Health effects of Baltic herring and salmon: a benefit-risk assessment
This page is a nugget.
The page identifier is Op_en7919 | |
|---|---|
| Moderator:Jouni (see all) | |
|
| |
| Upload data
|
Unlike most other pages in Opasnet, the nuggets have predetermined authors, and you cannot freely edit the contents. Note! If you want to protect the nugget you've created from unauthorized editing click here |
Health effects of Baltic herring and salmon: a benefit-risk assessment is a research manuscript about the Goherr assessment performed on the BONUS GOHERR project between 2015-2018. The manuscript is to be submitted to BMC Public Health [2].
Title page
- Title: Health effects of Baltic herring and salmon: a benefit-risk assessment
- Authors:
- Jouni T. Tuomisto, jouni.tuomisto[]thl.fi, (corresponding author), National Institute for Health and Welfare, Kuopio, Finland.
- Arja Asikainen, arja.asikainen[]thl.fi, National Institute for Health and Welfare, Kuopio, Finland.
- Päivi Meriläinen, paivi.merilainen@thl.fi, National Institute for Health and Welfare, Kuopio, Finland.
- Päivi Haapasaari, paivi.haapasaari[]helsinki.fi, University of Helsinki, Finland.
- Corresponding author: Jouni T. Tuomisto
Abstract
Background: Dioxin health risks from fish remains a complex policy issue, because especially Baltic fish contains relatively high concentrations of pollutants although it is otherwise healthy food. We studied the health benefits and risks of Baltic herring and salmon in four countries to identify critical uncertainties and facilitate evidence-based discussion on dioxin and fish policy.
Methods: We performed an online survey about consumers' fish consumption and its motivation in Denmark, Estonia, Finland, and Sweden. Dioxin concentrations were estimated based on a Finnish EU Fish II study and methylmercury concentrations from data from the Finnish Food Safety Authority. Exposure-response functions about several health endpoints were evaluated and quantified based on scientific literature. We also quantified infertility risk of men based on a recent European risk assessment of childhood dioxin exposure on sperm concentration later in life.
Results: Baltic herring and salmon contain omega-3 fatty acids, and their beneficial impact on cardiovascular risk clearly outweighs any risks of dioxins and methylmercury especially in people more than 45 years of age, but also in young men. The critical population subgroup is young women, who may expose their children to pollutants during pregnancy and breast feeding. However, even in this group the health risks are similar than health benefits. Value of information analysis demonstrated that the remaining scientific uncertainties are not large. In contrast, there are several critical uncertainties that are value judgements by nature, such as whether Baltic fish should be seen as primary or secondary source of nutrients; whether exceedance of tolerable weekly intake is an adverse outcome as such; and whether subgroup-specific restrictions are problematic or not.
Conclusions: Potential health risks from Baltic fish have decreased to less than a half in ten years. The new EFSA risk assessment clearly increases the fraction of population exceeding the tolerable dioxin intake, but the estimates of net health impacts change only marginally. Increased use of small herring (with less pollutants) is a no-regret option. Further value-based policy discussion rather than research is needed to clarify useful actions related to dioxins in fish.
Keywords
Fish consumption, dioxins, methylmercury, benefit-risk assessment, health impact, sperm concentration, Baltic Sea, knowledge crystal, food recommendation, European Food Safety Authority EFSA.
Background
Dioxins (polychlorinated dibenzo-p-dioxins and furans) and polychlorinated biphenyls (PCBs) are persistent environmental pollutants that are found at relatively high concentrations in fish. Fatty Baltic fish (notably Baltic herring, salmon, trout and lamprey) biomagnify dioxins and PCBs in the food chain and constitute the largest exposure source of these compounds in the Finnish population. These fish species often exceed the EU limits for dioxins and PCBs[1], but Finland and Sweden have a permanent derogation to sell these fish species on national market. Estonia is dealing with dioxins by selecting small Baltic herring with lower concentrations for human food.
The EU has had a long-term objective of reducing human exposure to these pollutants. Emission standards for industry have become stricter during the last decades, and also concentration limits for food and feed have eliminated the most contaminated items from the market. Although average exposure has decreased to a fraction of previous values, there is still concern about health effects of dioxin, especially related to fatty fish in the Baltic Sea.
European Commission therefore asked European Food Safety Authority EFSA to perform a risk assessment and derive an updated tolerable weekly intake (TWI) for dioxins and dioxin-like PCBs. The TWI was recently published, and it is seven times lower (2 pg/kg/week) than the previous value (14 pg/kg/week)[2].
Although there are previous benefit-risk assessments about Baltic fish[3], there were no studies that would have compared several countries and studied reasons and motivations for fish eating (or fish avoidance).
