Objective: To investigate the direct and indirect cost differences associated with eating a 'healthy' or 'unhealthy' diet. Design: Analysis of data from a baseline postal questionnaire for the UK Women's Cohort Study, including a detailed food frequency questionnaire (FFQ), supplemented by a telephone interview on a sub-sample. Subjects: The first 15 191 women who responded to the questionnaire, aged 35-69 years with similar numbers of meat eaters, fish eaters and vegetarians. Results: A healthy diet indicator (hdi), with values from 0 (lowest) to 8 (highest) was developed based on the WHO dietary recommendations. Direct monetary cost of the diet was calculated using prices from the 1995 National Food Survey and the Tesco home shopping catalogue. Women in the healthy diet group were almost four times as likely to be vegetarian and have a higher educational level. For direct costs, the difference between the most extreme hdi groups was £1.48 day −1 (equivalent to £540 year −1 ), with fruit and vegetable expenditure being the main items making a healthy diet more expensive. Forty-nine per cent of the food budget was spent on fruit and vegetables in hdi group 8 compared to 29% in hdi group 0. Interestingly, 52% of those questioned in both extreme hdi groups did not think that it was difficult to eat healthily. Conclusions: To achieve a particularly healthy diet independent predictive factors were spending more money, being a vegetarian, having a higher energy intake, having a lower body mass index (BMI) and being older.

Objective: To identify groups of subjects with similar food consumption patterns so that complex disease ± diet relationships can be investigated at the level of the whole diet, rather than just in terms of nutrient intake. Subjects: 33,971 women in the UK Women's Cohort Study. 60,000 women on the World Cancer Research Fund mailing list were initially invited to take part. Subjects were selected to include a high proportion of vegetarians. Design: The cohort completed a 217 item food frequency questionnaire. Cluster analysis was used to identify groups of women with similar food consumption patterns. Clusters were compared on socio-demographic characteristics, indicators of health and diet, and nutrient intakes. Results: Seven clusters were identi®ed including two vegetarian clusters. Groups appeared to be differentiated by differences in food types and in diversity of diet. Socio-demographic, health and diet characteristics and nutrient intakes all differed signi®cantly between groups. Conclusion: Classifying diets in more pragmatic terms than just nutrient intake should provide valuable insight into understanding complex diet-disease relationships. Dietary advice, whilst based on nutrient content of meals, needs to take account of the combinations of different food types that people naturally choose to use together. Sponsorship:World Cancer Research Fund. Descriptors: cohort studies; multivariate analysis; diet; food habits European Journal of Clinical Nutrition (2000) 54, 314±320 IntroductionIn nutritional research it is more common to consider food consumption in terms of nutrient intake, rather than type of food consumed. Conventionally nutrient intake is compared with recommended levels or adherence to dietary guidelines to identify groups of people with various levels of intake of speci®c nutrients. These groups can then be described in terms of their socio-demographic characteristics. This enables the researcher to identify individuals or more often subgroups of the population whose diets may be nutritionally de®cient in some way.Whilst researchers have concentrated on classifying people on the basis of their nutrient intake, consumers consider more than nutrient content when choosing food. In addition it is unlikely that the aetiology of diseases such as cancer can be explained by levels of single nutrients and it may be that non-nutritive substances, such as phytochemicals, may be involved. Most consumers eat foods in certain combinations or patterns, and interest is therefore now focussing on whole diets and lifestyles rather than single nutrients. Identi®cation of these patterns would be useful to explore complex diet-disease relationships (such as investigating potential effects of the Mediterranean diet) and also to intervene to provide relevant nutritional advice and education.Cluster analysis has been used in similar contexts to identify groups or clusters of people with similar characteristics (Wirfalt & Jeffery, 1997;Schroll et al, 1996;Tucker et al, 1992;Hulshof et al, 1992;Bisgrove et al, 198...

Objective: To explore the potential mis-reporting of speci®c food groups from food frequency questionnaire (FFQ) data and to examine the effect of using a weighting factor on estimated nutrient intake and ranking of subjects within the cohort according to nutrient intake. Design and subjects: A weighting factor was calculated for each of the individual 6572 women aged 35±69 y for four food groups, ®sh, meat, vegetables and fruit, using FFQ data and cross-check responses. Results: The vegetable weighting had most effect on median intakes, particularly of ®bre, vitamins A, C and E and folate. When all the weightings were applied, the median intakes of vitamins A and E were reduced by 35% and 27% respectively and the vitamin C intake was reduced by 44%. Ranking of subjects within the cohort according to nutrient intake was barely affected by the ®sh and meat weightings. The vegetable weighting had most effect on vitamin A with a rank correlation coef®cient of 0.88. When all the weightings were applied the rank correlations for vitamins A, C and E and folate were all less than 0.90. Conclusion: Inclusion of cross-check questions in FFQs can provide an additional source of information on food group intake. This can be compared with FFQ data to help identify possible over-reporting and then to adjust frequency of intake accordingly.