Developing logic models to inform public health policy outcome evaluation: an example from tobacco control

Abstract

Abstract Background The evaluation of large-scale public health policy interventions often relies on observational designs where attributing causality is challenging. Logic models—visual representations of an intervention’s anticipated causal pathway—facilitate the analysis of the most relevant outcomes. We aimed to develop a set of logic models that could be widely used in tobacco policy evaluation. Methods We developed an overarching logic model that reflected the broad categories of outcomes that would be expected following the implementation of tobacco control policies. We subsequently reviewed policy documents to identify the outcomes expected to result from the implementation of each policy and conducted a literature review of existing evaluations to identify further outcomes. The models were revised according to feedbacks from a range of stakeholders. Results The final models represented expected causal pathways for each policy. 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). Conclusions The use of logic models enables transparent and theory-based planning of evaluation analyses and should be encouraged in the evaluation of tobacco control policy, as well as other areas of public health.