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. 2013 May 23;16(11):1924–1932. doi: 10.1017/S1368980013001316

Changing patterns of fruit and vegetable intake in countries of the former Soviet Union

Sarah Krull Abe 1,*, Andrew Stickley 2, Bayard Roberts 1, Erica Richardson 3, Pamela Abbott 4, David Rotman 5, Martin McKee 1
PMCID: PMC10271555  PMID: 23701712

Abstract

Objective

To assess how the frequency of low fruit and vegetable consumption has changed in countries of the former Soviet Union (FSU) between 2001 and 2010 and to identify factors associated with low consumption.

Design

Cross-sectional surveys. A standard questionnaire was administered at both time points to examine fruit and vegetable consumption frequency. Logistic regression analysis was used to examine the relationship between demographic, socio-economic and health behavioural variables and low fruit and vegetable consumption in 2010.

Setting

Nationally representative population samples from Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia and Ukraine.

Subjects

Adults aged 18 years and older.

Results

Between 2001 and 2010 notable changes occurred in fruit and vegetable consumption in many countries resulting in a slight overall deterioration in diet. By 2010 in six countries about 40 % of the population was eating fruit once weekly or less often, while for vegetables the corresponding figure was in excess of 20 % in every country except Azerbaijan. A worse socio-economic situation, negative health behaviours (smoking and alcohol consumption) and rural residence were all associated with low levels of fruit and vegetable consumption.

Conclusions

International dietary guidelines emphasise the importance of fruit and vegetable consumption. The scale of inadequate consumption of these food groups among much of the population in many FSU countries and its link to socio-economic disadvantage are deeply worrying. This highlights the urgent need for a greater focus to be placed on population nutrition policies to avoid nutrition-related diseases in the FSU countries.

Keywords: Diet, Former Soviet Union, Fruit, Vegetables, Consumption

Globally, one of the main determinants of life expectancy is economic development, as demonstrated by the now well-known Preston curve( 1 ). However, some countries perform somewhat better than expected and others worse. Among the latter are many of the countries of the former Soviet Union (FSU)( 2 ). There are many reasons for this, but the leading explanations have been identified as alcohol, smoking, diet and health-care provision failures( 3 – 5 ). However, the situation is changing and, throughout this region, life expectancy has been improving during the 2000s( 6 ). The reasons remain inadequately understood although it is likely that there has been some improvement in all of the major risk factors. Our previous research has examined changes in smoking( 7 ) and access to health care( 8 , 9 ), and ongoing research is examining changes in alcohol consumption. There have been a number of relatively recent studies that have looked at some aspects of nutrition although mainly secondary to other issues( 10 – 15 ), but there has been little research specifically on changing diet in this region in the past decade outside Russia( 16 – 18 ). This gap is important as the experience of countries in Central Europe following the opening of markets in the 1990s suggests that changing diets are likely to have a significant impact on health( 19 , 20 ).

Earlier research in this region has characterised the traditional diet as high in fat and particularly low in fruit and vegetables, although differences in traditional diets in the South Caucasus and Central Asia were not explored( 3 ). Thus, research in the three Baltic states in the late 1990s reported median intakes of under 200 g/d( 21 ), compared with the WHO recommendation of at least 400 g/d( 22 ) or five servings of fruit and vegetables( 23 ). Many aspects of life in this region are, however, changing and food balance data from the FAO show substantial changes in the supply of fruit and vegetables since the late 1990s (Fig. 1)( 24 ). However, there are known discrepancies between these data and actual consumption levels( 25 ). Hence, it is necessary to complement them with data from surveys. In the present paper we seek partially to address this gap by reporting on the findings from surveys in nine FSU countries. This is now a priority: determining what people eat is an essential element of formulating evidence-based nutritional policies.

Fig. 1.

Fig. 1

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Trends in the per capita supply of fruit and vegetables (g/d) from 1992 to 2007 in countries of the former Soviet Union (data source: FAO( 24 ))

Methods

The data used in the present study are taken from two nationally representative cross-sectional household surveys conducted among adults in Armenia, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia and Ukraine in 2001 and again in 2010, when Azerbaijan was also included. Details of the earlier Living Conditions, Lifestyles and Health (LLH) survey conducted in October and November 2001 have been presented elsewhere( 10 , 26 ). In both the LLH and the more recent Health in Times of Transition (HITT) surveys, multistage random sampling with stratification by region and rural/urban settlement type was used. Within each primary sampling unit (about 50–200 (LLH), 100–200 (HITT) per country), households were selected by standardised random route procedures or selected by random sampling from a household list (in the case of Armenia in the earlier survey). Within each of the selected households one person (aged 18 years and over) was chosen (based on the nearest birthday). If there was no one at home after three visits (on different days and at different times), the next household on the route was selected.

