Health effects of Baltic herring and salmon: a benefit-risk assessment: Difference between revisions

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'''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.
'''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 motivation in Denmark, Estonia, Finland, and Sweden. Dioxin concentrations were estimated based on a Finnish EU Fish 2 study and methylmercury concentrations from data from the Finnish Food Safety Agency. 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.
'''Methods:''' We performed an online survey about consumers' fish consumption and motivation in Denmark, Estonia, Finland, and Sweden. Dioxin concentrations were estimated based on a Finnish EU Fish 2 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 smaller or 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 valuations 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.
'''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 smaller or 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 valuations 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 exeeding 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.
'''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 ==
== Keywords ==


Fish consumption, dioxins, methylmercury, benefit-risk assessment, health impact, sperm concentration, Baltic Sea, knowledge crystal, food recommendation, European Food Safety Agency EFSA.
Fish consumption, dioxins, methylmercury, benefit-risk assessment, health impact, sperm concentration, Baltic Sea, knowledge crystal, food recommendation, European Food Safety Authority EFSA.


== Background ==
== Background ==


The Background section should explain the background to the study, its aims, a summary of the existing literature and why this study was necessary or its contribution to the field.
''The Background section should explain the background to the study, its aims, a summary of the existing literature and why this study was necessary or its contribution to the field.


== Methods ==
== Methods ==
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     the type of statistical analysis used, including a power calculation if appropriate
     the type of statistical analysis used, including a power calculation if appropriate


A questionnaire to study consumer’s fish-eating habits in four Baltic Sea countries (Finland, Sweden, Estonia, and Denmark) was done 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 in detail in another article (Pihlajamäki et al, 2018, forthcoming).
=== 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 at current consumption levels. 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 the overview and links to the module pages, and all details can be found from there.<sup>a</sup>
 
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 defended later in the text). The model was run with 5000 iterations using R statistical software (version 3.5.1, https://cran.r-project.org).


Based on the results, a model was developed to predict distributions of individual long-term consumption (in grams per day) in subgroups defined by country, gender, and age (45 years was used as the cutpoint). In addition, people's reactions to several policies were predicted based on their answers (consumption of Baltic herring or salmon is recommended or restricted; or the availability and usability of these species is promoted). There decision scenarios were used to alter the business as usual scenario, and compare results. 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 of 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.
=== Consumption survey ===


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.
A questionnaire survey to study consumer’s fish-eating habits in four Baltic Sea countries (Finland, Sweden, Estonia, and Denmark) was done 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 in detail in another article (Pihlajamäki et al, 2018, forthcoming).


PCDD/F and PCB concentration distributions for each fish species and country were estimated in the following way:
Based on the results, a model was developed to predict distributions of individual long-term fish consumption (in grams per day) in subgroups defined by country, gender, and age (45 years was used as the cut point). In addition, people's reactions to several policies were predicted based on their answers on preferences (consumption of Baltic herring or salmon is recommended or restricted; or the availability and usability of these species is promoted). These decision scenarios were used to alter the business as usual scenario, and compare results. 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.
EU-kalat 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-kalat study, except that the means are scaled based on the area of interest relative to the reference (see table below).
The relative standard deviations are assumed to be the same for each fish population.
Baltic herring (>= 17 cm) and Small Baltic herring (<17 cm) are treated in the model as if they were separate species. Estonians are assumed to eat only small, others only large Baltic herring


Concentrations were calculated as dioxin toxic equivalency quantities (TEQ) by using WHO 2005 toxic equivalency factor (TEF) values.<ref>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.</ref> Levels of fatty acids and methylmercury in Baltic herring were based on measured data obtained from the Finnish food safety authority Evira and from literature for Baltic salmon.
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.


