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Article

Analysis of Correlation between the Socioeconomic Environment and Level of Nutrition in the Population of Serbia: A Part of the National Survey

by
Mladen Grujicic
1,†,
Marija Sekulic
2,*,†,
Milos Stepovic
3,*,
Natasa Zdravkovic
4,
Vladan Markovic
5,
Jagoda Gavrilovic
6,
Mirjana Veselinovic
4,
Jelena Vuckovic-Filipovic
4,
Katarina Nikolic
7,
Olivera Milovanovic
8,
Branimir Radmanovic
9,
Bojan Milosevic
10,
Rada Vucic
4,
Stefan Jakovljevic
10,
Vesna Ignjatovic
11 and
Snezana Radovanovic
12
1
Department of Hygiene and Epidemiology, Health Centar Bijeljina, 76300 Bijeljina, Republic of Srpska, Bosnia and Herzegovina
2
Department of Hygiene and Ecology, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia
3
Department of Anatomy, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia
4
Department of Internal Medicine, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia
5
Department of Radiology, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia
6
Department of Infectious Diseases, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia
7
Clinic for Dermatovenerology, University Clinical Centre Nis, 18000 Nis, Serbia
8
Department of Pharmacy, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia
9
Department of Psychiatry, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia
10
Department of Surgery, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia
11
Department of Nuclear Medicine, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia
12
Department of Social Medicine, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia
 
add Show full affiliation list
 
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*
Authors to whom correspondence should be addressed.
†
These authors have contributed equally to this manuscript.
Sustainability 2023, 15(14), 11189; https://doi.org/10.3390/su151411189
Submission received: 26 May 2023 / Revised: 4 July 2023 / Accepted: 12 July 2023 / Published: 18 July 2023
(This article belongs to the Section Health, Well-Being and Sustainability)
Review Reports Versions Notes

Abstract

:
Being overweight is one of the leading health problems of the 21st century. In different parts of Europe, different overweight statuses are noted. The aim of this study was to explore the correlation between the socioeconomic environment and the level of nutrition in the Serbian population. This research is part of the fourth national survey of the health of the population of Serbia. It was conducted as a descriptive, cross-sectional study. For the purposes of this research, the adult population over the age of 20 was included, and 12,439 respondents were analyzed. The nutritional status was assessed by the body mass index value—BMI. In order to investigate the differences between groups, the chi-squared test was used. The risk of being overweight was evaluated by calculating the odds ratio value, using univariate and multivariate regression. The prevalence of obesity was higher in females than in males, but pre-obesity was more common among male respondents. Males in the age category 55–64 years old, married, employed, with a higher level of education and material status were more likely to be overweight. As for the females, being overweight was most common among unemployed and economically inactive respondents, widowed/divorced, aged 65–74, with a primary school educational level and low material status. The level of nutrition is significantly associated with the socioeconomic environment.

1. Introduction

Being overweight and obesity are leading health problems of the 21st century, and their prevalence is reaching pandemic proportions. Obesity is a chronic, non-communicable disease, characterized by an increase in fat tissue, caused by an imbalance between energy intake and expenditure. Body mass index (BMI) is used for the purpose of assessing the level of nutrition of a person. BMI is a simple index that represents the ratio of body mass in kilograms (kg) to body height in meters (m2) and is used to classify being overweight and obesity [1]. Obesity is a result of a changed lifestyle and the combined effects of several different or conjoined factors such as genetic inheritance, metabolism, and different cultural, social and environmental factors [2].
The prevalence of overweight and obese people is registered in all countries, but the fastest increase is recorded in low- and middle-income countries [3]. A large number of studies have also found a high prevalence of obesity in people of low economic status compared to people with high incomes. The socioeconomic status of the family depends on the educational level of the family members; thus, the influence of the educational level, which is different in developed and underdeveloped countries, on the prevalence of overweight and obese people is often observed [4]. Studies conducted in developed countries have shown an inverse association between education and obesity, as employment and income depend on educational level [5]. Although the problem of obesity is multifaceted, one of the key factors that can influence the prevalence of obesity is marital status. Some studies document a higher prevalence of obesity in married people, especially in women [6]. Numerous non-communicable diseases are connected to obesity such as cancers, diabetes, hypertension and cardiovascular diseases, and obesity is one of the leading risk factors for premature death, as well as for the serious economic pressure of a family and the increase in costs within a society worldwide [7].
The prevalence of obesity has increased dramatically over the past 30 years [8]. According to the World Health Organization (WHO), the number of overweight adults in 2016 was 1.9 billion globally, of which 650 million were obese. It is estimated that the prevalence of obesity has reached 11% in males and 15% in females [9]. The mean BMI in obese men is the lowest in Ethiopia and Nigeria, approximately 21%, while the highest percentage is recorded in the USA and Australia, approximately 29%. According to these data, the lowest mean values of BMI in obese women are recorded in Japan and Ethiopia at about 21%, while the largest rates are recorded in small Pacific islands and Egypt at roughly 32%. Significant changes in the prevalence of obesity and pre-obesity have been observed at national and international levels. It is assumed that by the year 2030, 38% of the world’s adult population will be pre-obese, while one fifth of the world’s population (20%) is going to be obese [10,11].
The rising prevalence of obesity has been recorded during the last two decades in the EU. According to OECD data, the average obesity rate has increased from 11% in 2000 to 17% in 2018. More than half of the adult population in the EU (53%) were considered overweight (36% pre-obese and 17% obese) according to their body mass index (BMI) [12]. Across EU countries, obesity rates are differently distributed, with the highest percentage in Malta (26%) and the lowest percentage in Romania (10%) [13]. Comparing European obesity from the research in 2017–2018 to the research of the same ages in the US, it is estimated that Europeans have obesity levels that are three times lower: 43% vs. 13% [14]. As for specific countries, those rates were highest in Bulgaria and Romania for males (21%) and England for females (22%) [15].
Previous research articles that dealt with this subject showed similar trends in the rising number of overweight people in Serbia, where women were more likely to be obese and men were more likely to be overweight [16]. In 2017, the results showed a significant correlation of socioeconomic factors with obesity and overweight people older than 50 years [17]. Research studies expressed concerning facts about this pandemic trend in overweight people in European countries and its connection to global aging and the impact on healthcare costs [18,19]. Obesity in adults and children in Serbia is reaching epidemic proportions, as is the case with other countries in transition. In Serbia, in 2019, based on the measured BMI value, more than half (57.1%) of the population were overweight, pre-obese (36.3%) and obese (20.8%). In contrast to obesity, which was approximately equally prevalent in both sexes (men 21.7%, women 20.0%), pre-obesity was more common in men (43.4%) than in women (29.9%) [20].
According to the WHO, obesity in adults was more prevalent in females than in males in almost all regions of the world. Although the incidence of obesity varies according to gender, other socio-demographic and socioeconomic factors of obesity are also different for males and females [21,22].
The aim of this research is to investigate the impact of socio-demographic and socioeconomic characteristics on the nutritional status of adults in Serbia and to explore possible causes of differences in the prevalence of obesity and pre-obesity between genders.

