Goherr: Fish consumption study

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This page contains a detailed description about data management and analysis of an international survey related to scientific article Forage Fish as Food: Consumer Perceptions on Baltic Herring by Mia Pihlajamäki, Arja Asikainen, Suvi Ignatius, Päivi Haapasaari, and Jouni T. Tuomisto.[1] The results of this survey where also used in another article Health effects of nutrients and environmental pollutants in Baltic herring and salmon: a quantitative benefit-risk assessment by the same group.[2]


How Baltic herring and salmon are used as human food in Baltic sea countries? Which determinants affect on people’s eating habits of these fish species?



Survey of eating habits of Baltic herring and salmon in Denmark, Estonia, Finland and Sweden has been done in September 2016 by Taloustutkimus oy. Content of the questionnaire can be accessed in Google drive. The actual data can be found from the link below (see Data).



Original datafile File:Goherr fish consumption.csv.


The following assumptions are used:

Assumptions for calculations(-)
1freq1times /a0Never
2freq2times /a0.5 - 0.9less than once a year
3freq3times /a2 - 5A few times a year
4freq4times /a12 - 361 - 3 times per month
5freq5times /a52once a week
6freq6times /a104 - 2082 - 4 times per week
7freq7times /a260 - 3645 or more times per week
8amdish1g /serving20 - 701/6 plate or below (50 grams)
9amdish2g /serving70 - 1301/3 plate (100 grams)
10amdish3g /serving120 - 1801/2 plate (150 grams)
11amdish4g /serving170 - 2302/3 plate (200 grams)
12amdish5g /serving220 - 2805/6 plate (250 grams)
13amdish6g /serving270 - 400full plate (300 grams)
14amdish7g /serving400 - 550overly full plate (500 grams)
15ingredientfraction0.1 - 0.3Fraction of fish in the dish
16amside1g /serving20 - 701/6 plate or below (50 grams)
17amside2g /serving70 - 1301/4 plate (100 grams)
18amside3g /serving120 - 1801/2 plate (150 grams)
19amside4g /serving170 - 2302/3 plate (200 grams)
20amside5g /serving220 - 2805/6 plate (250 grams)
21change1fraction-1 - -0.8Decrease it to zero
22change2fraction-0.9 - -0.5Decrease it to less than half
23change3fraction-0.6 - -0.1Decrease it a bit
24change4fraction0No effect
25change5fraction0.1 - 0.6Increase it a bit
26change6fraction0.5 - 0.9Increase it over by half
27change7fraction0.8 - 1.3Increase it over to double
28change8fraction-0.3 - 0.3Don't know


This code is used to preprocess the original questionnaire data from the above .csv file and to store the data as a usable variable to Opasnet base. The code stores a data.frame named survey.

  • Model run 11.7.2018 [2]
  • Model run 27.3.2019 with country codes DK EE FI SE [3]

+ Show code


  • Sketches about modelling determinants of eating (spring 2018) [4]

Figures, tables and stat analyses for the first manuscript

  • Model run 8.5.2019 with thlVerse code (not run on Opasnet) [5] [6]
  • Model run 26.8.2018 with fig 6 as table [7]
  • Previous model runs are archived.

Methodological concerns:

+ Show code

Luke data about fish consumption in Finland [8][9]

Fish consumption as food in Finland(kg/a per person)
1domestic fishTotal6.
2domestic fishFarmed rainbow trout1.
3domestic fishBaltic herring0.
4domestic fishPike0.
5domestic fishPerch0.
6domestic fishVendace0.
7domestic fishEuropean whitefish0.
8domestic fishPike perch0.
9domestic fishOther domestic fish0.
10imported fishTotal6.
11imported fishFarmed rainbow trout0.
12imported fishFarmed salmon1.
13imported fishTuna (prepared and preserved)
14imported fishSaithe (frozen fillet)
15imported fishShrimps0.
16imported fishHerring and Baltic herring (preserved)
17imported fishOther imported fish2.

Descriptive statistics

Correlation matrix of all questions in the survey (answers converted to numbers).