Dioxin concentration distribution was estimated for Finnish catch of Baltic herring and salmon from the EU Fish 2 study. These distributions were then scaled by the ratio of the average concentration in a particular catch area vs. the Finnish catch area on the Bothnian Sea and Bay. 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. These areas were based on landing statistics.
=== Concentrations ===


=== Modelling ===
Size-specific PCDD/F and PCB concentration distributions for each fish species and country were estimated based on EU Fish II study and a hierarchical Bayesian model. The concentrations in Baltic herring were found out to be highly sensitive to fish size, while size-dependency was much weaker in salmon. The model assumed ca. 7 per cent annual decrease in dioxin concentrations, based on long time trend measured in Finland. The samples were caught between 2009 and 2010.


The assessment model is implemented in a modular way in Opasnet. In practice, this means that the data and code used for different parts, or modules, of the model is located on different pages at Opasnet. In this section, we give the overview and links to the module pages, and all details can be found from there. Each module in the assessment model is described in detail on its own knowledge crystal page at Opasnet. All relevant data and model code are available on those pages.<sup>a</sup>
The fish samples came mostly from the Bothnian Sea, which is an important area for Finnish and Swedish catch. The concentrations distributions for the studied countries ware 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<ref>REFERENCE ABOUT AVERAGE CONCENTRATIONS NEEDED!</ref>.


The whole idea of Opasnet web-workspace 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.
{{argument|relat1=relevant attack|selftruth1=true|id=arg8317|type=truth|content=Fix the claim to the [[Goherr assessment]] that small and large herring were treated as separate species!|sign=--[[User:Jouni|Jouni]] ([[User talk:Jouni|talk]]) 05:46, 20 November 2018 (UTC)}}


Concentrations were calculated as dioxin toxic equivalency quantities (TEQ) by using WHO 2005 toxic equivalency factor (TEF) values.<ref>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.</ref> Levels of fatty acids and methylmercury in Baltic herring were based on measured data obtained from the Finnish Food Safety Authority, and those in salmon are based on scientific literature.


The benefit-risk assessment was based on a modular Monte Carlo simulation model. 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-adjusted life years for non-typical endpoints such as tolerable weekly intakes of infertility, we used our own judgement and wide uncertainty bounds (these judgements are defended later in the text). These distributions where then sampled in a Monte Carlo simulation model. The model was run with 5000 iterations using R statistical software (version 3.5.1, https://cran.r-project.org).
Dioxin concentration distribution was estimated for Finnish catch of Baltic herring and salmon from the EU Fish 2 study. These distributions were then scaled by the ratio of the average concentration in a particular catch area vs. the Finnish catch area on the Bothnian Sea and Bay. 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. These areas were based on landing statistics.


=== Exposures ===
=== Exposures ===
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'''[[Burden of disease]]''' was estimated in two alternative ways: if the burden of a particular disease in the target population was known, the [[attributable risk|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 risk]]s 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.
'''[[Burden of disease]]''' was estimated in two alternative ways: if the burden of a particular disease in the target population was known, the [[attributable risk|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 risk]]s 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.


Abbreviations:
=== Disability weights ===
: DHA: docosahexaenic acid
: EPA: eicosapentaenic acid
 
Endnotes
 
<sup>a</sup> We refer to knowledge crystals using ''italics''. This indicates that there is a page with detailed data and codes at Opasnet (e.g. ''ERF of dioxin''), and that knowledge crystal can be accessed by the respective link (e.g. http://en.opasnet.org/w/ERF_of_dioxin).
 
DALYs


Childlessness is said to be "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, the impact is 0.1*50*0.5*0.5 DALY/case = 1.25 DALY/case, with rather high uncertainty (say, 0-2.5 DALY/case)
Childlessness is said to be "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, the impact is 0.1*50*0.5*0.5 DALY/case = 1.25 DALY/case, with rather high uncertainty (say, 0-2.5 DALY/case)
RESULTS
We can also consider men that have already decreased sperm concentrations from an unrelated reason. Dioxin is 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, and that increases to 0.48 at dioxin concentration 100 pg/g. This exposure-response is non-linear, with half of the effect occurring already at 10 pg/g. If ten percent of the population had this low semen concentration and if 20 % of boys exceed 10 pg/g (as seems to be the case with [[Goherr assessment]]), 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 and thus 50 DALY.