2. Materials and Methods

This research is part of the Health Research of the Population of Serbia conducted in the period from October to December 2019 by the Republic Institute of Statistics in cooperation with the Institute for Public Health of Serbia “Dr. Milan Jovanović Batut” and the Ministry of Health of the Republic of Serbia. The survey was conducted as a descriptive, analytical, cross-sectional study on a representative sample of the population of Serbia, and it did not include the population living in the territory of AP Kosovo and Metohija. The research was conducted using the methodology and instruments of the European Health Survey—third wave (EHIS-wave 3). Data were collected using three types of questionnaires: a household questionnaire, a questionnaire for adults aged 15 and older, and adult questionnaires filled out individually. Participants of this research were given a printed document with information about the content and purpose of the investigation and the participants gave their informative consent. Ethical standards in health research are in line with international (Declaration of Helsinki) and the specific legislation of the Republic of Serbia. The inclusion criteria were all adults older than 20 years who gave their informative consent, and the exclusion criteria were people under the age of 20, uncompleted surveys, and people with health problems causing not to be able to stand up. For this research, data on the adult population aged 20 and over were used. The total number of respondents was 12,439 (6032 males and 6407 females). As this is a descriptive, analytical study based on national data from 2019, it was not necessary to calculate the sample size. Sample size in national data is calculated according to the recommendations of the European Health Interview Survey—the EHIS wave 3. These precision requirements for all datasets of the EHIS wave are expressed in standard errors and are defined as continuous functions of the actual estimates and of the size of the statistical population in a country (https://ec.europa.eu/eurostat/documents/3859598/8762193/KS-02-18-240-EN-N.pdf/5fa53ed4-4367-41c4-b3f5-260ced9ff2f6) (accessed on 20 June 2023). Each member state ensures that the national sample size chosen fulfills the desired precision under the estimation strategy used.
The variables measured in the study were demographic characteristics (gender, age, marital status, type of settlement, and socio-economic characteristics (level of education, employment status, and material status of the respondents).
The nutritional status was assessed according to the body mass index value—BMI (Body Mass Index)—set by the WHO [23]. Body weight was measured using electronic balance for medical use. Body height was measured using an adjustable altimeter SECA. The measurement was carried out in all respondents except for immobile persons that could not straighten up and stand up by themselves. The formula for BMI is weight in kilograms divided by height in meters squared (weight (kg)/height (m2). According to BMI, the following categories were considered:
  • underweight: <18.5 kg/m2;
  • normal weight: 18.5–24.9 kg/m2;
  • pre-obese: 25.0–29.9 kg/m2;
  • obese: ≥30 kg/m2.
The term “overweight” was used for all respondents with a BMI ≥ 25 kg/m2 (i.e., obese and pre-obese).
To compare the differences between the groups, the chi-squared (χ2) test was used; for the data which did not follow a normal distribution, the Kruskal–Wallis test was applied. The risk was evaluated by calculating the value of OR (odds ratio) with a 95% confidence interval, using univariate and multivariate regression. The results were considered to be statistically significant when the probability was less than 5% (p < 0.05). All statistical calculations were performed using a commercial software package SPSS version 20.0 (The Statistical Package for Social Sciences software (SPSS Inc., version 20.0, Chicago, IL, USA)).

