Access to this full-text is provided by MDPI.
Content available from Nutrients
This content is subject to copyright.
Citation: Valsta, L.M.; Tapanainen,
H.; Kortetmäki, T.; Sares-Jäske, L.;
Paalanen, L.; Kaartinen, N.E.; Haario,
P.; Kaljonen, M. Disparities in
Nutritional Adequacy of Diets
between Different Socioeconomic
Groups of Finnish Adults. Nutrients
2022,14, 1347. https://doi.org/
10.3390/nu14071347
Academic Editor: Megan A. McCrory
Received: 16 February 2022
Accepted: 20 March 2022
Published: 23 March 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
nutrients
Article
Disparities in Nutritional Adequacy of Diets between Different
Socioeconomic Groups of Finnish Adults
Liisa M. Valsta 1, Heli Tapanainen 1,*, Teea Kortetmäki 2, Laura Sares-Jäske 1, Laura Paalanen 1,
Niina E. Kaartinen 1, Peppi Haario 1and Minna Kaljonen 3
1Department of Public Health and Welfare, Finnish Institute for Health and Welfare, 00271 Helsinki, Finland;
liisa.valsta@thl.fi (L.M.V.); laura.sares-jaske@thl.fi (L.S.-J.); laura.paalanen@thl.fi (L.P.);
niina.kaartinen@thl.fi (N.E.K.); peppi.haario@thl.fi (P.H.)
2Department of Social Sciences and Philosophy, University of Jyväskylä, 40014 Jyväskylä, Finland;
teea.kortetmaki@jyu.fi
3
Environmental Policy Centre, Finnish Environment Institute, 00790 Helsinki, Finland; minna.kaljonen@syke.fi
*Correspondence: heli.tapanainen@thl.fi; Tel.: +359-(0)29-524-8630
Abstract:
Information on dietary adequacy is needed to assess food and nutrition security in a
modern society, especially in the transition towards climate-friendly food systems. In this study,
differences in the nutritional adequacy of diets among Finnish adults were evaluated in population
groups of different education, income and urbanisation levels. The study used data from the FinDiet
2017 Survey (n= 1655, 18–74 years). Modelled usual intakes of foods and nutrients were evaluated
relative to food-based dietary guidelines issued by the National Nutrition Council of Finland (FNNC)
and with respect to nutrient adequacy following the Nordic Nutrition Recommendations and FNNC.
For about half of the nutrients studied, intakes were found to be adequate. Intakes of protein, fat,
saturated fatty acids and salt were estimated to be high. By contrast, inadequate intakes were seen in
folate and vitamins A, D, B1, B2 and C in almost all groups studied. Groups with a higher education
and income, groups that lived in urban areas and, in particular, women adhered more closely to
recommended food consumption and nutrient intakes than others. However, major challenges posed
by the Finnish diet are common to all groups studied, and only certain dietary features evaluated in
view of nutritional adequacy are associated with socioeconomic differences.
Keywords:
dietary intake; socioeconomic differences; urbanisation; 24 h dietary recall; usual
intake modelling; dietary guidelines; dietary recommendations; macronutrients; micronutrients;
climate-friendly
1. Introduction
The increasing prevalence of diet-related non-communicable diseases (NCDs) is one
important motivation to evaluate dietary adequacy as a baseline or a follow-up measure
for health and nutrition policy actions [
1
–
3
]. Dietary intake data that record dietary habits
and food consumption as well as nutrient intakes are needed to develop and evaluate
health, nutrition and food policy actions at the national level [
4
,
5
] and in international
coordinations [
6
–
9
]. Improvements in dietary assessment methods and greater harmonisa-
tion of surveillance activities have enhanced the accuracy of dietary assessments and the
comparability of results internationally [
10
–
12
]. At this point, more insight is needed into
the dietary differences between population groups that differ in their sociodemographic
and socioeconomic status (SES) [
13
–
15
], in order to mitigate SES-based inequalities of
health. This information is also increasingly important to predict the rate and the nutri-
tional consequences of the transition towards more climate-friendly diets, i.e., the expected
consumption increase in foods of vegetable origin and decrease in animal-based prod-
ucts [
16
–
18
]. Ensuring a socially just dietary transition requires attentiveness to existing
SES-based inequalities in nutrition [15].
Nutrients 2022,14, 1347. https://doi.org/10.3390/nu14071347 https://www.mdpi.com/journal/nutrients
Nutrients 2022,14, 1347 2 of 22
Efforts to harmonise dietary assessment methods and tools in Europe have made
progress during the past decade [
10
,
19
–
21
]. Today, researchers also have available im-
proved statistical modelling methods that permit the use of short-term individual food
consumption data for estimations of usual intake [
22
,
23
] and, consequently, the evaluation
of the dietary adequacy of whole population groups [
24
] using recent dietary recommenda-
tions and guidelines (e.g., [25,26]).
In Finland, gender differences in the quality of diets have been observed for sev-
eral decades already. Women’s diets have repeatedly been shown to be healthier than
men´s [27,28]
. SES-related health inequalities have received intense research interest during
the past decades as well, and measures such as the National Action Plan to Reduce Health
Inequalities 2008–2011 have been put forward [
29
]. However, no noticeable reduction in
health inequalities was seen during the Action Plan period [
30
]. Correspondingly, a more
recent study showed no narrowing of educational health inequalities in Finland between
2011 and 2017. Instead, according to some indicators, the disparities had widened [
31
]. SES
differences between diets have been investigated for decades, but studies in Finland have
been carried out based, for the most part, on information obtained by questionnaires on
meal patterns, consumption frequencies of indicator foods, such as fresh vegetables, meat
and meat products, butter and oil, or by comparing mean intakes at group
level [7,32–37]
.
Although studies on SES-based differences in food consumption and nutrient intake ex-
ist, recent data from Finland are scarce as most quantitative studies only include data
until 2007.
Results so far have shown generally higher vegetable and fruit consumption among
higher education and income groups. Educational differences in nutrient intakes during the
past decades have been most consistent for vitamin C intake, with a higher intake among
high education groups, but differences have also been seen for other nutrients [
28
,
38
–
40
].
Results from other countries are in line with findings from Finland and point to poorer
quality diets among individuals belonging to lower SES groups. In studies drawing on
data from Europe or other high-income countries, the consumption of vegetables and fruit
as well as the intake of vitamin C and vitamin D has tended to be lower for lower SES
groups, whereas sodium intake has generally been higher for them [14,41–43].
Regional differences in food consumption have been less extensively studied in Fin-
land, but the findings suggest more common use of vegetables and fruit in urban areas [
44
].
In addition, a recent study showed more common use of red and processed meat in rural
areas [45]. Similar findings have been observed in other countries as well [46,47].
Evaluations of dietary adequacy based on usual intake and on an average requirement
(AR) of micronutrients [
25
,
48
] are new in Europe [
49
] and have not been carried out for
different SES groups in Finland before.
The present study aims to evaluate the nutritional adequacy of adult diets in different
sociodemographic and socioeconomic population groups (education, income, urbanisation
level), using as its baseline usual intake modelling [
22
] or, where this is not applicable,
comparing mean intakes to recommended daily intake (RI) values by applying the most
recent dietary reference values used in the Nordic Countries [
25
] and at the national
level [
26
]. In addition, mean differences in food consumption, nutrient intakes and sources
of nutrients in adult diets among these same population groups are estimated. To gain
updated insights into the disparities between population groups, we used the most recent
food consumption data of the FinDiet 2017 Survey [
28
], which represent the Finnish
contribution to the EU Menu initiative of the European Food Safety Authority (EFSA) [
50
].
Based on previous findings [51,52], our hypothesis was that the proportion of individuals
whose diet complies with current nutrient recommendations is highest among the highest
educational and income groups.
Nutrients 2022,14, 1347 3 of 22
2. Materials and Methods
2.1. Study Population and Data Collection
The FinDiet 2017 Survey data were collected as a sub-sample of and in collabora-
tion with the FinHealth 2017 Study. The study population and data collection methods
have been reported in detail before [
50
,
53
]. In brief, the FinHealth 2017 Study, a national
health examination survey, was carried out in 50 study locations in mainland Finland
between January and May 2017. The sampling design of the survey was based on the
H
ealth 2000 Survey [54]
. For the FinHealth 2017 Study, a representative sample of adults
aged 18 years and above (n= 10,247) was drawn from the Population Register using one-
and two-stage stratified, random sampling. A 30% random sub-sample (n= 3099) among
those aged 18–74 years of the FinHealth 2017 Study sample was invited to participate
in the FinDiet 2017 Survey [
28
]. Their diet was assessed by two non-consecutive 24-h
dietary recalls and recorded by dietary interviewers using the in-house dietary software
Finessi (version 5.0.5, Finnish Institute for Health and Welfare, Helsinki, Finland) [
55
]. The
software included the food list and descriptors of the national food composition database
Fineli
®
[
56
]. Participants were first interviewed during the health examination part of
the survey; the second 24-h dietary recall took place by telephone between February and
October 2017, with a minimum interval of 8 days between recalls. A picture booklet of food
portions was used to estimate portion sizes. The use of food supplements was recorded
as well. The final food consumption data consisted of two accepted, non-consecutive 24-h
recalls from 1655 participants, i.e., 53% of the original sub-sample. The under-reporting
was evaluated following EU Menu methodology [
10
]; it was found to be on average 21%
for the face-to-face interviews and 18% for the telephone interviews.
Background data, e.g., gender and age, were obtained from the sampling frame, in-
formation for the SES background variables (education and income) was obtained from
the FinHealth 2017 Study questionnaires [
57
], and information on residential area was
obtained from the Population Register Centre (coordinates of the residence of the par-
ticipants) and Statistics Finland (categorisation of urbanisation level of residential area
based on these coordinates). The three educational categories used here—”low”, “middle”
and “high” education level—were created by dividing self-reported number of years of
fulltime studying (including primary school) in tertiles by sex and birth year. Questions on
total household income before tax deductions during the previous year, and on number
of adult and underage household members, were utilized to determine income group.
The household income question contained ten categories—from “less than EUR 15,000”
and “EUR 15,001–25,000” to “more than EUR 90,000” income per year. For this study, the
upper limits of the bottom nine original response categories (e.g., EUR 15,000 for the lowest
category and EUR 25,000 for the next category), and the lower limit of the highest category
(i.e., EUR 90,000) multiplied by two, were divided by weighted sum of household members,
assigning a weight of 1.0 to the first adult, 0.7 to additional adults and 0.5 to underage
household members [
58
]. The resulting individual values were grouped into sex-specific
quartiles (Qrt), which in turn were combined into three groups: “lowest Qrt”, “middle
Qrts” (2.–3. quartiles combined) and “highest Qrt”. Urbanisation levels were categorized
as follows: “urban” (urban areas), “semi-urban” (areas near urban areas and rural centres)
and “rural” (remote rural areas). The study population and categorisation into these groups
is described in Table 1.
