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|progression = Full draft
|progression = Full draft
|curator = THL
|curator = THL
|date = 2018-05-21
|date = 2020-08-12
}}
}}
'''For the final version of this assessment, see the scientific article.<ref name="tuomisto2020"/>
{{summary box
{{summary box
|question=What are the current individual and population level health benefits and risks of eating Baltic herring and salmon in Finland, Estonia, Denmark and Sweden? How would the health effects change in the future, if consumption of Baltic herring and salmon changes due to actions caused by a) fish consumption recommendations or limitations, b) different management scenarios of Baltic sea fish stocks, or c) selection of fish sizes for human consumption?
|question=What are the current individual and population level health benefits and risks of eating Baltic herring and salmon in Finland, Estonia, Denmark and Sweden? How would the health effects change in the future, if consumption of Baltic herring and salmon changes due to actions caused by a) fish consumption recommendations or limitations, b) different management scenarios of Baltic sea fish stocks, or c) selection of fish sizes for human consumption?
|answer=BONUS GOHERR project (2015-2018) looked at this particular question. A health impact assessment was performed based on fish consumption survey done in the four target countries; EU Fish 2 study about dioxin and PCB concentrations; a dynamic growth model about Baltic herring stock sizes and dioxin accumulation; scientific literature about exposure-response functions of several compounds found in Baltic fish; and online models produced in Opasnet web-workspace.
|answer=BONUS GOHERR project (2015-2018) looked at this particular question. A health impact assessment was performed based on fish consumption survey done in the four target countries; EU Fish 2 study about dioxin and PCB concentrations; a dynamic growth model about Baltic herring stock sizes and dioxin accumulation; scientific literature about exposure-response functions of several compounds found in Baltic fish; and online models produced in Opasnet web-workspace. This page describes the benefit-risk assessment performed.<ref name="tuomisto2020">Tuomisto, J.T., Asikainen, A., Meriläinen, P., Haapasaari, P. Health effects of nutrients and environmental pollutants in Baltic herring and salmon: a quantitative benefit-risk assessment. BMC Public Health 20, 64 (2020). https://doi.org/10.1186/s12889-019-8094-1</ref>
Dioxin and PCB concentrations have been constantly decreasing in Baltic fish for 40 years, and now they are mostly below EU limits. Also Baltic herring consumption has been decreasing during the last decades and is now a few grams per day, varying between age groups (old people eat more), genders (males eat more) and countries (Estonians eat more and Danes less than others). People reported that better availability of easy products, recipes, and reduced pollutant levels would increase their Baltic herring consumption. In contrast, recommendations to reduce consumption would have little effect on average.
Dioxin and PCB concentrations have been constantly decreasing in Baltic fish for 40 years, and now they are mostly below EU limits. Also Baltic herring consumption has been decreasing during the last decades and is now a few grams per day, varying between age groups (old people eat more), genders (males eat more) and countries (Estonians eat more and Danes less than others). People reported that better availability of easy products, recipes, and reduced pollutant levels would increase their Baltic herring consumption. In contrast, recommendations to reduce consumption would have little effect on average.
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In Goherr, the scope is wide and we are looking at scenarios about fragmented vs. integrated governance and high vs. low human impact at the Baltic Sea Region. These scenarios are described in more detail on a page about [[Goherr:_3.2._Online_description_of_the_scenarios_developed,_applicable_in_the_dioxin_model_(Task_5.1)_and_decision_support_model_of_WP6_(Month_16)._Responsible_partner:_UOULU. |management scenarios developed in Goherr WP3]].
In Goherr, the scope is wide and we are looking at scenarios about fragmented vs. integrated governance and high vs. low human impact at the Baltic Sea Region. These scenarios are described in more detail on a page about [[Goherr:_3.2._Online_description_of_the_scenarios_developed,_applicable_in_the_dioxin_model_(Task_5.1)_and_decision_support_model_of_WP6_(Month_16)._Responsible_partner:_UOULU. |management scenarios developed in Goherr WP3]].
In this assessment, will look at health only, but the decisions and options considered are based on the wider discussions about integrated governance. The main decisions considered are improvements about fish availability and recipes, food recommendations, and selection of fish sizes used for consumption. The changes caused by each option are estimated based on the [[Goherr: Fish consumption study|Goherr fish consumption study]]<ref>Asikainen A, Pihlajamäki M, Ignatius S, Meriläinen P, Haapasaari P, Tuomisto J. Human consumption of Baltic salmon and herring in four Baltic Sea countries: unravelling the embedded reasonings. Manuscript.</ref>
In this assessment, will look at health only, but the decisions and options considered are based on the wider discussions about integrated governance. The main decisions considered are improvements about fish availability and recipes, food recommendations, and selection of fish sizes used for consumption. The changes caused by each option are estimated based on the [[Goherr: Fish consumption study|Goherr fish consumption study]].<ref name="pihlajamaki2019"/>
* Info.improvements with options
* Info.improvements with options
** BAU (Business as usual): current availability of and chemical levels in herring and salmon.
** BAU (Business as usual): current availability of and chemical levels in herring and salmon.
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0|HELCOM|Info.improvements|BAU|effinfo||Replace|Current availability of and chemicals in herring and salmon|0
0|HELCOM|Info.improvements|BAU|effinfo||Replace|Current availability of and chemicals in herring and salmon|0
1|HELCOM|Info.improvements|Yes|effinfo||Identity|Better availability of and less chemicals in herring and salmon|
1|HELCOM|Info.improvements|Yes|effinfo||Identity|Better availability of and less chemicals in herring and salmon|
0|National food safety authority|Recomm.herring|BAU|effrecomm|Fish:Herring|Replace|Authorities recommendations about Baltic herring do not change|0
0|National food safety authority|Recomm.herring|BAU|effrecommRaw|Fish:Herring|Replace|Authorities recommendations about Baltic herring do not change|0
2|National food safety authority|Recomm.herring|Eat more|effrecomm|Fish:Herring;Recommendation:Eat less|Replace|Authorities recommend to eat more Baltic herring|0
2|National food safety authority|Recomm.herring|Eat more|effrecommRaw|Fish:Herring;Recommendation:Eat less|Replace|Authorities recommend to eat more Baltic herring|0
2|National food safety authority|Recomm.herring|Eat less|effrecomm|Fish:Herring;Recommendation:Eat more|Replace|Authorities recommend to eat less Baltic herring|0
2|National food safety authority|Recomm.herring|Eat less|effrecommRaw|Fish:Herring;Recommendation:Eat more|Replace|Authorities recommend to eat less Baltic herring|0
0|National food safety authority|Recomm.salmon|BAU|effrecomm|Fish:Salmon|Replace|Authorities recommendations about Baltic salmon do not change|0
0|National food safety authority|Recomm.salmon|BAU|effrecommRaw|Fish:Salmon|Replace|Authorities recommendations about Baltic salmon do not change|0
3|National food safety authority|Recomm.salmon|Eat more|effrecomm|Fish:Salmon;Recommendation:Eat less|Replace|Authorities recommend to eat more Baltic salmon|0
3|National food safety authority|Recomm.salmon|Eat more|effrecommRaw|Fish:Salmon;Recommendation:Eat less|Replace|Authorities recommend to eat more Baltic salmon|0
3|National food safety authority|Recomm.salmon|Eat less|effrecomm|Fish:Salmon;Recommendation:Eat more|Replace|Authorities recommend to eat less Baltic salmon|0