BONUS GOHERR project (2015-2018) looked at the particular question about dioxins in the Baltic fish and performed a health benefit-risk assessment, which is reported here. The project also studied social and cultural aspects of fishing and dioxins, and fisheries governance. This article is a part of the overview on the Baltic Sea, fishing, and dioxins.
Methods
Modelling
The overall aim of the study was to estimate health risks and benefits of important compounds (dioxins, dioxin-like PCBs, methylmercury, omega-3 fatty acids, and vitamin D) found in Baltic herring and salmon in the current situation. The assessment model was implemented in an open and modular way at Opasnet web-workspace. In practice, this means that all the data and code used for different parts, or modules, of the model are located on different pages at Opasnet. These pages are called knowledge crystals, as their structure and workflow follow certain rules (Tuomisto et al 2018, forthcoming). In this section, we give an overview of the model. Links to the module pages and all details can be found from the assessment page[4]. The whole model with data and codes is also available at IDA research data repository[5].
The benefit-risk assessment was based on a modular Monte Carlo simulation model, which had a hierarchical Bayesian module for estimating dioxin concentrations. The input distributions were derived either directly from data or from scientific publications. If no published information was available (as was the case with e.g. disability weights for non-typical endpoints such as tolerable weekly intakes or infertility), we used author judgement and wide uncertainty bounds (these judgements are described later in the text). The model was run with 5000 iterations using R statistical software (version 3.5.1, https://cran.r-project.org).
Consumption survey
A questionnaire survey was done to study consumers' fish-eating habits in four Baltic Sea countries (Finland, Sweden, Estonia, and Denmark) at the end of 2016. Around 500 respondents for internet panel were enrolled in each country by a professional survey company Taloustutkimus Ltd. The questionnaire included 32 questions about the consumption amount of Baltic herring and salmon, reasons to eat to not to eat those species, and policies that may affect the amount eaten. The questionnaire is described and results analysed in detail in another article (Pihlajamäki et al, 2018, forthcoming).
Individual long-term fish consumption (in grams per day) were estimated from consumption frequency and amount questions. Consumption distributions were produced for subgroups defined by country, gender, and age by random sampling (with replacement) of the individual estimates. People's reactions to several policies were predicted based on their answers (e.g. what if fish consumption is recommended or restricted; what if the availability and usability of these species improves; what if the price of fish changes). These decision scenarios were used to alter the business as usual scenario and compare results between scenarios.
Also a few technical scenarios were developed: what if nobody ate fish more than 3 g per day; and what if fish is considered a primary versus a secondary source of nutrients. The latter scenario is important if dose-responses are non-linear (as is the case with omega-3 fatty acids), because the incremental health benefits of a primary source are larger than those of a secondary source.
Concentrations
Fish-size-specific PCDD/F and dioxin-like PCB concentration distributions for each fish species and country were estimated based on EU Fish II study[6]. 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. A hierarchical Bayesian module was developed with the JAGS package of R software. The model assumed ca. 7 per cent annual decrease in dioxin concentrations, based on long time trends measured in Finland. The samples were caught between 2009 and 2010.
The concentrations in Baltic herring were found out to be highly sensitive to fish size, while size-dependency was much weaker in salmon. Herring sizes in different scenarios came from a fish growth model developed by SLU in BONUS GOHERR project[7].
The fish samples came mostly from the Bothnian Sea, which is an important area for Finnish and Swedish catch. The concentration distributions for the studied countries were derived from the concentration model results by scaling them with the average concentration on a catch area of interest relative to the average from Bothnian Sea. The Danish and Estonian catch areas were assumed to be Baltic west of Bornholm and Gulf of Finland, respectively. The Swedish catch areas for herring and salmon were assumed to be Baltic Main Basin and Bothnian Sea and Bay, respectively. The area selection was based on landing statistics.
Concentrations were weighted and summed up to toxic equivalency quantities (TEQ) by using WHO 2005 toxic equivalency factors (TEF)[8]. Levels of fatty acids and vitamin D in Baltic herring were based on measurement data obtained from the Finnish Food Safety Authority, and those in salmon are based on Fineli food database[9]. Methylmercury concentrations were based on Kerty database[10].
Exposure-responses
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. In the default scenario, nutrients were assumed to have an uncertain background intake from other sources; this was relevant for non-linear exposure-response function, in practice leading to a diminished health benefit of fish consumption.