== Results ==
== Results ==


''This should include the findings of the study including, if appropriate, results of statistical analysis which must be included either in the text or as tables and figures.
''This should include the findings of the study including, if appropriate, results of statistical analysis which must be included either in the text or as tables and figures.
We can also consider men that have already decreased sperm concentrations from an unrelated reason. Dioxin is 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, and that increases to 0.48 at dioxin concentration 100 pg/g. This exposure-response is non-linear, with half of the effect occurring already at 10 pg/g. If ten percent of the population had this low semen concentration and if 20 % of boys exceed 10 pg/g (as seems to be the case with [[Goherr assessment]]), 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 and thus 50 DALY.


[[File:Goherr benefit-risk assessment fig3.svg|thumb|500px|Dioxin concentrations have reduced a lot since 1990. The same trend have existed since the 1970's (data not shown).]]
[[File:Goherr benefit-risk assessment fig3.svg|thumb|500px|Dioxin concentrations have reduced a lot since 1990. The same trend have existed since the 1970's (data not shown).]]
Line 206: Line 197:
== List of abbreviations ==
== List of abbreviations ==


: EFSA: European Food Safety Agency
: DHA: docosahexaenic acid
: EFSA: European Food Safety Authority
: EPA: eicosapentaenic acid
: TEF: toxic equivalency factor
: TEF: toxic equivalency factor
: TEQ: toxic equivalency quantity
: TEQ: toxic equivalency quantity
Line 257: Line 250:
== Endnotes ==
== Endnotes ==


Endnotes should be designated within the text using a superscript lowercase letter and all notes (along with their corresponding letter) should be included in the Endnotes section. Please format this section in a paragraph rather than a list.
''Endnotes should be designated within the text using a superscript lowercase letter and all notes (along with their corresponding letter) should be included in the Endnotes section. Please format this section in a paragraph rather than a list.
 
<sup>a</sup> We refer to knowledge crystals using ''italics''. This indicates that there is a page with detailed data and codes at Opasnet (e.g. ''ERF of dioxin''), and that knowledge crystal can be accessed by the respective link (e.g. http://en.opasnet.org/w/ERF_of_dioxin).


== References ==
== References ==

Revision as of 05:46, 20 November 2018


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 [4].

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.

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 motivation in Denmark, Estonia, Finland, and Sweden. Dioxin concentrations were estimated based on a Finnish EU Fish 2 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 smaller or 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 valuations 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

The Background section should explain the background to the study, its aims, a summary of the existing literature and why this study was necessary or its contribution to the field.

Methods

   the aim, design and setting of the study
   the characteristics of participants or description of materials
   a clear description of all processes, interventions and comparisons.
   the type of statistical analysis used, including a power calculation if appropriate

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 at current consumption levels. 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 the overview and links to the module pages, and all details can be found from there.a

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 defended 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 to study consumer’s fish-eating habits in four Baltic Sea countries (Finland, Sweden, Estonia, and Denmark) was done 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 in detail in another article (Pihlajamäki et al, 2018, forthcoming).

Based on the results, a model was developed to predict distributions of individual long-term fish consumption (in grams per day) in subgroups defined by country, gender, and age (45 years was used as the cut point). In addition, people's reactions to several policies were predicted based on their answers on preferences (consumption of Baltic herring or salmon is recommended or restricted; or the availability and usability of these species is promoted). These decision scenarios were used to alter the business as usual scenario, and compare results. 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.

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.

Concentrations

Size-specific PCDD/F and PCB concentration distributions for each fish species and country were estimated based on EU Fish II study and a hierarchical Bayesian model. The concentrations in Baltic herring were found out to be highly sensitive to fish size, while size-dependency was much weaker in salmon. The model assumed ca. 7 per cent annual decrease in dioxin concentrations, based on long time trend measured in Finland. The samples were caught between 2009 and 2010.

The fish samples came mostly from the Bothnian Sea, which is an important area for Finnish and Swedish catch. The concentrations distributions for the studied countries ware 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[1].