3|National food safety authority|Recomm.salmon|Eat less|effrecommRaw|Fish:Salmon;Recommendation:Eat more|Replace|Authorities recommend to eat less Baltic salmon|0
0|HELCOM|Select.size|BAU|size|Fish:Baltic herring;Lower:70|Replace|Select herring size for consumption: only small, only large, or both|0
0|HELCOM|Select.size|BAU|size|Fish:Baltic herring;Lower:70|Replace|Select herring size for consumption: only small, only large, or both|0
0|HELCOM|Select.size|BAU|size|Fish:Baltic herring;Lower:120|Replace|Eat only large herring (>17 cm)|0
0|HELCOM|Select.size|BAU|size|Fish:Baltic herring;Lower:120|Replace|Eat only large herring (>17 cm)|0
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10|National food safety authority|Cons.policy|Increase|effinfo||Identity|Better availability of and less chemicals in herring and salmon|
10|National food safety authority|Cons.policy|Increase|effinfo||Identity|Better availability of and less chemicals in herring and salmon|
10|National food safety authority|Cons.policy|Reduce|effinfo||Replace|Current availability of and chemicals in herring and salmon|0
10|National food safety authority|Cons.policy|Reduce|effinfo||Replace|Current availability of and chemicals in herring and salmon|0
0|National food safety authority|Cons.policy|BAU|effrecomm|Fish:Herring|Replace|Authorities recommendations about Baltic herring do not change|0
0|National food safety authority|Cons.policy|BAU|effrecommRaw|Fish:Herring|Replace|Authorities recommendations about Baltic herring do not change|0
10|National food safety authority|Cons.policy|Increase|effrecomm|Fish:Herring;Recommendation:Eat less|Replace|Authorities recommend to eat more Baltic herring|0
10|National food safety authority|Cons.policy|Increase|effrecommRaw|Fish:Herring;Recommendation:Eat less|Replace|Authorities recommend to eat more Baltic herring|0
10|National food safety authority|Cons.policy|Reduce|effrecomm|Fish:Herring;Recommendation:Eat more|Replace|Authorities recommend to eat less Baltic herring|0
10|National food safety authority|Cons.policy|Reduce|effrecommRaw|Fish:Herring;Recommendation:Eat more|Replace|Authorities recommend to eat less Baltic herring|0
0|National food safety authority|Cons.policy|BAU|effrecomm|Fish:Salmon|Replace|Authorities recommendations about Baltic salmon do not change|0
0|National food safety authority|Cons.policy|BAU|effrecommRaw|Fish:Salmon|Replace|Authorities recommendations about Baltic salmon do not change|0
10|National food safety authority|Cons.policy|Increase|effrecomm|Fish:Salmon;Recommendation:Eat less|Replace|Authorities recommend to eat more Baltic salmon|0
10|National food safety authority|Cons.policy|Increase|effrecommRaw|Fish:Salmon;Recommendation:Eat less|Replace|Authorities recommend to eat more Baltic salmon|0
10|National food safety authority|Cons.policy|Reduce|effrecomm|Fish:Salmon;Recommendation:Eat more|Replace|Authorities recommend to eat less Baltic salmon|0
10|National food safety authority|Cons.policy|Reduce|effrecommRaw|Fish:Salmon;Recommendation:Eat more|Replace|Authorities recommend to eat less Baltic salmon|0
0|LUKE|Luke.scaling|BAU|amount||Identity|Use the Goherr fish consumption study data directly|
0|LUKE|Luke.scaling|BAU|amount||Identity|Use the Goherr fish consumption study data directly|
11|LUKE|Luke.scaling|Scale|amount|Fish:Salmon|Multiply|Average amount is scaled to actual Luke data (0.07 kg/a)|0.12
11|LUKE|Luke.scaling|Scale|amount|Fish:Salmon|Multiply|Average amount is scaled to actual Luke data (0.07 kg/a)|0.12
amount|Recommendation, Ages, Gender, oftenSource, muchSource, oftensideSource, muchsideSource, amountRawSource effinfoSource, effrecommSource, infoSource||sum|1|Remove redundant. Remove Recommendation with Eat more/less because it is replaced with more/less/BAU index.
effrecomm|Recommendation||sum|1|Change the increase/reduce to increase/BAU/reduce index of Cons.policy
conc|infoSource, conc_vitSource, conc_pcddfResult||sum|1|Remove redundant columns. Somehow model thinks conc_pcddfResult is marginal
conc|infoSource, conc_vitSource, conc_pcddfResult||sum|1|Remove redundant columns. Somehow model thinks conc_pcddfResult is marginal
expo_dir|infoSource, expo_bgSource, concSource||sum|1|Remove Source columns to save memory
expo_dir|infoSource, expo_bgSource, concSource||sum|1|Remove Source columns to save memory
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== Answer ==
== Answer ==
=== Results ===
=== Results ===
The results of this assessment have been published in a peer-reviewed scientific journal.<ref name="tuomisto2020"/>
;NOTE!: Not all of the following graphs are from the final model version (run at September, 2019). Please check the date of the graph before making conclusions.
<gallery heights=300 widths=400>
<gallery heights=300 widths=400>
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Goherr benefit-risk assessment fig21.svg|Burden of disease figures have a realistic x axis and are clearer than figures 18-20. Heart disease, stroke, and dioxin TWI (tolerable weekly intake) affect large fraction of population groups and are fairly large impacts, while other endpoints affect only a small fraction of the population. Dioxin TWI risk is very sensitive to the disability weight given.
Goherr benefit-risk assessment fig21.svg|Burden of disease figures have a realistic x axis and are clearer than figures 18-20. Heart disease, stroke, and dioxin TWI (tolerable weekly intake) affect large fraction of population groups and are fairly large impacts, while other endpoints affect only a small fraction of the population. Dioxin TWI risk is very sensitive to the disability weight given.
Goherr benefit-risk assessment fig22.svg|When looking that the net health benefits, it is clear that old age groups benefit a lot from eating fish despite risks. The impacts overall are much smaller in young age groups, and in women the critical issue is effects of child's intelligence quotient (IQ) and tooth defects, not the health impacts to the woman herself.
Goherr benefit-risk assessment fig22.svg|When looking that the net health benefits, it is clear that old age groups benefit a lot from eating fish despite risks. The impacts overall are much smaller in young age groups, and in women the critical issue is effects of child's intelligence quotient (IQ) and tooth defects, not the health impacts to the woman herself.
Goherr benefit-risk assessment fig22b.svg|Outcome of interest using different objectives. The default objective (the main assessment of this article) focusses on total net health effect in the whole population. The second objective focusses on young women only. Tolerable weekly intakes from 2001 and 2018 are converted to DALYs based on the number of people exceeding the guidance value.
Goherr benefit-risk assessment fig23.svg|For the majority of the population subgroups (except in Denmark), the net health impact deviates from zero. In many people (35-75 % of the old age groups and less than 15 % in the young age groups), the impact of Baltic fish is beneficial but for some people (less than 10 % of the subgroup in practically all subgroups except young Estonian women) the risks are sligthly greater.
Goherr benefit-risk assessment fig23.svg|For the majority of the population subgroups (except in Denmark), the net health impact deviates from zero. In many people (35-75 % of the old age groups and less than 15 % in the young age groups), the impact of Baltic fish is beneficial but for some people (less than 10 % of the subgroup in practically all subgroups except young Estonian women) the risks are sligthly greater.
Goherr benefit-risk assessment fig24.svg|Size selection of Baltic herring clearly reduces dioxin intake and also risks to youg women, but the overall picture of net benefits changes little because it is dominated by omega3 effects, which do not change in these scenarios.
Goherr benefit-risk assessment fig24.svg|Size selection of Baltic herring clearly reduces dioxin intake and also risks to youg women, but the overall picture of net benefits changes little because it is dominated by omega3 effects, which do not change in these scenarios.
