Benefit-risk assessment of Baltic herring and salmon intake

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For the final version of this assessment, see the scientific article.

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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. This page describes the benefit-risk assessment performed. 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.

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

Assessment presentation · show ready-made model results

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 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.

• 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.

Show details

Data updated successfully! Decisions(-) Obs Num Decision maker Decision Option Variable Cell Change Description Result 1 0 Adjust BAU exposure Add Add something to avoid zero 0.01 2 0 Adjust BAU incidence Multiply #/100000py --> #/py 0.00001 3 0 HELCOM Info.improvements BAU effinfo Replace Current availability of and chemicals in herring and salmon 0 4 1 HELCOM Info.improvements Yes effinfo Identity Better availability of and less chemicals in herring and salmon 5 0 National food safety authority Recomm.herring BAU effrecommRaw Fish:Herring Replace Authorities recommendations about Baltic herring do not change 0 6 2 National food safety authority Recomm.herring Eat more effrecommRaw Fish:Herring;Recommendation:Eat less Replace Authorities recommend to eat more Baltic herring 0 7 2 National food safety authority Recomm.herring Eat less effrecommRaw Fish:Herring;Recommendation:Eat more Replace Authorities recommend to eat less Baltic herring 0 8 0 National food safety authority Recomm.salmon BAU effrecommRaw Fish:Salmon Replace Authorities recommendations about Baltic salmon do not change 0 9 3 National food safety authority Recomm.salmon Eat more effrecommRaw Fish:Salmon;Recommendation:Eat less Replace Authorities recommend to eat more Baltic salmon 0 10 3 National food safety authority Recomm.salmon Eat less effrecommRaw Fish:Salmon;Recommendation:Eat more Replace Authorities recommend to eat less Baltic salmon 0 11 0 HELCOM Select.size BAU size Fish:Baltic herring;Lower:70 Replace Select herring size for consumption: only small, only large, or both 0 12 0 HELCOM Select.size BAU size Fish:Baltic herring;Lower:120 Replace Eat only large herring (>17 cm) 0 13 4 HELCOM Select.size Ban large size Fish:Baltic herring;Lower:170 Replace Eat only small herring (<17 cm) 0 14 4 HELCOM Select.size Ban large size Fish:Baltic herring;Lower:220 Replace Eat only small herring 0 15 4 HELCOM Select.size Ban large size Fish:Baltic herring;Lower:270 Replace Eat only small herring 0 16 4 HELCOM Select.size New products size Fish:Baltic herring Identity Eat both small and large herring 17 0 National food safety authority Background Yes expo_bg Identity Consider background from other sources 18 5 National food safety authority Background No expo_bg Replace Do not consider background from other sources 0 19 0 National food safety authority Limit No limit amount Identity Do not limit fish concumption of heavy eaters 20 6 National food safety authority Limit Max 3 g/d amount Identity This limits consumption to max 3 g per day (requires also a function in code) 21 0 Risk assessor Time 2018 time Replace Short timeline (2018 only) 2018 22 7 Risk assessor Time 2010 time Replace Long timeline (1990, 2000, 2010, 2018) 2010 23 7 Risk assessor Time 2000 time Replace Long timeline 2000 24 7 Risk assessor Time 1990 time Replace Long timeline 1990 25 0 Risk assessor Mixtures BAU conc Identity All compounds as mixture in the model 26 8 Risk assessor Mixtures MeHg conc Exposure_agent:MeHg Replace Consider situation without MeHg 0 27 8 Risk assessor Mixtures Dioxin conc Exposure_agent:PCDDF Replace Consider situation without PCDDF and PCB 0 28 8 Risk assessor Mixtures Dioxin conc Exposure_agent:PCB Replace Consider situation without PCDDF and PCB 0 29 8 Risk assessor Mixtures Dioxin conc Exposure_agent:TEQ Replace Consider situation without PCDDF and PCB 0 30 8 Risk assessor Mixtures Omega3 conc Exposure_agent:DHA Replace Consider situation without DHA and EPA 0 31 8 Risk assessor Mixtures Omega3 conc Exposure_agent:EPA Replace Consider situation without DHA and EPA 0 32 8 Risk assessor Mixtures Omega3 conc Exposure_agent:Omega3 Replace Consider situation without DHA and EPA 0 33 8 Risk assessor Mixtures Vitamin D conc Exposure_agent:Vitamin D Replace Consider situation without MeHg 0 34 9 Risk assessor Time 2009 time Replace Same time as EU Fish2 study (2009) 2009 35 9 Risk assessor Select.size All sizes size Fish:Baltic herring Identity Size fractions to include EU Fish 2 study 36 0 National food safety authority Cons.policy BAU effinfo Replace Current availability of and chemicals in herring and salmon 0 37 10 National food safety authority Cons.policy Increase effinfo Identity Better availability of and less chemicals in herring and salmon 38 10 National food safety authority Cons.policy Reduce effinfo Replace Current availability of and chemicals in herring and salmon 0 39 0 National food safety authority Cons.policy BAU effrecommRaw Fish:Herring Replace Authorities recommendations about Baltic herring do not change 0 40 10 National food safety authority Cons.policy Increase effrecommRaw Fish:Herring;Recommendation:Eat less Replace Authorities recommend to eat more Baltic herring 0 41 10 National food safety authority Cons.policy Reduce effrecommRaw Fish:Herring;Recommendation:Eat more Replace Authorities recommend to eat less Baltic herring 0 42 0 National food safety authority Cons.policy BAU effrecommRaw Fish:Salmon Replace Authorities recommendations about Baltic salmon do not change 0 43 10 National food safety authority Cons.policy Increase effrecommRaw Fish:Salmon;Recommendation:Eat less Replace Authorities recommend to eat more Baltic salmon 0 44 10 National food safety authority Cons.policy Reduce effrecommRaw Fish:Salmon;Recommendation:Eat more Replace Authorities recommend to eat less Baltic salmon 0 45 0 LUKE Luke.scaling BAU amount Identity Use the Goherr fish consumption study data directly 46 11 LUKE Luke.scaling Scale amount Fish:Salmon Multiply Average amount is scaled to actual Luke data (0.07 kg/a) 0.12 47 11 LUKE Luke.scaling Scale amount Fish:Herring Multiply Average amount is scaled to actual Luke data (0.31 kg/a) 0.22 48 12 OmegaERF Cohen ERF Response:CHD2 mortality;Observation:ERF;ER_function:RR Replace ERF from Cohen et al 2005 1 49 0 OmegaERF Cochrane ERF Response:CHD2 mortality;Observation:ERF;ER_function:Relative Hill Replace ERF from Cochrane 2018 0 50 12 OmegaERF Cohen ERF Response:Stroke mortality;Observation:ERF;ER_function:Relative Hill Identity ERF from Cohen et al 2005 51 0 OmegaERF Cochrane ERF Response:Stroke mortality;Observation:ERF;ER_function:Relative Hill Replace ERF from Cochrane 2018 0 Data updated successfully! Marginals(-) Obs Variable Index Probs Function dummy Description 1 effrecomm Recommendation sum 1 Change the increase/reduce to increase/BAU/reduce index of Cons.policy 2 amount Ages, Gender, oftenSource, muchSource, oftensideSource, muchsideSource, amountRawSource effinfoSource, effrecommSource, effrecommRawSource, infoSource sum 1 Remove redundant. 3 conc infoSource, conc_vitSource, conc_pcddfResult sum 1 Remove redundant columns. Somehow model thinks conc_pcddfResult is marginal 4 expo_dir infoSource, expo_bgSource, concSource sum 1 Remove Source columns to save memory 5 expo_indir f_ingSource, t0.5Source,f_mtocSource, BFSource sum 1 Remove redundant 6 PAF Exposcen, Adjust, populationSource, infoSource, frexposedSource, populationSource, RRSource, exposureSource, BWSource, incidenceSource sum 1 Remove redundant columns 7 BoD BoDtSource, incidenceSource, populationSource, case_burdenSource, InpBoDSource sum 1 Remove redundant columns 8 BoDattr Response, Adjust, BoDSource, ERFSource, doseSource, PAFSource, infoSource, populationSource sum 1 Remove redundant columns

Timing

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.

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.

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  Lengths of all fish species studied in EU Fish2 study. See Fig 2 for Baltic herring and salmon.

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  Fish lengths may vary a lot in different scenarios for Baltic herring. Goherr model is well in line with actual measurements in EU Fish2.

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  Dioxin concentrations have reduced a lot since 1990. The same trend have existed since the 1970's (data not shown).

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  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.

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  With active policy to not use large (>17 cm) Baltic herring, the concentrations on the plate would be even lower than nowadays.

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  Dioxin concentrations are strongly correlated with the size of Baltic herring, while the impact is much lower in salmon.

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  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.

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  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.

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  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.

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  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.

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  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).

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  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).

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  This is a detail from Fig 12 showing the subgroups that eat fish the most.

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  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).

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  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.

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  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)

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  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).

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  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.

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  Same as Fig 18 but the focus is on the upper corner of the figure, i.e. those with high impacts.

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  Heart disease and stroke are risks that actually affect large fractions of the older subgroups.

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  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.

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  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.

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  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.

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  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.

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  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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  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 but not later.

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  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.

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  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.

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  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.

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  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.

Rationale

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

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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). 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)
• Farmers and agricultural sector
• Consumers of fish
• Producers of fish oil and fish meal
• Baltic Sea RAC (Regional Advisory Concil) represents fishers (located in Copenhagen)
• Hydropower plant owners

Who will affect the decisions?

• Food safety organisations (EVIRA, Livsmedelsverket, ...)
• 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

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.

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.

Exposure-response functions were derived for all relevant pollutants and nutrients. Exposure-response functions of dioxins were derived for several endpoints. Tooth defects were based on an epidemiological study in Finland. Cancer morbidity was based on U.S.EPA dioxin risk assessment. Tolerable daily intake was based on EC Scientific Committee on Food recommendation. Exposure-response functions for omega-3 fatty acids on coronary heart disease and stroke mortalities were from a previous risk assessment. Exposure-response function of methylmercury on child's intelligence quotient was based on a previous risk assessment. Exposure-response function for vitamin D was a step function based on the daily intake recommendations for adults in Finland.

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.

