The ENCOMPASS framework: a practical guide for the evaluation of public health programmes in complex adaptive systems

Evaluating the co-creation process in public health interventions: the PROSECO framework

OBJECTIVES

To date, there is a lack of evaluation frameworks to guide the planning and conducting of the evaluation of co-creation in public health. This study aims to identify and set the components of the PROSECO framework (PROcesS Evaluation framework for CO-creation) to support the evaluation of co-creation processes in public health interventions.

STUDY DESIGN

A multi-step, iterative approach combining the outcomes of two scoping reviews, expert validation, and design refinement.

METHODS

The PROSECO framework was developed in three steps. Firstly, collecting results from two scoping reviews on process evaluation for co-creation and evaluation of co-creation methods. The scoping review results were analysed and refined by a selected group of experts and through a three-round anonymous survey. Based on those findings, the framework visualisation was developed and designed through multiple design iterations.

RESULTS

The PROcesS Evaluation framework for CO-creation, called the PROSECO framework, was developed to assist researchers and stakeholders in evaluating a co-creation process. The framework comprises a list of 37 components grouped under the five dimensions of Delivery, Participation, Experiential, Context, and Impact.

CONCLUSIONS

PROSECO is the first framework to offer a systematic approach to evaluating co-creation. By integrating a diverse set of evaluation components, encompassing delivery, participation, experiential, impact, and context, it offers a flexible and comprehensive approach to address the complex nature of co-creation.

Applying a participatory system dynamics approach to childhood overweight and obesity in the local context: reflections from the LIKE project

BACKGROUND

Methods based in system dynamics (SD) have gained prominence within public health research in recent years. SD is grounded in theory and explains how central principles, such as adaptation, dynamics and emergence can be used to understand and/or change complex systems. To date, few examples exist where this theory has been applied consistently in a prevention approach in a local context. This study aimed to reflect upon the application of theoretical SD principles in context of the Lifestyle Innovations Based on Youth Knowledge and Experience (LIKE) project.

METHODS

A multi-methods qualitative evaluation was conducted using the LIKE project, situated in Amsterdam, the Netherlands, as a case study. LIKE applied a participatory system dynamics approach for obesity prevention in youth, throughout the project during a time period of 6 years (2017-2023). Data collection included document reviews, a Ripple Effects Mapping workshop, and semi-structured interviews with involved stakeholders, followed by in-depth reflective analysis.

RESULTS

We identify three key lessons combining theory and practice: (1) theory: interdependency programme and context; lesson: avoid becoming overly focused on achieving a complete understanding of the system related to the topic under study (for example, obesity). Instead, ensure sufficient attention is given to comprehending the dynamics of the local context, including existing initiatives and policy processes; (2) theory: dynamic and adaptive character; lesson: while the ability to encompass real-world dynamics is a foundational strength of system dynamics theory, its practical application can be constrained by more static elements, such as budget planning, and the need for clearly defined roles and responsibilities; and (3) theory: strong governance; lesson: SD projects require strong governance including strategic planning and enduring commitment, but in the absence of clear milestones or measurable impact on the short term.

CONCLUSIONS

Applying SD principles in practice requires a collective shift in thinking and working for all parties involved. Challenges in particular relate to the many uncertainties that arise whereby everything continues to change over time, including the focus of the system under study; relevant stakeholders; and momentum for change. This necessitates strategies different from our accustomed linear research working practices, shifting instead towards more iterative approaches that accommodate complexity and uncertainty.

Understanding the underlying systems dynamics contributing to the continued predominance of the unhealthy motorway food environment in the Netherlands: identifying leverage points and actions for change

BACKGROUND

Motorway food environments are dominated by roadside restaurants and petrol station stores offering predominantly unhealthy quick-service meals and foods for on-the-go consumption. Improving these environments to promote healthier diets is necessary, but how to achieve this is not fully understood. Therefore, this study aims to identify the complex underlying systems dynamics contributing to the continued predominance of the unhealthy motorway food environment as well as to identify potential leverage points and corresponding actions for change to improve the healthiness of the motorway food environment.

METHODS

Two Group Model Building workshops were held in October 2023 with motorway food environment stakeholders (e.g. food providers, producers, national policymakers, truck drivers). In the first workshop, a Causal Loop Diagram (CLD) was created to identify the system that contributes to the continued predominance of the unhealthy motorway food environment. The research team then identified leverage points for change based on the CLD. During the second workshop, stakeholders formulated actions to improve the motorway food environment for each identified leverage point. Leverage points and actions were classified based on the Action Scales Model (ASM).

RESULTS

The resulting CLD comprised six interconnected subsystems (food providers, supply chain collaboration, government, social culture, road users, global trends) with six reinforcing feedback loops, underlying the continued predominance of the unhealthy motorway food environment. Additionally, 14 potential leverage points and 31 corresponding actions for change were identified at different levels of the system based on the ASM (i.e. events, structures, goals and beliefs).

CONCLUSIONS

The findings show many interrelated factors and mechanisms underlying the continued predominance of the unhealthy motorway food environment. Actions for change were proposed together with stakeholders aimed at leverage points at different system levels. The results show that the motorway food environment is shaped by broader societal goals and beliefs (e.g. the profitability of unhealthy products) and social-cultural beliefs particularly evident to the on-the-go setting, including the motorway food environment. Together they present the strongest potential for leveraging systems change. There is a need for a coherent multidimensional action plan targeting these leverage points, which is broadly supported by various stakeholders, to induce systemic change.

Monitoring and adaptation of a system dynamics approach to prevent childhood overweight and obesity: findings from the LIKE programme

BACKGROUND

There are few examples of public health programmes rooted in system dynamics methodology. The aim of this paper was to broaden the evidence-base on the implementation and evaluation of a system dynamics programme for obesity prevention, using the Lifestyle Innovations based on youth's Knowledge and Experience (LIKE) Programme as a case study. In LIKE, system dynamics principles were operationalized around three central pillars: the action programme is (1) rooted in a system-based understanding; (2) integrated in the local context and (3) dynamic.

METHODS

This study took place in an urban setting in Amsterdam, the Netherlands, as part of the LIKE programme. The action programme consisted of establishing thematic action groups around previously identified leverage points within the system of overweight-related behaviours among adolescents. An action monitoring register was used to monitor action development and implementation, including the targeted system level. To track action implementation and adaptation over time, we conducted an in-depth evaluation using ripple effects mapping and additional interviews for three action groups. This data was analysed by performing a thematic content analysis.

