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

From Opasnet
Jump to navigation Jump to search
No edit summary
No edit summary
 
(20 intermediate revisions by the same user not shown)
Line 4: Line 4:
{{nugget|moderator=Jouni
{{nugget|moderator=Jouni
|reference={{publication
|reference={{publication
|publishingyear=2018
|publishingyear=2019
|authors=Jouni T. Tuomisto, Arja Asikainen, Päivi Meriläinen, Päivi Haapasaari}}
|authors=Jouni T. Tuomisto, Arja Asikainen, Päivi Meriläinen, Päivi Haapasaari}}
}}
}}
'''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 was submitted to BMC Public Health [https://bmcpublichealth.biomedcentral.com/submission-guidelines/preparing-your-manuscript/research-article]. Thank you for your interest.
'''Health effects of nutrients and environmental pollutants in Baltic herring and salmon: a quantitative benefit-risk assessment''' is a research manuscript about the [[Goherr assessment]] performed on the BONUS GOHERR project between 2015-2018. The manuscript was submitted to BMC Public Health [https://bmcpublichealth.biomedcentral.com/submission-guidelines/preparing-your-manuscript/research-article]. Thank you for your interest.


== Title page ==
* [https://www.livsmedelsverket.se/om-oss/press/nyheter/pressmeddelanden/efsa-skarper-bedomningen-av-dioxiner-och-pcb Swedish Food Safety Authority about EFSA dioxin assessment]
 
* Title: Health effects of nutrients and environmental pollutants in 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 benefits are larger or similar than health risks. 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.<ref name="tuomisto2019">Tuomisto, J (2019). "Dioxins and dioxin-like compounds". WikiJournal Preprints. https://en.wikiversity.org/wiki/WikiJournal_Preprints/Dioxins_and_dioxin-like_compounds</ref> 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.<ref name="kiviranta2004">Kiviranta H, Ovaskainen M-L, Vartiainen T. (2004) Market basket study on dietary intake of PCDD/Fs, PCBs, and PBDEs in Finland. Environment International 30; 7; 923-932. https://doi.org/10.1016/j.envint.2004.03.002</ref> These fish species often exceed the EU limits for dioxins and PCBs<ref>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 22 Aug 2019.</ref>, but Finland and Sweden have a permanent derogation to sell these fish species on national market.<ref>Commission Regulation (EU) No 1259/2011 of 2 December 2011. https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2011:320:0018:0023:EN:PDF. Accessed 22 Aug 2019.</ref> Estonia is dealing with dioxins by selecting human food from small Baltic herring with lower concentrations.
 
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)<ref name="twi2018"/>.
 
Although there are previous benefit-risk assessments about Baltic fish<ref>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</ref>, 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 (en.opasnet.org). 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 2019, forthcoming). In this section, we give an overview of the model and describe the input data and assumptions used; the Result section consists of model results. Links to the module pages and all details can be found from the assessment page<ref>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 22 Aug 2019.</ref>. The whole model with data and codes is available on the page and also at Open Science Framework research data repository<ref>OSF. Data repository by the Centre for Open Science: BONUS GOHERR benefit-risk assessment dataset. https://osf.io/brxpt/. Accessed 22 Aug 2019.</ref>.
 
The benefit-risk assessment was based on a modular Monte Carlo simulation model, which had a hierarchical Bayesian module for estimating dioxin concentrations. The different modules are briefly described below, with references and links to further material.
 
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 10000 iterations using R statistical software (version 3.5.2, https://cran.r-project.org).
 
=== Consumption survey ===
 
[[File:Europe map with DK EE FI SE.png|thumb|250px|Figure 1. Countries where Goherr consumption survey was performed. Source: Europe with Countries - Single Color by FreeVectorMaps.com]]
 
The data used were from an internet-based survey that was conducted at the end of 2016. The survey focused on consumers’ eating habits of Baltic herring and salmon in four Baltic Sea countries: Denmark, Estonia, Finland, and Sweden (Figure 1). The questionnaire (Supplementary material S1) was designed and the results analysed by the authors, but the survey was administered by a professional market research company Taloustutkimus Oy, which has an established internet panel since 1997. The survey company recruited over 500 consumers from each country (total 2117) to respond to the survey questionnaire, which is above the required sample size to generalise the results to each case study country (with a 95% confidence level and 5% margin of error)<ref>Cowles, E.L., & Nelson, E. (2015). An introduction to Survey Research. New York: Business Expert Press.</ref>. The survey was targeted to adult population, i.e. 18 years or older.
 
The survey questionnaire comprised 32 questions, including sociodemographic questions as well as questions relating to fish consumption frequency and amount in general, and to Baltic herring and salmon in particular. There were also questions about reasons to eat or to not to eat those species, and policies that may affect the amount eaten. The questionnaire was translated into the national language of each case study country (Finnish, Swedish, Estonian and Danish). The country and gender of the respondents were provided directly by the internet panel and were therefore not included in the questionnaire.
 
Only those respondents who reported fish consumption in general were asked follow-up questions about herring and salmon consumption, and are included in the analysis presented in this paper. As the survey focused specifically on consumption of herring and salmon originating from the Baltic Sea, a distinction had to be made in the questionnaire between Baltic herring and herring originating from elsewhere, e.g. the North Sea or North Atlantic,  as well as between the salmonids (Baltic and Norwegian salmon, farmed salmon, rainbow trout). Regarding herring consumption, the respondents  that reported  eating  some type of herring were  asked explicitly whether they consume Baltic herring. Concerning Baltic salmon, the respondents were asked to choose from a list of salmonids, which ones they consume. In addition to the analysis presented in this paper, the survey was conducted for the purpose of a consumer perception and consumption study<ref name="pihlajamaki2019">Pihlajamäki M, Asikainen A, Ignatius S, Haapasaari P, Tuomisto JT. (2019) Forage Fish as Food: Consumer Perceptions on Baltic Herring. Sustainability 11(16) 4298. https://doi.org/10.3390/su11164298</ref> and therefore only part of the survey results are presented in this paper.
Individual long-term fish consumption (in kilograms per year) 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 ca. 1 kg per year; 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 where the benefits level off with increasing consumption. In such a case, the incremental health benefits of a primary source are larger than those of a secondary source.
 
The data analysis was conducted  using  R-program (version 3.5.1, http://cran.r-project.org). Because the survey was conducted on an internet panel rather than on a random sample from the general population, the respondents may not be fully representative of the actual population distributions of the countries. Therefore, the respondents were weighted based on actual age, gender, and region distributions of each country to produce population representative results.
 
To support transparency, the data and all the results will be available online: http://en.opasnet.org/w/Goherr:_Fish_consumption_study
 
=== 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<ref>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.</ref>. 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 fish 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 in BONUS GOHERR project<ref>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.</ref>.
 
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 provided by the International Council for the Exploration of the Sea (ICES)<ref>Report of the Herring Assessment Working Group for the Area South of 62˚N (HAWG) http://www.ices.dk/sites/pub/Publication%20Reports/Expert%20Group%20Report/acom/2016/HAWG/01%20HAWG%20Report%202016.pdf</ref><ref>Report of the Baltic Salmon and Trout Assessment Working Group (WGBAST) http://www.ices.dk/sites/pub/Publication%20Reports/Expert%20Group%20Report/acom/2016/WGBAST/wgbast_2016.pdf</ref>.
 
Dioxin and PCB concentrations were weighted and summed up to toxic equivalency quantities (TEQ) by using WHO 2005 toxic equivalency factors (TEF)<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 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<ref>National Institute for Health and Welfare. Fineli database https://www.fineli.fi. Accessed 22 Aug 2019.</ref>. Methylmercury concentrations were based on Kerty database<ref>Finnish Environment Institute. Kerty database http://www.syke.fi/fi-FI/Avoin_tieto/Ymparistotietojarjestelmat. Accessed 22 Aug 2019.</ref>.
 