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The assessment of '''[[Goherr:_Fish_consumption_study |consumption of fish]]''' was based on a survey conducted by Taloustutkimus Ltd in the four study countries. Around 500 adults were recruited from each country, and consumption of fish, especially that of Baltic salmon and herring, was asked. Also, we asked reasons for eating or not eating fish and factors that would increase or reduce fish consumption. Gender and age (18-45 or 45+) were separated in the model.
The assessment of '''[[Goherr:_Fish_consumption_study |consumption of fish]]''' was based on a survey conducted by Taloustutkimus Ltd in the four study countries. Around 500 adults were recruited from each country, and consumption of fish, especially that of Baltic salmon and herring, was asked. Also, we asked reasons for eating or not eating fish and factors that would increase or reduce fish consumption. Gender and age (18-45 or 45+) were separated in the model.
'''[[EU-kalat|Concentrations of dioxins and PCBs in fish]]''' were based on EU Fish 2 study conducted by THL in 2009. The results were based on pooled and individual fish samples (98 Baltic herring and 9 salmon samples) and analysed for 17 dioxin and 37 PCB congeners. Toxic equivalent quantities (TEQ) were used by multiplying each congener concentration with its potential to induce dioxin-like effects ([[Toxic equivalency factor|toxic equivalency factor]], TEF) and summing up. Size-specific concentration distributions were estimated for salmon and herring, and dioxin and PCB TEQs using linear regression and hierarchical Bayesian modelling using R (version 3.4.3 and JAGS package, http://cran.r-project.org/). Herring sizes and dioxin concentrations in different scenarios came from the fish growth model by SLU applied in BONUS GOHERR project; those results are published elsewhere<ref>Mia Pihlajamäki, Arja Asikainen, Suvi Ignatius, Päivi Haapasaari and Jouni T. Tuomisto. Forage Fish as Food: Consumer Perceptions on Baltic Herring. Sustainability 2019, 11(16), 4298; https://doi.org/10.3390/su11164298</ref>
'''[[EU-kalat|Concentrations of dioxins and PCBs in fish]]''' were based on EU Fish 2 study conducted by THL in 2009. The results were based on pooled and individual fish samples (98 Baltic herring and 9 salmon samples) and analysed for 17 dioxin and 37 PCB congeners. Toxic equivalent quantities (TEQ) were used by multiplying each congener concentration with its potential to induce dioxin-like effects ([[Toxic equivalency factor|toxic equivalency factor]], TEF) and summing up. Size-specific concentration distributions were estimated for salmon and herring, and dioxin and PCB TEQs using linear regression and hierarchical Bayesian modelling using R (version 3.4.3 and JAGS package, http://cran.r-project.org/). Herring sizes and dioxin concentrations in different scenarios came from the fish growth model by SLU applied in BONUS GOHERR project; those results are published elsewhere<ref name="pihlajamaki2019">Mia Pihlajamäki, Arja Asikainen, Suvi Ignatius, Päivi Haapasaari and Jouni T. Tuomisto. Forage Fish as Food: Consumer Perceptions on Baltic Herring. Sustainability 2019, 11(16), 4298; https://doi.org/10.3390/su11164298</ref>
'''Other concentrations'''. [[Concentrations of beneficial nutrients in fish]] were based on published data and a dataset obtained from the Finnish Food Safety Authority Evira. [[Mercury concentrations in fish in Finland]] were based on Kerty database produced by the Finnish Environment Institute.
'''Other concentrations'''. [[Concentrations of beneficial nutrients in fish]] were based on published data and a dataset obtained from the Finnish Food Safety Authority Evira. [[Mercury concentrations in fish in Finland]] were based on Kerty database produced by the Finnish Environment Institute.
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* Model run 1.9.2019 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=iwV2cemKKnFBl1uG]
* Model run 1.9.2019 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=iwV2cemKKnFBl1uG]
* Model run 10.9.2019 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=pTwPumzOaotRjlRN] [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=A9pSQtOn7PQsAnGI]
* Model run 10.9.2019 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=pTwPumzOaotRjlRN] [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=A9pSQtOn7PQsAnGI]
* Model run 11.9.2019 official [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=2816VWIKZPtMKBAn]
* Model run 19.9.2019 with agent-specific disease burdens [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=JaizK4lnwxBw631r]
* Model run 24.9.2019 with EPA 2004 CSF. New official [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=HwNhImyTS3iauapg]
In Opasnet many pages being worked on and are in different classes of progression. Thus the information on those pages should be regarded with consideration. The progression class of this page has been assessed:
This page is a full draft
This page has been written through once, so all important content is already where it should be. However, the content has not been thoroughly checked yet, and for example important references might still be missing.
The content and quality of this page is/was being curated by the project that produced the page.
The quality was last checked: 2020-08-12.
For the final version of this assessment, see the scientific article.[1]
What are the current individual and population level health benefits and risks of eating Baltic herring and salmon in Finland, Estonia, Denmark and Sweden? How would the health effects change in the future, if consumption of Baltic herring and salmon changes due to actions caused by a) fish consumption recommendations or limitations, b) different management scenarios of Baltic sea fish stocks, or c) selection of fish sizes for human consumption?
BONUS GOHERR project (2015-2018) looked at this particular question. A health impact assessment was performed based on fish consumption survey done in the four target countries; EU Fish 2 study about dioxin and PCB concentrations; a dynamic growth model about Baltic herring stock sizes and dioxin accumulation; scientific literature about exposure-response functions of several compounds found in Baltic fish; and online models produced in Opasnet web-workspace. This page describes the benefit-risk assessment performed.[1]
Dioxin and PCB concentrations have been constantly decreasing in Baltic fish for 40 years, and now they are mostly below EU limits. Also Baltic herring consumption has been decreasing during the last decades and is now a few grams per day, varying between age groups (old people eat more), genders (males eat more) and countries (Estonians eat more and Danes less than others). People reported that better availability of easy products, recipes, and reduced pollutant levels would increase their Baltic herring consumption. In contrast, recommendations to reduce consumption would have little effect on average.
Health benefits of Baltic herring and salmon clearly outweigh health risks in age groups over 45 years. Benefits are higher even in the most sensitive subgroup, women at childbearing age. The balance is close to even, if exceedance of the tolerable daily intake is given weight in the consideration and if other omega-3 sources are given priority over fish. The analysis was robust in a sense that we did not find uncertainties that could remarkably change the conclusions and suggest postponing decisions in hope of new information.
Overall, main arguments from this health assessment and other disciplines studied in Goherr are in favour of getting rid of dioxin-based food restrictions related to Baltic herring and salmon, and promoting human consumption of Baltic fish.
This assessment is part of the WP5 work in Goherr project. Purpose is to evaluate health benefits and risks caused of eating Baltic herring and salmon in four Baltic sea countries (Denmark, Estonia, Finland and Sweden). This assessment is currently on-going.
Question
What are the current individual and population level health benefits and risks of eating Baltic herring and salmon in Finland, Estonia, Denmark and Sweden? How would the health effects change in the future, if consumption of Baltic herring and salmon changes due to actions caused by a) fish consumption recommendations or limitations, b) different management scenarios of Baltic sea fish stocks, or c) selection of fish sizes for human consumption?
Intended use and users
Results of this assessment are used to inform policy makers about the health impacts of fish. Further, this assessment will be combined with the results of the other Goherr WPs to produce estimates of future health impacts of Baltic fish related to different policy options. Especially, results of this assessment will be used as input in the decision support model built in Goherr WP6.
