Benefit-risk assessment of Baltic herring and salmon intake

From Opasnet
Jump to: navigation, search
Schematic picture of the health benefit-risk model for Baltic herring and salmon intake.

Assessment presentation · show ready-made model results (without salmon)

+ Show code


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.


What are the current 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 different management scenarios of Baltic sea fish stocks?

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.


  • National institute for health and welfare (THL)
  • Goherr project group


  • 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)

Decisions and scenarios

Management scenarios developed in Goherr WP3 frames the following boundaries to the use and consumption of Baltic herring and salmon as human food. Effect of these scenarios to the dioxin levels and the human food use will be evalauted quantitatively and feed into the health benefit-risk model to assess the health effect changes.

  • Scenario 1: “Transformation to sustainability”
    • Hazardous substances, including dioxins, are gradually flushed out and the dioxin levels in Baltic herring are below or close to the maximum allowable level.
    • Fish stocks are allowed to recover to levels, which makes maximum sustainable yield possible and increases the total catches of wild caught fish. The catches of salmon by commercial fisheries has stabilized at low level, while the share of recreational catch increases slightly.
    • The use of the Baltic herring catch for food increases. A regional proactive management plan for the use of catch has increased the capacity of the fishing fleets to fish herring for food and through product development and joint marketing, have increased consumer demand for Baltic herring.
  • Scenario 2: “Business-as-usual”
    • The commercial catches of salmon continue to decrease. The demand for top predatory species, such as salmon and cod remains high, while the demand for herring decreased further as a result of demographic changes.
    • Most of the herring catch are used for fish meal and oil production in the region.
    • The use of Baltic herring from the southern parts of the Baltic Sea where the dioxin contents are not likely to exceed the maximum allowable level, are prioritised for human consumption. In the absence of the demand in many of the Baltic Sea countries, majority of the herring intended for direct human consumption are exported to Russia.
  • Scenario 3: “Inequality”
    • The nutrient and dioxins levels continue to decrease slowly.
    • The commercial catches of salmon have decreased further as the general attitudes favour recreational fishing, which has also resulted in decreased demand.
    • The herring catches have increased slightly, but the availability of herring suitable for human consumption remains low due to both, dioxin levels that remain above the maximum allowable limit in the northern Baltic Sea and the poor capacity to fish for food.
    • The use of the catch varies between countries. In Estonia, for example, where the whole catch has been traditionally used for human consumption, there is no significant change in this respect, but in Finland, Sweden and Denmark, herring fishing is predominantly feed directed.
  • Scenario 4: “Transformation to protectionism”
    • The level of hazardous substances also increases as emission sources are not adequately addressed.
    • Commercial salmon fisheries disappears almost completely from the Baltic Sea, although restocking keeps small scale fisheries going.
    • Many of the Baltic herring stocks are also fished above the maximum sustainable yield and total catches are declining.
    • Owing to the growing dioxin levels detected in herring, majority of the catch is used for aquaculture.


  • Model development during 2016 and 2017
  • First set of results in March 2017, draft publication in March 2018


This section will be updated as soon as preliminary results are available




Schematic picture of the health benefit-risk model for Baltic herring and salmon intake.
Detailed 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).


  • Policy makers
    • Food safety authorities
    • Fisheries management
  • Researchers
    • Food safety
    • Health
  • NGO's
    • WWF
    • Active consumers
    • Marine Stewardship Council
  • Baltic sea fishers and producers?