=== Exposures ===
 
Exposures to pollutants and nutrients were simply products of consumption amounts as assessed by the respondents of the survey and concentrations in the consumed fish, with possibly an uncertain background intake from other sources. 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 a toxicokinetic model.
 
Infant's exposure during pregnancy and breast-feeding was estimated with this equation:
 
:<math>C_{s,i} = \frac{I_{a,m} * t_{1/2,m} * f_m * FE}{ln2 * BF_i},</math>
 
where C<sub>s,i</sub> = serum concentration of dioxin in the infant in pg/g fat; I<sub>a,m</sub> = average daily intake of dioxin of the mother in absolute amounts pg/day; t<sub>1/2,m</sub> = the half-life of dioxin in the mother (2737.5 d = 7.5 a); f<sub>m</sub> = fraction of ingested dioxin actually absorbing from the gut in the mother (0.80); FE = fraction of mother's dioxin load that is transported to the infant during breast feeding (0.25); BF = body fat amount in the infant (into which the dioxin is evenly distributed) during the period when tooth and testis are sensitive to defects and the exposure at its highest (ca. six months of age) (1 kg)<ref>Tuomisto J.T. (2017) Infant's dioxin exposure. Opasnet. http://en.opasnet.org/w/Infant%27s_dioxin_exposure. Accessed 22 Aug 2019.</ref>
 
=== Exposure-responses ===
 
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.
 
{| {{prettytable}}
|+'''Table 1. Exposure-response functions used in the assessment.
|----
! Exposure agent|| Response|| Esposure-response unit|| Exposure-response function<br/>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 0.00006 (-0.000019, 0.00014)||mother's exposure must be converted to child's exposure (measured as pg /g fat)<ref name="minguez2017"/>
|----
|| TEQ (intake through placenta and mother's milk)|| developmental tooth defects|| log (pg /g) in child's body fat||linear; slope 0.0014 (0.00029, 0.0025)|| epidemiological study in Finland<ref name="alaluusua1996"/>
|----
|| TEQ|| cancer morbidity|| pg/kg/day|| linear; slope 0.000074 (0.000032, 0.00016)||U.S.EPA dioxin risk assessment<ref>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.</ref>.
|----
|| TEQ|| tolerable weekly intake 2001|| pg/kg/week||acceptable range below 14 ||EC Scientific Committee on Food recommendation<ref name="twi2001">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 22 Aug 2019.</ref>
|----
|| TEQ|| tolerable weekly intake 2018|| pg/kg/week||acceptable range below 2 ||EFSA recommendation<ref name="twi2018">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</ref>
|----
|| omega-3 fatty acids|| coronary heart disease mortality|| mg/day||ED50: -0.17 (-0.25, -0.091)|| a previous risk assessment<ref name="cohen2005"/>
|----
|| omega-3 fatty acids|| stroke mortality|| mg/day||ED50: -0.12 (-0.25, 0.0071)|| a previous risk assessment<ref name="cohen2005"/>
|----
|| vitamin D|| vitamin D recommendation|| µg/day||acceptable range 10 - 100|| a step function based on the daily intake recommendations for adults in Finland<ref>Finnish Nutrition Recommendations 2014 https://www.evira.fi/globalassets/vrn/pdf/ravitsemussuositukset_2014_fi_web.3_es-1.pdf. Accessed 22 Aug 2019.</ref>
|----
|| methylmercury|| loss in child's IQ points|| mg/kg/day||linear; slope 6.6 (-0.27, 14)|| a synthesis of EFSA TWI estimate<ref>EFSA Panel on Contaminants in the Food Chain (CONTAM). Scientific Opinion on the risk for public health related to the presence of mercury and methylmercury in food. (2012) EFSA Journal 2012;10(12):2985. https://doi.org/10.2903/j.efsa.2012.2985</ref> and a previous risk assessment<ref>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.</ref>.
|----
|| DHA|| loss in child's IQ points|| mg/day||linear; slope -0.0013 (-0.0018, -0.00081)||a previous risk assessment<ref name="cohen2005">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).</ref>.
|----
|}
 
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<ref>Mocarelli P et al. Dioxin exposure, from infancy to puberty, produces endocrine disruption and affects human semen quality. Environmental Health Perspectives 2008;116:1</ref><ref>Mocarelli P. et al. Perinatal exposure to low doses of dioxin can permanently  impair human semen quality. Environmental Health Perspectives 2011;119:5.</ref> and a Russian children's study<ref name="minguez2017">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.</ref>.
 
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<ref>Sharpe1 RM. Sperm counts and fertility in men: a rocky road ahead. EMBO Rep. 2012;13:398–403. doi:10.1038/embor.2012.50.</ref>. 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 <ref name="alaluusua1996">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.</ref>
<ref name="alaluusua1999">Alaluusua S, Lukinmaa PL, Torppa J, Tuomisto J, Vartiainen T. Developing teeth as biomarker of dioxin exposure. Lancet. 1999;353:206.</ref>
and children exposed during the Seveso accident<ref name="Alaluusua">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.</ref>.
 
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<ref>Prüss-Üstün A, Mathers C, Corvalán C, Woodward A. (2003). Assessing the environmental burden of disease at national and local levels: Introduction and methods. WHO Environmental Burden of Disease Series. 1. Geneva: World Health Organization. ISBN 978-9241546201.</ref> was estimated in one of two alternative ways: if an exposure agent affects the burden of a particular disease in relation to the background of the disease, the attributable fraction of a particular compound exposure was calculated. If the relation was not relative to background, 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.).
 
BoD<sub>i</sub> = BoD * AF<sub>i</sub> = BoD * (RR<sub>i</sub> - 1) / RR<sub>i</sub>, or
 
BoD<sub>i</sub> = N<sub>i</sub> * D * DW,
 
where BoD is the burden caused by the disease under study, i is an exposure agent increasing the risk of the disease, AF is attributable fraction, RR<sub>i</sub> is the relative risk that the population faces due to exposure to exposure agent i (as compared with a counterfactual scenario with no exposure), N is the number of disease cases attributed to exposure agent i, D is the duration of a disease incident, and DW is the disability weight of the disease (0=perfect health, 1=death).
 
{| {{prettytable}}
|+'''Table 2. Case burdens of different health reponses. Case burden is calculated as the product of disease-specific disability weights and disease durations.
! Response|| DALYs per case|| Description
|----
|| tooth defect|| 0 - 0.12|| disability weight 0.001 and duration 60 a with 100 % uncertainty. For comparison, IHME gives disability weight 0 for asymptomatic caries and 0.006 for mild other oral disorders with symptoms<ref name="disabilityweight">Institute for Health Metrics and Evaluation. (2019) Disability weights for GBD2017 study. http://ghdx.healthdata.org/record/global-burden-disease-study-2017-gbd-2017-disability-weights. Accessed 22 Aug 2019.</ref>.
|----
|| 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|| 0-5|| disability weight 0.1 and duration 50 a with 100 % uncertainty. See also text. Here we used a clearly higher disability weight than IHME (0.008)<ref name="disabilityweight"/>.
|----
|| child's IQ|| 0.11 (95 % CI 0.06 - 0.16)|| Mild intellectual disability (IQ&lt;70) has disability weight 0.043
(95 % CI 0.026-0.064) based on IHME<ref name="disabilityweight"/>. This is scaled to one IQ point with duration 75 a.
|----
|}
 
Background disease levels were needed for stroke and cardiovascular diseases and were obtained from The Institute for Health Metrics and Evaluation (IHME) (Table 3.)<ref>Institute for Health Metrics and Evaluation. https://healthdata.org. Accessed 22 Aug 2019</ref>. Also disability weights of diseases were based on their estimates, if available. Duration estimates 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). Therefore, we used 0.1*50*0.5 DALY/case = 2.5 DALY/case, with rather high uncertainty (0-5 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<ref>Eurostat. http://ec.europa.eu/eurostat. Accessed 22 Aug 2019.</ref>.
 
