Benefit-risk assessment of Baltic herring and salmon intake

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Scope

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 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.

Participants

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

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)

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.

Timing

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

Answer

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

Rationale

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

Stakeholders

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

Dependencies

Calculation of cases of disease

• totcases (Op_en2261/totcases on page Health impact assessment) with dose and RR, automatic intermediate variables. Indices: Age, Gender, Country, Response.
  • population, case-specific, from main model. Suggested indices: Age, Gender, Country.
  • disincidence,Incidence of the disease of interest. Op_en5917/initiate Disease risk. Suggested indices: Age, Gender, Country, Disease. ----#: . Should we use IHME data instead? --Jouni (talk) 16:03, 13 April 2017 (UTC) (type: truth; paradigms: science: comment) ←--#: . Done --Arja (talk) 12:07, 19 May 2017 (UTC) (type: truth; paradigms: science: defence)
    • Burden of disease as DALY in Finland ⇤--#: . Inactivate and merge? --Jouni (talk) 16:03, 13 April 2017 (UTC) (type: truth; paradigms: science: attack)
  • ERF and threshold, exposure-response functions. Op_en2031/initiate Exposure-response function. Existing indices: Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling. ←--#: . Done. --Jouni (talk) 14:22, 19 April 2017 (UTC) (type: truth; paradigms: science: defence)
    • Exposure-response functions of dioxins
    • Exposure-response functions of fatty acids
    • Exposure-response functions of methylmercury
    • ERFs of vitamins
  • frexposed, fraction of population that is exposed from Goherr: Fish consumption study. Suggested indices: Age, Gender, Country.
  • exposure, from the main model because has case-specific adjustments. Suggested indices: Age, Gender, Country, Compound.
    • amount, Consumption of fish. Existing indices: Gender, Age, Country, Fish.
    • EU-kalat: concentration, Pollutant and fatty acid concentrations in fish. Suggested indices: Fish_species → Fish, POP → Compound, Catch_square (Catch_site, Catch_location) → Area, Length_mean_mm → Length.
      • Persistent organic pollutant levels in Baltic herring ⇤--#: . Page only used to summarise data from EU-kalat. --Jouni (talk) 11:40, 19 April 2017 (UTC) (type: truth; paradigms: science: attack)
      • Persistent organic pollutant levels in Baltic salmon ⇤--#: . Page only used to summarise data from EU-kalat. --Jouni (talk) 11:40, 19 April 2017 (UTC) (type: truth; paradigms: science: attack)
      • Concentrations of beneficial nutrients in fish. Suggested indices: Fish, Compound. ----#: . Good data for Baltic herring, Salmon not taken into account yet --Arja (talk) 13:27, 13 March 2017 (UTC) (type: truth; paradigms: science: comment) ←--#: . Take data from Fineli. --Jouni (talk) 16:03, 13 April 2017 (UTC) (type: truth; paradigms: science: defence) ----#: . Omega-3 content in salmon: update and change the answer to point to the main page. --Jouni (talk) 16:03, 13 April 2017 (UTC) (type: truth; paradigms: science: comment)
      • Mercury concentrations in fish in Finland. Suggested indices: Fish, Location, Size, Year, Compound. ←--#: . update code by using Bayesian model on Kerty database. --Jouni (talk) 16:03, 13 April 2017 (UTC) (type: truth; paradigms: science: defence)
    • Toxic equivalency factor (TEF). Indices: TEFversion, Compound. ←--#: . Done. --Jouni (talk) 14:22, 19 April 2017 (UTC) (type: truth; paradigms: science: defence)

Calculation of DALYs:

• totcases (see above)
• Disability weights of health effects
• Length of disease

Background exposure(-)

