The objective of the present work was to quantify mothers’ misclassification of pre-school children’s weight status and to determine factors associated with the maternal misperception.

A representative sample of 2287 children aged 2–5 years was examined (GENESIS study). Mothers’ perceptions of their child’s weight status and the children’s and mothers’ anthropometric and other characteristics (sociodemographic and lifestyle) were recorded.

Almost 38 % of mothers underestimated their child’s weight status. The frequency of underestimation was much higher among ‘at risk of being overweight’ and ‘overweight’ children (88·3 % and 54·5 %, respectively) compared with ‘underweight/normal-weight’ children (18·0 %, P < 0·001). Multiple logistic regression modelling revealed that the likelihood of mothers’ underestimation of their child’s weight status was significantly higher in boys, in children engaging in physical activity for less than 3 h/week and in children whose mothers had low education status, compared with their counterparts. Moreover, the higher the BMI-for-age Z-score, the greater the odds that the mother would underestimate her child’s weight status.

The current study demonstrated that more than one-third of mothers misclassify their children’s weight status as being lower than the actual. Given that mother’s weight perception might be an important determinant of child’s body weight development, clinicians and health professionals should help mothers correctly classify their children’s weight status, which could potentially help in the early prevention of overweight and obesity.

Dietary assessment has been used for certification to receive food supplements or other nutrition services and to provide feedback for educational purposes. The proportion of individuals correctly certified as eligible is a function of the amount of error that exists in the dietary measures and the level of dietary intake used to establish eligibility. Whether individuals are correctly counselled to increase or decrease the consumption of selected foods or nutrients is a function of the same factors. It is not clear, however, what percentage of individuals would be correctly classified under what circumstances. The objective of this study is to demonstrate the extent to which measurement error and eligibility criteria affect the accuracy of classification.

Hypothetical distributions of dietary intake were generated with varying degrees of measurement error. Different eligibility criteria were applied and the expected classification rates were determined using numerical methods.

Simulation study.

Cut points of dietary intake at decreasing levels below the 50th percentile of true intake were associated with lower sensitivity and predictive value positive rates, but higher specificity and predictive value negative rates. The correct classification rates were lower when two cut points of dietary intake were used. Using a single cut point that was higher than the targeted true consumption resulted in higher sensitivity but lower predictive value positive, and lower specificity but higher predictive value negative.

Current methods of dietary assessment may not be reliable enough to attain acceptable levels of correct classification. Policy-makers and educators must consider how much misclassification error they are willing to accept and determine whether more intensive methods are necessary.

To assess the relative ability of principal components analysis (PCA)-derived dietary patterns to correctly identify cases and controls compared with other methods of characterising food intake.

Participants in this study were 232 endometrial cancer cases and 639 controls from the Western New York Diet Study, 1986–1991, frequency-matched to cases on age and county of residence.

Usual intake in the year preceding interview of 190 foods and beverages was collected during a personal interview using a detailed food-frequency questionnaire. Principal components analysis identified two major dietary patterns which we labelled ‘healthy’ and ‘high fat’. Classification on disease status was assessed with separate discriminant analyses (DAs) for four different characterisation schemes: stepwise DA of 168 food items to identify the subset of foods that best discriminated between cases and controls; foods associated with each PCA-derived dietary pattern; fruits and vegetables (47 items); and stepwise DA of USDA-defined food groups (fresh fruit, canned/frozen fruit, raw vegetables, cooked vegetables, red meat, poultry, fish and seafood, processed meats, snacks and sweets, grain products, dairy, and fats).

In general, classification of disease status was somewhat better among cases (54.7% to 67.7%) than controls (54.0% to 63.1%). Correct classification was highest for fruits and vegetables (67.7% and 62.9%, respectively) but comparable to that of the other schemes (49.5% to 66.8%).

Our results suggest that the use of principal components analysis to characterise dietary behaviour may not provide substantial advantages over more commonly used, less sophisticated methods of characterising diet.

Despite assumed similarities in Canadian and US dietary habits, some differences in food availability and nutrient fortification exist. Food-frequency questionnaires designed for the USA may therefore not provide the most accurate estimates of dietary intake in Canadian populations. Hence, we undertook to evaluate and modify the National Cancer Institute's Diet History Questionnaire (DHQ) and nutrient database.

Of the foods queried on the DHQ, those most likely to differ in nutrient composition were identified. Where possible these foods were matched to comparable foods in the Canadian Nutrient File. Nutrient values were examined and modified to reflect the Canadian content of minerals (calcium, iron, zinc) and vitamins (A, C, D, thiamin, riboflavin, niacin, B6, folate and B12). DHQs completed by 13 181 Alberta Cohort Study participants aged 35–69 years were analysed to estimate nutrient intakes using the original US and modified versions of the DHQ databases. Misclassification of intake for meeting the Dietary Reference Intake (DRI) was determined following analysis with the US nutrient database.

Twenty-five per cent of 2411 foods deemed most likely to differ in nutrient profile were subsequently modified for folate, 11% for vitamin D, 10% for calcium and riboflavin, and between 7 and 10% for the remaining nutrients of interest. Misclassification with respect to meeting the DRI varied but was highest for folate (7%) and vitamin A (7%) among men, and for vitamin D (7%) among women over 50 years of age.

Errors in nutrient intake estimates owing to differences in food fortification between the USA and Canada can be reduced in Canadian populations by using nutrient databases that reflect Canadian fortification practices.