The HITT survey was conducted between March and May 2010 (except in Kyrgyzstan, where political violence delayed the data collection until early 2011). Face-to-face interviews were conducted by trained fieldworkers in the respondents’ homes. Response rates varied from 71 % to 88 % in the LHH survey and from 47·3 % (Kazakhstan) to 83·0 % (Moldova) in the HITT survey. In the LHH study, approximately 2000 interviews were completed in each country with the exception of Russia (4000) and Ukraine (2400). For HITT there were 1800 respondents in each country, except in Russia (3000) and Ukraine (2000). As with the earlier study larger samples were collected in these countries to reflect their larger and more regionally diverse populations. In Georgia there was also a greater number of respondents (2200) as a result of a booster survey of 400 additional interviews that was undertaken in November 2010 to ensure a more representative sample. All participants gave informed consent prior to their inclusion in the study.

The HITT questionnaire included many of the same questions that had been used in the LLH survey to enable comparability. The draft questionnaire was forward- and back-translated into each of the languages in which it was administered, and then piloted before being finalised. Except in Russia and Belarus (where all interviews were conducted in Russian), respondents were given the choice of answering in Russian or a national language. Many of the questions used in both surveys are common survey questions. Other questions were designed specifically for use in these surveys based on our knowledge of the populations’ behaviour/lifestyles in this region( 26 ) (e.g. the question relating to garden plots). In the current study the primary outcomes of interest concern the consumption of fruit and vegetables, information about which was obtained in response to two questions: ‘How often in the past week have you eaten … fresh fruit/vegetables (except for potatoes)?’ In the LLH study interviewees were presented with the response options ‘daily’, ‘2 or 3 times per week’, ‘occasionally (1 time per week)’ and ‘extremely seldom’. In HITT the options were ‘daily/almost daily’, ‘several times per week’, ‘once a week’ and ‘less than once a week’.

As details of the LLH study population have been presented elsewhere( 26 ), here we will focus on characteristics of the HITT sample, which are presented in Table 1. Table 2 presents details of fruit and vegetable consumption in the countries in 2001 and 2010. Table 3 presents results from a logistic regression analysis that was performed to examine which factors were associated with consuming fruit and vegetables in 2010. In the regression analysis we examined factors associated with eating fruit and vegetables once weekly or less often where those individuals with inadequate diets were coded ‘1’ (while respondents giving other answers were coded ‘0’).

Table 1.

Sample characteristics of HITT 2010 study (n 18 000), by country†

Armenia Azerbaijan Belarus Georgia Kazakhstan Kyrgyzstan Moldova Russia Ukraine All countries
n % n % n % n % n % n % n % n % n % n %
Total sample 1800 100·0 1800 100·0 1800 100·0 2200 100·0 1800 100·0 1800 100·0 1800 100·0 3000 100·0 2000 100·0 18 000 100·0
