Talk:Benefit-risk assessment of Baltic herring and salmon intake

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EFSA information session on dioxin 13.11.2018

Event: EFSA information session on opinion on dioxins and dioxin-like PCBs oin food and feed
Location: EFSA, Parma
Notes by Jouni Tuomisto (not approved by anyone)
See also a blog text that was inspired by this meeting (in Finnish)

Comments will be published together with meeting notes. Links will be added here when they are available.

Juliane: Some people were unhappy that this meeting is not about scientific discussion about the issue but only an information event.

Italy: PCBs should be separated from dioxins because different issue.

PROMETHEUS: promoting methods for evidence use in science. An internal approach in EFSA.

  • Make an assessment plan
  • Perform plan
  • Document work
  • Document deviations

Luisa Bordajandi: Comments from member states

FoodEx2 was not used. Why? [This is a food classification system develped by EFSA]

Because data was collected when FoodEx1 was in place.

Zsuzsanna Horvàth: Assessment done

  • Data with 35000 food and feed samples was analysed.
  • FoodEx2 system was used.
  • After data cleaning, 20000 samples were available.
  • Only 43 samples from Finland!
  • Most samples from Germany, France and Norway.
  • Food consumption surveys: EFSA Comprehensive European Food Consumption Database.

Peter Fürst: Time trends of dioxin

  • During the last 10 years, there has not been a substantial decrease in dioxin exposure.
  • Dioxin concentration decrease is leveling out in dairy in Germany.

Jouni: Will you publish the original concentration and food consumption data? SHOULD, it is worth (tens of) millions of euros.

Juliane: Advisory forum is planning to open the raw data, and member states seem to be accepting.

Germany: Why did EFSA not use mother's milk because that is the most relevant?

Astrid Bulder RIVM: Foodex1 was used. Why not Foodex2? Zsuzsanne: Data is from 2010-2016 when Foodex1 was used.

Helle Knutsen: Human studies on dioxin

  • Russian children's study cannot distinguish between pre- and postnatal exposures.
  • High exposures to HCB in Russia because that was the product of the nearby plant. (25% percentile in study was 8 times higher than median in the U.S.)
  • Typically, adjusting for HCB, beta-HCH, DDE or NDL-PCB will result in steeper dose-response.
  • Retention time for pubertal maturity related to dioxin exposure. Not used in risk assessment, however, because only one study available.
  • Inclusion of pubertal delay would probably not have affected the TWI.

Ron Hoogendoom: Uncertainties linked to TEF.

  • PCB-126 contribues 55 % to total TEQ in Russian Children's Study (RCS).

EU-SYSTEQ study looked at TEFs and found that TEF of PCB-126 is lower than current 0.1, rather 0.003.

  • There are two possible approaches to TEFs. [Which?]

Italy: RCS takes place in an unfortunate place with lots of other exposures. Lead changes the intercept of the ERF curve and therefore this study cannot be used to estimate ERF for non-lead exposure situations. [Not clear how.] AHR mechanism is not enough demonstrated in this endpoint to be used. TWI derived from sperm concentration should not be used for all consumers but on the target group only.

Lars Rylander: RCS is not optimal. But we need to talk about importance of confounders.

Helle: I don't see your point related to lead in semen quality.

Dick, NL: I share some concerns of Italy. If you add TCDD+PCB in Seveso studies, what happens? There is probably effect of confounding [in RCS?]. Because of confounding, Seveso studies are better.

Ron: We only have TCCD from Seveso, so we cannot study other congeners. But I am not sure that this demonstrates the effect of confounding. EPA used the second Seveso study and derived a very low RfD.

Germany: If we look at RCS ERF, we do not know sensitive time window. So that's the real concern rather than confounders.

Lars (Barregård?, on phone): RCS had lead levels 3 ug/dl, but semen effects have only been seen above 10 ug/dl.

Ron: We normally always use the most sensitive age group and derive TWI based on that. It is interesting whether TWI should be applied to the whole population when it comes to e.g. fish consumption.

Juliane: Yes, but that issue is more for WHO to discuss rather than EFSA.

Astrid: The most sensitive endpoint drives the TWI, but when you make a risk assessment and give recommendations, we should have a broader risk assessment.

Helle: We come back to this after toxicokinetics. But this issue applies to fertile women, which is not a small subpopulation.

Ron: That's always the case that we need to consider populations vs subpopulations.

