European Perspectives |
The sociodemographic patterning of health in Estonia, Latvia, Lithuania and Finland
Ville Helasoja1, Eero Lahelma2, Ritva Prättälä1, Anu Kasmel3, Jurate Klumbiene4 and Iveta Pudule5
1 National Public Health Institute, Department of Epidemiology and Health Promotion, Finland
2 Department of Public Health, University of Helsinki, Finland
3 Estonian Centre for Health Promotion, Estonia
4 Institute for Biomedical Research, Kaunas University of Medicine, Lithuania
5 Health Promotion Centre, Latvia
Correspondence: Ville Helasoja, MSc, National Public Health Institute, Department of Epidemiology and Health Promotion, Mannerheimintie 166, FIN-00300 Helsinki, Finland, tel. +358 9 4744 1, e-mail: vheh{at}ktl.fi
Received August 18, 2003, accepted December 18, 2003
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Abstract |
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Background: Public health problems in the Baltic countries are typical of Eastern European transition economies. A common assumption is that the economic transition has been particularly difficult for previously disadvantaged groups, and comparative research on the health differences between sociodemographic groups in the Baltic countries is therefore needed. This study compared associations of health with gender, age, education, level of urbanization and marital status in three Baltic countries and Finland. Methods: The data were gathered from cross-sectional postal surveys conducted in 1994, 1996, 1998 and 2000 on adult populations (aged 20–64 years) in Estonia (n = 5052), Latvia (n = 4290), Lithuania (n = 7945) and Finland (n = 12796). Three self-reported health indicators were used: (i) perceived health, (ii) diagnosed diseases and (iii) symptoms. Results: The prevalence of less-than-good perceived health (average, rather poor or poor) was higher in the Baltic countries (men 66–56%, women 68–64%) than in Finland (men 35%, women 31%). The odds ratios (with 95% confidence intervals) of less-than-good perceived health among the low educated compared to the highly educated in Estonia, Latvia, Lithuania and Finland were 2.03 (1.49–2.77), 2.00 (1.45–2.76), 2.27 (1.78–2.89) and 1.89 (1.61–2.20) among men, and 3.32 (2.43–4.55), 2.77 (2.04–3.77), 2.07 (1.61–2.66) and 1.89 (1.63–2.20) among women, respectively. Diseases and symptoms were also more common among the lower educated men and women in all four countries. However, urbanization and marital status were not consistently related to the health indicators. Conclusions: The Baltic countries share a similar sociodemographic patterning of health with most European countries, i.e. the lower educated have worse health. The methodological considerations of this study point out, however, that further research is needed to support public health policies aimed at the most vulnerable population groups.
Keywords: Baltic countries, sociodemographic patterning of health
The three Baltic countries, Estonia, Latvia and Lithuania, received their independence from Russia after the First World War. In the aftermath of the Second World War they became part of the Soviet Union. They gained their independence back again at the beginning of the 1990s during the collapse of the Soviet regime. Currently these countries share public health problems typical of Eastern European transition economies: mortality and morbidity from cardiovascular and other non-communicable diseases is higher than in Western European countries.1,2
A common assumption is that the Eastern European transition has been particularly difficult for previously disadvantaged groups. Thus, the lower educated, poorer and older people, as well as those outside social support networks, may have suffered more than other groups. Psychosocial, behavioural and material determinants are likely to contribute to the poor overall health in these societies. These determinants are considered vital to the understanding of the reasons for the East–West health divide. Moreover, they reflect political and practical issues since substantial public health potential lies in the improvement of the situation among disadvantaged population groups and nations.3–7
In the 1990s and early 2000s, comparative studies on public health including all three Baltic countries have mainly focused on specific health behaviours such as smoking, drinking and food habits.8–15 These studies suggest that health behaviours are associated with key sociodemographic factors. Although health behaviours may not provide direct information on health outcomes, there are findings indicating that the sociodemographic patterning of health within the Baltic countries shows similarities with that of health behaviours.16–22
Further research is needed on sociodemographic differences in health using internationally comparative measures and data from the Baltic countries. Such studies are expensive and may need standardized clinical databases. An alternative is to use self-reported health from surveys.23 The Finbalt Health Monitor project24 provides comparative survey data from the Baltic countries. Additional comparable data are available from a stable but historically and geographically close EU country, Finland. Comparison with a typical Western European social patterning of health in Finland25 provides a yardstick for the interpretation of results from the three Baltic countries.26
The specific aim of this study was to examine whether the sociodemographic patterning of self-reported health is similar or dissimilar in Estonia, Latvia, Lithuania and Finland. From the existing body of research, we expected that the older, lower educated, rural and non-married people, would have worse health in all countries. In addition, we expected that ill health would be more prevalent in the Baltic countries than in Finland but the main focus of this paper was on the comparison of the relative sociodemographic patterns.
