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The aim of the study was to describe who ate 5 or more portions of fruit and vegetables per day (‘compliers’) in 1986–1987 and in 2000–2001.
We used data from the Dietary and Nutritional Surveys of British Adults. Each is a nationally representative dietary survey using 7 d weighed food records for men and women, aged 16–64 years, living in private households in Great Britain in 1986–1987 and in 2000–2001.
Data were analysed for 2197 adults in 1986–1987 and 1724 adults in 2000–2001.
In 1986–1987 12·7 % were classified as ‘compliers’ compared with 16·5 % in 2000–2001. Manual social classes, younger participants and people on benefits or outside paid employment were less likely to be ‘compliers’. Being divorced, widowed or separated was negatively related to being a ‘complier’, as was being in a household with dependant children or a lone parent with dependant children. Between 1986–1987 and 2000–2001 improvements were seen across social class groups and differences between men and women and between regions were reduced.
Only 12·7 % participants in the Dietary and Nutritional Surveys of British Adults were classified as ‘compliers’ in 1986–1987 compared with 16·5 % in 2000–2001. There have been some important changes in the distribution of ‘compliers’, but the low levels overall support the need for a reinvigorated policy drive to improve compliance with fruit and vegetable goals.
Using a nationally representative sample, to identify groups among British children aged 1½; – 4½; years who report similar patterns of diet.
Nationally representative dietary survey, using 4 d weighed dietary records, of girls and boys aged 1½; – 4½; years living in private households in Great Britain in 1992–1993. Cluster analysis was used to aggregate individuals into diet groups.
Eight hundred and forty-eight boys and 827 girls.
Three clusters were identified for girls and three for boys. Among boys the most prevalent cluster was ‘Healthy Diet’ (52·3 %), the second was ‘Convenience Diet’ (38·3 %) and the third was ‘Traditional Diet’ (9·3 %). Among girls, the most prevalent dietary cluster was ‘Healthy Diet’ (58·7 %), followed by a ‘Convenience Diet’ (36·6 %) and ‘Traditional Diet’ (4·3 %). There were important differences in nutrient profile, sociodemographic and behavioural characteristics between clusters.
Cluster analysis identified three groups among both girls and boys which differed not only in terms of reported dietary intake, but also with respect to nutrient intake, social and behavioural characteristics. The groups identified could provide a useful basis for the development, monitoring and targeting of public health nutrition policy for pre-school children in the UK. Further research is needed on the consequences for chronic disease in the future for these children.
To develop a food-frequency questionnaire (FFQ) useful for ranking of nutrient intakes.
Subjects consuming their regular diet completed 7 days of weighed intake registry (7-WIR). Foods for the FFQ were selected by stepwise multiple regression. The FFQ was then completed for each subject using data on individual food consumption from the 7-WIR. The correlation and agreement between the extrapolated FFQ and the 7-WIR data were assessed using Spearman's rank correlation coefficients (rS) and Bland and Altman's limits of agreement (LOA).
We studied 97 randomly selected 20–40-year-old subjects.
Sixty foods were selected for the FFQ. The 7-WIR and the extrapolated FFQ intake estimates correlated well. rS was 0.58 for energy, 0.53 for carbohydrate, 0.50 for total fat, and 0.48 for protein. For micronutrients, rS varied from 0.46 (manganese) to 0.71 (vitamin B12). FFQ average intake estimates were 83%, 80%, 86.2% and 86.4% of 7-WIR estimates for energy, carbohydrate, total fat and protein, respectively. LOA for these nutrients ranged between 45% and 165%. FFQ micronutrient intakes were on average 96% (median) of those from the 7-WIR, and the median lower and upper LOA were 50% and 203%. However, there was no indication that the degree of agreement varied with the level of intake.
According to our simulated validation, this FFQ may be useful to rank subjects by nutrient intake. Its validity against standard independent measurements and its applicability to other subsets of the Colombian population should be carefully considered.