Participants
University of Helsinki: Sakari Kuikka, Päivi Haapasaari, Suvi Ignatius, Kirsi Hoviniemi, Inari Helle, Annukka Lehikoinen, Mika Rahikainen
National Institute for Health and Welfare (THL): Jouni Tuomisto, Arja Asikainen, Päivi Meriläinen
University of Oulu: Timo P. Karjalainen, Simo Sarkki, Mia Pihlajamäki
Swedish University of Agricultural Sciences (SLU): Anna Gårdmark, Johan Östergren, Magnus Huss, Andreas Bryhn, Philip Jacobson
University of Aalborg/Innovative Fisheries Management (IFM-AAU): Alyne Delaney, Jesper Raakjaer
Stakeholders needed in the assessment: fisher's associations, agricultural/fisheries ministries in Finland, Sweden, Estonia, Denmark.
Boundaries
Four baltic sea countries (Denmark, Estonia, Finland, Sweden)
Current situation (fish use year 2016, pollutant levels in fish year 2010)
Estimation for future (not year specific)
Area considered: Sweden, Finland, Denmark, Estonia. For detailed herring/salmon stock modelling, only the Bothnian Sea and Gulf of Bothnia is considered.
Policies considered: See the table below.
This assessment looks at health only. Goherr as a project will consider wider objectives: threats to and state of the fish stocks; impacts and governance responses. See below.
Decisions and scenarios
In Goherr, the scope is wide and we are looking at scenarios about fragmented vs. integrated governance and high vs. low human impact at the Baltic Sea Region. These scenarios are described in more detail on a page about management scenarios developed in Goherr WP3.
In this assessment, will look at health only, but the decisions and options considered are based on the wider discussions about integrated governance. The main decisions considered are improvements about fish availability and recipes, food recommendations, and selection of fish sizes used for consumption. The changes caused by each option are estimated based on the Goherr fish consumption study.[2]
Info.improvements with options
BAU (Business as usual): current availability of and chemical levels in herring and salmon.
Yes: Better availability of and less chemicals in herring and salmon.
Recomm.herring with options
BAU: authorities recommendations about Baltic herring do not change.
Eat more: authorities recommend to eat more Baltic herring.
Eat less: authorities recommend to eat less Baltic herring.
Recomm.salmon with options
BAU: authorities recommendations about Baltic salmon do not change.
Eat more: authorities recommend to eat more Baltic salmon.
Eat less: authorities recommend to eat less Baltic salmon.
Coherent consumer policy (Cons.policy): This is a combination of the three decisions above, with options:
BAU: BAU is chosen for all three decisions.
Less: BAU for Info.improvements and Eat less for both herring and salmon recommendation
More: Yes for Info.improvements and Eat more for both herring and salmon recommendation
Inconsistent: All other combinations
There are also more technical scenarios: what if nobody eats fish more than 3 g/d on average, what time points will be considered, what pollutants will be considered, and will other sources of nutrients and pollutants be considered as primary or will fish be considered as primary source. The ordering in the latter scenario is important if exposure-response functions are non-linear, as is case with e.g. omega-3 fatty acids. The marginal omega-3 benefits are highest with low exposures, and marginal benefit becomes smaller as the exposure increases. Thus, the health impacts of a primary source are considered larger than those of a secondary source.
The assessment started in April 2015. The first stakeholder meeting was in February 2016 and the second in November 2017. The final results with the full assessment were finalised in June 2018.
Answer
Results
The results of this assessment have been published in a peer-reviewed scientific journal.[1]
NOTE!
Not all of the following graphs are from the final model version (run at September, 2019). Please check the date of the graph before making conclusions.
Baltic herring has larger variation in concentration because it depends on the fish size. Nowadays half or even more of Baltic herring and salmon is below the EU limit value.
Limiting fish use to 3 g/d per species would theoretically reduce pollutant exposures a lot and have an impact on threshold endpoints such as dioxin TWI (tolerable weekly intake). However, this policy is not realistic (see Figs 8-11). It is an important value judgement whether fish is seen as a secondary or primary source of nutrients, i.e. whether to calculate background intake or not, respectively, due to non-linear dose-responses like with stroke and heart disease; for health impacts, see Fig 27.
Baltic herring consumption shows several trends: men eat more than women; the old eat more than the young; Estonians eat more than others. For salmon, Danes and Estonians report much more consumption than what is available on the market; clearly they don't know the origin of their salmon.
Consumption policies (improved information, recommendations) seem effective in increasing Baltic herring (and somewhat salmon) consumption. However, they are not effective in reducing consumption as only a small fraction obeys this recommendation. See also Figs 10 and 11.
Individual change in consumption depends on previous consumption but not much on population subgroup. Also, although some people obey the recommendation to reduce consumption, almost an equal amount does the opposite, and most do not change.
Fig 10 is about people consuming large amounts of fish. The exactly same phenomenon is visible in subgroups that eat moderate amounts (i.e. 5 g/d or less).
There is large individual variation (almost hundredfold) in fish consumption in most subgroups. There is also a large fraction of people who do not eat these fishes at all (25-90 % with Baltic herring, 70-90 % with Baltic salmon).
Most people are below the recommended (vitamin D) or tolerable (methylmercury, dioxin) daily intake. The differences between countries are clearly larger with dioxin (TEQ) than with methylmercury (MeHg).
Dioxin exposures from Fig 14 are shown by subgroup and country. Few young females exceed the tolerable daily intake any more, as both concentrations and consumption have reduced.
Average exposures do not go beyond tolerable limits of dioxin except with the most active increasing policy in Denmark and Estonia (which countries also over-reported salmon intake). Methylmercury exposures are far from the limit, but still it has significant health impacts (see Fig 26)
Exposure-response functions of the health impacts considered in this assessment. With yes/no impacts no (0) means benefit (or no harm) and yes (1) means harm (or no benefit).
Figures with numbers of cases must be interpreted with caution. The model unrealistically assumes that the whole population subgroup behaves like one individual, so only the average numbers are realistic. However, these figures are useful in demonstrating how the risks are distributed within subgroups: most impacts come to very few people.
Burden of disease figures have a realistic x axis and are clearer than figures 18-20. Heart disease, stroke, and dioxin TWI (tolerable weekly intake) affect large fraction of population groups and are fairly large impacts, while other endpoints affect only a small fraction of the population. Dioxin TWI risk is very sensitive to the disability weight given.
When looking that the net health benefits, it is clear that old age groups benefit a lot from eating fish despite risks. The impacts overall are much smaller in young age groups, and in women the critical issue is effects of child's intelligence quotient (IQ) and tooth defects, not the health impacts to the woman herself.
Outcome of interest using different objectives. The default objective (the main assessment of this article) focusses on total net health effect in the whole population. The second objective focusses on young women only. Tolerable weekly intakes from 2001 and 2018 are converted to DALYs based on the number of people exceeding the guidance value.
For the majority of the population subgroups (except in Denmark), the net health impact deviates from zero. In many people (35-75 % of the old age groups and less than 15 % in the young age groups), the impact of Baltic fish is beneficial but for some people (less than 10 % of the subgroup in practically all subgroups except young Estonian women) the risks are sligthly greater.
Size selection of Baltic herring clearly reduces dioxin intake and also risks to youg women, but the overall picture of net benefits changes little because it is dominated by omega3 effects, which do not change in these scenarios.
Time trends show that in each country, the risks have come down a lot and especially the tooth defect risk has reduced. Indeed, these were observed in Finland in children born in the 1980's[3] but not later.
When one exposure agent is removed at a time, it is possible to see the impacts on endpoints that are affected by several compounds, in this case child's IQ. Without methylmercury in fish, the health benefits of omega3 would be roughly double than what they are now. Note that the scales on the y axes vary.