Nutrients 2022,14, 1347 4 of 22
Table 1. The participants of the FinDiet 2017 Survey by gender, education, income and urbanisation level.
Men Women
n%Mean Age,
Years
% Under-
Reporters
% BMI
≥30 kg/m2n%Mean Age,
Years
% Under-
Reporters
% BMI
≥30 kg/m2
Education
Low 259 33 50.7 26 26 269 31 50.3 29 33
Middle 258 33 50.1 30 23 305 35 51.7 30 26
High 256 33 54.0 24 22 285 33 52.7 15 22
Missing 7 1 16 2
Income
Lowest Qrt 200 26 45.4 26 28 187 21 45.5 33 28
Middle (2.–3. Qrt) 389 50 54.8 27 22 419 48 54.7 25 31
Highest Qrt 175 22 51.6 26 22 235 27 50.6 15 18
Missing 16 2 34 4
Urbanisation level
Urban 451 58 50.1 29 22 535 61 50.6 24 24
Semi-urban 207 27 53.3 26 26 192 22 52.7 28 28
Rural 122 16 54.9 19 29 147 17 53.8 25 40
Missing 0 0 1 0
All 780 100 51.7 27 24 875 100 51.6 25 27
BMI, body mass index; Qrt, quartile.
2.2. Food Consumption and Compatibility with Food-Based Dietary Guidelines
Consumption of foods and dishes was compared between the different SES groups at
the ingredient level after disaggregating the consumed foods according to the recipies of
the National Food Composition Database, Fineli
®
[
56
]. Exceptions to this principle were
processed meat products (e.g., sausages) and processed fish products (e.g., canned fish),
which were not disaggregated into ingredients but rather quantified as purchased. Food
consumption was classified according to the Fineli
®
food grouping system, of which results
are shown for 13 food groups. These food groups include groups that are highlighted
in the Finnish food-based dietary guidelines, namely vegetables and fruit, legumes, nuts
and seeds, potatoes, red and processed meat (further broken down into beef, pork and
sausages), fish and seafood, liquid milk products (including yogurts) and cheese, and
cereals. These are also food groups that are expected to change markedly during the dietary
transition towards more climate-friendly diets, i.e., the expected consumption increase in
foods of vegetable origin and decrease in animal-based products [16,59].
Consumption of four main food groups was compared to the food-based dietary
guidelines, which are as follows: vegetables and fruit consumption (500 g/day excluding
juices) and red and processed meat intake (no more than 500 g as cooked/week). For milk
products, the guideline (which is 5–6 dL of milk and 2–3 slices of cheese) was summed up
and expressed as raw milk needed to produce these amounts, taking into account the higher
energy intake of men compared to women; this resulted in an approximate guideline value
of 900 g/day of raw milk for men and 700 g/day for women. The amount of raw milk was
calculated for men as 6 dL (600 g) of liquid milk plus 3 slices, 30 g each, of cheese multiplied
by 10 as a commonly used conversion factor from milk to cheese; for women, this was
5 dL
(500 g) of liquid milk plus 2 slices, 20 g each, of cheese. The guidelines for cereal
products (
9 portion
s and 6 portions for men and women, respectively) were multiplied
by an average amount of cereal per portion, i.e., 27 g, for bread and porridge, and other
cereal product portions commonly used in Finland, resulting in an approximate rounded
guideline of 245 g/day and 160 g/day for men and women, respectively [26].
2.3. Nutrient Intake and Evaluation of Adequacy Relative to Reference Values
The mean intakes of 20 macro- and micronutrients from food alone (i.e., excluding
food supplements) were analysed and intake differences between the SES groups evaluated.
The adequacy evaluation followed a modification of an evaluation protocol previously
reported by Steenbergen et al. [
49
]. The AR of nutrients was used to estimate the proportion
of Finnish adults in different SES groups with inadequate intake, using modelled usual
Nutrients 2022,14, 1347 5 of 22
intake distributions and the AR cut-point method [
48
]. If the proportion of a population
group reaching the AR level was
≥
90% (i.e., the proportion below the AR level was <10%),
the nutrient intake was considered “probably adequate”. If <90% of the population group
met the AR level, the intake was judged “not adequate”. When the AR was not available,
the RI was used as advised by the Nordic Nutrition Recommendations (NNR2012) [
25
].
According to the NNR2012, if the mean intake of a group is at or above the RI, there is
probably a “low prevalence of inadequacy”, and if the mean intake is below the RI, “no
firm conclusions can be drawn regarding the prevalence of inadequacy at the group level”.
With respect to macronutrients, the RI ranges as % of total energy (E%) were considered.
The macronutrient intake of a SES group was considered to be adequate if inside the RI
range. Sodium intake was evaluated as salt intake and compared to the Finnish population
reference intake of 5 g/day [
26
]. For iron in pre-menopausal women, RI was used for
intake evaluation since one of the underlying assumptions of the AR cut-point method,
symmetrical requirement distribution, is not met by this group [48].
Upper limits were evaluated as well. If >2.5% of a population group exceeded the
upper limit of the RI range, the intake was judged to be “high”. For micronutrients, the
upper intake level (UL) reference values were adopted from the nutrient recommendations
of the National Nutrition Council of Finland [
26
] and were used to estimate the proportion
of Finnish adults that may potentially be at risk of adverse effects due to high intake of a
certain nutrient. If the proportion of a population group exceeding the UL was larger than
2.5% of the population, the intake was considered “high”; if below, it was considered to
be “safe”.
In addition, the usual intake distributions of macronutrients and micronutrients from
food only and from food and dietary supplements combined, for both men and women,
were evaluated for the whole sample by comparison with the AR values or RI values, as
described above.
2.4. Statistical Analyses
All analyses were performed for men and women separately. Non-participation bias
was corrected using weighting factors, which improves the representativeness of the results
with respect to the Finnish adult population overall [
60
]. The energy under-reporters were
identified following the instructions of EU Menu methodology [10].
Mean consumption or intake with 95% confidence intervals (CIs) was calculated using
the mean of the data for two days for each subject. Regression analysis was used to test the
mean differences between sociodemographic groups. Age was included as a covariate in
the regression models. Consumption or intake data were transformed prior to regression
analysis using either log or cube root transformation to achieve normality. For pair-wise
tests, multiple comparisons were taken into account using the Tukey–Kramer adjustment.
For some episodically consumed food groups, it was not possible to transform the con-
sumption data into a normal distribution. For these, the Kruskal–Wallis non-parametric
test was used. Pair-wise comparisons were not performed for non-parametric tests. For
all food groups, consumption adjusted for energy intake (g/MJ) was used in statistical
tests. Usual intake and the proportion of the population below or above the reference value
were estimated with statistical program SPADE (R package SPADE.RIVMNwCore 4.0.92,
RIVM, Bilthoven, The Netherlands) [
22
]. The 95% CIs of the proportions were generated
by the bootstrap function available in SPADE, with 500 iterations. Significant differences
in proportions between sociodemographic groups were evaluated by non-overlapping
95% CIs.
Nutrients 2022,14, 1347 6 of 22
3. Results
3.1. Population Characteristics
The characteristics of the whole population, including gender, SES groups and urban–
rural categorisation, are shown in Table 1. The sample was evenly divided among educa-
tional groups, but the proportions of subjects in the middle income and the urban groups
were larger compared to other groups within the income and urbanisation sets, respectively.
3.2. Food Consumption by Gender, SES Groups and Urbanisation
The consumption of different foods at ingredient level by gender, SES groups and
urbanisation level is shown in Table 2. The highest educated group consumed more
vegetables and fruit but less red and processed meat compared to the two lower educated
groups. The same pattern, i.e., the highest educated group consuming more foods of
vegetable origin and fewer of animal origin, could also be seen in the consumption of nuts
and seeds and pork (general test statistically significant). Similar findings with respect to
education level were seen within both genders. In addition, the lowest educated group of
women consumed less cheese compared to the middle education group.
The highest income group consumed more vegetables and fruit, whereas the consump-
tion of cereals was more likely to be lower among men and women in the highest income
group when compared to the lower income groups. Among men, potatoes were consumed
less in the highest income group compared to the middle income group (Table 2).
Urban men consumed more vegetables and fruit compared to semi-urban or rural men.
Rural men consumed more potatoes and liquid milk compared to urban men. Semi-urban
men consumed more red and processed meat than urban men. Rural women consumed
more potatoes, red and processed meat and milk fats compared to urban women. Semi-
urban women did not differ from urban women regarding potato and milk fat consumption,
but consumed more red and processed meat and liquid milk than urban women (Table 2).
3.3. Food Consumption in Relation to Food-Based Dietary Guidelines
The usual food consumption distribution obtained in this study for vegetables and
fruit, red and processed meat, milk products and cereals were compared to the ref-
erence consumption levels given by the Finnish food-based dietary guidelines. The
comparisons for vegetables and fruit and for red and processed meat are shown in
Figures 1and 2, respectively.
The vegetable and fruit consumption guideline (500 g/day) was met by 20–29% of the
highest educational and income groups of men and women as well as by 24% of the middle
educational group of women (Figure 1). In these SES groups, the proportion of those who
reached the guideline was significantly higher compared to other SES groups. On average,
8–15% of men in different urbanisation groups, and 16–23% of women in these groups, met
the vegetables and fruit consumption guideline, but differences between the urbanisation
groups were not statistically significant within genders.
Overall, 18–22% of men met the guideline value for red and processed meat con-
sumption (no more than 500 g/week as cooked). Among men, the groups adhering more
closely to the guideline than others were the highest educated and urban men (34% and
24%, respectively) (Figure 2). There was no significant difference by income level. Among
women, adherence to the meat guideline was generally higher; the overall proportion
not exceeding the recommended intake was highest among the highest educated of these
(83%) and also higher among urban women (77%) compared to other urbanization levels
(Figure 2, bottom). Again, income was not a significant factor.
Nutrients 2022,14, 1347 7 of 22
Table 2. Average consumption of food groups (g/day) in men and women by education, income and urbanisation level.