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Methods |
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The data were gathered from cross-sectional postal surveys arranged by the Finbalt Health Monitor project in 1994, 1996, 1998 and 2000 in the Baltic countries and Finland. The methodology and questionnaires used in the surveys have been harmonized between the participating countries.24,27 Each survey is based on a nationally representative random sample drawn from the population register containing all the citizens. The sampling unit has been the individual in all the surveys and substitution of non-respondents has not been used. A limitation of the data is that in the Baltic countries it is technically impossible to link the survey material to external morbidity or mortality registers.
Gender, age, level of education, level of urbanization and marital status were the sociodemographic determinants (table 1). For Estonia, Lithuania and Finland, education was measured as the total number of years of schooling given in the questionnaire, and was trichotomized as follows: (i) high, 14 years or more; (ii) intermediate, 10–13 years; and (iii) low, 9 years or less of education. Education in Latvia was not measured in years, but instead by educational levels (primary, secondary, vocational and university) which was nevertheless an ordinal measurement. Education was trichotomized as follows: (i) high, university; (ii) intermediate, secondary or vocational; and (iii) low, primary education. In all four countries the level of urbanization was based on an administrative classification of the address of the respondent. Ethnic origin was dichotomized as ‘natives’ and ‘others’. In the Baltic countries ‘others’ were mainly Russians.
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Three self-reported health indicators were included: (i) perceived health; (ii) diagnosed diseases; (iii) symptoms.
Perceived health was asked by the following question ‘How would you assess your present state of health?: 1) good, 2) reasonably good, 3) average, 4) rather poor, 5) poor’. It was categorized firstly by using a common dichotomization: less-than-good (3,4,5) and other (1,2). As the average category was very common in the Baltic countries, the analyses were secondly conducted using the following dichotomization: poor (4,5) and other (1,2,3) (table 2).
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The number of diseases was measured against a checklist of eight items: ‘Have you had any of the following diseases diagnosed or treated by a doctor during the last year? (yes/no): elevated blood pressure, diabetes, myocardial infarction, angina pectoris, heart failure, rheumatic arthritis, back problems or chronic bronchitis’. The number of symptoms was also measured against a checklist of eight items: ‘Have you had any of the following symptoms or complaints during the last month (30 days)? (yes/no): chest pain during exercise, joint pain, back pain, swelling in the feet, varicose veins, eczema, constipation or toothache’. This checklist also contained headache, insomnia and depression but these psychological items were excluded in order to add conceptual consistency of the summary measure. The checklists allowed ‘yes’ or empty response alternatives, except that in Estonia in 1996 and 2000 there was also a ‘no’ alternative. We categorized the empty responses as ‘no’. The two checklists were dichotomized as follows: (i) one or more diseases and other, (ii) three or more symptoms and other (table 2).
Logistic regression analysis28 was used to examine the patterning of sociodemographic differences in health. All models were fitted separately for each country and gender, and the main effects were fitted in their assumed temporal order. The overall effect was added first, followed by age, education, urbanization and marital status. Models consisting of the overall effect, age and each explanatory variable only were also examined. The statistical significance of the terms was assessed by scaled deviance and change of the degrees of freedom (
SD and DF). The results of the age-adjusted and fully adjusted models are presented as odds ratios (OR) and their 95% confidence intervals (CI) in tables 3
–6.
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Estonia and Latvia have substantial non-native populations, and the above models were also fitted by using ethnic origin as the first explanatory variable in these countries. The results of these models are not shown as we found only small and inconsistent ethnic differences. However, the fully adjusted ORs of the statistically significant differences between the ethnic groups are presented in the text.
