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Discovery and characterisation of dietary patterns in two Nordic countries

Using non-supervised and supervised multivariate statistical techniques to analyse dietary survey data

image of Discovery and characterisation of dietary patterns in two Nordic countries

This Nordic study encompasses multivariate data analysis (MDA) of preschool Danish as well as pre- and elementary school Swedish consumers. Contrary to other counterparts the study incorporates two separate MDA varieties - Pattern discovery (PD) and predictive modelling (PM). PD, i.e. hierarchical cluster analysis (HCA) and factor analysis (using PCA), helped identifying distinct consumer aggregations and relationships across food groups, respectively, whereas PM enabled the disclosure of deeply entrenched associations. 17 clusters - here defined as dietary prototypes - were identified by means of HCA in the entire bi-national data set. These prototypes underwent further processing, which disclosed several intriguing consumption data relationships: Striking disparity between consumption patterns of Danish and Swedish preschool children was unveiled and further dissected by PM. Two prudent and mutually similar dietary prototypes appeared among each of two Swedish elementary school children data subsets. Dietary prototypes rich in sweetened soft beverages appeared among Danish and Swedish children alike. The results suggest prototype-specific risk assessment and study design.

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Discussion

The study presented here shows selected findings and interpretations based on MDA of two dietary surveys – one conducted in Sweden and one in Denmark – focused on preschool and elementary school children. In this undertaking both non-supervised and supervised MDA techniques have found application. Thus, the focus has been strictly laid on dietary patterns, which are inherently complex and seemingly need more than a single MDA technique to become satisfactory deciphered. For example, most dietary patterns typically feature low consumption of some food groups, jointly with high consumption of other foods. Cluster Analysis, typically based on the K-means algorithm, and Factor Analysis, commonly in the form of PCA, have found increasing application in the area over the last decade.29 In a dietary survey context, Cluster Analysis gathers consumers into non-overlapping groups based on dietary similarity, whereas PCA identifies linear combinations of foods that are frequently consumed in combination. Thus, these two statistical techniques describe diets from different perspectives. A major technique applied to the study outlined here is an in-house built development of the K-means algorithm, featuring divisive-type multi-furcating clustering operation as well as output display of several hierarchical levels. This hierarchical design proved indeed very helpful in identifying and selecting relevant populations to dietary prototypes. From this inroad, two separate extensions – CMDS and HPBCA – were designed and likewise allowed to operate on the Danish and Swedish data sets. The PCA statistical technique also found application here, but mostly to support results derived by other methods. Moreover, predictive modelling – based on the widely acknowledged RF algorithm as well as the computational-efficient NSC – was applied to selected excerpts of the entire data set, i.e. Danish and Swedish preschool children.

English

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