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Data updated successfully! Background exposure(-) Obs Country Gender Exposure_agent Result Unit Description 1 FI Male Vitamin D 0 - 11.7 µg /d Finriski 12 - 0.3 silakasta 2 SE Male Vitamin D 0 - 11.7 µg /d Finriski 12 - 0.3 silakasta 3 EE Male Vitamin D 0 - 11.7 µg /d Finriski 12 - 0.3 silakasta 4 DK Male Vitamin D 0 - 11.7 µg /d Finriski 12 - 0.3 silakasta 5 Female Vitamin D 0 - 8.5 µg /d Finriski 8.7 - 0.2 silakasta 6 Male EPA 0 - 120 mg /d Finriski 125 - 4.6 silakasta 7 Female EPA 0 - 96 mg /d Finriski 100 - 3.9 silakasta 8 Male DHA 0 - 118 mg /d Finriski 125 - 6.7 silakasta 9 Female DHA 0 - 94 mg /d Finriski 100 - 5.4 silakasta 10 ALA 0 mg /d 11 PCDDF 0 pg /d (TEQ) 12 PCB 0 pg /d (TEQ) 13 MeHg 0 µg /d 14 logTEQ 0 log(pg /g) 15 Fish 0 g /d Data updated successfully! Exposure-response functions of interest(-) Obs Exposure_agent Resp Response ER_function Scaling Dummy 1 TEQ Tooth defect Yes or no dental defect ERS None 1 2 TEQ Cancer Cancer morbidity CSF BW 1 3 TEQ TWI 2001 Dioxin recommendation tolerable daily intake TDI BW 1 4 TEQ TWI 2018 Dioxin recommendation tolerable daily intake 2018 TDI BW 1 5 TEQ Infertility Sperm concentration ERS None 1 6 DHA Child's IQ Loss in child's IQ points ERS None 1 7 Omega3 Heart (CHD) CHD2 mortality Relative Hill None 1 8 Omega3 Heart (CHD) CHD2 mortality RR None 1 9 ALA Heart (CHD) CHD2 mortality RR None 1 10 Vitamin D Vitamin D intake Vitamin D recommendation Step None 1 11 MeHg Child's IQ Loss in child's IQ points ERS BW 1 12 Omega3 Cancer Breast cancer RR None 1 13 Fish Depression Depression RR None 1 14 Fish Mortality All-cause mortality RR None 1 Data updated successfully! DALYs of responses(DALY /case) Obs Response DALY Description 1 CHD2 mortality 5 - 15 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. Disability weights of IHME. Lower means the lower bound of the size group. Data updated successfully! Size distribution of fish species(mm,fraction) Obs Fish Lower Fraction 1 Baltic herring 70 0.112540 2 Baltic herring 120 0.070343 3 Baltic herring 170 0.344871 4 Baltic herring 220 0.472215 5 Baltic herring 270 0.000031 6 Baltic herring 320 0 7 Salmon 700 1 8 Salmon 1000 0 Data updated successfully! Diox conc fishing areas(TEQ) Obs Area Fish Result Concentrations used for countries 1 24 Herring 3.08 DK 2 25-29 Herring 1.95 SE 3 28 Herring 2.84 4 30-31 Herring 2.29 FI (reference: reflects EU-kalat study) 5 32 Herring 2.45 EE 6 24 Salmon 16.0 DK 7 25-29 Salmon 10.9 8 28 Salmon 10.9 9 30-31 Salmon 9.3 FI, SE (reference: reflects EU-kalat study) 10 32 Salmon 10.7 EE 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). Population data from Eurostat database Data updated successfully! Population(n) Obs Country Group Result 1 DK Male 18-45 1032266 2 DK Male >45 1207260 3 DK Female 18-45 1003587 4 DK Female >45 1296678 5 EE Male 18-45 252485 6 EE Male >45 237413 7 EE Female 18-45 239174 8 EE Female >45 339881 9 FI Male 18-45 970016 10 FI Male >45 1182808 11 FI Female 18-45 921887 12 FI Female >45 1339537 13 SE Male 18-45 1823042 14 SE Male >45 2065334 15 SE Female 18-45 1738408 16 SE Female >45 2199156 Landing of fish from different areas in Baltic Sea by country. Fish species and area Country DK FI EE SE 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 Data updated successfully! Model parametres(various) Obs Response Exposure_agent Type Subgroup Unit Result Description 1 Loss in child's IQ points incidence Group:Female 18-45 IQ /100000py 596000 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 Data updated successfully! Model dependencies(-) Obs Name Ident label type Description Child Page 1 addexposure Op_en7748/hia additional exposure 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

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]

Manuscript graphs

• [7]
• Model run 29.8.2019 [8]
• Model run 1.9.2019 [9]
• Model run 10.9.2019 [10] [11]
• Model run 11.9.2019 official [12]
• Model run 19.9.2019 with agent-specific disease burdens [13]
• Model run 24.9.2019 with EPA 2004 CSF. New official [14]