Model must contain predictors such as country, gender, age etc. Maybe we should first study what determinants are important? Model must also contain determinants that would increase or decrease fish consumption. This should be conditional on the current consumption. How? Maybe we should look at principal coordinates analysis with all questions to see how they behave.

Also look at correlation table to see clusters.

Some obvious results:

  • If reports no fish eating, many subsequent answers are NA.
  • No vitamins correlates negatively with vitamin intake.
  • Unknown salmon correlates negatively with the types of salmon eaten.
  • Different age categories correlate with each other.

However, there are also meaningful negative correlations:

  • Country vs allergy
  • Country vs Norwegian salmon and Rainbow trout
  • Country vs not traditional.
  • Country vs recommendation awareness
  • Allergy vs economic wellbeing
  • Baltic salmon use (4 questions) vs Don't like taste and Not used to
  • All questions between Easy to cook ... Traditional dish

Meaningful positive correlations:

  • All questions between Baltic salmon ... Rainbow trout
  • How often Baltic salmon/herring/side salmon/side herring
  • How much Baltic salmon/herring/side salmon/side herring
  • Better availability ... Recommendation
  • All questions between Economic wellbeing...Personal aims
  • Omega3, Vitamin D, and Other vitamins

Model runs

+ Show code

Bayes model

  • Model run 3.3.2017. All variables assumed independent. [11]
  • Model run 3.3.2017. p has more dimensions. [12]
  • Model run 25.3.2017. Several model versions: strange binomial+multivarnormal, binomial, fractalised multivarnormal [13]
  • Model run 27.3.2017 [14]
  • Other models except multivariate normal were archived and removed from active code 29.3.2017.
  • Model run 29.3.2017 with raw data graphs [15]
  • Model run 29.3.2017 with salmon and herring ovariables stored [16]
  • Model run 13.4.2017 with first version of coordinate matrix and principal coordinate analysis [17]
  • Model run 20.4.2017 [18] code works but needs a safety check against outliers
  • Model run 21.4.2017 [19] some model results plotted
  • Model run 21.4.2017 [20] ovariables produced by the model stored.
  • Model run 18.5.2017 [21] small updates
  • 13.2.2018 old model run but with new Opasnet [22]

+ Show code

Initiate ovariables

jsp taken directly from data WITHOUT salmpling

+ Show code

Amount estimated from a bayesian model
  • Model run 24.5.2017 [23]

+ Show code

Amount estimates directly from data rather than from a bayesian model
  • Initiation run 18.5.2017 [24]
  • Initiation run 24.2.2018: sampling from survey rather than each respondent once [25]

+ Show code

Initiate other ovariables
  • Code stores ovariables assump, often, much, oftenside, muchside, amount.
  • Model run 19.5.2017 [26]
  • Initiation run 24.5.2017 without jsp [27]
  • Model run 8.6.2017 [28]

+ Show code

Other code

This is code for analysing EFSA food intake data about fish for BONUS GOHERR manuscript Ronkainen L, Lehikoinen A, Haapasaari P, Tuomisto JT. 2019.

+ Show code


The survey data will be used as input in the benefit-risk assessment of Baltic herring and salmon intake, which is part of the WP5 work in Goherr-project.

See also



  1. 1.0 1.1 Mia Pihlajamäki, Arja Asikainen, Suvi Ignatius, Päivi Haapasaari and Jouni T. Tuomisto. Forage Fish as Food: Consumer Perceptions on Baltic Herring. Sustainability 2019, 11(16), 4298; https://doi.org/10.3390/su11164298
  2. Tuomisto, J.T., Asikainen, A., Meriläinen, P., Haapasaari, P. Health effects of nutrients and environmental pollutants in Baltic herring and salmon: a quantitative benefit-risk assessment. BMC Public Health 20, 64 (2020). https://doi.org/10.1186/s12889-019-8094-1

Related files

Goherr: Fish consumption study. Opasnet . [29]. Accessed 07 Dec 2022.