RESULTS

During the 6-year course of LIKE, 63 action ideas were formulated by 12 action groups, and 22 of these actions were implemented. Most of these implemented actions targeted lower system levels. A total of 9 of the 22 implemented actions were incorporated in existing initiatives. We observed that operationalization of system dynamics principles influenced the form of the action programme. Action ideas were dynamic in the sense that they changed over time or were abandoned because of growing system insights and/or factors within the wider context. This required shifting the focus from individual actions to the programme as a whole and formulating action ideas in terms of their function in changing the system, instead of on its form.

CONCLUSIONS

Using LIKE as a case study, this study provides an example of the output of a system dynamics action programme. We show how leverage points can be used as a starting point to develop action ideas that target lower and higher system levels. This demands monitoring and evaluation that facilitates continuous customization of the programme.

Evaluating a whole-school approach to addressing gender-based violence in Scottish secondary schools (Equally Safe at School): a study protocol for a type I hybrid effectiveness-implementation trial

INTRODUCTION

Equally Safe at School (ESAS) is a whole-school intervention to reduce gender-based violence (GBV) in secondary school. ESAS comprises self-assessment, student-led action group, two-tier staff training, curriculum enhancement and policy review. Schools set up key activities in Year 1 and embed them in Year 2. GBV, including sexual harassment, is common in secondary schools and disproportionately affects young women and lesbian, gay, bisexual, transgender and queer youth.

METHODS AND ANALYSIS

We will evaluate the effectiveness, cost-effectiveness, mechanisms of action and implementation of ESAS. We will recruit 36 schools across Scotland. The evaluation comprises three linked studies:Study 1: Pragmatic cluster randomised trial with 1:1 school allocation to either immediate ESAS intervention start (intervention schools) or 12-month delayed intervention start (control schools). Our primary outcome of student experience of sexual harassment will be measured at 12 months post-randomisation. Analysis of primary and secondary outcomes (student and school level) will be conducted on an intention to treat (ITT) basis comparing schools according to their original allocation.Study 2: Mixed-methods evaluation. Study 2A: Longitudinal follow-up will assess primary, secondary and intermediate outcomes at baseline, 12 months and 24 months of follow-up. Study 2B: Systems and realist-informed process evaluation will assess intervention and control school context, fidelity, dose and reach, acceptability and actor response, and how this varies by school and students. We will also assess implementation processes and mechanisms of action (beneficial or harmful), including if and how change is embedded over time, and if and how ESAS helps schools leverage other assets and resources.Study 3: Economic evaluation to assess the within-trial and longer term cost-effectiveness of ESAS.The methods include surveys in three out of six year groups (Years 2, 4 and 6) in all schools (baseline, 12 months and 24 months of follow-up); interviews with staff, students and other stakeholders; activity observations; brief surveys with key actors and analysis of trial documentation.

ETHICS AND DISSEMINATION

Ethical approval by University of Glasgow MVLS Ethics Committee (200220268). Findings will be disseminated via multiple channels to researchers, GBV and education sector stakeholders, study participants and the public.

TRIAL REGISTRATION NUMBER

ISRCTN29792495.

Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study

BACKGROUND

Modern lifestyle risk factors, like physical inactivity and poor nutrition, contribute to rising rates of obesity and chronic diseases like type 2 diabetes and heart disease. Particularly personalized interventions have been shown to be effective for long-term behavior change. Machine learning can be used to uncover insights without predefined hypotheses, revealing complex relationships and distinct population clusters. New data-driven approaches, such as the factor probabilistic distance clustering algorithm, provide opportunities to identify potentially meaningful clusters within large and complex datasets.

OBJECTIVE

This study aimed to identify potential clusters and relevant variables among individuals with obesity using a data-driven and hypothesis-free machine learning approach.

METHODS

We used cross-sectional data from individuals with abdominal obesity from The Maastricht Study. Data (2971 variables) included demographics, lifestyle, biomedical aspects, advanced phenotyping, and social factors (cohort 2010). The factor probabilistic distance clustering algorithm was applied in order to detect clusters within this high-dimensional data. To identify a subset of distinct, minimally redundant, predictive variables, we used the statistically equivalent signature algorithm. To describe the clusters, we applied measures of central tendency and variability, and we assessed the distinctiveness of the clusters through the emerged variables using the F test for continuous variables and the chi-square test for categorical variables at a confidence level of α=.001.

RESULTS

We identified 3 distinct clusters (including 4128/9188, 44.93% of all data points) among individuals with obesity (n=4128). The most significant continuous variable for distinguishing cluster 1 (n=1458) from clusters 2 and 3 combined (n=2670) was the lower energy intake (mean 1684, SD 393 kcal/day vs mean 2358, SD 635 kcal/day; P<.001). The most significant categorical variable was occupation (P<.001). A significantly higher proportion (1236/1458, 84.77%) in cluster 1 did not work compared to clusters 2 and 3 combined (1486/2670, 55.66%; P<.001). For cluster 2 (n=1521), the most significant continuous variable was a higher energy intake (mean 2755, SD 506.2 kcal/day vs mean 1749, SD 375 kcal/day; P<.001). The most significant categorical variable was sex (P<.001). A significantly higher proportion (997/1521, 65.55%) in cluster 2 were male compared to the other 2 clusters (885/2607, 33.95%; P<.001). For cluster 3 (n=1149), the most significant continuous variable was overall higher cognitive functioning (mean 0.2349, SD 0.5702 vs mean -0.3088, SD 0.7212; P<.001), and educational level was the most significant categorical variable (P<.001). A significantly higher proportion (475/1149, 41.34%) in cluster 3 received higher vocational or university education in comparison to clusters 1 and 2 combined (729/2979, 24.47%; P<.001).

CONCLUSIONS

This study demonstrates that a hypothesis-free and fully data-driven approach can be used to identify distinguishable participant clusters in large and complex datasets and find relevant variables that differ within populations with obesity.

'It's about collaboration': a whole-systems approach to understanding and promoting movement in Suffolk

BACKGROUND

Population-levels of physical activity have remained stagnant for years. Previous approaches to modify behaviour have broadly neglected the importance of whole-systems approaches. Our research aimed to (i) understand, (ii) map, (iii) identify the leverage points, and (iv) develop solutions surrounding participation in physical activity across an English rural county.

