing climatic conditions discussed above. Future human exposures, both direct and indirect, might differ significantly from current patterns. Moreover, new types of pathogens might emerge from a new ecological niche, taking advantage of favourable exposure pathways. These s pose considerable challenges to existing public health infrastructures. Amid competing demands, the allocation of scarce resources poses a predicament for public health. In order to alleviate this difficult decisionmaking process we have developed a decision support tool to estimate infection risks from climate. By making a quantitative assessment of the microbial risk of FWD, this decision support tool can assist decision makers in prioritising different adaptation options. Quantitative microbiological risk assessment (QMRA) has traditionally been used to estimate health impacts from exposure to pathogens (6-7). The interactive computational tool described here computes FWD risk due to climate under current and future climate 1

7 Climate and food and water-borne diseases A tool for QMRA TECHNICAL REPORT conditions for a number of exposure pathways. It quantifies anticipated impacts, such as the projected burden of additional cases of infectious disease under climate scenarios, and can therefore help assess the significance or relative importance of such events. The decision support tool can also estimate the degree to which adaptation options would be likely to reduce vulnerability to the anticipated impacts. It can be used to evaluate the effectiveness of adaptation interventions and the feasibility of implementing interventions in the context of current and planned programmes and activities. For example, a number of adaptation interventions can be considered to reduce water-borne disease: upgrading water treatment and distribution systems; reinforcing drinking water regulations (and compliance) to control the introduction and spread of waterborne diseases; beach closures; public notices advising that water should be boiled or surveillance programmes for waterborne disease outbreaks. Similarly, for food-borne diseases a number of adaptation options could be considered: stricter regulations (and compliance) in relation to food production, processing, transport and storage in order to control the introduction and spread of diseases; surveillance programmes for food-borne disease outbreaks or education programmes on appropriate food handling. The decision support tool can be used to estimate disease burden from FWD for a number of pathogen pathway combinations under different (IP) climate scenarios (8) and intervention strategies. The decision support tool is intended to stimulate collaboration between different government departments/agencies such as health, environment, agriculture, etc. Inter-sectoral collaboration can help anticipate and quantify potential impacts on society. Computing disability adjusted life years (DALYs) can also support prioritisation of different adaptation options, by focusing on exposure pathways rather than pathogens. Thus, it is a valuable resource for mapping the climate vulnerability of infectious disease risk, with versatile applications for a number of settings, geographic and climatic regions. 2

8 TECHNICAL REPORT Climate and food and water-borne diseases A tool for QMRA Objectives The objectives of the decision support tool for QMRA were: to design a comprehensive decision-making tool for relative assessment of the impact of climate on FWD in the different European Member States, to provide an interface for a broader audience of scientific users and stakeholders to process and use this decision-making tool for assessment of the impact of climate on FWD. Methodology QMRA is a disease-specific approach covering the characteristics and properties of a pathogen under certain environmental conditions. Various FWD may be affected by climate and the selection of pathogens was subjected to a set of criteria: extent of the health risk under certain climatic conditions, food- and waterborne transmission, availability of data needs for QMRA, representative examples of pathogens in each of the groups of microorganisms: viruses, protozoan parasites and bacteria. Pathogens for this analysis included: Salmonella spp., Campylobacter spp., Cryptosporidium spp., Vibrio (non cholera) spp. and norovirus. The sources of food- and waterborne pathogens can be zoonotic, environmental and/or anthropic and may relate to a variety of transmission routes. The QMRA integrates these parameters into different modules that may be shared by different pathogens. The procedure followed can be divided into four steps: identification of potential risks and exposure routes quantification of exposure to the pathogen for individuals determination of a dose-response model (based on literature and/or experimental data), where the response is defined by the probability of developing an adverse health event for a given dose estimation of exposure dose that describes the infection risk. In order to assess the in risk linked to changing climatic patterns, this approach estimates the relative risk due to the specific effects of climate. Each individual step may also contain descriptions of both current and anticipated local climate parameters as a result of climate in order to assess the risk variation. 3