Obs	Background	Country	Gender	Exposure_agent	Result	Unit	Description
1	Yes	FI	Male	Vitamin D	11.7	µg /d	Finriski 12 - 0.3 silakasta
2	Yes	SWE	Male	Vitamin D	11.7	µg /d	Finriski 12 - 0.3 silakasta
3	Yes	EST	Male	Vitamin D	11.7	µg /d	Finriski 12 - 0.3 silakasta
4	Yes	DK	Male	Vitamin D	11.7	µg /d	Finriski 12 - 0.3 silakasta
5	Yes		Female	Vitamin D	8.5	µg /d	Finriski 8.7 - 0.2 silakasta
6	Yes		Male	EPA	120	mg /d	Finriski 125 - 4.6 silakasta
7	Yes		Female	EPA	96	mg /d	Finriski 100 - 3.9 silakasta
8	Yes		Male	DHA	118	mg /d	Finriski 125 - 6.7 silakasta
9	Yes		Female	DHA	94	mg /d	Finriski 100 - 5.4 silakasta
10	Yes			PCDDF	0	pg /d (TEQ)
11	Yes			PCB	0	pg /d (TEQ)
12	Yes			MeHg	0	µg /d
13	Yes			logTEQ	0	log(pg /g)
14	No				0

Indices

• 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

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 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]