+ Show code- Hide code # This is code Op_en7748/pregraph on page [[Benefit-risk assessment of Baltic herring and salmon intake]] library(OpasnetUtils) library(ggplot2) library(reshape2) ##### Parameters from user interface BS <- 30 # This is a fork from code Op_en7919/ on page [[Health effects of Baltic herring and salmon: a benefit-risk assessment]] ############# VOI functions objects.latest("Op_en2480",code_name="VOI") # [[Value of information]] VOI, binning ################ Fetch all the model ovariables objects.latest("Op_en7748", code_name="model") # All objects of the model cat("CollapseMarginals for all runs.\n") oprint(margi) ############## Draw graphs ######## Check first that everything is in order if(FALSE) { oempty(all=TRUE) # Empty all ovariable outputs, marginals, and decision marks from openv DecisionTableParser(initiate(decsel=NULL)) InpBoD <- EvalOutput(InpBoD, verbose=TRUE) BoDattrPer1 <- EvalOutput(BoDattrPer1,verbose=TRUE) ### Plot and print each ovariable ovas <- list( # ovariables addexposure, amount, amountRaw, assump, BF, BoD, BoDattr, BoDattrPer1, BoDt, BW, case_burden, conc, conc.param, conc_mehg, conc_pcddf, conc_vit, dose, effinfo, effrecomm, ERF, ERF_diox, ERF_env, ERF_mehg, ERF_micr, ERF_omega3, ERF_vit, ERFchoice, expo_bg, expo_dir, expo_indir, exposure, f_ing, f_mtoc, frexposed, incidence, info, InpBoD, jsp, lengt, much, muchside, often, oftenside, PAF, population, RR, size, t0.5, time ) for(i in 1:length(ovas)) { tmp <- ovas[[i]] cat("Ovariable", tmp@name, "\n") print(tryCatch(oggplot(tmp)+theme_gray(base_size=16), error=function(e1) cat("Cannot plot ",tmp@name))) oprint(tryCatch(summary(tmp), error=function(e1) cat("Cannot print summary for ",tmp@name))) } } ########## Concentration # Long timeline # Initiate the model: update decisions and set N. oempty(all=TRUE) DecisionTableParser(initiate(decsel=c(7))) conc <- CheckCollapse(EvalOutput(conc)) ### Figure 3. tmp <- conc #tmp@output <- fillna(tmp@output, "Select.size") tmp <- (BAUt * tmp[ tmp$Exposure_agent %in% c("Vitamin D", "MeHg","TEQ","Omega3") & tmp$Iter %in% 1:1000,])@output fig3 <- ggplot(tmp, aes(x=Result, colour=Fish, linetype=Time))+ stat_ecdf(size=1.5)+theme_grey(base_size=BS)+ facet_wrap(~Exposure_agent, scales="free_x")+ scale_x_log10(labels=remzero)+ labs( # title="Concentrations of compounds in Baltic fish", x="Concentration (TEQ: pg/g; MeHg, vit D: µg/g; omega3: mg/g [f.w.])", y="Cumulative probability" )+scale_colour_manual(values=varit12)+ theme(axis.text.x=(element_text(hjust=1))) fig3 ## Different sizes # Initiate the model: update decisions. oempty(all=TRUE) DecisionTableParser(initiate(decsel=c(4))) InpBoD <- EvalOutput(InpBoD) BoDattrPer1 <- EvalOutput(BoDattrPer1) cat("VOI for BoD and Select.size.\n") tmp <- oapply( BoDattrPer1[!grepl("TWI", BoDattrPer1$Resp) , ], INDEX = c("Iter","Select.size","Group","Country"), FUN = sum ) oprint(VOI( tmp, decision = c("Select.size") )) ##### Amount of consumption # Initiate the model: update decisions and set N. #openv.setN(3000) oempty(all=TRUE) if(TRUE) { # TRUE: direct study results, FALSE: average amount scaled to match Luke statistics tmp <- initiate(decsel=c(10)) # Info.improvement, Recommendations, i.e. Cons.policy #, OmegaERF, Background } else { tmp <- initiate(decsel=c(5,10,11,12)) # Info.improvement, Recommendations, i.e. Cons.policy, OmegaERF, Background, Luke scaling } DecisionTableParser(tmp) InpBoD <- EvalOutput(InpBoD, verbose=FALSE) BoDattrPer1 <- EvalOutput(BoDattrPer1,verbose=FALSE) cat("Indices used in BoDattrPer1\n") tmp <- setdiff(colnames(BoDattrPer1@output)[BoDattrPer1@marginal],"Iter") oprint(data.frame( Index = tmp, Locations = sapply(tmp, function(i) paste(unique(BoDattrPer1@output[[i]]),collapse=", ")) )) cat("Disease burden of infertility at individual level (mDALY/a per person)\n") oprint(oapply( BoDattrPer1[BoDattrPer1$Resp=="Infertility"&BoDattrPer1$Time=="2018",], # info[info$Group=="Female 18-45",], c("Resp","Country","Group","Time"), mean )@output) cat("Disease burden of infertility at population level (DALY/a)\n") oprint(oapply( BoDattr*info[info$Group=="Female 18-45",], c("Resp","Country","Group","OmegaERF"), mean )@output) cat("Background disease burden\n") oprint(summary(oapply(BoD, NULL, sum, c("Group","BoDSource"))))#, marginals=c("Country","Response"))) amo <- info * amount * 365.25 / 1000 # g/d --> kg/a cat("Fish consumption average (kg/a).\n") oprint(oapply(amo*BAU,c("Country","Fish"),mean)@output) oprint(oapply(amo*BAU,c("Country","Group","Fish"),mean)@output) if(FALSE) { ova <- amo out <- aggregate( ova$Row, by = ova@output[c("Group")], FUN = function(x) { sample(x, 3000, replace=TRUE) } ) # FUN = function(x) { # apply(array(as.numeric(sample( # as.character(x), # 3000, # replace = TRUE # )), dim = c(1, 3000)), MARGIN = 2, FUN = mean) # }) temp <- melt(out[[length(out)]]) out[[length(out)]] <- 1:nrow(out) colnames(temp) <- c("Nrow", "Iter", "Row") out <- merge(out, temp, by.x = "x", by.y = "Nrow") out$x <- NULL tmp2 <- amo@output[order(amo$Iter),] tst <- merge( amo@output[ !duplicated(tmp2[c("Fish","Group","Row","Cons.policy")]) , # There may be several identical Rows on both sides colnames(tmp2)!="Iter"], out ) out <- Ovariable(output=tst, marginal=colnames(tst) %in% c("Group", colnames(amo@output)[amo@marginal])) out <- oapply(out, c("Group","Fish","Cons.policy","Iter"),FUN=sum) } else { # Endif out <- amo } amo.diff <- oapply(out*BAU, FUN=sum, cols="Cons.policy") amo.diff$Amorig <- amo.diff$Result amo.diff <- out - amo.diff amo.diff <- amo.diff[amo.diff$Cons.policy %in% c("Increase","Reduce"),] amo.diff <- amo.diff[amo.diff$Amorig < 5 , ] # Large values not interestng if(nrow(amo.diff@output)>0) { # Figure 5. fig10 <- ggplot(data=amo.diff@output[amo.diff$Iter %in% 1:3000,],#amo.diff@output, # Changes >10 g/d=3.6 kg/a are uninteresting. aes(x=Amorig, y=Result, colour=Group))+ geom_point()+#data=amo.diff@output[amo.diff$Iter %in% 1:3000,])+ geom_smooth(method="lm", se=FALSE)+ theme_gray(base_size=BS)+facet_grid(Fish~Cons.policy)+ theme(legend.position="bottom")+ coord_cartesian(ylim=c(-4,8),xlim=c(0,4))+ labs( # title="Individuals' fish intake after all consumption policies", x="Current fish intake (kg/a)", y="Change in fish intake (kg/a)" )+scale_colour_manual(values=varit12) # Figure 4. fig12 <- ggplot((BAU*amo+0.05)@output, aes(x=Result, colour=Country))+ stat_ecdf(size=1.5)+ geom_point( data = oapply(BAU*amo, c("Fish","Group","Country"),mean)@output, aes(x=Result, y=0.03, colour=Country), shape=1, size=5, stroke=3 )+ theme_grey(base_size=BS)+ theme(legend.position = "bottom")+ facet_grid(Fish~Group, scales="free_x")+scale_x_log10(labels=remzero)+ labs( # title="Consumption of Baltic fish by country and subgroup", x="Fish consumption of a random individual (kg/a)", y="Cumulative probability" )+scale_colour_manual(values=varit12) } ######## Exposure tmp <- info * unkeep(exposure[ paste(exposure$Exposure_agent, exposure$Exposure) %in% c( "MeHg To child", "Vitamin D To eater", "Omega3 To eater", "TEQ To eater" ),],sources=TRUE) # Figure 6. tmp <- BAU*tmp[tmp$Exposure_agent=="TEQ",]*7/70+0.1 fig15 <- ggplot(tmp@output, aes(x = Result, colour=Country))+ stat_ecdf(size=1.5) + scale_x_log10(labels=remzero) + geom_point( data = oapply(tmp, c("Group","Country"),mean)@output, aes(x=Result, y=0.03, colour=Country), shape=1, size=5, stroke=3 )+ theme_gray(base_size = BS)+ theme(legend.position = "bottom")+ geom_vline(data=recom[recom$Unit=="pg/kg/week",], aes(xintercept=Result, linetype=Line), colour="red", size=1)+ facet_wrap(~ Group)+ labs( # title="Exposure to dioxin from Baltic fish", y="Cumulative probability", x = "Exposure (pg/kg/week)", colour="", linetype="" )+scale_colour_manual(values=varit12) #### Burden of disease varit12[3] <- "#DDC0D3FF" # Make the pink brighter to separate depression from infertility # Figure 7. tmp <- oapply( BoDattrPer1[!grepl("TWI", BoDattrPer1$Resp) & BoDattrPer1$Background=="Yes" & BoDattrPer1$OmegaERF=="Cochrane",], INDEX=c("Resp","Group","Country","Cons.policy"), mean )@output fig22 <- ggplot(tmp, aes(x=Group, weight=BoDattrPer1Result, fill=Resp))+ geom_bar()+facet_grid(Cons.policy ~ Country)+ theme_grey(base_size=BS-8)+ theme( legend.position = "bottom", axis.text.x = element_text(angle = -90) )+ labs( # title="Disease burden of Baltic fish by country, group, and Cons.policy", y = "Disease burden (mDALY/a per person)", fill="" )+ scale_fill_manual(values=varit12) ######### Figure 8. tmp <- oapply(BoDattrPer1 * BAU, INDEX=c("Resp","Country","OmegaERF","Group"),mean)@output tmp <- rbind( cbind( Objective="Net health, default", tmp[!grepl("TWI",tmp$Resp),] ), cbind( Objective="Net health, young women", tmp[!grepl("TWI",tmp$Resp) & tmp$Group=="Female 18-45",] ), cbind( Objective=tmp$Resp, tmp )[grepl("TWI",tmp$Resp),] ) sums <- aggregate(tmp["Result"], by=tmp[c("Objective","Country","OmegaERF")],sum) ggplot(tmp, aes(x=Objective, weight=Result))+ geom_bar(aes(fill=Resp))+facet_grid(OmegaERF ~ Country)+ theme_grey(base_size=BS-8)+ theme( legend.position = "bottom", axis.text.x = element_text(angle = -90, hjust=0) )+ geom_text(data=sums, size=6, aes(label=round(Result,1),y=pmax(1,Result+1)))+ labs( title="Disease burden using different objectives for all", y = "Disease burden (mDALY/a per person)", fill="" )+ scale_fill_manual(values=varit12) fig22b <- ggplot(tmp[tmp$OmegaERF=="Cochrane",], aes(x=Objective, weight=Result))+ geom_bar(aes(fill=Resp))+facet_grid(. ~ Country)+ theme_grey(base_size=BS-8)+ theme( legend.position = "bottom", axis.text.x = element_text(angle = -90, hjust=0) )+ geom_text(data=sums[sums$OmegaERF=="Cochrane",], size=6, aes(label=round(Result,1),y=pmax(1,Result+1)))+ labs( # title="Disease burden using different objectives", y = "Disease burden (mDALY/a per person)", fill="" )+ scale_fill_manual(values=varit12) if(FALSE) { # Why are risks so high in Sweden? ggplot((info * BoDattr * BAU)@output, aes(x=Result, color=Country))+ stat_ecdf()+ facet_wrap(~Resp, scales="free_x") ggplot((info * BoDattrPer1 * BAU)@output, aes(x=Result, color=Country))+ stat_ecdf()+ facet_wrap(~Resp, scales="free_x") ggplot((info * exposure * BAU)@output, aes(x=Result, color=Country))+ stat_ecdf()+ facet_wrap(~Exposure_agent, scales="free_x") # oapply(info * exposure * BAU,c("Country","Group","Exposure_agent"),max)@output # oapply(info * amount * BAU,c("Country","Group","Fish"),max)@output ## There is young woman in Sweden who reports a lot of both Baltic herring and salmon eating, and her TEQ exposure ## is 2215 pg/d while the respective maxima in other countries are <660 pg/d. ## This explains the high risks specifically in Sweden: young women don't show much benefit, just risk. ## sort((-1*info*exposure[exposure$Exposure_agent=="TEQ",]*BAU)$Result) ggplot((info*much*BAU)@output,aes(x=Result, color=Country))+stat_ecdf()+facet_grid(Fish~Group) oprint(paramtable(info*much*BAU,c("Country","Group","Fish"))) ggplot((info*muchside*BAU)@output,aes(x=Result, color=Country))+stat_ecdf()+facet_grid(Fish~Group) oprint(paramtable(info*muchside*BAU,c("Country","Group","Fish"))) } ############### Value of information for Cons.policy cat("VOI for BoDattr and Cons.policy.\n") tmp <- oapply( info*BoDattr[!grepl("TWI", BoDattr$Resp) & BoDattr$Background=="Yes" & BoDattr$OmegaERF=="Cochrane" , ], INDEX = c("Iter","Cons.policy","BoDattrSource","Group","Country"), FUN = sum ) oprint(VOI( tmp, decision = c("Cons.policy"), scenario = "BoDattrSource" )) cat("VOI for BoDattr and Cons.policy for separate Groups.\n") oprint(VOI( tmp, decision = c("Cons.policy"), scenario = "Group" )) ################ Comparison to other environmental health risks # Disease burden statistics for Finland a<-opbase.data("Op_fi3944", subset="Tautitaakka Suomessa THL:n tutkimuksessa") # [[Tautitaakka Suomessa]] a$Result <- as.numeric(as.character(a$Result)) a <- a[order(-a$Result) , ] lab <- c( "Fine particles, death", "Noise, annoyance+sleep disorder", "Indoor radon, cancer", "Environmental tobacco smoke, CHD", "UV radiation, cancer", "Fine particles, chr bronchitis", "Fine particles, symptom days", "BONUS GOHERR estimate", "MeHg in fish, IQ in children", "Moisture in homes, asthma", "Moisture in homes, respir symptoms", "Lead, IQ in children", "Environmental tobacco smoke, cancer" ) levels(a$Impact)[1:12] <- lab[-8] levels(a$Source)[levels(a$Source)=="Food"] <- "Food source" # Figure 8. tmp <- info * BoDattr * BAU tmp <- oapply( tmp[tmp$Country=="FI" & !grepl("TWI", tmp$Resp),], cols="Iter", FUN=mean )@output levels(tmp$Resp)[levels(tmp$Resp) %in% c("Infertility","Tooth defect")] <- "Dioxin risk" colnames(tmp)[colnames(tmp)=="Resp"] <- "Source" tmp$Impact <- "BONUS GOHERR estimate" tmp <- orbind(tmp,a[a$Result >= 380 , ]) tmp$Impact <- factor(tmp$Impact, lab) tmp$Source <- factor(tmp$Source, sort(levels(tmp$Source))) fig28 <- ggplot(data=tmp, aes(x=Impact, weight=Result,fill=Source))+ geom_bar() + coord_flip() + theme_gray(base_size = BS) + labs( # title = "Environmental disease burden in Finland", x = "", y = "Disease burden (DALY/a in whole population)" ) + scale_fill_manual(values = varit12[c(5,6,3,8,14,2,11,1,7,4)]) + #4,5,8,1,6,7,2,3,9)]) + theme(legend.position = c(0.8, 0.7)) + guides(fill=guide_legend(title=NULL))+ theme(axis.title.x = element_text(hjust=1)) ################### Calculate ratio of benefits and risks at individual level # NOTE! THIS IS NOT A RATIO OF MARGINAL BENEFIT THAT WE SHOULD LOOK AT. RATHER, THIS IS ABSOLUTE AVERAGE RISK # DIFFERENCE BETWEEN TWO GROUPS: WINNERS AND LOSERS. if(FALSE) { ratio <- BoDattr[!grepl("TWI",BoDattr$Resp), ] * BAU * info ratio$Type <- ifelse(result(ratio)>0,"risk","benefit") ratio <- oapply(ratio, c("Type","Country","Iter","Group"),sum) net <- ratio[ratio$Type=="benefit" , colnames(ratio@output)!="Type"] + ratio[ratio$Type=="risk" , colnames(ratio@output)!="Type"] net@name <- "net" colnames(net@output)[colnames(net@output)=="Result"] <- "netResult" ratio <- -1 * ratio[ratio$Type=="benefit" , colnames(ratio@output)!="Type"] / ratio[ratio$Type=="risk" , colnames(ratio@output)!="Type"] ratio@name <- "ratio" colnames(ratio@output)[colnames(ratio@output)=="Result"] <- "ratioResult" ratio <- ratio + net ratio$Rank <- rank(ratio$ratioResult) cat("Benefit/risk ratio calculated at individual level and averaged for Groups and Countries.\n") oprint(oapply(ratio, c("Group","Country"),mean)@output) ggplot(ratio@output,aes(x=ratioResult, colour=Group))+stat_ecdf()+ scale_x_log10()+geom_vline(xintercept=1) ggplot(ratio@output,aes(x=netResult, colour=Group))+stat_ecdf() ggplot(ratio@output,aes(x=netResult,y=ratioResult,colour=Group))+ geom_point()+ geom_density_2d()+ scale_y_log10()+geom_hline(yintercept=1) # coord_cartesian(xlim=c(-2,2)) # facet_wrap(~Country) ggplot(ratio@output[ratio$Group=="Female 18-45",][1:100,], aes(x=ratioResult,y=Rank,colour=abs(netResult)))+ geom_point(size=3,shape=21)+ scale_x_log10()+geom_vline(xintercept=1) } ############################################# cat("Concentrations in fish: mean (95 % CI)\n") oprint(paramtable(info * conc,c("Country","Fish","Exposure_agent"))) cat("Exposure to pollutants and nutrients in fish: mean (95 % CI)\n") oprint(paramtable(info * exposure,c("Country","Exposure_agent"))) cat("Exposure response functions: mean (95 % CI)\n") oprint(paramtable(ERF[ERF$Observation=="ERF",],c("Exposure_agent","Resp"))) cat("Total burden of disease of selected causes from all risk factors (kDALY/a): mean (95 % CI)\n") oprint( reshape( paramtable( oapply(BoD/1000,NULL,sum,c("Group","BoDSource")) * ERFchoice, c("Country","Resp") ), timevar="Country", idvar="Resp",v.names="Estimate", direction="wide" ) ) cat("Individual attributable burden of disease of fish (mDALY/a per person): mean (95 % CI)\n") oprint(paramtable(BoDattrPer1*BAU,c("Resp","Group","Country"))) cat("Attributable burden of disease of fish by country (DALY/a): mean (95 % CI)\n") oprint( reshape( paramtable( info*BoDattr*BAU, c("Resp","Exposure_agent","Country") ), timevar="Country", idvar=c("Resp","Exposure_agent"),v.names="Estimate", direction="wide" ) ) cat("Burden of disease per case (DALY): mean (95 % CI)\n") oprint(paramtable(case_burden*ERFchoice,c("Resp"))) ################ Store results images <- c("expotable", "varit12", ls()[grepl("fig",ls())]) if(FALSE) { objects.store(list=images) cat("Objects", images, "stored.\n") } pre <- "" # "Goherr benefit-risk assessment " post <- ".pdf" do <- FALSE fig3 if(do) ggsave(paste0(pre,"Figure 3",post), width=15, height=10.5) fig12 if(do) ggsave(paste0(pre,"Figure 4",post), width=14, height=10.5) fig10 if(do) ggsave(paste0(pre,"Figure 5",post), width=11.5, height=10.5) fig15 if(do) ggsave(paste0(pre,"Figure 6",post), width=11.5, height=10.5) fig22 if(do) ggsave(paste0(pre,"Figure 7",post), width=11.5, height=10.5) fig22b if(do) ggsave(paste0(pre,"Figure 8",post), width=14, height=10.5) fig28 if(do) ggsave(paste0(pre,"Figure 9",post), width=14, height=10.5) ################ Insight network if(do) { gr <- scrape(type="assessment") objects.latest("Op_en3861", "makeGraph") # [[Insight network]] gr <- makeGraph(gr) export_graph(gr, "ruori.svg") render_graph(gr) }