9 Climate and food and water-borne diseases A tool for QMRA TECHNICAL REPORT Results A total of 22 mathematic components were developed for the Climate Change Modules Quantitative Microbial Risk Assessment (MQMRA). These modules break down the fate and behaviour of the selected microorganisms during their transmission from environmental sources to humans. The exposure pathways included drinking water, bathing water, oysters, eggs/egg products and chicken fillets. A total of 13 pathogen pathway combinations were created with the selected pathogens and exposure pathways. The individual QMRA modules were programmed using Mathematica 7 and presented in an interactive computational tool which runs on free software using Wolfram CDF Player ( Wolfram Research Inc). The QMRA tool is designed to be used with default values from literature or with local data according to user settings. The thirteen QMRA combinations can therefore also be fed with location-specific current climate conditions, projected climate conditions and specific data, depending on the module in order to estimate the direction (increase or decrease) and size of the relative infection risk for the selected pathogens as caused by climate. The final output of the decision support tool is an estimate of the relative infection risk for a particular pathogen pathway combination. Conclusion The decision support tool has been validated against published dose-response curves and found to perform well under different climate conditions. Due to model simplifications and parameter uncertainty, the computed absolute infection risks should be considered indicative. Nevertheless, the decision support tool can be used by decisionmakers, environmental engineers and scientists to prioritise certain intervention strategies based on the outcome of the risk assessment. 4

10 TECHNICAL REPORT Climate and food and water-borne diseases A tool for QMRA Quantitative microbial risk assessment (QMRA) tool Introduction When developing the tool for QMRA the main objective was to assess the relative risks of climate associated FWD for EU Member States. More specifically, the aim was : to create a conceptual model that identifies the major pathways of FWD in the environment and under climate variations, to design a comprehensive decision-making tool for relative assessment of the impact of climate on FWD in the different European Member States, and to develop an interface for a broader audience composed of scientific users and stakeholders to enable them to process and use this decision-making tool to assess the impact of climate on FWD. A mathematical decision support tool has been developed, known as the Climate Change Modules for Quantitative Microbial Risk Assessment (QMRA), with an interface to estimate risks under current and future climate scenarios. The decision support tool is designed to assess the risk of infectious diseases linked to climate. It offers technical support for the quantitative assessment of the potential impact of meteorological s in Europe on FWD. The mathematical background to the decision support tool and the functionalities of QMRA are described below. The user interface provides experts and decision-makers with a quantitative assessment of the impacts of FWD under various climate scenarios. It is intended for experts, institutions and policy makers working in the field of public health in Europe with a particular focus on climate and FWD diseases. This report is not designed to offer formal guidance or advice on the best strategies for adapting public health systems to climate. Rather, it is designed to provide a comparative risk assessment by weighing different risks and scenarios against one other. The QMRA tool has been developed to accommodate local conditions in order to give a quantitative assessment of the risk on a local scale. This decision support tool will assist with the guidance and development of comprehensive EU adaptation strategies in order to reduce health risks associated with FWD pathogens in relation to climatic impact. This report is particularly relevant for stakeholders within EU Member States, but it may also be useful to a broader international audience including public health institutions, public health policymakers, and FWD experts and specialists dedicated to the control of FWD pathogens or operating in academic and international institutions. This report was written to present the findings of the project entitled Impact of climate on FWD in Europe, which was commissioned by ECDC in accordance with its mandate to identify emerging threats to communicable disease control in the EU. The project was financed solely by the European Centre for Disease Prevention and Control (ECDC) and was undertaken by Rinjksinstituut voor Volkgezondheit en Milieu [National Institute for Public Health and the Environment] (RIVM) in the Netherlands. This ECDC report is based on the RIVM Report /2009 (Impact of Climate Change on Food- and Water-borne Infectious Diseases in Europe. Decisionmaking tool, RIVM, 2009). The first part of the report presents climate modules for QMRA. After outlining the framework and providing a general overview of the modules used, the decision support tool is presented using examples. Each one of the mathematical components and pathways are described in the annexes of the report. Risk assessment framework QMRA is used to estimate adverse health events from exposure of individuals to a hazard, such as a microbiological agent (9-10). Figure 1 presents a general framework for conducting QMRA involving four steps (11): First, the problem is defined and the potential risks and exposure routes identified (hazard identification). Then the exposure to the pathogen is quantitatively estimated for individuals, e.g. to yield an estimated ingested dose of the pathogen (exposure assessment). 5

