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dc.contributor.authorTaylor, A.-
dc.contributor.authorDal Grande, E.-
dc.contributor.authorWu, J.-
dc.contributor.authorShi, Z.-
dc.contributor.authorCampostrini, S.-
dc.identifier.citationPopulation Health Metrics, 2014; 12(1):31-1-31-13-
dc.description.abstractBACKGROUND: Understanding how risk factors (tobacco, alcohol, physical inactivity, unhealthy diet, high blood pressure, and high cholesterol) change over time is a critical aim of public health. The associations across the social gradient over time are important considerations. Risk factor surveillance systems have a part to play in understanding the epidemiological distribution of the risk factors so as to improve preventive measures and design public health interventions for reducing the burden of disease. METHODS: Representative, cross-sectional data were collected in South Australia using telephone interviews, conducted on a minimum of 600 randomly selected people (of all ages) each month. Data were collected from January 2004 to December 2013. Unadjusted prevalence over time, the relative percentage change over the 10 years, and the absolute change of the risk factors with sex, age group, and socio-economic status (SES) estimates are presented. RESULTS: In total 55,548 adults (≥18 years) were interviewed (mean age = 47.8 years, 48.8% male). Decreases were apparent for insufficient physical activity, inadequate fruit and vegetables, smoking, and soft drink consumption of ≥500 ml/day. Increases were found over the 10 years for obesity, high cholesterol, diabetes, and for those with no risk factors. Apparent differences were noticeable by different sex, age, and SES categories. While increases in physical activity and fruit and vegetable consumption and decreases in smoking prevalence and multiple risk factors are to be expected in 2020-2021, the prevalence of obesity, high blood pressure, high cholesterol, and diabetes are expected to increase. CONCLUSIONS: Public health efforts in increasing the proportion of the population undertaking appropriate risk factor behavior are showing signs of success, with data from 2004 to 2013 showing encouraging trends. Deriving comparable trends over time by key demographics and SES variables provides evidence for policymakers and health planners to encourage interventions aimed at preventing chronic disease.-
dc.description.statementofresponsibilityAnne W Taylor, Eleonora Dal Grande, Jing Wu, Zumin Shi and Stefano Campostrini-
dc.publisherBioMed Central-
dc.rights© 2014 Taylor et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.-
dc.subjectRisk factors-
dc.titleTen-year trends in major lifestyle risk factors using an ongoing population surveillance system in Australia-
dc.typeJournal article-
dc.identifier.orcidTaylor, A. [0000-0002-4422-7974]-
dc.identifier.orcidDal Grande, E. [0000-0002-5919-3893]-
dc.identifier.orcidShi, Z. [0000-0002-3099-3299]-
dc.identifier.orcidCampostrini, S. [0000-0002-7113-4458]-
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