Calculation of cases of disease

Calculation of DALYs:

ObsDecision makerDecisionOptionVariableCellChangeResultDescription
1Food safety authorityBackgroundYesbgexposureIdentityConsider background from other sources
2Food safety authorityBackgroundNobgexposureReplace0Do not consider background from other sources
3HELCOM?FoodpolicyYeseatchgavailIdentityBetter availability of herring and salmon
4HELCOM?FoodpolicyBAUeatchgavailReplace0Current availability of herring and salmon
5HELCOM?FoodpolicyYeseatchgchemicIdentityChemical concentrations are lower than in BAU
6HELCOM?FoodpolicyBAUeatchgchemicReplace0Chemical concentrations are lower than in BAU
Background exposure(-)
1FIMaleVitamin D11.7µg /dFinriski 12 - 0.3 silakasta
2SWEMaleVitamin D11.7µg /dFinriski 12 - 0.3 silakasta
3ESTMaleVitamin D11.7µg /dFinriski 12 - 0.3 silakasta
4DKMaleVitamin D11.7µg /dFinriski 12 - 0.3 silakasta
5FemaleVitamin D8.5µg /dFinriski 8.7 - 0.2 silakasta
6MaleEPA120mg /dFinriski 125 - 4.6 silakasta
7FemaleEPA96mg /dFinriski 100 - 3.9 silakasta
8MaleDHA118mg /dFinriski 125 - 6.7 silakasta
9FemaleDHA94mg /dFinriski 100 - 5.4 silakasta
10PCDDF0pg /d (TEQ)
11PCB0pg /d (TEQ)
12MeHg0µg /d
13logTEQ0log(pg /g)
Exposure-response functions of interest(-)
1TEQTooth defectYes or no dental defectERSNone1
2TEQCancerCancer morbidityCSFBW1
3TEQDioxin TDIDioxin recommendation tolerable daily intakeTDIBW1
4DHAChild's IQLoss in child's IQ pointsERSNone1
5Omega3Heart (CHD)CHD2 mortalityRelative HillNone1
6Omega3StrokeStroke mortalityRelative HillNone1
7Vitamin DVitamin D intakeVitamin D recommendationStepNone1
8MeHgChild's IQLoss in child's IQ pointsERSBW1
DALYs of responses(DALY /case)
1Heart (CHD)5 - 15Assumes DW 1 and D 10 U 50%
2Stroke5 - 15Assumes DW 1 and D 10 U 50 %
3Tooth defect0 - 0.12DW 0.001 D 60 U 100 %. 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?
4Cancer0 - 0.28DW 0.1 D 20, in addition loss of life expectancy 5 a. This comes from a lifetime exposure, so it is (linearly( assumed that 1/50 of this is caused by one-year exposure. U 100 %
5Vitamin D intake0.0001 - 0.0101DW 0.001 D 1 U 101x. 1 if not met
6Dioxin TDI0.0001 - 0.0101DW 0.001 D 1 U 101x. 1 if not met
7Child's IQ0.0517 (0.03 - 0.0817)Intellectual disability, mild (IQ<70): 0.031/IQ point (0.018-0.049) From IHME. D 50 U from IHME.
DW = disability weight
D = duration (a)
U = uncertainty
Diox conc fishing areas(TEQ)
ObsAreaFishResultConcentrations used for countries
430-31Herring2.29FI (reference: reflects EU-kalat study)
930-31Salmon 9.3FI, SE (reference: reflects EU-kalat study)

PCDD/F and PCB concentration distributions for each fish species and country are estimated in the following way:

  • EU-kalat 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-kalat study, except that the means are scaled based on the area of interest relative to the reference (see table above).
  • The relative standard deviations are assumed to be the same for each fish population.
  • Baltic herring (>= 17 cm) and Small Baltic herring (<17 cm) are treated in the model as if they were separate species. Estonians are assumed to eat only small, others only large Baltic herring. --# : It should be checked how realistic assumption this is. --Jouni (talk) 13:40, 10 October 2017 (UTC)

