Browsing Articles and other publications by RIVM employees by Subjects
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Importance of details in food descriptions in estimating population nutrient intake distributions.National food consumption surveys are important policy instruments that could monitor food consumption of a certain population. To be used for multiple purposes, this type of survey usually collects comprehensive food information using dietary assessment methods like 24-h dietary recalls (24HRs). However, the collection and handling of such detailed information require tremendous efforts. We aimed to improve the efficiency of data collection and handling in 24HRs, by identifying less important characteristics of food descriptions (facets) and assessing the impact of disregarding them on energy and nutrient intake distributions. In the Dutch National Food Consumption Survey 2007-2010, food consumption data were collected through interviewer-administered 24HRs using GloboDiet software in 3819 persons. Interviewers asked participants about the characteristics of each food item according to applicable facets. Food consumption data were subsequently linked to the food composition database. The importance of facets for predicting energy and each of the 33 nutrients was estimated using the random forest algorithm. Then a simulation study was performed to determine the influence of deleting less important facets on population nutrient intake distributions. We identified 35% facets as unimportant and deleted them from the total food consumption database. The majority (79.4%) of the percent difference between percentile estimates of the population nutrient intake distributions before and after facet deletion ranged from 0 to 1%, while 20% cases ranged from 1 to 5% and 0.6% cases more than 10%. We concluded that our procedure was successful in identifying less important food descriptions in estimating population nutrient intake distributions. The reduction in food descriptions has the potential to reduce the time needed for conducting interviews and data handling while maintaining the data quality of the survey.