Abstract
Introduction
Obesity is the most threatening non-infectious disease of our time, the basis of many chronic diseases, increasing the mortality rate. The Roma ethnic minority is particularly affected.
Materials and methods
Cross-sectional–questionnaire and physical–survey in rural settlements of Hungary, Romania, Slovakia, where Roma live with the non-Roma, Hungarian-speaking population (N = 1893).
Results
The average BMI of the Hungarian (P < 0.001) and Romanian (P = 0.018) samples was significantly higher than that of the Slovak sample. In the case of Roma and non-Roma subjects, we found a significant difference in Hungary (P = 0.006) based on body composition, as well as in the case of visceral fat (P < 0.001). The extremely obese (<40 BMI) are mostly low educated in Romania and Hungary (P < 0.001), while those in normal weight have a tertiary education in Slovakia (P = 0.027). Hungarian Roma and non-Roma participants show significant differences in the physical activity dimension of the SF-36 questionnaire (P < 0.001), as well as in Romania (P < 0.001) and Slovakia (P = 0.002).
Conclusions
In summary, it can be stated that rural Roma subjects in Hungary are in the worst situation in terms of obesity in the three countries studied. In our study, the results in Slovakia clearly suggest a healthier lifestyle.
Introduction
Obesity is spreading worldwide, posing an increased risk to health-related quality of life, the development of chronic diseases such as cardiovascular disease (CVD), type 2 diabetes (DM) or premature death. At least 2.8 million people worldwide die each year from overweight or obesity [1]. According to the WHO, obesity has tripled worldwide since 1975. According to the 2016 data, more than 1.9 billion adults were overweight, and more than 650 million of them were obese, meaning that 39% of adults are overweight, and 13% are obese, and even the number of obese children under the age of 5 is estimated at 39 million in 2020. It is also a fact that the majority of the world's population lives in countries where being overweight kills more people than malnutrition [2, 3].
In the context of the COVID-19 pandemic, which erupted in 2019, the scientific community has clearly demonstrated that obesity and chronic diseases that develop on its own exacerbate the symptoms, and the course of the disease adversely affects the prognosis of infected individuals. Recent experience and epidemiological reports of the epidemic from Wuhan, China suggest that metabolic syndrome (MS), hypertension, CVD and DM are more likely to cause death in infected individuals. Obesity is one of the main risk factors, especially in young and middle-aged adults, for a poor prognosis in an infected person [4–11].
Obesity has a negative impact on health-related quality of life in a number of areas, as it affects physical and mental well-being, but it also affects role in society as well as life satisfaction [12, 13].
There are several risk factors behind obesity, including a lack of physical activity, a positive energy balance due to poor nutrition and the intake of excess calories, but stress, smoking and alcohol consumption can also be mentioned. The event of the last two years has further increased physical inactivity, as the pandemic has also brought the use of food and processed food delivery, as well as teleworking to the forefront [14]. Obesity and improper lifestyle are more likely to lead to the development of chronic diseases such as CVD, DM and MS. Risk factors include gender, age, low educational attainment, and the resulting lack of knowledge, such as unemployment or lack of health insurance, and poor social conditions. In many cases, however, different health cultural habits or disadvantages caused by a lack of language skills, i.e. ethnicity, may also be at the root of the problems [15–17]. The problems mentioned above are particularly acute among the Roma minority in Europe [18].
The largest minority in Europe is the Roma minority, and their number is growing. There are 10–12 million Roma in Europe and an estimated 6 million live in the European Union (EU). Many countries have a high number of subjective poor, and their poor conditions also have serious consequences for their health [19]. Several subgroups can be distinguished in the Roma population from India, but they have integrated into mainstream society by preserving their cultural, religious, lifestyle, dietary and clothing traditions. The lifestyles and social status of the subgroups are also different, but most of them live segregated even today. Due to their Indian origin, their genetics differ from the genetics of Caucasian peoples. The majority of the Roma population lives in a socially disadvantaged situation, and this increases the level of smoking and alcohol consumption. The combined effect of these is behind the higher morbidity and mortality rates of the Roma ethnic minority [20].
It is well known that in many countries of the world, the health status of ethnic minorities is worse than that of the majority society, and their life expectancy is also lower than the national average. Their low level of education still results in illiteracy, making it difficult for them to find work, with the result that their housing conditions and lifestyle are unsatisfactory [21]. Malnutrition has been a problem of disadvantaged minorities for a long time, but obesity is also causing additional health problems.
