The dietary habits changed drastically since 1980 and the chemical composition of a lot of processed foods was altered to meet new requirements. This nutrition shift along with the industrialisation and the rise of the sedentary lifestyle, led to the spread of the obesity epidemi
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The dietary habits changed drastically since 1980 and the chemical composition of a lot of processed foods was altered to meet new requirements. This nutrition shift along with the industrialisation and the rise of the sedentary lifestyle, led to the spread of the obesity epidemic which still manifests in many countries, including European. Children are also affected from this epidemic, with the childhood overweight and obesity trends to grow. The OECD children average for 2017 reached a 15.5%. However, in each country different factors contribute to the manifestation of the phenomenon, composing its unique obesogenic environment. In the current thesis proposal, exploratory statistical modeling is employed, to identify the non-biological factors explaining children's weight for the Netherlands. The analysis is conducted with the use of a custom framework to capture factors which are linked with societal inequities. The outcome of this analysis is a group of factors which explain the weight in preschool children. Also, significant results for preschool children are derived by comparing different screening definitions for overweight and obesity, showing the clusters of risk-prone children. By using the insights from the analysis, data informed policies are proposed to aid the creation of a healthier urban environment for future population to thrive in. Data informed policies based on governmental data for the Netherlands can facilitate the decision making and make prevention and mitigation of the epidemic more effective, by targeting appropriately different population segments which are in need. Future research could focus on prediction models of obesity based on the significant factors identified in this study. Also, the expansion of the current models with more variables from the urban environment is needed to show more specific associations with urban features.