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Background Chronic stress increases chronic disease risk and may underlie the association between exposure to adverse socioeconomic conditions and adverse health outcomes. The relationship between exposure to such conditions and chronic stress is complex due to feedback loops between stressor exposure and psychological processes, encompassing different temporal (acute stress response to repeated exposure over the life course) and spatial (biological/psychological/social) scales. We examined the mechanisms underlying the relationship between exposure to adverse socioeconomic conditions and chronic stress from a complexity science perspective, focusing on amplifying feedback loops across different scales. Methods We developed a causal loop diagram (CLD) to interpret available evidence from this perspective. The CLD was drafted by an interdisciplinary group of researchers. Evidence from literature was used to confirm/contest the variables and causal links included in the conceptual framework and refine their conceptualisation. Our findings were evaluated by eight independent researchers. Results Adverse socioeconomic conditions imply an accumulation of stressors and increase the likelihood of exposure to uncontrollable childhood and life course stressors. Repetition of such stressors may activate mechanisms that can affect coping resources and coping strategies and stimulate appraisal of subsequent stressors as uncontrollable. We identified five feedback loops describing these mechanisms: (1) progressive deterioration of access to coping resources because of repeated insolvability of stressors; (2) perception of stressors as uncontrollable due to learned helplessness; (3) tax on cognitive bandwidth caused by stress; (4) stimulation of problem avoidance to provide relief from the stress response and free up cognitive bandwidth; and (5) susceptibility to appraising stimuli as stressors against a background of stress. Conclusions Taking a complexity science perspective reveals that exposure to adverse socioeconomic conditions implies recurrent stressor exposure which impacts chronic stress via amplifying feedback loops that together could be conceptualised as one vicious cycle. This means that in order for individual-level psychological interventions to be effective, the context of exposure to adverse socioeconomic conditions also needs to be addressed.

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

Background Inequalities in obesity pertain in part to differences in dietary intake in different socioeconomic groups. Examining the economic, social, physical and political food environment of low-income groups as a complex adaptive system – i.e. a system of multiple, interconnected factors exerting non-linear influence on an outcome, can enhance the development and assessment of effective policies and interventions by honouring the complexity of lived reality. We aimed to develop and apply novel causal loop diagramming methods in order to construct an evidence-based map of the underlying system of environmental factors that drives dietary intake in low-income groups. Methods A systematic umbrella review was conducted on literature examining determinants of dietary intake and food environments in low-income youths and adults in high/upper-middle income countries. Information on the determinants and associations between determinants was extracted from reviews of quantitative and qualitative studies. Determinants were organised using the Determinants of Nutrition and Eating (DONE) framework. Associations were synthesised into causal loop diagrams that were subsequently used to interpret the dynamics underlying the food environment and dietary intake. The map was reviewed by an expert panel and systems-based analysis identified the system paradigm, structure, feedback loops and goals. Results Findings from forty-three reviews and expert consensus were synthesised in an evidence-based map of the complex adaptive system underlying the food environment influencing dietary intake in low-income groups. The system was interpreted as operating within a supply-and-demand, economic paradigm. Five sub-systems (‘geographical accessibility’, ‘household finances’, ‘household resources’, ‘individual influences’, ‘social and cultural influences’) were presented as causal loop diagrams comprising 60 variables, conveying goals which undermine healthy dietary intake. Conclusions Our findings reveal how poor dietary intake in low-income groups can be presented as an emergent property of a complex adaptive system that sustains a food environment that increases the accessibility, availability, affordability and acceptability of unhealthy foods. In order to reshape system dynamics driving unhealthy food environments, simultaneous, diverse and innovative strategies are needed to facilitate longer-term management of household finances and socially-oriented practices around healthy food production, supply and intake. Ultimately, such strategies must be supported by a system paradigm which prioritises health.