Exposure-response functions were derived for several pairs of exposure agents and responses (see Table 1). We derived the exposure-response functions for infertility and tooth defects indirectly from published results, so the rational of those endpoints is described here in more detail.
| Exposure agent | Response | Esposure-response unit | Exposure-response function mean (95 % confidence interval) |
References and notes |
|---|---|---|---|---|
| TEQ (intake through placenta and mother's milk) | male infertility due to sperm concentration decrease | pg /g in boy's body fat | linear; slope 0.000061 (-0.000019, 0.00014) | mother's exposure must be converted to child's exposure (measured as pg /g fat)[11] |
| TEQ (intake through placenta and mother's milk) | developmental dental defects | log (pg /g) in child's body fat | linear; slope 0.0014 (0.0014, 0.0014) | epidemiological study in Finland[12] |
| TEQ | cancer morbidity | pg/kg/day | linear; slope 0.000073 (0.000032, 0.00016) | U.S.EPA dioxin risk assessment[13]. |
| TEQ | tolerable weekly intake 2001 | pg/kg/week | acceptable range below 14 | EC Scientific Committee on Food recommendation[14] |
| TEQ | tolerable weekly intake 2018 | pg/kg/week | acceptable range below 2 | EFSA recommendation[2] |
| omega-3 fatty acids | coronary heart disease mortality | mg/day | ED50: -0.17 (-0.25, -0.091) | a previous risk assessment[15] |
| omega-3 fatty acids | stroke mortality | mg/day | ED50: -0.12 (-0.25, 0.0099) | a previous risk assessment[15] |
| vitamin D | vitamin D recommendation | µg/day | acceptable range 10 - 100 | a step function based on the daily intake recommendations for adults in Finland[16] |
| methylmercury | loss in child's IQ points | mg/kg/day | linear; slope 6.5 (-0.67, 14) | a previous risk assessment[17]. |
| DHA | loss in child's IQ points | mg/day | linear; slope -0.0013 (-0.0018, -0.00081) | a previous risk assessment[15]. |
In humans, sperm concentrations have been shown to decrease permanently if boys are exposed to dioxins before nine years of age. The results come from Seveso[18][19] and a Russian children's study[11].
EFSA recently assessed this risk from the Russian children's study and concluded that significant effect was seen already in the second quartile with median PCDD/F TEQ concentration 10.9 pg/g fat, when measured from the serum of the boys at the age of ca. 9 years. Mean sperm concentration was ca. 65 (95 % CI 50-80) million/ml in the lowest quartile, while in all other quartiles the concentration was ca. 40 (95 % CI 30-55) million/ml. Due to the shape of the effect, we used a non-linear exposure-response curve with half of the maximum effect (effective dose 50, ED50) occurring at TEQ concentration 10 pg/g fat.
However, sperm concentration as such is not an adverse health effect. It only manifests itself if the concentration is low enough to prevent conception in a reasonable time window, say, five years. According to a review, the success rate of couples who try to get pregnant is 65 % in 6 months if the sperm concentration is above 40 million/ml[20]. Below that concentration, the probability is fairly proportional to the sperm concentration.
Based on this, we estimated that (assuming independent probabilities between 6-month periods), the probability of not getting pregnant in five years follows this curve:
P(infertility after 5 a) = (1 - 0.65 (1+ (-0.39c)/(c + 10 pg/g)))^10
where c is the dioxin concentration in boy's fat tissue. This curve is pretty linear below TEQ concentration 50 pg/g with slope ca. 0.00006 g/pg, meaning that for each 1 pg/g increase in dioxin concentration the boy's fat tissue (or serum fat), there is an incrementally increased probability of 0.00006 that he cannot get a child even after five years of trying.
Exposure-response function for tooth defect was also derived from several studies. Alaluusua and coworkers have studied dioxin exposure in small children and the development of permanent molar teeth. They have found defects in both general population in Finland from the exposures in the 1980's [12] [21] and children exposed during the Seveso accident[22]
Based on these studies, we approximated that the effect is linearly correlated with the logarithm of the dioxin concentration in the child.
Disease burden
Disease burden 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 attributable number of cases due to the exposure was estimated, and this was multiplied by the years under disease per case and the disability weight of the disease (Table 2.).
Background disease levels were needed for stroke and cardiovascular diseases and were obtained from The Institute for Health Metrics and Evaluation[23]. Also disability weights of diseases were based on their estimates, if available. Duration estimatess of diseases were based on our general understanding of pathological progresses of these diseases rather than direct epidemiological evidence. We tried to be realistic with estimates but also not to underestimate the risks of fish consumption, so that potential conclusions about safety of fish would not be unfounded.
With the non-typical health effects, namely exceedances of tolerable weekly intakes and deviation from the vitamin D recommendation, we used very wide uncertainty distributions, as it was unclear how much weight should be given to endpoints that are only indications of potential health risk rather than actual adverse effects. A value of information analysis was performed to test the importance of these uncertainties.
Childlessness can be viewed as tragedy of life, so the disability weight could be in the order of 0.1 DALY per year permanently (50 years). However, the disability weight applies to only half of the children (boys), and we can fairly assume that half of the couples get a satisfactory solution via adoption, in-vitro fertilisation or other treatments. Therefore, we used 0.1*50*0.5*0.5 DALY/case = 1.25 DALY/case, with rather high uncertainty (0-2.5 DALY/case).