Education Income Urbanisation Level
Low (1) Middle (2) High (3) General
Test
Pair-Wise
Comparison Lowest Qrt (1) Middle (2.–3. Qrt) (2) Highest Qrt (3) General
Test
Pair-Wise
Comparison Urban (1) Semi-Urban (2) Rural (3) General
Test
Pair-Wise
Comparison
Food Group Mean 95% CI Mean 95%CI Mean 95% CI p-
Value Sign. Diff. 1Mean 95% CI Mean 95% CI Mean 95% CI p-
Value Sign. Diff. 1Mean 95% CI Mean 95% CI Mean 95% CI p-
Value Sign. Diff. 1
Men
Veg. and fruit 289 265–314 283 257–310 371 340–402 <0.001 1, 2 < 3 280 246–313 313 289–337 360 329–391 <0.001 1, 2 < 3 327 304–349 298 267–329 271 229–312 0.001 1 > 2, 3
Potatoes 89 79–99 86 76–95 79 68–89 NS NS 84 73–95 91 82–100 71 60–83 0.040 2 > 3 80 72–87 85 77–93 107 91–124 0.021 1 <3
Legumes 8 5–10 13 7–20 16 10–22 NS 214 7–21 12 8–15 12 8–16 NS 215 11–20 8 5–11 7 4–10 NS 2
Nuts and
seeds 5 3–6 8 5–10 9 6–11 <0.001 27 4–10 6 5–7 8 5–11 0.001 28 6–10 6 4–8 4 2–5 0.017 2
Red and proc.
Meat 146 131–161 149 136–163 117 102–131 <0.001 1, 2 > 3 141 124–158 135 125–145 144 125–163 NS NS 133 122–144 150 133–167 139 119–160 0.007 1 < 2
Beef 27 21–33 41 32–51 31 23–39 0.008 237 27–47 28 23–34 39 29–48 NS 235 29–42 33 26–40 22 17–28 NS 2
Pork 42 32–51 42 35–50 24 18–30 <0.001 243 32–54 37 31–43 28 20–36 NS 235 29–41 43 32–54 32 24–41 NS 2
Sausages 40 32–47 31 26–37 35 25–45 NS 231 23–40 36 31–42 40 30–49 NS 231 25–36 41 32–49 46 33–59 0.001 2
Fish and
seafood 34 27–41 32 16–47 43 35–51 0.032 234 17–51 35 29–41 39 30–48 0.020 240 30–49 27 22–32 34 21–48 NS 2
Liquid milk 407 358–457 389 329–450 342 301–384 NS NS 444 385–503 355 320–389 358 301–416 NS NS 361 320–402 400 339–462 444 376–512 0.016 1 < 3
Milk fats 19 16–22 18 15–21 19 16–22 NS NS 18 14–22 19 17–21 18 15–21 NS NS 18 16–20 19 17–22 20 16–23 NS NS
Cheese 32 28–35 36 30–42 33 29–37 NS NS 33 27–40 35 31–38 32 27–38 NS NS 32 28–35 36 30–43 35 29–41 NS NS
Cereals 146 137–156 154 137–171 150 141–158 NS NS 157 138–176 152 144–159 139 128–150 0.035 2 > 3 151 140–162 144 133–154 156 144–169 NS NS
Women
Veg. and fruit 326 294–357 394 365–423 422 390–453 <0.001 1 < 2, 3 342 307–377 385 358–412 418 387–448 0.009 1 < 3 393 370–417 375 321–429 340 293–387 NS NS
Potatoes 64 57–71 64 55–73 58 52–65 NS NS 61 51–70 65 58–72 58 51–65 NS NS 59 53–65 60 53–67 76 66–87 0.004 1, 2 < 3
Legumes 11 8–14 14 8–19 15 11–20 0.038 214 10–19 14 10–19 12 9–16 NS 215 11–19 11 6–16 9 5–12 NS 2
Nuts and
seeds 6 4–8 8 6–10 11 9–13 <0.001 27 5–9 8 6–10 12 9–14 <0.001 210 8–11 7 5–9 7 4–9 0.005 2
Red and proc.
meat 79 72–86 72 64–80 61 55–68 <0.001 1, 2 > 3 76 66–85 71 66–77 65 57–73 NS NS 66 60–71 78 70–86 83 73–94 <0.001 1 < 2, 3
Beef 17 13–22 21 17–26 17 13–21 NS 218 13–24 18 15–21 20 16–25 NS 219 16–23 17 14–21 17 14–20 NS 2
Pork 20 16–24 19 15–23 14 11–18 0.015 222 16–28 18 15–22 14 11–17 0.016 215 13–18 21 16–26 25 17–33 0.006 2
Sausages 20 16–25 14 10–17 13 10–16 NS 219 14–24 16 13–19 11 8–14 NS 213 11–16 20 13–27 19 14–25 NS 2
Fish and
seafood 24 19–29 27 22–32 34 28–39 0.011 222 17–27 27 23–31 35 29–41 0.003 230 26–34 25 19–30 23 16–30 NS 2
Liquid milk 333 295–370 290 260–320 286 260–313 NS NS 297 257–338 306 282–331 307 274–341 NS NS 280 258–303 341 303–380 332 278–386 0.043 1 < 2
Milk fats 12 11–14 14 12–15 14 12–15 NS NS 11 9–14 14 12–15 14 12–16 NS NS 13 11–14 13 10–15 16 13–19 0.001 1, 2 < 3
Cheese 19 17–21 24 21–26 24 21–27 0.004 1 < 2 20 17–23 23 21–25 24 22–27 NS NS 22 21–24 22 19–25 22 19–26 NS NS
Cereals 110 103–117 108 101–116 116 109–122 NS NS 119 108–129 110 105–115 109 103–115 0.015 1 >2, 3 110 105–116 114 109–120 109 100–119 NS NS
1
Considered significantly different with group rankings as indicated, if for the general test p< 0.05 and for pair-wise comparison p< 0.05.
2
Pair-wise comparisons not produced for food
groups where the non-parametric general test had to be used. CI, confidence interval; Sign. Diff., significant difference; NS, not statistically significant; veg., vegetables; proc., processed.
Nutrients 2022,14, 1347 8 of 22
Nutrients 2022, 14, x FOR PEER REVIEW 9 of 25
Nutrients 2022, 14, x. https://doi.org/10.3390/xxxxx www.mdpi.com/journal/nutrients
3.3. Food Consumption in Relation to Food-Based Dietary Guidelines
The usual food consumption distribution obtained in this study for vegetables and
fruit, red and processed meat, milk products and cereals were compared to the reference
consumption levels given by the Finnish food-based dietary guidelines. The comparisons
for vegetables and fruit and for red and processed meat are shown in Figures 1 and 2,
respectively.
Figure 1. Usual intake distribution of vegetables and fruit consumption compared to the dietary
guideline (recommended daily intake (RI); marked as dashed line) of minimally 500 g/day among
men (upper figures) and women (lower figures) according to (a) educational group, (b) income
level.
Figure 1.
Usual intake distribution of vegetables and fruit consumption compared to the dietary
guideline (recommended daily intake (RI); marked as dashed line) of minimally 500 g/day among
men (upper figures) and women (lower figures) according to (
a
) educational group, (
b
) income level.
Nutrients 2022, 14, x FOR PEER REVIEW 10 of 25
Figure 2. Usual intake distribution of red and processed meat consumption compared to the dietary
guideline (RI; marked as dashed line) of a maximum of 500 g/week (= 71 g/day as cooked meat)
among men (upper figures) and women (lower figures) according to (a) educational group and (b)
urbanisation level.
The vegetable and fruit consumption guideline (500 g/day) was met by 20–29% of the
highest educational and income groups of men and women as well as by 24% of the mid-
dle educational group of women (Figure 1). In these SES groups, the proportion of those
who reached the guideline was significantly higher compared to other SES groups. On
average, 8–15% of men in different urbanisation groups, and 16–23% of women in these
groups, met the vegetables and fruit consumption guideline, but differences between the
urbanisation groups were not statistically significant within genders.
Overall, 18–22% of men met the guideline value for red and processed meat con-
sumption (no more than 500 g/week as cooked). Among men, the groups adhering more
closely to the guideline than others were the highest educated and urban men (34% and
24%, respectively) (Figure 2). There was no significant difference by income level. Among
women, adherence to the meat guideline was generally higher; the overall proportion not
exceeding the recommended intake was highest among the highest educated of these
(83%) and also higher among urban women (77%) compared to other urbanization levels
(Figure 2, bottom). Again, income was not a significant factor.
About half the men and women met the milk guideline, but no differences were seen
between SES groups. Among men, only 1–6% consumed enough cereals to reach the con-
sumption guideline. No differences were found among men between the SES groups.
Among women, the cereal consumption guideline was met best by the lowest income
group (15%) and worst among the highest income group (4%). There were no differences
between educational or urbanisation levels.
Figure 2.
Usual intake distribution of red and processed meat consumption compared to the dietary
guideline (RI; marked as dashed line) of a maximum of 500 g/week (= 71 g/day as cooked meat)
among men (upper figures) and women (lower figures) according to (
a
) educational group and
(b) urbanisation level.
Nutrients 2022,14, 1347 9 of 22
About half the men and women met the milk guideline, but no differences were
seen between SES groups. Among men, only 1–6% consumed enough cereals to reach the
consumption guideline. No differences were found among men between the SES groups.
Among women, the cereal consumption guideline was met best by the lowest income group
(15%) and worst among the highest income group (4%). There were no differences between
educational or urbanisation levels.
3.4. Nutrient Intake Differences and Intake Adequacy Evaluation Based on RI
The evaluation of nutrient intake differences and the adequacy evaluation for nutrients
that did not have AR values available and for which the evaluation was based on RI values
instead (i.e., macronutrients and salt) are shown in Table 3. In addition, RI was also used for
iron in pre-menopausal women. Proportions of population groups meeting the RI reference
intakes according to our data on intake distributions in men and women by education,
income and urbanisation level are presented in the Supplementary Material, Table S1.
Table 3.
Average nutrient intakes and adequacy evaluation based on recommended daily intake (RI)
values in men and women by education, income and urbanisation level.
A. Men Reference
Value
General
Test
Pair-Wise
Comparison Overall Adequacy Evaluation 2
Nutrient RI Mean 95% CI Mean 95% CI Mean 95% CI p-Value Sign. Diff. 1
Education Low (1) Middle (2) High (3)
Energy (MJ) - 9.4 9.0–9.8 9.5 8.9–10.2 9.5 9.1–9.8 NS NS -
Protein (E%) 10–20 17.8 17.4–18.3 18.1 17.6–18.6 18.1 17.5–18.7 NS NS High intake
Total Carbohydrates
(E%) 45–60 44.2 43.2–45.2 42.6 41.7–43.5 43.2 42.1–44.4 0.036 1 > 2 No firm conclusions can be drawn
Fibre (g) >35 21.9 20.7–23.2 21.1 20–22.1 24.4 23–25.8 0.001 3 > 1, 2 No firm conclusions can be drawn
Fat (E%) 25–40 38.0 37.1–38.9 39.3 38.5–40.1 38.6 37.6–39.7 0.036 1 < 2 Low prevalence of inadequacy
Saturated f.a. (SFA)
(E%) <10 15.1 14.6–15.5 15.4 14.8–15.9 14.7 14.2–15.2 NS NS High intake
Polyunsaturated f.a.