3. Results

Out of the total number of respondents, 6032 were males with an average age of 51.7 ± 17.5, and 6407 were females with an average age of 53.8 ± 17.8 (Table 1). According to BMI, 37.6% of the studied population had a normal weight (31.2% of males and 43% females), 30.9% were pre-obese (44.6% of males and 31.8% females), 22.7% were obese (23.3% of males and 22.2% females) and only 2% were categorized as underweight (0.9% of males and 3% females). A statistically significant difference in the level of nutrition (χ2 = 264.067, p < 0.001) was observed between males and females (Table 2).
There is a statistically significant difference in nutrition levels within all observed demographic characteristics in males. The prevalence of obesity increased with age and was highest among men aged 55 to 64 (28.9%) (χ2 = 213.148; p < 0.001). The average age of the obese is 54.4 ± 14.9 years, while the mean value of the number of years of normally fed people is 50.1 ± 19.6 (p < 0.001). In men who were not married or cohabiting, the prevalence of obesity was two times lower (13.1%) than in men who were married or cohabiting (26.9%) (χ2 = 213.496; p < 0.001). The highest percentage of obese men is observed in the Vojvodina Region (28.6%) (χ2 = 56.754; p < 0.001). The prevalence of obesity increased with the decreasing level of education in men (χ2 = 30.658; p < 0.001). Observed according to the well-being index, the highest prevalence of obesity was among the poorest men (24.9%). Among employed men, there was a higher proportion of obese people (24.2%) compared to those who were inactive (22.9%) or unemployed (21.8%) (χ2 = 30, 834; p = 0.015) (Table 3).
Obesity in women was most common between the ages of 65 and 74 (33.9%), with the average obesity being significantly higher in the category of obese women (60.7 ± 13.6) compared to women with normal nutrition (48.6 ± 18.1) (p < 0.001). The highest prevalence of obesity was among widows and divorced women (27.5%), and the lowest among women who had never been married or cohabited (5.5%). For all observed demographic variables, statistically significant differences were observed between the categories of nutrition according to body mass index in women. Differences in the prevalence of obesity between people of different socio-economic statuses were significantly more pronounced in women than in men. Women with the lowest level of education had a prevalence of obesity almost three times higher (33.7%) compared to women with the highest level of education (12.6%) (χ2 = 259.495; p < 0.001). Differences in the prevalence of obesity also existed according to financial status. The poorest women were significantly more often obese than the richest women (χ2 = 72.084; p < 0.001). Economically inactive women had two times the prevalence of obesity (29.2%) compared to that of employed women (12.9%) (χ2 = 285.528; p < 0.001) (Table 4).
Older age, living in the region of Šumadija and Western Serbia, unemployment, and marriage ending in divorce or separation are predictors of obesity in men in univariate analysis, while in women, these predictors encompass older age, cohabitation, divorce, separation, death of a partner, living in the regions of Vojvodina, southern and Eastern Serbia, and having a lower education and a lower material status (Table 5).
The multivariate model shows that the most important demographic and socioeconomic factors for men are age, cohabitation, divorce, separation, death of a partner, and unemployment, while for women, significant predictors are age, cohabitation, secondary level of education, living in the Vojvodina region, and unemployment (Table 5).

4. Discussion

A review of the literature shows that the prevalence of pre-obesity is greater in males [24]. When considering the data from the world population review, some countries show very similar obesity rates between genders, like Canada (approximately 29% for both gender), Israel (26%), and some Balkan countries like Croatia (24%), Montenegro (23%) and Albania (21%) [25]. Our results are consistent with these findings. Most of the studies show that the prevalence of obesity is much higher in the female gender [26,27]. On the contrary, in some European countries (Germany, Hungary, and Bulgaria, etc.), obesity is still more common in males [28,29,30].
The prevalence of overweight increases with age, and it is most common in people in the age group 64–74 years, after which it starts to decrease [31,32]. Both men and women are less obese in the age group below the age of 44, which can be explained by the different lifestyles and activities people undergo when they are younger, as these lead to differences in the amount of exercise, with people having more active time, and different stress levels and stress relief methods. Moreover, in our research, the largest number of obese women in this age group is recorded, which is similar to findings in recent national studies in Poland [33]. The reason for this may be the re-composition of the body in the elderly population, the decrease in muscle mass, and the increase in fat tissue. Age is a factor that determines lifestyle, including eating habits and physical activity. Considering that pre-obesity and obesity are risk factors for the occurrence of numerous non-communicable diseases, their presence has become more frequent with aging [34]. The elderly population should be considered one of the priority groups in the prevention of pre-obesity and obesity [35].
Previous studies have shown that married people are more obese and the lowest prevalence of obesity (8.4%) was observed in people who have never been married [16]. The results of our study also show that the highest percentage of BMI ≥ 25 kg/m2 was among married people in both genders, while the highest prevalence of underweight people was observed in the group of unmarried people. The explanation behind this higher percentage of overweight respondents who were married can be the lack of free time due to family responsibilities, their caring less about physical appearance, and less motivation for exercising, as well as by different nutritional habits in married people [36]. Other factors that are mentioned as resulting in overweight are stress about work, and partner and family problems, highlighting the influence of a healthy social environment on both obesity and overall health. The high percentage of obese females were also in the group of widows/divorced (27.5%). For this finding, the reason may be the social stigmatization of widows and their financial inequalities, as this may trigger higher stress levels that affect nutritional habits [37].
One of the most important socio-demographic factors contributing to the nutritional status of a population is educational level. Findings from previous investigations have shown that people with higher educational levels had a better health status [38]. As mentioned in the study of Tzotzas et al., a lower level of education in obese respondents is related to an increase in psychological distress, decreased physical activity, and unhealthy eating habits [39]. In our study, the highest percentage of overweight females (68.2%) had only primary education. Chung and Lim found that obesity was more than twice as prevalent among less-educated women (34.3%) than among highly educated women (16.0%) in South Korea [40]. However, the findings of our research show that when it comes to members of the male gender, as many as 68.9% of those who were overweight belonged to the middle educational category. The fact is a sedentary lifestyle with increased work hours may lead to this consequence [41]. The reason for this finding, women with a higher level of education being less likely to be overweight, contrary to that observed for men, is probably the fact that women with higher levels of education have a better knowledge of issues related to caloric intake and overweight. Additionally, the social pressure to be slim is more pronounced among educated women compared to men.
The findings of earlier studies revealed there is a positive correlation between BMI and employment. Thus, 27.7% of employees in the US are obese [42]. Our results also show a significant correlation between employment status and the prevalence of pre-obesity and obesity. More overweight males were observed in the employed category in comparison to the unemployed category. Nevertheless, the reverse outcome was found for females. This is probably because employed males have unhealthy eating habits and their occupations require sedentary behavior, leading them to engage in low levels of physical activity [43], whereas unemployed females usually engage in less physical activity because they spend more time at home and are exposed to food as they prepare it, which encourages them to eat more [44].
The literature states that income disparities have a greater effect on dietary quality [45]. The majority of pre-obese and obese respondents belonged to the first (the lowest) category in terms of economic well-being, which is in line with our results, where 58.6% of overweight females belonged to the poorest class of the population. Individuals with lower incomes consume fewer vegetables and fruits, a higher proportion of fat, and less fiber than individuals with higher incomes. Consequently, low income leads to unhealthy aspects of nutrition [46].
This research was conducted in 2019 and was one of four large population studies in Serbia that included a large number of respondents, which allowed for the continuation of research of this type but as well as the comparison of the results with those of countries in Europe and the rest of the world. The results of this research are, at the moment, the most recent results referring to the adult population in the Republic of Serbia and explore the influence of demographic and socio-economic factors on nutritional status.