{| {{prettytable}}
|+'''Table 3. Total burden of disease of selected causes<ref name="ihme"/>.
|----
! Country || Cause || DALYs, mean (95 % CI)
|----
|| Denmark || Stroke || 14000 (890, 29000)
|----
|| Estonia || Stroke || 5700 (210, 13000)
|----
|| Finland || Stroke || 14000 (950, 29000)
|----
|| Sweden || Stroke || 23000 (1200, 46000)
|----
|| Denmark || Heart (CHD) || 26000 (430, 66000)
|----
|| Estonia || Heart (CHD) || 16000 (180, 33000)
|----
|| Finland || Heart (CHD) || 40000 (530, 100000)
|----
|| Sweden || Heart (CHD) || 58000 (780, 140000)
|----
|}
 
=== Value of information analysis ===
 
Value of information is a mathematical method that compares the  difference of utility (money, DALYs or other measure of the objective) in two scenarios: that some additional information is obtained before a decision is made, or that the decision is made with the current information. This can be formulated as
 
VOI = E(max<sub>i</sub>(U(d<sub>i</sub>))) - max<sub>i</sub>(E(U(d<sub>i</sub>))),
 
where VOI is value of information, E is expected value, U is the utility of decision d, and i is an index of decision options<ref>Howard, R. (1966) Information Value Theory. IEEE Transactions on Systems Science and Cybernetics. 2 (1): 22–26. doi:10.1109/tssc.1966.300074.</ref>. In this study, we also estimated the value of including or excluding an option to the decision making.
 
== Results ==
 
[[File:Goherr benefit-risk assessment fig3.svg|thumb|500px|Figure 2. Cumulative concentration distributions of the four key exposure agents in Baltic herring and salmon. For dioxin, also the time trend since 1990 is shown.]]
[[File:Goherr benefit-risk assessment fig12.svg|thumb|500px|Figure 3. Cumulative fish consumption distributions of Baltic herring and salmon in different subgroups of the studied countries.]]
[[File:Goherr benefit-risk assessment fig10.svg|thumb|500px|Figure 4. Individual change in consumption after policies to either increase or reduce fish intake.]]
[[File:Goherr benefit-risk assessment fig15.svg|thumb|500px|Figure 5. Cumulative dioxin exposure distributions shown by subgroup and country.]]
[[File:Goherr benefit-risk assessment fig22.svg|thumb|500px|Figure 6. Disease burden of eating Baltic fish in Denmark, Estonia, Finland, and Sweden (expected value at individual level). Note that negative values mean improved health. mDALY: 0.001 disability-adjusted life years, CHD: coronary heart disease, IQ: intelligence quotient.]]
[[File:Goherr benefit-risk assessment fig22b.svg|thumb|500px|Figure 7. Outcome of interest using different objectives. The default net health assessment (the main assessment of this article) assumes background intake of omega-3 fatty acids, which reduces the marginal benefit of Baltic fish. The second assessment assumes no background. Tolerable weekly intakes from 2001 and 2018 are converted to DALYs based on the number of people exceeding the guidance value.]]
[[File:Goherr benefit-risk assessment fig28.svg|thumb|500px|Figure 8. Burden of disease of the most important environmental health factors in Finland. BONUS GOHERR results are from this study, others from a previous publication<ref name="asikainen2013">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 22 Aug 2019.</ref>.]]
 
Concentration distributions of the key exposure agents in Baltic herring and salmon are shown in Figure 2. 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 2.
 
Fish consumption varies a lot between countries and population subgroups, and also within each subgroup (Figure 3.). The average consumption of Baltic herring and salmon is 1.4 and 0.5 kg/a per person in Finland according to estimates based on our survey. There is large individual variation (almost hundredfold) in fish consumption within most subgroups.
 
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. Herring consumption is more accurately known, although the Danes are not sure whether their herring comes from the Baltic Sea or the North Sea. For example, in Finland a typical dish name contains the word ''herring'', if it contains Baltic herring. Therefore the species is often known to the consumer even if the dish is not self made. However, this is not true with Baltic salmon.
 
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 3.6 kg/year (10 g/day). This varies from a few percent in young people to up to ca. 30 % in old Estonians.
 
The Natural Resources Institute Finland reports (mostly based on landing statistics) that the consumption of Baltic herring and salmon were 0.31 and 0.07 kg/a per person, respectively<ref>Natural Resources Institute Finland. (2019) Fish consumption 2017. https://stat.luke.fi/en/fish-consumption-2017_en. Accessed 22 Aug 2019.</ref>. This implies that people tend to overestimate their long-term average consumption in general and for Baltic salmon in particular. Because of this discrepancy, we performed a sensitivity analysis where our consumption estimates in our assessment were scaled to match the Finnish statistics. The results of all variables were smaller, but the overall picture remained the same. Also, a notable fraction of population still exceeded the TWI 2018 value.
 
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 1.8 kg/year, where most of the population is.
 
Because of the large variation in fish consumption, also the dioxin exposure from Baltic fish varies more than hundred-fold within population subgroups (Figure 5.). 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 ranging from a few percent to a quarter exceed the EC Scientific Committee on Food TWI value from 2001<ref name="twi2001"/>. 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<ref name="twi2018"/>.
 
The main objective of this study was to compare health risks and benefits of Baltic fish consumption. Figure 6. shows a large variation between population subgroups. The most dominant feature is the cardiovascular benefits, which in old age groups clearly outdo all risks. In most population subgroups, the benefits are typically ten or hundred-fold larger than risks at individual level. In contrast, the risks and benefits in young women are both small, but at individual level, risks are often larger based on the model.
 
Figure 7. shows several different objectives that could be used as a basis for decision making. The first one is using net health effect, estimated like in the benefit-risk assessment performed here. The second objective ignores background exposure to omega-3 fatty acids, and the benefit estimates become clearly larger. This is because the exposure-response functions of omega-3 fatty acids are non-linear, and the incremental health benefit is larger in the beginning and levels off with increasing exposure; if background exposure is ignored, more of the impact is attributed to Baltic fish. Third and fourth objectives try to avoid exceedance of tolerable weekly intake values from 2001 and 2018, respectively.
 
When the whole population is considered (top row), net health objective recommends increasing rather than decreasing Baltic fish consumption in every country, while TWI approaches suggest that reducing 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 slightly larger risk than benefit. Because the subgroup of young women consumes the smallest amount of Baltic herring, the TWI exceedances have a minor impact in all scenarios except for TWI 2018 in Estonia, where the impact is clearly higher than in other countries.
 
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 then 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, factual actions to reduce dioxin emissions and consequently exposures have been very effective 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 8.). It is not even close to the largest ones from air pollution (which may be up to tens of thousands DALY in Finland alone), 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 8. Individual risk per mother would be 0.05 and 0.03 mDALY/a per person, respectively (compare to Figure 5). 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.
 
IHME estimates the disease burden of male infertility of all causes at only 52 DALY/a, and the value including female infertility is roughly a double of that<ref name="ihme">Institute for Health Metrics and Evaluation. (2019) Global Burden of Disease Results tool, GBD2017 resuls. http://ghdx.healthdata.org/gbd-results-tool. Accessed 22 Aug 2019.</ref>. So, it seems that our estimates are not understimating the sperm concentration problem.
 