Population data from Eurostat database

Landing of fish from different areas in Baltic Sea by country.
Fish species and area Country
Tot herring (1000t) 4.8 132.4 35.3 66.5
Total salmon (t) 122 367 9 315
Herring 24 73 % 1 %
Herring 25-29 6 % 20 % 21 % 76 %
Herring 28.1 47 %
Herring 30 73 % 21 %
Herring 31 3 % 0 %
Herring 32 0 % 3 % 33 % 0 %
Salmon 25-29 7 % 11 % 6 %
Salmon 30-31 78 % 94 %
Salmon 32 15 % 67 % 0 %
Salmon 28 22 %
Salmon 24 92 %
ICES subdivisions
Subdivision Name
21 Kattegat
22 Great Belt
23 Öresund
24 Baltic West of Bornholm
25 Southern Central Baltic West
26 Southern Central Baltic East
27 North West of Gotland
28 Eastern Main Basin
28.1 Gulf of Riga
28.2 Eastern Main Basin
29 Åland Sea
30 Bothnian Sea
31 Bothnian Bay
32 Gulf of Finland
Number of samples in EU-kalat study for PCDD/F and PCB measurements.
Catch location Subdivision Fish_species Number of samples
Hanko 29 Small Baltic herring (< 17 cm) 2
Kotka 32 Small herring 2
Oulu 31 Small herring 2
Pori 30 Small herring 32
Turku 29 Small herring 2
Vaasa 30 Small herring 2
Hanko 29 Large Baltic herring (>= 17 cm) 3
Kotka 32 Large herring 3
Oulu 31 Large herring 2
Pori 30 Large herring 42
Turku 29 Large herring 3
Vaasa 30 Large herring 3
Hanko 29 Salmon 1
Kotka 32 Salmon 2
Oulu 31 Salmon 2
Pori 30 Salmon 2
Turku 29 Salmon 2



  • Country (Denmark, Estonia, Finland, Sweden)
  • Year (current, future)
  • Gender
  • Age: 18-45 years or >45 years
  • Fish species (Baltic herring, Baltic salmon)
  • Health end-point, specified by name
  • Compound: TEQ (PCDD/F and PCB), Vitamin D, Omega3 (includes EPA and DHA), MeHg


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 above Dependencies section. Model results are presented as tables and figures when those are available.

  • 18.5.2017: Archived exposure model Op7748/exposure by Arja (used separate ovariables for salmon and herring) [1]

Health impact model (Monte Carlo)

  • Model run 13.3.2017: a simple copy of op_fi:Silakan hyöty-riskiarvio [2]
  • Model run 13.3.2017 with showLocations function [3]
  • Model run 13.3.2017 produces totcases results but are not meaningful yet [4]
  • Model run 14.3.2017 with exposure graph [5]
  • Model run 14.3.2017 bugs not fixed [6]
  • Model run 30.5.2017 [7]
  • Model run 12.6.2017 with 2D Monte Carlo [8]
  • Model run 8.9.2017 with known bugs fixed [9]
  • Model run 6.10.2017: added updated mc2d function, better exposure$Exposure (To eater or To child), Background as marginal after mc2d, MeHg concentrations; bugs fixed with conc_mehg and IQ disabilityweight [10]
  • Model run 6.10.2017: 1000 iterations [11]
  • Model run 11.10.2017: country-specific concentrations [12]
  • Model run 12.10.2017: Background bug fixed [13] objects.get("150780268107")
  • Model run 12.10.2017: with marginal background and new Limit decision [14] objects.get("150783160307")
  • Model run 19.10.2017: n = 2117 and mc2d$run2d = FALSE [15]
  • Model run 27.10.2017: salmon removed from amount. [16]
  • Model run 15.11.2017: salmon included [17]
  • Model run 21.11.2017: using EU-kalat concentration model from May 2017 [18]
  • Model run 22.11.2017: correcting for conc and Large herring, also debugged mc2d. [19]

# : We must check that Baltic herring, Large herring and Small herring are used in all variables coherently. There may be problems in conc. --Jouni (talk) 09:39, 21 November 2017 (UTC)

--# : This must be corrected back when the bayesian conc_pcddf works again. --Jouni (talk) 14:11, 22 November 2017 (UTC)

#: Country, Ages, and Gender are marginals in both RR an casesabs although mc2dparam$run2d==FALSE. Yet, in casesabs all combinations have 2117 rows, while in RR all combinations have 80-190 rows and only the combinations Exposcen*Limit*Background*Resp have 2117 rows each. So, RR works as expected with run2d==FALSE but casesabs does not. What is wrong? --Jouni (talk) 14:11, 22 November 2017 (UTC)

