Domestic fish consumption of the general population in Finland

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What is the average fish consumption of individuals in the general Finnish population? Imported fish may be considered. Sea and freshwater species may be considered separately. D↷




EFSA food database 2016

The EFSA Comprehensive European Food Consumption Database consists of data from many EU countries, collected between 1997 and 2012. The database was published 2016.[1] The Finnish data comes from the following studies:

  • Diabetes Prediction and Prevention Nutrition Study (DIPP) 2001-2009
  • Diabetes Prediction and Prevention Nutrition Study (DIPP) 2003 -2006
  • National FINDIET 2012 Survey
  • National Findiet Surveys
  • Nutrition and wellbeing of secondary school pupils
  • Special Turku Coronary Risk Factor Intervention Project

The most useful dataset is about the chronic consumption among consumers only, measured as g/d [2]. Another dataset is about the whole population including those who do not consume the product [3].

2007 study

Fish consumption by the population [2] for twelve various fish species commonly consumed in Finland as normal distributions (weighed mean, weighed SD) and additionally indexed by age categories and gender.

Fish consumption in the subpopulation of fish users (g/d).
Fish species Consumption by 0-2 years old Consumption by 2-18 years old Consumption by 18-55 years old
Baltic herring (Clupea harengus membras) 5.717 11.55 29.61
Herring (Clupea harengus) 0 3.333 19.84
Vendace (Coregonus albula) 7.858 8.884 37.08
Whitefish (Coregonus lavaretus) 10.05 19.4 52.57
Pike (Esox lucius) 3.636 3.684 26.13
Rainbow trout (Onchorhynchus mykiss) 9.686 17.59 66.31
Shrimp (Pandalus sp.) 0.663 4.328 19.94
Perch (Perca fluviatilis) 5.807 14.64 20.31
Saithe (Pollachius virens) 10.09 16.48 54.51
Atlantic salmon (Salmo salar) 13.82 18.79 24.95
Pike-perch (Sander lucioperca) 18.65 22.52 0
Tuna (Thunnus thynnus) 7.981 7.733 47.43

<anacode>5.717 5.897 11.55 12.96 29.61 39.02 0.000e+000 0.000e+000 3.333 2.47 19.84 15.22 7.858 7.6 8.884 10.2 37.08 49.41 10.05 8.227 19.4 23.57 52.57 64.87 3.636 2.402 3.684 3.106 26.13 32.6 9.686 7.075 17.59 14.06 66.31 48.59 0.663 1.339 4.328 4.142 19.94 26.41 5.807 3.15 14.64 29.2 20.31 31.37 10.09 7.458 16.48 14.44 54.51 32.06 13.82 14.23 18.79 19.34 24.95 19.84 18.65 18.83 22.52 17.77 0.000e+000 7.981 6.591 7.733 8.539 47.43 39.26</anacode>

Fisheries statistics 2006

There are two kinds of data. Actual consumption data and fishery catch data.[3] [4] [5] [6]

For the most important species (herring, salmon) the actual consumption estimates are available [7], but for the most of the species consumption estimates are calculated from fishery catch. [8] For those species, fishery catch is first transformed to filleted fish weight [9] and further with actual edible proportions [10]to fish consumption.

RKTL has data on overall fish consumption in Finland. This model yields a very similar estimate. D↷

Fish consumption as filleted weight (g/d) (or kg/a?)
Fish species Fish consumption
Farmed salmon (sea+inland) 3.83
Wild salmon 0.23
Herring(sea) 1.92
White fish(sea) 0.3
Sprat(sea) 0.11
Perch(sea) 0.44
Flounder(sea) 0.01
Pike-perch(sea) 0.2
Bream(sea) 0.07
Pike(sea) 0.35
Vendace(sea) 0.06
Burbot(sea) 0.02
Wild salmon(inland) 0.45
White fish(inland) 0.48
Perch(inland) 1.25
Pike-perch(inland) 0.27
Bream(inland) 0.31
Pike(inland) 1.53
Vendace(inland) 1.38
Burbot(inland) 0.16

