Difference between revisions of "Benefit-risk assessment of Baltic herring and salmon intake"
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library(ggplot2) | library(ggplot2) | ||
− | objects.latest("Op_en7749", code_name = "initiate") # [[Goherr: Fish consumption study]] | + | 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) | ||
+ | } | ||
+ | |||
+ | #objects.latest("Op_en7749", code_name = "initiate") # [[Goherr: Fish consumption study]] | ||
objects.latest("Op_en7749", code_name = "surveyjsp") # Uses jsp directly from survey data. | objects.latest("Op_en7749", code_name = "surveyjsp") # Uses jsp directly from survey data. | ||
− | openv.setN(max(as.numeric(as.character(jsp$Iter)))) # Adjust N to data size | + | openv.setN(max(as.numeric(as.character(jsp@data$Iter)))) # Adjust N to data size |
conc <- Ovariable( | conc <- Ovariable( | ||
"conc", | "conc", | ||
dependencies = data.frame( | dependencies = data.frame( | ||
− | Name = c("concentration" | + | Name = c( |
+ | "concentration", # [[EU-kalat]] | ||
+ | "vit" # [[Concentrations of beneficial nutrients in fish]] | ||
+ | ), | ||
Ident = c("Op_en3104/initiate", "Op_en1838/initiate") | Ident = c("Op_en3104/initiate", "Op_en1838/initiate") | ||
), | ), | ||
Line 165: | Line 182: | ||
# ^ This code does not exist yet. Use EU-kalat as example. | # ^ This code does not exist yet. Use EU-kalat as example. | ||
− | objects.latest("Op_en4017", code_name = "initiate") # [[TEF]] | + | #objects.latest("Op_en4017", code_name = "initiate") # [[TEF]] |
+ | |||
+ | conc <- EvalOutput(conc) | ||
+ | |||
+ | exposure <- Ovariable( | ||
+ | "exposure", | ||
+ | dependencies = data.frame( | ||
+ | Name = c("conc", "amount"), | ||
+ | Ident = c(NA, "Op_en7749/initiate") | ||
+ | ), | ||
+ | formula = function(...) { | ||
+ | # sumitem only works in this code | ||
+ | conc <- sumitem(conc, "Exposure_agent", c("TEQdx", "TEQpcb"), "TEQ") | ||
+ | levels(conc$Fish)[levels(conc$Fish) == "Baltic herring"] <- "Herring" | ||
+ | exposure <- amount * conc | ||
+ | return(exposure) | ||
+ | } | ||
+ | ) | ||
+ | |||
+ | 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() | ||
+ | |||
+ | |||
#!!++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ | #!!++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ | ||
Line 182: | Line 224: | ||
addexposure@output <- fillna(addexposure@output, c("Gender", "Exposure_agent")) | addexposure@output <- fillna(addexposure@output, c("Gender", "Exposure_agent")) | ||
− | + | addexposure <- sumitem(addexposure, "Exposure_agent", c("PCDDF","PCB"), "TEQ") | |
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | addexposure <- sumitem(addexposure, "Exposure_agent", c("PCDDF", "PCB"), "TEQ") | ||
addexposure <- sumitem(addexposure, "Exposure_agent", c("EPA", "DHA"), "Omega3") | addexposure <- sumitem(addexposure, "Exposure_agent", c("EPA", "DHA"), "Omega3") | ||
addexposure <- unkeep(addexposure, prevresults = TRUE, sources = TRUE) | addexposure <- unkeep(addexposure, prevresults = TRUE, sources = TRUE) | ||
Line 373: | Line 401: | ||
totcases2 <- Ovariable( | totcases2 <- Ovariable( | ||
"totcases", # This calculates the total number of cases in each population subgroup. | "totcases", # This calculates the total number of cases in each population subgroup. | ||
− | + | # The cases are calculated for specific (combinations of) causes. However, these causes are NOT visible in the result. | |
− | + | dependencies = data.frame( | |
− | + | Name = c( | |
− | + | "population", # Population divided into subgroups as necessary | |
− | + | "dose", # Exposure to the pollutants | |
− | + | "disincidence", # Incidence of the disease of interest | |
− | + | "RR", # Relative risks for the given exposure | |
− | + | "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_en5917/initiate", # [[Disease risk]] | |
− | + | "Op_en2261/RR", # [[Health impact assessment]] | |
− | + | "Op_en2031/initiate", # [[Exposure-response function]] | |
− | + | "Op_en2031/initiate", # [[Exposure-response function]] | |
− | + | "Op_en2261/frexposed" # [[Health impact assessment]] | |
− | + | ) | |
− | + | ), | |
− | + | formula = function(...) { | |
− | + | ||
− | + | test <- list() | |
− | + | ERF@marginal[colnames(ERF@output) %in% c("ERF_parameter", "Scaling")] <- FALSE # Make sure that these are not marginals | |
− | + | ||
− | + | ############### First look at the relative risks based on RR | |
− | + | ||
− | + | if(testforrow(RR, dose)) { # If an ovariable whose nrow(ova@output) == 0 | |
− | + | # is used in Ops, it is re-EvalOutput'ed, and therefore ERFrr*dose may have rows even if ERFrr doesn't. | |
− | + | ||
− | + | # takeout is a vector of column names of indices that ARE in population but NOT in the disease incidence. | |
− | + | # However, populationSource is kept because oapply does not run if there are no indices. | |
− | + | if(FALSE) { | |
− | + | print("RR") | |
− | + | print(head(dose@output)) | |
− | + | if(class(population) == "ovariable") { | |
− | + | takeout <- setdiff(colnames(population@output)[population@marginal], | |
− | + | colnames(disincidence@output)[disincidence@marginal] | |
− | + | ) | |
− | + | if(length(takeout) > 0) {# Aggregate to larger subgroups. | |
− | + | pop <- oapply(population, NULL, sum, takeout) | |
− | + | } else { | |
− | + | pop <- population | |
− | + | } | |
− | + | } else { | |
− | + | takeout <- character() | |
− | + | pop <- population | |
