Occupational Medicine Advance Access published April 11, 2014 Occupational Medicine doi:10.1093/occmed/kqu044
Effect of unemployment on cardiovascular risk factors and mental health P. Zagożdżon1, J. Parszuto2, M. Wrotkowska1 and D. Dydjow-Bendek1 Department of Hygiene and Epidemiology, Medical University of Gdansk, Gdynia 81-519, Poland, 2Regional Occupational Medicine Center, Gdansk 80-887, Poland.
1
Correspondence to: P. Zagożdżon, Department of Hygiene and Epidemiology, Medical University of Gdansk, ul. Powstania Styczniowego 9B, Gdynia 81-519, Poland. Tel: +48 583491820; fax: +48 583491928; e-mail: [email protected]
Aims
To assess the association between unemployment, major cardiovascular risk factors and mental health.
Methods
A cross-sectional study in which data were collected between 2009 and 2010 during preventive health examinations by an occupational medicine service in Gdansk, Poland. Data on blood pressure, resting heart rate, information about smoking habits, body mass index and history of use of mental health services were collected during these assessments. Multiple logistic regression was used during data analysis to adjust for age, gender, education and length of employment.
Results
Study participants comprised 3052 unemployed and 2059 employed individuals. After adjustment for age, gender, education and number of previous employments, the odds ratio (OR) for hypertension in relation to unemployment was 1.02 [95% confidence interval (95% CI) 0.84–1.23]. There was a statistically significant negative association between being overweight and unemployment (OR = 0.81, 95% CI: 0.66–0.99). Smoking was positively associated with unemployment after adjustment for age and sex (OR = 1.45, 95% CI: 1.25–1.67). There was a positive relationship between mental ill-health and unemployment among study participants (OR = 2.05, 95% CI: 0.91–4.65), but this was not statistically significant.
Conclusions The patterns of major cardiovascular risk factors differed between unemployed and employed individuals in Poland. Our observations suggest employment status is a predictor of specific disease risk profiles; consequently, specific preventive measures are needed in unemployed individuals. Key words
Cardiovascular risk factors; cross-sectional study; mental health; unemployment.
Introduction Unemployment became a major economic concern in Poland following economic transition. Poland started to implement transformation policies in 1989 and was the first country in Central and Eastern Europe to reestablish a market economy. A negative effect of unemployment on health has been documented by numerous authors, but relatively few studies have examined the health consequences of unemployment in Eastern Europe [1,2]. Recent meta-analysis of 42 studies on unemployment and mortality in Western countries showed an increased risk of death following job loss [3]. The effect of unemployment on mortality has also been
identified in Poland, where unemployed individuals had mortality rates twice those of employed individuals [4]. This doubled risk also applied to cardiovascular mortality in the same study [4]. The mechanisms responsible are thought to involve behavioural and lifestyle risk factors, such as smoking, poor diet, alcohol consumption and lack of exercise. According to cross-sectional data, these risk factors are more frequent among the unemployed [5]. Other pathophysiological mechanisms, linked to financial problems and changes in social relationships, can make individuals prone to psychosomatic disorders after job loss [6,7]. Lack of employment causes financial insecurity and adversely affects socio-economic status, which may lead to inappropriate health behaviours.
© The Author 2014. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: [email protected]
Downloaded from http://occmed.oxfordjournals.org/ at York University Libraries on August 12, 2014
Background Following the economic changes in Poland, increasing health discrepancies were observed during a period of 20 years, which may be partly attributable to the consequences of unemployment.
Page 2 of 6 Occupational Medicine
Methods This cross-sectional descriptive study used the registry of the Regional Occupational Medicine Centre in Gdansk. Data were collected between 2009 and 2010 during preventive health examinations undertaken as occupational medicine service tasks. According to legislation on the promotion of employment and labour market institutions, unemployed individuals may be referred for work practice. The health of such individuals should be checked before referring them for training or work. The study population comprised unemployed individuals recruited from the Gdansk Employment Office, while the control group comprised employees of Gdansk Shipyard and public administration clerks in Gdansk. The data collected from each participant comprised (i) a consultation with an occupational medicine specialist, including medical history and physical examination; (ii) an assessment of vital signs, including resting systolic and diastolic blood pressure; (iii) measurement of body mass index (BMI); (iv) self-reported history of diabetes and hypercholesterolemia; (v) a questionnaire regarding educational level and smoking habits and (vi) self-reported history of psychiatric treatment or psychotherapy during last year. Dummy variables were used to code educational level in four categories: university, secondary, vocational and elementary school. Data on the number and total length of previous employments were also collected. The data were
presented as means ± standard deviation (SD) or as percentages. Continuous variables with a normal distribution were compared with Student’s t-test for independent samples. If variables did not follow a normal distribution, Wilcoxon’s test was used for the comparison of independent measurements. Differences between categorical variables were tested by means of the chi-squared test. The odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated in a multivariate model. Multiple logistic regression analysis was applied to all variables that had at least a marginal univariate predictive value (P
Effect of unemployment on cardiovascular risk factors and mental health P. Zagożdżon1, J. Parszuto2, M. Wrotkowska1 and D. Dydjow-Bendek1 Department of Hygiene and Epidemiology, Medical University of Gdansk, Gdynia 81-519, Poland, 2Regional Occupational Medicine Center, Gdansk 80-887, Poland.