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Results |
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A comparison with census information suggests that our data slightly underrepresents the youngest age group in all countries except Latvia (table 1). Our data also contain more women than the official statistics.29–32 Educational levels are difficult to compare as can be seen from the proportions of those with at least third level education in 1996 in Estonia (53%), Latvia (60%), Lithuania (59%) and Finland (52%).29 Our trichotomous categorization is likely to distinguish the upper and lower end of the educational scale but the intermediate category is more heterogeneous. The proportion of urban population1 in 1997 in Estonia (70%), Latvia (65%), Lithuania (68%) and Finland (62%) corresponds to combined proportions of respondents in cities and towns in our data. A comparable estimate of the prevalence of marriage during our study period could not be obtained from the official statistics of the Baltic countries. The Finnish statistics32 indicated a lower proportion of married men (52%) and women (55%) than in our data. However, our ‘married’ category includes cohabitation. The ethnic distribution of our data was rather similar to the corresponding proportions of native citizens in official statistics from Estonia (65%), Latvia (56%) Lithuania (82%) and Finland (93%).29–32
Prevalence of ill health
The prevalence of less-than-good perceived health (average, rather poor or poor) was higher in the Baltic countries (men 56–66%, women 64–68%) than in Finland (men 35%, women 31%). This was mainly due to the average category being particularly prevalent in the Baltic countries, whereas differences in the prevalence of poor (rather poor or poor) health were small. The prevalence of one or more diagnosed disease was higher in Lithuania than in the other countries. There were no consistent gender differences, except that the prevalence of three or more symptoms was higher among women in all countries. The most common diseases were elevated blood pressure and back problems in all countries and the most common symptoms were joint pain and back pain (table 2).
There were no marked differences between the study years. However, the prevalence of one or more disease was higher among Estonian men in 2000 (32%) than in 1994 (21%). The prevalence of diseases and symptoms was higher among those with poor perceived health in all countries (data not shown).
Sociodemographic patterning of ill health
Perceived health
The mutual adjustment for the independent variables in the logistic regression models made only a small contribution to the patterning of less-than-good perceived health (table 3), and did not consistently decrease or increase the differences with any of the studied variables. This was also the case with the models of poor perceived health (table 4).
Perceived health was statistically significantly worse in the older age groups with both cut-off points (tables 3 and 4). Also, the main effect of age was statistically significant (P < 0.001) among men and women in all four countries.
In the fully adjusted models, less-than-good perceived health was statistically significantly more common among the lower educated men and women in all countries. The OR for less-than-good perceived health among those with a low level of education compared to those with a high level of education varied between 1.89 and 2.27 in men and between 1.89 and 3.32 in women. The educational gradient was smallest in Finland among both genders. Poor perceived health (table 4) was also more common among the lower educated, with the exception of Estonian men. Moreover, the differences between the high and intermediate level education groups were not statistically significant among Lithuanian and Finnish men, or Estonian women.
The main effect of education was statistically significant (P < 0.001) among men and women in all countries when less-than-good perceived health was examined, but non-significant among Estonian men and women and Lithuanian men when poor health was examined.
The differences between urban and rural areas were small and inconsistent with both cut-off points of perceived health especially among women. However, the OR was higher for rural areas among Estonian men and Lithuanian men and women when less-than-good perceived health was examined (table 3). Similar patterns were found for Estonian and Latvian men for perceived poor health (table 4). The main effect of urbanization was statistically significant (P < 0.001) in Lithuanian men when less-than-good perceived health was examined.
The OR was higher among the non-married in Finland with both cut-off points of perceived health. When poor perceived health was examined, the same pattern was found for both genders in all countries. However, the differences were not statistically significant for Estonian men or Latvian women. The main effect of marital status was statistically significant (P < 0.001) among Finnish men for both cut-off points, and also among Lithuanian men when poor health was examined.
The fully adjusted OR of less-than-good perceived health was higher among non-native men [1.33 (1.05–1.69)] and women [2.14 (1.74–2.65)] in Estonia. Among Estonian women, poor perceived health was also more common among the non-natives [1.96 (1.48–2.59)]. Less-than-good perceived health was similarly more common among non-native women in Latvia [1.36 (1.11–1.65)].