5. Conclusions

In our study, there is an association between nutritional status and demographic and socio-economic factors. There are significant differences in terms of age, marital status, education level, and employment and material status between overweight males and females. The prevalence of obesity is similar between both genders, but pre-obesity is more prevalent among males. Males in the age category 55–64 years, who are married, employed, and have a higher level of education and material status have a higher chance of being overweight. In the case of female respondents, overweight is most likely to occur among females without an active job, in the age category 65–74, in those who are widows/divorced, and have a primary education level and low material status. The public health importance of this investigation lies in the collection of new data, which provide results that may be important in the design of effective strategies for the prevention and control of obesity in Serbia and other developing countries with similar risk factors.

Author Contributions

Conceptualization, M.G. and M.S. (Marija Sekulic); methodology, M.G., M.S., S.R. and M.S. (Milos Stepovic); software, S.R. and O.M.; validation, M.G., M.S. (Marija Sekulic), S.R., M.S. (Milos Stepovic), N.Z., V.M., J.G., O.M. and K.N.; formal analysis, M.G., M.S. (Marija Sekulic), S.R., M.S. (Milos Stepovic), M.V., B.M. and B.R.; investigation, M.S. (Milos Stepovic), K.N., J.V.-F. and R.V.; resources, S.R. and M.S. (Marija Sekulic); data curation, M.G., M.S. (Marija Sekulic), S.R., M.S. (Milos Stepovic) and S.J.; writing—original draft preparation, M.G., M.S. (Marija Sekulic), S.R., M.S. (Milos Stepovic), J.G. and M.V.; writing—review and editing, M.G., M.S. (Marija Sekulic), S.R. and M.S. (Milos Stepovic); visualization, M.G., M.S. (Marija Sekulic), S.R., M.S. (Milos Stepovic), V.I., B.R. and R.V.; supervision, M.G., M.S. (Marija Sekulic), S.R., M.S. (Milos Stepovic), B.M., S.J. and V.I.; project administration, M.S. (Marija Sekulic), S.R., M.S. (Milos Stepovic), N.Z. and V.M.; funding acquisition, M.S. (Marija Sekulic), S.R. and J.V.-F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki. The database from the National Health Survey of 2019 was handed over for use for scientific research purposes to the University of Kragujevac by official letter from the Institute for Public Health of the Republic of Serbia “Milan Jovanović Batut”.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are unavailable due to privacy or ethical restrictions because the current owner of the rights, the Institute of Public Health of Serbia, “Milan Jovanović Batut” and the database was handed over to the University of Kragujevac with an official letter for the purpose of further research.