To identify socio-economic demographic and environmental factors that predict better height-for-age for children under 5 years of age in a Dhaka slum population.
A panel survey, conducted between 1995 and 1997. A random sample of households was selected. Socio-economic, demographic and environmental variables were collected monthly by questionnaire and nutritional status was assessed.
Dhaka slums in Bangladesh.
Three hundred and ninety-two children, surveyed in September–November 1996.
Main outcome measures:
Height-for-age Z-score (HAZ) above −2.
Thirty-one per cent of children had HAZ <−2. Logistic regression adjusted for cluster sampling showed that better nourished children were more likely to have taller mothers, to be from female-headed households and from families with higher income, electricity in the home, better latrines, more floor space and living in Central Mohammadpur. Better nourished children were less likely to have fathers who have taken days off from work due to illness.
Interest in ‘positive deviance’ is motivated by the recognition that a focus on the malnourished only – the bottom tail of the distribution – provides indications of how families fail, but not of how they succeed in maintaining child nutrition in the face of adversity. Our analysis provides an alternative perspective on nutrition and vulnerability in an urban slum setting.
To identify groups within Dhaka slums that report similar patterns of livelihood, and to explore nutritional and health status.
A random sample of households participated in a longitudinal study in 1995–1997. Socio-economic and morbidity data were collected monthly by questionnaire and nutritional status was assessed. Cluster analysis was used to aggregate households into livelihood groups.
Dhaka slums, Bangladesh.
Five-hundred and fifty-nine households.
Main outcome measures: Socio-economic and demographic variables, nutritional status, morbidity.
Four livelihood groups were identified. Cluster 1 (n = 178) was the richest cluster with land, animals, business assets and savings. Loans as well as income were higher, which shows that this group was credit-worthy. The group was mainly selfemployed and worked more days per month than the other clusters. The cluster had the second highest body mass index (BMI) score, and the highest children's nutrition status. Cluster 2 (n = 190) was a poor cluster and was mainly dependent selfemployed. Savings and loans were lower. Cluster 3 (n = 124) was the most vulnerable cluster. Members of this group were mainly casual unskilled, and 40% were femaleheaded households. Total income and expenditure were lowest amongst the clusters. BMI and children's nutritional status were lowest in the slum. Cluster 4 (n = 67) was the second richest cluster. This group comprised skilled workers. BMI was the highest in this cluster and children's nutritional status was second highest.
Cluster analysis has identified four groups that differed in terms of socioeconomic, demographic and nutritional status and morbidity. The technique could be a practically useful tool of relevance to the development, monitoring and targeting of vulnerable households by public policy in Bangladesh.
Using a national representative sample to identify groups within the UK male and female population over 65 years who report similar patterns of diet.
National representative dietary survey, using 4-day weighed dietary records of men and women aged over 65 years old and living in private households in Great Britain in 1994–1995. Cluster analysis was used to aggregate individuals into diet groups.
558 women and 539 men.
Main outcome measures:
Consumption of predefined food groups, nutrient intakes, socio-economic, demographic and behavioural characteristics.
Three large clusters comprising 86% of the male population and three large clusters comprising 83% of the female population were identified. Among men, the most prevalent cluster was a ‘mixed diet’ with elements from a traditional diet and some elements from a healthy diet (48% of the male population); the second was a ‘healthy diet’ (21% of the male population); and the third was a ‘traditional diet high in alcohol’ (17% of the male population). Among women, the most prevalent diet was a ‘sweet traditional diet’ (33% of the female population); the second was a ‘healthy diet’ (32% of the female population); and the last was a ‘mixed diet’ with elements of the traditional diet and the healthy diet (18% of the female population). There were important differences in average nutrient intakes, socio-demographic and behavioural characteristics across these diet clusters.
Cluster analysis identified three diet groups among men and three among women. These differed not only in terms of reported dietary intake, but also with respect to their nutrient content, social and behavioural profile. The groups identified could provide a useful basis for health promotion based upon the diet clusters.