Assessment Of The Prevalence of risky lifestyle and Its assocated Fac-ToesFor metsbolic Synbrome Among Adultys Jimma Town, South Wast Ethiopia

Abstract

Background: Globally, chronic non-communicable diseases (NCDs) are the leading causes of disabilities and deaths. Recently, NCDs were given much attention as the diseases were noted not only to be limited to the developed countries. The growing middle class and ever changing lifestyle in developing countries have led to the rapid increase in the burden of NCD; the epidemiological trend has caught up with the Ethiopian. With the current increase in trend of the diseases among all the different social categories of people, attention has begun to rise about major risk factors for metabolic syndrome and NCDs. In the Jimma Town, risky lifestyle behaviors might be among cause of burden of NCDs. Little is known about the prevalence of the risky lifestyles coupled with little data available on the smoking, risky drinking, unhealthy diet, and low physical activity and other lifestyle associated factors in the Town. Objectives: The general aim of this study was to assess the prevalence of risky lifestyles for metabolic syndrome. Methods: Community based cross-sectional study was conducted among 844 adults in Jimma Town, from March to May 2016. The study participants were selected from the target population using a multistage sampling technique. Data were collected using pre tested semi-structured questionnaire. The data were edited, coded and entered into Epidata 3.1 in double and exported, to SPSS for windows version 21.0 for cleaning and analyses. Prevalence of risky life styles for metabolic syndromes and associated factors were determined using frequencies, bivariate and multivariate logistic regression models. Results and discussion: The prevalence of tobacco uses, unhealthy diet, in adequate physical activity, sedentary behavior, risky drinking, unhealthy sleep, and chewing khat were, 23.4%, 66.7%, 29%, 46,3%, 18.7% ,40.1%, 48.5%, respectively. The prevalence of zero and all seven risky lifestyle score were 4.1%, 1%, respectively. The prevalence of simultaneous occurrence of two risk factors was 78.1%, whereas, the prevalence of high risky lifestyle score (as measured by the highest tertile of the score) was 31.7%. The results of multivariable logistic regression analyses showed that male, (AOR= 2.40 [95% CI: 1.338 to 3.597]), widowed (AOR= 0.21[95% CI: 0.051, 0.507]), age interval of 45-64 (AOR=1.74 [95% CI: 1.00 to 3.009]), student (AOR=2.85 [95%CI:1.478, 5.873]), having >4 children (AOR=0.17[95%CI: 0.06 ,0.55]), and living with family members (AOR=0.43 [95%CI:0.262, 0.765]) were independent predictors of high risk lifestyle score among adults of the town. Conclusion: The prevalence of high risky lifestyles for metabolic syndrome was considerably high in the study population. Our findings showed that 4 out of 5 adults had more than one risky score and one third had four or more risk factor score. The finding calls for a focused intervention through strengthening of both community and institution based behavior change communications to prevent the increase in metabolic syndrome and NCDs.