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 study country (Denmark, Estonia, Finland, and Sweden were not weighting by population), but using uncertainties for individual people. This approach ensures that value of information is not underestimated, because at population level many uncertainties average out and are smaller than at individual level.
 
Decision about selecting herring size has practically no expected value of perfect information (less than 2 DALY/a for a whole country) because ''Ban large'', i.e. switching from large 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. Therefore, the most important decision option to include in the decision process is to increase information and fish availability (200 DALY/a), while any of the other options can be excluded without much change in expected benefit.
 
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 120 DALY/a. At the same time, the total disease burden at stake is clearly smaller than with the whole population. These two results together show that the uncertainties about what to do with respect to young women are clearly larger than with other subgroups.
 
== 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 less than a kilogram per year, 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 the consumption of 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.
 
A benefit-risk assessment attempts to estimate the total benefits and total risks and then compare those. This results in a need to estimate all relevant endpoints, even if there is uncertainty about the mere existence of a causal effect. There is a reason for this: if an uncertain endpoint is rejected from further scrutiny, mathematically it implies certainty about zero impact. Therefore, it is necessary to avoid omissions and try to produce a balanced quantitative view of both risks and benefits on the one hand, and of their uncertainties on the other hand.
 
This is a different approach than in e.g. Cochrane reviews<ref>Cochrane. (2016) Charter of good management practice. https://community.cochrane.org/sites/default/files/uploads/inline-files/Cochrane%20Charter%20of%20Good%20Management%20Practice.pdf. Accessed 22 Aug 2019</ref>, where data that do not meet specific criteria are omitted. The two approaches reach scientific quality in different ways: Cochrane reviews using quality standards, and benefit-risk assessments by opening all details to fellow researchers for quantitative and qualitative criticism. A benefit-risk assessment can actually be seen as one step in an iterative process, where the understanding improves in an interaction with assessments and researchers, resulting in updated assessments in next steps.
 
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 our 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.
 
One critical question about this assessment is whether the beneficial effects are actually real. Elizabeth Pennisi reports several studies about genetic variation of fatty acid metabolism and links to cardiovascular risk<ref>Pennisi E. Is fish oil good for you? Depends on your DNA. Science 17 September 2015 [http://news.sciencemag.org/biology/2015/09/fish-oil-good-you-depends-your-dna]</ref>. The overall conclusion is that although these issues are not well understood, there seem to be genetic variation about the health benefits of omega-3 fatty acids. Lauritzen et al. made a review on docosahexaenoic acid (DHA) and concluded that it is especially important for the developing brain during fetal period and infancy, although there may be variation in intrinsic production and therefore in the need of DHA from food. <ref>Lotte Lauritzen, Paolo Brambilla, Alessandra Mazzocchi, Laurine B. S. Harsløf, Valentina Ciappolino and Carlo Agostoni. (2016) DHA Effects in Brain Development and Function. Nutrients 2016, 8(1), 6. {{doi|10.3390/nu8010006}} [https://www.mdpi.com/2072-6643/8/1/6/htm]</ref> Also this implies variation within the population.
 
Aung et al. conducted a meta-analysis of omega-3 supplement trials with more than 77000 individuals<ref>Aung T, Halsey J, Kromhout D, et al. Cardiovascular Disease Risks. Meta-analysis of 10 Trials Involving 77 917 Individuals. JAMA Cardiol. 2018;3(3):225-233. {{doi|10.1001/jamacardio.2017.5205}} [https://jamanetwork.com/journals/jamacardiology/fullarticle/2670752]</ref>. They found only weak, border-marginal cardiovascular benefits and concluded that the study did not support the use of dietary omega-3 supplements. The US NCCIH says more about these studies and also: "Moderate evidence has emerged about the health benefits of eating seafood. The health benefits of omega-3 dietary supplements are unclear."<ref>National Center for Complementary and Integrative Health. (2018) Omega-3 supplements in depth. Website edited May 2018. [https://nccih.nih.gov/health/omega3/introduction.htm] Accessed 2 July 2019.</ref>.
 
A previous benefit-risk assessment was performed on omega-3 fatty acids and dioxins<ref>van der Voet H, Mul A, van Klaveren JD. A probabilistic model for simultaneous exposure to multiple compounds from food and its use for risk–benefit assessment. Food and Chemical Toxicology 2007: 45 (8) 1496-1506. https://doi.org/10.1016/j.fct.2007.02.009</ref>. The study found scenarios where consumption of herring in the Netherlands would bring benefits of omega-3 but dioxin exposure would remain below tolerable intake of the time (2 pg/kg/d).
 
Another study concluded that Atlantic herring provides cardiovascular health benefits at consumption levels where the dioxin cancer risk remains acceptable<ref>Sidhu KS. Health benefits and potential risks related to consumption of fish or fish oil. Regulatory Toxicology and Pharmacology 2003: 38 (3) 336-344. https://doi.org/10.1016/j.yrtph.2003.07.002</ref>. Also a Chinese study concluded that herring containing PCB7 concentrations 12.5 ng/g fresh weight  can be used regularly and get health benefits without significant contaminant risks<ref>Du Z-Y, Zhang J, Wang C, Li L, Man Q, Lundebye A-K, Frøyland L. Risk–benefit evaluation of fish from Chinese markets: Nutrients and contaminants in 24 fish species from five big cities and related assessment for human health. Science of The Total Environment 2012: 416: 187-199. https://doi.org/10.1016/j.scitotenv.2011.12.020</ref>. This equals approximately 2-3 pg/g fresh weight of TEQ, assuming that PCB7/TEQ ratio is similar in Finnish and Chinese herrings.
 
However, The National Food Agency of Sweden published a report with a conclusion that increased herring consumption would unnecessarily increase dioxin risks, while it is possible to eat fish with less dioxins (e.g. smaller herring) and gain the same health benefit<ref>Glynn A, Sand S and Becker W. Risk and Benefit Assessment of Herring and Salmonid Fish from the Baltic Sea Area. The Swedish Food Agency, Rapport 21 - 2013 [https://www.livsmedelsverket.se/globalassets/publikationsdatabas/rapporter/2013/2013_livsmedelsverket_21_risk_benefit_herring_salmonid_fish_ver2.pdf], Accessed 2 July 2019.</ref>. The report did not assess how consumption would change in practice, if Sweden abandoned the exemption to use large herring.
 
None of these benefit-risk assessments compared the magnitude of risks and benefits quantitatively on a common scale such as DALY. Also, there are still uncertainties about how genetic variation or intake matrix (food vs supplement) affects the health benefits of omega-3 fatty acids. Therefore, we conclude that there is a specific need to perform such quantitative assessments and explicitly address remaining uncertainties. Further, omitting benefits by focusing only on risks would be a larger bias than including benefits with uncertainties.
 
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 still 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<ref>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 22 Aug 2019.</ref>.
 
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)<ref>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 22 Aug 2019</ref><ref>Ignatius S, Delaney A, Haapasaari P. Socio-cultural values as a dimension of fisheries governance: the cases of Baltic salmon and herring. Forthcoming.</ref>. 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<ref>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</ref>.
 
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 healthy 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
: IHME: Institute for Health Metrics and Evaluation
: IQ: intelligence quotient
: ICES: International Council for the Exploration of the Sea
: TEF: toxic equivalency factor
: TEQ: toxic equivalency quantity
: TWI: tolerable weekly intake
: 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 and according to the national guidelines, there was no need for ethical approval<ref>National Advisory Board of Research Ethics. Ethical principles of research in the humanities and social and behavioural sciences and proposals for ethical review. Helsinki; 2009. https://www.tenk.fi/sites/tenk.fi/files/ethicalprinciples.pdf. Accessed 22 Aug 2019.</ref>. The consent to participate was obtained from the study participants in writing.
 