We chose Hungarian-speaking Roma and non-Roma living in a non-urban environment in the Carpathian Basin, including Hungary, Romania and Slovakia, as the study population of our comparative research. In all three countries, there is a significant and growing trend of the number of Roma minorities [22, 23].
The aim of this publication is to present the prevalence of obesity among subjects in relation to their sociodemographic characteristics, subjective health status, biometric data, body composition and quality of life. Our complex research also allowed us to make a comparison between the data of Roma and non-Roma respondents living in different countries, in addition to examining the above correlations. Based on our original concept, we expected to experience the worst values for the results of people living in Romania, mainly Roma, and that Hungary was likely to be similar to the Slovak sample. Our assumption was based on WHO data, according to which Romania had the highest death rate due to non-infectious diseases in 2019, followed by Hungary and Slovakia [24].
Materials and Methods
Our present study was conducted in 21 settlements in Hungary, 15 in Romania and 6 in Slovakia among the population aged 18 and over, where Hungarian-speaking Roma and non-Roma residents lived mixed. Hungary was fully covered in five regions, including the participating municipalities. In Romania, the municipalities of the North-West and Central Romania, mainly in the counties of Bihor, Satu Mare, Harghita, Mures and Covasna, were included in the macro-region 1. In Slovakia, we surveyed participants in municipalities in the districts of Nitra, Banská Bystrica and Košice.
We reached the settlements mainly through social networks and Roma community leaders. In many cases, social workers helped to collect questionnaires. The questionnaires were filled out in advance in some cases, but in most of the cases we conducted personal interviews with the participants on site. Physical examinations were performed on site in all cases. Data collection took place between September 2020 and March 2022, and the introduction of and compliance with the COVID-19 epidemic and epidemiological restrictions were aggravating circumstances in our work. Our study involved Roma individuals who volunteered at the advertised time and place, undertook to complete the questionnaire, underwent physical examinations and signed an informed consent form after receiving detailed information about the course of the study.
Our questionnaire included questions on lifestyle and socio-economic status as well as subjective health, in addition to the questions in the standard SF-36 [25]. Quality of Life questionnaire (SF-36). During physical examinations, height measurement was taken, waist and hip abundance were checked with a centimetre tape, and then body composition was measured with an OMRON 511 Bio impedance (hereinafter BIA) device, and blood pressure and pulse were measured with an OMRON device.
Due to space constraints, only the dimensions of physical activity and role limitation due to physical problems, as well as mental health, are presented in this publication.
SPSS Statistics 25 was used for statistical analyses. We used it to perform descriptive statistics. Chi-squared test, t-test and ANOVA were used to examine the correlations and differences between the variables. Correlation tests were also used. The normality test was performed using the Kolmogorov-Smirnov test, a 95% confidence interval and a P < 0.05 significance were determined.
Results
The characteristics of the test sample are illustrated in Table 1.
Socio-demographic characteristics
| Hungary (n = 852) | Romania (n = 631) | Slovakia (n = 410) | ||||
| Roma (n = 430) | Non-Roma (n = 422) | Roma (n = 330) | Non-Roma (n = 301) | Roma (n = 204) | Non-Roma (n = 206) | |
| Gender | ||||||
| male | 108 (25.1%) | 128 (30.3%) | 72 (21.8%) | 118 (39.2%) | 58 (28.4%) | 43 (20.9%) |
| female | 322 (74.9%) | 294 (69.7%) | 258 (78.2%) | 183 (60.8%) | 146 (71.6%) | 163 (79.1%) |
| Age (yrs) | 44.61 ± 14.52 | 46.34 ± 15.38 | 39.07 ± 14.48 | 41.27 ± 16.87 | 39.42 ± 15.51 | 40.63 ± 14.55 |
| Education | ||||||
| 8 primary classes or less | 248 (57.7%) | 71 (16.8%) | 269 (81.5%) | 81 (26.9%) | 110 (53.9%) | 10 (4.9%) |
| Secondary vocational school | 90 (20.9%) | 60 (14.2%) | 50 (15.2%) | 47 (15.6%) | 50 (24.5%) | 31 (15.0%) |
| Secondary school | 62 (14.4%) | 124 (29.5%) | 9 (2.7%) | 86 (28.6%) | 37 (18.1%) | 62 (30.1%) |
| College/University | 30 (7.0%) | 167 (39.6%) | 2 (0.6%) | 87 (28.9%) | 7 (3.4%) | 103 (50.0%) |
Notes. Data in frequencies/percentages.