Germany: What do you say about critical windows where the impact occurs only after several years?

Ron: Interesting and important.

Helle: There is an ongoing study in Seveso about the second generation effects. No results yet.

Lunch discussion: Dieter: I don't believe the RCS results at all. The exposure assessment of alcohol and smonking is based on questionnaire and poor questions even. It is a weak study. There is good evidence about sperm effects, but ERF curve based on RCS is implausible (too much left).

Ron: Toxikokinetic modelling

  • Emond model was updated to R but does not estimate blood-fat ratio correctly and was therefore discarded.
  • CADM model was used. Modifications: children have same elimination rate; apparent difference is due to growth.
  • We stick to weekly basis (rather than monthly) because peak exposures during a day may be important but we don't know for sure.
  • Okay intake levels: 0.25 pg/kg/d for mother, 0.5 pg/kg/d for child.
  • If you look at the tooth defect, you would come to EHDI of 3 rather than 2 pg/kg/wk. So sperm concentration is not that far from the tooth effects.
  • If you use Faqi 1998 study, you will end up to 3 pg/kg/wk.
  • With Jämsä 2001 study on bone effects would give you ca 4 pg/kg/wk.

Jouni: What if the critical window is at 12 mo rather than 9 years?

Ron: If the breast feeding patterns is the same, the conclusion is the same.

Jouni: You could have looked at Alaluusua 1996 study that had good breast feeding estimates. Or compare to animal studies that have good toxicokinetics.

Ron/Helle: RCS did not have good breastfeeding data, only that it was 7.5 mo on average. There is new work that could be done on this.

Marco B: Now we have epi data on sperm concentrations, backed up by animal studies.

Germany: If we did not have a problem during earlier years, how can we have a problem now when the levels are much lower?

Astrid: I agree with Germany. We wonder the aplicability of TWI when it is calculated back to mother. We are uncomfortable with this to apply it to all population groups.

Ron: two-fold exposure in children is not an assumption, that's what we see in data.

Peter Fürst: Uncertainty and recommendations

  • Concentration data was produced to check for compliance, so it focussed on high values rather than measuring small concentrations reliably.
  • Feed data are needed because all crises have origninated from dioxin in feed.
  • Country-specific risk-benefit assessments should be done.

Iceland: How applicable is this TWI?

Leondios Greeece: There is a lot of feed and food coming from abroad, and this is not reported to EFSA.

Peter: Why do you not report it? We in Germany do not differentiate, if it is on the market.

Zsuzsanna: We have all kinds of samples of imported products. We are investigating country differences.

Jouni: Good to have benefit-risk assessment as recommendation for further work. We have done one, including EFSA estimates on sperm concentrations, and it had surprisingly little impact. EFSA should do those, too.

Jouni: I recommend to promote the use of quantitative estimates of uncertainty rather than pluses or minuses about possible direction of bias.

Juliane(?): That will be for the future.

[Jouni: I have discussed this since 2005, so we ARE in the future already.]

Klaus Abraham Germany: BfR opinion. 6 questions

  1. How robust is the parameter 'sperm concentration'?
  2. What is the critical time window?
  3. What is the mode of action?
  4. How do Seveso studies contribute to the effect?
  5. Does RCS provide an appropriate basis for health-based guidance value?
  6. Does dioxin effect align with the semen quality trends in Western countries?
  • In summary, sperm concentration is tricky and the 3 studies together are weak. Critical window is not known.
  • Priskorn study 2018: Danish sperm concentrations do not show change in 1996-2016 despite decrease in maternal smoking and dioxin. Why?

Jouni: We are developing a method to organise scientific discussions and disputes. I'd like to use this material as an example.

Tino, EFSA: Communicaton plan

Juliane: We'd like to publish comments. Bu we'd like to not express conflicting messages to public.

Germany: Of course there will be conflicting messages.

Italy: I agree with Germany.

Jouni: There will be conflict of messages because we have concentration results showing that dioxins are safer than ever. EFSA implies that dioxins are more dangerous than ever. We should increase understanding and show that different conclusions may be done from the same data.

Frans (phone): Milk formula factories will jump to this as it implies that mother's milk may be a health hazard.

Juliane: Meeting minutes will be sent to you this week. We must publish the opinion very soon: 19.11. communication, 20.11. publication, and that will happen before meeting minutes are published (sent to you next week, a few weeks for comments).