=== 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.
Changes to the revision 2019-09-24:


The datasets generated and analysed during the current study, together with the other material mentioned above, are available at  Open Science Framework research data repository by the Centre for Open Science. https://osf.io/brxpt/
In addition to the analysis presented in this paper, the survey was conducted for the purpose of a consumer perception and consumption study[11] and therefore only part of the survey results are presented in this paper.  


=== Competing interests ===
&rarr; The survey was designed and conducted for the purposes of this study and another study about consumer perception and consumption. The latter study[11] was published first, and it contains a more detailed description of the study methods, including the questionnaire.


The authors declare that they have no competing interests.
Due to these reasons and according to the national guidelines, there was no need for ethical approval[65].


=== Funding ===
&rarr; Due to these reasons and according to the national guidelines, there was no need for ethical approval.  (National Advisory Board of Research Ethics. Ethical principles of research in the humanities and social and behavioural sciences and proposals for ethical review. Helsinki; 2009. https://www.tenk.fi/sites/tenk.fi/files/ethicalprinciples.pdf.  Accessed 24 Sept 2019.)


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. Funding decision was based on a research plan submitted in an open call. After that, the funders did not have a say in the implementation of the study nor analysis, conclusions or publishing of the results.
22. Huan Yang, Pengcheng Xun, Ka He. Fish and Fish Oil Intake in Relation to Risk of Asthma: A Systematic Review and Meta-Analysis. PLOS November 12, 2013. https://doi.org/10.1371/journal.pone.0080048


=== Authors' contributions ===
22. Yang H, Xun P, He K (2013) Fish and Fish Oil Intake in Relation to Risk of Asthma: A Systematic Review and Meta-Analysis. PLOS ONE 8(11): e80048. https://doi.org/10.1371/journal.pone.0080048


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.
23. Asmaa S Abdelhamid, Tracey J Brown, Julii S Brainard, Priti Biswas, Gabrielle C Thorpe, Helen J Moore, Katherine HO Deane, Fai K AlAbdulghafoor, Carolyn D Summerbell, Helen V Worthington, Fujian Song, Lee Hooper. (2018) Omega‐3 fatty acids for the primary and secondary prevention of cardiovascular disease. Cochrane Systematic Review. https://doi.org/10.1002/14651858.CD003177.pub4


=== Acknowledgements ===
Abdelhamid  AS, Brown  TJ, Brainard  JS, Biswas  P, Thorpe  GC, Moore  HJ, Deane  KHO, AlAbdulghafoor  FK, Summerbell  CD, Worthington  HV, Song  F, Hooper  L. Omega‐3 fatty acids for the primary and secondary prevention of cardiovascular disease. Cochrane Database of Systematic Reviews 2018, Issue 11. Art. No.: CD003177. DOI: 10.1002/14651858.CD003177.pub4.


We thank all BONUS GOHERR researchers and stakeholder meeting participants, who participated in lively discussions about the importance of Baltic fisheries management and health.
24. Zheng J, Huang T, Yu Y, Hu X et al. Fish consumption and CHD mortality: an updated meta-analysis of seventeen cohort studies. Public Health Nutrition (2012) 15:4:725-737. DOI: https://doi.org/10.1017/S1368980011002254


=== Authors' information ===
61. Ignatius S, Delaney A, Haapasaari P. Socio-cultural values as a dimension of fisheries governance: the cases of Baltic salmon and herring. Forthcoming.


No specific information.
Ignatius, S. H. M., Haapasaari, P. E., & Delaney, A. (2017). Socio-cultural values as a dimension of fisheries management: the cases of Baltic salmon and herring. 52. Abstract from BONUS SYMPOSIUM: Science delivery for sustainable use of the Baltic Sea living resources, Tallinna, Estonia.


== Figures, tables and additional files ==
'''Additional changes due to an additional round of minor improvements


Figure 1. Study countries around the Baltic Sea.
* Ethical permission was not needed in any of the study countries, according to national guidelines for research and data protection: Finland, Sweden (Lag (2003:460) om etikprövning av forskning som avser människor [https://www.riksdagen.se/sv/dokument-lagar/dokument/svensk-forfattningssamling/lag-2003460-om-etikprovning-av-forskning-som_sfs-2003-460]), Denmark (National Videnskabsetisk Komité. Hvad skal jeg anmelde? http://www.nvk.dk/forsker/naar-du-anmelder/hvilke-projekter-skal-jeg-anmelde), and Estonia (Riigi Teataja. Isikuandmete kaitse seadus RT 2007, 24, 127. https://www.riigiteataja.ee/akt/130122010011). Accessed 28 Nov 2019.


