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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.

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.

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.

Little is known about socioeconomic differences in the association between the food environment and dietary behavior. We systematically reviewed four databases for original studies conducted in adolescents and adults. Food environments were defined as all objective and perceived aspects of the physical and economic food environment outside the home. The 43 included studies were diverse in the measures used to define the food environment, socioeconomic position (SEP) and dietary behavior, as well as in their results. Based on studies investigating the economic (n = 6) and school food environment (n = 4), somewhat consistent evidence suggests that low SEP individuals are more responsive to changes in food prices and benefit more from healthy options in the school food environment. Evidence for different effects of availability of foods and objectively measured access, proximity and quality of food stores on dietary behavior across SEP groups was inconsistent. In conclusion, there was no clear evidence for socioeconomic differences in the association between food environments and dietary behavior, although a limited number of studies focusing on economic and school food environments generally observed stronger associations in low SEP populations. (Prospero registration: CRD42017073587)

We conducted a systematic review to examine whether changes in social determinants can contribute to the prevention of depression, in order to provide input for policy development and to highlight research gaps. Social determinants were defined as the structural conditions in which people live that shape their health and were categorized according to whether they pertained to societal arrangements, material resources distributed through these arrangements, or social resources that follow from interactions between people. To capture all relevant evidence we included studies that measured depressive disorders, depressive symptoms, psychological distress, mental health and prescription rates of antidepressants. We searched three databases (Medline, Embase and Psychinfo) from their inception till December 2022 and supplemented our search by reference and citation searching of the included studies. Studies were synthesized qualitatively and we used the Validity Assessment tool for econometric studies to assess study quality. Prospero submission number CRD42021236132 A total of 31,103 titles were identified, 135 studies met our inclusion criteria. The majority of studies were conducted in the United States (n = 45) or the United Kingdom (n = 39). Studies used longitudinal data (n = 61); repeated cross-sectional data (n = 20); or evaluated an intervention study (n = 7). Study designs included natural experiments (n = 19), while some used propensity score matching to construct a quasi-experiment (n = 11). Analysis methods included difference-in-difference approaches (n = 30) or regression analysis in varying forms. We found evidence that strategies that promote paid employment and parental leave policies can reduce risk of depression whereas reduced entitlements to social welfare (particularly when accompanied by obligations to enter employment), loss of income, instability of housing and collective insecurity increase depression risk. A number of studies examined moderation by gender, age category or ethnicity and of these gender was the most commonly observed moderator. Few studies tested underlying causal mechanisms with formal mediation analyses. These studies provide important indications of how intervening on social determinants of health can shape risk for depression. However, the included studies do not fully capture the complexity of the relationships between determinants and the mechanisms driving them. Future studies could take this into account, for instance by using systems approaches.

Background Systems approaches are increasingly adopted in public health, commonly operationalised using system dynamics (SD). In public health, systems approaches have prioritised understanding the current system by describing the causes of a complex problem – e.g. obesity – as a system. It remains challenging to advance from understanding the current system producing undesired outcomes, towards responses to improve outcomes. Rather than creating models of the (entire) system, SD traditionally emphasises specific models to support policy development. While core concepts from SD have effectively been adopted in public health, there may be more to learn from SD when it comes to designing systems approaches that can fulfil the purpose of informing problem responses. Methods We reviewed seminal SD literature for clues on how to refine the focus of systems approaches, so that they lead to specific models supporting policy development. We conducted a narrative review, seeking a strategy that can be leveraged in systems approaches in public health. We concentrated on SD’s problem framing strategy, leading to two insights. Results Insight 1: Alongside the complex problem at stake (e.g. obesity), consider the intended result of the systems approach (coordination, learning, analysis or transformation). This helps recognise which system components are relevant to problem responses and make methodological decisions accordingly. Insight 2: If investigation of the current system reveals that only radical change can lead to improved outcomes, then proceed to envisioning how the system could be fundamentally transformed to support those desired outcomes. This next step helps to anticipate policy resistance, unintended consequences and counterintuitive behaviour by contemplating how the system would react due to proposed problem responses. Conclusions Applying a problem framing strategy, as is commonly done in SD, could make systems approaches in public health better positioned to inform problem responses. Problem framing stimulates the contribution of systems approaches to health policy, prioritising system components relevant to problem responses (Insight 1), which may not be part of the system (Insight 2).

The importance of cultural adaptations in behavioral interventions targeting ethnic minorities in high-income societies is widely recognized. Little is known, however, about the effectiveness of specific cultural adaptations in such interventions. To systematically review the effectiveness of specific cultural adaptations in interventions that target smoking cessation, diet, and/or physical activity and to explore features of such adaptations that may account for their effectiveness. Systematic review using MEDLINE, PsycINFO, Embase, and the Cochrane Central Register of Controlled Trials registers (1997-2009). a) effectiveness study of a lifestyle intervention targeted to ethnic minority populations living in a high income society; b) interventions included cultural adaptations and a control group that was exposed to the intervention without the cultural adaptation under study; c) primary outcome measures included smoking cessation, diet, or physical activity. Out of 44904 hits, we identified 17 studies, all conducted in the United States. In five studies, specific cultural adaptations had a statistically significant effect on primary outcomes. The remaining studies showed no significant effects on primary outcomes, but some presented trends favorable for cultural adaptations. We observed that interventions incorporating a package of cultural adaptations, cultural adaptations that implied higher intensity and those incorporating family values were more likely to report statistically significant effects. Adaptations in smoking cessation interventions seem to be more effective than adaptations in interventions aimed at diet and physical activity. This review indicates that culturally targeted behavioral interventions may be more effective if cultural adaptations are implemented as a package of adaptations, the adaptation includes family level, and where the adaptation results in a higher intensity of the intervention. More systematic experiments are needed in which the aim is to gain insight in the best mix of cultural adaptations among diverse populations in various settings, particularly outside the US.

