ORIGINAL COMMUNICATION
Obesity, diet, and poverty: trends in the Russian transition to market economy
L Jahns1, A Baturin2 and BM Popkin1*
1Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; and 2Institute of Nutrition, Russian Academy of Medical Sciences, Moscow, Russia
Objective: To examine trends in macronutrient intake, overweight, and obesity. Design: Cross-sectional samplesFcollected nine times between 1992 and 2000Ffrom the Russian Longitudinal Monitoring Survey provide interviewer-administered 24-h diet recalls and measured height and weight, together with detailed information regarding income and expenditures. Setting: The Russian Federation. Subjects: Women and men, aged 19–55y. Interventions: None. Methods: A nationally representative sample of working-age Russian adults was stratified by gender and income (per cent of regional poverty level). Secular trends in mean energy and macronutrient intake, as well as prevalence of overweight and obesity in the population are described over the first 8y of the Russian Federation. Results: Overall, energy intake increased slightly. Fat, as a percentage of energy (E%), decreased from 39.6 to 31.6% and protein, as a per cent of energy, decreased from 14.3 to 12.5%. Overweight (determined by body mass index (BMI) Z25kg/ m2) prevalence remained relatively stable at about 50% and obesity (BMI Z30kg/m2) prevalence increased from 13.3 to 16.0% of the adult population. Women consumed less energy than men and displayed higher prevalences of overweight and obesity in all time periods. There was an income effect among men in all time periods, with higher-income men consuming more calories, fat, and protein than lower-income men; this effect was not apparent in women except in the proportion of fat and protein intake. Conclusions: The adult Russian population appears to have escaped macronutrient privation during economic reform and has experienced increasing rates of obesity. Sponsorship: US National Institutes of Health (R01-HD30880 and R01-HD38700) European Journal of Clinical Nutrition (2003) 57, 1295–1302. doi:10.1038/sj.ejcn.1601691
Keywords: Russia; obesity trends; diet; fat intake; nutritional status
Introduction The Russian Federation (Russia) is the largest country of the former Soviet Union. It is a large, diverse and resource-rich country with a well-documented, rapidly changing food economy (Baturin, 1994; Cornia, 1994; Beliaev, 1996; Popkin et al, 1997a,b; WHO, 1998; Clarke et al, 2000). Shifts in food commodity consumption shown in Figure 1 (State Statistical Office, Moscow), occurring between 1950 and 1989, reflect the change from a diet dominated by starchy staples (bread and potatoes) to one with high meat, dairy products, and sugar. However, the shortages of the 1980s, coupled with the policies of perestroika (reform) and glasnost (openness) in the late 1980s, and followed by the dissolution of the Soviet State in December 1991, led to enormous shifts in the political, cultural, and economic spheres of the Russian Republic. The food economy was profoundly affected. Market subsidies for foodstuffs, parti- cularly meat products, were reduced or restructured as the arduous processes of liberalization and privatization began
Received 26 April 2002; revised 30 September 2002; accepted 23 October 2002
*Correspondence: BM Popkin, Carolina Population Center, University of North Carolina at Chapel Hill, CB # 8120 University Square, 123 W. Franklin St., Chapel Hill, NC 27516-3997, USA. E-mail: popkin@unc.edu Guarantor: B Popkin. Contributors: LJ led the project, discussed core ideas, analysed data, drafted and amended the paper. AB and BMP participated in the design and collection of RLMS data, discussed core ideas, and commented on drafts.