(PUFA) (E%) 5–10 6.6 6.4–6.8 6.7 6.5–7 7.1 6.8–7.3 0.023 3 > 1, 2 Low prevalence of inadequacy
N-3 PUFA (E%) 1 1.5 1.5–1.6 1.5 1.4–1.6 1.7 1.6–1.8 0.0163 3 > 2 Low prevalence of inadequacy
Salt (g) ≤5 8.8 8.4–9.1 9.0 8.3–9.7 8.4 8–8.8 NS NS High intake
Income Lowest Qrt (1) Middle (2) Highest Qrt (3)
Energy (MJ) - 9.9 9.2–10.5 9.3 9.0–9.7 9.4 9.0–9.8 NS NS -
Protein (E%) 10–20 18.0 17.4–18.6 17.6 17.2–18 18.8 18.1–19.5 0.041 2 < 3 High intake
Total Carbohydrates
(E%) 45–60 43.0 42–44 44.1 43.4–44.9 42.4 41–43.8 NS NS No firm conclusions can be drawn
Fibre (g) >35 21.3 19.8–22.8 23.0 21.9–24.1 23.1 21.6–24.7 0.036 1 < 3 No firm conclusions can be drawn
Fat (E%) 25–40 39.0 38–39.9 38.2 37.5–39 38.8 37.6–40 NS NS Low prevalence of inadequacy
Saturated f.a. (SFA)
(E%) <10 15.2 14.6–15.8 15.1 14.8–15.5 14.8 14.2–15.4 NS NS High intake
Polyunsaturated f.a.
(PUFA) (E%) 5–10 6.8 6.4–7.1 6.7 6.5–6.9 7.0 6.7–7.3 NS NS Low prevalence of inadequacy
N-3 PUFA (E%) 1 1.5 1.4–1.6 1.5 1.5–1.6 1.6 1.5–1.8 NS NS Low prevalence of inadequacy
Salt (g) ≤5 9.0 8.3–9.7 8.7 8.4–9 8.7 8.3–9.2 NS NS High intake
Urbanisation level Urban (1) Semi-urban (2) Rural (3)
Energy (MJ) - 9.4 9.0–9.8 9.6 9.1–10.0 9.6 9.1–10.0 NS NS -
Protein (E%) 10–20 18.2 17.8–18.6 18.2 17.5–18.9 16.8 16.2–17.3 0.003 1,2 > 3 High intake
Total Carbohydrates
(E%) 45–60 43.1 42.2–43.9 43.0 41.9–44.2 45.5 43.5–47.6 NS NS No firm conclusions can be drawn
Fibre (g) >35 22.4 21.4–23.4 22.0 20.5–23.5 23.0 20.9–25 NS NS No firm conclusions can be drawn
Fat (E%) 25–40 38.8 37.9–39.6 38.7 37.9–39.5 37.7 35.9–39.5 NS NS Low prevalence of inadequacy
Saturated f.a. (SFA)
(E%) <10 14.8 14.4–15.3 15.3 14.8–15.7 15.7 14.8–16.6 NS NS High intake
Polyunsaturated f.a.
(PUFA) (E%) 5–10 7.0 6.7–7.2 6.7 6.5–6.9 6.2 5.8–6.6 0.006 1 > 3 Low prevalence of inadequacy
N-3 PUFA (E%) 1 1.6 1.5–1.7 1.5 1.4–1.6 1.4 1.3–1.6 0.026 1 > 3 Low prevalence of inadequacy
Salt (g) ≤5 8.7 8.3–9.2 8.8 8.4–9.3 8.7 8.2–9.2 NS NS High intake
Nutrients 2022,14, 1347 10 of 22
Table 3. Cont.
B. Women Reference
value
General
test
Pair-wise
comparison Overall adequacy evaluation2
Nutrient RI Mean 95% CI Mean 95% CI Mean 95% CI p-Value Sign. Diff.1
Education Low (1) Middle (2) High (3)
Energy (MJ) - 7.1 6.8–7.4 7.2 6.9–7.5 7.9 7.6–8.1 0,0005 3 > 1, 2 -
Protein (E%) 10–20 17.6 17.1–18.1 17.7 17.2–18.2 17.1 16.5–17.6 NS NS High intake
Total Carbohydrates
(E%) 45–60 44.8 43.8–45.8 44.9 43.9–45.9 44.5 43.6–45.4 NS NS No firm conclusions can be drawn
Fibre (g) >25 18.5 17.5–19.6 20.8 19.7–22 22.2 21.1–23.3 0.000 3,2 > 1 No firm conclusions can be drawn
Fat (E%) 25–40 37.6 36.7–38.6 37.4 36.5–38.3 38.4 37.6–39.2 NS NS Low prevalence of inadequacy
Saturated f.a. (SFA)
(E%) <10 14.6 14–15.2 14.4 13.9–14.9 14.0 13.6–14.4 NS NS High intake
Polyunsaturated f.a.
(PUFA) (E%) 5–10 6.6 6.3–7 6.7 6.4–7 7.4 7.1–7.7 0.000 3 > 1, 2 Low prevalence of inadequacy
N-3 PUFA (E%) 1 1.6 1.5–1.7 1.6 1.5–1.7 1.8 1.7–1.9 0.0090 3 > 1, 2 Low prevalence of inadequacy
Salt (g) ≤5 6.3 6.1–6.6 6.2 5.9–6.5 6.7 6.4–6.9 NS NS High intake
Iron (18–50 years)
(mg) 15 9.0 8.5–9.5 10.2 9.6–10.8 10.9 10.3–11.5 0.0000 3, 2 > 1 No firm conclusions can be drawn
Income Lowest Qrt (1) Middle (2) Highest Qrt (3)
Energy (MJ) - 7.2 6.8–7.6 7.3 7.1–7.6 7.7 7.4–8.0 NS NS -
Protein (E%) 10–20 17.3 16.7–17.9 17.3 17–17.7 17.7 17.1–18.2 NS NS High intake
Total Carbohydrates
(E%) 45–60 45.8 44.6–46.9 45.1 44.3–45.8 43.5 42.3–44.6 0.021 3 < 1
1,2 low prevalence of inadequacy,
3 no firm conclusions can be
drawn
Fibre (g) >25 20.0 18.7–21.3 20.6 19.5–21.6 21.4 20–22.7 NS NS No firm conclusions can be drawn
Fat (E%) 25–40 36.9 35.6–38.2 37.6 36.9–38.3 38.9 37.7–40 NS 1 < 3 Low prevalence of inadequacy
Saturated f.a. (SFA)
(E%) <10 14.0 13.3–14.7 14.5 14.1–14.9 14.6 13.8–15.3 NS NS High intake
Polyunsaturated f.a.
(PUFA) (E%) 5–10 6.7 6.4–7.1 6.8 6.5–7 7.2 6.9–7.5 0.020 2 < 3 Low prevalence of inadequacy
N-3 PUFA (E%) 1 1.6 1.5–1.7 1.7 1.6–1.8 1.8 1.7–1.9 0.031 1 < 3 Low prevalence of inadequacy
Salt (g) ≤5 6.4 6.1–6.7 6.3 6.1–6.6 6.5 6.2–6.8 NS NS High intake
Iron (18–50 years)
(mg) 15 9.5 8.9–10.1 10.1 9.5–10.7 10.3 9.6–11.1 NS NS No firm conclusions can be drawn
Urbanisation level Urban (1) Semi-urban (2) Rural (3)
Energy (MJ) - 7.4 7.2–7.6 7.3 7.0–7.7 7.2 6.9–7.5 NS NS -
Protein (E%) 10–20 17.6 17.2–17.9 17.5 16.9–18.1 17.2 16.6–17.7 NS NS High intake
Total Carbohydrates
(E%) 45–60 44.3 43.6–45 45.6 44.1–47.2 45.2 43.9–46.5 NS NS
1 no firm conclusions can be
drawn, 2,3 low prevalence of
inadequacy
Fibre (g) >25 20.7 19.9–21.6 20.3 18.6–22 19.9 18.2–21.6 NS NS No firm conclusions can be drawn
Fat (E%) 25–40 38.2 37.5–38.8 36.9 35.4–38.4 37.6 36.5–38.7 NS NS Low prevalence of inadequacy
Saturated f.a. (SFA)
(E%) <10 14.3 13.9–14.6 14.3 13.3–15.3 14.9 14.3–15.4 NS NS High intake
Polyunsaturated f.a.
(PUFA) (E%) 5–10 7.1 6.8–7.4 6.6 6.3–6.8 6.5 6.2–6.9 0.011 1 > 2, 3 Low prevalence of inadequacy
N-3 PUFA (E%) 1 1.7 1.6–1.8 1.6 1.5–1.7 1.6 1.5–1.7 NS NS Low prevalence of inadequacy
Salt (g) ≤5 6.4 6.2–6.6 6.5 6.2–6.8 6.3 5.9–6.6 NS NS High intake
Iron (18–50 years)
(mg) 15 10.2 9.8–10.6 9.8 8.8–10.8 9.1 8.5–9.8 NS NS No firm conclusions can be drawn
1
Considered significantly different with group rankings as indicated, if for the general test p< 0.05 and for
pair-wise comparison p< 0.05.
2
Using RI reference values provided by Nordic Nutrition Recommendations
(NNR2012) [
25
]. If the mean intake of a group is at or above the RI, there is probably a “low prevalence of
inadequacy” and if it is below the RI, “no firm conclusions can be drawn regarding the prevalence of inadequacy
at the group level”, according to the NNR2012 [
25
]. RI, recommended daily intake; E%, % of total energy; f.a.,
fatty acids; N-3, omega-3.
3.4.1. Education
Energy intake varied between 9.4 MJ/day and 9.5 MJ/day among men across edu-
cational groups (NS). Energy intake varied between 7.1 MJ/day and 7.9 MJ/day among
women across educational groups, being highest for the highest educational group. In men,
fat intake was higher in the middle education group than in the lowest education group.
The highest educational group had the highest intakes of fibre and total polyunsaturated
fatty acid (PUFA). It also had higherintake of omega-3 (n-3) PUFA, compared to the middle
education group. Similar differences were seen in women. The intake of fibre was higher
in the highest and middle education groups compared to the lowest education group, and
the intake of PUFA and n-3 PUFA was highest among those in the highest education group
(Table 3).