All result graphs

+ 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) ##### Parameters from user interface decsel <- c(5,10) N <- 1000 groups <- function(o) {return(o)} # Not needed any more but function still shows up in the code objects.latest("Op_en7748", code_name="model") # All objects of the model rm(list=ls()[grepl("^Dec", ls())]) # Decisions and marginal collapses #decs <- deci[deci$Num %in% c("0",decsel) , ] # Choose decisions #if("10" %in% decsel) {decs <- decs[!decs$Decision %in% c("Info.improvements","Recomm.herring","Recomm.salmon"),]} # Cons.policy #covers these #DecisionTableParser(decs) initiate(c(5,10)) cat("Model iterations run:",N,"\n") cat("Decisions used in model:\n") oprint(deci) InpBoD <- EvalOutput(InpBoD) BoDattr <- EvalOutput(BoDattr) objects.latest("Op_en3104", code_name="preprocess") # [[EU-kalat]] eu2 eu2 <- EvalOutput(eu2) # Disease burden statistics for Finland a<-opbase.data("Op_fi3944", subset="Tautitaakka Suomessa THL:n tutkimuksessa") a$Result <- as.numeric(as.character(a$Result)) a <- a[order(-a$Result) , ] ############### Limits for fish concentrations (pg/g fresh weight) recomconc <- data.frame( Exposure_agent=c("PCDDF","TEQ"), Compound=c("PCDDF","TEQ"), Result=c(3.5,6.5) # http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:02006R1881-20140701 ) ############ Limits for dioxin intake (ug or pg/d per person) recomintake <- data.frame( Exposure_agent=c("MeHg","Vitamin D","TEQ"), Result = c( 13, # EFSA 2012: 1.3 µg/kg/wk, assuming 70 kg 7.5, # Recommendation for adults in Finland 140 # TWI 14 pg /kg /week, assuming 70 kg )) eu3 <- eu2[ eu2$Fish %in% c("Baltic herring","Salmon") & eu2$Compound %in% c("PCDDF","PCB","TEQ") , ] ########### Print basic output basicoutput <- function() return(oprint(data.frame( Parameter = c( "Number of iterations: ", "Info.improvements: ", "Recomm.salmon: ", "Recomm.herring: ", "Limit: ", "Select.size: ", "Time: ", "Background: ", "Mixtures: " ), Values = c( max(as.numeric(as.character(BoD$Iter))), paste(unique(BoD$Info.improvements), collapse=", "), paste(unique(BoD$Recomm.salmon), collapse=", "), paste(unique(BoD$Recomm.herring), collapse=", "), paste(unique(BoD$Limit), collapse=", "), paste(unique(BoD$Select.size), collapse=", "), paste(unique(BoD$Time), collapse=", "), paste(unique(BoD$Background), collapse=", "), paste(unique(BoD$Mixtures), collapse=", ") ) ) )) ############# VOI functions objects.latest("Op_en2480",code_name="VOI") # [[Value of information]] VOI, binning ############## Draw graphs ############## Fish length objects.get(runs[1]) # Long timeline if(sum(colnames(BoD@output) %in% "BoDResult")==2) { # This is needed for model runs before 20.4.2018 colnames(BoDRaw@output)[colnames(BoDRaw@output)=="BoDResult"] <- "BoDRawResult" BoDRaw@name <- "BoDRaw" colnames(BoD@output)[colnames(BoD@output)=="BoDResult"] <- c("BoDRawResult","BoDResult") } cat("Basic output for figures 3 - 4, 25\n") basicoutput() ########## Concentration fig3 <- ggplot((BAUt*conc)@output, aes(x=concResult, colour=Fish, linetype=Time))+ stat_ecdf()+theme_grey(base_size=BS)+ facet_wrap(~Exposure_agent, scales="free_x")+scale_x_log10()+ labs( title="Fig 3. Concentrations of relevant compounds in Baltic fish", x="Concentration (dioxin: pg/g; MeHg and vit D: µg/g; omega3: mg/g)", y="Cumulative probability" ) fig4 <- ggplot()+ stat_ecdf(data=eu3@output, aes(x=eu2Result, colour="Data 2009"))+ stat_ecdf(data=melt(exp(samps.j$pred[,,,1])), aes(x=value, colour="Bayes estimate"))+ stat_ecdf(data=conc_pcddf@output, aes(x=conc_pcddfResult, colour="Goherr estimate", linetype=Time))+ geom_vline(data=recomconc,aes(xintercept=Result), colour="pink", size=2)+ theme_grey(base_size=BS)+ facet_grid(Compound ~ Fish)+scale_x_log10()+ labs( title="Fig 4. Dioxin in Baltic fish based on EU Fish2 study or models", y="Cumulative probability", x="Concentration (pg/g fw)" ) ############### Burden of disease with time trend and size selection BoD <- groups(BoD) fig25 <- ggplot(oapply( BAUt*BoD[BoD$Group =="Female 18-45",], c("Group","Time","Resp","Country"), mean )@output, aes(x=Time, weight=Result, fill=Resp))+ geom_bar()+facet_grid(.~Country)+ theme_grey(base_size=BS)+ theme( legend.position = "bottom", axis.text.x = element_text(angle = -90) )+ labs( title="Fig 25. Burden of disease in young females by country and time", y = "Disease burden (mDALY/a per person)" )+ scale_fill_manual(values=varit12) ######## Size selections objects.get(runs[2]) # Herring size selections if(sum(colnames(BoD@output) %in% "BoDResult")==2) { # This is needed for model runs before 20.4.2018 colnames(BoDRaw@output)[colnames(BoDRaw@output)=="BoDResult"] <- "BoDRawResult" BoDRaw@name <- "BoDRaw" colnames(BoD@output)[colnames(BoD@output)=="BoDResult"] <- c("BoDRawResult","BoDResult") } cat("Basic output for figures 1 - 2, 5, 24\n") basicoutput() cat("VOI of BoDRaw using infertility rather than TWI by decision Select.size.\n") tmp <- oapply(BoDRaw[!BoDRaw$Resp %in% c("Dioxin TWI", "TWI 2018"),],c("Iter","Select.size","BoDSource"),sum) oprint(VOI(tmp, "Select.size",scenario="BoDSource")) fig1 <- ggplot()+ stat_ecdf(data=eu2@output, aes(x=Length, colour="EU fish data"))+ stat_ecdf(data=lengt@output, aes(x=lengtResult, colour=Select.size))+#, colour="Goherr model"))+ facet_wrap(~Fish, scales="free_x")+theme_gray(base_size=BS)+ labs( title="Fig 1. Fish length in the EU Fish 2 study or Goherr MC model", y="Cumulative probability", x="Fish length (mm)" ) fig2 <- ggplot()+ stat_ecdf(data=eu2@output[eu2$Fish %in% c("Baltic herring","Salmon"),], aes(x=Length, colour="EU fish data"))+ stat_ecdf(data=lengt@output, aes(x=lengtResult, colour=Select.size))+#, colour="Goherr model"))+ facet_wrap(~Fish, scales="free_x")+theme_gray(base_size=BS)+ labs( title="Fig 2. Fish length in the EU Fish 2 study or Goherr MC model", y="Cumulative probability", x="Fish length (mm)" ) fig5 <- ggplot()+ stat_ecdf(data=eu3@output[eu3$Compound=="PCDDF",], aes(x=eu2Result, colour="Data 2009"))+ stat_ecdf(data=melt(exp(samps.j$pred[,1,,1])), aes(x=value, colour="Bayes estimate"))+ stat_ecdf(data=conc@output[conc$Exposure_agent=="PCDDF",], aes(x=concResult, colour="Goherr estimate"))+ geom_vline(data=recomconc[recomconc$Exposure_agent=="PCDDF",], aes(xintercept=Result), colour="pink", size=2)+ theme_grey(base_size=BS)+ facet_grid(Select.size ~ Fish)+scale_x_log10()+ labs( title="Fig 5. PCDD/F in Baltic fish based on EU Fish2 study or models", y="Cumulative probability", x="Concentration (pg/g fw)" ) BoD <- groups(BoD) fig24 <- ggplot(oapply( BoD,#[BoD$Time == "2018",], INDEX=c("Resp","Group","Country","Select.size"), mean)@output, aes(x=Select.size, weight=BoDResult, fill=Resp))+ geom_bar()+facet_grid(Country ~ Group)+ theme_grey(base_size=BS)+ theme( legend.position = "bottom", axis.text.x = element_text(angle = -90) )+ labs( title="Fig 24. Burden of disease by country, group and herring size selection", y = "Disease burden (mDALY/a per person)" )+ scale_fill_manual(values=varit12) ######### All herring sizes in 2009 objects.get(runs[3]) # Herring size selections and Time==2009 cat("Basic output for figure 6\n") basicoutput() temp <- (lengt+conc_pcddf[ conc_pcddf$Time=="2009" & # Same as EU Fish2 study conc_pcddf$Compound!="TEQ" & conc_pcddf$Select.size=="All sizes",])@output if(nrow(temp)>0) { fig6 <- ggplot()+ geom_point(data=temp, cex=0.2, aes(x=lengtResult, y=conc_pcddfResult, colour="Goherr model"))+ geom_point(data=eu3@output, aes(x=Length, y=eu2Result, colour="EU Fish2 data"),cex=2)+ geom_smooth(data=eu3@output, aes(x=Length, y=eu2Result, colour="EU Fish2 data"),colour="blue")+ facet_grid(Compound~Fish, scales="free_x")+scale_y_log10()+ theme_gray(base_size=BS)+coord_cartesian(ylim=c(0.3,12))+ labs( title="Fig 6. Fish dioxins vs length by EU Fish2 data or model", y="Dioxin concentration (pg/g fw)", x="Fish length (mm)" ) } ######### Exposure with varying background and max intake limit objects.get(runs[4]) # Background, Limit if(sum(colnames(BoD@output) %in% "BoDResult")==2) { # This is needed for model runs before 20.4.2018 colnames(BoDRaw@output)[colnames(BoDRaw@output)=="BoDResult"] <- "BoDRawResult" BoDRaw@name <- "BoDRaw" colnames(BoD@output)[colnames(BoD@output)=="BoDResult"] <- c("BoDRawResult","BoDResult") } cat("Basic output for figure 7, 27.\n") basicoutput() cat("VOI for BoDRaw using infertility rather than TWI by decisions Background and Limit.\n") tmp <- oapply(BoDRaw[!BoDRaw$Resp %in% c("Dioxin TWI", "TWI 2018"),],c("Iter","Background","Limit","BoDSource"),sum) oprint(VOI(tmp, c("Background","Limit"),scenario="BoDSource")) tmp <- groups(unkeep(exposure[exposure$Exposure=="To eater",],sources=TRUE) * info) fig7 <- ggplot( oapply(tmp,INDEX=c("Limit","Exposure_agent","Background"),mean)@output, aes(x = Limit, weight=Result, fill=Background))+ geom_bar() + theme_gray(base_size = BS)+ facet_grid(Exposure_agent ~ Background, scales="free_y")+ labs( title="Fig 7. Exposure to compounds in fish by background and limit", y="Exposure (dioxin: pg/d; vit D & MeHg: µg/d; omega3: mg/d)" ) BoD <- groups(BoD) fig27 <- ggplot( oapply(BoD,INDEX=c("Limit","Resp","Group","Background"),mean)@output, aes(x = Limit, weight=BoDResult, fill=Resp))+ geom_bar() + theme_gray(base_size = BS)+ facet_grid(Group ~ Background, scales="free_y")+ scale_fill_manual(values=varit12)+ labs( title="Fig 27. Burden of disease for subgroups by background and limit", y="Disease burden (mDALY/a per person)" ) ##### Amount of consumption objects.get(runs[5]) # Info.improvement, Recommendations, i.e. Policy if(sum(colnames(BoD@output) %in% "BoDResult")==2) { # This is needed for model runs before 20.4.2018 colnames(BoDRaw@output)[colnames(BoDRaw@output)=="BoDResult"] <- "BoDRawResult" BoDRaw@name <- "BoDRaw" colnames(BoD@output)[colnames(BoD@output)=="BoDResult"] <- c("BoDRawResult","BoDResult") } cat("Basic output for figures 8 - 23, 28, 29.\n") basicoutput() cat("VOI for BoDRaw and Policy.\n") tmp <- oapply(groups(info*BoDRaw[!BoDRaw$Resp %in% c("Dioxin TWI", "TWI 2018"),]),c("Iter","Info.improvements","Recomm.herring", "Recomm.salmon","Policy","BoDSource"), sum) + disabilityweight[disabilityweight$Resp=="Dioxin TWI",] tmp@name <- "BoDRaw" oprint(VOI( unkeep(tmp,c("Resp","disabilityweightSource")), c("Info.improvements","Recomm.herring","Recomm.salmon","Policy"), "disabilityweightResult", "BoDSource" )) amount <- groups(amount) amount.diff <- unkeep(amount*BAU, prevresults=TRUE,sources=TRUE, cols=policies) amount.diff$Amorig <- amount.diff$Result amount.diff <- amount - amount.diff amount.diff <- amount.diff[ amount.diff$Policy %in% c("Increase","Reduce"),] #cat("87.5th percentile (top 1/8) of fish eating in different population groups. # There is a large subgroup of young Swedish females who say that they eat a lot of Baltic salmon. # (Probably they don't because it is not that much available.)