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)

Concentrations were calculated as dioxin toxic equivalency quantities (TEQ) by using WHO 2005 toxic equivalency factor (TEF) values.[2] Levels of fatty acids and methylmercury in Baltic herring were based on measured data obtained from the Finnish Food Safety Authority, and those in salmon are based on scientific literature.

Dioxin concentration distribution was estimated for Finnish catch of Baltic herring and salmon from the EU Fish 2 study. These distributions were then scaled by the ratio of the average concentration in a particular catch area vs. the Finnish catch area on the Bothnian Sea and Bay. 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. These areas were based on landing statistics.

Exposures

Intake of salmon and herring

A Bayesian module was created with the JAGS package of R software to calculate intake of salmon and herring in each country utilizing the questionnaire-based consumption data. The original data was used to calculate distribution parameters. Output of the module was the amount of fish eaten by a random individual, expressed as g/day. The module with its code is accessible online on the knowledge crystal page Goherr: Fish consumption study.

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 (##REF).

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-responses

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[3]. Cancer morbidity was based on U.S.EPA dioxin risk assessment[4]. Tolerable daily intake was based on EC Scientific Committee on Food recommendation[5]. Exposure-response functions for omega-3 fatty acids on coronary heart disease and stroke mortalities were from a previous risk assessment[6]. Exposure-response function of methylmercury on child's intelligence quotient was based on a previous risk assessment[7]. Exposure-response function for vitamin D was a step function based on the daily intake recommendations for adults in Finland[8].

Table ##. Exposure-response functions used in the assessment.
Exposure agent Response Esposure-response unit Exposure-response function References
TEQ (Intake through placenta and mother's milk) Male infertility due to sperm concentration decrease pg /g in boy's body fat 0.00006+-0.00004 [9] Mother's exposure must be converted to child's exposure (measured as pg /g fat) using ovariable Infant's dioxin exposure.
TEQ (Intake through placenta and mother's milk) Developmental dental defects log (pg /g) in child's body fat 0.26 +- 0.12
TEQ Cancer morbidity pg/kg/day 0.000032; 0.000035; 0.000156
TEQ Current EFSA dioxin recommendation: tolerable daily intake pg/kg/week 14
TEQ Suggested EFSA dioxin recommendation: tolerable daily intake pg/kg/week 2
DHA Loss in child's IQ points mg/day -0.0013 (-0.0018 - -0.0008)
Omega3 CHD2 mortality mg/day -0.17 (-0.25 - -0.088)
Omega3 Stroke mortality mg/day -0.12 (-0.25 - 0.01)
Vitamin D Vitamin D recommendation µg/day 100
MeHg Loss in child's IQ points mg/kg/day 6.533 (0 - 14)

Sperm concentration

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[10][11] and a Russian children's study[9].

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. Sperm concentration mean was ca. 65 (95 % CI 50-80) million/ml in the lowest quartile, while in all other quartiles the concentration was ca. 40 (96 % 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 occurring at TEQ concentration 10.5 pg/g fat.

However, sperm concentration as such is not a 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.[12] 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+ \frac{-0.39c}{c + 10.5 pg/g})^{10}

where c is the dioxin concentration in boy's fat tissue. For details, see the knowledge crystal ERF of dioxin at Opasnet.

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.

Tooth defects

Alaluusua and coworkers have published several studies on 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 [3] [13] and children exposed during the Seveso accident[14]

Based on these studies, we approximated that the effect is linearly correlated with the logarithm of the dioxin concentration in the child.

Background disease levels

Background disease levels were needed for stroke and cardiovascular diseases and were obtained from The Institute for Health Metrics and Evaluation (IHME).

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.

Disability weights

Childlessness is said to be "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, the impact is 0.1*50*0.5*0.5 DALY/case = 1.25 DALY/case, with rather high uncertainty (say, 0-2.5 DALY/case)

Results

This should include the findings of the study including, if appropriate, results of statistical analysis which must be included either in the text or as tables and figures.