Acknowledgments

This research is part of the Health Research of the Population of Serbia conducted in the period from October to December 2019 by the Republic Institute of Statistics, in cooperation with the Institute for Public Health of Serbia “Milan Jovanović Batut” and the Ministry of Health of the Republic of Serbia. This study was approved by the competent territorial Ethics Committees of the four main regions of Serbia with headquarters in the Republic Institute for Public Health in Belgrade. We would like to thank to the Ministry of Health of the Republic of Serbia and the Institutes of Public Health of Serbia “Milan Jovanovic Batut” on approval for using and analyzing data.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Nuttall, F.Q. Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutr. Today 2015, 50, 117–128. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Templin, T.; Hashiguchi, T.C.O.; Thomson, B.; Dieleman, J.; Bendavid, E. The overweight and obesity transition from the wealthy to the poor in low- and middle-income countries: A survey of household data from 103 countries. PLoS Med. 2019, 16, e1002968. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Ford, N.D.; Patel, S.A.; Narayan, K.M. Obesity in Low- and Middle-Income Countries: Burden, Drivers, and Emerging Challenges. Annu. Rev. Public Health 2017, 38, 145–164. [Google Scholar] [CrossRef] [Green Version]
  4. Dinsa, G.D.; Goryakin, Y.; Fumagalli, E.; Suhrcke, M. Obesity and socioeconomic status in developing countries: A systematic review. Obes. Rev. 2012, 13, 1067–1079. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Veghari, G.; Sedaghat, M.; Maghsodlo, S.; Banihashem, S.; Moharloei, P.; Angizeh, A.; Tazik, E.; Moghaddami, A. The correlation between educational levels and central obesity in the north of Iran: An epidemiologic study. ARYA Atheroscler. 2013, 9, 217–222. [Google Scholar] [PubMed]
  6. Liao, C.; Gao, W.; Cao, W.; Lv, J.; Yu, C.; Wang, S.; Pang, Z.; Cong, L.; Dong, Z.; Wu, F.; et al. Association of Educational Level and Marital Status With Obesity: A Study of Chinese Twins. Twin Res. Hum. Genet. 2018, 21, 126–135. [Google Scholar] [CrossRef] [Green Version]
  7. Zatońska, K.; Psikus, P.; Basiak-Rasała, A.; Stępnicka, Z.; Gaweł-Dąbrowska, D.; Wołyniec, M.; Gibka, J.; Szuba, A.; Połtyn-Zaradna, K. Obesity and Chosen Non-Communicable Diseases in PURE Poland Cohort Study. Int. J. Environ. Res. Public Health 2021, 18, 2701. [Google Scholar] [CrossRef]
  8. Kelly, T.; Yang, W.; Chen, C.S.; Reynolds, K.; He, J. Global burden of obesity in 2005 and projections to 2030. Int. J. Obes. 2008, 32, 1431–1437. [Google Scholar] [CrossRef] [Green Version]
  9. World Health Organisation, Geneva. Available online: https://www.who.int/news/item/04-03-2022-world-obesity-day-2022-accelerating-action-to-stop-obesity (accessed on 4 April 2023).
  10. WHO, Geneva. Available online: https://ourworldindata.org/obesity#:~:text=13%25%20of%20adults%20in%20the,of%20energy%20intake%20and%20expenditure (accessed on 4 April 2023).
  11. World Obesity Atlas. Available online: https://www.worldobesityday.org/assets/downloads/World_Obesity_Atlas_2022_WEB.pdf (accessed on 4 April 2023).
  12. Organisation for Economic Cooperation and Development. Obesity among Adults. Available online: https://www.oecd-ilibrary.org/sites/8cdeadfa-en/index.html?itemId=/content/component/8cdeadfa-en (accessed on 5 April 2023).
  13. Eurostat. Overweight and Obesity—BMI Statistics. Available online: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Overweight_and_obesity_-_BMI_statistics (accessed on 5 April 2023).
  14. Fryar, C.D.; Carroll, M.D.; Afful, J. Prevalence of Overweight, Obesity, and Severe Obesity among Adults Aged 20 and Over: United States, 1960–1962 through 2017–2018. NCHS Health E-Stats 2020. Available online: https://www.cdc.gov/nchs/data/hestat/obesity-adult-17-18/obesity-adult.htm (accessed on 5 April 2023).
  15. Stival, C.; Lugo, A.; Odone, A.; Brandt, P.A.V.D.; Fernandez, E.; Tigova, O.; Soriano, J.B.; López, M.J.; Scaglioni, S.; Gallus, S.; et al. Prevalence and Correlates of Overweight and Obesity in 12 European Countries in 2017–2018. Obes. Facts 2022, 15, 655–665. [Google Scholar] [CrossRef]
  16. Sekulic, M.; Vasiljevic, D.; Radevic, S.; Djonovic, N. Socioeconomic Inequalities in Overweight and Obesity in Serbia: Data from 2013 National Health Survey. Front. Pharmacol. 2017, 8, 967. [Google Scholar]
  17. Grujic, V.; Dragnic, N.; Mijatovic-Jovanovic, V.; Ukropina, S.; Harhaji, S.; Radic, I.; Kvrgic, S. Predictors of overweight and obesity among adults aged 50 years and above: Serbian national health survey. Vojnosanit. Pregl. 2017, 74, 38–45. [Google Scholar] [CrossRef] [PubMed]
  18. Jakovljevic, M.; Arsenijevic, J.; Pavlova, M.; Verhaeghe, N.; Laaser, U.; Groot, W. Within the triangle of health care legacies—Comparing the performance of South-Eastern European health systems. J. Med. Econ. 2017, 20, 483–492. [Google Scholar] [CrossRef] [PubMed]