Key contacts for the future:

  • Juliane Kleiner, chairman. Ask for concentration data.
  • Helle Knutsen, from Norway, talk with her about health impact.
  • Szuzsanne, ask for the conc data.
  • Dieter, greetings. Ask for comments about my synthesis.

Tony (EFSA; find contact info!): Uncertainty meeting by EFSA and ??. Probabilistic issues. See EFSA website. Around Feb 19-20, 2019.

Needs and updates 8.6.2017

Goherr: Fish consumption study

  • Amountissa on 2x liikaa olisiko assumpUnit ongelmana. Siellä kirjoitusvirhe: AssumpUnit -> assumpUnit. ←--#: . OK (type: truth, paradigm: science view) --Jouni (talk) 13:02, 8 June 2017 (UTC)

Benefit-risk assessment of Baltic herring and salmon intake

  • Ihd erf muutettava relative hill:ksi. RR ei voi mennä lähelle nollaa. BRA-annosvastetaulukkoon CHD2 mortality ja Relative Hill. ←--#: . OK (type: truth, paradigm: science view) --Jouni (talk) 13:02, 8 June 2017 (UTC)
    • Ihd af on järjetön ilmeisesti RR:stä johtuen. Tarkista kun annosvaste on korjattu.
  • D vit on mg eikä ug vaikka erf on ug. Korjaa conciin /100 -> *(1000/100)←--#: . OK (type: truth, paradigm: science view) --Jouni (talk) 13:02, 8 June 2017 (UTC) ⇤--#: . Tämä muuttaa mysö Omega3 pitoisuudet ug:ksi vaikka ne pitäisi säilyä mg (type: truth, paradigm: science view) --Arja (talk) 06:17, 14 June 2017 (UTC)
  • Exposure kuva alkakoon 1e-2 koodissa BRA/←--#: . OK (type: truth, paradigm: science view) --Jouni (talk) 13:02, 8 June 2017 (UTC)
  • Tarkasta missä responsien nimiä käytetään ettei tule mismatch.
  • Tee ERFs of interst-taulukkoon uusi sarake Resp jossa käyttönimet. Laskennassa kuitenkin käytetään Responsea paitsi BoDt ja disabilityweight (taulukko DALYs of responses), joissa muunnettava nimet joka tapauksessa.←--#: . OK (type: truth, paradigm: science view) --Jouni (talk) 13:02, 8 June 2017 (UTC)
  • Muuta tooth defect käyttämään ensimmäistä vastetta.←--#: . OK (type: truth, paradigm: science view) --Jouni (talk) 13:02, 8 June 2017 (UTC)
  • Onko tooth defect todennäköisyys, oletusarvo vai vasteen vakavuus? Pitää olla täsmällinen.
    • Tooth defectejä luokkaa 100000. Mistä näin paljon?
    • Tooth defect sisältää infiä ja na ta
  • Breast feeding ei näy dose-kuvassa. Pitää piirtää eri kuvaan ja selittää Exposure kummankin kuvan otsikossa.←--#: . OK (type: truth, paradigm: science view) --Jouni (talk) 13:02, 8 June 2017 (UTC)
  • Casesabs kuva katsoo vain itereitä eli yhtä maa/sukupuoli/ikäryhmää yhtenä yksikkönä. Niinpä määrätkin ovat vain tälle alaryhmälle ja summautuvat maksimissaan kahteen miljoonaan. Eivät siksi ole mielekkäitä ellei eritellä tarkasti mitä ryhmää katsotaan.
  • Miksi D-vitamiinisuositus on nolla kaikilla joilla ei ole backgroundia? Senhän pitäisi tuottaa 1 jos alittaa suosituksen ja ilman backgroundia näin käy monelle.
  • Miten change in child iq voi koskettaa niin monia? Sitä ei ole rajoitettu lapsiin. Vaikutustahan ei pitäisi tulla niissä iteraatioissa joissa ei ole nuori nainen. Tämä vaatii useampaa korjaamista:
    • exposuressa tehdään sarakkeeseen Exposure "To child" dx.expo.childin riveille ja NA expoRaw:n riveille.←--#: . OK (type: truth, paradigm: science view) --Jouni (talk) 13:02, 8 June 2017 (UTC)
    • frexposed luotaessa säilytetään Iter, jotta rivit säilyvät vaikka non-marginaalit häviäisivät. Tällöin esim. dosea ja amountia ei tarvitse välillä kertoa infolla. Exposure-sarakkeessa ehtona "To child".←--#: . OK (type: truth, paradigm: science view) --Jouni (talk) 13:02, 8 June 2017 (UTC)
    • ERF:ssä pitää ne Exposuret jotka liittyvät lapseen korvataan "To child" mutta muita ei tarvitse muuttaa. Sama thresholdissa.←--#: . OK (type: truth, paradigm: science view) --Jouni (talk) 13:02, 8 June 2017 (UTC)