Gender
Women 977 54·3 954 53·0 1015 56·4 1400 63·6 946 52·6 930 51·7 1003 55·7 1789 59·6 1157 57·9 10 171 56·5
Men 823 45·7 846 47·0 785 43·6 800 36·4 854 47·4 870 48·3 797 44·3 1211 40·4 843 42·2 7829 43·5
Age (years)
18–39 919 51·1 961 53·4 850 47·2 856 38·9 963 53·5 1033 57·4 794 44·1 1251 41·7 828 41·4 8455 47·0
40–59 625 34·7 663 36·8 547 31·9 817 37·1 571 31·7 568 31·6 651 36·2 1047 34·9 619 31·0 6135 34·1
60+ 256 14·2 176 9·8 376 20·9 527 24·0 266 14·8 199 11·1 355 19·7 702 23·4 553 27·7 3410 18·9
Education level
Complete higher education 325 18·1 323 18·0 396 22·0 796 36·2 428 23·8 320 17·8 332 18·5 662 22·2 484 24·4 4066 22·6
Less than complete higher education 1474 81·9 1468 82·0 1404 78·0 1403 63·8 1372 76·2 1480 82·2 1463 81·5 2326 77·8 1500 75·6 13 890 77·4
Household size
Mean and sd 4·5 1·5 4·3 1·6 2·9 1·3 3·8 1·8 4·0 1·8 4·7 2·2 3·1 1·5 2·9 1·4 3·0 1·4 3·6 1·8
Location
Urban 1393 77·4 1016 56·4 1323 73·5 1051 47·8 1000 55·6 820 45·6 687 38·2 2179 72·6 1396 69·8 10 865 60·4
Rural 407 22·6 784 43·6 477 26·5 1149 52·2 800 44·4 980 54·4 1113 61·8 821 27·4 604 30·2 7135 39·6
Economic situation
Good/very good 499 27·8 458 25·8 409 22·8 117 5·4 572 31·9 622 34·6 461 25·9 519 18·0 328 16·5 3985 22·4
Average 970 54·0 926 52·2 1158 64·5 1105 50·6 1097 61·1 994 55·3 873 49·0 1885 65·3 1188 59·9 10 196 57·3
Bad/very bad 328 18·3 391 22·0 228 12·7 964 44·1 126 7·0 181 10·1 447 25·1 484 16·8 467 23·6 3616 20·3
Limit food
Never 971 54·0 889 49·9 1356 75·5 783 35·7 1411 78·7 951 53·0 1182 66·0 2184 73·4 1163 58·4 10 890 60·8
Sometimes 662 36·8 640 36·0 376 20·9 1025 46·8 325 18·1 584 32·6 538 30·0 655 22·0 613 30·8 5418 30·3
Constantly 165 9·2 251 14·1 65 3·6 383 17·5 57 3·2 259 14·4 72 4·0 137 4·6 215 10·8 1604 9·0
Garden plot
No 1307 73·9 1128 65·2 674 37·7 871 41·5 663 36·9 592 32·9 532 30·2 1213 41·5 748 39·0 7728 44·0
Yes 462 26·1 603 34·8 1113 62·3 1128 58·5 1132 63·1 1208 67·1 1232 69·8 1707 58·5 1172 61·0 9857 56·1
Healthy diet
(Quite) important 1681 93·5 1752 97·7 1713 96·0 2148 98·7 1679 93·9 1759 97·9 1754 97·6 2732 93·5 1908 96·5 17 126 96·0
(Rather) unimportant 116 6·5 41 2·3 72 4·0 29 1·3 109 6·1 38 2·1 43 2·4 190 6·5 69 3·5 707 4·0
Cigarettes
0 1296 72·2 1408 78·7 1330 74·2 1692 77·0 1275 71·0 1387 77·3 1442 80·6 2063 69·3 1452 73·5 13 345 74·5
1–10/d 103 5·7 57 3·2 262 14·6 139 6·3 238 13·3 202 11·3 165 9·2 356 12·0 207 10·5 1729 9·7
11+/d 397 22·1 325 18·2 200 11·2 366 16·7 283 15·8 206 11·5 182 10·2 556 18·7 317 16·0 2832 15·8
Alcohol
Never 386 24·8 1440 80·4 241 13·4 653 29·7 599 33·3 899 49·9 289 16·2 661 22·1 449 22·7 5617 31·8
1 time/week or less 1058 68·0 285 15·9 1387 77·3 1379 62·8 1080 60·1 849 47·2 1030 57·5 2049 68·6 1275 64·5 10 392 58·8
2–3 times/week or more 111 7·1 66 3·7 167 9·3 164 7·5 118 6·6 52 2·9 471 26·3 277 9·3 254 12·8 1680 9·5
Self-reported health
Very good/good 975 54·2 1059 58·9 622 34·6 538 24·5 795 44·2 955 53·1 666 37·3 1027 34·5 653 32·8 7290 40·6
Average 613 34·1 441 24·5 911 50·6 848 38·6 832 46·2 637 35·4 697 39·0 1469 49·4 896 45·1 7344 40·9
Very bad/bad 211 11·7 298 16·6 267 14·8 810 36·9 173 9·6 208 11·6 424 23·7 480 16·1 440 22·1 3311 18·5
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HITT, Health in Times of Transition.