European Journal of Clinical Nutrition (2003) 57, 1295–1302 & 2003 Nature Publishing Group All rights reserved 0954-3007/03 $25.00 www.nature.com/ejcn
(WHO, 1998). Figure 2 indicates how income inequality and poverty increased in the 1990s. A large segment of the population living in poverty lost considerable earning power and many experienced varying degrees of food insecurity. Additionally, increasing income inequality added yet an- other layer of insult to vulnerable households (Tulchinsky & Varavikova, 1996; Walberg et al, 1998; Lokshin & Popkin, 1999; Laatikainen et al, 2002). The health crisis of Russia has received considerable attention as both communicable and noncommunicable disease (NCD) rates have increased. A recent WHO report warns of the widening health gap between Western and Eastern European countries, citing poverty as the main factor contributing to the complex health problems facing the countries of the former Soviet Union. Unbalanced diet and unfavourable health beha- viours, including huge increases in alcohol and tobacco use, may be considered proximate determinates of health which have their roots in larger social and economic problems. (Bobak & Marmot, 1996; Tulchinsky & Varaviko- va, 1996; Cockerham, 2000; WHO, 2002). Russia, in particular, has experienced precipitous drops in life expec- tancy over the last decade (Shkolnikov et al, 1997, 2001). In 1998, the Government of the Russian Federation adopted its first broad nutrition policy: ‘Concept of Federal Healthy Nutrition Policy in Russia by the Year 2005’. This policy, developed by the Ministry of Science, Ministry of Health, Academy of Medical Sciences, and Academy of Agriculture of Russia, draws attention to aspects of a healthy diet, regular physical activity, prevention of obesity, and food safety issues (Kniazhev et al, 1998; Pokrovskiy et al, 1995, 2002). It is widely accepted, although controversial in the details, that there are links between diet (especially aspects of dietary fat intake), environment, excess adiposity, and NCDFespe- cially coronary heart disease (CHD)F(Bray & Popkin, 1998; Krauss et al, 1998; Visscher & Seidell, 2001). Therefore, we focus our report on the broad aspects of these potential health risks. Our hypothesis is that energy and diet quality would decrease, and the prevalence of overweight in the population would diminish from 1992 to 2000. In this report, we update and expand previous work to present changes in poverty, macronutrient intake, and nutritional status in a nationally representative sample of adults in the Russian Federation during the first 8y of the reform period (1992–2000).
Methods Data The Russian Longitudinal Monitoring Survey (RLMS) is a household-based, individual survey and is the first nation- ally representative sample taken across the Russian Federa- tion. Data were collected in two phases. Phase I consisted of surveys conducted in (a) September 1992 (Round 1, n¼17150), (b) February 1993 (Round 2, n¼16378), (c) August 1993 (Round 3, n¼15563), and (d) November 1993 (Round 4, n¼15429). Phase II consisted of surveys con- ducted in (a) December 1994 (Round 5, n¼11568), (b) October 1995 (Round 6, n¼10911), (c) October 1996 (Round 7, n¼10158), (d) November 1998 (Round 8, n¼9607), and (e) October 2000 (Round 9, n¼9006). Phase I (Rounds 1–4) surveyed 6000–7000 households providing 20 primary sample units (PSUs). In contrast, the second phase (Rounds 5–9) surveyed approximately 3000–4000 house- holds with a new sample with effectively 65 PSUs. While there is a longitudinal component to the survey, we use the nationally representative cross-sectional sample in each round. Since Rounds 2, 3, and 4 were all collected in 1993, we present only Rounds 1, 4, 5, 6, 7, 8, and 9. Round 4 was retained for 1993 as the data collection month (November) was seasonally comparable with the other data collection months. This minimized potential bias because of season- ality of dietary intake.
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Figure 1 Development of food consumption: Annual intake of selected foods, Soviet Union, 1950–1989: , milk; F, bread; , potatoes; , red meat; , sugar (reprinted with permission from Mortality patterns and Adulth Health Interventions in the Newly Independent States. Copyright 1995 by the National Academy of Sciences. Courtesy of the National Academy Press, Washington DC.)
