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This study evaluates the contribution of energy-dense, nutrient-poor ‘extra’ foods to the diets of 16–24-month-old children from western Sydney, Australia.
An analysis of cross-sectional data collected on participants in the Childhood Asthma Prevention Study (CAPS), a randomised trial investigating the primary prevention of asthma from birth to 5 years. We collected 3-day weighed food records, calculated nutrient intakes, classified recorded foods into major food groups, and further classified foods as either ‘core’ or ‘extras’ according to the Australian Guide to Healthy Eating.
Pregnant women, whose unborn child was at risk of developing asthma because of a family history, were recruited from all six hospitals in western Sydney, Australia. Data for this study were collected in clinic visits and at participants’ homes at the 18-month assessment.
Four hundred and twenty-nine children participating in the CAPS study; 80% of the total cohort.
The mean consumption of ‘extra’ foods was ∼150 g day− 1 and contributed 25–30% of the total energy, fat, carbohydrate and sodium to the diets of the study children. ‘Extra’ foods also contributed around 20% of fibre, 10% of protein and zinc, and about 5% of calcium. Children in the highest quintile of ‘extra’ foods intake had a slightly higher but not significantly different intake of energy from those in the lowest quintile. However, significant differences were evident for the percentage of energy provided by carbohydrate and sugars (higher) and protein and saturated fat (lower). The intake of most micronutrients was also significantly lower among children in the highest quintile of consumption. The intake of ‘extra’ foods was inversely associated with the intake of core foods.
The high percentage of energy contributed by ‘extra’ foods and their negative association with nutrient density emphasise the need for dietary guidance for parents of children aged 1–2 years. These preliminary data on commonly consumed ‘extra’ foods and portion sizes may inform age-specific dietary assessment methods.
To examine statistical models that have been used to predict the cessation of breast–feeding.
In nutritional epidemiology, a knowledge of risk factors that lead to breast-feeding cessation is essential to promote optimal infant health by increasing or sustaining breast–feeding rates. However, a number of methodological issues complicate the measurement of such risk factors. It is important when building multivariate models that variables entered into the model are not intervening variables, factors on the causal pathway or surrogate outcomes. Inclusion of these types of variable can lead to inaccurate models and biased results. A factor often cited to predict breast–feeding is ‘intention to breast–feed’ prior to the birth of the infant, although this factor is directly on the causal decision–making pathway. Another factor often cited is the age of introduction of formula feeding, which is actually part of the outcome variable because formula feeding defines the difference between full, complementary and no breast-feeding. Rather than include these as risk factors in multivariate models, factors removed from the causal pathway such as influences of educational practices, including advice to complementary feed, and beliefs and attitudes of families and health-care practitioners should be measured.
The accurate quantification of modifiable risk factors is essential for designing public health education campaigns that are effective in sustaining or increasing breast–feeding duration.
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