†

Data are presented in the form of numbers and percentages unless stated otherwise.

Table 2.

Prevalence of fruit and vegetable consumption in 2001 and 2010, by country†

n n Daily/almost daily (%) Several times per week (%) Once weekly (%) Less than once weekly (%)
2001 2010 2001 95 % CI 2010 95 % CI 2001 95 % CI 2010 95 % CI 2001 95 % CI 2010 95 % CI 2001 95 % CI 2010 95 % CI
Fruit
Armenia 2000 1790 35·0 32·9, 37·1 35·3 33·0, 37·5 31·7 29·6, 33·7 33·2 31·0, 35·4 21·0 19·2, 22·8 21·5 19·6, 23·3 12·4 10·9, 13·8 10·1 8·7, 11·5
Azerbaijan – 1786 – – 43·3 41·0, 45·6 – – 35·3 33·1, 37·5 – – 16·7 15·0, 18·5 – – 4·6 3·6, 5·6
Belarus 1995 1788 21·7 19·8, 23·5 22·4 20·4, 24·3 33·9 31·9, 36·0 36·9 34·7, 39·2 26·8 24·8, 28·7 20·5 18·6, 22·3 17·6 16·0, 19·3 20·3 18·4, 22·1
Georgia 2007 2176 37·9 35·7, 40·0 19·3 17·7, 21·0 37·4 35·3, 39·5 33·3 31·3, 35·3 15·8 14·2, 17·4 18·8 17·2, 20·5 8·9 7·7, 10·2 28·5 26·6, 30·4
Kazakhstan 1999 1786 17·2 15·5, 18·8 31·7 29·6, 33·9 24·6 22·7, 26·5 28·5 26·4, 30·6 29·9 27·9, 31·9 20·0 18·2, 21·9 28·4 26·4, 30·3 19·7 17·9, 21·6
Kyrgyzstan 1997 1800 38·6 36·4, 40·7 21·1 19·2, 23·0 34·2 32·1, 36·2 30·8 28·7, 33·0 18·8 17·1, 20·5 21·8 19·9, 23·7 8·5 7·3, 9·7 26·3 24·2, 28·3
Moldova 1996 1775 33·5 31·4, 35·5 23·7 21·7, 25·6 38·3 36·1, 40·4 27·9 25·9, 30·0 19·8 18·0, 21·5 20·2 18·4, 22·1 8·5 7·2, 9·7 28·2 26·1, 30·3
Russia 3994 2967 14·9 13·8, 16·0 31·2 29·5, 32·9 24·6 23·3, 25·9 37·5 35·8, 39·3 30·3 28·9, 31·8 22·3 20·8, 23·8 30·2 28·7, 31·6 9·0 8·0, 10·0
Ukraine 2379 1979 25·9 24·1, 27·7 26·6 24·7, 28·6 29·3 27·4, 31·1 34·3 32·2, 36·4 26·5 24·7, 28·3 20·8 19·0, 22·6 18·4 16·8, 19·9 18·3 16·6, 20·0
Vegetables
Armenia 2000 1793 28·6 26·6, 30·6 41·7 39·4, 43·9 40·6 38·4, 42·7 36·5 34·3, 38·8 20·6 18·8, 22·4 16·8 15·1, 18·6 10·3 8·9, 11·6 5·0 4·0, 6·0
Azerbaijan – 1770 – – 47·9 45·6, 50·2 – – 32·8 30·6, 35·0 – – 12·8 11·3, 14·4 – – 6·4 5·3, 7·6
Belarus 1998 1786 37·0 34·9, 39·2 25·1 23·1, 27·2 41·0 38·9, 43·2 43·5 41·2, 45·8 17·2 15·6, 18·9 18·8 17·0, 20·6 4·7 3·8, 5·6 12·5 11·0, 14·1
Georgia 2008 2192 39·1 37·0, 41·3 27·8 25·9, 29·7 36·2 34·1, 38·3 40·9 38·9, 43·0 17·2 15·6, 18·9 17·5 15·9, 19·1 7·4 6·3, 8·6 13·8 12·4, 15·3
Kazakhstan 1998 1797 37·0 34·9, 39·2 42·5 40·2, 44·8 39·3 37·2, 41·5 31·3 29·1, 33·4 17·7 16·0, 19·3 15·4 13·7, 17·0 6·0 4·9, 7·0 10·9 9·4, 12·3
Kyrgyzstan 2000 1800 45·1 42·9, 47·3 34·2 32·0, 36·4 32·8 30·7, 34·9 33·1 30·9, 35·2 15·4 13·8, 16·9 17·0 15·3, 18·7 6·8 5·7, 7·8 15·7 14·0, 17·4
Moldova 1999 1775 36·3 34·2, 38·4 24·6 22·6, 26·6 44·1 41·9, 46·3 32·1 29·9, 34·2 14·9 13·3, 16·4 19·4 17·5, 21·2 4·8 3·9, 5·7 24·0 22·0, 26·0
Russia 3999 2967 44·6 43·0, 46·1 35·5 33·8, 37·2 34·0 32·5, 35·5 43·3 41·6, 45·1 15·7 14·5, 16·8 15·9 14·6, 17·2 5·8 5·1, 6·5 5·3 4·5, 6·1
Ukraine 2384 1987 42·1 40·1, 44·1 44·3 42·2, 46·5 34·0 32·1, 35·9 34·6 32·5, 36·7 17·3 15·8, 18·8 12·5 11·0, 13·9 6·6 5·6, 7·6 8·6 7·3, 9·8
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Total samples: in 2001, fruit (n 18 367), vegetables (n 18 386); in 2010, fruit (n 17 847), vegetables (n 17 867).

†

Data are presented in the form of numbers, and percentages with 95 % confidence intervals.

Table 3.

Factors associated with the consumption of fruit and vegetables once weekly or less often in nine former Soviet countries in 2010

