Difference between revisions of "Benefit-risk assessment of Baltic herring and salmon intake"
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conc <- sumitem(conc, "Exposure_agent", c("PCDDF", "PCB"), "TEQ") | conc <- sumitem(conc, "Exposure_agent", c("PCDDF", "PCB"), "TEQ") | ||
levels(conc$Fish)[levels(conc$Fish) == "Baltic herring"] <- "Herring" | 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. | # Empty values ("") in indices must be replaced by NA so that Ops works correctly. | ||
levels(addexposure$Gender)[levels(addexposure$Gender) == ""] <- NA | levels(addexposure$Gender)[levels(addexposure$Gender) == ""] <- NA | ||
+ | levels(addexposure$Country)[levels(addexposure$Country) == ""] <- NA | ||
levels(addexposure$Exposure_agent)[levels(addexposure$Exposure_agent) == ""] <- NA | levels(addexposure$Exposure_agent)[levels(addexposure$Exposure_agent) == ""] <- NA | ||
addexposure@output <- fillna(addexposure@output, c("Country", "Gender", "Exposure_agent")) | addexposure@output <- fillna(addexposure@output, c("Country", "Gender", "Exposure_agent")) | ||
Line 209: | Line 210: | ||
addexposure <- CollapseMarginal(addexposure, cols = "Background", fun = "sample") | addexposure <- CollapseMarginal(addexposure, cols = "Background", fun = "sample") | ||
− | + | expoRaw <- expoRaw + addexposure | |
− | return( | + | return(expoRaw) |
} | } | ||
) | ) | ||
Line 237: | Line 238: | ||
expoRaw$Exposure <- "Other" | expoRaw$Exposure <- "Other" | ||
dx.expo.child$Exposure <- "Breast feeding" | dx.expo.child$Exposure <- "Breast feeding" | ||
− | return(combine(expoRaw, dx.expo.child | + | return(combine(expoRaw, dx.expo.child)) |
} | } | ||
) | ) | ||
− | + | ||
# Limit the infant health responses to 10 % of females at age 18-45 a | # Limit the infant health responses to 10 % of females at age 18-45 a | ||
# (assuming 10 % probability to give birth during a year) | # (assuming 10 % probability to give birth during a year) | ||
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BoDt <- Ovariable( # Total burden of disease | BoDt <- Ovariable( # Total burden of disease | ||
"BoDt", | "BoDt", | ||
− | ddata = "Op_en5917", | + | ddata = "Op_en5917", # [[:op_en:Disease risk]] |
subset = "IHME_Goherr" | subset = "IHME_Goherr" | ||
) | ) | ||
− | |||
− | |||
− | |||
− | |||
# WHY ARE THERE RESPONSES THAT DO NOT MATCH WITH EXPOSURES? | # WHY ARE THERE RESPONSES THAT DO NOT MATCH WITH EXPOSURES? | ||
Line 319: | Line 316: | ||
concentration <- unkeep(concentration, prevresults = TRUE, sources = TRUE) * skaala | concentration <- unkeep(concentration, prevresults = TRUE, sources = TRUE) * skaala | ||
− | + | ||
− | BoD <- Ovariable( | + | ############# 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( | dependencies = data.frame( | ||
Name = c( | Name = c( | ||
"population", # Population divided into subgroups as necessary | "population", # Population divided into subgroups as necessary | ||
"dose", # Exposure to the pollutants | "dose", # Exposure to the pollutants | ||
− | |||
− | |||
"ERF", # Other ERFs than those that are relative to background. | "ERF", # Other ERFs than those that are relative to background. | ||
"threshold", # exposure level below which the agent has no impact. | "threshold", # exposure level below which the agent has no impact. | ||
Line 335: | Line 334: | ||
"Op_en2261/population", # [[Health impact assessment]] | "Op_en2261/population", # [[Health impact assessment]] | ||
"Op_en2261/dose", # [[Health impact assessment]] | "Op_en2261/dose", # [[Health impact assessment]] | ||
− | |||
− | |||
"Op_en2031/initiate", # [[Exposure-response function]] | "Op_en2031/initiate", # [[Exposure-response function]] | ||
"Op_en2031/initiate", # [[Exposure-response function]] | "Op_en2031/initiate", # [[Exposure-response function]] | ||
Line 343: | Line 340: | ||
), | ), | ||
formula = function(...) { | formula = function(...) { | ||
− | + | temp <- list() | |
− | + | UR <- ERF[ERF$ERF_parameter %in% c("UR", "CSF", "ERS") , ] | |
− | ERF | ||
− | |||
− | |||
# testforrow could be simplified a bit. | # testforrow could be simplified a bit. | ||
# Dose could be simplified with combine. | # Dose could be simplified with combine. | ||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
if(testforrow(UR, dose)) { # See RR for explanation. | if(testforrow(UR, dose)) { # See RR for explanation. | ||