Nutrients 2022,14, 1347 11 of 22
The mean intakes of total fat, protein, total PUFA and n-3 PUFA met the lower bound
of the RI reference values. However, total fat, protein, saturated fatty acid (SFA) and salt in-
takes were found to be high both in men and women. The higher level of recommended pro-
tein intake (20 E%) was exceeded by 18–25% of men and by 4–19% of women; there were no
differences between educational levels in this regard (S
upplementary Material, Table S2
).
Mean total carbohydrate and fibre intakes were both below the RI reference values, but
based on this fact alone, no firm conclusions can be drawn about the adequacy of intakes
either in men or in women (Table 3) [
25
]. The intake of salt in both men and women
exceeded the population goal (5 g/day) in over 95% of men and in about 85–90% of women
in different educational groups. The mean iron intake of pre-menopausal women fell below
the RI reference value; thus, no firm conclusions can be drawn about the adequacy of their
intake [25] (Table 3).
3.4.2. Income
Energy intake by income group ranged between 9.3 and 9.9 MJ/day among men
and between 7.2 MJ/day and 7.7 MJ/day among women; differences between income
groups within genders were not statistically significant. Intake by income did differ notably,
however, for several important nutrients. Thus, with respect to fibre, the lowest income
group of men had the lowest intake. For protein, the intake was lower in the middle income
group compared to the highest income group of men (Table 3). Intakes of fat, PUFA and n-3
PUFA were highest in the highest income group of women, while intake of carbohydrates
was highest in the lowest income group of women (Table 3).
The intakes of total fat, protein, total PUFA and n-3 PUFA met the minimum rec-
ommendations in all income groups of men and women. The intakes of total fat and
protein were sufficient and indeed exceeded recommended levels in all income groups
of men and women. The higher level of recommended protein intake was exceeded
by 16–33% of men, with the greatest excess recorded in the highest income group, and
by 11–21% of women in the income groups, but without differences between groups
(S
upplementary Material, Table S2
). SFA and salt intakes were found to be high through-
out: salt intake in both men and women exceeded the population goal (5 g/day) in over
95% of men and in about 85% of women. The mean iron intake of pre-menopausal women
in all income groups was below the RI reference value; thus, no firm conclusions can be
drawn about the adequacy of their intake (Table 3).
3.4.3. Urbanisation Level
Energy intake was between 9.4 MJ/day and 9.6 MJ/day among men and between
7.2 MJ/day
and 7.4 MJ/day among women based on urbanisation level and did not differ
by urbanisation level for either gender. In men, the intakes of PUFA and n-3 PUFA were
lower in rural areas compared to urban areas. The protein intake of rural men was also
lower in comparison to urban and semi-urban men. In a similar trend, in women, urban
residents had higher PUFA intakes compared to semi-urban or rural residents (Table 3).
Intakes of total fat, protein, total PUFA and n-3 PUFA met the recommendations
in all urbanisation groups. Practically all men and women met the lower limit of the
protein intake recommendation range, i.e., 10 E%. Except for semi-urban women, in all
population groups evaluated, far below 10% met the saturated fatty acid recommendation,
i.e.,
<10 E%
. Thus, total fat, protein and saturated fatty acid intakes were evaluated to be
high in both men and women. The intake of salt exceeded the population goal (5 g/day)
in more than 95% of all men and in about 80–85% of women in all urbanisation groups
(Su
pplementary Material, Table S2
). The mean iron intake of pre-menopausal women at all
urbanisation levels was below the RI reference value, so no firm conclusions can be drawn
about intake adequacy (Table 3).
Nutrients 2022,14, 1347 12 of 22
3.5. Nutrient Intake Differences and Adequacy Evaluation Based on the AR Cut-Point Method
The nutrient intake differences for micronutrients are shown in
Supplementary Material
,
Table S3. The adequacy evaluations based on the AR cut-point method [
48
] are shown in
Tables 4–6.
3.5.1. Education
In men, intakes of vitamin E, folate, vitamin C and iron were higher in the highest
educational group compared to the two other groups. In women, intakes of vitamin A,
folate and vitamin C were higher in the highest and middle education groups compared to
the lowest education group. In addition, the intake of vitamin E was highest in the highest
education group (Supplementary Material, Table S3).
The intakes of vitamin E, vitamin B12, calcium, iodine and zinc met the AR reference
values in all educational groups of men and women. In addition, both men and post-
menopausal women met the iron requirement and women the B2 requirement. Intakes
of vitamins A, D, B1 and folate were not adequate (Table 4). In addition, among men, the
intake of vitamin B2 was not adequate.
3.5.2. Income
The lowest income group of men had the lowest vitamin C intake. For iron, the intake
was lower in the lowest income group compared to the highest, whereas for B12, the intake
was lower in the middle income group compared to the highest. In women, the intakes of
vitamin D, vitamin E, vitamin B2, folate, vitamin B12 and vitamin C were highest in the
highest income group (Supplementary Material, Table S3).
The intakes of vitamin E, vitamin B12, calcium, iodine and zinc met the adequacy
criteria in all income groups of men and women. In addition, the adequacy criteria for iron
were met in all income groups of men and in post-menopausal women. Vitamins A, D, B1,
B2, folate and vitamin C levels were evaluated as not adequate, with the exception of the
highest income group of men (vitamin C and D), the highest and middle income groups of
women (vitamin B2) and vitamin C in all income groups of women (Table 5).
3.5.3. Urbanisation Level
Only a few differences in nutrient intakes for micronutrients by urbanisation level were
seen. Calcium intake was higher among rural men compared to urban men. By contrast,
vitamin C intake was higher among urban compared to semi-urban men. In women, urban
women had higher folate intakes compared to rural women (S
upplementary Material, Table S3
).
The intakes of vitamin E, vitamin B12, calcium, iodine and zinc met the adequacy
criteria in all urbanisation level groups. In addition, all urbanisation level groups of men
and post-menopausal women met the iron recommendations. Under 90% of the population
met the AR reference values of vitamin A, D, B1, B2, folate and vitamin C. These intakes are
thus considered “not adequate”. The exceptions are the intake of vitamin C among urban
women and of vitamin B2 among urban and semi-urban women (Table 6).
3.5.4. Case Vitamin C
Of all nutrients evaluated, vitamin C intake distributions differed the most between
different SES groups. Vitamin C intakes were not adequate for any of the educational
groups of men or for the lowest educational group of women. Vitamin C intakes were also
not adequate for the two lowest income groups of men or for men at any urbanisation
level; semi-urban and rural women had inadequate vitamin C intakes as well (Figure 3). In
contrast, in other population groups, over 90% of participants exceeded the AR reference
value of vitamin C.
3.6. Nutrient Sources of the Different Population Groups Studied
The highest educated group obtained more nutrients from vegetables, fruit and berries,
from legumes and nuts, and from fish compared to the lowest educated group, for whom
Nutrients 2022,14, 1347 13 of 22
fats, meats, cereals, potatoes and beverages ranked as more important nutrient sources
(data only shown for folate in Supplementary Material, Figure S1a–c).
Across income groups, the picture was more mixed. For many nutrients, both the
lowest and highest income groups or else two adjacent groups shared the same important
food sources; e.g., meat, eggs and legumes served as an important source of folate, and
vegetables as an important source of vitamin C, among both the lowest and the highest
income groups of men). In this inspection of nutrient sources, meats or legumes were not
divided into detailed sub-categories. Among women, the important food groups of the
highest income group were more often vegetables, fruit and berries, fish, legumes and nuts,
milk, sugars and sweets, whereas with the lower income groups, cereals, meat, potatoes,
eggs and fats served more often as important nutrient sources.
Some important food sources of nutrients among urban men and women were fish,
vegetables, legumes and nuts, fats, and fruit and berries; by contrast, in semi-urban and
rural population groups, meat, eggs, and sugary ingredients and confectionary were seen to
be more common. In addition, milk, cereals and potatoes were especially common nutrient
sources in the rural population groups.
Table 4.
Proportion of population groups reaching the average requirement (AR) values, and
adequacy evaluation based on usual intake distributions in men and women by education.
Reference
Value Low (1) Middle (2) High (3) Sign. Diff. 1
≥90% of
Population
Group > AR
Overall Adequacy
Evaluation 2
Nutrient AR % 95% CI % 95% CI % 95% CI Yes/No
Men
Vitamin A (µg RE) 600 81 72–90 71 67–77 77 69–85 NS No Not adequate
Vitamin D (µg) 7.5 89 85–95 86 79–93 86 80–92 NS No Not adequate
Vitamin E (mg) 6 96 94–99 96 95–98 99 97–100 NS Yes Adequate
Vitamin B1 (mg) 1.2 65 59–72 65 59–72 64 58–70 NS No Not adequate
Vitamin B2 (mg) 1.4 85 80–89 82 78–87 80 76–85 NS No Not adequate
Folate (µg) 200 65 59–71 67 61–75 80 73–87 3 > 1 No Not adequate
Vitamin B12 (µg) 1.4 100 100–100 100 100–100 100 100–100 NS Yes Adequate
Vitamin C (mg) 60 70 63–77 72 66–80 82 76–88 NS No Not adequate
Calcium (mg) 500 97 95–99 97 95–99 98 96–100 NS Yes Adequate
Iron (mg) 7 95 92–99 93 90–97 95 93–98 NS Yes Adequate
Iodine (µg) 100 100 99–100 99 98–100 100 99–100 NS Yes Adequate
Zinc (mg) 6 99 98–100 99 98–100 99 98–100 NS Yes Adequate
Women
Vitamin A (µg RE) 500 84 74–94 86 80–93 89 82–100 NS No Not adequate3
Vitamin D (µg) 7.5 69 63–77 70 63–78 69 63–75 NS No Not adequate
Vitamin E (mg) 5 97 94–99 96 94–99 100 99–100 3 > 2 Yes Adequate
Vitamin B1 (mg) 0.9 72 66–77 76 70–83 79 73–86 NS No Not adequate
Vitamin B2 (mg) 1.1 90 86–95 91 88–95 91 88–95 NS Yes Adequate
Folate (µg) 200 45 38–52 60 55–67 74 68–81 2 > 1, 3 > 1, 2 No Not adequate
Vitamin B12 (µg) 1.4 100 100–100 100 100–100 100 100–100 NS Yes Adequate
Vitamin C (mg) 50 88 82–93 94 91–98 96 94–99 3 > 1 No (1), Yes (2, 3) 1 not adequate, 2,3
adequate
Calcium (mg) 500 96 94–99 98 97–100 98 97–99 NS Yes Adequate
Iron (51–74 years) (mg) 6 98 94–100 94 91–98 96 93–100 NS Yes Adequate
Iodine (µg) 100 99 97–100 99 98–100 99 98–100 NS Yes Adequate
Zinc (mg) 5 100 99–100 100 99–100 99 98–100 NS Yes Adequate
1
Significant differences in proportions between educational groups were evaluated by non-overlapping 95%
CI.