\n") # 85 % of young female swedes say that they eat more than 13 g/d of Baltic salmon. # This is unlikely but it is the main reason why net health loss is positive # in this group. #oprint(oapply( # unkeep(amount*BAU, sources=TRUE), # INDEX=c("Country","Group","Fish"), # FUN=function(x) quantile(x, 0.875) #)@output) fig8 <- ggplot(oapply(amount, INDEX=c("Country","Group","Fish","Policy"),mean)@output, aes(x=Policy, weight=amountResult, fill=Fish))+geom_bar()+ facet_grid(Country~Group)+ theme_gray(base_size=BS)+ theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5))+ labs( title="Fig 8. Consumption of Baltic fish by subgroup and policy", x="Population subgroup", y="Average fish consumption (g/d)" ) if(nrow(amount.diff@output)>0) { fig9 <- ggplot(amount.diff@output, aes(x=Result, colour=Group))+stat_ecdf()+ theme_grey(base_size=BS)+ theme(legend.position = "bottom")+ coord_cartesian(xlim=c(-5,10))+facet_grid(Fish~Policy)+ labs( title="Fig 9. Fish intake change due to consumption policies", x="Change in amount consumed by a random individual (g/d)", y="Cumulative probability" ) } if(nrow(amount.diff@output)>0) { fig10 <- ggplot(amount.diff@output[amount.diff$Amorig<50,], # Changes >50 are unrealistic because intake always <100. aes(x=Amorig, y=Result, colour=Group))+geom_point()+geom_smooth()+ theme_grey(base_size=BS)+facet_grid(Fish~Policy)+ coord_cartesian(ylim=c(-40,60))+ labs( title="Fig 10. Change in individual fish intake after all consumption policies", x="Current fish intake (g/d)", y="Change in fish intake (g/d)" ) fig11 <- ggplot(amount.diff@output[amount.diff$Amorig<5,], # Changes >50 are unrealistic because intake always <100. aes(x=Amorig, y=Result, colour=Group))+geom_point()+geom_smooth()+ theme_grey(base_size=BS)+facet_grid(Fish~Policy)+ coord_cartesian(ylim=c(-5,6))+ labs( title="Fig 11. Change in individual fish intake after all consumption policies", x="Current fish intake (g/d)", y="Change in fish intake (g/d)" ) } fig12 <- ggplot((BAU*amount+0.05)@output, aes(x=Result, colour=Country))+stat_ecdf()+ theme_grey(base_size=BS)+ facet_grid(Fish~Group, scales="free_x")+scale_x_log10()+ labs( title="Fig 12. Consumption of Baltic fish by country and group", x="Fish consumption of a random individual in BAU (g/day)", y="Cumulative probability" ) fig13 <- ggplot((BAU*amount+0.05)@output, aes(x=Result, colour=Country))+stat_ecdf()+ theme_grey(base_size=BS)+ facet_grid(Fish~Group, scales="free_x")+scale_x_log10()+ labs( title="Fig 13. Consumption of Baltic fish by country and group", x="Fish consumption of a random individual in BAU (g/day)", y="Cumulative probability" )+coord_cartesian(xlim=c(10,100),ylim=c(0.8,1)) ######## Exposure tmp <- groups(unkeep(exposure[ paste(exposure$Exposure_agent, exposure$Exposure) %in% c( "MeHg To child","Vitamin D To eater","Omega3 To eater","TEQ To eater" ),],sources=TRUE) * info) fig14 <- ggplot((BAU*tmp+0.1)@output, aes(x = Result, colour=Country))+ stat_ecdf() + scale_x_log10() + theme_gray(base_size = BS)+ geom_vline(data=recomintake, aes(xintercept=Result), colour="pink", size=2)+ facet_wrap(~ Exposure_agent, scales="free_x")+ labs( title="Fig 14. Exposure to compounds from Baltic fish and background", y="Cumulative probability", x = "Exposure (dioxin: pg/d; vit D & MeHg: µg/d; omega3: mg/d)" ) fig15 <- ggplot((BAU*tmp[tmp$Exposure_agent=="TEQ",]+0.1)@output, aes(x = Result, colour=Country))+ stat_ecdf() + scale_x_log10() + theme_gray(base_size = BS)+ geom_vline(data=recomintake[recomintake$Exposure_agent=="TEQ",], aes(xintercept=Result), colour="pink",size=2)+ facet_wrap(~ Group)+ labs( title="Fig 15. Exposure to dioxin from Baltic fish", y="Cumulative probability", x = "Exposure (pg/d)" ) fig16 <- ggplot(oapply(tmp,INDEX=c("Country","Exposure_agent","Policy"),mean)@output, aes(x = Policy, weight=Result, fill=Policy))+ geom_bar() + theme_gray(base_size = BS)+ geom_hline(data=recomintake, aes(yintercept=Result), colour="pink",size=2)+ facet_grid(Exposure_agent ~ Country, scales="free_y")+ theme( legend.position = "bottom", axis.text.x = element_text(angle = -90) )+ labs( title="Fig 16. Exposure to compounds from fish and background", y="Exposure (dioxin: pg/d; vit D & MeHg: µg/d; omega3: mg/d)" ) ######### Table with exposures if(TRUE) { BAU <- Ovariable(data=data.frame( Info.improvements = "BAU", Recomm.herring = "BAU", Recomm.salmon = "BAU", Limit = "No limit", Select.size = "BAU", Time = 2018, Mixtures = "BAU", Background = "Yes", Result = 1 )) } else { BAU <- 1 } mc2dparam$run2d<-TRUE mc2dparam$newmarginals<-"Country" mc2dparam$N2 <- 1000 expotable <- mc2d(exposure[exposure$Exposure=="To eater"&exposure$Exposure_agent=="TEQ",]*BAU*info) #summary(tmp) # Error with colnames?! expotable <- rbind( data.frame( oapply( expotable, c("Exposure_agent","Country","Exposure"), FUN=function(x) quantile(x, probs=0.05) )@output, Prob = 0.05 ), data.frame( oapply( expotable, c("Exposure_agent","Country","Exposure"), FUN=function(x) quantile(x, probs=0.25) )@output, Prob = 0.25 ), data.frame( oapply( expotable, c("Exposure_agent","Country","Exposure"), FUN=function(x) quantile(x, probs=0.75) )@output, Prob=0.75 ), data.frame( oapply( expotable, c("Exposure_agent","Country","Exposure"), FUN=function(x) quantile(x, probs=0.95) )@output, Prob = 0.95 ) ) expotable$Result <- expotable$Result/70 expotable <- expotable[order(expotable$Country),] cat("Exposure to dioxin (pg/kg/day in TEQ)") oprint(expotable) ####### Numbers of cases fig18 <- ggplot(groups(BAU*casesabs*info)@output, aes(x=Result, colour=Group))+ stat_ecdf()+theme_grey(base_size=BS-2)+ facet_grid(Country~ Resp, scales="free_x")+ theme( legend.position = "bottom", axis.text.x = element_text(angle = -90) )+ labs( title="Fig 18. Numbers of cases for non-relative responses (individual=population)", y="Cumulative probability", x="# of cases/a (for IQ: each change by 1 point is a case)" ) fig19 <- ggplot(groups(BAU*casesabs*info)@output, aes(x=Result, colour=Group))+ stat_ecdf()+theme_grey(base_size=BS-2)+ facet_grid(Country~ Resp, scales="free_x")+ coord_cartesian(ylim=c(0.8,1))+ theme( legend.position = "bottom", axis.text.x = element_text(angle = -90) )+ labs( title="Fig 19. Numbers of cases for non-relative responses (individual=population)", y="Cumulative probability", x="# of cases/a (for IQ: each change by 1 point is a case)" ) fig20 <- ggplot(groups(BAU*casesrr*info)@output, aes(x=Result, colour=Group))+ stat_ecdf()+theme_grey(base_size=BS)+ facet_grid(Country~ Resp)+ coord_cartesian(xlim=c(-12000,0))+ theme( legend.position = "bottom" )+ labs( title="Fig 20. Numbers of cases for relative responses (individual=population)", y="Cumulative probability", x="# of cases/a " ) #### Burden of disease BoD <- groups(BoD) fig21 <- ggplot((BAU*BoD)@output, aes(x=BoDResult, colour=Group))+stat_ecdf()+ theme_grey(base_size=BS)+ facet_wrap(~ Resp, scales = "free_x")+ labs( title="Fig 21. Burden of disease by response and group", x="Disease burden (mDALY/a per a random individual)", y="Cumulative probability") fig22 <- ggplot((oapply(BoD, INDEX=c("Resp","Group","Country","Policy"), mean))@output, aes(x=Group, weight=BoDResult, fill=Resp))+ geom_bar()+facet_grid(Country ~ Policy)+ theme_grey(base_size=BS)+ theme( legend.position = "bottom", axis.text.x = element_text(angle = -90) )+ labs( title="Fig 22. Burden of disease by country, group and policy", y = "Disease burden (mDALY/a per person)" )+ scale_fill_manual(values=varit12) fig23 <- ggplot((BAU * info * oapply(BoD, cols=c("Resp","Response"), FUN = sum))@output, aes(x=Result, colour=Group))+stat_ecdf()+ theme_grey(base_size=BS)+ coord_cartesian(xlim=c(-10,10))+facet_wrap(~Country)+ labs( title="Fig 23. Net disease burden by country and population group", x="Disease burden (mDALY/a per a random individual)", y="Cumulative probability" ) tmp <- BoD[BoD$Policy!="Inconsistent" & BoD$Country=="FI",] tmp$Impact <- ifelse(tmp$Resp %in% c("Tooth defect","Cancer","Dioxin TWI","TWI 2018"),"Risk","Benefit") # Does IQ go right in the code as it may be risk or benefit? tmp$Impact[tmp$Resp %in% c("Dioxin TWI", "TWI 2018")] <- "TWI dioxin" fig29 <- ggplot((oapply(tmp, INDEX=c("Resp","Group","Country","Policy","Impact"), mean))@output, aes(x=Group, weight=BoDResult, fill=Impact))+ geom_bar()+facet_wrap( ~ Policy)+ theme_grey(base_size=BS)+ theme( legend.position = "bottom", axis.text.x = element_text(angle = -90) )+ labs( title="Fig 29. Burden of disease by group and policy in Finland", y = "Disease burden (mDALY/a per person)" )+ scale_fill_manual(values=varit12[c(1,4,3)]) ################ Comparison to other environemental health risks fig28 <- ggplot()+ geom_bar(data=oapply(BoDRaw[BoDRaw$Country=="FI",]*BAU, cols="Iter",FUN=mean)@output, aes(x="Goherr MC model estimate", weight=Result,fill=Resp))+ geom_bar(data=a[a$Result >= 380 , ], aes(x = Impact, weight = Result, fill = Source)) + coord_flip() + theme_gray(base_size = BS-4) + labs( title = "Fig 28. Environmental burden of disease in Finland", x = "", y = "Disease burden (DALY/a in whole population)" ) + scale_fill_manual(values = varit12[c(3,4,6,10,2,9,8,1,5,7,11,12)]) + theme(legend.position = c(0.8, 0.7)) ####################### Comparison of intake to EFSA tolerable weekly intakes tmp <- expoRaw[expoRaw$Exposure_agent=="TEQ",] + amount[amount$Fish=="Baltic herring",] fig30 <- ggplot(tmp@output, aes(x=amountResult*30/100, y=expoRawResult/70*7))+geom_smooth()+ coord_cartesian(xlim=c(0,7),ylim=c(0,20))+ geom_hline(yintercept=c(2,14))+ labs(title="How much can you eat herring and still stay below TWI?", x="Consumption of herring (100 g portions/mo)", y="Exposure to TEQ (pg/kg/week)") ###################### Mixtures objects.get(runs[6]) # Mixtures if(sum(colnames(BoD@output) %in% "BoDResult")==2) { # This is needed for model runs before 20.4.2018 colnames(BoDRaw@output)[colnames(BoDRaw@output)=="BoDResult"] <- "BoDRawResult" BoDRaw@name <- "BoDRaw" colnames(BoD@output)[colnames(BoD@output)=="BoDResult"] <- c("BoDRawResult","BoDResult") } cat("Basic output for figure 26.\n") basicoutput() BoD <- groups(BoD) fig26 <- ggplot(oapply(BoD, c("Group","Resp","Mixtures"),mean)@output, aes(x=Mixtures, weight=BoDResult, fill=Resp))+ geom_bar()+facet_wrap( ~ Group, scales="free_y")+ theme_grey(base_size=BS-2)+ theme( legend.position = "bottom", axis.text.x = element_text(angle = -90) )+ labs( title="Fig 26. Burden of disease by group and one compound removed at a time", y = "Disease burden (mDALY/a per person)", x="Which compound is removed from the model?" )+ scale_fill_manual(values=varit12) ######## Exposure-response functions objects.get(runs[7]) # Exposure-response functions fig17 <- ggplot(erfs, aes(x=Dose,y=Result,colour=Resp,group=Group,linetype=Expo))+ geom_line(size=2)+facet_wrap(~Group, scales="free_y")+ scale_x_log10()+theme_gray(base_size=BS-2)+ labs( title="Fig 17. Exposure-response functions for health impacts", y="Relative risk (CHD, stroke) or number of cases", x="Dose (Omega3: mg/d, TEQ: pg/kg/d except tooth pg/d, MeHg: µg/kg/d, vitamin D: µg/d)" ) #################### VOI for mcd2=TRUE and Policy objects.get(runs[8]) # Info.improvement, Recommendations, i.e. Policy with mc2d=TRUE cat("VOI for BodRaw and Policy separately for Gender, Age, Country subgroups.\n") tmp <- oapply(groups(info*BoDRaw[!BoDRaw$Resp %in% c("Dioxin TWI", "TWI 2018"),]),c("Iter","Info.improvements","Recomm.herring", "Recomm.salmon","Policy","BoDSource"), sum) + disabilityweight[disabilityweight$Resp=="Dioxin TWI",] tmp@name <- "BoDRaw" oprint(VOI( unkeep(tmp,c("Resp","disabilityweightSource")), c("Info.improvements","Recomm.herring","Recomm.salmon","Policy"), "disabilityweightResult", c("Gender","Age","Country") ))