METHODS

A systems-consortium of partners from regional and local government, charities, providers, deliverers, advocacy groups, and health and social care, and public health engaged in our research, which consisted of two-phases. Within Phase 1, we used secondary data, insight-work, a narrative review, participatory workshops, and interviews in a pluralistic style to map the system-representing physical activity. Phase 2 began with an initial analysis using markers from social network analysis and the Action Scales Model. This analysis informed a participatory workshop, to identify leverage points, and develop solutions for change within the county.

RESULTS

The systems-map is constructed from biological, financial, and psychological individual factors, interpersonal factors, systems partners, built, natural and social environmental factors, and policy and structural factors. Our initial analysis found 13 leverage points to review within our participatory workshop. When appraised by the group, (i) local governing policies, (ii) shared policies, strategies, vision, and working relationships, (iii) shared facilities (school, sport, community, recreation), and (iv) funding were deemed most important to change. Within group discussions, participants stressed the importance and challenges associated with shared working relationships, a collective vision, and strategy, the role of funding, and management of resources. Actions to leverage change included raising awareness with partners beyond the system, sharing policies, resources, insight, evidence, and capacity, and collaborating to co-produce a collective vision and strategy.

CONCLUSIONS

Our findings highlight the importance and provide insight into the early phase of a whole-systems approach to promoting physical activity. Our whole-systems approach within Suffolk needs to consider methods to (i) grow and maintain the systems-consortium, (ii) create a sustainable means to map the system and identify leverage points within it, and (iii) monitor and evaluate change.

How national healthcare change initiatives balance emergent and deliberate change: A principles-focused evaluation

Principles-focused evaluation reflects on the change process itself through examination of its underlying principles. The Centre for Advancing Collaborative Healthcare & Education (CACHE) worked to build interprofessional education programs and tools that attended to the Team Primary Care (TPC) principles. Our internally directed principles-focused evaluation, presented here, asks how CACHE adhered to these principles in the programs and tools it delivered to the TPC project. The article's main contribution is the creation of a new concept, organizational critically reflective practice, which describes an approach health leaders can use to mitigate the limitations of short-term initiatives while pursuing transformational change. We propose specific tools and steps that will help health leaders attempting to enact organizational critically reflective practice.

If things were simple, word would have gotten around. Can complexity science help us improve pediatric research?

Complexity science is a discipline which explores how complex systems behave and how we interact with them. Though it is widely implemented outside medicine, particularly in the sciences involving human behavior, but also in the natural sciences such as physics and biology, there are only a few applications within medical research. We propose that complexity science can provide new and helpful perspectives on complex pediatric medical problems. It can help us better understand complex systems and develop ways to cope with their inherent unpredictabilities. In this article, we provide a brief introduction of complexity science, explore why many medical problems can be considered 'complex', and discuss how we can apply this perspective to pediatric research. IMPACT: Current methods in pediatric research often focus on single mechanisms or interventions instead of systems, and tend to simplify complexity. This may not be appropriate. Complexity science provides a framework and a toolbox to better address complex problems. This review provides a starting point for the application of complexity science in pediatric research.

The impact of a patient-centred care pathway for patients treated with oral anticancer drugs: A multicentre pre-posttest study in Flanders

RATIONALE

In the Collaborative Network To Take Responsibility for Oral Anticancer Therapy (CONTACT) project, an evidence-based and patient-centred care(PCC) pathway was implemented in 12 oncology departments in Flanders. The care pathway was developed in cocreation by an interdisciplinary project team, and tailored to the local hospital context.

AIMS AND OBJECTIVES

In this study, the impact of the care pathways on quality of PCC and other patient outcomes was investigated.

METHOD

A pre-posttest study was performed in nine of the participating oncology departments. The primary outcome was quality of PCC. Furthermore, level of patient self-management, medication adherence, satisfaction with information about the oral anticancer drug and quality of life were measured as secondary outcomes. Linear mixed models were used to investigate differences in outcomes between the pre- and posttest group.

RESULTS

Quality of PCC, as well as all secondary outcomes improved after implementation of the care pathway. However, the changes in pre- and posttest scores were not significant. The overall quality of PCC increased from 3.72 to 3.88, measured on a five-point Likert scale (p = 0.124).

CONCLUSION

This study showed small, however, no significant improvements in the quality of PCC and other patient outcomes. The lack of significant changes can be attributed to the complexity of the care pathway development, poor or unstable implementation of the care pathway and limited changes in follow-up care. More insight in the actual implementation of the care pathway and potential contextual factors influencing its effect is needed to help understand the outcomes of this pre-posttest study.

Understanding bidirectional and transactional processes of child eating behaviours and parental feeding practices explaining poor health outcomes across infancy and early childhood in Australia: protocol for the Longitudinal Assessment of Children's Eating (LACE) study

INTRODUCTION

Child eating behaviours develop through interactions between the child's characteristics, psychological factors and the child's social environment and this affects the child's diet and weight. To examine the currently existing birth cohort studies examining child eating behaviours, a review was conducted. There are currently no birth cohorts that concurrently examine child eating behaviours, dietary intake, growth and parental feeding practices from birth into early childhood. Therefore, the primary objective of the Longitudinal Assessment of Children's Eating (LACE) study is to examine the bidirectional and transactional processes of child eating behaviours and parental feeding practices explaining poor dietary intake and excess weight across infancy and early childhood.

METHODS AND ANALYSIS

The LACE study will be a prospective, longitudinal parent-reported study following infants from younger than 4 months of age across nine waves of data collection: younger than 4 months, 4 months, 6 months, 9 months, 12 months, 18 months, 2 years, 3 years and 5 years. Participants will be included if they are parents of infants younger than 4 months, 18 years or older, fluent in English and living in Australia at baseline. A sample size of 1210 is proposed. Participants will be recruited online via paid social media (Facebook and Instagram) advertisements. The study will examine child eating behaviours, body mass index Z-score, dietary intake, screen time, temperament, parent feeding practices and styles, and demographics. The data will be obtained using the online survey software Qualtrics. Data analyses will be conducted using Stata.