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Both phases of the RLMS are based on stratified three-stage cluster samples of residential addresses. The sample was designed to allow analysis of both household and individual level data. The household (defined as ‘a group of people who live together in a given domicile, and who share common income and expenditures’) was the targeted unit, and attempts were made to survey all individuals in each household at a given address. Therefore, in each round of Phases 1 and 2, the same household was revisited, regardless of whether the same household or a new household occupied the dwelling. Briefly, the sampling frames for both surveys have identical aims (a nationally representative random sample) but slightly different designs. Phase 1, stage 1, consisted of stratifying 2335 official regions (PSUs) into 10 regions based upon geographical factors, ethnicity, and level of urbanization data from Goskomstat (the Russian State Statistical Bureau). Probability-proportional-to-size (PPS) was used to select 20 PSUs, however Moscow and St Petersburg were selected with certainty. In stage 2, voting districts within each PSU were ordered according to size and PPS used to select 10 districts within each PSU, yielding 200 secondary sampling units (SSU). In stage 3, a list of all household addresses in each SSU was compiled, and interviewers used a random selection method to systematically select 36 house- holds , providing a random sample of about 7200 house- holds. Interviewers returned at least three times to each household to complete questionnaires for each resident. The household response rate was 88.8% for Round 1 and by Round 4 had declined only to 76.0% of the original sample. Owing to a change in collaborators, in 1994 a new sample was drawn ( RLMS Phase 2). The same multistage probability sample method was used; however, the number of PSU drawn was increased to 38, thus increasing precision of estimates. In each of the regions, the basis for the number of PSU was level of urbanization. Within each PSU, the population was stratified into urban and rural substrata, and the target sample size was allocated proportionately. For instance, if 40% of the region was rural, then 40% of the households were drawn from rural villages. The target sample of households for this sample was 4000, and again the response rate exceeded 80% in Round 5 (the first round using the Phase 2 sample). By Round 9, the response rate was 73.9%. A detailed discussion of response rates, reasons for nonresponse, and potential bias is provided by Swafford and Kosolapov (2002). The legal age of retirement is 55y for females and 60y for males; therefore, we chose the lower age (55y) to make the estimates comparable for working-age Russians. We present cross-sectional data from 19.0 to 54.9- y-old subjects, stratified by gender and tertile of household regional poverty index (income). All results were adjusted by using sample weights to ensure that results are comparable across rounds and nationally representative of the Russian population. Details of the design and implementation of the RLMS have been described elsewhere (Mroz & Popkin, 1995; Popkin et al, 1997b; Zohoori et al, 1998; Lokshin & Popkin, 1999).
Economic measures The dissolution of the Soviet Union and the attendant transition to a market economy led to monthly inflation levels averaging 15% from June 1992 to December 1994. The consumer price index reflects changes in the overall cost of living for all Russians, but it is an aggregate measure, and might not accurately measure the relevant prices faced by poor individuals. To overcome this shortcoming, we used the official Russian poverty lines to define the incidence of poverty in Russia. These measures, developed by two of the authors of this paper, in conjunction with other Russian officials and researchers, reflect the average cost of food items in a Russian food basket for low-income persons. We have refined the all-Russia poverty lines to incorporate regional price variations in a standard food basket, adjusted for family size and nutrient requirements of household members. The adjusted poverty lines reflect the cost of living for low-income persons, similar to the ‘thrifty food plan’ used by the United States Department of Agriculture (USDA) to determine food assistance program eligibility (Andrews et al, 2001). Therefore, to meaningfully orient our analyses, income measures in this report, for all years, refer to the household poverty index for the income measure of each individual (including comprehensive measures of nonwage income). This household poverty index is derived as: nominal household income/sum of all individual poverty lines in household, adjusted for household size. Details on the income and price measures can be found elsewhere (Lokshin & Popkin, 1999).
Dietary measures Individual energy intake and the proportion of macronu- trients consumed were derived from the RLMS dietary intake data. A single 24-h recall was collected in all rounds except Round 7, when two consecutive 24-h recalls were obtained. In this analysis, the day 1 recall is presented. For each survey, trained interviewers conducted a standard 24-h recall for each member in the household, using color photos of foods to assist in assessing portion sizes. Phase I data were edited and processed by the Russian Center for Preventive Medicine and Phase II data by the Russian Institute of Nutrition, Russian Academy of Medical Sciences (RIN). The most recent RIN food composition table is used in all rounds.
Anthropometry Subjects were weighed, wearing light clothing and no shoes, and measured in their homes by trained interviewers. BMI was calculated as BMI¼weight/height2 (kg/m2). We use the 1997 WHO recommendations to define overweight for adults based on BMI as follows: ‘underweight’ o18.5, ‘overweight’ Z25.0 and ‘obese’ Z30.0 (International Obesity TASK Force, 1998).
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Statistics All analyses were carried out using Stata Corporation statistical software v.7.0 (Stata Corp., 2001). All results are weighted and adjusted for the complex sampling design using Stata SVY commands.
