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.



Executive summary

The study outlined in this report strived at disclosing pertinent patterns in dietary surveys by means of an array of multivariate data analysis (MDA) techniques. The overall purpose was thus to unveil embedded patterns in selected data material, but also to generally demonstrate feasibility of new computational technology in this area. The material selected for this purpose encompasses food consumption survey data from Sweden and Denmark. The first among those compilations is known as Riksmaten – barn 2003, harbouring children of three age groups (four, eight and eleven years of age), whereas the latter data set is an excerpt – holding preschool children (four to five years of age) – of the Danish National Survey of Diet and Physical Activity, compiled over several years until 2008. These sets of food consumption data have previously been subjected to classical statistical analysis, but were – prior to embarking on this exercise – devoid of scrutiny by means of more advanced computational techniques. The analytical exercises described in this report encompass two major fields of MDA, which can be summarised as Unsupervised Learning/Descriptive modelling, on the one hand, and Supervised Learning/Predictive Modelling, on the other.


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