Fruit once weekly or less often Vegetables once weekly or less often
Model 1† Model 2‡ (n 16 592) Model 1† Model 2‡ (n 16 607)
n % OR 95 % CI OR 95 % CI n % OR 95 % CI OR 95 % CI
Gender
Women 3761 37·3 1·00 Ref. 1·00 Ref. 2687 26·6 1·00 Ref. 1·00 Ref.
Men 3080 39·7 1·10 1·04, 1·18** 1·10 1·02, 1·20* 2167 27·9 1·07 1·00, 1·15 1·09 1·00, 1·18
Age (years)
18–39 2740 32·6 1·00 Ref. 1·00 Ref. 2067 24·6 1·00 Ref. 1·00 Ref.
40–59 2481 40·8 1·42 1·33, 1·53*** 1·23 1·14, 1·33*** 1720 28·2 1·20 1·11, 1·30*** 1·05 0·96, 1·15
60+ 1620 48·3 1·93 1·76, 2·12*** 1·37 1·23, 1·54*** 1067 31·6 1·41 1·28, 1·56*** 1·04 0·91, 1·17
Education
High 1150 28·4 1·00 Ref. 1·00 Ref. 834 20·7 1·00 Ref. 1·00 Ref.
Low 5681 41·3 1·33 1·27, 1·39*** 1·20 1·15, 1·26*** 4012 29·1 1·25 1·20, 1·31*** 1·15 1·10, 1·21***
Household size 6832 38·4 0·96 0·94, 0·98*** 0·99 0·97, 1·02 4847 27·2 0·96 0·94, 0·99** 0·98 0·95, 1·00
Location
Urban 3531 32·8 1·00 Ref. 1·00 Ref. 2541 23·6 1·00 Ref. 1·00 Ref.
Rural 3310 46·8 1·81 1·59, 2·05*** 1·59 1·37, 1·84*** 2313 32·7 1·57 1·36, 1·82*** 1·37 1·16, 1·62***
Economic situation
Good 1077 27·1 1·00 Ref. 1·00 Ref. 841 21·2 1·00 Ref. 1·00 Ref.
Average 3716 36·7 1·56 1·40, 1·74*** 1·37 1·23, 1·53*** 2590 25·6 1·28 1·15, 1·43*** 1·21 1·08, 1·36**
Poor 1992 55·9 3·41 2·97, 3·91*** 2·23 1·93, 2·59*** 1383 38·6 2·34 2·04, 2·69*** 1·68 1·43, 1·97***
Limit food
Never 3392 31·3 1·00 Ref. 1·00 Ref. 2404 22·2 1·00 Ref. 1·00 Ref.
Sometimes 2549 47·6 1·99 1·81, 2·19*** 1·69 1·52, 1·87*** 1804 33·5 1·76 1·59, 1·95*** 1·62 1·45, 1·81***
Constantly 881 55·6 2·74 2·37, 3·17*** 2·07 1·76, 2·43*** 636 40·0 2·33 1·99, 2·72*** 2·03 1·70, 2·42***
Garden plot
Yes 4204 43·0 1·00 Ref. 1·00 Ref. 2910 29·7 1·00 Ref. 1·00 Ref.
No 2515 32·8 0·65 0·58, 0·72*** 0·97 0·87, 1·09 1850 24·1 0·75 0·67, 0·85*** 1·01 0·89, 1·14
Healthy diet
Important 6473 38·1 1·00 Ref. 1·00 Ref. 4581 26·9 1·00 Ref. 1·00 Ref.
Unimportant 296 42·3 1·19 1·00, 1·42 1·23 1·02, 1·48* 231 32·9 1·33 1·10, 1·60** 1·42 1·17, 1·74**
Cigarettes
0 4973 37·6 1·00 Ref. 1·00 Ref. 3571 26·9 1·00 Ref. 1·00 Ref.
1–10/d 650 37·8 1·01 0·91, 1·12 1·03 0·92, 1·15 435 25·3 0·92 0·81, 1·04 0·91 0·80, 1·04
11+/d 1179 42·0 1·20 1·10, 1·31*** 1·20 1·08, 1·34** 822 29·4 1·13 1·03, 1·25* 1·16 1·03, 1·31*
Alcohol
Never 2070 37·1 1·00 Ref. 1·00 Ref. 1527 27·3 1·00 Ref. 1·00 Ref.
1 time/week or less 3936 38·2 1·04 0·95, 1·15 1·04 0·95, 1·14 2780 27·0 0·98 0·88, 1·09 0·99 0·90, 1·09
2–3 times/week or more 756 45·5 1·41 1·23, 1·63*** 1·20 1·02, 1·41* 511 30·7 1·18 1·00, 1·38* 0·96 0·80, 1·14
Self-reported health
Good 2284 31·5 1·00 Ref. 1·00 Ref. 1732 23·9 1·00 Ref. 1·00 Ref.
Average 2847 39·1 1·39 1·28, 1·51*** 1·06 0·97, 1·16 1914 26·3 1·14 1·04, 1·24** 0·97 0·89, 1·07
Bad 1684 51·7 2·32 2·08, 2·60*** 1·21 1·07, 1·38** 1191 36·3 1·82 1·62, 2·03*** 1·17 1·02, 1·34*
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Ref., referent category.

*P < 0·05; **P < 0·01; ***P < 0·001.

†Model 1: Bivariate analysis.

‡Model 2: Multivariate analysis adjusted for country and for all other variables in the model.

The independent variables examined in the analysis included demographic factors: sex; age (18–39/40–59/60+ years); educational attainment (completed higher education/less than completed higher education); household size (i.e. number of members – a continuous variable); and residential location (urban/rural). Socio-economic situation was assessed using variables relating to: economic well-being, measured through an item on self-rated household economic situation (categorised as ‘good’/‘very good’/‘average’/‘bad’/‘very bad’); information about the extent to which the respondent's household was required to limit its basic food intake in the past 12 months (‘never’/‘sometimes’/‘constantly’); and possessing a garden plot (yes/no). Health beliefs were assessed using information on attitudes towards having a healthy diet (dichotomised as ‘important’ and ‘quite important’/‘rather unimportant’ and ‘unimportant’); smoking behaviour, i.e. smoking/non-smoking and the number of cigarettes smoked each day among smokers (1–10/11+); and the frequency of alcohol consumption (‘never’/‘once per week or less’/‘2–3 times per week or more’). We also examined the relationship between self-reported health (categorised as ‘good’ and ‘very good’/‘average’/‘bad’ and ‘very bad’) and diet.