Figure 2. 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 3. 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 4. Individual change in consumption after policies to either increase or reduce fish intake.
: Caption: Individuals' fish intake after all consumption policies
Figure 5. Cumulative dioxin exposure distributions shown by subgroup and country.
: Caption: Exposure to dioxin from Baltic fish
Figure 6. Disease burden attributable to eating Baltic fish in Denmark, Estonia, Finland, and Sweden (expected value at individual level). Note that negative values mean improved health. mDALY: 0.001 disability-adjusted life years, CHD: coronary heart disease, IQ: intelligence quotient.
: Caption: Disease burden attributable to Baltic fish by country, group, and policy
Figure 7. Outcome of interest using different objectives. The default net health assessment (the main assessment of this article) assumes background intake of omega-3 fatty acids, which reduces the marginal benefit of Baltic fish. The second assessment assumes no background. Tolerable weekly intakes from 2001 and 2018 are converted to DALYs based on the number of people exceeding the guidance value.
: Caption: Disease burden using different objectives
Figure 8. Burden of disease of the most important environmental health factors in Finland. BONUS GOHERR results are from this study, others from a previous publication<ref name="asikainen2013">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 22 Aug 2019.</ref>.
: Caption: Environmental disease burden in Finland
== References ==
<references/>
== See also ==
* Model run 28.3.2019: uncertainty added to tooth defect ERF, child IQ disability weight updated [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=gdyr6DQa4s9hshoo]
** Country codes changed to DK EE FI SE already from the upstream
* Model run with better parameter tables 29.3.2019 [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=KaQsyaPnYQxj0DiD]
* Final figure and VOI runs for the article manuscript based on this [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=HxrrEkAIxDJeX8tF main model].
** Cons.policy and Background (10000 iterations) [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=W2Pk5BilOCPCBoR5]
** Cons.policy, Background, and Luke.scaling (5000 iterations) [http://en.opasnet.org/en-opwiki/index.php?title=Special:RTools&id=6TBA6p1aaG2XNmHH]
=== Code for figures and tables ===
<rcode label="Code for figures and tables" graphics=1>
# This is code Op_en7919/ on page [[Health effects of Baltic herring and salmon: a benefit-risk assessment]]
library(OpasnetUtils)
library(ggplot2)
library(reshape2)
############# VOI functions
objects.latest("Op_en2480",code_name="VOI") # [[Value of information]] VOI, binning
####### Download data
##### Model runs 13.10.2018 / 27.3.2019
runs <- c(
  '153942283661', #  'u2Iv3zi04KfafzeL' # Long timeline 1990-2018 (5000 iter)
  '155388872848', #  'AiLDacsNvauZRJZh' # Select.size Ban large/New products (5000 iter)
  '153942751277', #  '0kRBZhcTLbkeQwoL' # Time 2009+2018, Select.size All sizes (5000 iter)
  '153942016925', #  '5x2FBxDCgP0UNsRD' # Background yes/no, Limit 0 or 3 g/d (5000 iter)
  '155385954551', #  'TgnBCdDI0glk6Eur' # Background, Cons.policy (Info.improvements, Recommendations) (5000 iter)
  '153942475082', #  'By1XVhwN4l6siUjP' # Mixtures (5000 iter)
  '153942148644', #  'PGei5FcnuUPwcTcz' # Exposure-response functions (100 iterations)
  '155398213810', #  'ZG61hjn7IP3t4Z57' # Cons.policy, Background, Luke scaling (5000 iter)
  '155401096341'  #  'HxrrEkAIxDJeX8tF' # Background, Cons.policy (10000 iterations)
  #  '' # '' # Cons.policy (Info.improvements, Recomm.herring, Recomm.salmon) (5000 iter)] but with mc2d=TRUE
)
############### Limits and recommendations
recom <- data.frame(
  Line=c(rep("Concentration limit",2),"TWI 2001","TWI 2018","MeHg TWI","recommended vitamin D"),
  Exposure_agent=c("PCDDF",rep("TEQ",3),"MeHg","Vitamin D"),
  Compound=c("PCDDF",rep("TEQ",3),"MeHg","Vitamin D"),
  Unit=c(rep("pg/g fresh weight",2),rep("pg/kg/week",2),"µg/d","µg/d"),
  Result=c(
    3.5, # http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:02006R1881-20140701
    6.5,
    14, # SCF 2001 TWI 14 pg /kg /week
    2,  # EFSA 2018 TWI 2 pg/kg/week
    13, # EFSA 2012: 1.3 µg/kg/wk, assuming 70 kg
    7.5 # Recommendation for adults in Finland
  )
)
###### Preprocess
remzero <- function(n){
  gsub("\\.0+$","", format(n, scientific = FALSE))
}
groups <- function(o) {
  o$Group <- factor(paste(o$Gender, o$Ages), levels = c(
    "Female 18-45",
    "Male 18-45",
    "Female >45",
    "Male >45"
  ))
  #  o$Country <- factor(as.character(o$Country))
  if("Resp" %in% colnames(o@output)) {
    levels(o$Resp)[levels(o$Resp)=="Dioxin TWI"] <- "TWI 2001"
    o$Resp <- factor(o$Resp, levels=c(
      "Heart (CHD)",
      "Stroke",
      "Vitamin D intake",
      "Child's IQ",
      "Infertility",
      "Tooth defect",
      "Cancer",
      "TWI 2001",
      "TWI 2018"
    ))
  }
  return(o)
}
BS <- 30
policies <- c(
  "Info.improvements",
  "Recomm.herring",
  "Recomm.salmon",
  "Cons.policy",
  "Limit",
  "Select.size",
  "Time",
  "Background",
  "Mixtures"
)
BAU <- Ovariable(
  "BAU",
  output = data.frame(
    Cons.policy = "BAU",
    Info.improvements = "BAU",
    Recomm.herring = "BAU",
    Recomm.salmon = "BAU",
    Limit = "No limit",
    Select.size="BAU",
    Time="2018",
    Background="Yes",
    Mixtures="BAU",
    BAUResult = 1
  ),
  marginal = c(rep(TRUE,8),FALSE)
)
BAUt <- BAU
BAUt$Time <- NULL
varit12 <- c(
  '#519b2f', # tummanvihreä
  '#29a0c1', # syaaninsininen
  '#be3f72', # rubiininpunainen
  '#faa61a', # oranssi
  '#2f62ad', # tummansininen uudet THL-värit, koko väripaletti.
  '#7bc143', # vaaleanvihreä
  '#cc77ac', # roosa
  '#606060', # tumma harmaa
  '#c3c2c6', # keskiharmaa
  '#dcdfe2', # vaalea harmaa
  '#cc7acc', # 7
  '#244911' # 12
)
colors <- c("#74AF59FF","#2F62ADFF","#D692BDFF","#29A0C1FF","#BE3F72FF","#FBB848FF") # From [[Goherr: Fish consumption study]]
varit12 <- c(colors, varit12[7:12])
##############  Draw graphs
########## Concentration
objects.get(runs[1]) # Long timeline
### Figure 2.
fig3 <- ggplot((BAUt*conc[
  conc$Exposure_agent %in% c("Vitamin D", "MeHg","TEQ","Omega3") & conc$Iter %in% 1:1000,])@output,
  aes(x=concResult, colour=Fish, linetype=Time))+
  stat_ecdf(size=1.5)+theme_grey(base_size=BS)+
  facet_wrap(~Exposure_agent, scales="free_x")+
  scale_x_log10(labels=remzero)+
  labs(
    #    title="Concentrations of compounds in Baltic fish",
    x="Concentration (TEQ: pg/g; MeHg, vit D: µg/g; omega3: mg/g [f.w.])",
    y="Cumulative probability"
  )+scale_colour_manual(values=varit12)+
  theme(axis.text.x=(element_text(hjust=1)))
cat("Disease burden of infertility at individual level (mDALY/a per person)\n")
oprint(oapply(
  groups(BoD[BoD$Resp=="Infertility"&BoD$Time=="2018",]*
          info[info$Gender=="Female"&info$Ages=="18-45",]),
  c("Resp","Time","Country","Group"),
  mean
)@output)
cat("Disease burden of infertility at population level (DALY/a)\n")
oprint(oapply(
  groups(BoDRaw[BoDRaw$Resp=="Infertility"&BoD$Time=="2018",]*
          info[info$Gender=="Female"&info$Ages=="18-45",]),
  c("Resp","Time","Country","Group"),
  mean
)@output)
############### Value of information for Select.size
objects.get(runs[2]) # Select.size, 5000 iterations
cat("VOI for BoDRaw and Select.size.\n")
tmp <- oapply(
  groups(info*BoDRaw[!grepl("TWI", BoDRaw$Resp) & BoDRaw$Background=="Yes" , ]),
  INDEX = c("Iter","Select.size","Group","Country"),
  FUN = sum
)
oprint(VOI(
  tmp,
  decision = c("Select.size")
))
##### Amount of consumption
if(TRUE) { # TRUE: direct study results, FALSE: average amount scaled to match Luke statistics
  objects.get(runs[9]) # Info.improvement, Recommendations, i.e. Cons.policy, Background
} else {
  objects.get(runs[8]) # Info.improvement, Recommendations, i.e. Cons.policy, Background, Luke scaling
}
cat("Indices used in BoD\n")
tmp <- setdiff(colnames(BoD@output)[BoD@marginal],"Iter")
oprint(data.frame(
  Index = tmp,
  Locations = sapply(tmp, function(i) paste(unique(BoD@output[[i]]),collapse=", "))
))
amount <- groups(amount)*365.25/1000 # g/d --> kg/a
cat("Fish consumption average (kg/a).\n")
oprint(oapply(amount*BAU,c("Country","Fish"),mean)@output)
oprint(oapply(amount*BAU,c("Country","Group","Fish"),mean)@output)
amount.diff <- unkeep(amount*BAU, prevresults=TRUE,sources=TRUE, cols=policies)
amount.diff$Amorig <- amount.diff$Result
amount.diff <- amount - amount.diff
amount.diff <- amount.diff[
  amount.diff$Cons.policy %in% c("Increase","Reduce"),]
if(nrow(amount.diff@output)>0) {
 
  # Figure 4.
 
  fig10 <- ggplot(amount.diff@output, # Changes >10 g/d=3.6 kg/a are uninteresting.
                  aes(x=Amorig, y=Result, colour=Group))+
    geom_point(data=amount.diff@output[amount.diff$Iter %in% 1:1000,])+
    geom_smooth()+
    theme_gray(base_size=BS)+facet_grid(Fish~Cons.policy)+
    theme(legend.position="bottom")+
    coord_cartesian(ylim=c(-4,8),xlim=c(0,4))+
    labs(
      #      title="Individuals' fish intake after all consumption policies",
      x="Current fish intake (kg/a)",
      y="Change in fish intake (kg/a)"
    )+scale_colour_manual(values=varit12)
 