Diseases
The differences between the age-adjusted and fully adjusted models for one or more diseases were only minor (table 5). In all countries diseases were statistically significantly more common among older men and women and the main effect of age was also statistically significant (P < 0.001). In the fully adjusted models, the OR was generally higher among lower educated men and women in all countries, except among Latvian women. A statistically significant gradient was found in Lithuanian and Finnish men and women, and also among Estonian women. These differences between those with low level of education as compared to those with high level varied between 1.45 and 1.74 in men and between 1.55 and 1.60 in women. The educational gradient tended to be similar in Estonian and Latvian men, but the differences were statistically non-significant. The main effect of education was also statistically significant (P < 0.001) in Lithuanian and Finnish men and women. The differences between urban and rural areas were mainly small and inconsistent. However, the OR was highest among Latvian rural men and among Lithuanian men living in towns. There were no consistent differences by marital status, but the OR was lower among the non-married Finnish men. The fully adjusted OR was higher among non-native men [1.35 (1.06–1.71)] and women [1.46 (1.20–1.77)] in Estonia.
Symptoms
The age-adjusted and fully adjusted models for three or more symptoms were mostly similar (table 6). In all countries, symptoms were statistically significantly more common among older men and women and the main effect of age was also statistically significant (P < 0.001). The OR was higher among the lower educated in all countries, but these differences were not statistically significant among Estonian and Latvian men. Among women, the gradient was more consistent in all countries and the difference between those with a low level of education as compared to a high level of education varied between 1.44 and 2.05. The main effect of education was statistically significant (P < 0.001) among Lithuanian men and women and Finnish men. The differences between urban and rural areas were small and inconsistent, the OR being highest among Estonian rural men. There were no differences by marital status, except that the OR was lower among non-married Estonian men. The fully adjusted OR was higher among non-native women in both Estonia [1.56 (1.27–1.93)] and Latvia [1.54 (1.18–2.00)].
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Discussion |
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We found that the gradient of perceived health by age and education was similar in all four countries studied, i.e. poor health was more common among the older and the lower educated men and women. However, against our expectations, differences between urban and rural areas, as well as between married and non-married men and women, were small and inconsistent in all four countries. In addition, the indicators of ill health were not consistently more prevalent in the Baltic countries than in Finland.
The response rates in our study were comparatively high, but non-response is still a potential cause of bias. We lack comparable data about non-response for different sociodemographic groups but late response as well as unit and item non-response among the respondents have been analysed in detail. The bias has been found to be generally small and its direction similar in all countries. However, it is likely that people with social problems are overrepresented among the non-respondents.27 Therefore, poor health might be more prevalent if non-respondents had responded.
This study did not address changes over time for two reasons: firstly, the main focus was to examine the key sociodemographic factors with sufficient statistical power, and secondly, the years 1994–2000 are a period of relative social stabilization after the major shock effects of economic transition of the early 1990s in the Baltic countries. This situation is also reflected by mortality indicators.33,34
We were unable to quantify to what extent the observed educational differences might be related to purely material conditions. However, education is likely to represent people's long-term life-chances better than more volatile income or employnent status, and therefore is suitable for cross-sectional cross-country comparison across the whole study period. Education provides human capital, also in times of social and economic transformation.35 Moreover, inequalities in health within individual Eastern European countries have been found to be related to education.5,36
The direct cross-country comparisons of the prevalence levels should be made with caution. Nevertheless, a high prevalence of the average category of perceived health was found consistently in all three Baltic countries but not in Finland. Reasons for this may be linguistic, cultural or even political.37 For example it has been argued that it was beneficial during the communist regime to be ‘normal’ or ‘average’, and people may still be unwilling to use the extreme ends of scales in surveys.26,38 Comparable analyses from the Baltic countries are scarce, but in a study of seven post-communist countries,16 the overall prevalence of poor health varied between 12 and 16% in the Baltic countries. These figures are close to ours. There is also previous evidence on the prevalence of average perceived health in Estonia (men 51%, women 53%),18 and these figures, too, are close to our estimates.
A potential explanation for the difference between the Baltic countries and Finland in average perceived health is that the respondents perceive ‘good’ health as more than just lack of ill health. It has been suggested that psychosocial factors become more important in societies where basic material needs have been met.39 Thus, differences between the Baltic countries and Finland might reflect a gap in the general well-being. This was the case at least in the early 1990s, when perceived health and life control as well as economic satisfaction were worse in the Baltic countries than in Finland.17
A similar gap between Finland and the Baltic countries is found for premature mortality,1 and one might have expected that it would also be visible for poor perceived health, diseases and symptoms. Our data indicated, however, mainly differences in good perceived health. A reason for the inconsistency between self-reported poor health and mortality may be that the external,33 causes of death are not reflected in our data. In addition, it may be that people's own health perceptions do not refer exactly to a similar physical health status in the Baltic countries and Finland. It has been found that age peers are an important point of reference for perceiving one's own health.40 Similarly, a respondent with health problems in a population with a generally deteriorated health may rate his/her own health as average which is actually ‘true’ for his/her own reference group. In contrast, a respondent with physiologically similar health status may assess his/her health as poor if he/she belongs to a ‘healthy’ reference group. Finally, the association between health perception and mortality may be related with other factors, such as weak sense of mastery,41 which could not be assessed in our study.