If background is not considered, i.e. fish is seen as the primary source of omega3, the health benefits are clearly larger than in the business-as-usual scenario of this model. It is a value judgement to decide whether fish or other sources are considered primary. Limiting fish intake to 3 g/d would reduce risks a lot, but benefits would also clearly decrease.
In a bigger picture, Baltic fish and its health hazards are only one of the many environmental health risks. It is not even close to the largest ones, but it may be in the top 10 list.
This is a simplified version of Fig 22: it is only about Finland, inconsistent policies have been omitted, and health impacts have been categorised into only three groups: benefits, risks, and TWI dioxin (which is actually not a health endpoint even if we have given it a disability value.)
Value of information analyses
For detailed results, see model run on 18.11.2018. Value of information was looked at in three parts, where a bunch of similar decisions were considered together. In these VOI analyses, infertility was used as the outcome for the sperm concentration effect, while tolerable weeksly intakes (both the current "Dioxin TWI" and the new suggested "TWI 2018") were ignored.
Value of information was calculated for the total burden of disease in a random population subgroup in the four study countries, but using uncertainties for individual people. This approach ensures that value of information is not underestimated, because at population level many uncertainties are smaller than at individual level.
Select herring size
There is practically no expected value of perfect information (EVPI) (only 1.5 DALY/a) because Ban large, i.e. switching to small herring is in most cases better than other alternatives. However, also other options are beneficial, and the expected value of including that option is 16 DALY/a.
If that option is excluded, EVPI increases to 8 DALY/a.
Consider background and limit maximal fish intake to 3 g/d are evaluated at the same time.
EVPI is slightly higher than with herring size, 51 DALY. This is because there is no obvious single decision option to choose.
Dropping the option Background=No would cost 1880 DALY/a, demonstrating that that is clearly a good choice. However, whether background should be considered or not is not an actionable decision but rather a value judgement about how the situation should be seen. In practice, if you consider background intake (Background=Yes), you ignore a large amount of health benefits from omega3 fatty acids in fish. Some people may say that ignoring it is exactly what you should do because those omega3 fatty acids can easily be received from sources that do not have pollutants (the default in this assessment), while others say that fish and other natural foods are the primary source, and omega3 pills and other food supplements should only be used if undernourished.
If you always consider background intake, then the model uncertainties decrease, and your EVPI is lower (10 DALY/a). The largest EVPI (42 DALY/a) is obtained when fish intake is not limited to 3 g/d; this is because there is more room for benefits leveling off and relative importance of risks increasing, thus increasing uncertainty to decision making.
Improved information (including availability and usability of fish) and consumption recommendations.
EVPI with these decisions is 40 DALY/a, so there is some uncertainty about what to do.
The most important decision option is to increase information and fish availability (145 DALY/a), while any of the other options can be excluded without much change in expected value.
In a previous analysis we used tolerable weeksly intake instead of infertility (model run on 20.4.2018, data not shown). The disability weight used for tolerable weekly dioxin intake is highly uncertain in the model (hundredfold uncertainty 0.0001 - 0.01 DALY/case of exceedance). Therefore, presumably it would be very important to know the actual value that the society wants to allocate to this impact. But actually it is not, as knowing the value has expected value of partial perfect information (EVPPI) of only 12 DALY/a. The reason for this seems to be that this disability weight rarely becomes so high that a decision maker would actually regret fish-promoting policies.
Conclusions
Overall, main arguments from this health assessment and other disciplines studied in Goherr are in favour of getting rid of dioxin-based food restrictions related to Baltic herring and salmon, and promoting human consumption of Baltic fish.
Fish is healthy food, and its use should be promoted. This applies to Baltic herring and salmon as well, even when considering the dioxin and methylmercury concentrations and vulnerable subgroups. This assessment has shown several reasons that support this conclusion:
Net health benefits are clear in older age groups with increased risk of cardiovascular diseases.
Even in the subgroup of young females, the risks are close to or smaller than benefits.
We also considered the tolerable weekly intake (TWI) of dioxin in the assessment. This is actually not a health risk per se, but an indicator that the exposure is approaching levels where actual health harm may occur. However, we did give it a reasonable weight in the model, as we thought that it is something that people don’t want to exceed. Even when it is considered in the assessment, the previous conclusions prevail. If this outcome is ignored, the health risks become so small that there is little uncertainty about the conclusion.
Dioxin concentrations have decreased dramatically since the 1990's, which was the starting point of our scenarios. In fact, the decrease started even earlier during the 1970's and 1980's when industrial emissions started to improve. The current levels are roughly one tenth of the worst levels. So, the remaining dioxin problem is way smaller than its historical reputation implies.
Young women are the risk group but they eat less Baltic herring and salmon than other population subgroups. Therefore, fish promotion policies are likely to increase the health benefits in other subgroups (especially the elderly) much more than health risks in young women.
According to the fish consumption survey (Task 5.3), policies promoting Baltic fish consumption seem to encourage people to increase their fish intake substantially. In contrast, discouraging policies were effective only in a minority, while some would paradoxically increase fish intake, and the average change would be negligible.
Recommendations to limit Baltic herring intake can be targeted to the risk group of young women only. However, it is possible that as a side effect, these recommendations affect also other age groups even if they would benefit from increasing herring consumption. The other groups could face risks that are larger than benefits in young women, and the overall health impact could be poor. (However, while people responded that such recommendations would not reduce their herring intake, herring consumption has been decreasing for decades in parallel with the discussion on the dioxin risks of herring. Thus, definitive conclusion on this point is not possible.)
There are still large uncertainties in both scientific and value-based issues. However, our results show that robust conclusions about decision options can be made with the current information. Also, it seems that there is no single source of additional information that would reveal crucial insights into this issue. In other words, there seems to be little or no value in postponing recommendations in the hope that we would, in the near future, learn something that would help us make wiser decisions.
The conclusions above are based on health considerations only. If we include economic, cultural, and food security aspects into this consideration, we can argue that:
The price of Baltic herring for food is clearly higher than price of herring for feed. Therefore, for fishing industry it would be beneficial to start catching more fish for human consumption and therefore effects are synergistic with those of health.
In many areas around the Baltic Sea, Baltic herring has high cultural value as a food and as a tradition. An increase in consumption would help maintaining the culture, and vice versa.
Baltic Sea could be an important source of human food, but currently it is rather a source of animal feed. If dioxin-based food regulations were abandoned, it would be easier to develop the Baltic Sea as a food reserve.
Error creating thumbnail: Unable to save thumbnail to destinationSchematic picture of the health benefit-risk model for Baltic herring and salmon intake.Error creating thumbnail: Unable to save thumbnail to destinationDetailed modelling diagram of the health benefit-risk model. Green nodes are original data, red nodes are based on scientific literature, and blue nodes are computational nodes. Those with a number are generic nodes designed to be used in several assessments. the number refers to the page identifier in Op_en wiki (this Opasnet wiki). Nodes with red borders are places where it is possible to aggregate an assessment of random individuals to population-level examination. At those points, individual variation disappears and only population-level uncertainty remains in the model. This aggregation is done with mc2d function, but it is NOT used in the default model runs and therefore all results currently presented on this page are based on individual-level assessments.
Stakeholders
Who will be affected by the decisions?
Professional fishers and their organisations
Anglers and other recreational fishers and their organisations
Land owner fishers and their organisation (they have inherited rights)
Saami people also have inherited rights (in Sweden only?)
Food and fish industry.