Typically, Roma in Romania (59.1%) do not have health insurance in our sample (P < 0.001). In Hungary and Slovakia, the proportion of Roma and non-Roma is similar among those without insurance, but their numbers are low.
Nevertheless, in our sample, Roma living in Romania are reported to have a higher proportion of diseases diagnosed by a physician (P = 0.015). In Hungary, Roma respondents are more likely to be diagnosed with a (P = 0.015) as well, while in Slovakia the proportion of those diagnosed with a is higher among the Roma and the majority of participants (Fig. 1).
Health-related indicators by respondent number and percentage, N = 1893
Notes. Data in frequencies/percentages.
Citation: Developments in Health Sciences 6, 1; 10.1556/2066.2022.00047
Roma typically do not go for screening in Hungary (62.4%) (P < 0.001), there is no significant difference between Roma and non-Roma participants in the other two countries.
Participants were asked how they subjectively perceived their health status. In the case of the Hungarian (P < 0.001), Romanian (P < 0.001) and Slovak (P = 0.011) samples, a significantly higher proportion of non-Roma participants feel mostly healthy. In the case of Hungarian and Romanian participants, a large percentage of Roma often feel ill (Fig. 2).
Subjective sense of health by respondent number, N = 1893
Notes. Data in percentages.
Citation: Developments in Health Sciences 6, 1; 10.1556/2066.2022.00047
Results of physical examinations
The results of blood pressure measurement are similar for the three countries, with a slight increase. Although the difference is not significant, we measured the highest values for both systolic and diastolic blood pressure in the Slovak sample. The results of further physical examinations and nutritional indices are shown in Table 2.
Nutritional status, N = 1893
| Hungary (n = 852) | Romania (n = 631) | Slovakia (n = 410) | ||||||||||
| Roma (n = 430) | Non-Roma (n = 422) | Roma (n = 330) | Non-Roma (n = 301) | Roma (n = 204) | Non-Roma (n = 206) | |||||||
| BMI (kg m−2) | 29.77 ± 7.43 | 28.43 ± 6.6 | 29.22 ± 7.47 | 28.19 ± 6.57 | 28.07 ± 7.92 | 27.25 ± 6.03 | ||||||
| Visceral fat | 10.35 ± 5.82 | 9.40 ± 4.76 | 9.23 ± 4.89 | 9.14 ± 5.30 | 9.08 ± 4.96 | 8.43 ± 4.78 | ||||||
| Waist circumference (cm) | M | F | M | F | M | F | M | F | M | F | M | F |
| 100.74 ± 16.48 | 94.64 ± 18.34 | 99.80 ± 16.00 | 91.27 ± 15.02 | 101.23 ± 16.50 | 94.35 ± 17.50 | 97.51 ± 14.78 | 91.54 ± 18.06 | 96.41 ± 17.39 | 91.47 ± 15.30 | 95.46 ± 12.35 | 89.48 ± 15.90 | |
Notes. Data in mean ± standard deviation; M: male, F: female.
Based on the body mass index (hereinafter BMI) value, our sample is predominant. The highest value was found in the case of Hungarian and Romanian Roma, and the lowest in the Slovak sample, especially in the case of majority participants. Regarding BMI, the average BMI of Hungarians is significantly higher than that of Slovaks (P < 0.001), and the average BMI of Slovakia is significantly lower than the average BMI of Romania (P = 0.018). Within countries, we found a significant difference between the average BMI of Roma and non-Roma (P = 0.006). Within countries, however, we found no difference in BMI between men and women of Roma and non-Roma origin.