Exposure-response function

    • Exposure_unit ei ole marginaali, joten muutetaan määrittelyä. ←--#: . OK (type: truth, paradigm: science view) --Jouni (talk) 13:02, 8 June 2017 (UTC)

Health impact assessment and Burden of disease

  • AF laskenta on väärin jos rr<1. Korjaa koodi olioihin BoDpaf ja casesrr.
  • HIA laskennassa pitäisi preferoida aina taustatautitaakan käyttö, jos se vain on saatavilla.

ERF of omega-3 fatty acids and ERF of methylmercury

  • Childs iq. Mitä tämä tarkasti tarkoittaa? Äo pistettä vai tapausta? Pistettä kai.
    • Erf pitäisikö olla + aina haittaa ja - aina hyötyä? Eli äo muutos onkin menetys. Korjaa termi ja etumerkki.←--#: . OK (type: truth, paradigm: science view) --Jouni (talk) 13:02, 8 June 2017 (UTC)

ERF of dioxin

  • TEQ ERF rivit 1 ja 2 ovat oikeasti samassa yksikössä mutta taulukossa lukee eri. Käytä mieluummin jälkimmäistä.←--#: . ok (type: truth, paradigm: science view) --Jouni (talk) 13:02, 8 June 2017 (UTC)

Disease risk

  • BoDt-ovariablen Response muutetaan Resp:ksi ja vastaamaan BRA:n lokaatioita.←--#: . OK (type: truth, paradigm: science view) --Jouni (talk) 13:02, 8 June 2017 (UTC)


Vanhempia havaintoja ongelmista (tarkista että kaikki kunnossa nyt):

  • Frexposed country puuttuu ok
  • A c g puuttuvat dosesta mutta ei dose2. Ok
  • Casesrr on c muttei muita ja sekin marginal.
  • Casesabs cag on mutta c marginal
  • Bodpaf c on marginal ok
  • Bidcsse c on marginal. Samoin bod ok
  • Bodcase abs on nelinkertainen rr on vajausta. Ok
  • Bodt iteroituu 1000. Pitää muuttaa openv N. Ok
  • Funktio joka käy läpi dependencyt ja kirjaa uudet ja tiopuvat indeksit sekä tippuvat lokaatiot. Sources ja prevresults true näyttää myös ne mutta oletuksena on false. OK
    • Onko tärkeää näyttää se mistä ovariablesta indeksit tulivaat? Ei kai jos jokaisesta näytetään mitä on. Paramsetrit drops ja news ja currenta näyttävä tippuvat uudet ja nykyiset indeksit. Oletuksena true. OK
    • Currentcols sarakkeeseen lokaatiot jos ne ovat muuttuneet mutta muuten ei vaan tieto löytyy loctablesista.
  • RR$Age täytyy tarkistaa. Voiko sen poistaa rr n sisällä? Tai voiko sen olla tekemättä ja toteuttaa fiksummin? Ok