Initiate model

• Model run 27.7.2019 with oempty [15]
• Model run 21.8.2019 [16]
• Model run 29.8.2019 without groups function [17]
• Model run 4.9.2019 with paramtable [18]
• Model run 9.9.2019 with new InpBoD from IHME [19]
• Model run 11.9.2019 official [20]
• Model run 23.9.2019 [21]

+ Show code- Hide code # This is code Op_en7748/model on page [[Benefit-risk assessment of Baltic herring and salmon intake]] library(OpasnetUtils) ##### Parameters from user interface decsel <- NULL # c(5,10) N <- 10 ############### Limits and recommendations recom <- data.frame( Line=c(rep("Concentration limit",2),"TWI 2001","TWI 2018","MeHg TWI","recommended vitamin D"), Exposure_agent=c("PCDDF",rep("TEQ",3),"MeHg","Vitamin D"), Compound=c("PCDDF",rep("TEQ",3),"MeHg","Vitamin D"), Unit=c(rep("pg/g fresh weight",2),rep("pg/kg/week",2),"µg/d","µg/d"), Result=c( 3.5, # http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:02006R1881-20140701 6.5, 14, # SCF 2001 TWI 14 pg /kg /week 2, # EFSA 2018 TWI 2 pg/kg/week 13, # EFSA 2012: 1.3 µg/kg/wk, assuming 70 kg 7.5 # Recommendation for adults in Finland ) ) ###### Preprocess (these are typically needed when graphs are plotted) initiate <- function(decsel =NULL) { # Decisions and marginal collapses rm(list=ls(envir=.GlobalEnv)[grepl("^Dec", ls(envir=.GlobalEnv))], envir=.GlobalEnv) decs <- deci[deci$Num %in% c("0",decsel) , ] # Choose decisions if("10" %in% decsel) {decs <- decs[!decs$Decision %in% c("Info.improvements","Recomm.herring","Recomm.salmon"),]} # Cons.policy covers these cat("Decisions used in model:\n") oprint(decs) return(decs) } remzero <- function(n){ gsub("\\.0+$","", format(n, scientific = FALSE)) } paramtable <- function(ova, INDEX) { tmp <- cbind( oapply( ova, INDEX=INDEX, FUN=function(x) quantile(x, c(0.025, 0.5, 0.975)) )@output, oapply( ova, INDEX=INDEX, FUN= mean )@output ) tmp1 <- gsub("\\.$","",gsub("(0*)$", "", sprintf("%.6f", signif(tmp[[(length(INDEX)+1)*2]], 2)))) tmp2 <- gsub("\\.$","",gsub("(0*)$", "", sprintf("%.6f", signif(tmp[[length(INDEX)+1]][,1], 2)))) tmp3 <- gsub("\\.$","",gsub("(0*)$", "", sprintf("%.6f", signif(tmp[[length(INDEX)+1]][,3], 2)))) tmp$Estimate <- ifelse(tmp1==tmp2 & tmp2==tmp3,tmp1, paste0(tmp1, " (",tmp2, ", ",tmp3,")")) return(tmp[c(INDEX,"Estimate")]) } policies <- c( "Info.improvements", "Recomm.herring", "Recomm.salmon", "Cons.policy", "Limit", "Select.size", "Time", "Background", "Mixtures" ) BAU <- Ovariable( "BAU", data = data.frame( Cons.policy = "BAU", Info.improvements = "BAU", Recomm.herring = "BAU", Recomm.salmon = "BAU", Limit = "No limit", Select.size="BAU", Time="2018", Background="Yes", Mixtures="BAU", OmegaERF="Cochrane", BAUResult = 1 ) # marginal = c(rep(TRUE,8),FALSE) ) BAUt <- Ovariable( "BAU", data = data.frame( Cons.policy = "BAU", Info.improvements = "BAU", Recomm.herring = "BAU", Recomm.salmon = "BAU", Limit = "No limit", Select.size="BAU", Background="Yes", Mixtures="BAU", BAUResult = 1 ) # marginal = c(rep(TRUE,8),FALSE) ) resplev <- c( "Mortality", "Heart (CHD)", "Depression", "Vitamin D intake", "Cancer", "Child's IQ", "Infertility", "Tooth defect", "TWI 2001", "TWI 2018" ) varit12 <- c( "#74AF59FF", # green Colours 1-6 from [[Goherr: Fish consumption study]] "#2F62ADFF", # blue "#D692BDFF", # pink "#29A0C1FF", # light blue "#BE3F72FF", # red "#FBB848FF", # yellow "#cc77ac", # roosa # Colours 7-12 from THL old palette? "#606060", # tumma harmaa "#cc77ac", # roosa "#cc77ac", # roosa "#c3c2c6", # keskiharmaa "#dcdfe2", # vaalea harmaa "#cc7acc", # 7 "#244911" # 12 ) ################3# Decisions and marginal collapses deci <- opbase.data("Op_en7748", subset = "Decisions") # Decisions and marginal collapses decs <- deci[deci$Num %in% c("0",decsel) , ] # Choose decisions if("10" %in% decsel) {decs <- decs[!decs$Decision %in% c("Info.improvements","Recomm.herring","Recomm.salmon"),]} # Cons.policy covers these DecisionTableParser(decs) cat("Model iterations run:",N,"\n") cat("Decisions used in model:\n") oprint(deci) margi <- as.data.frame(sapply(opbase.data("Op_en7748", subset = "Marginals"), as.character),stringsAsFactors=FALSE) CollapseTableParser(margi) cat("Marginals summed up in model:\n") oprint(margi) # For development and testing, use smaller ovariables openv.setN(N) size <- Ovariable("size", ddata="Op_en7748", subset="Size distribution of fish species") time <- Ovariable( "time", data = data.frame(Result=c(2018)) ) conc <- Ovariable( "conc", dependencies = data.frame( Name = c( "conc_pcddf", # [[EU-kalat]] "conc_vit", # [[Concentrations of beneficial nutrients in fish]] "conc_mehg", # data from [[Mercury concentrations in fish in Finland]] "info" ), Ident = c( "Op_en3104/initiate", "Op_en1838/initiate", "Op_en4004/conc_mehg", NA ) ), formula = function(...) { ############ Adjust PCDD/F concentrations with country ## coeff: country-specific coefficients d <- opbase.data("Op_en7748", subset = "Diox conc fishing areas") coeff <- Ovariable(output = data.frame( Fish = rep(c("Baltic herring", "Salmon"), each = 4), Country = c("DK", "EE", "FI", "SE"), Result = c( d$Result[d$Fish=="Herring"&d$Area=="24"], d$Result[d$Fish=="Herring"&d$Area=="32"], d$Result[d$Fish=="Herring"&d$Area=="30-31"], d$Result[d$Fish=="Herring"&d$Area=="25-29"], d$Result[d$Fish=="Salmon"&d$Area=="24"], d$Result[d$Fish=="Salmon"&d$Area=="32"], d$Result[d$Fish=="Salmon"&d$Area=="30-31"], d$Result[d$Fish=="Salmon"&d$Area=="30-31"] ) )) / Ovariable(output = data.frame( Fish = c("Baltic herring", "Salmon"), Result = c( d$Result[d$Fish=="Herring"&d$Area=="30-31"], d$Result[d$Fish=="Salmon"&d$Area=="30-31"] ) )) pcddf <- conc_pcddf * coeff colnames(pcddf@output)[colnames(pcddf@output)=="Compound"] <- "Exposure_agent" pcddf <- unkeep(pcddf, sources = TRUE) ################### Adjust conc_vit and conc_mehg levels(conc_vit$Exposure_agent)[levels(conc_vit$Exposure_agent) == "D_vitamin"] <- "Vitamin D" conc_vit <- conc_vit / 100 # From /100g to /g result(conc_vit)[conc_vit$Exposure_agent == "Vitamin D"] <- result(conc_vit)[conc_vit$Exposure_agent == "Vitamin D"] * 1000 # from mg to ug out <- combine(conc_vit, conc_mehg) out@output <- fillna(out@output, marginals = "Country") levels(out$Fish)[levels(out$Fish)=="Herring"] <- "Baltic herring" out <- combine(out, pcddf) if("Time" %in% colnames(out@output)) out@output <- fillna(out@output, "Time") result(out) <- pmax(0.01,result(out)) return(out * info) }, unit = "PCDDF, PCB, TEQ: pg /g; Vitamin D, MeHg: µg /g; DHA, EPA, Omega3: mg /g fresh weight" ) ######## Standard amount but adjusted for country-specific herring size objects.latest("Op_en7749", code_name = "initiate") # [[Goherr: Fish consumption study]] ## Variables assump, often, much, oftenside, muchside, amountRaw, effinfo, effrecomm, amount amountOrigFormula <- amount@formula amount@formula <- function(...) { amount <- amountOrigFormula() result(amount)[result(amount)>3 & amount$Limit=="Max 3 g/d"] <- 3 levels(amount$Fish)[levels(amount$Fish)=="Herring"] <- "Baltic herring" amount <- info * amount amount@marginal[colnames(amount@output)=="Recommendation"]<- FALSE return(amount) } amount@meta$unit <- "g /d" ## Exposure with background exposure but without mother's exposure to child expo_dir <- Ovariable( "expo_dir", dependencies=data.frame(Name=c("amount","conc","expo_bg")), formula = function(...) { out <- conc[conc$Exposure_agent=="TEQ",] * 0 + 1 out$Exposure_agent <- "Fish" out <- combine(conc, out, name="conc") out <- oapply(amount * out, cols="Fish", FUN=sum) out <- Ovariable(output = data.frame( Exposcen = c("BAU", "No exposure"), Result = c(1, 0) ), marginal=c(TRUE,FALSE)) * out + expo_bg out$Exposure <- as.factor( ifelse( out$Exposure_agent %in% c("DHA", "MeHg"), "To child", "To eater" ) ) return(out) }, unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d; Fish: g /d" ) ## Background-exposure to vitamin D and omega-3 addexposure <- Ovariable( "addexposure", ddata = "Op_en7748", # [[Benefit-risk assessment of Baltic herring and salmon intake]] subset = "Background exposure", unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d" ) # Should the background be specific for gender and country? At the moment it is. expo_bg <- Ovariable( "expo_bg", dependencies = data.frame(Name="addexposure","info"), formula = function(...) { out <- addexposure # Empty values ("") in indices must be replaced by NA so that Ops works correctly. levels(out$Gender)[levels(out$Gender) == ""] <- NA levels(out$Country)[levels(out$Country) == ""] <- NA levels(out$Exposure_agent)[levels(out$Exposure_agent) == ""] <- NA out@output <- fillna(out@output, c("Country", "Gender", "Exposure_agent")) temp1 <- out[out$Exposure_agent %in% c("PCDDF","PCB") , ] temp1 <- oapply(temp1, cols = "Exposure_agent", FUN = sum) temp1$Exposure_agent <- "TEQ" temp2 <- out[out$Exposure_agent %in% c("EPA", "DHA") , ] temp2 <- oapply(temp2, cols = "Exposure_agent", FUN = sum) temp2$Exposure_agent <- "Omega3" out <- combine(out, temp1, temp2) out <- unkeep(out * info, prevresults = TRUE, sources = TRUE) return(out) }, unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d" ) # Stores non-marginal columns for further use. info <- Ovariable( "info", dependencies = data.frame(Name = c("jsp")), formula = function(...) { out <- jsp out$Group <- factor( paste(out$Gender, out$Ages), levels = c("Female 18-45", "Male 18-45", "Female >45", "Male >45") ) out$Country <- factor(out$Country, ordered=FALSE) out <- unique(out@output[c("Iter","Country","Group","Gender","Row")]) out$Result <- 1 return(out) } ) exposure <- Ovariable( "exposure", dependencies = data.frame( Name = c( "expo_dir", # direct exposure, i.e. the person eats or breaths the exposure agent themself "expo_indir", # indirect exposure, i.e. the person (typically fetus or infant) is exposed via someone else (mother) "mc2d" # 2D Monte Carlo function ), Ident = c( NA, "Op_en7797/expo_indir", # [[Infant's dioxin exposure]] # expo_indir "Op_en7805/mc2d") # [[Two-dimensional Monte Carlo]] ), formula = function(...) { out <- combine(expo_dir, expo_indir) out <- unkeep(out, "Source.1", sources=TRUE) out <- mc2d(out) return(out) }, unit = "PCDDF, PCB, TEQ: (To eater: pg /day; to child: pg /g fat); Vitamin D, MeHg: µg /day; DHA, EPA, Omega3: mg /day" ) # Parameters for 2-dimensional Monte Carlo (not used by default) mc2dparam<- list( N2 = 1000, # Number of iterations in the new Iter strength = 50, # Sample size to which the fun is to be applied. Resembles number of observations run2d = FALSE, # Should the mc2d function be used or not? info = info, # Ovariable that contains additional indices, e.g. newmarginals. newmarginals = c("Group","Exposure"), # Names of columns that are non-marginals but should be sampled enough to become marginals method = "bootstrap", # which method to use for 2D Monte Carlo? Currently bootsrap is the only option. fun = mean # Function for aggregating the first Iter dimension. ) # Pick only the interesting dose-responses (saves time in calculations) ERFchoice <- Ovariable( "ERFchoice", ddata = "Op_en7748", # [[Benefit-risk assessment of Baltic herring and salmon intake]] subset = "Exposure-response functions of interest" ) # 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 = c("jsp","exposure","info")), formula = function(...) { out <- merge( unique(jsp@output[c("Gender", "Ages", "Country", "Iter")]), unique(exposure@output["Exposure"]) ) out$Result <- ifelse( out$Exposure == "To child", ifelse( out$Gender == "Female" & out$Ages == "18-45", 0.1, # Probability of birth during a year. 0 ), 1 ) out$Result[is.na(out$Result)] <- 1 return(info * Ovariable(output=out)) }, unit="fraction" ) ###### ERF objects.latest("Op_en2031", code_name = "ERF2") # [[Exposure-response function]] ERF, subgrouping ERFOrigFormula <- ERF@formula ERF@formula <- function(...) { out <- ERFOrigFormula() out$Exposure <- as.factor(ifelse( out$Exposure %in% c( "Maternal intake through placenta", "Maternal ingested intake", "Intake through placenta and mother's milk" ), "To child", "To eater" )) out$Resp <- out$Resp <- factor(out$Resp, levels=resplev) out@marginal[colnames(out@output) %in% c("ER_function","Exposure_unit","Scaling")] <- FALSE return(out) } ERF@meta$unit <- "several" dat <- opbase.data("Op_en7748", subset="Model parametres")[-1] incidence <- Ovariable( "incidence", data = subgrouping(dat[dat$Type=="incidence" , c("Response","Subgroup","Result")], "Subgroup"), unit="# /person-year" ) #### POPULATION population <- Ovariable( "population", ddata = "Op_en7748", subset = "Population", unit="#" ) case_burden <- Ovariable( "case_burden", ddata = "Op_en7748", subset = "DALYs of responses", unit="DALY /case" ) objects.latest("Op_en5917", code_name="InpBoD") # [[Disease risk]] InpBoD InpBoD <- EvalOutput(InpBoD) # Evaluated because is not a dependency but an Input BoDattrPer1 <- Ovariable( "BoDattrPer1", dependencies = data.frame(Name="BoDattr", Ident="Op_en2261/BoDattr2"), # [[Health impact assessment]] BoDattr formula = function(...) { out <- info * BoDattr / population * 1000 return(out) }, unit="mDALY per person/year with continuous exposure" ) BoDattrPer1 <- EvalOutput(BoDattrPer1, verbose=TRUE) BoDattrOrigFormula <- BoDattr@formula BoDattr@formula <- function(...) { out <- BoDattrOrigFormula() out$Resp <- as.character(out$Resp) # Subtracts mortalities from total mortality. tmp <- -1 * out[out$Resp %in% c("Heart (CHD)", "Cancer"),] tmp$Resp <- "Mortality" out <- combine(out, tmp) out$Resp <- factor(out$Resp, levels=resplev) out <- info * out # Make Group non-marginal as it is return(out) } BoDattr@meta$unit="DALY /a per subpopulation" ### Plot and print each ovariable ovas <- list( # ovariables addexposure, amount, amountRaw, assump, BF, BoD, BoDattr, BW, case_burden, conc, conc.param, conc_mehg, conc_pcddf, conc_vit, dose, effinfo, effrecomm, ERF, ERF_diox, ERF_env, ERF_mehg, ERF_micr, ERF_omega3, ERF_vit, ERFchoice, expo_bg, expo_dir, expo_indir, exposure, f_ing, f_mtoc, frexposed, incidence, info, InpBoD, jsp, lengt, much, muchside, often, oftenside, PAF, population, RR, size, t0.5, time ) for(i in 1:length(ovas)) { tmp <- ovas[[i]] cat("Ovariable", tmp@name, "\n") print(tryCatch(oggplot(tmp)+theme_gray(base_size=16), error=function(e1) cat("Cannot plot ",tmp@name))) oprint(tryCatch(summary(tmp), error=function(e1) cat("Cannot print summary for ",tmp@name))) } ################ Insight network #gr <- scrape(type="assessment") #objects.latest("Op_en3861", "makeGraph") # [[Insight network]] #gr <- makeGraph(gr) #export_graph(gr, "ruori.svg") #render_graph(gr) #objects.latest("Op_en6007", code_name="diagnostics") #oprint(showLoctable()) #oprint(showind()) ### Empty each ovariable for storage and store oempty(all=TRUE) objects.store(list=setdiff(ls(), "wiki_username")) cat("Objects stored without output:", setdiff(ls(), "wiki_username"), ".\n")

