Adapting to the social environment that we create together: How complexity science changes the way we understand health inequalities

Socioeconomic inequalities in health are persisting. Because health inequalities are dependent on many interacting causes, it is increasingly argued that traditional epidemiological methods do not suffice. A shift in research paradigm to complexity science has been proposed, where causes and their interactions are conceptualised as constituting a system that operates across spatio-temporal scales, from cells to society. This perspective implies that health inequalities stem from deeper, underlying problems that require systemic action. This thesis aimed to further clarify how complexity science may change the way we understand health inequalities. Part 1 focused on how to best make two complexity science methods – conceptual causal loop diagrams and computational system dynamics models – applicable and accessible to public health (Chapters 1 and 2). Part 2 examined how society-level causes play a role in the relationship between socioeconomic status and health, i.e. (i) feelings of inferiority resulting from social comparisons (Chapter 3) and (ii) chronic stress resulting from adverse socioeconomic conditions (Chapter 4). Part 3 utilised a computational system dynamics model to investigate interactions between society-level social norms and individual-level weight-related behaviour (Chapter 5) and explored whether such models can help advocate for society-level interventions (Chapter 6). The presented results show that the effect of socioeconomic status on health cannot be fully understood if interactions between causes at different scales are disregarded. They indicate that complexity science can (i) advance understanding of dynamics involving society-level causes, (ii) demonstrate the impact of interventions on such dynamics and (iii) improve mental models – shifting the focus to society-level causes. Complexity science can thus serve as the foundation for pinpointing and eventually intervening on structural drivers of health inequalities.