Population data for each country for year 2016 was available from Eurostat database. Data was separated for gender and age (18 – 45 years and > 45 years) groups[24].
| Response | DALYs per case | Description |
|---|---|---|
| coronary heart disase mortality | 5 - 15 | disability weight 1 and duration 10 a, with 50% uncertainty |
| stroke mortality | 5 - 15 | disability weight 1 and duration 10 a with 50 % uncertainty |
| tooth defect | 0 - 0.12 | disability weight 0.001 and duration 60 a with 100 % uncertainty. |
| cancer | 0 - 0.28 | disability weight 0.1 and duration 20 a, and in addition loss of life expectancy 5 a. This comes from a lifetime exposure, so it is (linearly) assumed that 1/50 of this is caused by one-year exposure. With 100 % uncertainty |
| vitamin D intake | 0.0001 - 0.01 | disability weight 0.001 and duration 1 a with 100-fold log-uniform uncertainty |
| TWI 2001 | 0.0001 - 0.01 | disability weight 0.001 and duration 1 a with 100-fold log-uniform uncertainty |
| TWI 2018 | 0.0001 - 0.01 | disability weight 0.001 and duration 1 a with 100-fold log-uniform uncertainty |
| infertility | 0-2.5 | disability weight 0.1 and duration 50 a with two-fold uncertainty. See also text. |
| child's IQ | 0.0517 (95 % CI 0.03 - 0.0817) | Intellectual disability, mild (IQ<70) has disability weight 0.031 (95 % CI 0.018-0.049) based on Institute for Health Metrics and Evaluation, Seattle. This is scaled to one IQ point with duration 50 a. |
Results
Concentration distributions of the key exposure agents in Baltic herring and salmon are shown in Figure 1. Baltic herring has lower concentrations than salmon for most exposure agents studied, but for vitamin D the levels in salmon are lower. Dioxin concentrations have reduced a lot since 1970, and the trend since 1990 is shown in Figure 1.
Fish consumption varies a lot between countries and population subgroups, and also within each subgroup (Figure 2.). Only about a quarter of people report any wild Baltic salmon consumption. Many people also say that they do not know where their salmon comes from and whether it is salmon or rainbow trout. Landing statistics imply that the consumption of wild Baltic salmon is actually lower than reported (data not shown).
Herring consumption is more accurately known, although the Danes are not sure whether their herring comes from the Baltic Sea or the North Sea. There is large individual variation (almost hundredfold) in fish consumption in most subgroups.
There is also large variation between population subgroups. Estonians eat clearly more Baltic herring and Danes eat less than individuals from other countries. Males tend to eat more, and young people eat less than other population subgroups. These differences are rather similar in all countries, although at different levels. The fraction of people that do not eat Baltic herring at all varies remarkably between subgroups: it is only 25 % in old male Estonians, while it is more than 90 % in young female Danes. There is also a sizable fraction who eat Baltic herring more than 10 g/day. This varies from a few percent in young people to up to ca. 30 % in old Estonians.
Because of the large variation in fish consumption, also the dioxin exposure from fish varies more than hundred-fold within population subgroups (Figure 3.). The variation between subgroups is also large. In the model, many people have apparent zero exposure because other dioxin sources than Baltic fish were not included. A fraction from a few percent to a quarter exceed the EC Scientific Committee on Food TWI value from 2001[14]. The fraction is much higher, from 20 to up to 75 %, when the new EFSA TWI value of 2 pg/kg/week from 2018 is used as the criterion[2].
We also asked in the questionnaire, how the respondent would change fish intake if an increase or decrease of fish consumption was recommended by authorities (Figure 4.). The outcome depends on previous consumption but not much on population subgroup. If increase is recommended, a clear and systematic increase is seen in the average response. In contrast, a recommendation to reduce intake results in inconsistent effects. Some people follow the recommendation, but almost an equal number does the opposite, and most do not change fish intake. This phenomenon is seen already at current intake levels below 5 g/day, where most of the population is.
The main objective of this study was to compare health risks and benefits of Baltic fish consumption. Figure 5. shows a large variation between population subgroups. The most dominant feature is the cardiovascular benefits which in old age groups clearly outdo all risks. This is even more prominent if Baltic fish is considered as the primary source of omega-3 fatty acids and thus have larger marginal benefits (data not shown); in the default assessment other sources are considered primary. In young women, the risks and benefits are close to each other, if infertility is used as the health outcome for sperm concentration.
Figure 6. shows several different objectives that could be used as a basis for decision making. The first one is using net health effect, measured like in the benefit-risk assessment performed here. Two alternative objectives try to avoid exceedance of tolerable weekly intake values from 2001 and 2018. When the whole population is considered (top row), net health objective suggests eating Baltic fish in the current way rather than quitting in every country, while TWI approaches suggest that stopping fish consumption is a better option. If only the target population of young women is considered (bottom row), all impact values are close to zero, but net health impact may sometimes show larger risk than benefit. Because young women is the subgroup with least Baltic herring consumption, the TWI exceedances have a small impact in all scenarios except for TWI 2018 in Estonia, where the impact is 0.8 mDALY/a per person.
