Purposes Dietary free sugars (FS) are the most important risk factor for dental caries and can contribute to excess energy intake. Measuring FS intake is limited by food composition databases and appropriate dietary assessment methods. The aim of this analysis was to estimate total sugar (TS) and FS intakes for Irish pre-schoolers and examine the proportion of dietary TS and FS captured using a short food questionnaire (SFQ). Methods This is a secondary analysis of 3-year-old children from two national surveys; Growing Up in Ireland (GUI), N = 9793 of whom 49% were girls and the National Preschool Nutrition Survey (NPNS), N = 126 and 52% were girls. GUI used SFQs and NPNS used semi-weighed food diaries to collect dietary data from 3-year-old children. Dietary intake databases were linked using an established approach. Mean daily TS and FS intakes and frequency were calculated, and consumption patterns from foods and meals are presented. The proportion of foods that were covered or non-covered by the GUI SFQ was calculated by comparison with the NPNS food diary. Results 75% of 3 year-olds had FS intake greater than the maximum recommended by WHO guidelines for free sugar intake, while 4% met the lower threshold. The median frequency of TS and FS consumption was 5.0 (4.0-6.0) and 4.0 (3.0-5.0) times/day. Less than one-quarter of TS intake (g/day) was non-covered by the GUI SFQ while less than one-third of FS intake was non-covered. Conclusions A large majority of 3-year-old Irish children do not meet the WHO recommended guidelines for FS intake and almost none meet the desired conditional recommendation. SFQs only capture two-thirds of FS intake at this early age.
A poor quality diet may be a common risk factor for both obesity and dental problems such as caries. The aim of this paper is to use classification tree analysis (CTA) to identify predictors of dental problems in a nationally representative cohort of Irish pre-school children. CTA was used to classify variables and describe interactions between multiple variables including socio-demographics, dietary intake, health-related behaviour, body mass index (BMI) and a dental problem. Data were derived from the second (2010/2011) wave of the ‘Growing Up in Ireland’ study (GUI) infant cohort at 3 years, n = 9793. The prevalence of dental problems was 5.0% (n = 493). The CTA model showed a sensitivity of 67% and specificity of 58.5% and overall correctly classified 59% of children. Ethnicity was the most significant predictor of dental problems followed by longstanding illness or disability, mother’s BMI and household income. The highest prevalence of dental problems was among children who were obese or underweight with a longstanding illness and an overweight mother. Frequency of intake of some foods showed interactions with the target variable. Results from this research highlight the interconnectedness of weight status, dental problems and general health and reinforce the importance of adopting a common risk factor approach when dealing with prevention of these diseases.
Collecting accurate and detailed dietary intake data is costly at a national level. Accordingly, limited dietary assessment tools such as Short Food Questionnaires (SFQs) are increasingly used in large surveys. This paper describes a novel method linking matched datasets to improve the quality of dietary data collected. Growing Up in Ireland (GUI) is a nationally representative longitudinal study of infants in the Republic of Ireland which used a SFQ (with no portion sizes) to assess the intake of “healthy” and “unhealthy” food and drink by 3 years old preschool children. The National Preschool Nutrition Survey (NPNS) provides the most accurate estimates available for dietary intake of young children in Ireland using a detailed 4 days weighed food diary. A mapping algorithm was applied using food name, cooking method, and food description to fill all GUI food groups with information from the NPNS food datafile which included the target variables, frequency, and amount. The augmented data were analyzed to examine all food groups described in NPNS and GUI and what proportion of foods were covered, non-covered, or partially-covered by GUI food groups, as a percentage of the total number of consumptions. The term non-covered indicated a specific food consumption that could not be mapped using a GUI food group. “High sugar” food items that were non-covered included ready-to-eat breakfast cereals, fruit juice, sugars, syrups, preserves and sweeteners, and ice-cream. The average proportion of consumption frequency and amount of foods not covered by GUI was 44 and 34%, respectively. Through mapping food codes in this manner, it was possible, using density plots, to visualize the relative performance of the brief dietary instrument (SFQ) compared to the more detailed food diary (FD). The SFQ did not capture a substantial portion of habitual foods consumed by 3-year olds in Ireland. Researchers interested in focussing on specific foods, could use this approach to assess the proportion of foods covered, non-covered, or partially-covered by reference to the mapped food database. These results can be used to improve SFQs for future studies and improve the capacity to identify diet-disease relationships.
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