Without intervening, over the next decade 40% of the Dutch adults will suffer from a chronic non-communicable lifestyle-related disease (NCD). Multiple NCDs in an adverse environmental context can result in a double hit with synergistic effects. This is referred to as “ecosyndemics”, i.e. disease interactions that result from environmental changes caused by humans. While (socioeconomically) disadvantaged groups are at a higher risk of developing NCDs and more often live in adverse environmental contexts, these groups may differ in vulnerability for the same conditions. The aim of this project is to unravel processes of health deterioration by looking specifically for tipping points where population health could regress into an ecosyndemic. On the basis of artificial intelligence combining both routine health- and social care data as well as environmental data, the clarification of tipping points aims to deliver a set of knobs and buttons which policymakers, healthcare professionals and citizens can use to utilize effective interventions to improve the lifestyle and living environment of the population. To achieve this, we will first identify clusters of NCDs, and visualize (spatial) hotspots to detect where constellations of NCDs and adverse environmental conditions (ecosyndemics) emerge. Second, together with citizens and societal stakeholders we will identify potential tipping points that could lead to an ecosyndemic and empirically evaluate these with machine learning techniques. Third, we will build capacity in three living labs by translating the results into lifestyle interventions on a policy, healthcare and community level to reduce the impact of adverse environmental contexts. Finally, we will invest in human capital by setting up a learning community together with stakeholders.