PurposeEthnic minority groups usually have a more unfavourable disease risk profile than the host population. In Europe, ethnic inequalities in health have been observed in relatively small studies, with limited possibilities to explore underlying causes. The aim of the Healthy Life in an Urban Setting (HELIUS) study is to investigate the causes of (the unequal burden of) diseases across ethnic groups, focusing on three disease categories: cardiovascular diseases, mental health and infectious diseases.ParticipantsThe HELIUS study is a prospective cohort study among six large ethnic groups living in Amsterdam, the Netherlands. Between 2011 and 2015, a total 24 789 participants (aged 18–70 years) were included at baseline. Similar-sized samples of individuals of Dutch, African Surinamese, South-Asian Surinamese, Ghanaian, Turkish and Moroccan origin were included. Participants filled in an extensive questionnaire and underwent a physical examination that included the collection of biological samples (biobank).Findings to dateData on physical, behavioural, psychosocial and biological risk factors, and also ethnicity-specific characteristics (eg, culture, migration history, ethnic identity, socioeconomic factors and discrimination) were collected, as were measures of health outcomes (cardiovascular, mental health and infections). The first results have confirmed large inequalities in health between ethnic groups, such as diabetes and depressive symptoms, and also early markers of disease such as arterial wave reflection and chronic kidney disease, which can only just partially be explained by inequalities in traditional risk factors, such as obesity and socioeconomic status. In addition, the first results provided important clues for targeting prevention and healthcare.Future plansHELIUS will be used for further research on the underlying causes of ethnic differences in health. Follow-up data will be obtained by repeated measurements and by linkages with existing registries (eg, hospital data, pharmacy data and insurance data).

Background In Western European countries, the prevalence of depressive symptoms is higher among ethnic minority groups, compared to the host population. We explored whether these inequalities reflect variance in the way depressive symptoms are measured, by investigating whether items of the PHQ-9 measure the same underlying construct in six ethnic groups in the Netherlands. Methods A total of 23,182 men and women aged 18–70 of Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish or Moroccan origin were included in the HELIUS study and had answered to at least one of the PHQ-9 items. We conducted multiple group confirmatory factor analyses (MGCFA), with increasingly stringent model constraints (i.e. assessing Configural, Metric, Strong and Strict measurement invariance (MI)), and regression analysis, to confirm comparability of PHQ-9 items across ethnic groups. Results A one-factor model, where all nine items reflect a single underlying construct, showed acceptable model fit and was used for MI testing. In each subsequent step, change in goodness-of-fit measures did not exceed 0.015 (RMSEA) or 0.01 (CFI). Moreover, strict invariance models showed good or acceptable model fit (Men: RMSEA = 0.050; CFI = 0.985; Women: RMSEA = 0.058; CFI = 0.979), indicating between-group equality of item clusters, factor loadings, item thresholds and residual variances. Finally, regression analysis did not indicate potential ethnicity-related differential item functioning (DIF) of the PHQ-9. Conclusions This study provides evidence of measurement invariance of the PHQ-9 regarding ethnicity, implying that the observed inequalities in depressive symptoms cannot be attributed to DIF.

Digital transformation in health care has a lot of opportunities to improve access and quality of care. However, in reality not all individuals and communities are benefiting equally from these innovations. People in vulnerable conditions, already in need of more care and support, are often not participating in digital health programs. Fortunately, numerous initiatives worldwide are committed to make digital health accessible to all citizens, stimulating the long-cherished global pursuit of universal health coverage. Unfortunately initiatives are not always familiar with each other and miss connection to jointly make a significant positive impact. To reach universal health coverage via digital health it is necessary to facilitate mutual knowledge exchange, both globally and locally, to link initiatives and apply academic knowledge into practice. This will support policymakers, health care providers and other stakeholders to ensure that digital innovations can increase access to care for everyone, leading towards Digital health for all.