Regarding visceral fat measured with BIA, we found that Hungary showed significantly higher (P < 0.001) results than Romania and Slovakia (P < 0.001). Examining the average visceral fat among Roma and non-Roma participants within the countries, we found that the average of Roma was significantly (P = 0.010) higher in Hungary compared to non-Roma. This variable was further examined by gender. In the case of Hungarian Roma, the average visceral fat was significantly higher in men than in women (P < 0.001), as well as in the case of non-Roma men and women (P < 0.001). There were similar significant differences between Roma (P < 0.001) and non-Roma (P < 0.001) men and women living in Romania. In Slovakia, the average visceral fat of non-Roma men was significantly (P = 0.007) higher than that of women. When comparing the examined subjects of Roma origin, we found that the average of Hungarian Roma was significantly higher than the average of Roma living in Slovakia (P = 0.008), as we obtained the same result for non-Roma (P = 0.032).
Regarding the examination of waist circumference, we found that the average of the Hungarian participants was significantly (P < 0.001) higher than that of the Slovak subjects, just like the average of the waist circumference of the Romanian subjects which was higher (P = 0.004) than that of the Slovak sample. Comparing the waist circumference of Roma and non-Roma within the countries, a significant difference appeared only in the Hungarian sample, where the average of Roma is significantly higher (P = 0.046) than that of non-Roma. During the comparison of the Roma groups, we found that the average waist circumference in Hungary was significantly (P = 0.027) higher than that of Roma in Slovakia. The same result was found for non-Roma (P = 0.023), and the average waist circumference of non-Roma in Slovakia was significantly lower than in the non-Roma sample in Romania (P = 0.031). Breaking down the data further, BMI and waist circumference of Roma men are higher than those of non-Roma men, and this difference is even more significant for Roma and non-Roma women. The differences by country can be clearly seen, in the case of Roma men we found the highest BMI and the highest waist circumference among those living in Romania, but the averages of Hungarian Roma men do not differ significantly, while BMI and waist circumference of Roma men living in Slovakia are much higher, remaining below the averages of the other two countries. In the case of women, however, Hungarian Roma women presented the highest averages, although surprisingly, Roma women in Romania were barely below the averages, with the most favourable values for Roma women in Slovakia. The proportion is similar for non-Roma women.
In the case of Roma, the proportion of extremely obese people was significantly (P = 0.037) higher among those with low education. In the case of non-Roma, the majority of those in the lean category had a high school diploma (P = 0.002). Breaking it down by country, there is no significant difference between body weight and education among Roma, while non-Roma in Slovakia have a significantly higher proportion (P = 0.027) of tertiary education. In Romania, even among non-Roma, the extremely obese typically (P < 0.001) completed eight or fewer primary school classes.
Divided into age groups, individuals in the lean category have a significantly (P < 0.001) higher incidence in the 18–24 age group, while extreme obesity is characteristic in the 40–64 age group.
The impact of obesity on some dimensions of quality of life
The average values for each dimension are shown in Table 3.
Mean of SF-36 priority dimensions, N = 1893
| Hungary (n = 852) | Romania (n = 631) | Slovakia (n = 410) | ||||
| Roma (n = 430) | Non-Roma (n = 422) | Roma (n = 330) | Non-Roma (n = 301) | Roma (n = 204) | Non-Roma (n = 206) | |
| Physical activity | 72.17 ± 28.49 | 82.72 ± 23.36 | 74.75 ± 28.52 | 81.87 ± 24.86 | 76.34 ± 28.86 | 84.56 ± 24.04 |
| Social role limit due to physical problems | 68.89 ± 38.98 | 77.84 ± 35.40 | 65.30 ± 42.93 | 72.01 ± 38.55 | 68.75 ± 39.08 | 81.06 ± 31.90 |
| Mental health | 54.50 ± 16.21 | 59.31 ± 11.96 | 50.38 ± 15.40 | 53.70 ± 12.94 | 54.19 ± 18.06 | 56.27 ± 14.75 |
Notes. Data in mean ± standard deviation.
Only a few of the eight dimensions of the SF-36 quality of life questionnaire are presented in this study. In general, we found that the dimension of physical activity has a lower average for Roma than for non-Roma (Table 3). Within the Roma group, Roma participants from the Hungary scored the lowest, indicating a decrease in their physical activity, but there was no significant difference between the averages of the three countries (P = 0.140) on this dimension. When looking at Roma and non-Roma participants by country, we found significant differences in Hungary (P < 0.001), Romania (P < 0.001) and Slovakia (P = 0.002). When Roma and non-Roma were compared by origin, the mean for the Roma was significantly lower (P < 0.001).
