Background: Group model building is a process of engaging stakeholders in a participatory modeling process to elicit their perceptions of a problem and explore concepts regarding the origin, contributing factors, and potential solutions or interventions to a complex issue. Recently, it has emerged as a novel method for tackling complex, long-standing public health issues that traditional intervention models and frameworks cannot fully address. However, the extent to which group model building has resulted in the adoption of evidence-based practices, interventions, and policies for public health remains largely unstudied. The goal of this systematic review was to examine the public health and healthcare applications of GMB in the literature and outline how it has been used to foster implementation and dissemination of evidence-based interventions. Methods: We searched PubMed, Web of Science, and other databases through August 2022 for studies related to public health or health care where GMB was cited as a main methodology. We did not eliminate studies based on language, location, or date of publication. Three reviewers independently extracted data on GMB session characteristics, model attributes, and dissemination formats and content. Results: Seventy-two studies were included in the final review. Majority of GMB activities were in the fields of nutrition (n = 19, 26.4%), health care administration (n = 15, 20.8%), and environmental health (n = 12, 16.7%), and were conducted in the United States (n = 29, 40.3%) and Australia (n = 7, 9.7%). Twenty-three (31.9%) studies reported that GMB influenced implementation through policy change, intervention development, and community action plans; less than a third reported dissemination of the model outside journal publication. GMB was reported to have increased insight, facilitated consensus, and fostered communication among stakeholders. Conclusions: GMB is associated with tangible benefits to participants, including increased community engagement and development of systems solutions. Transdisciplinary stakeholder involvement and more rigorous evaluation and dissemination of GMB activities are recommended.

Background: Tobacco and alcohol use are major behavioural risks in developing countries like Nepal, which are contributing to a rapid increase in non-communicable diseases (NCDs). This causal relationship is further complicated by the multi-level social determinants such as socio-political context, socio-economic factors and health systems. The systems approach has potential to facilitate understanding of such complex causal mechanisms. The objective of this paper is to describe the role of tobacco and alcohol use in the interaction of social determinants of NCDs in Nepal.Method: The study adopted a qualitative study design guided by theSystemic Interventionmethodology. The study involved key informant interviews (n = 63) and focus group discussions (n = 12) at different levels (national, district and/or community) and was informed by the adaptedSocial Determinants of Health Framework. The data analysis involved case study-based thematic analysis using framework approach and development of causal loop diagrams. The study also involved three sense-making sessions with key stakeholders.Results: Three key themes and causal loop diagrams emerged from the data analysis. Widespread availability of tobacco and alcohol products contributed to the use and addiction of tobacco and alcohol. Low focus on primary prevention by health systems and political influence of tobacco and alcohol industries were the major contributors to the problem. Gender and socio-economic status of families/communities were identified as key social determinants of tobacco and alcohol use.Conclusion: Tobacco and alcohol use facilitated interaction of the social determinants of NCDs in the context of Nepal. Socio-economic status of families was both driver and outcome of tobacco and alcohol use. Health system actions to prevent NCDs were delayed mainly due to lack of system insights and commercial influence. A multi-sectoral response led by the health system is urgently needed.

The field of quality-of-life (QOL) research would benefit from learning about and integrating systems science approaches that model how social forces interact dynamically with health and affect the course of chronic illnesses. Our purpose is to describe the systems science mindset and to illustrate the utility of a system dynamics approach to promoting QOL research in chronic disease, using diabetes as an example. We build a series of causal loop diagrams incrementally, introducing new variables and their dynamic relationships at each stage. These causal loop diagrams demonstrate how a common set of relationships among these variables can generate different disease and QOL trajectories for people with diabetes and also lead to a consideration of non-clinical (psychosocial and behavioral) factors that can have implications for program design and policy formulation. The policy implications of the causal loop diagrams are discussed, and empirical next steps to validate the diagrams and quantify the relationships are described.