Health impacts overall are much smaller in young age groups, and in women the critical issues are effects on child's intelligence quotient (IQ), tooth defects, and sperm concentration, not the health impacts on the woman herself. These risk emerge due to dioxin and methylmercury exposures during pregnancy and breast feeding. Child's own diet during early years may also have an impact although the exposure is typically much lower. These risks are in the same range as the health benefits, and the overall balance depends mostly on the disability weights of distinct outcomes and other value judgements such as whether Baltic fish is considered as a primary source of omega-3 fatty acids.
The policy of recommending increased consumption seems to be somewhat effective, while a recommended consumption reduction is indistinguishable from the business-as-usual scenario. In contrast, actual dioxin policies implemented on emission reduction have reduced the dioxin exposures and thus risk effectively during the last 40 years.
In a bigger picture, Baltic fish and its health hazards are only one of the many environmental health risks (Figure 7.). It is not even close to the largest ones from air pollution (which may be up to tens of thousands DALY), but it may be in the top 10 list.
It is possible that we are overoptimistic about the current sperm concentrations, as reduction from subfertile levels could increase the probability of infertility more than our model predicts. So, we did a sensitivity analysis with men that have already decreased sperm concentrations from an unrelated reason. Dioxins are likely to reduce that even further. For example, if the sperm concentration is 10 million/ml, the probability of infertility in five years is 0.32 based on the equation above. That increases to 0.4 at dioxin concentration 10 pg/g. If ten percent of the population had such low semen concentration and if 20 % of boys exceed 10 pg/g (as seems to be the case according to our model), then we would see for example in Finland 25000 boys/year * 0.1 with low fertility * 0.2 with high dioxin * 0.08 absolute increase in infertility = 40 cases per year, each 1.25 DALY and thus 50 DALY in total. This is more than the 29 DALY from the default model, but does not change the overall picture in Figure 7. Individual risk per mother would be 0.05 and 0.03 mDALY/a per person, respectively (compare to Figure 4). They are also much smaller than the 25000 boys/year * 0.1 with low fertility * 0.32 absolute probability of infertility * 1.25 DALY = 1000 DALY due to infertility from all other causes of low sperm concentration in our sensitivity analysis.
Value of information was looked at for specific decision scenarios, where a group of similar decisions were considered together.
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.
Decision about selecting herring size has practically no expected value of perfect information (only 1.5 DALY/a) because Ban large, i.e. switching to small herring is in most cases better than other alternatives.
Decision about improved information (including availability and usability of fish) and consumption recommendations has expected value of perfect information with these decisions of 40 DALY/a, so there is some uncertainty about what to do. The maximum net benefit is usually achieved by increasing Baltic fish intake. Thefore, the most important decision option to include in the decision process is to increase information and fish availability (140 DALY/a), while any of the other options can be excluded without much change in expected value.
The analysis was also performed for the young female subgroup separately, assuming a situation where subgroup-specific policies are plausible and effective and do not affect other people. The expected value of perfect information was ca. 80 DALY/a. This implies that there is somewhat more value (80 vs. 40 DALY/a) in improving scientific information in this case than in the case where the whole population is treated in the same way. Interestingly, the absolute difference is not large although a decision maker is more uncertain about which option is the best in the case of young women; this is because the total health impacts in the whole population are so much larger.
Discussion
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 studied). 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 similar to risks in the most sensitive subgroup, women at childbearing age. The balance depends on value assumptions: risks prevail if exceedance of the tolerable weekly intake (especially the new 2018 value) is given weight in the consideration; but benefits are larger if other omega-3 sources are considered secondary to Baltic fish. The analysis was robust in the sense that we did not find factual uncertainties that could remarkably change the conclusions and would suggest postponing decisions in hope of new crucial information.
We found some no-regret policies: promoting small Baltic herring rather than large ones brings all health benefits but reduces exposures to pollutants. Promoting Baltic fish to other population subgroups than young females brings more health than harm. And reducing dioxin emissions to atmosphere will reduce concentrations in fish as well as in dairy and meat products.
The results of the study should not be considered as exact magnitudes of the properties studied. We attempted to quantify actual, measurable properties but acknowledge that these are just humble estimates of the actual truth sometimes produced with few data. We also tried to use probability distributions systematically to reflect out ignorance and also actual variation in populations. We had to make several assumptions about e.g. actual impacts of policies, how representative Finnish measurements are for fish in other countries, what background exposures to use, and how to derive disability weights or durations. In any case, we had to convert all outcomes into a single metric for policy and value-of-information analyses, and DALY seemed to be usable. We had to stretch the definition slightly to include non-disease outcomes, and we also had to use author judgement to estimate durations of diseases and impacts of competing risks, which are not directly observable from epidemiological data. Previous assessments have shown large health benefits related to fish, so when under uncertainty, we tried to be realistic but also tried to avoid underestimating risks, because that bias might cause erroneous conclusions.