Thus, there may be a smaller gap in poor self-reported health than what is found for morbidity or mortality because of different reporting due to social comparison. Similarly, our data may also underestimate differences between urban and rural areas. It may also be difficult to determine an ideal cut-off point for perceived health in the Baltic countries, but our findings suggests that the difference between good and average health may be more relevant than, for example, in Russia.42
Women reported slightly more diseases and symptoms than men, but the sociodemographic differences for all studied health indicators were mainly similar among both genders. Health problems related to low socioeconomic status usually cumulate in men in the post-communist countries,6 and therefore one might have expected to find a stronger association with education among men. However, this was not the case, as a statistically significant educational gradient was found even more consistently among women in all of the countries. In addition, the magnitude of relative educational differences was rather similar among both genders. The lack of gender differences in self-reported health was also observed in another study from Estonia.18 There is evidence that the transition period has been problematic for women's health in all three Baltic countries.43
Overall, the educational gradient was mainly similar across all studied countries and health indicators but the most consistent patterns were found with less-than-good perceived health. Among Estonian men the educational differences in poor perceived health, diseases as well as symptoms were generally weakest even though one might have expected a clear gradient.44 This may support that in our self-reported data the lack of good perceived health is possibly an important indicator of actual health problems. However, in spite of these methodological reservations, the main message was clear, i.e. that the lower educated men and women reported poorer health. This has mainly been the case in earlier studies from the Baltic countries as well,16,18,34,45–47 but there are findings suggesting that a high level of education might be a stronger protective factor for health in a stable market economy than in a post-communist society.17,37,48 Our results from the Baltic countries did not support a differential effect of social stratification on health.
As with most other transition economies, the Baltic countries, especially Estonia and Latvia, have also faced difficulties with the privatization of agriculture. Therefore we expected that the multiple social changes and pressures might result in poorer health within the rural areas. However, we did not find a universal pattern, although health was somewhat poorer among rural Estonian and Latvian men and also Lithuanian men and women. These findings are also in line with results from other studies from Estonia18 and Lithuania.34 The generally smaller than expected differences between urban and rural areas can also be due to the fact that in our study we were able to adjust the figures for education, which is not always possible in mortality studies.
Our results concerning marital status were somewhat mixed. It has been argued that ‘neo-traditional’ networks are of particular importance in the post-communist countries, especially for the health of men.49 This should have resulted in poorer health among non-married men in the Baltic countries, but we found indications of this only for poor perceived health, with no gender differences. However, it has been found that the prevalence of poor health in Lithuania is even lower among non-married men, possibly due to health problems accumulating among divorced men.38 This could lie behind our results too, but because of a limited number of divorced respondents we were unable to analyse them as a separate category.
Finally, we did not find consistent differences in the sociodemographic patterning of health between the natives and the non-natives in Estonia and Latvia. Therefore, it is possible that the sociodemographic factors have broadly similar effects among all ethnic groups in these countries. However, all observed statistically significant differences between ethnic groups were similar: health was worse among the non-natives. This was also found in another study in Estonia.18
In sum, the Baltic countries share a similar sociodemographic patterning of health with most European countries, i.e. the lower educated have worse health. Policy makers should take seriously the growing challenge of large inequalities in health. However, the findings of this study are likely to show only the tip of the iceberg of the actual inequalities. Our study calls for further research on the sociodemographic determinants of health to support efficient evidence-based public health policies aimed at the most vulnerable population groups in the studied countries.
Key points
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Acknowledgments |
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Source(s) of support in the form of grants, equipment, drugs etc. was from ‘Population, Health and Living Conditions’ graduate school programme, University of Helsinki, Finnish National Public Health Institute and Finnish Ministry of Social Affairs and Health.
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