2
If the proportion of the group reaching the average requirement (AR) level was
≥
90%, the intake was
considered “adequate”. If <90% of the group met the AR level, the intake was considered “not adequate”. If over
2.5% of the group exceeded the upper limit of the RI range of macronutrients as E% or exceeded the UL level of
micronutrients, the intake was considered “high”.
3
Based on the confidence interval, the highest educational
group is close to adequate vitamin A intake. RE, retinol equivalents.
Nutrients 2022,14, 1347 14 of 22
Table 5.
Proportion of population groups reaching the average requirement (AR) values, and
adequacy evaluation according to usual intake distributions in men and women by income.
Reference
Value Lowest Qrt (1) Middle (2.–3.
Qrt) (2) Highest Qrt (3) Sign. Diff. 1
≥90% of
Population
Group > AR
Overall Adequacy
Evaluation 2
Nutrient AR % 95% CI % 95% CI % 95% CI Yes/No
Men
Vitamin A (µg RE) 600 77 69–88 72 67–80 81 74–90 NS No Not adequate
Vitamin D (µg) 7.5 88 80–94 83 77–89 90 84–98 NS Yes (3), No (1, 2) 3 adequate, 1 and 2 not
adequate
Vitamin E (mg) 6 98 96–99 96 94–98 99 99–100 3 > 2 Yes Adequate
Vitamin B1 (mg) 1.2 63 56–70 63 58–70 67 60–73 NS No Not adequate
Vitamin B2 (mg) 1.4 85 80–89 80 75–85 88 83–92 NS No Not adequate
Folate (µg) 200 70 62–77 67 62–74 85 76–92 3 > 2 No Not adequate
Vitamin B12 (µg) 1.4 100 100–100 100 100–100 100 100–100 NS Yes Adequate
Vitamin C (mg) 60 72 65–79 70 65–75 93 87–98 3 > 1, 2 Yes (3), No (1, 2) 3 adequate, 1,2 not
adequate
Calcium (mg) 500 98 96–99 97 95–99 97 95–99 NS Yes Adequate
Iron (mg) 7 94 90–98 95 92–97 97 95–99 NS Yes Adequate
Iodine (µg) 100 99 99–100 100 99–100 100 99–100 NS Yes Adequate
Zinc (mg) 6 99 98–100 99 98–100 99 99–100 NS Yes Adequate
Women
Vitamin A (µg RE) 500 88 80–100 89 83–96 85 78–94 NS No Not adequate
Vitamin D (µg) 7.5 64 58–73 71 64–78 69 61–80 NS No Not adequate
Vitamin E (mg) 5 96 94–99 97 95–99 100 99–100 3 > 2 Yes Adequate
Vitamin B1 (mg) 0.9 72 65–81 76 70–81 78 72–86 NS No Not adequate
Vitamin B2 (mg) 1.1 87 82–92 92 88–95 95 93–98 3 > 1 No (1), Yes (2, 3) 1 not adequate, 2,3
adequate
Folate (µg) 200 51 45–60 57 52–62 78 71–87 3 > 1, 2 No Not adequate
Vitamin B12 (µg) 1.4 100 100–100 100 100–100 100 100–100 NS Yes Adequate
Vitamin C (mg) 50 91 86–97 93 89–96 95 93–98 NS Yes Adequate
Calcium (mg) 500 95 92–99 98 96–99 99 98–100 NS Yes Adequate
Iron (51–74 years) (mg) 6 94 90–99 94 91–97 99 98–100 3 > 2 Yes Adequate
Iodine (µg) 100 99 97–100 98 96–99 100 99–100 3 > 2 Yes Adequate
Zinc (mg) 5 99 98–100 100 99–100 100 100–100 NS Yes Adequate
1
Significant differences in proportions between income groups were evaluated by non-overlapping 95% CI.
2
If
the proportion of the group reaching the AR level was
≥
90%, the intake was considered “adequate”. If <90% of
the group met the AR level, the intake was evaluated to be “not adequate”. If over 2.5% of the group exceeded the
upper limit of the RI range of macronutrients as E% or exceeded the UL level of micronutrients, the intake was
evaluated to be “high”.
Nutrients 2022, 14, x FOR PEER REVIEW 18 of 25
3.5.4. Case Vitamin C
Of all nutrients evaluated, vitamin C intake distributions differed the most between
different SES groups. Vitamin C intakes were not adequate for any of the educational
groups of men or for the lowest educational group of women. Vitamin C intakes were also
not adequate for the two lowest income groups of men or for men at any urbanisation
level; semi-urban and rural women had inadequate vitamin C intakes as well (Figure 3).
In contrast, in other population groups, over 90% of participants exceeded the AR refer-
ence value of vitamin C.
Figure 3. Usual intake distribution of vitamin C intakes compared to the average requirement (AR)
value among men (average requirement (AR) = 60 mg/day, upper figures) and women (AR = 50
mg/day, lower figures) according to (a) educational group, (b) income level and (c) urbanization
level.
3.6. Nutrient Sources of the Different Population Groups Studied
The highest educated group obtained more nutrients from vegetables, fruit and ber-
ries, from legumes and nuts, and from fish compared to the lowest educated group, for
whom fats, meats, cereals, potatoes and beverages ranked as more important nutrient
sources (data only shown for folate in Supplementary Material, Figure S1a–c).
Across income groups, the picture was more mixed. For many nutrients, both the
lowest and highest income groups or else two adjacent groups shared the same important
food sources; e.g., meat, eggs and legumes served as an important source of folate, and
vegetables as an important source of vitamin C, among both the lowest and the highest
income groups of men). In this inspection of nutrient sources, meats or legumes were not
divided into detailed sub-categories. Among women, the important food groups of the
highest income group were more often vegetables, fruit and berries, fish, legumes and
nuts, milk, sugars and sweets, whereas with the lower income groups, cereals, meat, po-
tatoes, eggs and fats served more often as important nutrient sources.
Some important food sources of nutrients among urban men and women were fish,
vegetables, legumes and nuts, fats, and fruit and berries; by contrast, in semi-urban and
rural population groups, meat, eggs, and sugary ingredients and confectionary were seen
to be more common. In addition, milk, cereals and potatoes were especially common nu-
trient sources in the rural population groups.
Figure 3.
Usual intake distribution of vitamin C intakes compared to the average require-
ment (AR) value among men (average requirement (AR) = 60 mg/day, upper figures) and
women (
AR = 50 mg/day
, lower figures) according to (
a
) educational group, (
b
) income level and
(c) urbanization level.
Nutrients 2022,14, 1347 15 of 22
Table 6.
Proportion of population groups reaching the average requirement (AR) values, and
adequacy evaluation according to usual intake distributions in men and women by ur
banisation leve
l.
Reference
Value Urban (1) Semi-Urban
(2) Rural (3) Sign. Diff. 1
≥90% of
Population
Group > AR
Overall Adequacy
Evaluation 2
Nutrient AR % 95% CI % 95% CI % 95% CI Yes/No
Men
Vitamin A (µg RE) 600 74 68–79 83 70–97 72 66–79 NS No Not adequate
Vitamin D (µg) 7.5 86 80–91 87 81–93 86 80–93 NS No Not adequate
Vitamin E (mg) 6 98 96–99 95 93–98 96 94–100 NS Yes Adequate
Vitamin B1 (mg) 1.2 59 53–64 67 61–75 65 60–74 NS No Not adequate
Vitamin B2 (mg) 1.4 81 77–86 82 77–87 86 81–95 NS No Not adequate
Folate (µg) 200 73 67–78 62 56–69 68 62–77 NS No Not adequate
Vitamin B12 (µg) 1.4 100 100–100 100 100–100 100 100–100 NS Yes Adequate
Vitamin C (mg) 60 78 72–83 67 59–74 68 63–77 NS No Not adequate
Calcium (mg) 500 97 95–98 97 95–99 99 99–100 3 > 1 Yes Adequate
Iron (mg) 7 94 91–96 97 94–100 92 88–97 NS Yes Adequate
Iodine (µg) 100 99 99–100 100 99–100 100 100–100 NS Yes Adequate
Zinc (mg) 6 99 98–100 99 98–100 100 99–100 NS Yes Adequate
Women
Vitamin A (µg RE) 500 86 81–92 86 77–96 84 72–96 NS No Not adequate
Vitamin D (µg) 7.5 67 61–74 66 61–73 72 64–80 NS No Not adequate
Vitamin E (mg) 5 98 96–99 98 96–100 96 93–98 NS Yes Adequate
Vitamin B1 (mg) 0.9 75 70–81 71 65–77 76 68–83 NS No Not adequate
Vitamin B2 (mg) 1.1 90 87–94 94 90–97 87 84–91 NS Yes (1, 2), No (3) 1, 2 adequate, 3 not
adequate
Folate (µg) 200 65 61–71 56 49–64 44 38–49 3 < 1, 2 No Not adequate
Vitamin B12 (µg) 1.4 100 100–100 100 99–100 100 100–100 NS Yes Adequate
Vitamin C (mg) 50 95 93–98 89 85–94 88 84–93 NS Yes (1), No (2, 3) 1 adequate, 2, 3 not
adequate
Calcium (mg) 500 98 97–99 98 97–99 94 92–97 3 < 1, 2 Yes Adequate
Iron (51–74 years) (mg) 6 95 92–99 94 90–98 98 96–100 NS Yes Adequate
Iodine (µg) 100 99 97–100 99 98–100 99 97–100 NS Yes Adequate
Zinc (mg) 5 100 99–100 99 99–100 100 100–100 NS Yes Adequate
1
Significant differences in proportions between urbanisation level groups were evaluated by non-overlapping
95% CI.
2
If the proportion of the group reaching the AR level was
≥
90%, the intake was considered “adequate”. If
<90% of the group met the AR level, the intake was considered “not adequate”. If over 2.5% of the group exceeded
the upper limit of the RI range of macronutrients as E% or exceeded the UL level of micronutrients, the intake
was considered “high”.
4. Discussion
In this study, the adequacy of the Finnish diet among adults in different SES groups
was evaluated to be close to adequate—or the prevalence of inadequacy to be low—in the
case of total fat, PUFA, n-3 PUFA, vitamin E, vitamin B12, calcium, iodine and zinc intakes
of the whole population and of iron among men and post-menopausal women. On the other
hand, improvements are clearly needed to address the levels of high saturated fatty acid
and salt intake in all population groups studied. Inadequate intakes were seen for folate,
vitamin A, vitamin D, vitamin B1, vitamin B2 as well as vitamin C in almost all SES groups
studied. Additionally, protein intake was unnecessarily high, and total carbohydrate and
fibre intakes were prone to being below the recommended level across groups.