Role limitation due to physical problems also shows similar results, but a significant (P = 0.008) difference was found between the Slovak and Romanian means, as the mean of the Hungarian sample was higher (P = 0.017) than the Romanian sample. Romania had the lowest score, while Slovakia had the highest on this indicator. When looking at Roma and non-Roma participants within the countries, we found significant differences in Hungary (P < 0.001), Romania (P = 0.040) and Slovakia (P < 0.001). The average of Roma was significantly lower than the average of majority participants (P < 0.001) only when Roma and non-Roma were compared. The analysis was also performed for Roma and non-Roma breakdowns to obtain more precise results. It shows a significant (P = 0.005) difference between Slovak and Romanian non-Roma as well as between the mean of the Hungarian and Romanian non-Roma (P = 0.031). For non-Roma, we found a significant difference between the Slovak and Romanian samples (P = 0.014).
For the mental health dimension, there is a significant difference between Hungary and Romania (P < 0.001) and between Slovakia and Romania (P < 0.001). Hungary shows the highest score, followed by Slovakia and finally Romania. Within the countries, when looking at Roma and non-Roma participants, we found significant differences in Hungary (P < 0.001) and Romania (P = 0.004). Only when Roma and non-Roma are compared is the average for Roma is significantly lower than the average for majority participants (P < 0.001). As for Roma, a significant difference is found between the mean scores of Hungarian and Romanian participants (P < 0.001), and between Slovak and Romanian samples (P = 0.009). For non-Roma, there is a significant difference between Hungarian and Romanian (P < 0.001) and Hungarian and Slovak (P = 0.006) respondents (P < 0.001), with the highest mean score for Hungarians and a significant difference between Slovak and Romanian samples (P = 0.028).
Relationships between variables
In Hungary, the correlation was also stronger for BMI (r = 0.233, P < 0.001) and visceral fat (r = 0.376, P < 0.001). In the Slovak sample, age showed a very weak correlation with BMI (r = 0.177, P = 0.011) and visceral fat (r = 0.183, P = 0.009).
Further decomposing the correlation into Roma and non-Roma participants, we found that for Roma, there is a weak correlation with BMI (r = 0.241, P < 0.001) and visceral fat (r = 0.344, P < 0.001). For most subjects, age is weakly correlated with BMI (r = 0.276, P < 0.001) and moderately correlated with visceral fat (r = 0.404, P < 0.001).
When examining the relationship between age and BMI by country for Roma participants, we found the strongest correlation for Romanian participants (r = 0.323, P < 0.001), as it was the strongest correlation for visceral fat (r = 0.379, P < 0.001). In Slovakia, BMI shows a very weak correlation with age (r = 0.177, P < 0.001) and visceral fat (r = 0.264, P < 0.001).
In Romania, a stronger correlation between age and BMI (r = 0.373, P < 0.001) is observed for most participants, as well as for visceral fat (r = 0.547, P < 0.001).
In general, we found the following stronger associations in our sample. A strong correlation was found between systolic and diastolic blood pressure values (r = 0.715, P < 0.001), and a strong correlation was also found between BMI and waist circumference (r = 0.833, P < 0.001).
Further correlations are illustrated in Table 4.
Additional correlation relationships, N = 1893
| Age | BMI | Visceral fat | Waist circumference | Physical activity | |
| BMI | 0.250** | ||||
| Visceral fat | 0.367** | ||||
| Physical activity | −0.353** | −0.273** | −0.232** | −0.303** | |
| Social role limit due to physical problems | −0.317** | −0.205** | −0.194** | −0.235** | 0.599** |
| Mental health | 0.174** |
Notes. Data in Pearson's correlation coefficients (r), **P < 0.001.
Discussion
Social situation
Our sample also demonstrates the disadvantaged situation of the Roma minority in all three countries. The majority of Roma typically completed eight or fewer primary school classes, compared to non-Roma participants, who have a higher proportion of vocational qualifications and higher levels of educational attainment. Roma living in Romania also show the highest proportion of low education attainment in our sample, still with high illiteracy rates, although this is mainly typical of the older age group. Although international data show an improvement in the educational attainment of Roma, the proportion of uneducated Roma is still many times higher than in the majority population [26, 27]. According to Slovak data, Roma people live in a marginalized situation, in poor health, low educational attainment and, therefore, have a poor labour market situation [28].
