Abstract
Background In epidemiological studies there exists the evidence of relationship between socio-economic status (SES) and health. The suspicious cause of these differences is life-style. Objectives The aim of the presented study was to identify the relationships between SES and life-style factors and health status. Methods A structured questionnaire was distributed to a random sample of population aged 25–70 and collected by postal delivery. The relationship between SES and life style factors and health status was analysed using the logistic regression. Results The total number of analysed questionnaires was 634. The lowest response-rate was in the group with the basic education. The sample was homogenous by sex and age. Significant differences were found between the group of healthy people and respondents with a chronic disease by all socio-economic factors except of family status and housing conditions. The significant differences in health status (adjusted for sex, age, education, occupation, family, economic situation, density of housing) were identified between groups by sex (women; OR = 0.54; 95%CI 0.36–0.80) by age (the number of people with a disease increased with age), by education (basic education; OR = 2.84; 95%CI 1.28–6.31; baseline university), by economic activity (non-active; OR = 1.92; 95%CI 1.17–3.15) and financial situation of family (average; OR = 0.47; 95%CI 0.28–0.76; baseline below average). Health status was significantly correlated with passivity (OR = 2.05; 95%CI 1.28–3.26), lack of psychical well-being (OR = 2.01; 95%CI 1.30–3.11), risk behaviour (OR = 0.37; 95%CI 0.24–0.57) and lack of contentment (OR = 2.92; 95%CI 1.83–4.68). Passivity (passive way of life-style) was in a significant correlation with education (basic; OR = 3.74; 95%CI 1.59–8.80; baseline university) and economical situation (average; OR = 0.54; 95%CI 0.31–0.93; baseline below average). Risk behaviour significantly decreased with age (age 51–60; OR = 0.39; 95%CI 0.17–0.90; age >61; OR = 0.16; 95%CI 0.50–0.54; baseline < 30), was higher in people living alone (OR = 3.28; 95%CI 1.77–6.05), in people living in overcrowded houses and in people economically active. Risk behaviour appeared significantly less in people with the over average economical situation of family (OR = 0.15; 95%CI 0.36–0.64) comparing with people below average. The negative impact of risk factors of life-style on health status was not confirmed in our study, the contrary in ill people risk behaviour appeared less often than in healthy people. As the study was cross-sectional one, we do not know the information of risk behaviour history. Less frequent risk behaviour in ill respondents might resulted from the change behaviour after the appearance of disease. Conclusions Overall results of the project demonstrate that health status was affected foremost by age; was better in the groups with higher education, in people with an active life-style, with the increasing economical standard of the family. Leisure time activities increased with education, but risk behaviour was in no relationship with education. Health status was more affected by active life-style than by risk behaviour. Life-style was predicted by economical situation of respondents, family and their education. Acknowledgement: This study was realised within the grant Nr. NJ/6139–3 funded by the Czech MoH.
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