Model bug fixes 30.8.-7.9.2017

  • Op_en7805/mc2d Mitä tämä sisältää? Funktion mc2d joka 1) arpoo yksilötason datasta havaintoja, 2) yhdistää ne annetulla funktiolla 3) siten että alkuperäinen Iter korvautuu väestötason Iterillä.
  • mc2d-lista on seuraavien ovariablejen dependency: RR, casesabs. Miksi? Koska niistä eteenpäin oliot ovat populaatiokohtaisia.
  • Ovatko pitoisuuksien yksiköt oikein? ←--#: . Done (type: truth, paradigm: science view) --Jouni (talk) 11:22, 6 September 2017 (UTC)
    • Omega3 herring on 9990 ja sen pitäisi olla noin 1 % joten yksikkö näyttää olevan ug/g. ERF tarvitsee annoksen mg eli koska kerrotaan syöntigrammoilla, tämä pitäisi jakaa tuhannella.
    • PCB ja PCDDF herring ovat 1.5 ja 2.8 ja näiden pitäisi olla noin 1-2 pg/g. ERF tarvitsee pg eli ovat oikein.
    • Vitamin D herring on 0.16 ja pitäisi olla luokkaa 0.1 ug/g eli on oikein. Myös ERF tarvitsee ug.
    • conc_vit-arvot ovat kymmenesosa eli 0.015 (d-vitamiini) ja 998 (omega3) koska ne ovat mg/100 g.
    • Korjaa siis conc@formula:an rivi conc_vit <- conc_vit / 100 ; result(conc_vit)[conc_vit$Exposure_agent == "D-vitamin"] <- result(conc_vit)[conc_vit$Exposure_agent == "D_vitamin"] * 1000 # From /100g to /g and D_vitamin from mg to ug
  • conc: lisää metaan eri altisteiden yksiköt. ←--#: . Done. (type: truth, paradigm: science view) --Jouni (talk) 13:30, 7 September 2017 (UTC)
  • casesabs: ←--#: . Done (type: truth, paradigm: science view) --Jouni (talk) 11:22, 6 September 2017 (UTC)
    • DHA- ja omega-tulokset vääristyneet pitoisuusvirheen takia. Vaikuttaa vasteisiin Child's IQ, Heart(CHD), Stroke. Kaksi jälkimmäistä leikkautuvat pois koska perustuvat RR:ään.
    • Lineaarisessa vasteessa on virhe. Pitää olla: out <- (threshold + temp * ERF * frexposed) * population. Tämä koskee Child's IQ, Cancer, Tooth defect. jotka muuttuvat paljon järkevämmiksi nyt.
    • pitää summata pois myös altistukseen liittyvät sarakkeet, koska ne voivat erotella erilaiset altisteet, vaikka nyt ne pitää yhdistää. Korjattava siis out <- oapply(out, NULL, sum, c("Exposure_agent", "Exposure", "ER_function", "Scaling"))
  • Tämä non-marginal-juttu on selitettävä arviointisivulla.
  • Miksi RR on pienempi Heart (CHD):lle jos Exposcen on No exposure kuin BAU? Sama Strokelle? Tämä menee väärin päin. Syy ei ole virhe dosessa, jossa bgexposure koskee VAIN No exposure -ryhmää mutta ei BAU-ryhmää; tämä johtuu siitä, että BAU-ryhmässä on jo Background mukana periytyneenä expoRaw:sta. Sen sijaan expoRaw'ssa on virhe, koska Background-sarake periytyy infosta eikä sisällä mitään tietoa ja dependenciesissä on addexposure eikä bgexposure. Pitää siis korjata dependencies ja lisätä bgexposure formulaan ←--#: . Done. (type: truth, paradigm: science view) --Jouni (talk) 13:30, 7 September 2017 (UTC)
  • casesabs Tooth defect 1.5 M! Miksi? Koska ERF skaalataan log10, ja tämän takia dose on -3.8. Sen sijaan No exposure on -6 eli pienelläkin altistuksella tulee altistusta 2.2 ja tämä kerrottuna ERF:llä (0.25) tuottaa vaikutusta 0.55 per lapsi. ERF on siis mietittävä uusiksi ja katsottava esim Silakka-mallista: siellä Response oli "Yes or no dental defect" eli Alaluusuan lineaarinen versio. Toisaalta saattaa korjaantua kun tuo yllä mainittu Background-virhe korjataan. ←--#: . Ei korjaantunut, otetaan lineaarinen versio käyttöön. (type: truth, paradigm: science view) --Jouni (talk) 13:38, 8 September 2017 (UTC)
  • incidence on vain dummy vakio 0.1, vaikka eri taudit tietysti esiintyvät kovin erilaisina määrina eri-ikäisissä, eri sukupuolilla ja eri maissa. Tämä pitäisi päivittää. ----#: . Ei ole kriittinen tässä, koska suositaan BoDT:hen perustuvia arvoja, mutta pitää muistaa että välivaiheen casesrr on väärin. (type: truth, paradigm: science view) --Jouni (talk) 13:38, 8 September 2017 (UTC)
  • BoD menee väärin, koska sieltä tippuvat BoDcasen vasteet pois. Ilmeisesti vika on koodissa, joka suosii BoDpafia. ←--#: . Done. (type: truth, paradigm: science view) --Jouni (talk) 13:38, 8 September 2017 (UTC)
  • Exposure=NA on ongelmallinen. Pitäisi muuttaa OpasnetUtilsiin järkevämpi tapa, vaikka "Any location" tarkoittaisi että mätsää kaikkien lokaatioiden kanssa.

