Qualitative systems mapping for complex public health problems: A practical guide

Abstract

Systems mapping methods are increasingly used to study complex public health issues. Visualizing the causal relationships within a complex adaptive system allows for more than developing a holistic and multi-perspective overview of the situation. It is also a way of understanding the emergent, self-organizing dynamics of a system and how they can be influenced. This article describes a concrete approach for developing and analysing a systems map of a complex public health issue drawing on well-accepted methods from the field of social science while incorporating the principles of systems thinking and transdisciplinarity. Using our case study on HIV drug resistance in sub-Saharan Africa as an example, this article provides a practical guideline on how to map a public health problem as a complex adaptive system in order to uncover the drivers, feedback-loops and other dynamics behind the problem. Qualitative systems mapping can help researchers and policy makers to gain deeper insights in the root causes of the problem and identify complexity-informed intervention points.

Fig 1. Causal loop diagram example.

Thermostat room temperature regulation as a (simplified) example of a causal loop diagram.

Fig 2. Graphical abstract.

Overview of the described methodology, consisting of four iterative building blocks and continuously requiring the researchers to adopt a transdisciplinary approach and to be aware of their disciplinary biases.

Fig 3. Mental model example.

Example of a mental model of an interviewee, visualizing the elements and connections which came up during the interview and which are perceived to be true by the interviewee. The researcher tried to bring some first structure in the model by using a color code.

Fig 4. Different ways of visualizing a system.

The elements and connections in A and B are exactly the same. In A the system is organized according to the different layers ranging from biology on the micro level to the individual level, the social context, the healthcare system and overarching factors at the macro level. In B, the elements are divided in thematic clusters and the relationships between clusters are visualized. Figure adapted from Kiekens et al. [19] and for illustrative purposes only.

Fig 5. In-degree.

Illustration of mapping choices to be made by the researchers and the consequences for the in-degree metric.

Fig 6. Summarizing a complex system.

A) Detailed system of factors influencing HIVDR. The main feedback loops or subsystems are highlighted with colored circles. B) The same system, condensed into the main feedback loops and with the main exogenous factors represented on the outside. Each cluster in panel A is represented as a single element in panel B, represented with the same color in the core of the element. All connections between two clusters in panel A are represented as one connection in panel B. This way, the main dynamics of the system are represented in a more condensed and comprehensible format. Figure adapted from Kiekens et al. [19] and for illustrative purposes only.