We can also consider men that have already decreased sperm concentrations from an unrelated reason. Dioxin is 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, and that increases to 0.48 at dioxin concentration 100 pg/g. This exposure-response is non-linear, with half of the effect occurring already at 10 pg/g. If ten percent of the population had this low semen concentration and if 20 % of boys exceed 10 pg/g (as seems to be the case with Goherr assessment), 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 and thus 50 DALY.

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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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Dioxin concentration distributions in Baltic fish after size-related policies. With active policy to not use large (>17 cm) Baltic herring ("Ban large"), the concentrations on the plate would be clearly lower than nowadays, while the promotion of smaller herring size has a smaller effect ("New products") compared with the business as usual scenario ("BAU").
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Individual change in consumption after a recommendation to either increase or reduce the fish intake. The outcome 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. This phenomenon is seen already at current intakes below 5 g/day, where most of the population is.
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Fish consumption distributions of Baltic herring and salmon in different subgroups of the studied countries. 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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Cumulative dioxin exposure distributions shown by subgroup and country. Few young females exceed the current tolerable daily intake any more, as both concentrations and consumption have decreased. The new tolerable weekly intake is exceeded by a much larger fraction. arg6931: . Change these from TDI to TWI. And add new TWI line. --Jouni (talk) 06:29, 19 November 2018 (UTC) (type: truth; paradigms: science: relevant attack)
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Burden of disease of eating Baltic fish (expected value on the individual level). 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.arg6931: . Remove policy "inconsistent". --Jouni (talk) 06:29, 19 November 2018 (UTC) (type: truth; paradigms: science: relevant attack)
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Burden of disease of the most important environmental health factors in Finland. 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.

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.

Discussion

This section should discuss the implications of the findings in context of existing research and highlight limitations of the study.

Conclusions

This should state clearly the main conclusions and provide an explanation of the importance and relevance of the study reported.

List of abbreviations

DHA: docosahexaenic acid
EFSA: European Food Safety Authority
EPA: eicosapentaenic acid
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.

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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 in the IDA research data repository, [###PERSISTENT WEB LINK TO DATASETS]

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.

Endnotes

Endnotes should be designated within the text using a superscript lowercase letter and all notes (along with their corresponding letter) should be included in the Endnotes section. Please format this section in a paragraph rather than a list.

a We refer to knowledge crystals using italics. This indicates that there is a page with detailed data and codes at Opasnet (e.g. ERF of dioxin), and that knowledge crystal can be accessed by the respective link (e.g. http://en.opasnet.org/w/ERF_of_dioxin).

References

  1. ↑ REFERENCE ABOUT AVERAGE CONCENTRATIONS NEEDED!
  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.
  3. ↑ 3.0 3.1 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.
  4. ↑ 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.
  5. ↑ 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 [1]
  6. ↑ Cohen, J.T., PhD, Bellinger, D.C, PhD, W.E., MD, Bennett A., and Shaywitz B.A. 2005b. A Quantitative Analysis of Prenatal Intake of n-3 Polyunsaturated Fatty Acids and Cognitive Development. American Journal of Preventive Medicine 2005;29(4):366–374).
  7. ↑ Cohen JT, Bellinger DC, Shaywitz BA. A quantitative analysis of prenatal methyl mercury exposure and cognitive development. Am J Prev Med. 2005 Nov;29(4):353-65.
  8. ↑ Finnish Nutrition Recommendations 2014 [2]
  9. ↑ 9.0 9.1 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.
  10. ↑ Mocarelli P et al. Dioxin exposure, from infancy to puberty, produces endocrine disruption and affects human semen quality. Environmental Health Perspectives 2008;116:1
  11. ↑ Mocarelli P. et al. Perinatal exposure to low doses of dioxin can permanently impair human semen quality. Environmental Health Perspectives 2011;119:5.
  12. ↑ Sharpe1 RM. Sperm counts and fertility in men: a rocky road ahead. EMBO Rep. 2012;13:398–403. doi:10.1038/embor.2012.50.
  13. ↑ Alaluusua S, Lukinmaa PL, Torppa J, Tuomisto J, Vartiainen T. Developing teeth as biomarker of dioxin exposure. Lancet. 1999;353:206.
  14. ↑ 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.

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