ETHICS AND DISSEMINATION

Ethical approval was granted by the Deakin University Human Ethics Advisory Group, Faculty of Health (HEAG-H 120_2022). The findings from this study will be disseminated via presentations at scientific conferences and published manuscripts in peer-reviewed journals. Findings will be disseminated to the general public via mainstream media and to participants of the study with a summary of the findings.

A complex systems lens can help us understand drivers of emerging challenges in work and health

Emergent health challenges related to work Work is not only central to population health but is also a significant driver of social inequality in health (1). In a recent Lancet series on work and health, the authors outlined six emergent challenges concerning work: the impact of technology, the intersection of work with sociodemographic health determinants, migrant work, precarious employment, long working hours, and climate change (1). The authors of the Lancet series also presented recommendations for future research, advocating for the utilization of mixed-methods, innovative analytical approaches (eg, causal modeling), realist evaluation, and interdisciplinary collaboration. Although each of these approaches are highly relevant, their integrated application was only vaguely outlined.

We believe that each of these work and health challenges show features of complex adaptive systems. They are multifaceted, constantly evolving, and emerge from our complex and disordered real world, which is often characterized by interactions, non-linearity, interference, feedback loops, and adaptation. Consequently, future research on work and health may benefit from adopting a complex systems perspective to obtain a comprehensive understanding of the drivers of these challenges (2–4). We have recently developed an interdisciplinary framework for knowledge production aimed at understanding complex health issues within the domain of public health, rooted in complex systems theory (5). This framework can serve to organize our thinking, formulate research questions, and integrate methodologies related to each of these six work and health challenges.

Briefly outlined, the Health Complexity framework relates to three core dimensions in which complex health issues may be conceptualized: patterns, mechanisms, and dynamics (5). Patterns: Looking for specific patterns of disease or risk factors allows us to empirically identify health issues that emerge from the mechanisms and dynamics of the underlying systems, eventually allowing us to discover vulnerable subgroups, and thereby set boundaries for targeted interventions. Mechanisms: Understanding the core mechanisms that give rise to these emergent health patterns and how they are connected across scales through interactions and interference can help us identify potential leverage points for intervention. Dynamics: Building evidence on the dynamics that make patterns and mechanisms change over time will allow us to identify vicious circles associated with particularly high morbidity.

Between them, these dimensions cover seven key features of complex systems (emergence, interactions, non-linearity, interference, feedback loops, adaptation, and evolution), which we have highlighted as central to public health. The Health Complexity framework builds upon the ideas of methodological pluralism (6–8) and is intended to be an overarching framework for interdisciplinary and collaborative research on complex health issues, also in the field of work and health. As an illustration, we will outline the elements needed to examine one of these challenges – precarious employment – through a complex systems lens, particularly highlighting how this approach influences the way we phrase research questions on health problems that do justice to the complexity of the real world.

Precarious employment viewed through a complex systems lens With globalization and technological advancements, there has been a shift towards a gig economy. This has led to an increase in temporary, part-time, and freelance work, which often lacks stability and benefits. Precarious employment specifically refers to such work characterized by employment insecurity, income inadequacy, and lack of rights and protection (9). The lack of stability and benefits associated with precarious employment combined with poor working conditions have been shown to have negative effects on physical and mental health (10–13). Workers may experience higher levels of stress, depression, and other health problems due to financial insecurity and lack of access to healthcare, which collectively may be an important driver of health inequality and of health decline. In a life course perspective, there may also be important feedback mechanisms exacerbating such inequality, with poor health not only being a consequence of precarious employment, but workers with poor health may be more likely to be excluded from stable work (14). Overall, the increasing prevalence of precarious employment represents a substantial challenge for public health, which can be seen as a sort of byproduct of larger societal trends. We believe that employing a complex systems lens can help us generate relevant scientific knowledge about the fundahttps://www.sjweh.fi/editoi.sjweh.fi/pics/update_u_3.gifmental drivers of this problem. This essentially entails three interlinked steps organized around the three core dimensions of the Health Complexity Framework (figure 1).

Patterns: As a first step, we need to zoom out and understand the health effects associated with emergent patterns of precarious employment in their context across time and space, asking questions such as: •How does precarious employment change over time, and how does this changing pattern affect population health? •Are there certain population groups, defined, eg, by socioeconomic status, age, occupation, migrant status, or geographical regions who experience more adverse health effects by precarious employment than others?

Systematically evaluating health patterns associated with precariousness can help us define boundaries for targeted prevention. Employing classical epidemiological surveillance methods alongside data science techniques for uncovering patterns within multidimensional large-scale datasets serves as key examples of such pattern identification.

Mechanisms: As a second step, we need to understand what mechanisms underlying the health effects of precariousness and how elements of these mechanisms are connected across scales, from cells to society, asking questions such as: •How do mechanisms interact across biological, behavioral, social, and societal scales to create the rising public health problems associated with precarious employment? •Does precarious employment and its associated health problems cluster and spread across social networks and/or across occupational and economic sectors?

Systematically evaluating the interconnectedness between mechanisms and individuals across various scales can help us identify leverage points for intervention. Whereas biomedical studies can contribute to uncovering the biological mechanisms linked to precarious employment, such as the embodiment of stress (15), the social sciences may offer profound insights into macro-scale mechanisms involving political, economic, and social structures.

Dynamics: As a third step, we need to explore how the health effects of precarious employment change over time due to dynamic processes like adaptation and feedback, asking questions such as: •How do national political and social contexts adapt to historical changes in the labor market including the increase in precarious employment, and what is the impact of this adaptation when it comes to how and to what extent precarious employment can affect the health of individuals and populations? •Is there a reinforcing feedback mechanism between social disadvantage, precarious employment, and health? This mechanism could create a vicious circle—for example, social disadvantage increasing the likelihood of precarious employment, which then leads to health consequences that may further reinforce social disadvantage.

Systematically assessing such dynamism can help us intervene on vicious circles that generate excessive burdens of disease in specific population groups. Systems methodology, including formal conceptual model building and computational simulations, are essential in creating such evidence.

Integrating interdisciplinary knowledge across these dimensions will provide a systematic and comprehensive understanding of the patterns of precarious employment and health, the underlying connected mechanisms generating these patterns, and the dynamics that makes them change over time. Some dimensions, like the patterns of precarious employment and health, may already be well-researched, while other dimensions such as dynamics require further investigation. We argue that it is essential to systematically explore all these dimensions to comprehensively understand a complex issue. Leaving out one of these core dimensions may leave blind spots that will render our understanding of precarious employment and health incomplete and thereby impact the efficiency of future interventions. In this editorial, we have focused on how to phrase research questions when applying a complex systems lens on precarious employment and health. This clearly needs to be matched by the integration of an interdisciplinary set of methods and data. An overview of such methods and data can be found in Rod et al (5).