Mink and fox farmers
All people utilising recreational values of the Baltic Sea (relates to eutrophication)
Ministries (of agriculture, environment, and commerce)
EU: DG Mare, EU Parliament?
SWAM (Swedish Agency for Marine and Water Management)
Who are interested in the decisions?
Scientists
Environmental NGOs
Bureaucrats
ICES (International Council for the Exploration of the Sea)
Helcom
Dependencies
The assessment model is implemented in a modular way in Opasnet. In practice, this means that the data and code used for different parts of the model is located at different pages in Opasnet. In this section, we give the overview and links to the module pages, and all details can be found from there. The whole idea of Opasnet is that different modules can be used in different assessments simultaneously, and that updates in any module are fully reflected in all assessments when they are rerun.
The assessment of consumption of fish was based on a survey conducted by Taloustutkimus Ltd in the four study countries. Around 500 adults were recruited from each country, and consumption of fish, especially that of Baltic salmon and herring, was asked. Also, we asked reasons for eating or not eating fish and factors that would increase or reduce fish consumption. Gender and age (18-45 or 45+) were separated in the model.
Concentrations of dioxins and PCBs in fish were based on EU Fish 2 study conducted by THL in 2009. The results were based on pooled and individual fish samples (98 Baltic herring and 9 salmon samples) and analysed for 17 dioxin and 37 PCB congeners. Toxic equivalent quantities (TEQ) were used by multiplying each congener concentration with its potential to induce dioxin-like effects (toxic equivalency factor, TEF) and summing up. Size-specific concentration distributions were estimated for salmon and herring, and dioxin and PCB TEQs using linear regression and hierarchical Bayesian modelling using R (version 3.4.3 and JAGS package, http://cran.r-project.org/). Herring sizes and dioxin concentrations in different scenarios came from the fish growth model by SLU applied in BONUS GOHERR project; those results are published elsewhere[2]
Exposures to pollutants and nutrients were simply products of consumption amounts and concentrations in the consumed fish. An exception to this were the infant's exposures to dioxin and methylmercury during pregnancy and breast-feeding, as they were derived from the mother's exposure using simple toxicokinetic models.
Burden of disease was estimated in two alternative ways: if the burden of a particular disease in the target population was known, the attributable fraction of a particular compound exposure was calculated. If it was not known, the excess number of cases due to the exposure was estimated using health impact assessment, and this was multiplied by the years under disease per case and the disability weight of the disease. If the exposure-response function was relative to background risk of the disease, disease risks from Finland were used for all countries. Population data was from Eurostat (http://ec.europa.eu/eurostat). Disability weights and durations of diseases were based on the estimates from the Institute for Health Metrics and Evaluation (https://healtdata.org), adjusted using author judgement when appropriate estimates were not available.
Model parameters
We assume that the background exposure is a uniform distribution between zero and the average Finnish intake for nutrients (according to the Finriski study). The typical nutrient intake from Baltic herring is subtracted from the average to avoid double counting.
Assumes disability weight 1 and duration 10 a (all durations are in years) with 50% uncertainty
2
Stroke mortality
5 - 15
Assumes disability weight 1 and duration 10 a with 50 % uncertainty
3
Yes or no dental defect
0 - 0.12
disability weight 0.001 and duration 60 a with 100 % uncertainty. Or should we use this: Developmental defect: caries or missing tooth, 0.008 (0.003 - 0.017), Periodontitis weight from IHME. D: 1. U from IHME?
4
Cancer morbidity
0.3937391 (0.3566650 - 0.4356150)
IHME estimate for breast cancer (the most important cancer in this analysis): 19.7 (95% CI 21.8 - 17.8) YLL/case. This comes from a lifetime exposure, so it is (linearly( assumed that 1/50 of this is caused by one-year exposure.
5
Vitamin D recommendation
0.0001 - 0.0101
disability weight 0.001 and duration 1 a with 101-fold uncertainty. 1 if not met
6
Dioxin recommendation tolerable daily intake
0.0001 - 0.0101
disability weight 0.001 and duration 1 a with 101-fold uncertainty. 1 if not met
7
Dioxin recommendation tolerable daily intake 2018
0.0001 - 0.0101
disability weight 0.001 and duration 1 a with 101-fold uncertainty. 1 if not met
8
Sperm concentration
0-5
disability weight 0.1 and duration 50 a in boys, with 2-fold uncertainty. See ERF of dioxin
9
Loss in child's IQ points
0.11 (0.06 - 0.16)
Mild intellectual disability (IQ<70) has disability weight 0.043 (95 % CI 0.026-0.064) based on IHME. This is scaled to one IQ point with duration 75 a.
PCDD/F and PCB concentration distributions for each fish species and country are estimated in the following way:
EU Fish 2 study is used as the reference, because it has a large number of measurements. They come mostly from Bothnian Sea (subdivision 30).
The distributions are like those from the EU Fish 2 study, except that the means are scaled based on the area of interest relative to the reference (see table above).
On average, a population has ca. 6 IQ points per person below 100: mean(abs(rnorm(10000, 100,15)-100))/2
2
Cancer morbidity
incidence
# /100000py
657
Statistics Finland https://tilastot.syoparekisteri.fi/syovat/ applies to all subgroups because cancer is lifetime risk
3
Dioxin recommendation tolerable daily intake
incidence
# /100000py
11000
Average over the whole Goherr population is 11 %
4
Dioxin recommendation tolerable daily intake 2018
incidence
# /100000py
32000
Average over the whole Goherr population is 32 %
5
Yes or no dental defect
incidence
Group:Female 18-45
# /100000py
22400
Alaluusua et al 2004 found 11/49 cases in two lowest groups
6
Sperm concentration
incidence
Group:Female 18-45
# /100000py
7000
Male infertility rate is 7 % (Wikipedia)
7
Vitamin D recommendation
incidence
# /100000py
22000
Valsta et al.: 14 and 30 % of Finnish males and females, respectivey, are below average need (7.5 ug/d) http://urn.fi/URN:ISBN:978-952-343-238-3
Tooth defect disease burden comes also from Disease risk#Calculations. But also incidence is needed because the ERF is not related to background: Alaluusua et al 2004 found 11/49 cases in two lowest groups
Exposure to relevant agents from other sources than Baltic fish
2
amount
Op_en7748/hia
consumption amount
key ovariable
Amount of fish consumed
exposure
3
amountRaw
Op_en7748/hia
Amount of fish consumed without background exposure
4
assump
Op_en7748/hia
Assumptions about fish meal sizes and other things
5
bgexposure
Op_en7748/hia
background exposure
Subgroup-specific background intakes
6
BoD
Op_en7748/hia
disase burden
objective
Total burden of disease (the objective of the assessment)
7
BoDcase
Op_en7748/hia
disease burden based on cases
Burden of disease for responses that are estimated using numbers of cases
8
BoDpaf
Op_en7748/hia
disase burden based on PAF
Burden of disease for responses that are estimated using population attributable fraction
9
BoDRaw
Op_en7748/hia
Burden of disease calculated as if people in each subgroup are identical
10
BoDt
Op_en7748/hia
observerd disase burden
Disease-specific burden of disease due to all causes
11
BW
Op_en7748/hia
body weight
Body weight of an individual (nominal 70 kg)
12
casesabs
Op_en7748/hia
cases of absolute ERFs
Number of cases from responses that have background-independent ERFs
13
casesrr
Op_en7748/hia
cases of relative ERFs
Number of cases from responses that have background-dependent ERFs
14
conc
Op_en7748/hia
concentrations in fish
key ovariable
Concentrations of relevant exposure agents
exposure
15
conc_mehg
Op_en7748/hia
Concentration of methylmercury
16
conc_pcddf
Op_en7748/hia
Concentration of dioxin and PCB
17
conc_vit
Op_en7748/hia
Concentration of vitamin D
18
Decamount
Op_en7748/hia