Model bug fixes 4.10.2017-

  • Add units to all ovariables. ←--#: . Units added for amount, exposure and BoD, the key output variables. (type: truth, paradigm: science view) --Jouni (talk) 09:31, 6 October 2017 (UTC)
  • Remove arrow from incidence to RR (why is it there?)
  • Child's IQ and tooth defect are the same for <45 and >45 year olds. Correct! This is because combine in casesabs@formula drops them. Cannot use orbind instead, because also uses oapply and immediately after mc2d is run where Age etc. are needed.
    • Solution: Modify mc2d function so that before sampling Iter2, the ovariable is multiplied by an informative ovariable containing additional indices (in the Goherr model, it is called info). ←--#: . Done. (type: truth, paradigm: science view) --Jouni (talk) 21:12, 4 October 2017 (UTC)
    • This does not solve the issue that Ages, Gender and Country remain non-marginals. The adjustment in mc2d is done correctly but they are changed to non-marginals because the parent frexposed has them as non-marginals. Therefore, they must be converted back to marginals in the assessment code. ←--#: . Done. (type: truth, paradigm: science view) --Jouni (talk) 09:31, 6 October 2017 (UTC)
  • Should background be in mc2dparam$newmarginals? ←--#: . Yes. Done. (type: truth, paradigm: science view) --Jouni (talk) 09:31, 6 October 2017 (UTC)
  • Column Exposure does not work properly. We want to separate direct effects to the fish eater from those that are mediated to her child via placenta or mother's milk. However, NA as the location is problematic and should be replaced by "To eater". This has to be done in ovariable Exposure so that it inherits correctly. ←--#: . Done. (type: truth, paradigm: science view) --Jouni (talk) 21:12, 4 October 2017 (UTC)
    • There is a related problem: ERF$Exposure has to reflect the exposure. This is done by manually converting ERF$Exposure to either "To eater" or "To child" based on whether the Response affects eater or child. The same applies to threshold. ←--#: . Done. (type: truth, paradigm: science view) --Jouni (talk) 21:12, 4 October 2017 (UTC)
  • MeHg is missing from conc. Add. ←--#: . Done. (type: truth, paradigm: science view) --Jouni (talk) 09:31, 6 October 2017 (UTC)
  • Change sign for Child's IQ disabilityweight. Numbers are for loss of IQ. ←--#: . Done. (type: truth, paradigm: science view) --Jouni (talk) 09:31, 6 October 2017 (UTC)
  • Which indices should be marginal in BoD? Because it is scaled to the population, does it make a difference whether Ages and Gender are marginals or not? In a graph, it is scaled by Result/openv$N so the actual numbers will scale correctly for each marginal subgroup, i.e. Ages*Gender*Country*Background, but not for non-marginals. It is meaningful both to sum up and not to sum up with Age, Gender, and Country, but Background MUST be shown separately. ←--#: . Agreed. This is how it is currently implemented. (type: truth, paradigm: science view) --Jouni (talk) 09:31, 6 October 2017 (UTC)
  • expoRaw contains data for sumiteming PCDDF+PCB to TEQ. This should happen in conc_pcddf already. ←--#: . Changed. (type: truth, paradigm: science view) --Jouni (talk) 08:02, 12 October 2017 (UTC)
  • conc_pcddf now has Fish locations Baltic herring, Large herring and Small herring, among many others. In expoRaw Baltic herring -> Herring but we do not want this. Ovariable amount conc already has Large herring, Small herring, and Salmon; but conc_vit and conc_mehg have Herring and Salmon. Therefore, in conc we should convert Herring to Small herring and Large herring (assuming that vitamin and MeHg concentrations are equal in small and large herrings). Downstream of conc, Baltic herring should drop out. ←--#: . Done (type: truth, paradigm: science view) --Jouni (talk) 08:02, 12 October 2017 (UTC)
  • Index Background is made a non-marginal (with sampling) in bgexposure. I don't think this is wise. Instead, bgexposure should contain the data from the table, and Background == No should be a decision option where bgexposure is multiplied by 0. ←--#: . Corrected. (type: truth, paradigm: science view) --Jouni (talk) 20:04, 12 October 2017 (UTC)
  • A decision should be built where Herring and Salmon intake are restricted to 3 g/d == one portion per month (each). This probably has to be implemented in the code, like in op_fi:Silakan hyöty-riskiarvio-model. ←--#: . Done. (type: truth, paradigm: science view) --Jouni (talk) 20:04, 12 October 2017 (UTC)
  • Why is DK missing from exposure data? Because in conc it is spelled Dk. ←--#: . Corrected. (type: truth, paradigm: science view) --Jouni (talk) 20:04, 12 October 2017 (UTC)