Other data

The aim is to find probability distributions of daily consumption of various sea and freshwater fish species in six subgroups of the general Finnish population. The subgroups of interest are males and females in two age categories 0-2yr and 2-18yr, and adults (males and females combined) aged 18-55yr and 55yr+. The data used to derive these distributions comes from [2] where the summary statistics (mean, sd and percentiles: 5th, 25th, 50th, 75th, 95th) of daily consumption of twelve fish species among fish users and information on the percentage of users are given. The data is provided separately for males and females in the following age groups: 1yr, 3yr, 6-9yr, 25-34yr, 35-44yr, 45-54yr and 55-64yr. There is a single data point of daily intake of whitefish by women aged 25-34yr and 35-44yr, and by males aged 45-54yr. This information is not enough to create probability distribution. Thus, it was assumed that in each of these three sex/age groups the intake of whitefish is the same as intake of vendace. First, linear interpolation was used to approximate values of 5th, 25th, 50th, 75th, 95th percentiles of daily fish consumption among consumers in missing age/sex groups. For example, the i-th percentile of daily consumption of species j in a group of 2 year old males was found by linear interpolation between points corresponding to i-th percentiles of daily consumption of species j in groups of 1 year old and 3 year old males (for interpolation the mid age of every subgroup was used). The same technique was applied to age groups 4-5yr, 10-13yr, 14-17yr, 18-24yr. The i-th percentile of daily fish consumption by 0yr old children (users only) was approximated by the arithmetic mean of values of i-th percentiles for months 7 through 11 obtained by linear interpolation between i-th percentiles of fish consumption at age of 6 months and at age of 1yr (mid age of 1.5yr). Since young children don’t eat fish until they are 6 months old the fish consumption and the percentage of fish users at age of 6 months is zero. Moreover, it was assumed that the fish consumption and the percentage of fish consumers in age group 65-74yr and in age group 75yr+ is the same as in group 55-64yr. In the similar way the percentage of users of particular fish species in every missing age/sex group was estimated. Then, the available data (percentiles, mean and variance) was used to reconstruct the probability distributions of daily fish consumption among users in every age/sex group. The following procedure was used for that purpose. First, for a given fish species, age and gender the starting/initial cumulative distribution function (CDF) of daily consumption was constructed by linear interpolation between known percentiles. In general, this distribution will not reproduce the mean and sd as provided by the data. Therefore, the constrained optimization was further used to find the CDF that satisfies the percentiles given and whose mean and variance are as close as possible to the mean and variance from the data. For age groups for which the mean and variance were unknown only the first step of this procedure was applied. After that, information about the percentage of users was used to determine the probability distribution of daily consumption of particular fish species among all individuals in a certain sex/age group (users and non-users). The resulting distributions were further grouped and combined (first by age and then, only in case of adults, by gender; population data can be found here [11]) to derive the fish consumption distributions in six target age/sex groups. Finally, since in [2] the fish habitat was not differentiated the species specific data on sea and inland intake proportions (assumed to be the same as proportion of sea and inland fishery catch and extracted from [4]) was used to estimate consumption of different sea and inland species by target subgroups.

Unit: g/d


EFSA food database 2016

EFSA data was fetched and preprocessed. Ovariable amount was created to be used in health impact assessments. The preprocess code does not work at Opasnet.

You can find both codes at Github.

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Fisheries statistics 2006

<anacode>Farmed_salmon_table+(Fish_onsumption_tabl/Population_of_fin)+Herring_consumption_ +(Wild_salmon_consumpt/population_of_fin)</anacode>

See also


  1. EFSA Comprehensive European Food Consumption Database. (2016) [1]
  2. 2.0 2.1 2.2 Tero Hirvonen. 2007.
  3. Kalatalous tilastoina 2006 RKTL, page 23
  4. 4.0 4.1 Kalatalous tilastoina 2006 RKTL, pages 3 and 9
  5. RKTL 2006(a) Estimates for filleting factors
  6. RKTL 2006(b) Estimates for factors of edible proportions
  7. Methyl mercury: Kalatalous tilastoina 2006 RKTL, page 23
  8. Methyl mercury: Kalatalous tilastoina 2006 RKTL, pages 3 and 9
  9. Methyl mercury: RKTL 2006(a) Estimates for filleting factors
  10. Methyl mercury: RKTL 2006(b) Estimates for factors of edible proportions
  11. Population of Finland years 1980 - 2007.