11 ANALYSI S Climate and food and water-borne diseases A tool for QMRA TECHNICAL REPORT Next, a dose-response model is obtained, either from literature or from experimental data depending on availability, which describes the relation between a dose being ingested and the probability of developing an adverse health event. Finally, the estimated dose from the exposure assessment is related to the dose-response model to characterise the risk of infection. Figure 1. Conceptual framework for risk assessment according to ILSI (International Life Sciences Institute) Risk Science Institute Pathogen Risk Assessment Working Group Pathogen risk assessment Problem formulation Characterisation o f exposure Characterisation of human health effect Risk characterisation This general framework is also based on a succession of steps that can be modelled. For example, firstly pathogen concentrations in the source are estimated. Then, s in the pathogen concentration during processing (e.g. slaughter) or treatment (e.g. drinking water production) are modelled to estimate an individual risk of an adverse health event, such as infection or illness. The framework can also be used to evaluate the effect of climate on microbiological health risks, although some adaptation is required. For this purpose, exposure within a general framework can be assessed in various ways. One method is to apply the modular process risk model (MPRM) (12). In its origin, the MPRM is a QMRA tool that provides a modular structure for transmission models of food production chains. The decision support tool can take into account the variability and uncertainty of the parameters describing the transmission pathway. To summarise, each MPRM module is a mathematical description of a process that relates a pathogen input to a pathogen output. Combining successive modules gives a description of the transmission pathway and can be used to estimate an ingested dose. The original MPRM approach comprises seven steps (12): defining the statement or purpose of the model describing the pathway building the MPRM model structure by splitting up the pathway into modules collecting the available data and expert opinions depending on the model structure developed selecting the mathematical model to be used within each module feeding the available data into the model and assessing the exposure. As previously stated, the MPRM was originally developed to model the transmission of bacteria through food chains. However, its concept is also valid for other areas. This statement also applies to climate, where different regions experience different effects. By having available an electronic library of modules describing the theoretical effects of FWD impact under climate, a specific exposure assessment model can be constructed, combining modules as relevant. Here, the mathematical modelling is based on a specific literature review covering each component of a modular process risk model. A pilot version has been tested by eight FWD experts to evaluate the quality and practical application of the decision support tool and the clarity of the results presented. In addition to modifications to the MPRM approach, the general QMRA framework needs to be modified to estimate a relative risk due to the effects of climate. This requires the modules to be designed to enable both a description of the current state as a baseline and an effect of health risks due to anticipated local climate. So, the current QMRA concept can be modified according to objectives to ensure that it is possible to estimate a in risk due to climate. 6

12 Salmonella spp. Campylobacter spp. Vibrio spp. Norovirus Cryptosporidium spp. TECHNICAL REPORT Climate and food and water-borne diseases A tool for QMRA Climate quantitative microbiological risk assessment (QMRA) tool an overview of the structure A quantitative microbial risk assessment is a disease-specific approach detailing the characteristics and properties of a pathogen under the influence of environmental factors. A number of FWD may be affected by climate s; pathogens for this analysis were therefore selected on the basis of the following criteria: extent of the health risk under climatic parameters, food- and waterborne transmission, availability of data required for quantitative microbial risk assessment. At least one pathogen was selected from each of the groups of microorganisms: viruses, protozoan parasites and bacteria. The selected pathogens included Salmonella spp., Campylobacter spp., Cryptosporidium spp., Vibrio (non cholera) spp. and norovirus (see Table 2 for environmental and climatic features associated with these pathogens.) Table 1. Selected FWD, their host and environmental and climatic characteristics Host Human yes yes yes yes yes Animal yes yes yes no yes Multiplication in the environment yes not likely yes no no Link to climate parameters Change in: Temperature growth, growth, survival survival survival inactivation survival Extreme precipitation water water growth, water water contamination contamination survival contamination contamination UV light survival survival growth inactivation survival Drought no effect survival no effect inactivation survival Salinity no effect no effect growth no effect no effect Relative humidity survival, growth survival, growth no effect inactivation survival The MQRMA tool consists of a series of 22 mathematic modules which can be run separately or combined to reproduce 13 pathways of exposure to a given FWD. Outputs of the MQRMA are presented as relative infection risks for a selection of FWD, comparing infection risks under current and future climate conditions, (-) and (+) respectively. The entire architecture of the decision support tool is presented, covering the modular combination for four selected pathogens. A description of input parameters is available in the annexes for each module section, with a detailed procedure for the mathematical modelling. 7

