OP90 Using logic models to inform tobacco control policy outcome evaluation. (3rd September 2019)

• Record Type: Journal Article
• Authors: Langley, T; Gillespie, D; Lewis, S; Eminson, K; Docherty, G; Young, B
• Is Part Of: Journal of epidemiology and community health. Volume 73(2019)Supplement 1
• Page Start: A44
• Page End: A45
• Publication Date: 2019-09-03
• Subjects: logic models -- policy evaluation -- public health; Public health -- Periodicals; Epidemiology -- Periodicals; 614.4
• DOI: 10.1136/jech-2019-SSMabstracts.94
• Languages: English
• ISSNs: 0143-005X

Abstract

Abstract : Background: A key challenge in the evaluation of population-level public health policies is understanding how each policy is likely to work and in whom. This is particularly challenging in settings where several policies are implemented in a short period. Logic models are a visual representation of the anticipated causal pathway of an intervention and are useful in identifying the key measures of policy impact. We aimed to develop a set of logic models that could be widely used in tobacco control policy evaluation. Methods: We developed logic models for policies recently implemented in England, including smokefree legislation, changes to age of sale laws and point-of-sale display bans. We used an iterative process to develop models for each policy, before combining outcomes into a single overarching model. We initially reviewed policy documents to identify the outcomes expected to result from the implementation of each policy, and then conducted a literature review of existing policy evaluations to identify further outcomes. The models were refined through research team meetings, and revised according to feedback from a range of stakeholders including a public involvement group and national tobacco control policymakers. Results: The final models represented expected causal pathways for each policy and identified the populations in which outcomes were expected to occur. The models included short-term outcomes (such as policy awareness, compliance and social cognitive outcomes), intermediate outcomes (such as changes in smoking behaviour) and long-term outcomes (such as mortality, morbidity and health service usage). Conclusion: The logic models guided the development of hypotheses and choice of outcome measures in subsequent evaluations of tobacco control policies. The use of logic models enables prospective and theory-based planning of evaluation analyses, which in turn enhances the transparency of policy evaluation. The use of logic models should be encouraged in the evaluation of tobacco control policy, as well as in other areas of public health. This study is funded by NIHR PRP (PR-R14-1215-24001). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health and Social Care, arms length bodies or other government department.