Socio-Economic Differences in Cardiovascular Health: Findings from a Cross-Sectional Study in a Middle-Income Country

被引:24
作者
Jankovic, Janko [1 ]
Eric, Milos [2 ]
Stojisavljevic, Dragana [3 ]
Marinkovic, Jelena [4 ]
Jankovic, Slavenka [5 ]
机构
[1] Univ Belgrade, Fac Med, Inst Social Med, Belgrade, Serbia
[2] Singidunum Univ, Fac Econ Finance & Adm, Ctr European Integrat & Publ Management, Belgrade, Serbia
[3] Inst Publ Hlth, Banja Luka, Republic Of Srp, Bosnia & Herceg
[4] Univ Belgrade, Fac Med, Inst Med Stat & Informat, Belgrade, Serbia
[5] Univ Belgrade, Fac Med, Inst Epidemiol, Belgrade, Serbia
来源
PLOS ONE | 2015年 / 10卷 / 10期
关键词
RISK-FACTORS; EUROPEAN COUNTRY; EARLY RETIREMENT; DISEASE; ASSOCIATIONS; POPULATION; ADULTS; PREVALENCE; EDUCATION; MORTALITY;
D O I
10.1371/journal.pone.0141731
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background A relatively consistent body of literature, mainly from high-income countries, supports an inverse association between socio-economic status (SES) and risk of cardiovascular disease (CVD). Data from low- and middle-income countries are scarce. This study explores SES differences in cardiovascular health (CVH) in the Republic of Srpska (RS), Bosnia and Herzegovina, a middle-income country. Methods We collected information on SES (education, employment status and household's relative economic status, i.e. household wealth) and the 7 ideal CVH components (smoking status, body mass index, physical activity, diet, blood pressure, total cholesterol, and fasting blood glucose) among 3601 participants 25 years of age and older, from the 2010 National Health Survey in the RS. Based on the sum of all 7 CVH components an overall CVH score (CVHS) was calculated ranging from 0 (all CVH components at poor levels) to 14 (all CVH components at ideal levels). To assess the differences between groups the chi-square test, t-test and ANOVA were used where appropriate. The association between SES and CVHS was analysed with multivariate linear regression analyses. The dependent variable was CVHS, while independent variables were educational level, employment status and wealth index. Results According to multiple linear regression analysis CVHS was independently associated with education attainment and employment status. Participants with higher educational attainment and those economically active had higher CVHS (b = 0.57; CI = 0.29-0.85 and b = 0.27; CI = 0.10-0.44 respectively) after adjustment for sex, age group, type of settlement, and marital status. We failed to find any statistically significant difference between the wealth index and CVHS. Conclusion This study presents the novel information, since CVHS generated from the individual CVH components was not compared by socio-economic status till now. Our finding that the higher overall CVHS was independently associated with a higher education attainment and those economically active supports the importance of reducing socio-economic inequalities in CVH in RS.
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页数:14
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