Statistical analysis

The associations between these variables and fruit and vegetable consumption were examined by conducting logistic regression analyses using the statistical software package STATA version 12·1. Two models were examined. In Model 1 we examined the association between each independent variable and the outcome variable (inadequate diet). In Model 2 we examined the association between each independent variable and having an inadequate diet using a fully adjusted analysis where each variable is controlled for the effects of all the other variables in the model and for the possible country effects. The analysis was adjusted for clustering to account for the survey's clustered design. The results are presented in the form of odds ratios with 95 % confidence intervals. The level of statistical significance was set at P < 0·05.

Results

In the 2010 HITT survey there were more female than male respondents (56·5 % v. 43·5 %) in all of the study countries (Table 1). Georgia had a particularly high ratio of females to males (64:36), although this has been found in all recent surveys and is believed to reflect large-scale labour migration in the post-Soviet period( 27 , 28 ). In most countries just under two-thirds of the population lived in urban locations. The proportion of respondents who had completed their higher education ranged from about 18 % in Armenia, Azerbaijan, Kyrgyzstan and Moldova to 36·2 % in Georgia. There was a large variation in the range of respondents who felt their households were in a bad/very bad economic situation with the figure varying from 7·0 % in Kazakhstan to 44·1 % in Georgia. Large differences were also observed in the percentage of respondents who constantly had to limit their food intake and regarding the possession of a garden plot, with figures for the former ranging from 3·2 % (Kazakhstan) to 17·5 % (Georgia) and for the latter from 26·1 % (Armenia) to 69·8 % (Moldova). In every country over 90 % of interviewees thought that having a healthy diet was important although there was a sizeable number of heavy smokers (15·8 %) and frequent drinkers (9·5 %) across the countries. In total, 18·5 % of the respondents reported their health as being bad/very bad with the figures ranging from 9·6 % (Kazakhstan) to 36·9 % (Georgia).

The prevalence of fruit and vegetable consumption varied greatly among the FSU countries between 2001 and 2010 (Table 2). In terms of daily/almost daily consumption of fruit a three-way pattern was clearly visible: in three countries (Armenia, Belarus and Ukraine) the prevalence remained essentially unchanged, in three countries (Georgia, Kyrgyzstan and Moldova) it decreased sharply while in Kazakhstan and Russia it increased significantly. This meant that by 2010 about 20 % of the population (or more) in six countries were eating fruit less than once weekly, with large increases in Georgia (8·9 % to 28·5 %), Kyrgyzstan (8·5 % to 26·3 %) and Moldova (8·5 % to 28·2 %; Table 2). In contrast, the decline in those eating fruit less than once weekly between 2001 and 2010 was marked in Kazakhstan (28·4 % to 19·7 %) and especially in Russia (30·2 % to 9·0 %; Table 2). The country with the highest level of fruit consumption in 2010 was Azerbaijan, where 43·3 % of the population was consuming fruit on a daily/almost daily basis.

In contrast to fruit consumption, most countries (five) recorded a decrease in daily/almost daily vegetable consumption (of about 10–12 %) between 2001 and 2010. The exceptions were Ukraine, Kazakhstan and especially Armenia, where this figure rose from 28·6 % in 2001 to 41·7 % in 2010. By this later date, 24·0 % of the population was eating vegetables less than once weekly in Moldova while this figure was between 5·0 % and 15·7 % in the other countries. As with fruit consumption, the country with the highest level of daily/almost daily vegetable consumption in 2010 was Azerbaijan (47·9 %). In terms of the overall pattern in daily/almost daily fruit and vegetable consumption across the period, only one country – Kazakhstan – experienced a notable increase in consumption of both types of food, while three countries (Georgia, Kyrgyzstan and Moldova) all experienced a sharp decline in both fruit and vegetable consumption.

In the regression analysis (Table 3) a number of variables were significantly associated with both inadequate fruit and vegetable consumption. Men were more likely to eat fruit once weekly or less often compared with women (OR = 1·10; 95 % CI 1·02, 1·20), as were respondents aged 40–59 years (OR = 1·23; 95 % CI 1·14, 1·33) and 60+ years (OR = 1·37; 95 % CI 1·23, 1·54) compared with those aged 18–39 years. Those individuals who had a lower level of education were also more likely to eat fruit (OR = 1·20; 95 % CI 1·15, 1·26) and vegetables (OR = 1·15; 95 % CI 1·10, 1·21) once weekly or less often. Compared with the economically advantaged, those who reported that their economic situation was average or poor were significantly more likely to have lower levels of fruit (OR = 1·37; 95 % CI 1·23, 1·53 and OR = 2·23; 95 % CI 1·93, 2·59, respectively) and vegetable consumption (OR = 1·21; 95 % CI 1·08, 1·36 and OR = 1·68; 95 % CI 1·43, 1·97, respectively). As expected, there was a linear relationship between food limitation and inadequate diet with the odds for low fruit consumption increasing by 1·7 times (OR = 1·69; 95 % CI 1·52, 1·87) among those who sometimes limited their food intake and more than doubling (OR = 2·07; 95 % CI 1·76, 2·43) among those who constantly limited food intake compared with those who never did; while an almost identical odds gradient was noted among these groups for inadequate vegetable consumption (OR = 1·62; 95 % CI 1·45, 1·81 and OR = 2·03; 95 % CI 1·70, 2·42, respectively). Living in a rural location also significantly increased the risk for both low fruit (OR = 1·59; 95 % CI 1·37, 1·84) and vegetable (OR = 1·37; 95 % CI 1·16, 1·62) consumption. In terms of the ‘health environment’, smoking heavily (eleven or more cigarettes daily) increased the likelihood of having an inadequate fruit (OR = 1·20; 95 % CI 1·08, 1·34) and vegetable intake (OR = 1·16; 95 % CI 1·03, 1·31), while not believing that diet was important increased the risk for inadequate fruit and vegetable consumption by 1·23 (95 % CI 1·02, 1·48) and 1·42 (95 % CI 1·17, 1·74) times, respectively. Respondents who assessed their own health as bad were also significantly more likely to eat fruit (OR = 1·21; 95 % CI 1·07, 1·38) and vegetables (OR = 1·17; 95 % CI 1·02, 1·34) once weekly or less often compared with those individuals whose health was good. No consumption effect was observed for household size or for possessing a garden plot. Finally, those individuals who drank alcohol more frequently had a 1·2 times increased risk of eating fruit once weekly or less often compared with non-drinkers (OR = 1·20; 95 % CI 1·02, 1·41).