Are we at the brink of a ‘complexity turn’ in public health? We believe that we are witnessing a shift in public health away from the traditional model of evidence, which primarily focused on empirically testing predefined hypotheses of single exposures and outcomes. Instead, there is a growing recognition of public health issues as complex, involving the complex interactions of biological, social, psychological, economic, and other processes across various levels and time scales (2–5, 16–20). These dynamics may show nonlinearity and adaptability. This paradigm shift is particularly important to our understanding of the relationship between work and health, including the emergent challenges outlined in the Lancet series, where contextual factors and interactions across micro-, meso- and macro-levels emerge as main drivers of dynamic change in employment condition. Formalizing this turn towards complexity in public health requires not only a realignment of our research questions as outlined for precarious employment above, but also necessitates the integration of traditional epidemiological methods with systems methodologies, such as computational simulation modeling (3, 18). Furthermore, it calls for sustained support for interdisciplinary collaboration and substantial investment in a diverse array of data types. These include multi-scale data, spatial data, time-series data, life-course data, network data, and multi-generational data, among others. This shift in our understanding of public health also impacts our approach to evidence synthesis. Traditionally, evidence synthesis has been relatively straightforward, typically summarized in systematic reviews or meta-analyses focusing on single isolated risk factors. However, with a complex systems perspective, we must transition towards a dynamic evidence synthesis framework. This approach involves an ongoing process of data-driven discoveries, hypothesis testing, and theory building. By adopting this dynamic approach, we can effectively synthesize evidence on complex research questions while continuously assessing which dimensions remain unresolved and understudied. These unresolved or understudied aspects should serve as guiding principles for future studies and research programs, also on work and health.

Funding NHR acknowledge funding from the European Union (ERC, LAYERS, project no. 101124807). The views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.

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Systems thinking for local food environments: a participatory approach identifying leverage points and actions for healthy and sustainable transformations

BACKGROUND

Current local food environments encourage poor diets, posing a significant threat to public and planetary health. Acknowledging and addressing its inherent complexity is vital to making meaningful improvements to the food environment. Using a participatory approach with local stakeholders, this study aims to gain insight into the factors and mechanisms underlying the local food environment and to identify leverage points and system-based actions to foster healthy and sustainable local food environments.

METHODS

A systems-thinking approach was used in a Dutch municipality in 2022. Two group model building (GMB) workshops were held with community stakeholders (e.g. local policymakers, retailers and residents). During the first workshop (June 2022), factors and mechanisms influencing the local food environment were identified and visualized through a causal loop diagram (CLD). During the second workshop, leverage points and system-based actions to improve food environments were identified by the stakeholders. Four months after (October 2022), an action-implementation meeting was organized to stimulate the implementation of selected actions. Progress was monitored through brief telephone interviews 6 and 12 months after the second workshop.

RESULTS

The CLD visualises the factors and mechanisms influencing the local food environment from the point of view of the community stakeholders. The CLD consists of 46 factors shaping the local food environment, which were categorized into four identified subsystems: societal factors, individual, socio-economic factors, commercial factors and political factors. Eight leverage points were identified within the CLD, for example, 'lobby from food industry', 'governmental food policies' and 'e-commerce and platform economy'. Stakeholders formulated 20 actions targeting the identified leverage points. During the action-implementation meeting, long-term plans were created for five actions. After 1 year, only one participant (policy advisory role) remained actively engaged in three of these actions.

CONCLUSIONS

This study yields insight into the numerous factors and mechanisms underlying the local food environment and identified system-based actions as perceived by local stakeholders to improve this food environment locally. The CLD offers stakeholders valuable insights on employing a systems approach when enhancing food environments. More research is necessary, especially into the long-term processes and effects of implementing system-oriented actions to improve local food environments.

Applying systems thinking in youth-centred participatory action research for health promotion in an underserved neighbourhood

Purpose

Childhood overweight is considered a complex problem influenced by a range of factors, including energy balance-related behaviours (EBRBs) and interacting drivers of these behaviours. There is growing support that applying a systems approach is required to tackle complex problems resulting in actions that attempt to change the system's dynamics. Additionally, a participatory approach is advocated to include the lived experience of the population of interest both in the understanding of the system as well as the development, implementation and evaluation of relevant actions. We therefore combined Intervention Mapping, Participatory Action Research (PAR) and system dynamics in the development, implementation and evaluation of actions contributing to healthy EBRBs together with adolescents.

Methods

Four PAR groups comprising of 6-8 adolescent co-researchers (10-14 years) and 1-2 adult facilitators met weekly during 3-4 years. The structured Intervention Mapping protocol guided the process of the systematic development, implementation and evaluation of actions. System dynamics tools were included for the creation of Causal Loop Diagrams and development of systemic actions.

Results

Our approach comprised six steps that were executed by the PAR groups: (1) build Causal Loop Diagrams for each EBRB through peer research and identify overarching mechanisms, (2) determine leverage points using the Intervention Level Framework, (3) develop action ideas, (4) develop detailed actions including an implementation plan, (5) implement and, (6) evaluate the actions. PAR ensured that the actions fitted the lived experience of the adolescents, whilst system dynamics promoted actions at different levels of the system. The Intervention Mapping protocol ensured that the actions were theory-based. The main challenge involved integrating system dynamics within our practise in cooperation with adolescent co-researchers.

Conclusion

We experienced that combining Intervention Mapping, PAR and system dynamics worked well in developing, implementing and evaluating actions that target different levels of the system that drive adolescents' EBRBs. This study serves as an example to other studies aimed at developing, implementing and evaluating actions using a participatory and systems approach.