13 Climate and food and water-borne diseases A tool for QMRA TECHNICAL REPORT Table 2. Overview of mathematical modules and pathways for the selected FWD Pathways Modules Salmonella spp. Campylobacter spp. Vibrio spp. Cryptosporidium spp. Norovirus Drinking Bathing Chicken Bathing Drinking Bathing Drinking Bathing Eggs water water Oysters fillet water Oysters water water Oysters water water Oysters Temperature dependent growth in Bathing Water (GBW) GBW Temperature dependent bacterial growth in Oysters (GOY) GOY Prevalence in Poultry Flocks (PPF) PPF Growth of Salmonella Enteritidis in Eggs (GE) GE Combined Sewer Overflow (CSO) CSO CSO CSO CSO CSO CSO CSO CSO CSO Surface Run-off of water from land into surface water (RO) RO RO RO RO RO RO Temperature dependent inactivation or die-off in Bathing Water (IBW) IBW IBW IBW Temperature dependent inactivation or die-off in Surface Water (ISW) ISW ISW ISW ISW ISW ISW Temperature dependent inactivation or die-off in Oysters ( IOY) IOY IOY IOY Drinking water treatment (TDW) TDW TDW TDW Volume of unboiled drinking water consumption per person per day (VDW) VDW VDW VDW Volume of swallowed Bathing Water (VBW) VBW VBW VBW VBW Consumption of Oyster, raw/undercooked (COY, Egg, CE, CE COY F COY COY COY raw/undercooked Chicken Fillet F) Dose-response model (DR) DR DR DR DR DR DR DR DR DR DR DR DR DR Note: The MQMRA tool consists of 22 pathogen specific modules, subsets of which can be linked consecutively to estimate the relative infection risks for 13 pathogen pathway combinations (see column in the table above). The annexes contain the mathematical background for each of the modules and explain how to enter data in the decision support tool and how to interpret results. The tool in practice The MQRMA is an interactive computational tool to calculate the effects of climate on infection risks resulting from exposure to FWD. The section below contains a general description of the workflow using an example to facilitate the overall understanding of the decision support tool. More detailed explanations for each module and pathway or combination of modules are presented in Annex 1 and 2. Further information is also available in the Help menu for the computational decision support tool. The tool is programmed in Mathematica 7 (Wolfram Inc) and converted to run in Wolfram CDF Player (Wolfram Research Inc).The player is freely available from Wolfram Inc. 1 After installing the player and running the program file (MQRA Mathematica Player Notebook), the welcome screen of MQRMA will appear. This has a number of sub-headings and functionalities under four main tabs (see Figure 2). Welcome screen Introduction: presents general information under three sub-headings: MQRMA: overview : Climate information with three sub-tabs (temperature, annual precipitation and heavy rainfall) QMRA: Presents the four stages of the general framework used to carry out a QMRA: hazard identification, exposure assessment, dose response estimation and risk characterisation. Help: contains a glossary and information on the entire MQRMA. The Climate scenario and Pathogen pathway tabs describe the framework of the decision support tool. More detailed information on each module is available under the Module tab. This includes information under three sub-headings ( Description, Assumptions and How to enter data ). The two last tabs relate to the list of scientific references and contributors. The MQRA decision support tool is located under Climate scenario. When selecting this item, a second tab Pathogen pathway appears on the left containing the workflow for a full climate microbial risk assessment. After entering the details of a climate scenario (current and future conditions), the Pathogen pathway enables users to select a pathogen and pathway and compute the risk assessment

14 TECHNICAL REPORT Climate and food and water-borne diseases A tool for QMRA Figure 2. Climate modules for QMRA tool main tabs Welcome screen Introduction tab General information on MQMRA Climate and Quantitative Microbial Risk Assessment Help tab Glossary Climate scenario Pathogen pathways Modules References About Climate scenario Selection of current and future climateconditions Then, selectionof a pathogen pathway tab to run the risk assessment. The general workflow of the MQRMA follows a succession of steps that are illustrated in an example below using a number of parameters for Vibrio spp. 9

15 Climate and food and water-borne diseases A tool for QMRA TECHNICAL REPORT Climate: current conditions and future scenario Open the tab Climate scenario. Climate scenario (see Figure 3) presents current climate conditions in a blue font (-) and future climate conditions in red (+). All parameters are set by default but can be d by the user to fit local meteorological patterns for a specific risk assessment. The current climate conditions are set by default (daily average temperature and precipitation). However, future climate conditions will to High and dry under the preset option. For this example, daily average temperature has been d to 29 C and 27 C for air and water respectively. Future climate conditions are set by default to Moderate, wet but custom values or pre-selections can be used by clicking on the corresponding items. We are applying a 5 C temperature and an annual precipitation of 80%. If the frequency of heavy rainfall has been modified, the heavy rainfall graph should be shuffled to represent a random occurrence per quarter of the year (blue peaks are current frequency, red are future estimated frequency). Figure 3. Climate scenario screenshot The second step is to run the QMRA for a specific pathogen under these selected climatic conditions. The proposed functionalities enable each individual user to set the QMRA to specific conditions corresponding to local settings. Consequently the decision support tool can be used in a broad range of settings covering weather conditions across the European Union. Pathogen: selection of pathway Open the Pathogen pathway tab (see Figure 4). This page enables the user to select one pathogen and its pathway. Norovirus has been selected as a default, however the pathogen can be d by clicking on the left of the pathogen name (highlighted in green). It is important to note that not all pathways are available for a given pathogen and that this depends on pathogen ecology and the pathway of exposure. For example, Vibrio (non-cholera) spp. has only two pathways available ( bathing water and oyster ). The other pathways cannot be selected because they are not relevant for this pathogen. An example is presented based on the Vibrio pathogen to illustrate the functionalities of QMRA. After selecting Vibrio and Bathing water the modules used for the QMRA are selected automatically and a comprehensive flow chart, known as the QMRA tree, summarises the combination of modules used on the right-hand side of the page. 10