  # Figure 3.
 
fig12 <- ggplot((BAU*amount+0.05)@output, aes(x=Result, colour=Country))+
  stat_ecdf(size=1.5)+
  theme_grey(base_size=BS)+
  theme(legend.position = "bottom")+
    facet_grid(Fish~Group, scales="free_x")+scale_x_log10(labels=remzero)+
    labs(
      #    title="Consumption of Baltic fish by country and subgroup",
      x="Fish consumption of a random individual (kg/a)",
      y="Cumulative probability"
    )+scale_colour_manual(values=varit12)
}
######## Exposure
tmp <- groups(unkeep(exposure[
  paste(exposure$Exposure_agent, exposure$Exposure) %in% c(
    "MeHg To child",
    "Vitamin D To eater",
    "Omega3 To eater",
    "TEQ To eater"
  ),],sources=TRUE) * info)
# Figure 5.
fig15 <- ggplot((7/70*BAU*tmp[tmp$Exposure_agent=="TEQ",]+0.1)@output,
                aes(x = Result, colour=Country))+
  stat_ecdf(size=1.5) + scale_x_log10(labels=remzero) +
  theme_gray(base_size = BS)+
  theme(legend.position = "bottom")+
  geom_vline(data=recom[recom$Unit=="pg/kg/week",], aes(xintercept=Result, linetype=Line), colour="red", size=1)+
  facet_wrap(~ Group)+
  labs(
    #    title="Exposure to dioxin from Baltic fish",
    y="Cumulative probability",
    x = "Exposure (pg/kg/week)",
    colour="",
    linetype=""
  )+scale_colour_manual(values=varit12)
#### Burden of disease
BoD <- groups(BoD)
# Figure 6.
tmp <- oapply(
  BoD[!grepl("TWI", BoD$Resp) & BoD$Background=="Yes",],
  INDEX=c("Resp","Group","Country","Cons.policy"),
  mean
)@output
fig22 <- ggplot(tmp,
  aes(x=Group, weight=BoDResult, fill=Resp))+
  geom_bar()+facet_grid(Cons.policy ~ Country)+
  theme_grey(base_size=BS-8)+
  theme(
    legend.position = "bottom",
    axis.text.x = element_text(angle = -90)
  )+
  labs(
    #    title="Disease burden of Baltic fish by country, group, and Cons.policy",
    y = "Disease burden (mDALY/a per person)",
    fill=""
  )+
  scale_fill_manual(values=varit12)
######### Figure 7.
tmp <- BoD
tmp@output <- rbind(
  cbind(tmp@output,Focusgroup = "All"),
  cbind(tmp@output[tmp$Group=="Female 18-45",],Focusgroup = "Young women")
)
tmp <- oapply(
  tmp[tmp$Cons.policy=="BAU",],
  INDEX=c("Resp","Background","Focusgroup","Country"),
  mean
)@output
tmp <- rbind(
  cbind(
    Objective="Net health default",
    tmp[tmp$Background=="Yes" & !grepl("TWI",tmp$Resp),]
  ),
  cbind(
    Objective="Net health, no bg",
    tmp[tmp$Background=="No" & !grepl("TWI",tmp$Resp),]
  ),
  cbind(
    Objective=tmp$Resp,
    tmp
  )[grepl("TWI",tmp$Resp),]
)
sums <- aggregate(tmp["BoDResult"], by=tmp[c("Objective","Focusgroup","Country")],sum)
fig22b <- ggplot(tmp, aes(x=Objective, weight=BoDResult))+
  geom_bar(aes(fill=Resp))+facet_grid(Focusgroup ~ Country)+
  theme_grey(base_size=BS-8)+
  theme(
    legend.position = "bottom",
    axis.text.x = element_text(angle = -90, hjust=0)
  )+
  geom_text(data=sums, size=6, aes(label=round(BoDResult,1),y=pmax(1,BoDResult+1)))+
  labs(
    #    title="Disease burden using different objectives",
    y = "Disease burden (mDALY/a per person)",
    fill=""
  )+
  scale_fill_manual(values=varit12)
############### Value of information for Cons.policy
cat("VOI for BoDRaw and Cons.policy.\n")
tmp <- oapply(
  groups(info*BoDRaw[!grepl("TWI", BoDRaw$Resp) & BoDRaw$Background=="Yes" , ]),
  INDEX = c("Iter","Cons.policy","BoDSource","Group","Country"),
  FUN = sum
)
oprint(VOI(
  tmp,
  decision = c("Cons.policy"),
  scenario = "BoDSource"
))
cat("VOI for BoDRaw and Cons.policy for separate Groups.\n")
oprint(VOI(
  tmp,
  decision = c("Cons.policy"),
  scenario = "Group"
))
################ Comparison to other environmental health risks
# Disease burden statistics for Finland
a<-opbase.data("Op_fi3944", subset="Tautitaakka Suomessa THL:n tutkimuksessa")
a$Result <- as.numeric(as.character(a$Result))
a <- a[order(-a$Result) , ]
lab <- c(
  "Fine particles, death",
  "Noise, annoyance+sleep disorder",
  "Indoot radon, cancer",
  "Environmental tobacco smoke, CHD",
  "UV radiation, cancer",
  "Fine particles, chr bronchitis",
  "Fine particles, symptom days",
  "BONUS GOHERR estimate",
  "MeHg in fish, IQ in children",
  "Moisture in homes, asthma",
  "Moisture in homes, respir symptoms",
  "Lead, IQ in children",
  "Environmental tobacco smoke, cancer"
)
levels(a$Impact)[1:12] <- lab[-8]
levels(a$Source)[levels(a$Source)=="Food"] <- "Food source"
# Figure 8.
tmp <- BoDRaw * BAU * info
tmp <- oapply(
  tmp[tmp$Country=="FI" & !tmp$Resp %in% c("Dioxin TWI", "TWI 2018"),],
  cols="Iter",
  FUN=mean
)@output
levels(tmp$Resp)[levels(tmp$Resp) %in% c("Cancer","Infertility","Tooth defect")] <- "Dioxin risk"
colnames(tmp)[colnames(tmp)=="Resp"] <- "Source"
tmp$Impact <- "BONUS GOHERR estimate"
tmp <- orbind(tmp,a[a$Result >= 380 , ])
tmp$Impact <- factor(tmp$Impact, lab)
tmp$Source <- factor(tmp$Source, sort(levels(tmp$Source)))
fig28 <- ggplot(data=tmp, aes(x=Impact, weight=Result,fill=Source))+
  geom_bar() +
  coord_flip() +
  theme_gray(base_size = BS) +
  labs(
    #    title = "Environmental disease burden in Finland",
    x = "",
    y = "Disease burden (DALY/a in whole population)"
  ) +
  scale_fill_manual(values = varit12[c(4,5,8,1,6,7,2,3)]) +
  theme(legend.position = c(0.8, 0.7)) +
  guides(fill=guide_legend(title=NULL))+
  theme(axis.title.x = element_text(hjust=1))
################### Calculate ratio of benefits and risks at individual level
ratio <- groups(BoD[!grepl("TWI",BoD$Resp), ] * BAU * info)
ratio$Type <- ifelse(result(ratio)>0,"risk","benefit")
ratio <- oapply(ratio, c("Type","Country","Iter","Group"),sum)
net <- ratio[ratio$Type=="benefit" , colnames(ratio@output)!="Type"] +
  ratio[ratio$Type=="risk" , colnames(ratio@output)!="Type"]
net@name <- "net"
colnames(net@output)[colnames(net@output)=="Result"] <- "netResult"
ratio <- -1 * ratio[ratio$Type=="benefit" , colnames(ratio@output)!="Type"] /
  ratio[ratio$Type=="risk" , colnames(ratio@output)!="Type"]
ratio@name <- "ratio"
colnames(ratio@output)[colnames(ratio@output)=="Result"] <- "ratioResult"
ratio <- ratio + net
ratio$Rank <- rank(ratio$ratioResult)
cat("Benefit/risk ratio calculated at individual level and averaged for Groups and Countries.\n")
oprint(oapply(ratio, c("Group","Country"),mean)@output)
ggplot(ratio@output,aes(x=ratioResult, colour=Group))+stat_ecdf()+
  scale_x_log10()+geom_vline(xintercept=1)
ggplot(ratio@output,aes(x=netResult, colour=Group))+stat_ecdf()
ggplot(ratio@output,aes(x=netResult,y=ratioResult,colour=Group))+
  geom_point()+
  geom_density_2d()+
  scale_y_log10()+geom_hline(yintercept=1)
#  coord_cartesian(xlim=c(-2,2))
#  facet_wrap(~Country)
ggplot(ratio@output[ratio$Group=="Female 18-45",][1:100,],
      aes(x=ratioResult,y=Rank,colour=abs(netResult)))+
  geom_point(size=3,shape=21)+
  scale_x_log10()+geom_vline(xintercept=1)
#############################################
paramtable <- function(ova, INDEX) {
  tmp <- cbind(
    oapply(
      ova,
      INDEX=INDEX,
      FUN=function(x) quantile(x, c(0.025, 0.5, 0.975))
    )@output,
    oapply(
      ova,
      INDEX=INDEX,
      FUN= mean
    )@output
  )
  tmp1 <- gsub("\\.$","",gsub("(0*)$", "", sprintf("%.6f", signif(tmp[[(length(INDEX)+1)*2]], 2))))
  tmp2 <- gsub("\\.$","",gsub("(0*)$", "", sprintf("%.6f", signif(tmp[[length(INDEX)+1]][,1], 2))))
  tmp3 <- gsub("\\.$","",gsub("(0*)$", "", sprintf("%.6f", signif(tmp[[length(INDEX)+1]][,3], 2))))
  tmp$Estimate <- ifelse(tmp1==tmp2 & tmp2==tmp3,tmp1, paste0(tmp1, " (",tmp2, ", ",tmp3,")"))
  return(tmp[c(INDEX,"Estimate")])
}
cat("Exposure response functions: mean (95 % CI)\n")
oprint(paramtable(ERF,c("Exposure_agent","Resp")))
cat("Total burden of disease of selected causes (DALY): mean (95 % CI)\n")
oprint(paramtable(BoDt,c("Country","Resp")))
cat("Burden of disease per case (DALY): mean (95 % CI)\n")
oprint(paramtable(disabilityweight,c("Resp")))
################ Store results
images <- c("expotable", "varit12", ls()[grepl("fig",ls())])
if(FALSE) {
  objects.store(list=images)
  cat("Objects", images, "stored.\n")
}
pre <- "" # "Goherr benefit-risk assessment "
post <- ".pdf"
do <- FALSE
fig3
if(do) ggsave(paste0(pre,"Figure 2",post), width=15, height=10.5)
fig12
if(do) ggsave(paste0(pre,"Figure 3",post), width=14, height=10.5)
fig10
if(do) ggsave(paste0(pre,"Figure 4",post), width=11.5, height=10.5)
fig15
if(do) ggsave(paste0(pre,"Figure 5",post), width=11.5, height=10.5)
fig22
if(do) ggsave(paste0(pre,"Figure 6",post), width=11.5, height=10.5)
fig22b
if(do) ggsave(paste0(pre,"Figure 7",post), width=14, height=10.5)
fig28
if(do) ggsave(paste0(pre,"Figure 8",post), width=14, height=10.5)
</rcode>
* [https://www.livsmedelsverket.se/om-oss/press/nyheter/pressmeddelanden/efsa-skarper-bedomningen-av-dioxiner-och-pcb Swedish Food Safety Authority about EFSA dioxin assessment]