Notes from Goheer meetings 30.10. and 13.-15.11.2017

  • Why is vitamin D going to positive DALY side? Fish eating should have benefit on vitamin D.
    • The optimum dose is 7.5 - 100 ug/d. If Background is No, dose is 0 and if Yes it is 11.7.

30.10.2017 Goherr

Timo Assmuthilla on kuva dioksiinigervnancesta. Missä? Etsi

Ruokaturvaan ei ole alueellisia organisaatioita. Mutta koska silakan kysyntä sekä kotona että ulkomailla kasvaisi jos voisi tuottaa turvalliseksi todettua ja luotettua kalaa, tätä luotettavuuden ja matalien dioksiinipitoisuuksien kalaa kannattaisi organisoida alueellisesti.

Kuinka menevät ruokaturvaprosessit? Tästä pitäisi olla kaavio. Näiden pitäisi pystyä kytkeytymään kalapäätöksentekoon.

Politiikkaprosessien propertyjä: suosittelee taholla asiasta päättää asiasta

Päästörajojen päätösprosessi. Miten menee, onko kaaviota Ruokaturvapäätökset: miten prosessit menevät?

TOpics Intergration of salmon and herring management: Mitä

Multiannual plan Mikä on paras/pahin mahdollinen tulevaisuus jos tämä otetaan/ei oteta käyttöön? Mitkä ovat tärkeimpiä edistäviä / estäviä tekijöitä tämän muutoksen saavuttamisessa?

Dioksiinien integrointi kalakantojen managerointiin. Tätähän tehdään jo koska on asetettu dioksiiniraja-arvoja kalalle. Mutta onko tämä paras tapa integroida managerointia? Pitäisikö tämä myös ottaa huomioon TACeissa ja jos niin miten?

Background: Mitä asioita pitäisi esitellä jotta ihmiset ymmärtävät mikä on governancea ja mikä managementia?

Taustatiedot:

  • nykyiset governance systeemit (esim TAC-prosessi auttaisi ymmärtämään multiannual plania)
  • lohen ja silakan vuorovaikutus liittyen dioksiinien kertymiseen. Myös planktonin bioakkumulaatio
  • dioksiiniongelma silakassa ja lohessa. Terveys, kalastuksen rajoite, ekologinen: hylkeille, ongelma kaupalle,

Mikä näkökulma: Mitä sinusta on tärkeää nostaa esille? Mistä asioista puhutaan liian vähän?


Effect of improved information --> human consumption of salmon / herring Net burden of disease --> Food socurity and safety Food recommendations of: herring amount, salmon amount, herring size


Goherr 15.11.2017

Joka solmulle oma frekvenssitaulukko, josta suhteet opitaan. Katso oobn.

Jatkuva jakauma annetaan (ehdolla vanhemmat) esim 1000 iteraatiota

  • Herring intake
  • Salmon intake
  • Omega3 intake ym
  • Omega3 intake total
  • Dioxin intake total (h+s)
  • Net burden of disease

Luokitellut jakaumat annetaan

  • Effect of improved information
  • Country
  • Gender
  • Age

Jakaumaparametrit annetaan

  • Other intake of Omega3

Omega3 intake total: lasketaan summana

Net burden of disease

  • Srakkeet Age, Gender, Omega3 intale total, Dioxin intake total.

Effect of improved information: Tämä on mietittävä sen pohjalta miltä kyselydata näyttää.

  • Mitkä ovat päätösmuuttujan Information improvement tilat?
  • Oletetaan että vaikuttaa loheen ja silakkaan samalla tavalla.

Hanki GeNie ja puhu Päivi M.n kanssa ohjelman käyttämisestä.

Testaa löytyykö R-pakettia joka lukee oobn.