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To describe four different methods of identifying indicator foods that are high, medium or low in fat with reference to dietary patterns and to use these indicator foods to test three sets of definitions of ‘high’, ‘medium’ and ‘low’ in fat from ‘banding schemes’ developed by the Coronary Prevention Group (CPG), the Food Standards Agency (FSA) and Sainsbury’s.
Indicator foods were developed using food intake data from the UK National Diet and Nutrition Survey and two parameters: (i) probability of the food being consumed by an individual with a high-fat diet (Method 1); and (ii) the contribution of the food to the fat intake of the average diet of consumers (Methods 3 and 4). Method 2 used both parameters. The three banding schemes were tested by assessing their levels of agreement with methods in categorising indicators.
Sensitivity in identifying high, medium and low fat indicators was highest with the CPG banding scheme (high and medium fat indicators) and Sainsbury’s scheme (low fat indicators) (Methods 2, 3 and 4). The levels of agreement (kappa coefficient) were 0·68 for the CPG scheme; 0·51 for the Sainsbury’s scheme; and 0·41 for the FSA scheme (Method 3).
It is possible to use indicator foods related to dietary patterns of a specific population to generate more rational definitions of ‘high’, ‘medium’ and ‘low’ in fat. This could be the starting point for the development of indicator foods for testing more complex nutrient profile models (i.e. those that consider more than one nutrient).
To examine the nature of the link between food advertising in UK magazines aimed at children and young people and Internet food marketing, to establish whether consideration should be given to tightening existing controls.
A review and descriptive analysis of food advertising found in a sample of the top five magazine titles aimed at a range of ages of children and young people between November 2004 and August 2005 and of the Internet food marketing sites to which readers were directed.
Food advertising appeared as ‘cover-mount’ free gifts and as part of the main bound issue. Children aged 6–10 years were the most frequent recipients of food-based free gifts, all of which were confectionery. No food advertising was found in magazines aimed at pre-school children and it formed a small percentage of total advertising in the magazines aimed at children of school age and above. Most food advertisements were for ‘less healthy’ foods, although advertisements for ‘healthier’ food products did appear infrequently. Almost half of food advertisements directed readers towards Internet food marketing sites. We found evidence that these sites are using at least some of the ‘marketing tricks’ which have been identified as a cause for concern.
Proposed restrictions on broadcast media may lead to more food advertising via other non-broadcast means. We suggest monitoring the effect of such changes in print and online advertising and that consideration be given to restricting marketing techniques used on websites aimed at children and young people.
To compare nutrient profile models with a standard ranking of 120 foods.
Over 700 nutrition professionals were asked to categorise 120 foods into one of six positions on the basis of their healthiness. These categorisations were used to produce a standard ranking of the 120 foods. The standard ranking was compared with the results of applying eight different nutrient profile models to the 120 foods: Models SSCg3d and WXYfm developed for the UK Food Standards Agency, the Nutritious Food Index, the Ratio of Recommended to Restricted nutrients, the Naturally Nutrient Rich score, the Australian Heart Foundation's Tick scheme, the American Heart Association's heart-check mark and the Netherlands tripartite classification model for foods. Rank correlation was assessed for continuous models, and dependence was assessed for categorical models.
The continuous models each showed good correlation with the standard ranking (Spearman's ρ = 0.6–0.8). The categorical models achieved high χ2 results, indicating a high level of dependence between the nutrition professionals' and the models' categorisations (P < 0.001). Models SSCg3d and WXYfm achieved higher scores than the other models, implying a greater agreement with the standard ranking of foods.
The results suggest that Models SSCg3d and WXYfm rank and categorise foods in accordance with the views of nutrition professionals.
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