  19. Jakovljević, M. Population Ageing alongside Health Care Spending Growth. Srpski arhiv za celokupno lekarstvo 2017, 145, 534–539. [Google Scholar] [CrossRef] [Green Version]
  20. Republicki Zavod za statistiku Republike Srbije. Istrazivanje Zdravlja Stanovnistva Srbije 2019. Godine. Available online: https://publikacije.stat.gov.rs/G2021/pdf/G20216003.pdf (accessed on 6 April 2023).
  21. World Health Organization. Global Status Report on Noncommunicable Diseases 2014; World Health Organization: Geneva, Swizerland; Available online: https://reliefweb.int/report/world/global-status-report-noncommunicable-diseases-2014-attaining-nine-global?gclid=CjwKCAiA3pugBhAwEiwAWFzwdatfxU_KCgPKhQhtSPGudxlR23NtvWj0HEtqaPY9FjqPtgjZ8o67ZBoCHJEQAvD_BwE (accessed on 6 April 2023).
  22. Monteiro, C.A.; Moura, E.C.; Conde, W.L.; Popkin, B.M. Socioeconomic status and obesity in adult populations of developing countries: A review. Bull. World Health Organ. 2004, 82, 940–946. [Google Scholar]
  23. World Health Organization. A Healthy Lifestyle—WHO Recommendations. Available online: https://www.who.int/europe/news-room/fact-sheets/item/a-healthy-lifestyle---who-recommendations (accessed on 7 April 2023).
  24. Kim, K.B.; Shin, Y.A. Males with Obesity and Overweight. J. Obes. Metab. Syndr. 2020, 29, 18–25. [Google Scholar] [CrossRef]
  25. World Population Review. Available online: https://worldpopulationreview.com/country-rankings/most-obese-countries. (accessed on 7 April 2023).
  26. Cooper, A.J.; Gupta, S.R.; Moustafa, A.F.; Chao, A.M. Sex/Gender Differences in Obesity Prevalence, Comorbidities, and Treatment. Curr. Obes. Rep. 2021, 10, 458–466. [Google Scholar] [CrossRef]
  27. Kanter, R.; Caballero, B. Global gender disparities in obesity: A review. Adv. Nutr. 2012, 3, 491–498. [Google Scholar] [CrossRef] [Green Version]
  28. Schienkiewitz, A.; Kuhnert, R.; Blume, M.; Mensink, G.B.M. Overweight and obesity among adults in Germany—Results from GEDA 2019/2020-EHIS. J. Health Monit. 2022, 7, 21–28. [Google Scholar] [CrossRef]
  29. Bácsné Bába, É.; Ráthonyi, G.; Müller, A.; Ráthonyi-Odor, K.; Balogh, P.; Ádány, R.; Bács, Z. Physical Activity of the Population of the Most Obese Country in Europe. Hungary. Front. Public Health 2020, 8, 203. [Google Scholar] [CrossRef]
  30. World Health Organization. Nutrition, Physical Activity and Obesity Bulgaria. Available online: https://www.euro.who.int/__data/assets/pdf_file/0020/243290/Bulgaria-WHO-Country-Profile.pdf. (accessed on 7 April 2023).
  31. National Center for Health Statistics. Available online: https://www.cdc.gov/nchs/products/databriefs/db360.htm#:~:text=Among%20men%2C%20the%20prevalence%20of,those%20aged%2060%20and%20over (accessed on 7 April 2023).
  32. Najafipour, H.; Yousefzadeh, G.; Forood, A.; Karamouzian, M.; Shadkam, M.; Mirzazadeh, A. Overweight and obesity prevalence and its predictors in a general population: A community-based study in Kerman, Iran (Kerman coronary artery diseases risk factors studies). ARYA Atheroscler 2016, 12, 18–27. [Google Scholar]
  33. Stoś, K.; Rychlik, E.; Woźniak, A.; Ołtarzewski, M.; Jankowski, M.; Gujski, M.; Juszczyk, G. Prevalence and Sociodemographic Factors Associated with Overweight and Obesity among Adults in Poland: A 2019/2020 Nationwide Cross-Sectional Survey. Int. J. Environ. Res. Public Health 2022, 19, 1502. [Google Scholar] [CrossRef] [PubMed]
  34. Ariaratnam, S.; Rodzlan Hasani, W.S.; Krishnapillai, A.D.; Abd Hamid, H.A.; Jane Ling, M.Y.; Ho, B.K.; Shariff Ghazali, S.; Tohit, N.M.; Mohd Yusoff, M.F. Prevalence of obesity and its associated risk factors among the elderly in Malaysia: Findings from The National Health and Morbidity Survey (NHMS) 2015. PLoS ONE 2020, 15, e0238566. [Google Scholar] [CrossRef] [PubMed]
  35. Marques, A.; Peralta, M.; Naia, A.; Loureiro, N.; de Matos, M.G. Prevalence of adult overweight and obesity in 20 European countries, 2014. Eur. J. Public Health 2018, 28, 295–300. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Teachman, J. Body Weight, Marital Status, and Changes in Marital Status. J. Fam. Issues 2016, 37, 74–96. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Li, C.S.; Lee, J.H.; Liu, C.C.; Chan, Y.L.; Wen, C.; Chiu, M.L.; Tsai, M.K.; Tsai, S.P.; Wai, J.P.M.; Tsao, C.K.; et al. Physical activity to overcome the adversity of widowhood: Benefits beyond physical health. Medicine 2016, 95, e4413. [Google Scholar] [CrossRef]
  38. Raghupathi, V.; Raghupathi, W. The influence of education on health: An empirical assessment of OECD countries for the period 1995–2015. Arch. Public Health 2020, 78, 20. [Google Scholar] [CrossRef] [Green Version]
  39. Tzotzas, T.; Vlahavas, G.; Papadopoulou, S.K.; Kapantais, E.; Kaklamanou, D.; Hassapidou, M. Marital status and educational level associated to obesity in Greek adults: Data from the National Epidemiological Survey. BMC Public Health 2010, 10, 732. [Google Scholar] [CrossRef] [Green Version]
