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      A systematic review on the clustering and co-occurrence of multiple risk behaviours

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          Abstract

          Background

          Risk behaviours, such as smoking and physical inactivity account for up to two-thirds of all cardiovascular deaths, and are associated with substantial increased mortality in many conditions including cancer and diabetes. As risk behaviours are thought to co-occur in individuals we conducted a systematic review of studies addressing clustering or co-occurrence of risk behaviours and their predictors. As the main aim of the review was to inform public health policy in England we limited inclusion to studies conducted in the UK.

          Methods

          Key databases were searched from 1990 to 2016. We included UK based cross-sectional and longitudinal studies that investigated risk behaviours such as smoking, physical inactivity, unhealthy diet. High heterogeneity precluded meta-analyses.

          Results

          Thirty-seven studies were included in the review (32 cross-sectional and five longitudinal). Most studies investigated unhealthy diet, physical inactivity, alcohol misuse, and smoking. In general adult populations, there was relatively strong evidence of clustering between alcohol misuse and smoking; and unhealthy diet and smoking. For young adults, there was evidence of clustering between sexual risk behaviour and smoking, sexual risk behaviour and illicit drug use, and sexual risk behaviour and alcohol misuse.

          The strongest associations with co-occurrence and clustering of multiple risk behaviours were occupation (up to 4-fold increased odds in lower SES groups) and education (up to 5-fold increased odds in those with no qualifications).

          Conclusions

          Among general adult populations, alcohol misuse and smoking was the most commonly identified risk behaviour cluster. Among young adults, there was consistent evidence of clustering found between sexual risk behaviour and substance misuse. Socio-economic status was the strongest predictor of engaging in multiple risk behaviours.

          This suggests the potential for interventions targeting multiple risk behaviours either sequentially or concurrently particularly where there is evidence of clustering. In addition, there is potential for intervening at the social or environmental level due to the strong association with socio-economic status.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12889-016-3373-6) contains supplementary material, which is available to authorized users.

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          Most cited references63

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          Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

          David Moher and colleagues introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses
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            The prevalence and clustering of four major lifestyle risk factors in an English adult population.

            The aim of this study was to examine the clustering of four major lifestyle risk factors (smoking, heavy drinking, lack of fruit and vegetables consumption, and lack of physical activity), and to examine the variation across different socio-demographic groups in the English adult population. The study population was derived from the 2003 Health Survey for England (n=11,492). Clustering was examined by comparing the observed and expected prevalence of the different possible combinations. A multinomial multilevel regression model was conducted to examine the socio-demographic variation in the clustering of the four risk factors. The study found that, when using British health recommendations, a majority of the English population have multiple lifestyle risk factors at the same time. Clustering was found at both ends of the lifestyle spectrum and was more pronounced for women than for men. Overall, multiple risk factors were more prevalent among men, lower social class households, singles, and people who are economically inactive, but less prevalent among home owners and older age groups. The clustering of multiple risk factors provides support for multiple-behavior interventions as opposed to single-behavior interventions.
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              Tools for assessing quality and susceptibility to bias in observational studies in epidemiology: a systematic review and annotated bibliography.

              Assessing quality and susceptibility to bias is essential when interpreting primary research and conducting systematic reviews and meta-analyses. Tools for assessing quality in clinical trials are well-described but much less attention has been given to similar tools for observational epidemiological studies. Tools were identified from a search of three electronic databases, bibliographies and an Internet search using Google. Two reviewers extracted data using a pre-piloted extraction form and strict inclusion criteria. Tool content was evaluated for domains potentially related to bias and was informed by the STROBE guidelines for reporting observational epidemiological studies. A total of 86 tools were reviewed, comprising 41 simple checklists, 12 checklists with additional summary judgements and 33 scales. The number of items ranged from 3 to 36 (mean 13.7). One-third of tools were designed for single use in a specific review and one-third for critical appraisal. Half of the tools provided development details, although most were proposed for future use in other contexts. Most tools included items for selection methods (92%), measurement of study variables (86%), design-specific sources of bias (86%), control of confounding (78%) and use of statistics (78%); only 4% addressed conflict of interest. The distribution and weighting of domains across tools was variable and inconsistent. A number of useful assessment tools have been identified by this report. Tools should be rigorously developed, evidence-based, valid, reliable and easy to use. There is a need to agree on critical elements for assessing susceptibility to bias in observational epidemiology and to develop appropriate evaluation tools.
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                Author and article information

                Contributors
                nick.meader@york.ac.uk
                kristel.a.king@gmail.com
                moe.byrne@york.ac.uk
                kath.wright@york.ac.uk
                hilary.graham@york.ac.uk
                mark.petticrew@lshtm.ac.uk
                christine.power@ucl.ac.uk
                martin.white@mrc-epid.cam.ac.uk
                amanda.sowden@york.ac.uk
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                29 July 2016
                29 July 2016
                2016
                : 16
                : 657
                Affiliations
                [1 ]Centre for Reviews and Dissemination, University of York, York, YO10 5DD UK
                [2 ]Department of Health Sciences, University of York, York, UK
                [3 ]Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine, London, UK
                [4 ]Population, Policy and Practice, UCL Institute of Child Health, London, UK
                [5 ]UKCRC Centre for Diet and Activity Research (CEDAR), MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
                Article
                3373
                10.1186/s12889-016-3373-6
                4966774
                27473458
                b347e9c1-7ae2-4174-9fca-1e08c6fb5529
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 10 December 2015
                : 26 July 2016
                Funding
                Funded by: Department of Health, UK
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Public health
                multiple risk behaviours,systematic review,clustering,co-occurrence
                Public health
                multiple risk behaviours, systematic review, clustering, co-occurrence

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