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. Keywords: Socioeconomic status, Causal loop diagram, Chronic stress, Feedback loops
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.
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.
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.
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.
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).
Abstract
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.
To investigate whether items of the SF-12, widely used to assess health outcome in clinical practice and public health research, provide unbiased measurements of underlying constructs in different ...demographic groups regarding gender, age, educational level and ethnicity.
We included 23,146 men and women aged 18-70 of Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish, or Moroccan origin from the HELIUS study. Both 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 were conducted to establish comparability of SF-12 items across demographic groups.
MI regarding gender, age and education was tested in the ethnic Dutch group (N = 4,615). In each subsequent step of testing MI, change in goodness-of-fit measures did not exceed 0.010 (RMSEA) or 0.004 (CFI). Moreover, goodness-of-fit indices showed good fit for strict invariance models: RMSEA<0.055; CFI>0.97. Regarding ethnicity, RMSEA values of metric and subsequent models fell above 0.055, indicating violation of measurement invariance in factor loadings, thresholds and residual variances. Regression analysis revealed possible age-, education- and ethnicity-related DIF. Adjustment for this DIF had little impact on the magnitude of age and educational differences in physical and mental health, but ethnic inequalities in physical health-and to a lesser extent mental health-were reduced after DIF adjustment.
We found no evidence of violation of measurement invariance of the SF-12 regarding gender, age and educational level. If minor DIF would remain undetected in our MGCFA analyses, we showed that this would have negligible effect on the magnitude of demographic health inequalities. Regarding ethnicity, the SF-12 was not measurement invariant. After accounting for DIF, we observed a reduction of ethnic inequalities in health, in particular in physical health. Caution is warranted when comparing SF-12 scores across population groups with various ethnic backgrounds.
Public health is to a large extent determined by non-health-sector policies. One approach to address this apparent paradox is to establish healthy public policies. This requires policy makers in ...non-health sectors to become more aware of the health impacts of their policies, and more willing to adopt evidence-informed policy measures to improve health. We employed a knowledge broker to set the agenda for health and to specify health-promoting policy alternatives. This study aimed at gaining in-depth understanding of how this knowledge broker approach works.
In the context of a long-term partnership between the two universities in Amsterdam and the municipal public health service, we employed a knowledge broker who worked part-time at a university and part-time for an Amsterdam city district. When setting an agenda and specifying evidence-informed policy alternatives, we considered three individual policy portfolios as well as the policy organization of the city district. We evaluated and developed the knowledge broker approach through action research using participant observation.
Our knowledge brokering strategy led to the adoption of several policy alternatives in individual policy portfolios, and was especially successful in agenda-setting for health. More specifically, health became an issue on the formal policy agenda as evidenced by its uptake in the city district's mid-term review and the appointment of a policy analyst for health. Our study corroborated the importance of process factors such as building trust, clearly distinguishing the knowledge broker role, and adequate management support. We also saw the benefits of multilevel agenda-setting and specifying policy alternatives at appropriate policy levels. Sector-specific responsibilities hampered the adoption of cross-sectoral policy alternatives, while thematically designed policy documents offered opportunities for including them. Further interpretation revealed three additional themes in knowledge brokering: boundary spanning, a ripple effect, and participant observation.
The employment of a knowledge broker who works simultaneously on both agenda-setting for health as well as the specification of health-promoting policy alternatives seems to be a promising first step in establishing local healthy public policies. Future studies are needed to explore the usefulness of our approach in further policy development and policy implementation.
The aim of this study was to examine whether changes over time in reported area crime and perceived area safety were related to self-rated general health and physical activity (PA), in order to ...provide support for a causal relationship between social safety and health. Additionally, we investigated whether social cohesion protects the residents against the negative impact of unsafe areas on health and PA. Multilevel logistic regression analyses were performed on Dutch survey data, including 47,926 respondents living in 2974 areas. An increase in area level unsafety feelings between 2009 and 2011 was associated with more people reporting poor general health in 2012 in that area, but was not related to PA. Changes in reported area crime were not related to either poor general health or PA. The social cohesion in the area did not modify the effect of changes in social safety on health and PA. The results suggest that tackling feelings of unsafety in an area might contribute to the better general health of the residents. Because changes in area social safety were not associated with PA, we found no leads that such health benefits were achieved through an increase in physical activity.
Background: During the 1990s, inequalities in smoking prevalence by socioeconomic status (SES) have widened in Europe. Since then, many tobacco control policies have been implemented. Yet, European ...overviews of recent trends in smoking inequalities are lacking. This paper aims to provide an overview of longterm trends of socioeconomic inequalities in smoking cessation in Europe. Methods: We used data for 11 countries taken from Eurobarometer surveys from 1987 to 1995 and 2002—2012, with a total study sample of 63 737 respondents. We performed multilevel logistic regression to model associations of the quit ratio (proportion former smokers of ever smokers) with SES, measured by education and occupation separately, with adjustments for age, sex and time. Results: We found a significant, positive association for education and occupation with the quit ratio. The strength of the association decreased slightly from 1987 to 1995 and increased again from 2002 to 2012. Inequalities increased between the two periods in most countries and decreased in only one country. While in 1987-1995, the quit ratio increased among all SES groups and most strongly among the low SES group, in 2002-2012 it increased only among the high-education group (OR=1.38, 95% CI 1.02 to 1.87), and nonmanual occupation group (OR=1.59, 95% CI 1.19 to 2.12). Conclusions: Socioeconomic inequalities in smoking cessation rates have strongly increased since the 1990s and during the 2000s. This suggests that the tobacco control policies implemented during the 2000s have not been able to counter the trend in increasing inequalities.
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.
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.
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.
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.
Abstract
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.
PDF