In the case of most nutrients, either all or none of the studied groups exceeded or
did not reach a recommended upper or lower reference intake. This shows that the major
challenges in the Finnish diet cover all the groups studied here, and that only a few dietary
features evaluated for nutritional adequacy are associated with SES differences. One such
exception was the highest income group of men, which had adequate vitamin C and D
intakes (>90% of the group reaching the AR value), while the lower income groups did not.
Among women, such exceptions were seen in vitamin B2 and C intakes. For vitamin B2,
differences in nutritional adequacy were seen by income groups and urbanisation level and
for vitamin C by education and urbanisation levels. It was also seen that even when none
of the population groups met the threshold for nutritional adequacy set in the evaluation
for a certain nutrient, there were differences between the proportions reaching the reference
intake. For example, PUFA intakes, which were evaluated to have low prevalence of
inadequacy in all studied groups, were nevertheless higher among the highest educational
and urban groups compared to the lower educational and rural population groups. Thus,
Nutrients 2022,14, 1347 16 of 22
this study shows that attention needs to be paid to nutrition policy actions ensuring the
availability of nutritious food for the whole population, but especially for lower SES groups
and those living in semi-urban or rural areas. In this study, we covered education, income
and urbanisation levels as SES indicators. It may be, though, that age should be a factor of
concern as well, insofar as the elderly may need special attention [61].
In general, nutrient intakes in Finland do not differ very much from general Euro-
pean levels [
62
]. Vitamin D is an exception. In the European context, Finland has one
of the highest vitamin D intakes [
7
]. This is due to the Finnish vitamin D fortification
program that was started in the 1940s and was upgraded in 2002 [
5
,
63
]. Despite this effort,
we estimate vitamin D intake to be adequate only in the highest income group of men
(
≥90% of the grou
p reaching the AR reference value). However, the differences in the
within-group proportion of SES population groups reaching the average vitamin D require-
ment were shown to be small in our study, insofar as a proportion of over 80% was reached
in all groups studied. This is the situation when only food sources of vitamin D are taken
into account (excluding food supplement sources). The main sources of
vitamin D
in the
Finnish diet are fortified foods and fish [
28
,
64
]. Similarly, there were very few differences
between population groups in the proportion of subjects reaching the AR of iodine intake.
Iodine has been shown before to be an important nutrient showing lower intakes in lower
SES groups in Europe [
14
]. Again, the small differences in iodine uptake between SES
groups in Finland today are due to a salt fortification program in place since the 1940s and
upgraded in 2015 [
65
]. These examples show that food fortification is an effective tool of
nutrition policy when it comes to mitigating dietary disparities between different SES or
other population groups.
The differences observed in vitamin C intakes here resemble those seen in earlier years
in Europe [
14
]. Vegetables and fruit are the best sources of vitamin C, providing 63% and
71% of vitamin C for men and women, respectively, in the Finnish diet [
28
]. In this study,
men in the highest educational and income groups and urban men and women in the two
highest educational groups, women in all income groups and urban women consumed
more of these foods compared to other groups within the same gender. Significantly higher
proportions in these population groups also met the AR reference value for vitamin C
intake. Moreover, although none of the studied groups exceeded the 90% proportion
for reaching the AR of folate, a significantly higher proportion of higher-educated men
and women and in higher income groups met the AR reference intake for this nutrient.
An increase in vegetable and fruit consumption is important, therefore, from the point
of view of nutritional equity, health and climate-friendly diets that are rich in foods of
vegetable origin.
Protein intake in the Finnish diet stems mainly from animal-based foods (close to
70%) [
28
]. The fact that red and processed meat intakes among 70–90% of men and among
20–40% of women are above food-based dietary guideline levels in most population groups
studied, and that vegetable and fruit consumption is still below guideline levels for about
80% of these groups, provides evidence and motivation for the need to move further
towards a more vegetable-based, healthier diet.
We see in this study that women
´
s food consumption and dietary intakes are closer to
dietary guidelines and nutrition recommendations in the case of certain foods (e.g., red and
processed meat) and nutrients (e.g., sodium or salt). These are foods and nutrients whose
recommended intake represents an upper limit; the same reference criteria (maximum
amounts) are given for both genders [
25
,
26
]. In this study, the average energy intake of
women was 7.3 MJ/day and of men 9.5 MJ/day [
28
]. This means that staying at or below
the maximum recommended intake would require greater dietary adjustments in men, e.g.,
consumption of less meat/MJ or less salty food compared to women, to ensure similar
adequacy outcomes in both genders. In the case of the vegetables and fruit guideline
(500 g/day) and certain micronutrients (e.g., vitamin D, folate and calcium) that have
the same minimum requirement set for men and women, men have an advantage over
women in adequacy evaluation simply because they should and do eat more food. The
Nutrients 2022,14, 1347 17 of 22
same applies with respect to different adult age groups, when the same absolute reference
values are applied over a broad age range with varying energy needs. In the case of
macronutrients (e.g., protein and fatty acids) different energy intakes are taken into account
since recommendations are expressed relative to total energy intakes [
25
,
26
]. It might
be useful in the future to consider whether differences in energy intake between genders
should be taken into account more comprehensively in setting dietary guidelines or nutrient
recommendations than is done today.
One limitation of this study is that it covered nutrient intake from foods only, excluding
food supplements. We address this in the Supplementary Materials, where information
on combined intakes of foods and food supplements, and an evaluation of proportions
reaching the AR reference values of men and women based on these additional data, are
provided (Supplementary Material, Table S4). These data show that some inadequate
intakes, e.g., in the case of vitamin D, are resolved by the use of food supplements; but for
most nutrients this is not the case.
A known limitation of dietary studies based on self-reporting is the possibility of
misreporting, especially an under-reporting of energy consumed. In this study, under-
reporting was estimated to be on average about 25–27% for men and about 24–26% for
women. These figures are lower than those of national FinDiet Surveys in the past [
66
]. In
certain previous studies, high educational level has shown to predict under-reporting [
66
],
but in other settings, results have been reported to the effect that under-reporting is more
prevalent among individuals in lower SES groups [
67
]. In this study, the reason why higher
energy intake among women with high education compared to other educational groups
is probably due to differences in reporting of dietary intake. Indeed, women with low or
middle educational groups were found to under-report their dietary intake more often
than women with high education. This is supported also by the observation of obesity
(BMI
≥
30 kg/m
2
) prevalence being lower among the highest educated women and not
the other way around, which is concordant with earlier findings [
68
]. The fact that our
data included approximately 25% of energy under-reporters may have also affected the
nutrient adequacy evaluation. It may be possible that the proportion of inadequate diets
according to this study is an overestimation of the real situation. This would mean that
among women, the lower SES groups would have adequate diets more commonly than
was found by this study. If that was the case, this would mean that the disparities between
SES groups would be even less among Finnish women than evaluated here. Among men,
under-reporting did not differ between the SES groups.
The strengths of this study were the very careful sampling design based on population
register data; the data collection methods, involving a selection of supporting tools for
data collection; extensive quality controls; standardization interviews for the dietary inter-
viewers throughout the data collection period using the method reported by Gavrieli and
co-workers [
21
]; availability of a broad set of background data substantiating SES variables;
data management and weighting methods used to tackle the non-response bias [
50
,
53
]. The
data collection methods used were in line with the European guidance on methodology
for harmonised food consumption data collection in EU member states put forward by
EFSA [
10
]. Advanced modelling methods were used to obtain usual food consumption
and nutrient intake estimates and population distributions based on short-term data col-
lection [
22
], which enabled more accurate dietary adequacy evaluations, using also the
AR-based cut-point method [48].
While addressing nutritional disparities and the related health inequalities has been
acknowledged as an important societal goal in itself, the increasing pressure to transition
to more climate-friendly diets will make this issue even more urgent in the future. Active
interventions are needed to achieve these goals in tandem. According to the present study,
such interventions should certainly aim for an increase in vegetable and fruit consump-
tion across the entire population; initiatives such as the inclusive school meal program
in Finland [
69
–
71
] still deserve to be fostered. Moreover, the successful fortification pro-
grams implemented since the 1940s (Vitamin D, iodine) may become an important way
Nutrients 2022,14, 1347 18 of 22
to make sure lower SES classes are quickly brought on board. However, policy makers
should also consider more targeted nutritional interventions with respect to meat and dairy
consumption. Meanwhile, our results underline the need to update nutritional guidelines
while taking into account a more nuanced understanding of SES-based differences. Finally,
any initiatives taxing foods on the basis of climate or health impacts should consider their
impacts on different SES groups.
5. Conclusions
This study shows that the major challenges in the Finnish diet apply to all population
groups studied; only certain dietary features affecting nutritional adequacy are associated
with SES differences. Urban, higher educated population groups with a higher income
–and especially women among these groups—adhere more closely to dietary guidelines
and recommended nutrient intakes than other population groups, but not even these
groups reach the desired reference intakes of all nutrients evaluated. The dietary transition
towards healthier and more climate-friendly diets is more advanced among these trailblazer
population groups. Meanwhile, those groups whose protein intake is, for the most part,
still based on red and processed meat, i.e., men in general and less educated and non-urban
men in particular, will need to make the greatest adjustments.
Means to mitigate nutritional disparities must be applied broadly and pragmatically
to safeguard equal opportunities to achieve adequate nutrition and health regardless of a
person’s income, education or place of residence. The transition to climate-friendly diets
needs to ensure the right to good nutrition for all.
Supplementary Materials:
The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/nu14071347/s1. Figure S1: Food groups as sources of
folate in the diet of men and women according to (a) educational group, (b) income level and
(c) urbanisatio
n level (% of daily intake); Table S1: Proportions of population groups reaching the
recommended daily intake (RI) values according to usual intake distributions in men and women
by education, income and urbanisation level; Table S2: Proportions of population groups exceeding
the upper value of macronutrient RI range (E%) or the UL value [
26
], and evaluation of the intakes;
Table S3: Average nutrient intakes in men and women by education, income and urbanisation level;
Table S4: Nutrient intakes from food and combined intakes from food and food supplements, and
proportion of men and women reaching the dietary reference intakes, modified from [28].
Author Contributions:
Conceptualization, L.M.V., H.T., T.K. and M.K.; methodology, H.T., L.M.V.
and N.E.K.; formal analysis, H.T.; investigation, L.M.V., P.H., L.S.-J. and L.P.; resources, N.E.K., H.T.,
L.S.-J. and L.M.V.; data curation, H.T., L.S.-J. and L.M.V.; writing—original draft preparation, L.M.V.;
writing—review and editing, P.H., H.T., L.P., L.S.-J., T.K., M.K. and N.E.K.; visualization, H.T. and
L.S.-J.; supervision, L.M.V.; project administration L.M.V. and M.K.; funding acquisition, L.M.V., M.K.
and T.K. All authors have read and agreed to the published version of the manuscript.