  40. Chung, W.; Lim, S. Factors contributing to educational differences in obesity among women: Evidence from South Korea. BMC Public Health 2020, 20, 1136. [Google Scholar] [CrossRef]
  41. Witkam, R.; Gwinnutt, J.M.; Humphreys, J.; Gandrup, J.; Cooper, R.; Verstappen, S.M.M. Do associations between education and obesity vary depending on the measure of obesity used? A systematic literature review and meta-analysis. SSM Popul. Health 2021, 15, 100884. [Google Scholar] [CrossRef]
  42. Jankovic, J.; Marinkovic, J.; Simic, S. Utility of data from a national health survey: Do socioeconomic inequalities in morbidity exist in Serbia? Scand. J. Public Health 2011, 39, 230–238. [Google Scholar] [CrossRef]
  43. Di Tecco, C.; Fontana, L.; Adamo, G.; Petyx, M.; Iavicoli, S. Gender differences and occupational factors for the risk of obesity in the Italian working population. BMC Public Health 2020, 20, 706. [Google Scholar] [CrossRef] [PubMed]
  44. Ndambo, M.K.; Nyondo-Mipando, A.L.; Thakwalakwa, C. Eating behaviors, attitudes, and beliefs that contribute to overweight and obesity among women in Lilongwe City, Malawi: A qualitative study. BMC Women’s Health 2022, 22, 21. [Google Scholar] [CrossRef] [PubMed]
  45. French, S.A.; Tangney, C.C.; Crane, M.M.; Wang, Y.; Appelhans, B.M. Nutrition quality of food purchases varies by household income: The SHoPPER study. BMC Public Health 2019, 19, 231. [Google Scholar] [CrossRef] [Green Version]
  46. Mndala, L.; Kudale, A. Distribution and social determinants of overweight and obesity: A cross-sectional study of non-pregnant adult women from the Malawi Demographic and Health Survey (2015–2016). Epidemiol. Health 2019, 41, e2019039. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Table
Table 1. Socio-demographic characteristics of respondents.
Table 1. Socio-demographic characteristics of respondents.
VariableGenderp
MaleFemale
n%n%
Average age ± SD51.7 ± 17.553.8 ± 17.8p < 0.001
Age (years)
20–34126020.9114917.9<0.001
35–4499116.495815
45–5499116.499815.6
55–6411618.7126119.7
65–74107217.8122519.1
75+5929.881612.7
Marital status
Married/common-law union394165.3390361.1<0.001
Never married/unmarried community140923.483013.0
Divorce, separation, death of a partner67111.1165926.0
Region
Belgrade region136322.6154924.20.108
Region of Vojvodina134322.3145022.6
The region of Šumadija and Western Serbia197732.8200031.2
Region of Southern and Eastern Serbia134922.4140822.0
Education level
Primary and lower school117419.5189629.6<0.001
Middle school373962.1327051.0
College and university111218.5124019.4
Household income
Low income241440.0260840.70.334
Middle income120620.0131920.6
High income241240.0248038.7
Employment Status
Employed 258642.9206232.2<0.001
Unemployed116819.4110317.2
Inactive217136.0319249.8
Total603248.5640751.5
Table
Table 2. Prevalence of obesity according to gender.
Table 2. Prevalence of obesity according to gender.
VariablesNutrition Levelsp
UnderweightNormal NutritionPre-ObesityObesity
n%n%n%n%
Gender
Male420.9151231.2216044.6112823.3<0.001
Female1563.0227043.0168031.8117422.2
Total1982.0378237.6384030.9230222.7
Table
Table 3. Prevalence of obesity according to sociodemographic characteristics of the adult population, male gender.
Table 3. Prevalence of obesity according to sociodemographic characteristics of the adult population, male gender.
VariablesNutrition Levelsp
UnderweightNormal NutritionPre-ObesityObesity
n%n%n%n%
Average age ± SD48.2 ± 22.350.1 ± 19.652.6 ± 16.454.4 ± 14.9
Age (years)
20–34181.943645.936538.513013.7<0.001
35–4410.121126.739850.318122.9
45–5450.621226.435444.123228.0
55–6440.422524.443647.225928.9
65–7480.924126.641545.824226.7
75+61.318739.919240.98417.9
Marital status
Married/common-law union200.681925.3152447.187126.9<0.001
Never married/unmarried community171.650346.941238.414113.1
Divorce, separation, death of a partner50.918935.722041.511621.9
Region
Belgrade region40.529936.236143.716219.6<0.001
Region of Vojvodina111.126225.246845.129728.6
The region of Šumadija and Western Serbia110.657532.482946.736020.3
Region of Southern and Eastern Serbia161.337631.350241.730925.7
Education level
Primary and lower school131.332533.437338.426126.9<0.001
Middle school240.890330.2136045.570023.4
College and university50.628432.242748.416718.9
Household income
Low income221.163632.083542.049624.90.003
Middle income111.128829.645646.821922.5
High income90.558831.387046.341322.0
Employment Status
Employed 160.857327.897447.249924.20.015
Unemployed111.234636.438640.620721.8
Inactive150.956932.477043.840222.9
Total420.9151231.2216044.6112823.3
Table
Table 4. Prevalence of obesity according to sociodemographic characteristics of the adult population, female gender.
Table 4. Prevalence of obesity according to sociodemographic characteristics of the adult population, female gender.