Qualitative systems mapping in promoting physical activity and cardiorespiratory fitness: Perspectives and recommendations

The purpose of this report is to provide a perspective on the use of qualitative systems mapping, provide examples of physical activity (PA) systems maps, discuss the role of PA systems mapping in the context of iterative learning to derive breakthrough interventions, and provide actionable recommendations for future work. Systems mapping methods and applications for PA are emerging in the scientific literature in the study of complex health issues and can be used as a prelude to mathematical/computational modeling where important factors and relationships can be elucidated, data needs can be prioritized and guided, interventions can be tested and (co)designed, and metrics and evaluations can be developed. Examples are discussed that describe systems mapping based on Group Model Building or literature reviews. Systems maps are highly informative, illustrate multiple components to address PA and physical inactivity issues, and make compelling arguments against single intervention action. No studies were identified in the literature scan that considered cardiorespiratory fitness the focal point of a systems maps. Recommendations for future research and education are presented and it is concluded that systems mapping represents a valuable yet underutilized tool for visualizing the complexity of PA promotion.

Development of an action programme tackling obesity-related behaviours in adolescents: a participatory system dynamics approach

System dynamics approaches are increasingly addressing the complexity of public health problems such as childhood overweight and obesity. These approaches often use system mapping methods, such as the construction of causal loop diagrams, to gain an understanding of the system of interest. However, there is limited practical guidance on how such a system understanding can inform the development of an action programme that can facilitate systems changes. The Lifestyle Innovations Based on Youth Knowledge and Experience (LIKE) programme combines system dynamics and participatory action research to improve obesity-related behaviours, including diet, physical activity, sleep and sedentary behaviour, in 10-14-year-old adolescents in Amsterdam, the Netherlands. This paper illustrates how we used a previously obtained understanding of the system of obesity-related behaviours in adolescents to develop an action programme to facilitate systems changes. A team of evaluation researchers guided interdisciplinary action-groups throughout the process of identifying mechanisms, applying the Intervention Level Framework to identify leverage points and arriving at action ideas with aligning theories of change. The LIKE action programme consisted of 8 mechanisms, 9 leverage points and 14 action ideas which targeted the system's structure and function within multiple subsystems. This illustrates the feasibility of developing actions targeting higher system levels within the confines of a research project timeframe when sufficient and dedicated effort in this process is invested. Furthermore, the system dynamics action programme presented in this study contributes towards the development and implementation of public health programmes that aim to facilitate systems changes in practice.

Developing and Evaluating a Data-Driven and Systems Approach to Health Promotion Among Vocational Students: Protocol for the Data Health Study

BACKGROUND

Vocational school students exhibit significant risk behaviors in terms of poor diet, frequent use of nicotine products, inadequate fruit and vegetable intake, low levels of physical activity, and poor mental health. This makes vocational students vulnerable to the development of noncommunicable diseases. Therefore, effective health promotion programs targeting vocational students are required.

OBJECTIVE

The Danish study "Data-driven and Systems Approach to Health Promotion Among Vocational Students" (Data Health) aims to develop, implement, and evaluate a systems approach to support vocational schools, municipalities, and local communities in implementing locally relevant health promotion actions among and for vocational students. This paper describes the Data Health program and how implementation and preliminary effectiveness will be evaluated.

METHODS

The Data Health program offers an iterative 5-step process to develop changes in the systems that shape health behavior and well-being among vocational students. The program will be implemented and evaluated in 8 Danish vocational schools in 4 municipalities. The implementation of the process and actions will be explored using a systems-based evaluation design that assesses contextual differences and the mechanisms through which the program leads to changes in the systems. Preliminary effectiveness at the individual level (students' self-reported health behavior and well-being) and organizational level (school organizational readiness reported by school staff) will be assessed using a quasi-experimental design, and cross-sectional data will be collected at all 8 schools simultaneously 4 times during the 2-year study period.

RESULTS

This study was launched in 2021, and data collection is expected to be completed in June 2024. The first results are expected to be submitted for publication in January 2024.

CONCLUSIONS

We expect that the Data Health study will make significant contributions to complex intervention research by contributing to the paucity of research studies that have used systems approaches in school settings. The study will also provide evidence of successful elements for systems change and effectiveness to determine whether a national scale-up can be recommended.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05308459; https://clinicaltrials.gov/study/NCT05308459.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/52571.

Guiding prevention initiatives by applying network analysis to systems maps of adverse childhood experiences and adolescent suicide

Suicide is a leading cause of death in the United States, particularly among adolescents. In recent years, suicidal ideation, attempts, and fatalities have increased. Systems maps can effectively represent complex issues such as suicide, thus providing decision-support tools for policymakers to identify and evaluate interventions. While network science has served to examine systems maps in fields such as obesity, there is limited research at the intersection of suicidology and network science. In this paper, we apply network science to a large causal map of adverse childhood experiences (ACEs) and suicide to address this gap. The National Center for Injury Prevention and Control (NCIPC) within the Centers for Disease Control and Prevention recently created a causal map that encapsulates ACEs and adolescent suicide in 361 concept nodes and 946 directed relationships. In this study, we examine this map and three similar models through three related questions: (Q1) how do existing network-based models of suicide differ in terms of node- and network-level characteristics? (Q2) Using the NCIPC model as a unifying framework, how do current suicide intervention strategies align with prevailing theories of suicide? (Q3) How can the use of network science on the NCIPC model guide suicide interventions?

Using network analysis to identify leverage points based on causal loop diagrams leads to false inference

Network analysis is gaining momentum as an accepted practice to identify which factors in causal loop diagrams (CLDs)-mental models that graphically represent causal relationships between a system's factors-are most likely to shift system-level behaviour, known as leverage points. This application of network analysis, employed to quantitatively identify leverage points without having to use computational modelling approaches that translate CLDs into sets of mathematical equations, has however not been duly reflected upon. We evaluate whether using commonly applied network analysis metrics to identify leverage points is justified, focusing on betweenness- and closeness centrality. First, we assess whether the metrics identify the same leverage points based on CLDs that represent the same system but differ in inferred causal structure-finding that they provide unreliable results. Second, we consider conflicts between assumptions underlying the metrics and CLDs. We recognise six conflicts suggesting that the metrics are not equipped to take key information captured in CLDs into account. In conclusion, using betweenness- and closeness centrality to identify leverage points based on CLDs is at best premature and at worst incorrect-possibly causing erroneous identification of leverage points. This is problematic as, in current practice, the results can inform policy recommendations. Other quantitative or qualitative approaches that better correspond with the system dynamics perspective must be explored.