=== Code for estimating TEQ from chinese PCB7 ===
=== Code for estimating TEQ from chinese PCB7 ===

Latest revision as of 13:58, 28 November 2019


Health effects of nutrients and environmental pollutants in Baltic herring and salmon: a quantitative benefit-risk assessment is a research manuscript about the Goherr assessment performed on the BONUS GOHERR project between 2015-2018. The manuscript was submitted to BMC Public Health [2]. Thank you for your interest.

Changes to the revision 2019-09-24:

In addition to the analysis presented in this paper, the survey was conducted for the purpose of a consumer perception and consumption study[11] and therefore only part of the survey results are presented in this paper.

→ The survey was designed and conducted for the purposes of this study and another study about consumer perception and consumption. The latter study[11] was published first, and it contains a more detailed description of the study methods, including the questionnaire.

Due to these reasons and according to the national guidelines, there was no need for ethical approval[65].

→ Due to these reasons and according to the national guidelines, there was no need for ethical approval. (National Advisory Board of Research Ethics. Ethical principles of research in the humanities and social and behavioural sciences and proposals for ethical review. Helsinki; 2009. https://www.tenk.fi/sites/tenk.fi/files/ethicalprinciples.pdf. Accessed 24 Sept 2019.)

22. Huan Yang, Pengcheng Xun, Ka He. Fish and Fish Oil Intake in Relation to Risk of Asthma: A Systematic Review and Meta-Analysis. PLOS November 12, 2013. https://doi.org/10.1371/journal.pone.0080048

22. Yang H, Xun P, He K (2013) Fish and Fish Oil Intake in Relation to Risk of Asthma: A Systematic Review and Meta-Analysis. PLOS ONE 8(11): e80048. https://doi.org/10.1371/journal.pone.0080048

23. Asmaa S Abdelhamid, Tracey J Brown, Julii S Brainard, Priti Biswas, Gabrielle C Thorpe, Helen J Moore, Katherine HO Deane, Fai K AlAbdulghafoor, Carolyn D Summerbell, Helen V Worthington, Fujian Song, Lee Hooper. (2018) Omega‐3 fatty acids for the primary and secondary prevention of cardiovascular disease. Cochrane Systematic Review. https://doi.org/10.1002/14651858.CD003177.pub4

Abdelhamid AS, Brown TJ, Brainard JS, Biswas P, Thorpe GC, Moore HJ, Deane KHO, AlAbdulghafoor FK, Summerbell CD, Worthington HV, Song F, Hooper L. Omega‐3 fatty acids for the primary and secondary prevention of cardiovascular disease. Cochrane Database of Systematic Reviews 2018, Issue 11. Art. No.: CD003177. DOI: 10.1002/14651858.CD003177.pub4.

24. Zheng J, Huang T, Yu Y, Hu X et al. Fish consumption and CHD mortality: an updated meta-analysis of seventeen cohort studies. Public Health Nutrition (2012) 15:4:725-737. DOI: https://doi.org/10.1017/S1368980011002254

61. 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. H. M., Haapasaari, P. E., & Delaney, A. (2017). Socio-cultural values as a dimension of fisheries management: the cases of Baltic salmon and herring. 52. Abstract from BONUS SYMPOSIUM: Science delivery for sustainable use of the Baltic Sea living resources, Tallinna, Estonia.

Additional changes due to an additional round of minor improvements


Code for estimating TEQ from chinese PCB7

See Du et al, 2012.

+ Show code

Retrieved from "https://en.opasnet.org/index.php?title=Health_effects_of_Baltic_herring_and_salmon:_a_benefit-risk_assessment&oldid=43464"