+ Show code

Second part

  • Model run 4.6.2017 [20]
  • Model run 11.6.2017 with 2D Monte Carlo [21]
  • Model run 6.10.2017 [22] barchart shows blocks wrong in Opasnet but correctly on own computer
  • Model run 12.10.2017 [23]
  • Model run 16.10.2017: ggplot objects stored [24]
  • Model run 21.11.2017: plots stored [25]

#: These are the equal sizes for different graphics settings. A typically good base_size is 24:

  • Opasnet graphics
  • png(width = 1024, height=768) # (in pixels)
  • pdf(width = 14, height=10.5) # (in inches) --Jouni (talk) 15:52, 21 November 2017 (UTC)

+ Show code

Plot concentrations and survey

  • Requires codes Op_en7748/bayes and indirectly Op_en7748/preprocess.
  • Model run 1.3.2017 [26]

+ Show code

Interface for BBN model

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. This section describes how data from BRA is managed to fit BBN.

Data files and indices needed:

  • Consumer country (out.population from population)
    • Country (DK, EST, FI, SWE): total population size for each country
  • Consumer gender (out.population from population)
    • Gender (Female, Male): gender-specific number of people for each country
  • Consumer age group (out.population from population)
    • Age (18-45, 45>): gender and age-specific number of people for each country
  • Effect of improved information
    • Information improvements (policy options to be determined)
    • Country, age, and gender-specific distributions --# : Is it realistic be so specific? --Jouni (talk) 12:47, 22 November 2017 (UTC)
  • Human consumption of salmon (out.salmonintake from amount[Fish=="Salmon",])
    • Country, age, and gender-specific consumption (g/day)
    • The same with or without salmon recommendation policy given improved information (to be determined)
  • Human consumption of herring: same as for salmon (out.herringintake from amount[Fish!="Salmon",])
  • Omega3 intake from salmon and herring (mg/day) (out.omegaintake from exposure[Exposure_agent=="Omega3"&Background=="No",])
  • Human intake of dioxin from herring (out.herringdioxinexp from amount * conc)
  • Human intake of dioxin from salmon (out.salmondioxinexp from amount * conc)
  • Dioxin intake total (out.dioxinintake from exposure[Exposure_agent=="TEQ",])
  • Other intake of omega3 (out.omegatotal$Background from exposure[Exposure_agent=="Omega3",])
  • Omega3 intake total (out.omegatotal from exposure[Exposure_agent=="Omega3",])
  • Net burden of disease (out.BoD from BoD summed over Response)

+ Show code



See also

Goherr Research project 2015-2018: Integrated governance of Baltic herring and salmon stocks involving stakeholders

GOHERR logo NEW.png Goherr public website

Workpackages including task description and follow-up:
WP1 Management · WP2 Sociocultural use, value and goverrnance of Baltic salmon and herring · WP3 Scenarios and management objectives · WP4 Linking fish physiology to food production and bioaccumulation of dioxin · WP5 Linking the health of the Baltic Sea with health of humans: Dioxin · WP6 Building a decision support model for integrated governance · WP7 Dissemination

Other relevant pages in Opasnet: GOHERR assessment · Relevant literature

Relevant data: Exposure response functions of dioxins · Fish consumption in Sweden · POP concentrations in Baltic sea fish · Exposure response functions of Omega3 fatty acids

Relevant methods: Health impact assessment · OpasnetBaseUtils‎ · Modelling in Opasnet

Relevant assessments: Benefit-risk assessment of Baltic herring · Benefit-risk assessment on farmed salmon · Benefit-risk assessment of methyl mercury and omega-3 fatty acids in fish · Benefit-risk assessment of fish consumption for Beneris · Benefit-risk assessment of Baltic herring (in Finnish)

Bonus logo.png
Eu logo.png

Related files