There is a growing consensus among public health researchers that the complexities underlying population health and its distribution cannot be unravelled by traditional methods; a shift in research paradigm to systems thinking is imperative to move this field forward. As Rutter et al state “complex systems approaches will be essential for improving population health and reducing health inequalities” (Rutter et al. 2017, p. 2) . The expertise in this field is still limited, however.
To build this expertise, our proposal will take up the challenge of applying systems thinking to the very relevant issue of socio-economic inequalities in health. The link between socio-economic status and health is complex, involving multifaceted, dynamic causal pathways. As a result, socio-economic inequalities have proven to be intransigent. A systems approach that encompasses the complexity of these pathways, can support public health policy and practice in effectively tackling health inequalities.
The general objectives of our proposal are, using a systems science approach,
1. to analyse the pathways that link socio-economic status to health, based on an interdisciplinary approach that includes epidemiological and ethnographic studies and computational modelling;
2. to identify leverage points within the system that have the potential to reduce socio-economic inequalities in health.
We will use socio-economic inequalities in type 2 diabetes (T2D) as a case-study, and will reflect on the applicability of models to other outcomes. As lower socio-economic groups are increasingly multi-ethnically composed due to immigration from low- and middle-income countries over the past decades, we will pay specific attention to the factor ‘ethnicity’ in the production of socio-economic inequalities in health.
The theoretical starting point of our proposal is the well-known ‘Fundamental Cause Theory’ of Link & Phelan . According to this theory, socio-economic status involves access to resources such as money, knowledge, power and prestige, which are considered critical to maintaining a health advantage. The pathways through which these resources are linked to specific health outcomes need to be elucidated. This is exactly what this proposal aims to do. To this end, a multidisciplinary project group of public health, social and complex system scientists will collaborate, and combine a range of problem mapping and data acquisition strategies with advanced, cutting-edge simulation modelling.
The project will be implemented through five workpackages. In a first step, a Causal Loop Diagram (conceptual model) and Systems Dynamic Model (computational model) will be defined on three levels: a population level, an individual level representing the socio-economic status of each person in the population, and a physiological level representing the health of each individual in terms of their risk of T2D. At each level, domain experts will contribute by defining the key elements represented in the model and their connections. The model will then be refined in a cyclic process alternating data acquisition, model refinement and simulations. Identified gaps in the evidence will be filled using a mixed methods approach involving empirical studies (quantitative and qualitative) and computational modelling. For the quantitative studies, we will primarily use data from the HELIUS study, a multi-ethnic, population-based cohort study of around 25,000 participants. Data will be enriched with ethnographic studies on countervailing mechanisms, and computational modelling on social networks.
The integration of the knowledge from the epidemiological, ethnographic and computational studies will lead to a fundamentally new understanding of the complexity and dynamics of the pathways underlying socio-economic inequalities in health. From the resulting System Dynamic Model, we will identify leverage points within the system that can be amenable to policy actions to decrease socio-economic inequalities in T2D and, potentially, other health outcomes. In line with the Fundamental Cause Theory, we will thereby focus on strategies that improve the socio-economic status of lower strata, or change the access to resources such as money and knowledge across socio-economic groups.
In this way, we will contribute to a renewed evidence base for the identification of public health policies, taking real-world population level complexities into account. Our work will facilitate the generation of evidence for important questions such as: “how do the health effects of interventions that increase one’s amount of education compare to the health impact of income measures?”, and “does an increase in income also result in better health within a strong welfare state?”. Ultimately, this may help shift the current public health paradigm from single solutions towards a more realistic approach that considers what works for whom and in which context.