Results Shifts in diet and obesity status: 1992–2000 We find differences in energy intake by gender and income group (Figure 3) among the RLMS participants. Energy intake is directly related to income among men but not among women. Total energy intake increased for both men and women. Energy consumption in men increased with some small fluctuations, but never dropped to as low as 1992 levels. Mean intake among women hovered between 6.7 and 7.7MJ (1593kcal for low-income women in 1992, and 1823kcal for high-income women in 2000). Changes for men occurred consistently across income groups, although the 1995 dip was less pronounced among higher-income men and more dramatic among the lower-income men. The income-to-energy intake relation for women is not as direct, although women in the lowest income tertile consistently have the lowest energy intake. The difference in energy intake for men between the lower- and higher-income tertiles is about 325kcal/day or 14% (tertile 1 vs tertile 3 in 2000). The differences between women of various income groups are about half those of men (150kcal or 9%). However, similar to men, those women in the lowest income tertile consume the least energy. Despite the differences seen in energy intake between the genders, macronutrient composition and trends were similar for men and women, so we present a total adult intake. Fat intake decreased dramatically and steadily for all genders and income groups (Figure 4). The proportion of fat in the diet in 1992 was almost identical for all income groups (39.1, 39.8, and 39.8% for tertiles 1, 2, and 3, respectively). However, over time there was a decline in energy from fat among all income groups, with the lowest incomes declining most steeply until 1998, when lower and middle income intakes appear to level out. By October 2000, the average percent of energy from fat was 29.4, 32.9, and 33.4%, respectively. Protein intake as a per cent of energy displayed very similar trends to fat intake, decreasing among all groups, but with the lowest-income groups consuming the least protein in all years, and continuing to decrease after the
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Figure 3 Mean daily energy intake (MJ) by income tertile, Russian men and women aged 19–55y. , men: lower income; F, men: middle income; , men: higher income; –B–, women: lower income; ––, women: middle income; –n–, women: higher income. Sources: Russia Longitudinal Monitoring Survey, Rounds 1–9.
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Figure 4 Macronutrient composition of the diet of Russian adults aged 19–55y. Stratified by income tertile and year. ’, carbohydrate; &, protein; , fat.
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European Journal of Clinical Nutrition middle and higher income groups stabilized. These decreases in fat and protein were compensated for by commensurate increases in carbohydrate intake. There are two main points regarding overweight and obesity in the 1990s. First, women have always displayed higher prevalence rates of both overweight and obesity than men (Figures 5 and 6). There has been very little change in the prevalence of overweight (overall, from 44.6 to 44.9%), although the figure is high (41% of men and 49% of women overweight in 2000); but of the overweight, the proportion of adults who are obese increased (13.3–16.0%). The prevalence of obesity among men increased from 7.1% in 1992 to 10.3% in 2000, while in women the prevalence increased from 19.1 to 21.6%. Second, there is a strong,
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Figure 5 Overweight (BMI 25.0–29.9kg/m2) and obesity (BMIZ30kg/m2) of Russian men aged 19–55y by income tertile and year. ’, BMI 25.0–29.9kg/m2; &, BMIZ30.0kg/m2. Sources: Russia Longitudinal Monitoring Survey, Rounds 1–9.
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European Journal of Clinical Nutrition direct relation between income and overweight among men. The prevalence of overweight is highest among higher- income men with the lowest prevalence among lower- income men; this pattern is not apparent among women. The prevalence of underweight is higher in women than in men, but is low for both genders (1–2% in men and 3–5% in women for all time periods; results not shown).
Discussion This paper presents a broad description of the diet and overweight status of the Russian Federation population as it shifts into the global economy and free market system. We wish to emphasize that because of the ecologic nature of our data, these descriptive patterns by no means infer causality, but are meant to provide a sense of the changes during the last 10 years. We describe two main effects: (a) the overall cross-sectional trends in diet and overweight prevalence for working-age men and women over an 8-y period and (b) the differential effects by gender and income tertile. We have found that while the overall trends are similar in all groups, there are important degrees of severity of the trends among income groups, and this severity differs by gender. Overall, we find energy intake increasing slightly, fat decreasing, and obesity increasing over the 1992–2000 time period. Income affects men more than women, except in the case of the proportion of energy-providing macronutrients. While overweight prevalence has remained stable, that proportion of the overweight classified as obese has increased by an average of 0.34 percentage points per year, which is consistent with the increase seen in other countries over the last decade (Mokdad et al, 1999; Molarius et al, 2000; Visscher et al, 2002). Likewise, our findings are consistent with those of a 10-y longitudinal study in PolandFconducted during a period of similar economic reformsFthat young adults are