Decision about limiting intake to 10 g/d or not
19
Decbgexposure
Op_en7748/hia
Decision about considering background exposures from other sources or not
20
Deceffinfo
Op_en7748/hia
Decision about better availability and less chemicals in herring and salmon
21
Deceffrecomm
Op_en7748/hia
Decision about recommending more, less or BAU amount of consumption
22
Decsizes
Op_en7748/hia
Decision about the size range of consumed fish
23
disabilityweight
Op_en7748/hia
disability weights
Disability weights for relevant diseases
24
dose
Op_en7748/hia
dose
key ovariable
Dose of relevant exposure agents (scaled to body weight if necessary)
BoD
25
duration
Op_en7748/hia
duration of disease
Technical ovariable for nominal disease duration (always 1 because burden is in disabilityweight)
26
dx.expo.child
Op_en7748/hia
dioxin exposure in child
key ovariable
Child exposure to dioxin during pregnancy and breast feeding
27
effinfo
Op_en7748/hia
effectiveness of information
Effectiveness of information on fish consumption
28
effrecomm
Op_en7748/hia
effectiveness of recommendations
Effectiveness of recommendations on fish consumption
29
ERF
Op_en7748/hia
exposure-response function
key ovariable
Exposure-response functions for relevant agents and responses
BoD
30
ERF_diox
Op_en7748/hia
ERF for dioxin (Ahr-mediated)
31
ERF_env
Op_en7748/hia
ERF for several environmental pollutants (not used in this model)
32
ERF_mehg
Op_en7748/hia
ERF of methyl mercury on child IQ
33
ERF_omega3
Op_en7748/hia
ERF of omega-3 fatty acids on child IQ, coronary heart disease and stroke
34
ERF_vit
Op_en7748/hia
ERF of vitamin D on intake recommendation compliance
35
ERFchoice
Op_en7748/hia
Technical ovariable slicing the relevant ERFs for this model
36
expoRaw
Op_en7748/hia
Exposure to relevant agents, does not include child's exposure
37
exposure
Op_en7748/hia
exposure
key ovariable
Exposure to relevant agents, includes child's exposure
38
f_ing
Op_en7748/hia
Fraction of dioxins actually absorbed from food
39
f_mtoc
Op_en7748/hia
Fraction of mother's dioxins entering the child during pregnancy and breast feeding
40
frexposed
Op_en7748/hia
fraction of exposed population
Fraction of the exposed population
41
Hg
Op_en7748/hia
mercury data
Data of mercury concentrations from Kerty database
Op_en4004
42
incidence
Op_en7748/hia
incidence
Incidences of relevant diseases (CHD and stroke in this assessment)
43
info
Op_en7748/hia
Technical ovariable with non-marginal information that may drop during calculations (age, gender, country)
44
jsp
Op_en7748/hia
key ovariable
Results of the key questions in the Goherr consumer survey
amount
45
lengt
Op_en7748/hia
length
Length of the fish consumed (h missing to avoid confusion with function length)
46
mc2d
Op_en7748/hia
Function for 2D Monte Carlo
47
mc2dparam
Op_en7748/hia
Model parameters for 2D Monte Carlo
48
much
Op_en7748/hia
how much fish
How much fish is eaten during a meal (Goherr consumer survey)
49
muchside
Op_en7748/hia
how much side dishes
How much side meal fish is eaten during a meal (Goherr consumer survey)
50
often
Op_en7748/hia
how often fish
How often fish meals are eaten fish (Goherr consumer survey)
51
oftenside
Op_en7748/hia
how often side dishes
How often fish is eaten as side dish (Goherr consumer survey)
52
population
Op_en7748/hia
population size
Population size in subgroups (country, age, gender)
53
RR
Op_en7748/hia
relative risk
Relative risk of relevant responses given the modelled dose
54
samps.j
Op_en7748/hia
Parameters from Bayesian model for EU Fish study
55
sizes
Op_en7748/hia
Fractions of fish populations in each size group
56
sumExposcen
Op_en7748/hia
Function for exposure scenarios
57
survey1
Op_en7748/hia
consumer survey
Data of the Goherr consumer survey
Op_en7749
58
t0.5
Op_en7748/hia
dioxin half-life
Half-life of dioxin in mother during breast-feeding (elimination+excretion to milk)
59
threshold
Op_en7748/hia
threshold
Threshold for ERF of all relevant agents
60
threshold_diox
Op_en7748/hia
Threshold for dioxin ERF
61
threshold_env
Op_en7748/hia
Threshold for ERF of several pollutants (not used in this model)
62
threshold_mehg
Op_en7748/hia
Threshold for mrthylmercury ERF
63
threshold_omega3
Op_en7748/hia
Threshold for omega-3 fatty acid ERF
64
threshold_vit
Op_en7748/hia
Threshold for ERF of vitamin D
65
time
Op_en7748/hia
time
Time points considered in the assessment
66
vit.param
Op_en7748/hia
Parameters from Bayesian model for vitamin concentration data
67
fig1
Op_en7748/plots
Fish length in the EU Fish 2 study of Goherr MC model
lengt
Op_en7748
68
fig2
Op_en7748/plots
Fish length in the EU Fish 2 study of Goherr MC model, salmon and herring
lengt
Op_en7748
69
fig3
Op_en7748/plots
Concentrations of relevant compounds in Baltic fish
conc
Op_en7748
70
fig4
Op_en7748/plots
Dioxin in Baltic fish based on EU Fish 2 study or models
conc_pcddf
Op_en7748
71
fig5
Op_en7748/plots
PCDD/F in Baltic fish based on EU Fish 2 study of models, salmon and herring
conc
Op_en7748
72
fig6
Op_en7748/plots
Fish dioxins vs length by EU Fish 2 data or model
conc_pcddf
Op_en7748
73
fig7
Op_en7748/plots
Exposure to compounds in fish by background and limit decisions
exposure
Op_en7748
74
fig8
Op_en7748/plots
Consumption of Baltic fish by subgroup and policy
amount
Op_en7748
75
fig9
Op_en7748/plots
Fish intake change due to consumption policies
amount
Op_en7748
76
fig10
Op_en7748/plots
Change in individual fish intake after all consumption policies
amount
Op_en7748
77
fig11
Op_en7748/plots
Change in individual fish intake after all consumption policies
amount
Op_en7748
78
fig12
Op_en7748/plots
Consumption of Baltic fish by country and group
amount
Op_en7748
79
fig13
Op_en7748/plots
Consumption of Baltic fish by country and group, detail
amount
Op_en7748
80
fig14
Op_en7748/plots
Exposure to compounds from Baltic fish and background
exposure
Op_en7748
81
fig15
Op_en7748/plots
Exposure to dioxin from Baltic fish
exposure
Op_en7748
82
fig16
Op_en7748/plots
Exposure to compounds from fish and background
exposure
Op_en7748
83
fig17
Op_en7748/plots
Exposure-response functions for health impacts
ERF
Op_en7748
84
fig18
Op_en7748/plots
Numbers of cases for non-relative responses. All individuals are identical in population
casesabs
Op_en7748
85
fig19
Op_en7748/plots
Numbers of cases for non-relative responses, detail. All individuals are identical in population
casesabs
Op_en7748
86
fig20
Op_en7748/plots
Numbers of cases for relative responses. All individuals are identical in population
casesrr
Op_en7748
87
fig21
Op_en7748/plots
Burden of disease by response and group
BoD
Op_en7748
88
fig22
Op_en7748/plots
Burden of disease by country, group and policy
BoD
Op_en7748
89
fig23
Op_en7748/plots
Net disease burden by country and population group
BoD
Op_en7748
90
fig24
Op_en7748/plots
Burden of disease by country, group and herring size selection
BoD
Op_en7748
91
fig25
Op_en7748/plots
Burden of disease in young females by country and time
BoD
Op_en7748
92
fig26
Op_en7748/plots
Burden of disease by group and one compound removed at a time
BoD
Op_en7748
93
fig27
Op_en7748/plots
Burden of disease for subgroups by background and limit
BoD
Op_en7748
94
fig28
Op_en7748/plots
Environmental burden of disease in Finland
BoDRaw
Op_en7748
95
fig29
Op_en7748/plots
Burden of disease by group and policy in Finland
BoD
Op_en7748
Analyses
Indices
Country (Denmark, Estonia, Finland, Sweden)
Year (current [concentration data from 2009 but projected to 2018], future)
Gender (female, male)
Age (18-45 years, >45 years)
Fish species (Baltic herring, Baltic salmon)
Health end-point (coronary heart disease and stroke mortality, tooth defect caused by dioxins, intelligence quotient change in child of a pregnant or nursing woman, exceedance of tolerable daily intake of dioxin, cancer morbidity, and compliance with vitamin D recommendation)
Compound (TEQ (PCDD/F and PCB), Vitamin D, Omega3 (includes EPA and DHA), MeHg)
Calculations
This section will have the actual health benefit-risk model (schematically described in the above figure) written with R. The code will utilise all variables listed in the Dependencies section above. Model results are presented as tables and figures, and the most important ones are shown on this page.