16 TECHNICAL REPORT Climate and food and water-borne diseases A tool for QMRA Figure 4. Climate scenario screenshot The pathogen pathway screen lists the pathogen and the related pathways that can be selected. A number of modules are pre-selected for each pathogen pathway. If the selection square is white, then it can be (de)selected (non selectable in grey). This feature prevents the estimation of relative risks for non-realistic combinations of pathogens and pathways. Quantitative Microbial Risk Assessment The following step requires the user to enter data into the QMRA. Although predefined values are available as a default, they can be modified according to individual user requirements. In the example shown, by clicking on the tab QMRA: Vibrio x bathing water the module Growth in bathing water (GBW) will appear with default values from the literature (see Figure 5). Depending on the required purpose and the user s expertise, it is possible to modify a certain number of biological parameters. The three environmental parameters embedded in the GBW module are minimum and maximum concentration in bathing water (N/litre maximum d to 10 6 N/l) and growth temperature (C degree modified to 21 C). The values can modified using a drop-down menu and s are automatically applied and displayed in a graphic interface on the right-hand side of the screen. Figure 5. Pathway combining Vibrio X bathing water (environmental parameters) Note that climate scenario setting selected in the previous step is summarised at the bottom of the page. 11

17 Climate and food and water-borne diseases A tool for QMRA TECHNICAL REPORT Output The final step is to calculate the results of the QMRA under these conditions using Monte Carlo simulation. This is done by clicking on Do Monte Carlo simulation under the QMRA Bathing water tab which summarises the parameter values. The results of the QMRA are displayed on a final screen gathering all general information on the output of the model (see Figure 6). In our example, only one module is necessary, and thus, only one Monte Carlo simulation. The screenshot below displays output information, in particular the infection risk and its 95% confidence interval. The absolutes values are, of course, indicative and the target output is the estimated ratio of risk between future and current conditions. The results are reported on the right-hand side of the screen (box containing plotted figure and histogram of probabilities, ratio +/-). Figure 6. Pathway combining Vibrio X bathing water results The output of the decision support tool is an estimate of the relative infection risk for a particular pathogen pathway combination. The effects of climate on infection risks are determined by comparing calculated infection risks under current and future climate conditions. In this example, the difference in the ratio between 12