The Hungarian sample shows similar proportions as documented by the Hungarian Central Statistical Office, with a very low proportion of Roma in disadvantaged position in the labour market, as four-fifths of Roma employees completed eight or fewer primary school classes. The situation of Roma women is even more disadvantaged both in terms of job opportunities and education attainment. This is exacerbated partly by their culture and partly by the spatial distribution of the Roma population and the large number of children [29]. This is also strongly reflected in our Romanian sample.
Previous research in Hungary has also shown that if the level of education of Roma in Hungary improves, it becomes more and more important for them to get an education and a profession, which in turn decreases the number of children in the family, thereby improving their labour market situation [30].
The proportion of Roma with health insurance in our sample is fully in line with the results of the second EU survey, where 54% of Roma in Romania had no health insurance, compared to the favourable figures for Roma in Slovakia and Hungary (94–100%) [20].
Roma people are also more likely to feel ill in terms of their subjective perception of health [31]. Similar results were found in all three countries, with non-Roma people tending to feel healthier.
The link between obesity and quality of life
Obesity is not only a high health risk, but it also negatively affects quality of life, as pointed out by several studies [32, 33]. However, it has also been shown that a decline in quality of life can be a predictor of obesity, and not just a weight gain. The link is therefore two-way [34]. Our study also confirms that as weight gain increases, quality of life, especially physical health, deteriorates in proportion. There is a significant decrease in the mean score of the physical activity dimension relative to normal body weight in overweight, obese and extremely obese individuals. Similar results were obtained for the dimension of limitation due to physical problems. These results confirm the findings of previous studies that physical health declines more rapidly and to a greater extent among overweight or obese people [35].
The quality-of-life survey also looks at mental health changes caused by obesity. A growing body of research demonstrates that obesity causes negative changes primarily in physical components rather than in mental dimensions. Nor can it be overlooked that the decline in mental health is primarily seen in individuals with a BMI of 40+ [12]. In our study, similar results were obtained, although the differences were not significant, but it is clear that obese individuals, especially with extreme (>40 BMI) obesity, showed a decrease in the mean scores of the mental health dimension, as well as a decrease in the dimension of role limitation due to emotional problems. We have also found that the negative effects of obesity on mental health decline more slowly than those on physical health, which is already significantly more pronounced in the case of overweight. There is also evidence that weight loss clearly improves the physical dimensions of quality of life, while mental dimensions improve more slowly [36].
Ethnicity and quality of life
Previous research has shown that ethnic minorities score worse on quality of life tests than members of the majority society, regardless of age. Previous surveys have shown that their health is not rated as very poor, but that their mental health is nevertheless worse. The limits set by the dimensions of quality of life are based on individual perceptions, i.e., how individuals subjectively experience their daily lives. This also depends on the culture of origin and ethnicity, as this varies from population to population [37].
Our results also confirmed that the averages of individuals of Roma origin are significantly worse than those of non-Roma participants. In addition, for Hungarian-speaking Roma living in Romania and Slovakia, a double minority status is detectable, which is also reflected in the results of the mental health dimension obtained in the survey (this double disadvantage is evident especially for Roma in Romania). It can be concluded that ethnicity clearly influences the subjective perception of quality of life. For all three countries, we examined the means of Roma and majority participants in each dimension and found clearly significant differences between the means of dimensions of the quality of life.
The impact of age
Weight increases with age, although it is not clear that all older people with higher BMI scores are overweight or obese [38].
A previous study of Roma minority in Romania has also shown that malnutrition appears as early as in the 18–29 age group, and obesity in the older age group, which is all the more so for Roma women than for Roma men [39]. Similar results have been obtained in previous studies in Hungary [40], and several Roma studies in Slovakia have confirmed this observation with regard to body weight trends [41].
Our results have also confirmed this. Body weight and visceral fat levels increase with age, and also that lean individuals are typically found among young people, while obesity is predominant among 40–65-year-olds.
The positive impact of education
Evidence suggests that people of low socio-economic status, such as low education attainment, are more likely to be obese [42, 43]. However, previous research has shown that this was not the case in Hungary, where obesity was also more prevalent among people with higher levels of education [44]. However, our results show that extreme obesity was more prevalent among those with low levels of education, especially among Roma, including Roma in Romania. In contrast to other studies, extreme obesity was also dominant among the low-educated in Hungary.