1
Correspondence to: P. Zagożdżon, Department of Hygiene and Epidemiology, Medical University of Gdansk, ul. Powstania Styczniowego 9B, Gdynia 81-519, Poland. Tel: +48 583491820; fax: +48 583491928; e-mail: [email protected]
Aims
To assess the association between unemployment, major cardiovascular risk factors and mental health.
Methods
A cross-sectional study in which data were collected between 2009 and 2010 during preventive health examinations by an occupational medicine service in Gdansk, Poland. Data on blood pressure, resting heart rate, information about smoking habits, body mass index and history of use of mental health services were collected during these assessments. Multiple logistic regression was used during data analysis to adjust for age, gender, education and length of employment.
Results
Study participants comprised 3052 unemployed and 2059 employed individuals. After adjustment for age, gender, education and number of previous employments, the odds ratio (OR) for hypertension in relation to unemployment was 1.02 [95% confidence interval (95% CI) 0.84–1.23]. There was a statistically significant negative association between being overweight and unemployment (OR = 0.81, 95% CI: 0.66–0.99). Smoking was positively associated with unemployment after adjustment for age and sex (OR = 1.45, 95% CI: 1.25–1.67). There was a positive relationship between mental ill-health and unemployment among study participants (OR = 2.05, 95% CI: 0.91–4.65), but this was not statistically significant.
Conclusions The patterns of major cardiovascular risk factors differed between unemployed and employed individuals in Poland. Our observations suggest employment status is a predictor of specific disease risk profiles; consequently, specific preventive measures are needed in unemployed individuals. Key words
Cardiovascular risk factors; cross-sectional study; mental health; unemployment.
Introduction Unemployment became a major economic concern in Poland following economic transition. Poland started to implement transformation policies in 1989 and was the first country in Central and Eastern Europe to reestablish a market economy. A negative effect of unemployment on health has been documented by numerous authors, but relatively few studies have examined the health consequences of unemployment in Eastern Europe [1,2]. Recent meta-analysis of 42 studies on unemployment and mortality in Western countries showed an increased risk of death following job loss [3]. The effect of unemployment on mortality has also been
identified in Poland, where unemployed individuals had mortality rates twice those of employed individuals [4]. This doubled risk also applied to cardiovascular mortality in the same study [4]. The mechanisms responsible are thought to involve behavioural and lifestyle risk factors, such as smoking, poor diet, alcohol consumption and lack of exercise. According to cross-sectional data, these risk factors are more frequent among the unemployed [5]. Other pathophysiological mechanisms, linked to financial problems and changes in social relationships, can make individuals prone to psychosomatic disorders after job loss [6,7]. Lack of employment causes financial insecurity and adversely affects socio-economic status, which may lead to inappropriate health behaviours.
© The Author 2014. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: [email protected]
Downloaded from http://occmed.oxfordjournals.org/ at York University Libraries on August 12, 2014
Background Following the economic changes in Poland, increasing health discrepancies were observed during a period of 20 years, which may be partly attributable to the consequences of unemployment.
Page 2 of 6 Occupational Medicine
Methods This cross-sectional descriptive study used the registry of the Regional Occupational Medicine Centre in Gdansk. Data were collected between 2009 and 2010 during preventive health examinations undertaken as occupational medicine service tasks. According to legislation on the promotion of employment and labour market institutions, unemployed individuals may be referred for work practice. The health of such individuals should be checked before referring them for training or work. The study population comprised unemployed individuals recruited from the Gdansk Employment Office, while the control group comprised employees of Gdansk Shipyard and public administration clerks in Gdansk. The data collected from each participant comprised (i) a consultation with an occupational medicine specialist, including medical history and physical examination; (ii) an assessment of vital signs, including resting systolic and diastolic blood pressure; (iii) measurement of body mass index (BMI); (iv) self-reported history of diabetes and hypercholesterolemia; (v) a questionnaire regarding educational level and smoking habits and (vi) self-reported history of psychiatric treatment or psychotherapy during last year. Dummy variables were used to code educational level in four categories: university, secondary, vocational and elementary school. Data on the number and total length of previous employments were also collected. The data were
presented as means ± standard deviation (SD) or as percentages. Continuous variables with a normal distribution were compared with Student’s t-test for independent samples. If variables did not follow a normal distribution, Wilcoxon’s test was used for the comparison of independent measurements. Differences between categorical variables were tested by means of the chi-squared test. The odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated in a multivariate model. Multiple logistic regression analysis was applied to all variables that had at least a marginal univariate predictive value (P