experiencing increasing prevalence of overweight (Dennis et al, 2000). However, an examination of the association between educational level and relative body weight in the WHO MONICA study reported a decrease in obesity pre- valence in Moscow adults over a 10-y period (from 14 to 8% in men, from 33 to 21% in women), but an increase among Siberian men (from 13 to 15%), while the prevalence among Siberian women remained stable at 43%. Russians in the MONICA survey had some of the highest levels of education of all countries. In the final survey (1992–1995), there was a significant negative effect of education on BMI among women, but not among men (Molarius et al, 2000). Pomerleau et al, in a study of the Baltic Republics, did not find an income effect on prevalence of obesity, but reported excessive fat intake and inadequate fruits and vegetable intakes among adults (Pomerleau et al, 2000, 2001). We can only hypothesize the reasons for the observed income effects among men in our data, or conversely, why it is not apparent in women. These differences open a new question: what is it about the economic environment that seems to affect men more than women? It is possible that stress and inactivity related to unemployment and under- employment may be critical in this relation (Bobak et al, 2000; Marmot & Bobak, 2000). We could hypothesize that low-income females suffer less than low-income males because of alternative coping mechanisms utilizing family supports. Fully one-half of all single-parent females (tradi- tionally low-income) in Russia live with parents or other adults (Lokshin et al, 2001). It is difficult to study diet in Russia’s recent history. During Soviet times, agricultural production was large, but distribu- tion was uneven and stores were often empty. Individuals of all income levels in both pre- and post-Soviet Russia made use of social trade networks and kolkhoz (collective farm) markets, home production on small plots on their dachas (country plots and/or homes), and the ‘shadow economy’ to supplement their diet. An interaction between urban/rural residence and income may exist, because income is traditionally lower in rural areas, but has less effect on access to food. Delayed or suspended payment of wages contributes to irregular income and difficulty in estimation. Stillman reports in a careful analysis that urban households which experience an exogenous income shock (loss of income) of 10% change both food and total nondurable expenditures by 7–11% (Stillman, 2001). Biloukha and Utermohlen (2001) identified ‘cost’ as the main barrier to healthy eating among 65% of highly educated Ukrainian adults in a recent study of perceived influences on food choice. As we have noted in greater detail elsewhere, there are important data limitations that do not allow us to have a clear national representation of Russia in the pre-1990 era. In the mid-1970s, the Lipid Research Clinic (LRC) Program prevalence study investigated CHD risk factors in a sample of US and Russian adults. They reported that Russian men living in Moscow and Leningrad had lower fat as a proportion of energy and BMI than US men, but with a higher energy/kg body weight intake (Ingram et al, 1985). However, Russian women in the study had a higher BMI than their US counterparts, along with a higher energy intake. There was no significant difference in fat intake between the samples of women (Dennis et al, 1988). If energy expenditure has decreased in Russia without a concomitant decrease in energy intake, then the increase in obesity was inevitable. The RLMS is a powerful survey of health effects, but potential weaknesses are the single 24-h recall and the fact that all seasons of the year are not represented. On the other hand, a single 24-h recall is adequate for examining population-level changes, and although not seasonally representative, the survey rounds are comparable to each other, allowing us to better determine changes in winter intake. We have not explored aspects of physical activity, which along with diet determine body weight; nor have we explored the contribution of alcohol intake to caloric intake in this analysis. Although alcohol consumption is low
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European Journal of Clinical Nutrition among women, it can be considerable among men. The nutrient density in the diet of lower-income men may be quite low because of dilution by large alcohol consumption. Also, we have not accounted for smoking in the population, which is higher in men than women. Potential bias because of nonresponse over the years of the survey includes attrition in the urban areas, which was partially offset by targeting a slightly higher sample in urban areas. Comparison of responder characteristics to the 1997 microcensus was quite encouraging. Percentages of urban and rural households of various sizes, as well as age/gender and education level in the RLMS surveys were similar to the proportions found in the microcensus (Swafford & Kosola- pov, 2002). In conclusion, we find a direct effect of income on the diet and overweight status of Russian men. We also find that obesity is increasing. As in all countries, public health efforts may find difficulty in simultaneously targeting disparate problems among different segments of the population, including inadequate and excessive nutrition.
Acknowledgements We thank Andrew Yim, Anna Safronova, Rosalind King, and Sonya Jones for their comments, Tom Swasey for help with the graphics, Karin Gleiter and Laura Henderson for help with the database work, and Frances Dancy for her help with the manuscript. Source of Support: US National Institutes of Health (NIH) (R01-HD30880 and R01-HD38700).
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