VariablesNutrition Levelsp
UnderweightNormal NutritionPre-ObesityObesity
n%n%n%n%
Average age ±SD44.5 ± 21.148.6 ± 18.157.4 ± 15.860.7 ± 13.6
Age (years)
20–34707.859165.618520.5556.1<0.001
35–44253.246959.519324.510112.8
45–5491.137544.028533.418421.6
55–64181.733431.340137.631329.4
65–74171.627426.439638.135233.9
75+172.722735.922034.816926.7
Marital status
Married/common-law union591.8136841.4111033.676723.2<0.001
Never married/unmarried community599.641868.010416.9345.5
Divorce, separation, death of a partner372.747735.346534.437227.5
Region
Belgrade region373.750450.629129.216416.5<0.001
Region of Vojvodina322.742436.039133.233028.0
The region of Šumadija and Western Serbia522.886947.457031.134318.7
Region of Southern and Eastern Serbia352.747337.242833.633726.5
Education level
Primary and lower school352.246929.654734.553433.7<0.001
Middle school843.1127547.083830.951619.0
College and university373.852653.629530.012412.6
Household income
Low income753.484238.073133.056625.6<0.001
Middle income242.244941.534431.826424.4
High income572.997949.360530.534417.3
Employment Status
Employed 452.794356.147628.321612.9<0.001
Unemployed464.942545.627929.918219.5
Inactive652.588733.691834.777329.2
Total1563.0227043.0168031.8117422.2
Table
Table 5. Cross-over ratios (ORs) and 95% confidence intervals (CIs) for the association of demographic and socio-economic characteristics of subjects with overweight, both gender.
Table 5. Cross-over ratios (ORs) and 95% confidence intervals (CIs) for the association of demographic and socio-economic characteristics of subjects with overweight, both gender.
VariablesMaleFemale
Univariate ModelMultivariate ModelUnivariate ModelMultivariate Model
OR (95%CI)OR (95%CI)OR (95%CI)OR (95%CI)
Age (years)
20–341111
35–442.51 (1.54–4.32) *2.17 (1.24–3.36) *24.23 (2.54–4.62) *3.92 (2.43–6.75) *
45–543.98 (2.53–6.17) *3.19 (1.94–5.21) *7.29 (4.39–11.14) *5.27 (3.73–9.07) *
55–644.87 (3.19–7.59) *3.75 (2.27–6.41) *8.41 (4.85–14.16) *8.83 (4.28–14.65) *
65–744.31 (2.54–7.37) *3.38 (1.29–7.08) *10.11 (5.17–19.24) *10.92 (5.54–17.41) *
75+3.78 (1.97–6.54) *2.92 (2.67–5.36) *7.17 (3.57–11.21) *4.90 (2.94–7.58) *
Marital status
Married/common-law union1111
Never married/unmarried community0.37 (0.35–0.43) *0.35 (0.30–0.41) *00.24 (0.19–0.29) *0.26 (0.21–0.32) *
Divorce, separation, death of a partner1.61 (0.50–0.74) *0.64 (0.53–0.79) *1.27 (1.12–1.46) *0.89 (0.77–1.03)
Region
Belgrade region1111
Region of Vojvodina0.81 (0.67–0.97) **0.78 (0.63–0.96)1.88 (1.58–2.24) *1.66 (1.37–2.00) *
The region of Šumadija and Western Serbia1.35 (1.12–1.63) **1.33 (1.09–1.61) **1.16 (0.99–1.36)0.95 (0.80–1.13)
Region of Southern and Eastern Serbia0.959 (0.82–1.12)0.97 (0.82–1.15)1.79 (1.51–2.12) *1.39 (1.15–1.69) **
Education level
Primary and lower school1111
Middle school0.93 (0.77–1.13)0.88 (0.71–1.10)2.89 (2.45–3.42)1.46 (1.19–1.79) *
College and university1.09 (0.93–1.28)1.05 (0.88–1.25)1.33 (1.14–1.554)1.00 (0.85–1.18)
Household income
Low income1111
Middle income0.96 (0.84–1.09)1.04 (0.88–1.22)1.14 (1.40–1.80) *1.14 (0.98–1.33)
High income1.07 (0.91–1.27)1.08 (0.91–1.29)1.39 (1.20–1.62) *1.16 (0.98–1.37)
Employment Status
Employed1111
Unemployed0.66 (0.56–0.78) *0.75 (0.62–0.89) **1.48 (1.25–1.74) *1.37 (1.15–1.65) **
Inactive0.81 (0.69–0.92) *0.73 (0.63–0.85) *2.59 (2.28–2.95) *2.15 (1.85–2.49) *
one and two asterix means that there is a statistical significance, the diference is that where is one asterix-statistic sig-nifican level is less then 0.001 and two asterix is with sgnificance level less then 0.05.
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Grujicic, M.; Sekulic, M.; Stepovic, M.; Zdravkovic, N.; Markovic, V.; Gavrilovic, J.; Veselinovic, M.; Vuckovic-Filipovic, J.; Nikolic, K.; Milovanovic, O.; et al. Analysis of Correlation between the Socioeconomic Environment and Level of Nutrition in the Population of Serbia: A Part of the National Survey. Sustainability 2023, 15, 11189. https://doi.org/10.3390/su151411189

AMA Style

Grujicic M, Sekulic M, Stepovic M, Zdravkovic N, Markovic V, Gavrilovic J, Veselinovic M, Vuckovic-Filipovic J, Nikolic K, Milovanovic O, et al. Analysis of Correlation between the Socioeconomic Environment and Level of Nutrition in the Population of Serbia: A Part of the National Survey. Sustainability. 2023; 15(14):11189. https://doi.org/10.3390/su151411189

Chicago/Turabian Style

Grujicic, Mladen, Marija Sekulic, Milos Stepovic, Natasa Zdravkovic, Vladan Markovic, Jagoda Gavrilovic, Mirjana Veselinovic, Jelena Vuckovic-Filipovic, Katarina Nikolic, Olivera Milovanovic, and et al. 2023. "Analysis of Correlation between the Socioeconomic Environment and Level of Nutrition in the Population of Serbia: A Part of the National Survey" Sustainability 15, no. 14: 11189. https://doi.org/10.3390/su151411189

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