UR <- threshold + UR * dose * frexposed # Actual equation | UR <- threshold + UR * dose * frexposed # Actual equation | ||
# threshold is here interpreted as the baseline response (intercept of the line). It should be 0 for | # threshold is here interpreted as the baseline response (intercept of the line). It should be 0 for | ||
# UR and CSF but it may have meaningful values with ERS | # UR and CSF but it may have meaningful values with ERS | ||
− | + | ||
UR <- oapply(UR, NULL, sum, "Exposure_agent") | UR <- oapply(UR, NULL, sum, "Exposure_agent") | ||
UR <- population * UR | UR <- population * UR | ||
− | + | temp <- c(temp, UR) | |
} | } | ||
UR <- NULL | UR <- NULL | ||
− | |||
# Step estimates: value is 1 below threshold and above ERF, and 0 in between. | # Step estimates: value is 1 below threshold and above ERF, and 0 in between. | ||
# frexposed cannot be used with Step because this may be used at individual and maybe at population level. | # frexposed cannot be used with Step because this may be used at individual and maybe at population level. | ||
− | # Step function is essentially an individual function and cannot be used for BoD calculations??? | + | # Step function is essentially an individual function and cannot be used for BoD calculations??? |
Step <- ERF[ERF$ERF_parameter %in% c("Step", "ADI", "TDI", "RDI", "NOAEL") , ] | Step <- ERF[ERF$ERF_parameter %in% c("Step", "ADI", "TDI", "RDI", "NOAEL") , ] | ||
if(testforrow(Step, dose)) { # See RR for explanation. | if(testforrow(Step, dose)) { # See RR for explanation. | ||
Line 388: | Line 367: | ||
test <- c(test, Step) | test <- c(test, Step) | ||
} | } | ||
− | |||
− | |||
##################################################################### | ##################################################################### | ||
# Combining effects | # Combining effects | ||
− | + | if(length(temp) == 0) return(data.frame()) | |
− | if(length( | + | if(length(temp) == 1) out <- temp[[1]] |
− | if(length( | + | if(length(temp) == 2) out <- combine(temp[[1]], temp[[2]]) |
− | if(length( | + | |
− | + | # # 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? | # 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)) { | if("Exposcen" %in% colnames(out@output)) { | ||
out <- out * Ovariable( | out <- out * Ovariable( | ||
Line 421: | Line 423: | ||
return(out) | 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$Response)) | ||
+ | if(length(doublecount>0)) warning(paste( | ||
+ | "The response(s)", | ||
+ | doublecount, | ||
+ | "are probably double counted from both disease burdens and incidences." | ||
+ | )) | ||
+ | } | ||
+ | return(out) | ||
+ | |||
} | } | ||
) | ) | ||
Line 427: | Line 506: | ||
ggplot(BoD@output, aes(x = Response, weight = totcasesResult, fill = Gender))+geom_bar(position="dodge") | ggplot(BoD@output, aes(x = Response, weight = totcasesResult, fill = Gender))+geom_bar(position="dodge") | ||
+ | |||
+ | ####################################### | ||
+ | |||
+ | dx.expo.child <- Ovariable( | ||
+ | "dx.expo.child", | ||
+ | dependencies = data.frame( | ||
+ | Name = c( | ||
+ | "expoRaw", # Mother's exposure to dioxin (pg/d) | ||
+ | "t0.5", # Half-life of dioxin in mother (d) | ||
+ | "f_ing", # Fraction of dioxin ingested by mother that is actually absorbed | ||
+ | "f_mtoc", # Fraction of mother's dioxin body burden that enters the child during 6 mo breast feeding. | ||
+ | "BF" # Body fat amount (g) in the infant at 6 mo of age | ||
+ | ) | ||
+ | ), | ||
+ | formula = function(...) { | ||
+ | out <- expoRaw * f_ing * t0.5 / log(2) # Body burden in mother after long constant exposure | ||
+ | out <- out * f_mtoc / BF # Dioxin concentration in child | ||
+ | out <- log(out + 0.1) # Convert to logarithm for ERF compatibility (avoid log(0)) | ||
+ | |||
+ | # Technical conversion | ||
+ | out <- out[out$Exposure_agent == "TEQ" , ] | ||
+ | out$Exposure_agent <- "logTEQ" | ||
+ | return(out) | ||
+ | } | ||
+ | ) | ||
</rcode> | </rcode> | ||
Revision as of 13:51, 29 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