We have made the data, code, and reasoning available at Opasnet to facilitate the work of potential critics to find mistakes and false interpretations and also offer a place to publish critique.
The very recent EFSA TWI recommendation for dioxin (2 pg/kg/week as compared with the previous TWI 14 pg/kg/week) dramatically increases the fraction of non-compliant population in all countries studied. However, the implications of this fact are far from clear and require further discussion. We encourage both researchers and administrators to pay much more attention to comparing risks and benefits instead of only considering risks isolated from real complicated situation and perhaps leading to health disadvantages.
First, the most sensitive outcome, namely sperm concentration decrease, is only relevant for young women whose future children may be affected. Should the TWI be applied to all population subgroups?
Second, as dioxin exposure strongly relates to diet consisting of otherwise healthy Baltic fish (especially Baltic herring) in the Nordic countries, should these health benefits be considered when dioxin policy is designed? For example the Swedish food safety authority did not raise this issue in their commentary about the new EFSA TWI[26]
Third, Baltic herring has also other important values than health: economic (Baltic herring is the most abundant catch species by weight in the Baltic Sea), ecological (sustainable yield of Baltic herring is large and catch removes nutrients from the sea), climate (Baltic herring could replace red meat and other climate-unfriendly food sources), social (Baltic herring is inexpensive local food), and cultural (Baltic herring and salmon are an important part of coastal culture)[27][28]. Should these values be considered when dioxin policy is designed? BONUS GOHERR project found that all of these issues are considered important in the society[29].
This study was not designed to answer these value-based questions. But it is useful to understand that the value of information is low for the remaining scientific uncertainties about dioxin risks, and the critical questions are the ones mentioned above. Of course, different parts of Europe and the world have their own dioxin sources and risk-benefit comparisons, but because of biomagnification, fish is a typical source of many persistent pollutants everywhere. Political discussion and deliberation is needed. Scientific facts are crucial, but not the only crucial, elements in that discussion.
Conclusions
In conclusion, despite the new evidence and the new EFSA TWI recommendation, Baltic fish is still safe and health beneficial food for most population subgroups in the Nordic countries. A special subgroup, namely young women planning to have children, is of special concern. The health benefits are smaller than in older age groups, and also there are potential risks to the child that is exposed during pregnancy and breast feeding. Experts do not agree on conclusions about this subgroup, but the scientific uncertainties actually do not play a large role. In contrast, value judgements are crucial when designing policies for dioxins or Baltic fish. These questions should be carefully discussed and deliberated among decision makers, experts, citizens, and other stakeholders.
List of abbreviations
- CHD: coronary heart disease
- CI: confidence interval
- DALY: disability-adjusted life year
- DHA: docosahexaenic acid
- ED50: effective dose 50
- EFSA: European Food Safety Authority
- EPA: eicosapentaenic acid
- IQ: intelligence quotient
- TEF: toxic equivalency factor
- TEQ: toxic equivalency quantity
- WHO: World Health Organisation
Declarations
Ethics approval and consent to participate
An online survey was performed to adult consumers in Denmark, Estonia, Finland, and Sweden by Taloustutkimus Ltd. We asked about fish eating habits but not about health or other sensitive issues. We did not ask or collect identity information of the respondents, except age, gender, and country, which were used for classification in analyses. The survey did not involve any interventions. Due to these reasons, there was no need for ethical approval according to the THL guidance.
Consent for publication
Not applicable.
If your manuscript contains any individual person’s data in any form (including any individual details, images or videos), consent for publication must be obtained from that person, or in the case of children, their parent or legal guardian. All presentations of case reports must have consent for publication.
You can use your institutional consent form or our consent form if you prefer. You should not send the form to us on submission, but we may request to see a copy at any stage (including after publication).
See our editorial policies for more information on consent for publication.
If your manuscript does not contain data from any individual person, please state “Not applicable” in this section.
Availability of data and materials
The whole benefit-risk assessment was performed online at http://en.opasnet.org/w/Goherr_assessment, and all details (including data, code, results, descriptions, and discussions) are openly available, except for the personal data from the consumer survey. The consumer survey data was converted to and published as synthetic data, i.e. data that does not represent any real individuals but that has similar statistical properties as the actual data.
The datasets generated and analysed during the current study, together with the other material mentioned above, are available at the IDA research data repository, https://ida.fairdata.fi
Competing interests
The authors declare that they have no competing interests.