− | + | } | |
− | + | ||
− | + | # pci is the proportion of cases across different population subroups | |
− | + | # based on differential risks and | |
− | + | # population sizes. pci sums up to 1 for each larger subgroup found in disincidence. | |
− | + | # See [[Population attributable fraction]]. | |
− | + | pci <- population * RR | |
− | + | print("pci1") | |
− | + | print(head(pci@output)) | |
− | + | # Divide pci by the values of the actually exposed group (discard nonexposed) | |
− | + | # The strange Ovariable thing is needed to change the name of temp to avoid problems later. | |
− | + | temp <- pci * Ovariable(data = data.frame(Result = 1)) | |
− | + | if ("Exposcen" %in% colnames(temp@output)) { | |
− | + | temp@output <- temp@output[temp@output$Exposcen == "BAU" , ] | |
− | + | temp <- unkeep(temp, cols = "Exposcen", prevresults = TRUE, sources = TRUE) | |
− | + | } | |
− | + | temp <- unkeep(temp, prevresults = TRUE, sources = TRUE) | |
− | + | if(length(takeout) > 0) temp <- oapply(temp, NULL, sum, takeout) | |
− | + | #if(length(takeout) > 0) temp <- ooapply(temp, cols = takeout, FUN = "sum", use_plyr = TRUE) | |
− | + | # if(length(takeout) > 0) temp <- osum(temp, cols = takeout) | |
− | + | print("temp") | |
− | + | print(head(temp@output)) | |
− | + | pci <- pci / temp | |
− | + | temp <- NULL | |
− | + | } # if(FALSE) | |
− | + | # The cases are divided into smaller subgroups based on weights in pci. | |
− | + | # This is why the larger groups of population are used (pop instead of population). | |
− | + | out1 <- disincidence * population * (RR-1)/RR #unkeep(pci, prevresults = TRUE, sources = TRUE) | |
− | + | # out1 <- unkeep(out1, cols = "populationResult") # populationResult comes from pop and not from pci that actually contains | |
− | + | # the population weighting for takeout indices. Therefore it would be confusing to leave it there. | |
− | + | #print("out1") | |
− | + | #print(head(out1@output)) | |
− | + | test <- c(test, out1) | |
− | + | } | |
− | + | out1 <- NULL | |
− | + | ||
− | + | ########################################################################## | |
− | + | ############# This part is about absolute risks (i.e., risk is not affected by background rates). | |
− | + | ||
− | + | # Unit risk (UR), cancer slope factor (CSF), and Exposure-response slope (ERS) estimates. | |
− | + | ||
− | + | UR <- ERF | |
− | + | UR@output <- UR@output[UR@output$ERF_parameter %in% c("UR", "CSF", "ERS") , ] | |
− | + | 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 | |
− | + | print("UR") | |
− | + | print(head(dose@output)) | |
− | + | ||
− | + | UR <- oapply(UR, NULL, sum, "Exposure_agent") | |
− | + | ||
− | + | UR <- population * UR | |
− | + | test <- c(test, 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 <- ERF | |
− | + | Step@output <- Step@output[Step@output$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) | |
− | + | } | |
− | + | Step <- NULL | |
− | + | ||
− | + | ##################################################################### | |
− | + | # Combining effects | |
− | + | if(length(test) == 0) return(data.frame()) | |
− | + | if(length(test) == 1) out <- test[[1]]@output | |
− | + | if(length(test) == 2) out <- orbind(test[[1]], test[[2]]) | |
− | + | if(length(test) == 3) out <- orbind(orbind(test[[1]], test[[2]]), test[[3]]) | |
− | + | ||
− | + | # Find out the right marginals for the output | |
− | + | 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)) | |
− | + | ||
− | + | 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) | |
− | + | } | |
) | ) | ||
Revision as of 20:19, 23 May 2017
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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 (year 2020?)
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
Results
Conclusions
Rationale
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) ←# : Done --Arja (talk) 12:07, 19 May 2017 (UTC)
- Burden of disease as DALY in Finland ⇤# : Inactivate and merge? --Jouni (talk) 16:03, 13 April 2017 (UTC)
- 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)
- 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)
- Persistent organic pollutant levels in Baltic salmon ⇤# : Page only used to summarise data from EU-kalat. --Jouni (talk) 11:40, 19 April 2017 (UTC)
- 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) ←# : Take data from Fineli. --Jouni (talk) 16:03, 13 April 2017 (UTC) --# : Omega-3 content in salmon: update and change the answer to point to the main page. --Jouni (talk) 16:03, 13 April 2017 (UTC)
- 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)
- Toxic equivalency factor (TEF). Indices: TEFversion, Compound. ←# : Done. --Jouni (talk) 14:22, 19 April 2017 (UTC)
Calculation of DALYs:
- totcases (see above)
- Disability weights of health effects
- Length of disease
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 |
Analyses
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 (ICD-10 code?, name? something else? --# : This need to be matched for disability weight, duration of disease, background incidence and disease burden data, dose-responses --Arja (talk) 09:39, 22 September 2016 (UTC)
- 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]
Plot concentrations and survey
- Requires codes Op_en7748/bayes and indirectly Op_en7748/preprocess.
- Model run 1.3.2017 [7]
References
Keywords
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