Discussion

Between 2001 and 2010 there were notable changes in the consumption of fruit and vegetables in many countries of the FSU. Overall, the situation seems to have become slightly worse as only one country – Kazakhstan – recorded an increase in the daily/almost daily consumption of fruit and vegetables while three others (Georgia, Kyrgyzstan and Moldova) experienced sharp declines in both. The scale of the problem can be gauged by the fact that in 2010 in two-thirds of the countries about 40 % or more of the population was eating fruit once weekly or less often while in every country except Azerbaijan at least 20 % of the population was eating vegetables once weekly or less often, with this figure being significantly higher in Moldova. Regression analyses highlighted that a number of factors were associated with both low fruit and vegetable consumption. Specifically, living in a rural location, being economically disadvantaged and engaging in negative health behaviours were all associated with having an inadequate diet.

Before discussing the main findings of our study, there are several limitations which must be considered. First, and most obviously, neither survey was designed specifically to capture a comprehensive picture of dietary behaviour and the results can only provide an indication of the scale and nature of the situation, especially as the respective survey questions were not validated dietary measures. It would have required substantially greater resources than were available to administer food frequency or dietary recall surveys. Moreover, except in Armenia, food composition databases used in this region date from the Soviet period and, as we have previously shown in Estonia, are now obsolete( 29 ). Nevertheless, in the absence of any other published analyses of survey data we believe that the present results have some value in a region that has been the subject of remarkably little public health research. Second, as with most surveys those individuals who are socially marginalised and who may be most at risk of poor diet (e.g. homeless people) will have been missed, which may underestimate the prevalence of inadequate dietary intake. Third, although the country samples were nationally representative their size was nevertheless comparatively small when compared with the total population, which means that we may have missed important dietary variations across country (sub)populations. Moreover, as a result of the relatively small size of the country samples and a need to maximise statistical power in the regression analysis we were unable to perform male–female and country-specific analyses when examining the factors associated with inadequate fruit and vegetable consumption. Had this been possible, it might have further increased our understanding of the issue of consumption within these countries. Fourth, recall bias may also have been a possible problem. The answers came from respondents’ self-reports which may have lacked accuracy when compared with the more usual dietary data collection methods. Fifth, the question on consumption had slightly differently worded response categories in the two survey years which may have biased the comparative analyses of changes in the prevalence of fruit and vegetable consumption across the two time points. Sixth, as diet is influenced by seasonality, using information from one specific time point may have resulted in a biased picture of dietary intake for the whole year. Moreover, as the LLH and HITT data were collected at different points in the year this may have affected our across-time comparisons. Finally, it should also be noted that we were not able to examine other factors such as the role of agricultural subsidies and international trade agreements, which may have affected consumption in differing ways in the individual countries in this region.

The present findings are both surprising and alarming. Surprising, because food balance data suggest that several countries have experienced increases in supply. There is also some, albeit very limited, data from one part of Russia suggesting an increase between 1992 and 2007( 30 ). However, it is possible that more up-to-date data might show that the recent decline in food supply in some countries, shown in Fig. 1, has accelerated, possibly related to the global financial crisis, although Kazakhstan, with its oil revenues, may have seen a smaller relative decline than many of the other countries since 2008. Alarming because, as noted above, the situation was bad relative to much of the rest of the world to begin with.