20.8.2019: Archived previous models that used old HIA model with casesabs and casesrr ovariables. The following codes were removed from the current page:
18.5.2017: Archived exposure model Op7748/exposure by Arja (used separate ovariables for salmon and herring) [5]
Sketches about modelling determinants of eating (spring 2018) [6]
Health risk-benefit assessment model (BRA) implemented on this page produces also data for the overall Goherr Bayesian belief network model (BBN). In brief, the models are built in a way that they share the important nodes. The following ovariables are used to produce the data for BBN: bgexposure, amount, expoRaw, conc, lengt, BoD. There are merged and then the following information are selected:
Information improvements
Food recommendations herring amount
Food recommendations salmon amount
Food recommendations for herring size
Consumer country
Consumer gender
Consumer age group
Size of an indiv. herring in human diet
Effect of improved information herring
Human consumption of herring
Human consumption of salmon
Effect of improved information salmon
Other intake of Omega3
Omega3 intake total
Dioxin conc herring
Dioxin conc salmon
Human intake of dioxin from herring
Human intake of dioxin from salmon
Dioxin intake total
Net burden of disease
Omega3 from herring
Omega3 from salmon
As a result a data.frame with 22 columns is produced. This is used to produce 13 csv files, one for each BBN node.
Sara M. Pires, Géraldine Boué, Alan Boobis, Hanna Eneroth, Jeljer Hoekstra, Jeanne-Marie Membré, Inez Maria Persson, Morten Poulsen, Juliana Ruzante, Jacobvan Klaverena, Sofie T. Thomsen, Maarten J. Nauta. Risk Benefit Assessment of foods: Key findings from an international workshop. Food Research International, Available online 10 September 2018. doi:10.1016/j.foodres.2018.09.021
Risk and Benefit Assessment of Herring and Salmonid Fish from the Baltic Sea Area: National Food Agency in Sweden performed a risk assessment about the Baltic herring exemption in 2011 and concluded that "In conclusion, a cessation of the exemption from maximum limits would be more beneficial from a public health point-of view than a continued exemption. In the case of no exemption there would be a decreased exposure of the population to dioxins and dl-PCB without any limitation of the intake of beneficial nutrients." The conclusion was based on two criteria: comparison of TWI and intake in women in childbearing age and in children; and a view that large herring with high dioxin levels can be banned and replaced with small herring with low dioxin levels without any other change.[10]
Swedish Market Basket 2010: Swedish Food Agency performed a market basket study and concluded that the per capita dioxin+PSB intake was 39 (28-49) pg/d TEQ in 2010. This is about one fourth of the tolerable weekly intake (140 pg/d TEQ in a 70-kg person), and therefore it was concluded that such levels do not markedly increase the risk of health effects[11].
Assmuth Timo and Jalonen Pauliina 2005: Risks and management of dioxin-like compounds in Baltic Sea fish: An integrated assessment. Nordic Council of Ministers, Copenhagen. Assmuth Jalonen Dioxin risk assessment 2005[26]
EFSA 2012. Update of the monitoring of levels if dioxins and PCBs in food and feed. EFSA Journal 10(7):2832. doi: 10.2903/j.efsa.2012.2832
References
↑ 1.01.11.2Tuomisto, J.T., Asikainen, A., Meriläinen, P., Haapasaari, P. Health effects of nutrients and environmental pollutants in Baltic herring and salmon: a quantitative benefit-risk assessment. BMC Public Health 20, 64 (2020). https://doi.org/10.1186/s12889-019-8094-1
↑ 2.02.1Mia Pihlajamäki, Arja Asikainen, Suvi Ignatius, Päivi Haapasaari and Jouni T. Tuomisto. Forage Fish as Food: Consumer Perceptions on Baltic Herring. Sustainability 2019, 11(16), 4298; https://doi.org/10.3390/su11164298
↑ 3.03.1Satu Alaluusua, Pirjo-Liisa Lukinmaa, Terttu Vartiainen, Maija Partanen, Jorma Torppa, Jouko Tuomisto. (1996) Polychlorinated dibenzo-p-dioxins and dibenzofurans via mother's milk may cause developmental defects in the child's teeth. Environmental Toxicology and Pharmacology Volume 1, Issue 3, 15 May 1996, Pages 193-197. doi:10.1016/1382-6689(96)00007-5
↑U.S.EPA. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisory. Volume 2: Risk Assessment and Fish Consumption Limits, 3rd Edition. 2000. Table 3-1.
↑EC Scientific Committee on Food. (2001) Opinion of the Scientific Committee on Food on the risk assessment of dioxins and dioxin-like PCBs in food. CS/CNTM/DIOXIN/20 final [1]
↑Cohen, J.T., PhD, Bellinger, D.C, PhD, W.E., MD, Bennett A., and Shaywitz B.A. 2005b. A Quantitative Analysis of Prenatal Intake of n-3 Polyunsaturated Fatty Acids and Cognitive Development. American Journal of Preventive Medicine 2005;29(4):366–374).
↑Cohen JT, Bellinger DC, Shaywitz BA. A quantitative analysis of prenatal methyl mercury exposure and cognitive development. Am J Prev Med. 2005 Nov;29(4):353-65.
↑Anders Glynn, Salomon Sand and Wulf Becker. Risk and Benefit Assessment
of Herring and Salmonid Fish from the Baltic Sea Area. Report 21/2013. Livsmedelsverket, Sweden.[3]
↑National Food Agency. Market Basket 2010 - chemical analysis, exposure estimation and health-related assessment of nutrients and toxic compounds in Swedish food baskets. Report 7/2012. Livsmedelsverket, Sweden. [4]
Keywords
Terminology
Normative scenarios
paths you need to take to reach a defined goal
Expolorative scenarios
identify key uncertainties and dependencies to describe coherent paths into the future.
Governance types
How things are managed (e.g. top down command or co-management).
Management action
Actions to be taken based on decision-maker's decision (i.e. decisions)