18 TECHNICAL REPORT Climate and food and water-borne diseases A tool for QMRA future climate conditions (+) and current climate conditions (-) does not demonstrate a significant increase in infection risk. This description of the workflow is limited to one example. For other pathogens or pathways, different modules are available, as presented in Table 2. Specific parameters can be set for each of them and a dedicated Monte Carlo simulation carried out. The results are then displayed automatically by the decision support tool. In each of the different modules available for other complex pathways the user is able to specify a set of parameters. The risk assessment (QMRA) model can also automatically select an ad hoc combination by linking the required modules for this specific pathway. In this case, the complex succession of the module is automatically controlled by the decision support tool in a step-by-step procedure whereby each module is represented as a tab. Each tab takes the initial of a mathematical module. As described in the example of Vibrio x bathing water, numerous parameters can be set by the user in each module, as well as current and future climate conditions under the Climate scenario tab. Discussion This first version of the MQMRA tool includes a total of 13 QMRA combinations, each consisting of a set of consecutively linked modules selected from the 22 modules created. The decision support tool is highly flexible and the QMRA combinations can be run for various location-specific climate conditions throughout Europe, both current and projected, and for specific data depending on the selected modules. Decision support tool outcomes are estimates of relative infection risks for current and future climate conditions. The decision support tool also estimates the direction (increase or decrease) and magnitude of the relative infection risks for the selected pathogens due to climate and includes a confidence interval. The estimates of infection risks should, however, be regarded as indicative since they are point estimates (single average values) from simplified models (due to data deficiencies and assumptions). There may be insufficient data to complete a specific QMRA. In such cases, default values may be selected for the QMRA, but the representative nature of the default values for the specific situation needs to be carefully assessed and correctly documented. The estimated relative risks are as accurate as possible on the basis of the data entered. While the MQMRA is based on current scientific literature, in the event of insufficient data being available, the decision support tool can be used to identify the data gaps and direct future data collection. This is done by selecting the desired pathogen pathway combination and writing down the required entry fields for each of the modules. The MQMRA tool can also be used to answer what if questions for specific pathogen pathway combinations. For instance, it is possible to assess the required improvement in treatment efficiency of drinking water production so that it does not exceed a certain threshold infection risk due to the consumption of unboiled drinking water. Similarly, the effect of distance travelled from a source of contamination to a bathing area can be analysed. Another example of a what if scenario relates to the percentage of Salmonella-contaminated eggs. Currently, data are lacking to relate changing climatic conditions to the prevalence of Salmonella-contaminated eggs. Nevertheless, if a user is interested in the potential increase in infection risk if the prevalence of Salmonella-contaminated eggs increases from 5 10%, then the respective QMRA could be completed twice using the same climate scenarios, once for the 5% prevalence scenario and once for the 10% scenario. The relative infection risk is then calculated by dividing the estimate for the 10% prevalence scenario after climate by the estimate for the 5% prevalence scenario. In general, a variety of what if scenarios can be investigated as a basis for defining adaptation strategies and preventive measures by comparing relative risks from varying location-specific parameter values. The current decision support tool can be improved by including more and better estimates of parameter values for the fate and behaviour of pathogens as these become available. The inclusion of additional growth or inactivation kinetics models is more complex. For instance, the current growth model for Vibrio spp. is a simple model and this may be replaced by a growth model that accounts for temperature, ph, salinity, several Vibrio species (i.e. V. paraheamolyticus and V. vulnificus), and the occurrence of Vibrio in water once suitable data become available. Nevertheless, the decision support tool can accommodate new information which can be included in future versions of the decision support tool. The decision support tool is designed for use on a local scale, i.e. for a community or small region and cannot estimate the relative infection risks for the whole of Europe instantaneously. To obtain an indication of the estimates for Europe, location-specific data (climatic and non-climatic) can be obtained for smaller regions in Europe (e.g. from municipal health services). These data could subsequently be entered in the QMRA for selected pathogen pathway combinations. In addition, the resulting array of relative risks could be used to generate maps of Europe showing the differences in estimated relative infection risks by colour variation. These maps would 13

19 Climate and food and water-borne diseases A tool for QMRA TECHNICAL REPORT indicate the areas estimated to be most affected by climate in terms of FWD and would therefore yield valuable information for public health authorities, enabling them to include FWD in their adaptation strategies based on expertise in QMRA modelling. This decision support tool estimates the relative infection risk for each of the pathogens. For public health authorities, it may also be useful to add estimates of the disease burden caused by the pathogen pathway combinations to the tool. For instance, estimates of disability adjusted life years (DALYs) caused or saved by climate could support decisions on prioritisation of adaptation strategies for FWB pathogens. Furthermore, by adding DALY estimates, it will also be possible to estimate not only a pathogen-specific reduction or increase in relative risk, but also the combined effect for a set of pathogens. For instance, the presence of norovirus, Vibrio ssp. and hepatitis A virus in oysters may differ under specific conditions of climate. If the burden for Vibrio spp. increases and the burden for the other two pathogens decreases, then the total disease burden due to specific climate s may evolve towards a decrease or increase. Such information will help public health authorities to prioritise for pathogen pathways rather than pathogens, which is relevant for developing adaptation strategies. 14