Test, evidence, transition projects in Scotland: developing the evidence needed for transition of effective interventions in cancer care from innovation into mainstream practice

BACKGROUND

A robust evidence base is required to assist healthcare commissioners and providers in selecting effective and sustainable approaches to improve cancer diagnosis and treatment. Such evidence can be difficult to build, given the fast-paced and highly pressured nature of healthcare delivery, the absence of incentives, and the presence of barriers in conducting pragmatic yet robust research evaluations. Cancer Research UK (CRUK) has played an active part in building the evidence base through its funding of programmes to identify, evaluate and scale-up innovative approaches across the UK. The aim of this paper is to describe and explain the research design and intended approach and activities for two cancer services improvement projects in Scotland funded by CRUK.

METHODS

A hybrid effectiveness-implementation study design will assess both the efficiency of the new pathways and their implementation strategies, with the aim of generating knowledge for scale-up. A range of implementation, service and clinical outcomes will be assessed as determined by the projects' Theories of Change (ToCs). A naturalistic case study approach will enable in-depth exploration of context and process, and the collection and synthesis of data from multiple sources including routine datasets, patient and staff surveys, in-depth interviews and observational and other data. The evaluations are informed throughout by a patient/public representatives' group, and by small group discussions with volunteer cancer patients.

DISCUSSION

Our approach has been designed to provide a holistic understanding of how (well) the improvement projects work (in relation to their anticipated outcomes), and how they interact with their wider contexts. The evaluations will help identify barriers, facilitators, and unanticipated consequences that can impact scalability, sustainability and spread. By opting for a pragmatic, participatory evaluation research design, we hope to inform strategies for scaling up successful innovations while addressing challenges in a targeted manner.

Comprehensive application of a systems approach to obesity prevention: a scoping review of empirical evidence

A systems approach to obesity prevention is increasingly urged. However, confusion exists on what a systems approach entails in practice, and the empirical evidence on this new approach is unclear. This scoping review aimed to identify and synthesise studies/programmes that have comprehensively applied a systems approach to obesity prevention in intervention development, delivery/implementation, and evaluation. By searching international databases and grey literature, only three studies (10 publications) met inclusion criteria, which might be explained partially by suboptimal reporting. No conclusion on the effectiveness of this approach can be drawn yet due to the limited evidence base. We identified common features shared by the included studies, such as measuring ongoing changes, in addition to endpoint outcomes, and supporting capacity building. Some facilitators and barriers to applying a comprehensive systems approach in practice were identified. More well-designed and reported studies are needed, especially from low- and middle-income countries.

Understanding the system dynamics of obesity-related behaviours in 10- to 14-year-old adolescents in Amsterdam from a multi-actor perspective

Introduction and Methods

To develop an understanding of the dynamics driving obesity-related behaviours in adolescents, we conducted systems-based analysis on a causal loop diagram (CLD) created from a multi-actor perspective, including academic researchers, adolescents and local stakeholders.

Results

The CLD contained 121 factors and 31 feedback loops. We identified six subsystems with their goals: (1) interaction between adolescents and the food environment, with profit maximisation as goal, (2) interaction between adolescents and the physical activity environment, with utility maximisation of outdoor spaces as goal, (3) interaction between adolescents and the online environment, with profit maximisation from technology use as goal, (4) interaction between adolescents, parenting and the wider socioeconomic environment, with a goal focused on individual parental responsibility, (5) interaction between healthcare professionals and families, with the goal resulting in treating obesity as an isolated problem, and (6) transition from childhood to adolescence, with the goal centring around adolescents' susceptibility to an environment that stimulates obesity-related behaviours.

Discussion

Analysis showed that inclusion of the researchers' and stakeholders' perspectives contributed to an understanding of how the system structure of an environment works. Integration of the adolescents' perspective enriched insights on how adolescents interact with that environment. The analysis further showed that the dynamics driving obesity-related behaviours are geared towards further reinforcing such behaviours.

Human Factors in Leveraging Systems Science to Shape Public Policy for Obesity: A Usability Study

Background: despite a broad consensus on their importance, applications of systems thinking in policymaking and practice have been limited. This is partly caused by the longstanding practice of developing systems maps and software in the intention of supporting policymakers, but without knowing their needs and practices. Objective: we aim to ensure the effective use of a systems mapping software by policymakers seeking to understand and manage the complex system around obesity, physical, and mental well-being. Methods: we performed a usability study with eight policymakers in British Columbia based on a software tool (ActionableSystems) that supports interactions with a map of obesity. Our tasks examine different aspects of systems thinking (e.g., unintended consequences, loops) at several levels of mastery and cover common policymaking needs (identification, evaluation, understanding). Video recordings provided quantitative usability metrics (correctness, time to completion) individually and for the group, while pre- and post-usability interviews yielded qualitative data for thematic analysis. Results: users knew the many different factors that contribute to mental and physical well-being in obesity; however, most were only familiar with lower-level systems thinking concepts (e.g., interconnectedness) rather than higher-level ones (e.g., feedback loops). Most struggles happened at the lowest level of the mastery taxonomy, and predominantly on network representation. Although participants completed tasks on loops and multiple pathways mostly correctly, this was at the detriment of spending significant time on these aspects. Results did not depend on the participant, as their experiences with the software were similar. The thematic analysis revealed that policymakers did not have a typical workflow and did not use any special software or tools in their policy work; hence, the integration of a new tool would heavily depend on individual practices. Conclusions: there is an important discrepancy between what constitutes systems thinking to policymakers and what parts of systems thinking are supported by software. Tools may be more successfully integrated when they include tutorials (e.g., case studies), facilitate access to evidence, and can be linked to a policymaker’s portfolio.

Promoting the health of vulnerable populations: Three steps towards a systems-based re-orientation of public health intervention research

This paper proposes a novel framework for the development of interventions in vulnerable populations. The framework combines a complex systems lens with syndemic theory. Whereas funding bodies, research organizations and reporting guidelines tend to encourage intervention research that (i) focuses on singular and predefined health outcomes, (ii) searches for generalizable cause-effect relationships, and (iii) aims to identify universally effective interventions, the paper suggests that a different direction is needed for addressing health inequities: We need to (i) start with exploratory analysis of population-level data, and (ii) invest in contextualized in-depth knowledge of the complex dynamics that produce health inequities in specific populations and settings, while we (iii) work with stakeholders at multiple levels to create change within systems.