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Select and run Test run 21.7.2019 [22] Model parameters Choose which decisions to add to model (BAU of each is always considered): Information improvements: BAU (no), yes (size:2x) Recommendations about herring: BAU (current), eat more, eat less (size:3x) Recommendations about salmon: BAU (current), eat more, eat less (size:3x) Select herring size for human consumption: BAU (large only), ban large, new products (both) (size:3x) Should background be considered? BAU (yes), no (size:2x) Is fish consumption limited to 3 g/day? BAU (no), yes (size:2x) Long timeline: BAU (2018), 1990, 2000, 2010 (size:4x) Compound mixture: BAU (all), MeHg, Dioxin, Omega3 (size:4x) Scenario for length vs TEQ (all sizes in 2009) Coherent consumer policy: BAU, increase, reduce (size:3x) Scale average amount to Luke data: BAU (size:1x) How many iterations to run?: + Show code- Hide code # This is code Op_en7748/ on page [[Benefit-risk assessment of Baltic herring and salmon intake]] library(OpasnetUtils) objects.latest("Op_en7748", code_name="model") # All objects of the model rm(list=ls()[grepl("^Dec", ls())]) BAU <- Ovariable( "BAU", output = data.frame( Info.improvements = "BAU", Recomm.herring = "BAU", Recomm.salmon = "BAU", Limit = "No limit", Select.size="BAU", Time="2018", Background="Yes", Mixtures="BAU", BAUResult = 1 ), marginal = c(rep(TRUE,8),FALSE) ) BAUt <- BAU BAUt$Time <- NULL # Decisions and marginal collapses decs <- deci[deci$Num %in% c("0",decsel) , ] # Choose decisions if("10" %in% decsel) {decs <- decs[!decs$Decision %in% c("Info.improvements","Recomm.herring","Recomm.salmon"),]} # Cons.policy covers these DecisionTableParser(decs) cat("Model iterations run:",N,"\n") cat("Decisions used in model:\n") oprint(deci) InpBoD <- EvalOutput(InpBoD) BoDattr <- EvalOutput(BoDattr) BS <- 24 oggplot(BoDattr) fig21 <- ggplot((BAU*BoDattr)@output, aes(x=BoDattrResult, colour=Group))+stat_ecdf()+ theme_grey(base_size=BS)+ facet_wrap(~ Resp, scales = "free_x")+ labs( title="Fig 21. Burden of disease by response and group", x="Disease burden (mDALY/a per a random individual)", y="Cumulative probability") fig22 <- ggplot((oapply(BoDattr, INDEX=c("Resp","Group","Country","Policy"), mean))@output, aes(x=Group, weight=BoDattrResult, fill=Resp))+ geom_bar()+facet_grid(Country ~ Policy)+ theme_grey(base_size=BS)+ theme( legend.position = "bottom", axis.text.x = element_text(angle = -90) )+ labs( title="Fig 22. Burden of disease by country, group and policy", y = "Disease burden (mDALY/a per person)" )#+ # scale_fill_manual(values=varit12) fig23 <- ggplot((BAU * info * oapply(BoDattr, cols=c("Resp","Response"), FUN = sum))@output, aes(x=Result, colour=Group))+stat_ecdf()+ theme_grey(base_size=BS)+ coord_cartesian(xlim=c(-10,10))+facet_wrap(~Country)+ labs( title="Fig 23. Net disease burden by country and population group", x="Disease burden (mDALY/a per a random individual)", y="Cumulative probability" ) Exposure-response functions Model run 12.4.2018 [23] 152354803523 Model run 13.10.2018 with infertility [24] + Show code- Hide code # This is code Op_en7748/erf on page [[Goherr assessment]] library(OpasnetUtils) library(ggplot2) openv.setN(100) # Fetch objects to show objects.latest("Op_en2261",code_name="RR") # [[Health impact assessment]] RR objects.latest("Op_en2261",code_name="casesabs") # [[Health impact assessment]] casesabs # Pick only the interesting dose-responses (saves time in calculations) ERFchoice <- Ovariable( "ERFchoice", ddata = "Op_en7748", # [[Benefit-risk assessment of Baltic herring and salmon intake]] subset = "Exposure-response functions of interest" ) dose <- Ovariable( "dose", data=data.frame( Exposure_agent=rep(c("DHA","Omega3","MeHg","TEQ","Vitamin D"),each=18), Expo=rep(c("DHA","Omega3","MeHg","TEQ","Vit D"),each=18), Dose = c(0.03, 0.1, 0.3, 1, 1.1, 1.9, 2, 2.1, 3, 7, 8, 10, 30, 100, 110, 300, 1000, 3000), Result = c(0.03, 0.1, 0.3, 1, 1.1, 1.9, 2, 2.1, 3, 7, 8, 10, 30, 100, 110, 300, 1000, 3000) ) ) mc2dparam<- list( run2d = FALSE # Should the mc2d function be used or not? ) RR <- EvalOutput(RR) casesabs <- EvalOutput(casesabs) erfs <- unkeep(combine(RR,casesabs), sources=TRUE) erfs$Sex <- NULL erfs <- oapply(erfs, cols="Iter",FUN=mean)@output erfs$Group <- paste(erfs$Resp, "by", erfs$Expo) ggplot(erfs, aes(x=Dose,y=Result,colour=Resp,group=Group,linetype=Expo))+ geom_line(size=2)+facet_wrap(~Group, scales="free_y")+ scale_x_log10()+theme_gray(base_size=18)+ labs( title="Fig 17. Exposure-response functions for health impacts", y="Relative risk (CHD, stroke) or number of cases", x="Dose (Omega3: mg/d, TEQ: pg/kg/d except tooth pg/d, MeHg: µg/kg/d, vitamin D: µg/d)" ) objects.store(erfs) cat("Data.frame erfs stored.\n") Initiate goherr assessment ovariable + Show code- Hide code # This is code Op_en7748/goherr on page [[Benefit-risk assessment of Baltic herring and salmon intake]] library(OpasnetUtils) goherr <- Ovariable( "goherr", dependencies = data.frame(opbase.data("Op_en7748", subset="Model dependencies"))[-1], formula = function(...) { return(BoD) } ) colnames(goherr@dependencies)[colnames(goherr@dependencies)=="Result"] <- "Page" objects.store(goherr) cat("Assessment ovariable goherr stored.\n") Plot concentrations and survey Requires codes Op_en7748/bayes and indirectly Op_en7748/preprocess. Model run 1.3.2017 [25] + 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 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. 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. + Show code- Hide code #This is code Op_en7748/ on page [[Benefit-risk assessment of Baltic herring and salmon intake]] library(OpasnetUtils) library(reshape2) objects.latest("Op_en7748", code_name="hia") # http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=9aoTj5dnW9vGL0wX with 5000 iterations if(TRUE) { BAU <- Ovariable(data=data.frame( Info.improvements = "BAU", Recomm.herring = "BAU", Recomm.salmon = "BAU", Limit = "No limit", Select.size = "BAU", Time = 2018, Mixtures = "BAU", Background = "Yes", Result = 1 )) } else { BAU <- 1 } # Collect all ovariables together that contain input for BBN. # Without Iter, it contains 72 unique rows: Exposure_agent 2, Fish 3 (effectively 2), # Info.improvements 2, Recomm.herring 3, Recomm.salmon 3 d3 <- BAU + bgexposure[bgexposure$Exposure_agent %in% c("TEQ", "Omega3"),] + amount + expoRaw + conc + lengt + oapply(BoD, cols=c("Resp","Response"), FUN=sum) d3 <- d3@output[c( "Iter", "Country", "Gender", "Ages", "Exposure_agent", "Fish", "bgexposureResult", "amountResult", "expoRawResult", "concResult", "BoDResult", "Info.improvements", "Recomm.herring", "Recomm.salmon", "effinfoResult", "Select.size", "lengtResult" ) ] d3$Group <- paste(d3$Fish, d3$Exposure_agent, sep="_") d4 <- reshape( d3, v.names=c( "concResult", "amountResult", "expoRawResult", "bgexposureResult", "effinfoResult", "lengtResult" ), idvar=c("Iter","Info.improvements","Recomm.herring","Recomm.salmon","Select.size"), timevar="Group", drop=c("Exposure_agent","Fish"), direction="wide" ) # Match the columns above to the final columns needed (below) d5 <- d4[c( "Info.improvements", # 1 "Recomm.herring", "Recomm.salmon", "Select.size", "Country", # 5 "Gender", "Ages", "lengtResult.Baltic herring_TEQ", "effinfoResult.Baltic herring_TEQ", # 9a "amountResult.Baltic herring_TEQ", # 10 "amountResult.Salmon_TEQ", "effinfoResult.Salmon_TEQ", # 9b "bgexposureResult.Baltic herring_Omega3", "Iter", "concResult.Baltic herring_TEQ", "concResult.Salmon_TEQ", "expoRawResult.Baltic herring_TEQ", "expoRawResult.Salmon_TEQ", "Iter", "BoDResult", "expoRawResult.Baltic herring_Omega3", "expoRawResult.Salmon_Omega3" )] colnames(d5) <- c( "Information_improvements", "Food_recommendations_herring_amount", "Food_recommendations_salmon_amount", "Food_recommendations_for_herring_size", "Consumer_country", # 5 "Consumer_gender", "Consumer_age_group", "Size_of_an_indiv._herring_in_human_diet", "Effect_of_improved_information_herring", # 9a "Human_consumption_of_herring", # 10 "Human_consumption_of_salmon", "Effect_of_improved_information_salmon", # 9b "Other_intake_of_Omega3", "Omega3_intake_total", "Dioxin_conc_herring", # 15 "Dioxin_conc_salmon", "Human_intake_of_dioxin_from_herring", "Human_intake_of_dioxin_from_salmon", "Dioxin_intake_total", "Net_burden_of_disease", # 20 "Omega3_from_herring", "Omega3_from_salmon" ) d5$Omega3_intake_total <- d5$Omega3_from_herring + d5$Omega3_from_salmon d5$Dioxin_intake_total <- d5$Human_intake_of_dioxin_from_herring + d5$Human_intake_of_dioxin_from_salmon write.csv(d5[c(4,5,8)], "Size_of_an_indiv._herring_in_human_diet.csv", row.names=FALSE) write.csv(d5[c(1,5,6,7,9)], "Effect_of_improved_information_herring.csv", row.names=FALSE) write.csv(d5[c(1,5,6,7,12)], "Effect_of_improved_information_salmon.csv", row.names=FALSE) write.csv(d5[c(2,5,6,7,9,10)], "Human_consumption_of_herring.csv", row.names=FALSE) write.csv(d5[c(3,5,6,7,12,11)],"Human_consumption_of_salmon.csv", row.names=FALSE) write.csv(d5[c(13)], "Other_intake_of_Omega3.csv", row.names=FALSE) write.csv(d5[c(10,11,13,14)], "Omega3_intake_total.csv", row.names=FALSE) write.csv(d5[c(5,8,15)], "Dioxin_conc_in_herring.csv", row.names=FALSE) write.csv(d5[c(5,16)], "Dioxin_conc_in_salmon.csv", row.names=FALSE) write.csv(d5[c(10,15,17)], "Human_intake_of_dioxin_from_herring.csv", row.names=FALSE) write.csv(d5[c(11,16,18)], "Human_intake_of_dioxin_from_salmon.csv", row.names=FALSE) write.csv(d5[c(17,18,19)], "Dioxin_intake_total.csv", row.names=FALSE) write.csv(d5[c(6,7,14,19,20)], "Net_burden_of_disease.csv", row.names=FALSE)

See also

• 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
• Goherr:5.4 Benefit-risk assessment of previous, current and future fish intake
• Omega-3 content in salmon
• 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.
• 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.
• Riksmaten 2010
• Danskernes kostvaner 2011-2013

• 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

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)

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
• Shared understanding of Goherr policy situation using RDF approach (a detail about Baltic herring) [27]

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

Error creating thumbnail: Unable to save thumbnail to destination 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) Error creating thumbnail: Unable to save thumbnail to destination Error creating thumbnail: Unable to save thumbnail to destination http://www.bonusportal.org/ http://www.bonusprojects.org/bonusprojects