20 Norovirus TECHNICAL REPORT Climate and food and water-borne diseases A tool for QMRA Annex 1: Climate modules for Quantitative Microbial Risk Assessment tool For a summary of the mathematical module names and pathogens see Table 1 above. For each module, Annex 1 presents: the mathematical description scientific assumptions in the modelling process the procedure for completing each field with local parameters or values from scientific literature reviews. Table 3. Annex overview Abbreviationsin alphabetic order Module name Salmonella spp. Campylobacter spp. Vibrio spp. Cryptosporidium spp. F Consumption of raw/undercooked chicken fillet CE Consumption of raw/undercooked egg x COY Consumption of oysters x x x x CSO Combined sewer overflow x x x DR Dose response x x x x x GBW Growth in bathing water x GE Growth in eggs x GOY Growth in oysters x IBW Inactivation in bathing water x x x IOY Inactivation in oysters x x x ISW Inactivation in surface water x x x PPF Prevalence in poultry flocks x RO Run-off from agricultural land x TDW Treatment of drinking water x x VBW Volume of swallowed bathing water x x x x VDW Volume of unboiled drinking water x x x 15

21 Climate and food and water-borne diseases A tool for QMRA TECHNICAL REPORT Module F: Consumption raw/undercooked chicken fillet Pathogens Campylobacter spp. Mathematical description This module is part of the QMRA pathogen X chicken fillet screen. From the pathogen concentrations in chicken fillet and the consumption of undercooked/raw chicken fillet, the dose D (exposure) is calculated, which is the number of pathogens ingested per person per meal. How to enter data in the decision support tool A concentration of Campylobacter spp. in chicken fillet of 1 (Low: L), 10 (Medium: M), 100 (High: H), or any value (Set) can be entered and for consumption, a value of 20, 50, 100 or 200 grams can be entered on the QMRA pathogen X oyster screen. 16

22 TECHNICAL REPORT Climate and food and water-borne diseases A tool for QMRA Module CE: Consumption raw/undercooked egg Pathogens Salmonella spp. Mathematical description This module is part of the QMRA pathogen X egg screen. From the pathogen concentrations in egg (product) and the consumption of undercooked/raw egg or egg products, the dose D (exposure) is calculated, which is the number of pathogens ingested per person per meal. How to enter data in the decision support tool One egg (52 grams), one 100-gram egg product or one 200-gram egg product can be entered on the QMRA pathogen X egg screen. 17

23 Climate and food and water-borne diseases A tool for QMRA TECHNICAL REPORT Module COY: Consumption of oysters Pathogens Campylobacter spp., Vibrio spp., Cryptosporidium spp. and norovirus. Mathematical description This module is part of the QMRA pathogen X oyster screen. From the pathogen concentrations in oysters and the consumption of oysters the dose D (exposure) is calculated, which is the number of pathogens ingested per person per meal of oysters. How to enter data in the decision support tool A value of 20, 50, 100 or 200 grams can be entered on the QMRA pathogen X oyster screen. 18

24 TECHNICAL REPORT Climate and food and water-borne diseases A tool for QMRA Module CSO: Combined sewer overflow Pathogens Campylobacter spp., Cryptosporidium spp. and norovirus. Mathematical description The pathogens presented in this module (norovirus, Campylobacter spp. and Cryptosporidium spp.) are excreted by infected humans and transported to a wastewater treatment plant by the sewerage system. Treatment of the wastewater at the treatment plant reduces the concentrations of pathogens, usually by a factor of (1 2 log10 units). The treated wastewater is discharged into surface water (river/stream/canal), resulting in a dilution of pathogens depending on the size of the surface water. In combined sewers, the household wastewater is mixed with rainwater before it reaches the wastewater treatment plant. In the case of heavy rainfall, the wastewater treatment plant may reach maximum capacity, leading to an accumulation of untreated sewage and sewer overflow. During this combined sewer overflow (CSO), untreated wastewater is discharged into surface water, leading to peak concentrations of pathogens in the surface water. Depending on the efficiency of the wastewater treatment in removing pathogens, these peak concentrations can be several orders of magnitude higher, thus representing a period of increased risk. In a climate scenario with an increase in the number of heavy rainfall events, it is assumed that there is an equal increase in the amount of CSOs. If in the climate scenario annual precipitation has increased, there is more dilution of the discharged wastewater in the surface water. Thus, there are days with and without combined sewer overflow for current and future climatic conditions (indicated by indices 0 and 1 respectively) and these days are described by four equations to calculate the pathogen concentration in the surface water at the discharge point, C sw. The parameters used in the following formulae are explained in Table 4. Table 4. Key parameters used in the CSO module Parameters Unit Symbol Wastewater Treatment Plant Pathogen untreated wastewater concentration N/litre Wastewater treatment Log 10 Wastewater treatment, standard deviation Log 10 C in Log 10 Z StDev Discharge rate of treated wastewater m 3 /day Q wtp Combined Sewer Overflow Change in pathogen untreated wastewater concentration during CSO N/litre f in C in Discharge rate of untreated+treated wastewater m 3 /day r peak 365 n ry r peak peak Q wtp Surface water (river/stream/canal) Flow rate 10 3 m 3 /day Q sw Width Depth m m w sw d sw 19

