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Background

Smoking, alcohol consumption, poor diet and lack of physical exercise are leading and potentially modifiable risk factors for non-communicable diseases around the world. The frequent co-existence of unhealthy behaviours suggests that their effects are synergistic, and changing one habit might affect individual’s lifestyle as a whole. Evidence is therefore needed on prevalence and dynamics of unhealthy behavioural patterns to support novel effective public health initiatives.

The clusters of lifestyle behaviours, uptake of health interventions and adverse health outcomes are likely to differ by socio-economics status (SES). There is a duality in this complex inter-relationship: lifestyle choices could affect future adverse health outcomes, while adverse health outcomes could motivate a behavioural change. Certain behaviours might be also affected by genetic predisposition. The aim of the project is to develop evidence on the synergy of unhealthy behaviours and health and healthcare outcomes in population subgroups. Economic theories of individual health behaviour, such as the Becker-Murphy model of rational addiction and the Grossman model of health investment, could inform our approach to estimation. 

Data from UK Biobank (www.ukbiobank.ac.uk; 500,000 UK residents aged 40-69 years with data on socio-demographic and lifestyle characteristics, biomarkers, disease history and genetics, linked with death and cancer registries and hospital admission data); English Longitudinal Study of Ageing (ELSA, http://www.elsa-project.ac.uk/); and Survey of Health, Ageing and Retirement in Europe (SHARE, http://www.share-project.org/) could be used for this project.

Research Experience, Research Methods and Training

The project will use econometric, epidemiological and statistical/mathematical methods to address some of the following questions:

(1) identify clusters of unhealthy life behaviours, and investigate associations between behaviour pattern, further risk factors (such as disease history) and/or socio-economic status;

(2) assess effects of behavioural clusters on future health outcomes, life expectancy and healthcare costs, taking into account the time-varying relationships between clusters, other risk factors and outcomes;

(3) explore heterogeneity of findings by SES and other characteristics deemed important (eg. gender, previous disease history).

Field Work, Secondments, Industry Placements and Training

Training will be offered as required, eg in advanced statistical, econometrics and epidemiological methods, software, and working with complex datasets. Attendance of seminars, conferences and interactions with other researchers will be encouraged.

Prospective Candidate

Training will be offered as required, eg in advanced statistical, econometrics and epidemiological methods, software, and working with complex datasets. Attendance of seminars, conferences and interactions with other researchers will be encouraged.

 

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