Funding:
The data collection was funded in addition to THL, partially by the European Food Safety
Authority (EFSA), contract OC/EFSA/DATA/2015/03 CT 01 (EU Menu, Lot 2, Finland/Adults)
* and by the Strategic Research Council (SRC) of the Academy of Finland, grant number 327370.
* Disclaimer: The publication is produced by the Finnish Institute for Health and Welfare (THL)
and authors and not by EFSA and only represents the views of THL and the authors and not
EFSA’s position.
Institutional Review Board Statement:
The FinHealth 2017 Study (including the FinDiet 2017 Sur-
vey) was conducted in accordance with the Declaration of Helsinki and approved by the Coordinating
Ethics Committee at the Hospital District of Helsinki and Uusimaa (Reference 37/13/03/00/2016,
approved on 22 March 2016) for studies involving humans.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the
study before the first interview.
Data Availability Statement:
The data presented in this study (The FinDiet 2017 Survey data) are
available on request from THL Biobank at: https://thl.fi/en/web/thl-biobank/for-researchers (ac-
Nutrients 2022,14, 1347 19 of 22
cessed on 14 February 2022). The individual-level data are not publicly available due to
privacy restrictions.
Acknowledgments:
We acknowledge the expert work carried out by the FinHealth 2017 Study team
and the FinDiet 2017 Survey team throughout the data collection process, Anne Juolevi for the
data compilation, Tommi Härkänen for the expert support in statistical methods, the Fineli team
for the challenging task of keeping the food and nutrient composition data up-to-date, and Maria
Vaalavuo and Tuija Martelin for expert support in compiling the socioeconomic background data.
Christine Bartels is gratefully acknowledged for the language editing of the manuscript. We thank
the participants of the FinDiet 2017 Survey.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
GBD 2015 Obesity Collaborators; Afshin, A.; Forouzanfar, M.H.; Reitsma, M.B.; Sur, P.; Estep, K.; Lee, A.; Marczak, L.;
M
okdad, A.H.
; Moradi-Lakeh, M.; et al. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N. Engl. J. Med.
2017,377, 13–27. [PubMed]
2.
GBD 2017 Risk Factor Collaborators. Global, Regional, and National Comparative Risk Assessment of 84 Behavioural, Environ-
mental and Occupational, and Metabolic Risks or Clusters of Risks for 195 Countries and Territories, 1990–2017: A Systematic
Analysis for the Global Burden of Disease Study 2017. Lancet 2018,392, 1923–1994.
3.
Roth, G.A.; Mensah, G.A.; Johnson, C.O.; Addolorato, G.; Ammirati, E.; Baddour, L.M.; Barengo, N.C.; Beaton, A.Z.;
Benjamin, E.J
.;
Benziger, C.P.; et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update from the GBD 2019 Study. J.
Am. Coll. Cardiol. 2020,76, 2982–3021. [CrossRef] [PubMed]
4.
Pietinen, P.; Männistö, S.; Valsta, L.M.; Sarlio-Lahteenkorva, S. Nutrition Policy in Finland. Public Health Nutr.
2010
,13, 901–906.
[CrossRef] [PubMed]
5.
Raulio, S.; Erlund, I.; Männistö, S.; Sarlio-Lahteenkorva, S.; Sundvall, J.; Tapanainen, H.; Vartiainen, E.; Virtanen, S.M. Successful
Nutrition Policy: Improvement of Vitamin D Intake and Status in Finnish Adults over the Last Decade. Eur. J. Public Health
2017
,
27, 268–273. [CrossRef] [PubMed]
6.
Roos, G.; Prättälä, R. Disparities in Food Habits—Review of Research in 15 European Countries; Publications of the National Public
Health Institute B24/1999: Helsinki, Finland, 1999.
7.
Rippin, H.L.; Hutchinson, J.; Greenwood, D.C.; Jewell, J.; Breda, J.J.; Martin, A.; Rippin, D.M.; Schindler, K.; Rust, P.; Fagt, S.; et al.
Inequalities in Education and National Income Are Associated with Poorer Diet: Pooled Analysis of Individual Participant Data
across 12 European Countries. PLoS ONE 2020,15, e0232447. [CrossRef]
8.
Powles, J.; Fahimi, S.; Micha, R.; Khatibzadeh, S.; Shi, P.; Ezzati, M.; Engell, R.E.; Lim, S.S.; Danaei, G.; Mozaffarian, D.; et al.
Global, Regional and National Sodium Intakes in 1990 and 2010: A Systematic Analysis of 24 H Urinary Sodium Excretion and
Dietary Surveys Worldwide. BMJ Open 2013,3, e003733. [CrossRef]
9.
Wang, Q.; Afshin, A.; Yakoob, M.Y.; Singh, G.M.; Rehm, C.D.; Khatibzadeh, S.; Micha, R.; Shi, P.; Mozaffarian, D.; Global
Burden of Diseases Nutrition and Chronic Diseases Expert Group (NutriCoDE). Impact of Nonoptimal Intakes of Saturated,
Polyunsaturated, and Trans Fat on Global Burdens of Coronary Heart Disease. J. Am. Heart Assoc. 2016,5, 2891. [CrossRef]
10. EFSA (European Food Safety Authority). Guidance on the EU Menu Methodology. EFSA J. 2014,12, 3944. [CrossRef]
11.
Micha, R.; Khatibzadeh, S.; Shi, P.; Andrews, K.G.; Engell, R.E.; Mozaffarian, D.; Global Burden of Diseases Nutrition and Chronic
Diseases Expert Group (NutriCoDE). Global, Regional and National Consumption of Major Food Groups in 1990 and 2010: A
Systematic Analysis including 266 Country-Specific Nutrition Surveys Worldwide. BMJ Open 2015,5, e008705. [CrossRef]
12.
Burrows, T.L.; Ho, Y.Y.; Rollo, M.E.; Collins, C.E. Validity of Dietary Assessment Methods When Compared to the Method of
Doubly Labeled Water: A Systematic Review in Adults. Front. Endocrinol. 2019,10, 850. [CrossRef] [PubMed]
13.
Darmon, N.; Drewnowski, A. Does Social Class Predict Diet Quality? Am. J. Clin. Nutr.
2008
,87, 1107–1117. [CrossRef] [PubMed]
14.
Novakovic, R.; Cavelaars, A.; Geelen, A.; Nikolic, M.; Altaba, I.I.; Vinas, B.R.; Ngo, J.; Golsorkhi, M.; Medina, M.W.;
B
rzozowska, A.; et al.
Socio-Economic Determinants of Micronutrient Intake and Status in Europe: A Systematic Review. Public
Health Nutr. 2014,17, 1031–1045. [CrossRef] [PubMed]
15.
Kaljonen, M.; Kortetmäki, T.; Tribaldos, T.; Huttunen, S.; Karttunen, K.; Maluf, R.S.; Niemi, J.; Saarinen, M.; Salminen, J.;
V
aalavuo, M.; et al
. Justice in Transitions: Widening Considerations of Justice in Dietary Transition. Environ. Innov. Soc. Transit.
2021,40, 474–485. [CrossRef]
16.
Willett, W.; Rockstrom, J.; Loken, B.; Springmann, M.; Lang, T.; Vermeulen, S.; Garnett, T.; Tilman, D.; DeClerck, F.;
Wood, A.; et al
.
Food in the Anthropocene: The EAT-Lancet Commission on Healthy Diets from Sustainable Food Systems. Lancet
2019
,393,
447–492. [CrossRef]
17.
Hemler, E.C.; Hu, F.B. Plant-Based Diets for Personal, Population, and Planetary Health. Adv. Nutr.
2019
,10, S275–S283.
[CrossRef]
18.
Bock, A.K.; Bontoux, L.; Rudkin, J. Concepts for a Sustainable EU Food System, EUR 30894 EN; Publications Office of the European
Union: Luxembourg, 2022.
Nutrients 2022,14, 1347 20 of 22
19.
Ocké, M.; de Boer, E.; Brants, H.; van der Laan, J.; Niekerk, M.; van Rossum, C.; Temme, L.; Freisling, H.; Nicolas, G.;
C
asagrande, C.; et al
. PANCAKE—Pilot Study for the Assessment of Nutrient Intake and Food Consumption among Kids in
Europe. EFSA Support. Publ.
2012
,9, 339E. Available online: www.efsa.europa.eu/publications (accessed on 14 February 2022).
[CrossRef]
20.
Ambrus, Á; Horváth, Z.; Farkas, Z.; Cseh, J.; Petrova, S.; Dimitrov, P.; Duleva, V.; Rangelova, L.; ChikovaIscener, E.;
Ovaskainen, M.; et al.
Pilot Study in the View of a Pan-European Dietary Survey—Adolescents, Adults and Elderly. EFSA
Support. Publ.
2013
,10, 508E. Available online: http://www.efsa.europa.eu/en/supporting/pub/508e.htm (accessed on 14
February 2022).
21.
Gavrieli, A.; Naska, A.; Berry, R.; Roe, M.; Harvey, L.; Finglas, P.; Glibetic, M.; Gurinovic, M.; Trichopoulou, A. Dietary Monitoring
Tools for Risk Assessment. EFSA Support. Publ.
2014
,11, 607E. Available online: www.efsa.europa.eu/publications (accessed on
14 February 2022). [CrossRef]
22.
Dekkers, A.L.; Verkaik-Kloosterman, J.; van Rossum, C.T.; Ocke, M.C. SPADE, a New Statistical Program to Estimate Habitual
Dietary Intake from Multiple Food Sources and Dietary Supplements. J. Nutr. 2014,144, 2083–2091. [CrossRef]
23.
Tooze, J.A.; Midthune, D.; Dodd, K.W.; Freedman, L.S.; Krebs-Smith, S.M.; Subar, A.F.; Guenther, P.M.; Carroll, R.J.; Kipnis, V. A
New Statistical Method for Estimating the Usual Intake of Episodically Consumed Foods with Application to Their Distribution.
J. Am. Diet. Assoc. 2006,106, 1575–1587. [CrossRef] [PubMed]
24.
Souverein, O.W.; Dekkers, A.L.; Geelen, A.; Haubrock, J.; de Vries, J.H.; Ocke, M.C.; Harttig, U.; Boeing, H.; v
an‘t Veer, P
.;
EFCOVAL Consortium. Comparing Four Methods to Estimate Usual Intake Distributions. Eur. J. Clin. Nutr.
2011