Funding
This work resulted from the BONUS GOHERR project (Integrated governance of Baltic herring and salmon stocks involving stakeholders, 2015-2018) that was supported by BONUS (Art 185), funded jointly by the EU, the Academy of Finland and the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning.
Authors' contributions
JT planned the assessment design, performed most of the analyses, and wrote the first draft of the manuscript based on input from other authors. PH coordinated the project and participated in designing and linking of this work to other parts of the project. AA designed and performed the questionnaire study. PM participated in the discussions about the design and interpretation of results. All authors read and approved the final manuscript.
Acknowledgements
We thank all BONUS GOHERR researchers and stakeholder meeting participants, who participated in lively discussions about the importance of Baltic fisheries management and health.
Authors' information
No specific information.
Figures, tables and additional files
Figure 1. Cumulative concentration distributions of the four key exposure agents in Baltic herring and salmon. For dioxin, also the time trend since 1990 is shown.
- Caption: Concentrations of compounds in Baltic fish
Figure 2. Cumulative fish consumption distributions of Baltic herring and salmon in different subgroups of the studied countries.
- Caption: Consumption of Baltic fish by country and subgroup
Figure 3. Cumulative dioxin exposure distributions shown by subgroup and country.
- Caption: Individuals' fish intake after all consumption policies
Figure 4. Individual change in consumption after a recommendation to either increase or reduce fish intake is given.
- Caption: Exposure to dioxin from Baltic fish
Figure 5. Disease burden of eating Baltic fish in Denmark, Estonia, Finland, and Sweden (expected value on the individual level). Note that negative values mean improved health. mDALY=0.001 disability-adjusted life years. CHD: coronary heart disease, IQ: intelligence quotient, TWI: tolerable weekly intake.
- Caption: Disease burden of Baltic fish by country, group, and policy
Figure 6. Outcome of interest in different scenarios.
- Caption: Disease burden using different focus groups and objectives
Figure 7. Burden of disease of the most important environmental health factors in Finland. BONUS GOHERR results are from this study, others from a previous publication[25]
- Caption: Environmental disease burden in Finland
References
- ↑ Commission Regulation (EC) No 1881/2006 of 19 December 2006 setting maximum levels for certain contaminants in foodstuffs http://data.europa.eu/eli/reg/2006/1881/2014-07-01. Accessed 3 Dec 2018.
- ↑ 2.0 2.1 2.2 EFSA. Risk for animal and human health related to the presence of dioxins and dioxin‐like PCBs in feed and food. EFSA Journal 2018;16:5333. doi:10.2903/j.efsa.2018.5333
- ↑ Tuomisto JT, Niittynen M, Turunen A, Ung-Lanki S, Kiviranta H, Harjunpää H, et al. Itämeren silakka ravintona – Hyöty-haitta-analyysi [Baltic herring as food - a benefit-risk assessment]. Helsinki: Evira 03/2015, ISBN 978-952-225-141-1
- ↑ Tuomisto JT, Asikainen A, Meriläinen P, Haapasaari P. Benefit-risk assessment of Baltic herring and salmon intake. Opasnet: http://en.opasnet.org/w/Goherr_assessment. Accessed 3 Dec 2018.
- ↑ IDA. Data repository by the Ministry of Education and Culture, Finland. https://ida.fairdata.fi. Accessed 3 Dec 2018.
- ↑ Airaksinen R, Hallikainen A, Rantakokko P, Ruokojärvi P, Vuorinen PJ, Parmanne R, et al. Time trends and congener profiles of PCDD/Fs, PCBs, and PBDEs in Baltic herring off the coast of Finland during 1978-2009. Chemosphere 2014;114:165-71 doi:10.1016/j.chemosphere.2014.03.097.
- ↑ Jacobson P. Effects of size dependent predator-prey interactions and fisheries on population dynamics and bioaccumulation of dioxins and PCBs in Baltic salmon, Salmo salar L., and its fish prey. Aqua Introductory Research Essay 2016:2.
- ↑ Van den Berg M, Birnbaum LS, Denison M, De Vito M, Farland W, Feeley M, et al. The 2005 World Health Organization Reevaluation of Human and Mammalian Toxic Equivalency Factors for Dioxins and Dioxin-Like Compounds. Toxicological Sciences 2006;93:223–241 doi:10.1093/toxsci/kfl055.
- ↑ National Institute for Health and Welfare. Fineli database https://www.fineli.fi. Accessed 3 Dec 2018.
- ↑ Finnish Environment Institute. Kerty database http://www.syke.fi/fi-FI/Avoin_tieto/Ymparistotietojarjestelmat. Accessed 3 Dec 2018.
- ↑ 11.0 11.1 Minguez-Alarcon L. et al. A longitudinal study of peripubertal serum organochlorine concentrations and semen parameters in young men: the Russian children's study. Environmental Health Perspectives 2017;125:3.