Development and Validation of a Machine Learning Wrist-worn Step Detection Algorithm with Deployment in the UK Biobank

Background

Step count is an intuitive measure of physical activity frequently quantified in a range of health-related studies; however, accurate quantification of step count can be difficult in the free-living environment, with step counting error routinely above 20% in both consumer and research-grade wrist-worn devices. This study aims to describe the development and validation of step count derived from a wrist-worn accelerometer and to assess its association with cardiovascular and all-cause mortality in a large prospective cohort study.

Methods

We developed and externally validated a hybrid step detection model that involves self-supervised machine learning, trained on a new ground truth annotated, free-living step count dataset (OxWalk, n=39, aged 19-81) and tested against other open-source step counting algorithms. This model was applied to ascertain daily step counts from raw wrist-worn accelerometer data of 75,493 UK Biobank participants without a prior history of cardiovascular disease (CVD) or cancer. Cox regression was used to obtain hazard ratios and 95% confidence intervals for the association of daily step count with fatal CVD and all-cause mortality after adjustment for potential confounders.

Findings

The novel step algorithm demonstrated a mean absolute percent error of 12.5% in free-living validation, detecting 98.7% of true steps and substantially outperforming other recent wrist-worn, open-source algorithms. Our data are indicative of an inverse dose-response association, where, for example, taking 6,596 to 8,474 steps per day was associated with a 39% [24-52%] and 27% [16-36%] lower risk of fatal CVD and all-cause mortality, respectively, compared to those taking fewer steps each day.

Interpretation

An accurate measure of step count was ascertained using a machine learning pipeline that demonstrates state-of-the-art accuracy in internal and external validation. The expected associations with CVD and all-cause mortality indicate excellent face validity. This algorithm can be used widely for other studies that have utilised wrist-worn accelerometers and an open-source pipeline is provided to facilitate implementation.

Challenges in evaluating implementation and effectiveness in real-world settings: evaluation proposal for school-based health-promoting intervention

There are various research designs and approaches to investigate how health-promoting activities are implemented in complex, real-world systems, and to identify potential health effects that might occur following implementation. Although literature describes guidelines to perform and report about implementation research and effect evaluations, no specific guidelines exist on analysing and reporting about the combination of effectiveness data and implementation data collected as part of intervention evaluation in complex and diverse settings. This paper describes the evaluation of primary school-based health-promoting activities in complex systems. Furthermore, an approach for data categorization inspired by Rogers' Diffusion of Innovations theory is presented that can facilitate structuring the study's results and relating the degree of implementation to any impact on effectiveness outcomes that might be observed. Researchers interested in using this approach for data categorization have to ensure that the following three conditions are met: (i) data on an intervention's efficacy in a controlled setting with optimal implementation is available; (ii) key points that define an intervention's optimal implementation are available and (iii) an evaluation study is performed, collecting both effectiveness data and implementation data in a real-world context. This data categorization approach can be useful to generate more insight into an intervention's effectiveness under varying circumstances, and optimal support and advice can be provided to stakeholders to achieve maximum impact of population-based health-promoting interventions in complex, real-world systems. However, the proposed approach is a first suggestion and further testing and adaptation is necessary to increase its usefulness. Knowledge and experience sharing among researchers performing comparable research can increase the knowledge base regarding this subject.

The association between the presence of fast-food outlets and BMI: the role of neighbourhood socio-economic status, healthy food outlets, and dietary factors

Background

Evidence on the association between the presence of fast-food outlets and Body Mass Index (BMI) is inconsistent. Furthermore, mechanisms underlying the fast-food outlet presence-BMI association are understudied. We investigated the association between the number of fast-food outlets being present and objectively measured BMI. Moreover, we investigated to what extent this association was moderated by neighbourhood socio-economic status (NSES) and healthy food outlets. Additionally, we investigated mediation by frequency of fast-food consumption and amount of fat intake.

Methods

In this cross-sectional study, we used baseline data of adults in Lifelines (N = 149,617). Geo-coded residential addresses were linked to fast-food and healthy food outlet locations. We computed the number of fast-food and healthy food outlets within 1 kilometre (km) of participants’ residential addresses (each categorised into null, one, or at least two). Participants underwent objective BMI measurements. We linked data to Statistics Netherlands to compute NSES. Frequency of fast-food consumption and amount of fat intake were measured through questionnaires in Lifelines. Multivariable multilevel linear regression analyses were performed to investigate associations between fast-food outlet presence and BMI, adjusting for individual and environmental potential confounders. When exposure-moderator interactions had p-value < 0.10 or improved model fit (∆AIC ≥ 2), we conducted stratified analyses. We used causal mediation methods to assess mediation.

Results

Participants with one fast-food outlet within 1 km had a higher BMI than participants with no fast-food outlet within 1 km (B = 0.11, 95% CI: 0.01, 0.21). Effect sizes for at least two fast-food outlets were larger in low NSES areas (B = 0.29, 95% CI: 0.01, 0.57), and especially in low NSES areas where at least two healthy food outlets within 1 km were available (B = 0.75, 95% CI: 0.19, 1.31). Amount of fat intake, but not frequency of fast-food consumption, explained this association for 3.1%.

Conclusions

Participants living in low SES neighbourhoods with at least two fast-food outlets within 1 km of their residential address had a higher BMI than their peers with no fast-food outlets within 1 km. Among these participants, healthy food outlets did not buffer the potentially unhealthy impact of fast-food outlets. Amount of fat intake partly explained this association. This study highlights neighbourhood socio-economic inequalities regarding fast-food outlets and BMI.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13826-1.

A Theoretical Perspective on Why Socioeconomic Health Inequalities Are Persistent: Building the Case for an Effective Approach

Despite policy intentions and many interventions aimed at reducing socioeconomic health inequalities in recent decades in the Netherlands and other affluent countries, these inequalities have not been reduced. Based on a narrative literature review, this paper aims to increase insight into why socioeconomic health inequalities are so persistent and build a way forward for improved approaches from a theoretical perspective. Firstly, we present relevant theories focusing on individual determinants of health-related behaviors. Thereafter, we present theories that take into account determinants of the individual level and the environmental level. Lastly, we show the complexity of the system of individual determinants, environmental determinants and behavior change for low socioeconomic position (SEP) groups and describe the next steps in developing and evaluating future effective approaches. These steps include systems thinking, a complex whole-system approach and participation of all stakeholders in system change.