OBJECTIVES: Careful examination of the risk factors for cardiovascular disease (CVD) may enable clinicians to develop a reasonably preventive programme among the elderly. The main purpose of this paper was to examine the effects of income and education on CVD risk factors in an elderly population who had lived most of their lives in Communist times. METHODS: The CINDI (Countrywide Integrated Non-communicable Diseases Intervention) Programme questionnaire was used to collect data from an elderly (65+ years) population in Lodz, a large industrial city in Poland. The study population (n = 1,461) was selected at random, and the overall response rate was 57.1%. The following risk factors were evaluated: hypertension, obesity, elevated cholesterol, elevated blood sugar and smoking. RESULTS: Hypertension was the most frequently observed CVD risk factor (83.4% of participants) followed by hypercholesterolaemia (70.2%), obesity (30.5%), diabetes (18.4%) and smoking (8.5%). Hypertension and hypercholesterolaemia were related to age (OR=0.91, 95%CI: 0.88-0.95 and OR=0.95, 95%CI: 0.92-0.98, respectively). The younger people in the study population exhibited the highest prevalence of hypertension and hypercholesterolaemia; hypercholesterolaemia was observed more frequently among widowed respondents. Obesity and diabetes were associated with education level (OR=0.52, 95%CI: 0.34-0.79 and OR=0.60, 95%CI: 0.37-0.97, respectively). Younger single males with a lower level of education and income exhibited the highest prevalence of smoking. Multivariate analysis showed that age, education and gender were the best predictors for the cumulative risk factors of CVD. CONCLUSIONS: Education is more strongly associated with CVD risk factors than material status in the elderly. The best predictors of risk factors were age, sex and education. As we gain knowledge about CVD risk factors, we may be able to target preventive services in the elderly population more accurately and effectively, and help older adults make health decisions to reduce risk factors and increase their quality of life.
OBJECTIVES: Careful examination of the risk factors for cardiovascular disease (CVD) may enable clinicians to develop a reasonably preventive programme among the elderly. The main purpose of this paper was to examine the effects of income and education on CVD risk factors in an elderly population who had lived most of their lives in Communist times. METHODS: The CINDI (Countrywide Integrated Non-communicable Diseases Intervention) Programme questionnaire was used to collect data from an elderly (65+ years) population in Lodz, a large industrial city in Poland. The study population (n = 1,461) was selected at random, and the overall response rate was 57.1%. The following risk factors were evaluated: hypertension, obesity, elevated cholesterol, elevated blood sugar and smoking. RESULTS:Hypertension was the most frequently observed CVD risk factor (83.4% of participants) followed by hypercholesterolaemia (70.2%), obesity (30.5%), diabetes (18.4%) and smoking (8.5%). Hypertension and hypercholesterolaemia were related to age (OR=0.91, 95%CI: 0.88-0.95 and OR=0.95, 95%CI: 0.92-0.98, respectively). The younger people in the study population exhibited the highest prevalence of hypertension and hypercholesterolaemia; hypercholesterolaemia was observed more frequently among widowed respondents. Obesity and diabetes were associated with education level (OR=0.52, 95%CI: 0.34-0.79 and OR=0.60, 95%CI: 0.37-0.97, respectively). Younger single males with a lower level of education and income exhibited the highest prevalence of smoking. Multivariate analysis showed that age, education and gender were the best predictors for the cumulative risk factors of CVD. CONCLUSIONS: Education is more strongly associated with CVD risk factors than material status in the elderly. The best predictors of risk factors were age, sex and education. As we gain knowledge about CVD risk factors, we may be able to target preventive services in the elderly population more accurately and effectively, and help older adults make health decisions to reduce risk factors and increase their quality of life.
Authors: Pawel Nadrowski; Ewa Podolecka; Andrzej Pajak; Agnieszka Dorynska; Wojciech Drygas; Wojciech Bielecki; Magdalena Kwasniewska; Andrzej Tykarski; Arkadiusz Niklas; Tomasz Zdrojewski; Michal Skrzypek; Wojciech Wojakowski; Krystyna Kozakiewicz Journal: Cardiol J Date: 2018-03-23 Impact factor: 2.737
Authors: Ahmad Bahonar; Nizal Sarrafzadegan; Roya Kelishadi; Shahin Shirani; Mohammad Arash Ramezani; Mohammad Hossein Taghdisi; Mojgan Gharipour Journal: Int J Public Health Date: 2010-02-12 Impact factor: 3.380
Authors: S Yavuz Sanisoglu; Cagatay Oktenli; Adnan Hasimi; Mehmet Yokusoglu; Mehmet Ugurlu Journal: BMC Public Health Date: 2006-04-10 Impact factor: 3.295
Authors: Mohammadreza Amiri; Hazreen Abdul Majid; FarizahMohd Hairi; Nithiah Thangiah; Awang Bulgiba; Tin Tin Su Journal: BMC Public Health Date: 2014-11-24 Impact factor: 3.295