25 Climate and food and water-borne diseases A tool for QMRA TECHNICAL REPORT Equations used in the CSO module: Current climate conditions, C C sw,0 peak in Z wtp Q Q in sw n days with CSO: C sw,0 f in C in Z Future climate conditions, C C C sw,1 in Assumptions Z wtp wtp Qin f Q r f sw 365 n days no CSO: peak (1) (1) out Q Q in sw with days no CSO: with days with CSO: 365 n (2) (3) peak wtp sw f incin Z wtp r (4), 1 peak ry r peak f rqsw Q All the values of the parameters in equations 1-4 are assumed to be constant, with the exception of the wastewater treatment Log 10 Z. This parameter is assumed to have a normal distribution with standard deviation StDev. The wastewater treatment efficiency varies from day to day and therefore Monte Carlo samples are taken from the log-normal distribution to mimic variation in the relative risk estimation. It is assumed that CSO occurs on each heavy rainfall day and that the discharge rate of untreated plus treated wastewater is proportional to the increased precipitation on a heavy rainfall day. How to enter data in the decision support tool Parameter values can be entered by choosing a number of preset values (for example, Low, Medium, High or Small, Medium, Large see Figure 6). These preset values are values taken from literature (see Figure 7). Using the Set buttons it is possible to enter location-specific values. This type of data can be obtained from managers of wastewater treatment plants, for example. For the other part, the user can select from a range of values in a dropdown menu. Figure 7. Combined sewer overflow screenshot (module CSO) MQMRA: Climate Change Modules for Quantitative Microbial Risk Assessment Welcome Introduction Help Climate scenario Pathogen pathway QMRA: Norovirus Drinking Water CSO ISW Wastewater Treatment Plant Pathogen raw wastewater concentration N liter L M H Set : 1000 Log 10 Z StDev Wastewater treatment Default Set : 1.8 Discharge of treated wastewater m 3 day Combined Sewer Overflow S M L Set : 2000 Change in pathogen raw wastewater concentration: Discharge of raw treated wastewater: 4945m 3 day Surface water River Stream Canal S M L Set Flow rate 10 3 x m 3 day Width m Depth m Temperature Climate scenario Annual precipitation 2 C 20 2 Times more heavy rainfall days per year N liter Norovirus concentration in surface water at discharge 1 Norovirus Quarter of the year 20

26 TECHNICAL REPORT Climate and food and water-borne diseases A tool for QMRA On the right-hand side of the screen, there is a button marked Do Monte Carlo. Pressing this button calculates the pathogen concentration at the surface point of discharge for current and future climate conditions using the values set on the left-hand side of the screen. A graph appears that shows the pathogen surface water concentration for each day of the year. Each time a parameter value is d the Do Monte Carlo button reappears and must be pressed again to update pathogen concentrations. Furthermore, if parameter values in the Climate scenario screen are altered, or if another selection is made in the Pathogen pathway screen, then the Do Monte Carlo button needs to be pressed again. In the graph, pathogen concentrations are at two levels. The lower level represents discharge without CSO, varying on a day-to-day basis. The higher level represents the CSO events that are concurrent with heavy rainfall events as set in the Climate scenario screen. Current and future climate conditions are indicated in blue and red respectively. Table 5: Values of key parameters used in CSO module Parameter Dimension Value Reference Wastewater treatment plant Pathogen untreated wastewater concentration N/litre Low, Medium, High, Set Norovirus Campylobacter spp. Cryptosporidium spp. (13-14) L M H Wastewater treatment Log 10 Wastewater treatment, standard deviation Log 10 Log 10 Z * Default from literature or range from 0 5 Default from literature or range from 0 Log Z / 10 2 Combined sewer overflow CSO Change in pathogen untreated wastewater concentration during CSO ** Discharge rate of treated wastewater Surface water (river/stream/canal) Flow rate N/litre m 3 /day 10 3 X m 3 /day 0.1, 0.2, 0.5, 1, 2, 5, 10 Calculated proportional to heavy rainfall 86, 2200, Width m 10, 50, 125 Depth m 1.5, 2.6, 3.8 (5) * Z represents the fraction of pathogens that passes treatment ** In case these data are available. If not, then the default value of 1 can be used, indicating that the pathogen concentration in untreated wastewater does not during a CSO. 21