Targeting multiple levels of 'the smoking cessation system' using novel scientific approaches


This programme will develop and apply novel scientific approaches to increase the population rate of smoking cessation.

Background

Tobacco smoking causes 20% of cancers worldwide. The UK will not achieve the Government's target of being 'smoke-free' by 2030 on the current trajectory, particularly in disadvantaged groups.

Introduction

This programme, led by University College London (UCL), is developing and applying novel scientific approaches to increase the population rate of smoking cessation, targeting multiple levels by:

  • integrating theory and data from population surveys and experimental studies in computational models
  • optimising and evaluating wide-reach, hybrid human-digital-pharmacological interventions

The University of Sheffield is principally involved in the first of these aims. We are developing a novel agent-based model (ABM) of smoking cessation and using this model to understand how novel nicotine products (e.g. e-cigarettes) are affecting smoking rates and to estimate the impact of new policy action in this space.

Agent-based modelling

Our scientific approach is informed by the overarching 'COM-B' model of behaviour change, developed by our collaborators at UCL. The model represents how behaviour (e.g. making a quit attempt) is influenced by a person's capabilities, opportunities and motivations in relation to that behaviour, and how those factors are influenced by the wider social context.

Developing the behavioural science of smoking cessation requires integrating the insights from overarching theories and empirical data into empirically realistic computational models of behaviours in real-world contexts. 

The Sheffield team will build on existing work to develop a new agent-based model of smoking cessation, informed by the COM-B model and parameterised by research on our unique population-based and digital datasets. Our application of the model will generate and prioritise novel approaches for systems-level interventions and evaluations to support the 2030 smoke-free target.

Team

The project is led by Professor Jamie Brown and Professor Lion Shahab at University College London with a team at the University of Sheffield, led by Professor Robin Purshouse, developing the agent-based models.

This project is funded by Cancer Research UK.

Dates

April 2022 – March 2027

Funding

£531,098

Principal investigators

Professor Jamie Brown (UCL)
Professor Lion Shahab (UCL)
Professor Robin Purshouse (Sheffield)

Institutions involved

University of Sheffield
University College London

Key contact

robin.purshouse@sheffield.ac.uk

SPIRE

'Scoping of Policy Impacts for Regulating E-cigarettes (SPIRE): a data and decision analytic model mapping project' aims to learn what type of research about vapes would be most useful for policy making, what data already exists to go into this type of analysis and what new data are needed.

ODHIN

Optimising Delivery of Healthcare Interventions (ODHIN) was an EU-wide project focusing on understanding how best to translate the results of clinical research into everyday primary health care.