+ Show code- Hide code # This is code Op_en7748/hia on page [[Benefit-risk assessment of Baltic herring and salmon intake]] library(OpasnetUtils) library(ggplot2) # amount does not know where to find jsp. objects.latest("Op_en7749", code_name = "surveyjsp") # Uses jsp directly from survey data. openv.setN(max(as.numeric(as.character(jsp@data$Iter)))) # Adjust N to data size conc <- Ovariable( "conc", dependencies = data.frame( Name = c( "concentration", # [[EU-kalat]] "vit" # [[Concentrations of beneficial nutrients in fish]] ), Ident = c("Op_en3104/initiate", "Op_en1838/initiate") ), formula = function(...) { colnames(concentration@output)[colnames(concentration@output)=="Compound"] <- "Exposure_agent" out <- combine(concentration, vit) return(out) } ) #objects.latest("Op_en4004", code_name = "initiate") # [[Mercury concentrations in fish in Finland]] # ^ This code does not exist yet. Use EU-kalat as example. # Exposure with fish-related exposure and additional exposure from other sources. # Does NOT contain infant's exposure from mother. expoRaw <- Ovariable( "expoRaw", dependencies = data.frame( Name = c("conc", "amount", "addexposure"), Ident = c(NA, "Op_en7749/initiate", NA) ), formula = function(...) { sumitem <- function( ova, # ovariable that has locations to sum cond, # index column that contains the locations to sum condvalue, # vector of locations to sum sumvalue # location to be given to the rows with the sums ) { d <- ova d@output <- d@output[d@output[[cond]] %in% condvalue , ] d <- oapply(d, cols = cond, FUN = sum) d@output[[cond]] <- sumvalue ova@output <- orbind(ova, d) return(ova) } conc <- sumitem(conc, "Exposure_agent", c("PCDDF", "PCB"), "TEQ") levels(conc$Fish)[levels(conc$Fish) == "Baltic herring"] <- "Herring" expoRaw <- amount * conc # Empty values ("") in indices must be replaced by NA so that Ops works correctly. levels(addexposure$Gender)[levels(addexposure$Gender) == ""] <- NA levels(addexposure$Country)[levels(addexposure$Country) == ""] <- NA levels(addexposure$Exposure_agent)[levels(addexposure$Exposure_agent) == ""] <- NA addexposure@output <- fillna(addexposure@output, c("Country", "Gender", "Exposure_agent")) addexposure <- sumitem(addexposure, "Exposure_agent", c("PCDDF","PCB"), "TEQ") addexposure <- sumitem(addexposure, "Exposure_agent", c("EPA", "DHA"), "Omega3") addexposure <- unkeep(addexposure, prevresults = TRUE, sources = TRUE) # Make the background exposure uncertain rather than an index. addexposure <- CollapseMarginal(addexposure, cols = "Background", fun = "sample") expoRaw <- expoRaw + addexposure return(expoRaw) } ) ## Background-exposure to vitamin D and omega-3 addexposure <- Ovariable( "addexposure", ddata = "Op_en7748", subset = "Background exposure" ) exposure <- Ovariable( "exposure", dependencies = data.frame( Name = c( "expoRaw", "dx.expo.child" ), Ident = c( NA, "Op_en7797/initiate" # [[Infant's dioxin exposure]] ) ), formula = function(...) { expoRaw$Exposure <- "Other" dx.expo.child$Exposure <- "Breast feeding" return(combine(expoRaw, dx.expo.child)) } ) # Limit the infant health responses to 10 % of females at age 18-45 a # (assuming 10 % probability to give birth during a year) frexposed <- Ovariable( "frexposed", dependencies = data.frame(Name = "exposure"), formula = function(...) { out <- unique(exposure[c("Gender", "Ages", "Exposure")]) out$Result <- ifelse( out$Exposure == "Breast feeding", ifelse( outGender == "Female" & out$Ages == "18-45", 0.1, # Probability of birth during a year. 0 ), 1 ) return(out) } ) ######################### bgexposure <- addexposure ######################################################### VÄESTÖ # Tarkastellaan totcases-laskennassa aluksi yksilöriskiä, ja vasta lopussa kerrotaan yksilötulokset yksilöiden edustamien ryhmien koolla. population <- Ovariable( "population", data = data.frame( Country = c("DK", "EST", "FI", "SWE"), Result = c(2400000, 500000, 2700000, 4000000) # Number for one gender ) ) # Embed this into HIA ovariable dependencies objects.latest("Op_en2031", code_name = "initiate") # [[Exposure-response function]] BoDt <- Ovariable( # Total burden of disease "BoDt", ddata = "Op_en5917", # [[:op_en:Disease risk]] subset = "IHME_Goherr" ) # WHY ARE THERE RESPONSES THAT DO NOT MATCH WITH EXPOSURES? ggplot(concentration@output, aes(x=concentrationResult, Colour=Compound))+geom_density()+ facet_wrap(~Fish, scales="free_y")+scale_x_log10() ggplot(amount@output, aes(x=amountResult, Colour=Country))+stat_ecdf()+ facet_wrap(~Fish, scales="free_y")+scale_x_log10() ggplot(totcases@output, aes(x = Response, weight = totcasesResult, fill = Gender))+ geom_bar()+coord_flip() + theme_gray(base_size = 24) ggplot(exposure@output, aes(x = Result, colour = Gender))+ geom_density() + scale_x_log10() + theme_gray(base_size = 24)+ facet_wrap(~ Country)+labs(title = "Dioxin exposure (pg/d)") ### Muutetaan exposure yksikköön g /d kaikkien Exposure_agentien osalta. skaala <- Ovariable( output = data.frame( Exposure_unit = "g /d", Exposure_agent = c("Vitamin_D", "EPA", "DHA", "Omega3", "PCDDF", "PCB", "TEQ", "MeHg"), Result = c(1E-6, 1E-3, 1E-3, 1E-3, 1E-12, 1E-12, 1E-12, 1E-6)), marginal = c(FALSE, TRUE, FALSE) ) exposure <- exposure * skaala concentration <- unkeep(concentration, prevresults = TRUE, sources = TRUE) * skaala ############# This part is about absolute risks (i.e., risk is not affected by background rates). # Unit risk is essentially an individual function and cannot be used for BoD.?? # Unit risk (UR), cancer slope factor (CSF), and Exposure-response slope (ERS) estimates. # threshold is not used for RR. And the interpretation is strange for ERS (use as X intercept instead). casesabs <- Ovariable( "casesabs", # This calculates the burden of disease for endpoints using ERS. dependencies = data.frame( Name = c( "population", # Population divided into subgroups as necessary "dose", # Exposure to the pollutants "ERF", # Other ERFs than those that are relative to background. "threshold", # exposure level below which the agent has no impact. "frexposed" # fraction of population that is exposed ), Ident = c( "Op_en2261/population", # [[Health impact assessment]] "Op_en2261/dose", # [[Health impact assessment]] "Op_en2031/initiate", # [[Exposure-response function]] "Op_en2031/initiate", # [[Exposure-response function]] "Op_en2261/frexposed" # [[Health impact assessment]] ) ), formula = function(...) { temp <- list() UR <- ERF[ERF$ERF_parameter %in% c("UR", "CSF", "ERS") , ] # testforrow could be simplified a bit. # Dose could be simplified with combine. if(testforrow(UR, dose)) { # See RR for explanation. UR <- threshold + UR * dose * frexposed # Actual equation # threshold is here interpreted as the baseline response (intercept of the line). It should be 0 for # UR and CSF but it may have meaningful values with ERS UR <- oapply(UR, NULL, sum, "Exposure_agent") UR <- population * UR temp <- c(temp, UR) } UR <- NULL # Step estimates: value is 1 below threshold and above ERF, and 0 in between. # frexposed cannot be used with Step because this may be used at individual and maybe at population level. # Step function is essentially an individual function and cannot be used for BoD calculations??? Step <- ERF[ERF$ERF_parameter %in% c("Step", "ADI", "TDI", "RDI", "NOAEL") , ] if(testforrow(Step, dose)) { # See RR for explanation. Step <- 1 - (dose >= threshold) * (dose <= Step) # Actual equation # Population size should be taken into account here. Otherwise different population indices may go unnoticed.(?) Step <- oapply(Step, NULL, sum, "Exposure_agent") test <- c(test, Step) } ##################################################################### # Combining effects if(length(temp) == 0) return(data.frame()) if(length(temp) == 1) out <- temp[[1]] if(length(temp) == 2) out <- combine(temp[[1]], temp[[2]]) # # Find out the right marginals for the output. Do we need to do this manually? # marginals <- character() # nonmarginals <- character() # for(i in 1:length(test)) { # marginals <- c(marginals, colnames(test[[i]]@output)[test[[i]]@marginal]) # nonmarginals <- c(nonmarginals, colnames(test[[i]]@output)[!test[[i]]@marginal]) # } # test <- NULL # out <- out[!colnames(out) %in% c("populationSource", "populationResult")] # These are no longer needed. # out <- Ovariable(output = out, marginal = colnames(out) %in% setdiff(marginals, nonmarginals)) # Do we need to adjust marginals manually? if("Exposcen" %in% colnames(out@output)) { out <- out * Ovariable( output = data.frame(Exposcen = c("BAU", "No exposure"), Result = c(1, -1)), marginal = c(TRUE, FALSE) ) out <- oapply(out, NULL, sum, "Exposcen") } return(out) } ) casesrr <- Ovariable( "casesrr", # This calculates the burden of disease for endpoints using RR. dependencies = data.frame( Name = c( "population", # Population divided into subgroups as necessary "RR", # Relative risks for the given exposure "incidence" # incidence of responses ), Ident = c( "Op_en2261/population", # [[Health impact assessment]] "Op_en2261/RR", # [[Health impact assessment]] "Op_en5917/initiate" # [[Disease risk]] ) ), formula = function(...) { out <- population * incidence * (RR - 1)/RR if("Exposcen" %in% colnames(out@output)) { out <- out * Ovariable( output = data.frame(Exposcen = c("BAU", "No exposure"), Result = c(1, -1)), marginal = c(TRUE, FALSE) ) out <- oapply(out, NULL, sum, "Exposcen") } return(out) } ) BoDpaf <- Ovariable( "BoDpaf", # This calculates the burden of disease for endpoints using PAF. dependencies = data.frame( Name = c( "population", # Population relative to the scale (often 1 or 1M) of BoDt "BoDt", # Total burden of disease of the studied responses per a defined time interval "RR" # Relative risks for the given exposure ), Ident = c( "Op_en2261/population", # [[Health impact assessment]] "Op_en5917/BoDt", # [[Disease risk]] "Op_en2261/RR" # [[Health impact assessment]] ) ), formula = function(...) { out <- BoDt * population * (RR-1)/RR return(out) } ) BoDcase <- Ovariable( "BoDcase", # This calculates the burden of disease for endpoints using numbers of cases. dependencies = data.frame( Name = c( "casesrr", # Number of cases from relative endpoints with RR. "casesabs", # Number of cases from absolute ERFs. "disabilityweight", # Disability weights for each response. "duration" # Duration of a response case. ), Ident = c( "Op_en2261/casesrr", # [[Health impact assessment]] "Op_en2261/casesabs", # [[Health impact assessment]] "Op_en2307/disabilityweight", # [[Disability weights]] "Op_en2307/duration" # [[Disability weights]] ) ), formula = function(...) { out <- combine(casesrr, casesabs) out <- out * disabilityweight * duration return(out) } ) # Note! There may double counting if there are responses that have both BoDt and incidence. # Some warning against double counting should be created. BoD <- Ovariable( "BoD", # This calculates the burden of disease for endpoints using numbers of cases. dependencies = data.frame( Name = c( "BoDpaf", # Burden of disease as calculated from PAF "BoDcase" # Burden of disease as calculated from number of cases ), Ident = c( "Op_en2261/BoDpaf", # [[Health impact assessment]] "Op_en2261/BoDcase" # [[Health impact assessment]] ) ), formula = function(...) { out <- combine(BoDpaf, BoDcase) if(exists("BoDpaf") & exists("casesrr")) { doublecount <- intersect(unique(BoDpaf$Response), unique(caserr$Response)) if(length(doublecount>0)) warning(paste( "The response(s)", doublecount, "are probably double counted from both disease burdens and incidences." )) } return(out) } ) BoD <- EvalOutput(BoD) ggplot(BoD@output, aes(x = Response, weight = totcasesResult, fill = Gender))+geom_bar(position="dodge") ####################################### dx.expo.child <- Ovariable( "dx.expo.child", dependencies = data.frame( Name = c( "expoRaw", # Mother's exposure to dioxin (pg/d) "t0.5", # Half-life of dioxin in mother (d) "f_ing", # Fraction of dioxin ingested by mother that is actually absorbed "f_mtoc", # Fraction of mother's dioxin body burden that enters the child during 6 mo breast feeding. "BF" # Body fat amount (g) in the infant at 6 mo of age ) ), formula = function(...) { out <- expoRaw * f_ing * t0.5 / log(2) # Body burden in mother after long constant exposure out <- out * f_mtoc / BF # Dioxin concentration in child out <- log(out + 0.1) # Convert to logarithm for ERF compatibility (avoid log(0)) # Technical conversion out <- out[out$Exposure_agent == "TEQ" , ] out$Exposure_agent <- "logTEQ" return(out) } )