Background Half of the estimated annual 28 million non-communicable diseases (NCDs) deaths in low- and middle-income countries (LMICs) are attributed to weak health systems. Current health policy responses to NCDs are fragmented and vertical particularly in the African region. The World Health Organization (WHO) led NCDs Global action plan 2013–2020 has been recommended for reducing the NCD burden but it is unclear whether Africa is on track in its implementation. This paper synthesizes Africa’s progress towards WHO policy recommendations for reducing the NCD burden. Methods Data from the WHO 2011, 2014 and 2015 NCD reports were used for this analysis. We synthesized results by targets descriptions in the three reports and included indicators for which we could trace progress in at least two of the three reports. Results More than half of the African countries did not achieve the set targets for 2015 and slow progress had been made towards the 2016 targets as of December 2013. Some gains were made in implementing national public awareness programmes on diet and/or physical activity, however limited progress was made on guidelines for management of NCD and drug therapy and counselling. While all regions in Africa show waning trends in fully achieving the NCD indicators in general, the Southern African region appears to have made the least progress while the Northern African region appears to be the most progressive. Conclusion Our findings suggest that Africa is off track in achieving the NCDs indicators by the set deadlines. To make sustained public health gains, more effort and commitment is urgently needed from governments, partners and societies to implement these recommendations in a broader strategy. While donors need to suit NCD advocacy with funding, African institutions such as The African Union (AU) and other sub-regional bodies such as West African Health Organization (WAHO) and various country offices could potentially play stronger roles in advocating for more NCD policy efforts in Africa.

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.

Background Systems thinking embraces the complexity of public health problems, including childhood overweight and obesity. It aids in understanding how factors are interrelated, and it can be targeted to produce favourable changes in a system. There is a growing call for systems approaches in public health research, yet limited practical guidance is available on how to evaluate public health programmes within complex adaptive systems. The aim of this paper is to present an evaluation framework that supports researchers in designing systems evaluations in a comprehensive and practical way. Methods We searched the literature for existing public health systems evaluation studies. Key characteristics on how to conduct a systems evaluation were extracted and compared across studies. Next, we overlaid the identified characteristics to the context of the Lifestyle Innovations Based on Youth Knowledge and Experience (LIKE) programme evaluation and analyzed which characteristics were essential to carry out the LIKE evaluation. This resulted in the Evaluation of Programmes in Complex Adaptive Systems (ENCOMPASS) framework. Results The ENCOMPASS framework includes five iterative stages: (1) adopting a system dynamics perspective on the overall evaluation design; (2) defining the system boundaries; (3) understanding the pre-existing system to inform system changes; (4) monitoring dynamic programme output at different system levels; and (5) measuring programme outcome and impact in terms of system changes. Conclusions The value of ENCOMPASS lies in the integration of key characteristics from existing systems evaluation studies, as well as in its practical, applied focus. It can be employed in evaluating public health programmes in complex adaptive systems. Furthermore, ENCOMPASS provides guidance for the entire evaluation process, all the way from understanding the system to developing actions to change it and to measuring system changes. By the nature of systems thinking, the ENCOMPASS framework will likely evolve further over time, as the field expands with more completed studies.

There has been growing interest in the adoption of complex systems approaches to tackle major public health challenges such as obesity. This perspective redirects focus from individually oriented and narrowly focused interventions towards strategies that reshape structural drivers of health and disease across multiple levels, offering novel avenues for enhancing population health and tackling health inequalities. Despite growing consensus on the use of a complex systems model of evidence to support these kinds of approaches, there remains limited agreement on the types of evidence required both to understand and to tackle complex public health problems. In this paper, we propose a complex systems model of evidence that combines three types of evidence—causal, intervention, implementation—across three dimensions of complex systems—mechanisms, dynamics and patterns. This model thus identifies nine categories of evidence: causal evidence, which explains the mechanisms behind public health problems, the dynamics driving changes therein and emerging health patterns; intervention evidence, which focuses on the set of actions that can modify these mechanisms and dynamics, including their intended and unintended consequences on health outcomes and implementation evidence, which addresses what is needed to implement systems change effectively, how systems adapt to these changes and how systems changes contribute to changes in health patterns. This complex systems model of evidence may serve as a guide to researchers and decision-makers when designing research programmes and evidence-based policies in response to complex public health problems.