- ↑ 12.0 12.1 Alaluusua S, Lukinmaa PL, Vartiainen T, Partanen M, Torppa J, Tuomisto J. Polychlorinated dibenzo-p-dioxins and dibenzofurans via mother's milk may cause developmental defects in the child's teeth. Environ Toxicol Pharmacol. 1996;1:193-7.
- ↑ U.S.EPA. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisory. Volume 2: Risk Assessment and Fish Consumption Limits, 3rd Edition. 2000. Table 3-1.
- ↑ 14.0 14.1 EC Scientific Committee on Food. (2001) Opinion of the Scientific Committee on Food on the risk assessment of dioxins and dioxin-like PCBs in food. CS/CNTM/DIOXIN/20 final https://ec.europa.eu/food/sites/food/files/safety/docs/cs_contaminants_catalogue_dioxins_out90_en.pdf. Accessed 3 Dec 2018.
- ↑ 15.0 15.1 15.2 Cohen, J.T., PhD, Bellinger, D.C, PhD, W.E., MD, Bennett A., and Shaywitz B.A. 2005. A Quantitative Analysis of Prenatal Intake of n-3 Polyunsaturated Fatty Acids and Cognitive Development. American Journal of Preventive Medicine 2005;29:366–374).
- ↑ Finnish Nutrition Recommendations 2014 https://www.evira.fi/globalassets/vrn/pdf/ravitsemussuositukset_2014_fi_web.3_es-1.pdf. Accessed 3 Dec 2018.
- ↑ Cohen JT, Bellinger DC, Shaywitz BA. A quantitative analysis of prenatal methyl mercury exposure and cognitive development. Am J Prev Med. 2005 Nov;29:353-65.
- ↑ Mocarelli P et al. Dioxin exposure, from infancy to puberty, produces endocrine disruption and affects human semen quality. Environmental Health Perspectives 2008;116:1
- ↑ Mocarelli P. et al. Perinatal exposure to low doses of dioxin can permanently impair human semen quality. Environmental Health Perspectives 2011;119:5.
- ↑ Sharpe1 RM. Sperm counts and fertility in men: a rocky road ahead. EMBO Rep. 2012;13:398–403. doi:10.1038/embor.2012.50.
- ↑ Alaluusua S, Lukinmaa PL, Torppa J, Tuomisto J, Vartiainen T. Developing teeth as biomarker of dioxin exposure. Lancet. 1999;353:206.
- ↑ Alaluusua S, Calderara P, Gerthoux PM, Lukinmaa P-L, Kovero O, Needham L, et al. Developmental dental aberrations after the dioxin accident in Seveso. Environ Health Perspect. 2004;112:1313-8.
- ↑ Institute for Health Metrics and Evaluation. https://healthdata.org. Accessed 3 Dec 2018
- ↑ Eurostat. http://ec.europa.eu/eurostat. Accessed 3 Dec 2018.
- ↑ 25.0 25.1 Asikainen A, Hänninen O, Pekkanen J. Ympäristöaltisteisiin liittyvä tautitaakka Suomessa. [Disease burden related to environmental exposures in Finland.] Ympäristö ja terveys 2013;5:68-74. http://urn.fi/URN:NBN:fi-fe201312057566. Accessed 3 Dec 2018.
- ↑ Livsmedelsverket. EFSA skärper bedömningen av dioxiner och PCB. [EFSA makes the dioxin and PCB assessment stricter.] https://www.livsmedelsverket.se/om-oss/press/nyheter/pressmeddelanden/efsa-skarper-bedomningen-av-dioxiner-och-pcb. Accessed 3 Dec 2018.
- ↑ ICES. Baltic Sea Ecoregion – Fisheries overview. International Council for the Exploration of the Sea. ICES; 2018. http://www.ices.dk/sites/pub/Publication%20Reports/Advice/2018/2018/BalticSeaEcoregion_FisheriesOverviews_2018.pdf. Accessed 3 Dec 2018
- ↑ Ignatius S, Delaney A, Haapasaari P. Socio-cultural values as a dimension of fisheries governance: the cases of Baltic salmon and herring. Forthcoming.
- ↑ Ignatius S, Haapasaari P. Justification theory for the analysis of the socio-cultural value of fish and fisheries: The case of Baltic salmon. Marine Policy 2018;88:167-173 doi:10.1016/j.marpol.2017.11.007
See also
- Swedish Food Safety Authority about EFSA dioxin assessment
- arg8317: . Fix the claim to the Goherr assessment that small and large herring were treated as separate species! --Jouni (talk) 05:46, 20 November 2018 (UTC) (type: truth; paradigms: science: relevant attack)
- arg7715: . Update VOI function to display ncuu for each option. --Jouni (talk) 14:46, 23 November 2018 (UTC) (type: truth; paradigms: science: relevant attack)