Obesity is not only based on BMI
BMI alone does not give a definite indication of overweight or obesity; a more accurate picture is obtained by measuring waist circumference. In our sample, it is clear that both men and women, regardless of country and origin, show apple-type weight gain, with an average higher than the recommended values. For men, a waist circumference of less than 94 cm is recommended, above which overweight is confirmed, and a waist circumference above 102 cm is a high risk factor. For women, a waist circumference of less than 80 cm is optimal, and above 88 cm it is already a high risk factor for obesity and cardiovascular disease [45, 46].
In our overall sample, women have a higher risk factor for waist circumference despite having a similar average BMI as men. BMI and waist circumference are the biggest risk factors for Roma [47], especially for Roma men in Romania and Roma women in Hungary. Roma in Slovakia are the most protected in this respect for both sexes. In our study, we observed more balanced results among non-Roma subjects for the parameters concerned. As in our research abdominal-type obesity has been found in several previous surveys of Roma [29, 48]. This was also confirmed by the mean visceral fat measured by BIA, which presented higher values than the normal ranges. The highest average was found in native Roma men and women. Non-Roma men had more equal means in all three countries. However, the national averages for non-Roma women were higher than in the Romanian and Slovak samples. Our results were also in line with those of a previous Roma cohort study [49]. In summary, systolic and diastolic blood pressure increases with increasing body weight, as do waist circumference and visceral fat, supporting the higher prevalence of obesity, the major risk factor for CVD. For the SF-36 dimension, we also found that the scores for each dimension are lower in the underweight than in the normal-body mass index, but as the body mass index increases, the score again worsens.
Limitations of and lessons learned from the research
One limitation of our research is that women were overrepresented. Unfortunately, in all three countries we found clear passivity on the part of Roma men. Even if they accompanied their wives, they themselves were not willing to participate in the study. The lower participation rate of men was also observed in the non-Roma control group typically because they were working at the time of the study. The pandemic during the research period further complicated the conduct of the studies. On the one hand, it was difficult to organize the measurements at the sites during and in view of the restrictions, but in many cases the misconception that vaccination was administered was also a factor that reduced the willingness to participate among Roma participants, who in turn were mostly reluctant to take the vaccine.
During our survey, we found in many municipalities a fear of physical examinations on the part of both Roma and non-Roma residents, who did not want to be confronted with the possibility of health-related issues. The principle of "what we do not know about does not exist", describes the situation best, although our experience was rather subjective.
Conclusions
Our results show that, irrespectively of country and ethnic origin, overweight is present among the inhabitants of the three Carpathian Basin countries and has all its negative consequences in terms of health and quality of life, and in the case of the Roma population, this disadvantage is more pronounced. However, our preliminary assumption that the results for Hungarian Roma and non-Roma people are better in the three countries studied was not confirmed, and our research results clearly disproved this. Unfortunately, in the Hungarian rural Roma and non-Roma sample obesity is clearly an increased risk. Our present research did not cover the examination of the causes of the differences, but there are differences from the data, based on which it turns out that the difference in socio-demographic conditions is the explanation for the results. Furthermore, the results also reflect that the Covid-19 pandemic further worsened the obesity rate among the population, and this produced even worse results for the disadvantaged.
Authors' contribution
ÉK writing original draft. Preparation, creation of the published work, specifically writing the initial draft. HJF contributed to the critical revision of the manuscript, critical review, including pre- publication stages, supervised the study and finalized the manuscript.
Ethical approval
Ethical clearance: ETT TUKEB IV/3495-4/2021/EKU.
Conflicts of interest/Funding
The authors declare no conflict of interest.
“Pre-Doctoral Scholarship” grant (EFOP-3.6.3-VEKOP-16-2017-00009).
Acknowledgement
We would like to thank the Maltese Charity and Caritas to help with organisation and provision of sites. We wish to thank, Zorka Greksza, Zsófia Molnár, Noémi Mózes, Zsófia Négyökrű and Dorina Sípos-Bordán for their work on site.
Abbreviations
| BMI | body mass index |
| BIA | Bio Impedance Analyser |
| CVD | cardiovascular diseases |
| WHO | World Health Organization |
| EU | European Union |
| SF-36 | Short Form 36 |
| DM | diabetes mellitus |
| MS | metabolic syndrome |
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