Meet the Researcher: Karien Stronks, Prof. Dr. 


Karien Stronks, who graduated as a health scientist and political scientist, seeks to generate evidence on how the position of an individual in society impacts his/her health and – at the population level – leads to inequalities in health. More specifically, she has focused on understanding why health inequalities arise and how to effectively combat them. Since 2006, she has undertaken this task as Professor of Public Health at the Medical Faculty of the University of Amsterdam/Amsterdam UMC.

1. How did you come to be involved in the Centre? 

I became involved in the Centre through my role as an Associate of the Institute for Advanced Study (IAS) of the University of Amsterdam. Since the start, the Centre for Urban Mental Health has been closely linked to the IAS. At the IAS, researchers from a wide range of fields work together on all kinds of complex scientific and societal challenges, including health issues. Since the start of the Centre for Urban Mental Health, I have been a member of the board and one of the principal investigators.

2. What made you want to be involved in the Centre? 

In my research, I aim to understand why health inequalities arise and how to effectively combat them. These inequalities, e.g. between socioeconomic or ethnic groups, are the outcome of a complex interplay between elements, such as individuals, foodshops, health-care services, and local government—at multiple levels (e.g., individual, physical environment) that are interconnected (e.g., neighbours might share social norms on drinking habits) and interact (e.g., the interaction between individual norms on dietary habits and the food environment). That conceptualization of public health issues perfectly matches the proposition of the Centre, namely that, in order to improve urban mental health, the aforementioned complexities and dynamics of health problems need to be taken into account. 

Complexity science is the backbone of the Centre. It offers the tools to actually study the complexities and dynamics of public health issues. Let me illustrate this by an example. ‘Mainstream’ research on inequalities in health, including many of the studies I have done myself, assumes that inequalities in health between groups manifest as risk factors at the individual level. As a result, the collective processes that actually drive inequalities in health between these groups remain invisible in this kind of research. For example, the clustering of unhealthy behaviours in certain groups could reflect group processes that result from interactions between individuals: e.g., social norms on healthy eating are likely to shape the dietary habits of individuals within such a group. Therefore, policies that aim to promote a healthy food environment will not necessarily lead to healthier dietary habits if the group processes underlying this collective behaviour are not addressed.

How can complexity science tools help us in understanding these complexities and dynamics? I will illustrate this with an example from our own work, on the issue of overweight and obesity. Although policies to prevent overweight and obesity often start with a commitment to deal with group-level determinants, they frequently end up with the implementation of individualized interventions aimed at changing individual weight-related behaviour, such as providing information on a healthy diet. A failure to tackle group-level determinants is partly because the knowledge base regarding the impact of interventions addressing these determinants is limited. I am convinced that complexity science is likely to speed up knowledge production in this field. We have, e.g., recently used system dynamic models (SDMs) to test the hypothesis that as overweight becomes normal, the norm might be counteracting health awareness in shaping individual weight-related behaviour.[1] The results of that study show that the prevalence of overweight at a group level shifts to a lesser extent in all groups if norms have an influence on weight-related behaviour. This is thus a confirmation that norms are counteracting health awareness in shaping this behaviour. Particularly in groups where overweight is the norm, our results show that people’s perceptions towards overweight and obesity hold group-level weight close to overweight, despite of health awareness. This is just one illustration of the value of complexity science tools, in the field of overweight and obesity. I expect these tools to have similar value for mental public health issues, which is exactly what the Centre aims to achieve.

3. In what way can your expertise and field of work contribute to the Centre? 

The mental health problems the Centre focuses on, such as depression, are unequally distributed across populations, with, e.g., an up to 3 times higher risk in lower socioeconomic groups. So far, prevention of mental ill-health has mainly focused on addressing individual-level factors such as coping. Meta-analyses show that such interventions can indeed reduce depression incidence by 20%. However, this leaves 80% unaccommodated for. In addition, continuing to focus on individual-level factors is likely, as has been the case for other health conditions, to further widen inequalities. In order to prevent depression, we also need to focus on factors that go beyond the individual, i.e. social determinants, including poverty, social security system, and discrimination. Given the focus on social determinants of health in my work, I hope to contribute with research on this topic to the success of the Centre.

4. Do you have any new articles that have been published? What were the most important findings? 

Despite the growing call for complexity approaches in public health research, there is limited practical guidance available on how to evaluate public health programmes using such an approach. We have recently developed an evaluation framework that supports researchers in designing systems evaluations in a comprehensive and practical way. We expect this so-called ENCOMPASS framework [2] to contribute towards developing better evaluation standards and practices that can in turn generate evidence that accounts for the complexity of the real world and improve health. And my hope is that this framework will also be of use for and that it will actually be used by researchers working in the Centre for Urban Mental Health!

5. What is the best piece of (academic) advice you have ever received?

“Embrace the complexity of the real world in your research!” Complexity science offers a range of concepts and methods that have the potential to further this aim. I am the first to admit that whether the promise can be fulfilled is still unknown. But one thing is certain, we will never know until we try!

[1] Crielaard, L., Dutta, P., Quax, R., Nicolaou, M., Merabet, N., Stronks, K., & Sloot, P. M. A. (2020). Social norms and obesity prevalence: From cohort to system dynamics models. Obesity Reviews, 21(9), e13044.

[2] Luna Pinzon, A., Stronks, K., Dijkstra, C. et al. The ENCOMPASS framework: a practical guide for the evaluation of public health programmes in complex adaptive systems. Int J Behav Nutr Phys Act 19, 33 (2022).

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