Background Physical activity and sedentary behaviour are associated with health and wellbeing. Studies indicate that ethnic minority groups are both less active and more sedentary than the majority population and that factors influencing these behaviours may differ. Mapping the factors influencing physical activity and sedentary behaviour among ethnic minority groups living in Europe can help to identify determinants of physical activity and sedentary behaviour, research gaps and guide future research. Methods A systematic mapping review was conducted to map the factors associated with physical activity and sedentary behaviour among ethnic minority groups living in Europe (protocol PROSPERO ID = CRD42014014575). Six databases were searched for quantitative and qualitative research published between 1999 and 2014. In synthesizing the findings, all factors were sorted and structured into clusters following a data driven approach and concept mapping. Results Sixty-three articles were identified out of 7794 returned by the systematic search. These included 41 quantitative and 22 qualitative studies. Of these 58 focused on physical activity, 5 on both physical activity and sedentary behaviour and none focused on sedentary behaviour. The factors associated with physical activity and sedentary behaviour were grouped into eight clusters. Social & cultural environment ( n  = 55) and Psychosocial (39) were the clusters containing most factors, followed by Physical environment & accessibility (33), Migration context (15), Institutional environment (14), Social & material resources (12), Health and health communication (12), Political environment (3). An important finding was that cultural and religious issues, in particular those related to gender issues, were recurring factors across the clusters. Conclusion Physical activity and sedentary behaviour among ethnic minority groups living in Europe are influenced by a wide variety of factors, especially informed by qualitative studies. More comparative studies are needed as well as inclusion of a wider spectrum of the diverse ethnic minority groups resettled in different European countries. Few studies have investigated factors influencing sedentary behaviour. It is important in the future to address specific factors influencing physical activity and sedentary behaviour among different ethnic minority groups in order to plan and implement effective interventions.

We examined whether the role of maternal education in children's unhealthy snacking diet is moderated by other socio-economic indicators. Participants were selected from the Amsterdam Born Children and their Development cohort, a large ongoing community-based birth cohort. Validated Food Frequency Questionnaires (FFQ) (n = 2782) were filled in by mothers of children aged 5.7±0.5yrs. Based on these FFQs, a snacking dietary pattern was derived using Principal Component Analysis. Socio-economic indicators were: maternal and paternal education (low, middle, high; based on the highest education completed) household finance (low, high; based on ability to save money) and neighbourhood SES (composite score including educational level, household income and employment status of residents per postal code). Cross-sectional multivariable linear regression analysis was used to assess the association and possible moderation of maternal education and other socio-economic indicators on the snacking pattern score. Analyses were adjusted for children's age, sex and ethnicity. Low maternal education (B 0.95, 95% CI 0.83;1.06), low paternal education (B 0.36, 95% CI 0.20;0.52), lower household finance (B 0.18, 95% CI 0.11;0.26) and neighbourhood SES (B -0.09, 95% CI -0.11;-0.06) were independently associated with higher snacking pattern scores (p<0.001). The association between maternal education and the snacking pattern score was somewhat moderated by household finance (p = 0.089) but remained strong. Children from middle-high educated mothers (B 0.44, 95% CI 0.35;0.52) had higher snacking pattern scores when household finance was low (B 0.49, 95% CI 0.33;0.65). All socio-economic indicators were associated with increased risk of unhealthy dietary patterns in young children, with low maternal education conferring the highest risk. Yet, within the group of middle-high educated mothers, lower household finance was an extra risk factor for unhealthy dietary patterns. Intervention strategies should therefore focus on lower educated mothers and middle-high educated mothers with insufficient levels of household finance.

In old age, sufficient protein intake is important to preserve muscle mass and function. Around 50% of older adults (65+ y) consumes ≤1.0 g/kg adjusted body weight (BW)/day (d). There is no rapid method available to screen for low protein intake in old age. Therefore, we aimed to develop and validate a short food questionnaire to screen for low protein intake in community-dwelling older adults. We used data of 1348 older men and women (56-101 y) of the LASA study (the Netherlands) to develop the questionnaire and data of 563 older men and women (55-71 y) of the HELIUS study (the Netherlands) for external validation. In both samples, protein intake was measured by the 238-item semi-quantitative HELIUS food frequency questionnaire (FFQ). Multivariable logistic regression analysis was used to predict protein intake ≤1.0 g/kg adjusted BW/d (based on the HELIUS FFQ). Candidate predictor variables were FFQ questions on frequency and amount of intake of specific foods. In both samples, 30% had a protein intake ≤1.0 g/kg adjusted BW/d. Our final model included adjusted body weight and 10 questions on the consumption (amount on average day or frequency in 4 weeks) of: slices of bread (number); glasses of milk (number); meat with warm meal (portion size); cheese (amount and frequency); dairy products (like yoghurt) (frequency); egg(s) (frequency); pasta/noodles (frequency); fish (frequency); and nuts/peanuts (frequency). The area under the receiver operating characteristic curve (AUC) was 0.889 (95% CI 0.870-0.907). The calibration slope was 1.03 (optimal slope 1.00). At a cut-off of ≤0.8 g/kg adjusted BW/d, the AUC was 0.916 (96% CI 0.897-0.936). Applying the regression equation to the HELIUS sample, the AUC was 0.856 (95% CI 0.824-0.888) and the calibration slope 0.92. Regression coefficients were therefore subsequently shrunken by a linear factor 0.92. To conclude, the short food questionnaire (Pro55+) can be used to validly screen for protein intake ≤1.0 g/kg adjusted BW/d in community-dwelling older adults. An online version can be found at www.proteinscreener.nl. External validation in other countries is recommended.