Plot concentrations and survey

• Requires codes Op_en7748/bayes and indirectly Op_en7748/preprocess.
• Model run 1.3.2017 [7]

+ Show code- Hide code #This is code Op_en7748/ on page [[Benefit-risk assessment of Baltic herring and salmon intake]] library(OpasnetUtils) library(ggplot2) library(reshape2) objects.latest("Op_en7748", code_name = "bayes") #: pcd.pred, ans.pred, mu.pred objects.latest("Op_en3104", code_name = "preprocess") # [[EU-kalat]]: eu pl <- melt(pcd.pred) mul <- melt(mu.pred) ql <- melt(ans.pred) ############## PCDDF concentrations plotted ggplot(eu@output, aes(x = euResult, colour = Fish))+geom_density(size = 1) + scale_x_log10() + facet_wrap(~ Congener, scales = "free_y") + coord_cartesian(xlim = c(1E-3,1E2))+ labs(title = "Probability densities of PCDD/F concentrations directly from data") ggplot(eu@output, aes(x = euResult, colour = Congener))+geom_density(size = 1) + scale_x_log10() + facet_wrap(~ Fish, scales = "free_y") + coord_cartesian(xlim = c(1E-3,1E2))+ labs(title = "Probability densities of PCDD/F concentrations directly from data") ggplot(pl, aes(x = value, colour = Fish))+geom_density(size = 1) + scale_x_log10() + facet_wrap(~ Congener, scales = "free_y") + coord_cartesian(xlim = c(1E-3,1E2))+ labs(title = "Probability densities of PCDD/F concentrations from bayes model") ggplot(pl, aes(x = value, colour = Congener))+geom_density(size = 1) + scale_x_log10() + facet_wrap(~ Fish, scales = "free_y") + coord_cartesian(xlim = c(1E-3,1E2))+ labs(title = "Probability densities of PCDD/F concentrations from bayes model") ggplot(pl[pl$Fish == "Baltic herring",], aes(x = Iter, y = value, group = Seed, colour = Seed))+ geom_line() + scale_y_log10() + facet_wrap(~ Congener, scales = "free_y") + labs(title = "Mixing of model. Runs 1..1000, no thinning") # aggregate(pl["value"], by = pl[c("Congener", "Fish")], FUN = mean) ggplot(pl[pl$Fish %in% c("Baltic herring", "Rainbow trout", "Pike-perch", "Pike", "Salmon", "Sprat") , ], aes(x = value, colour = Congener))+geom_density(size = 1) + scale_x_log10() + facet_wrap(~ Fish, scales = "free_y") + coord_cartesian(xlim = c(1E-3,1E2))+ labs(title = "Selected fish species") ########## Questionnaire plotted for(i in unique(ql$Question)) { print(ggplot(ql[ql$Question == i,], aes(x = value, fill = Gender))+ geom_bar(position = "dodge") + facet_grid(Country ~ Age)+ labs(title = paste(i, "from bayes survey model"))) } ##################### Mu plotted ggplot(mul, aes(x = value, colour = Congener))+stat_ecdf(size = 1) + scale_x_log10() + facet_wrap(~ Fish, scales = "free_y") + #coord_cartesian(xlim = c(1E-3,1E2))+ labs(title = "Mu of PCDD/F concentrations from bayes model") ggplot(mul[mul$Fish == "Baltic herring",], aes(x = Iter, y = value, group = Seed, colour = Seed))+ geom_line() + scale_y_log10() + facet_wrap(~ Congener, scales = "free_y") + labs(title = "Mixing of model. Runs 1..1000, no thinning") #tef <- Ovariable("tef", ddata = "Op_en4017", subset = "TEF values") #tef <- EvalOutput(tef) #levels(eu$Congener) <- gsub("HCDD", "HxCDD", levels(eu$Congener)) #levels(eu$Congener) <- gsub("HCDF", "HxCDF", levels(eu$Congener)) #levels(eu$Congener) <- gsub("CoPCB", "PCB", levels(eu$Congener)) #euteq <- eu * tef

See also

• Risk and Benefit Assessment of Herring and Salmonid Fish from the Baltic Sea Area
• Swedish Market Basket 2010
• Riksmaten 2010
• Danskernes kostvaner 2011-2013

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

Goherr public website Workpackages 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 pages in Opasnet GOHERR assessment · Goherr flyer · Goherr scenarios · Relevant literature: policy · dioxins · values Data Exposure- response functions of dioxins · Fish consumption in Sweden · POP concentrations in Baltic sea fish · Exposure-response functions of Omega3 fatty acids Methods Health impact assessment · OpasnetBaseUtils‎ · Modelling in Opasnet Other 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) http://www.bonusportal.org/ http://www.bonusprojects.org/bonusprojects