The present study has highlighted the close link between socio-economic disadvantage and poor diet in the countries in this region as those in a poor economic situation were at significantly greater risk of eating fruit and vegetables less often. While not a surprising finding, it is nevertheless deeply worrying given the sharp growth in poverty that occurred in many of these countries in the early post-Soviet period( 31 ) and continuing high levels of poverty( 32 ). Indeed, poverty, beyond what is captured in our variables, might also partly explain the relationship we observed between rural location and inadequate diet, as some evidence suggests that there may have been a ‘ruralization’ of poverty in some of these countries( 33 ) and that rates of rural poverty are higher than those in urban areas in several of our study countries( 34 – 36 ). The association between area of residence and diet is complex and almost certainly influenced by other contextual factors. For example, it has been shown that rural Americans living in poverty had lower-quality diets which was associated with food insecurity( 37 ), although diet among rural inhabitants of the Baltic states was better than their urban counterparts, in large part because they could grow their own food( 38 ). However, in the countries included here, there are identifiable problems that may be impacting on rural diet. Financial difficulties facing rural enterprises have seen the non-payment, late payment and even ‘payment in kind’ of wages in some countries( 34 ) which might also have impacted on the ability to consume fruit and vegetables on a regular basis.

In terms of individual risk factors, those who regarded a healthy diet as being unimportant were more likely to have an inadequate diet. This finding accords with that from a recent study in Ireland which has shown that those with poorer fruit and vegetable consumption levels also have more negative attitudes towards healthy eating( 39 ). Our study revealed, however, that not only negative health attitudes but also worse health behaviours were associated with an inadequate diet – as both heavier smokers and those who consumed alcohol more frequently had lower levels of fruit and vegetable consumption. This finding is consistent with earlier research which has highlighted how negative health behaviours (smoking, drinking, poor diet and physical inactivity) ‘cluster’ in some individuals( 40 ). Alternatively, it is possible that economic factors may underlie the relationship between these health risk behaviours, with smokers and drinkers (particularly more frequent drinkers) spending money on alcohol and cigarettes rather than fresh fruit and vegetables. Some evidence also indicates that while the price of food increased throughout the transition period, alcohol seems to have become comparatively cheaper( 41 ). These opposing trends might not only have fuelled the exceptionally large increase in alcohol consumption in some countries like Russia in the post-Soviet period( 42 ) but also resulted in an inadequate diet for some people.

The finding that men and those with a lower education have less adequate diets mirrors previous results from the region( 14 , 43 ) and provides further support for the notion that men( 44 ) and those with a lower level of education( 18 , 45 ) may have been particularly disadvantaged in terms of health outcomes in the transition period. Similarly, the association we observed between having poor health and an inadequate diet was not unexpected given the role of diet in physical well-being – although determining the direction of the association was not possible in our cross-sectional study. It is possible, for example, that physical incapacity could limit income and/or access to food outlets and thus result in a more inadequate diet. One unexpected finding, however, was the lack of association between owning a garden plot and diet. This seems to contradict the idea advanced previously of the importance of garden plots for food provision in this region( 46 – 49 ). However, other research has highlighted the complexity of the relationship between garden plots, where they are situated and what they produce( 15 , 50 ) and the difference between subsistence food provision and the provision of a sustainably good diet( 51 ). Lack of nutritional knowledge and seasonality probably influence the relationship( 15 ). It has also been suggested in this context that people's dietary behaviour is heavily influenced by deeply embedded practices that are taken for granted( 52 ). This may mean that a greater availability of fruit and vegetables might not necessarily translate into a better diet. This suggests the need for more research on this phenomenon in the countries in this region to determine exactly how garden plots are being used and the role they are playing in terms of population diet in the FSU countries.

The present paper provides the first comparable information on the way several aspects of diet have changed in the countries of the FSU in the period between 2001 and 2010. It has shown that overall the situation in these FSU countries was worse in 2010 than it was in 2001. This is a matter of considerable concern. Although life expectancy has been improving, at least on the basis of these data it seems unlikely that diet is playing a major role in this improvement. Indeed, it may be storing up further problems for the future, given the evidence of high levels of overweight and obesity in some of our study countries( 53 ). However, it is only the first step in developing agendas for research and policy that will reverse the observed trends and thus contribute to more rapidly closing the health gap with other countries at similar levels of development. Future research should therefore build upon the present study by collecting more detailed information on diet from larger population samples within the individual countries. In particular, the FFQ should be validated in the countries in this region and additional information collected using food diaries over different periods of the year so as to capture the effects of seasonality. While the present paper has provided an important overview of fruit and vegetable intake in the countries in this region, there is now an urgent need for more detailed, in-depth, country-specific portraits in order to better understand diet and its effect on population health in the FSU.

Acknowledgements

Sources of funding: The HITT Project was funded by the European Union's Seventh Framework Programme, project HEALTH-F2-2009-223344. The European Commission cannot accept any responsibility for any information provided or views expressed. Conflicts of interest: The authors have no conflicts of interest to declare. Ethics: The research was approved by the Ethics Committee of the London School of Hygiene and Tropical Medicine. Authors’ contributions: S.K.A. developed the study idea, conducted the analyses and was the principal author of the paper. A.S. provided comments on the manuscript. B.R. helped conceive the study idea, provided statistical expertise and commented on the manuscript. E.R. helped contextualise the study idea and provided comments on the manuscript. P.A. and D.R. commented on the manuscript. M.M. helped conceive the study idea, wrote sections of the manuscript and commented on the manuscript for intellectual content. Acknowledgements: The authors are grateful to all members of the HITT project study teams who participated in the coordination and organisation of data collection for this paper.

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