Evidence of low vitamin D intakes in the Australian population points to a need for data-driven nutrition policy for improving population vitamin D status.

BACKGROUND: Nearly one in four Australian adults is vitamin D deficient (serum 25-hydroxyvitamin D concentrations [25(OH)D] < 50 nmol L-1 ) and current vitamin D intakes in the Australian population are unknown. Internationally, vitamin D intakes are commonly below recommendations, although estimates generally rely on food composition data that do not include 25(OH)D. We aimed to estimate usual vitamin D intakes in the Australian population. METHODS: Nationally representative food consumption data were collected for Australians aged ≥ 2 years (n = 12,153) as part of the cross-sectional 2011-2013 Australian Health Survey (AHS). New analytical vitamin D food composition data for vitamin D3 , 25(OH)D3 , vitamin D2 and 25(OH)D2 were mapped to foods and beverages that were commonly consumed by AHS participants. Usual vitamin D intakes (µg day-1 ) by sex and age group were estimated using the National Cancer Institute method. RESULTS: Assuming a 25(OH)D bioactivity factor of 1, mean daily intakes of vitamin D ranged between 1.84 and 3.25 µg day-1 . Compared to the estimated average requirement of 10 µg day-1 recommended by the Institute of Medicine, more than 95% of people had inadequate vitamin D intakes. We estimated that no participant exceeded the Institute of Medicine's Upper Level of Intake (63-100 µg day-1 , depending on age group). CONCLUSIONS: Usual vitamin D intakes in Australia are low. This evidence, paired with the high prevalence of vitamin D deficiency in Australia, suggests that data-driven nutrition policy is required to safely increase dietary intakes of vitamin D and improve vitamin D status at the population level.

Accuracy of energy and nutrient intake estimation versus observed intake using 4 technology-assisted dietary assessment methods: a randomized crossover feeding study.

BACKGROUND: Technology-assisted 24-h dietary recalls (24HRs) have been widely adopted in population nutrition surveillance. Evaluations of 24HRs inform improvements, but direct comparisons of 24HR methods for accuracy in reference to a measure of true intake are rarely undertaken in a single study population. OBJECTIVES: To compare the accuracy of energy and nutrient intake estimation of 4 technology-assisted dietary assessment methods relative to true intake across breakfast, lunch, and dinner. METHODS: In a controlled feeding study with a crossover design, 152 participants [55% women; mean age 32 y, standard deviation (SD) 11; mean body mass index 26 kg/m2, SD 5] were randomized to 1 of 3 separate feeding days to consume breakfast, lunch, and dinner, with unobtrusive weighing of foods and beverages consumed. Participants undertook a 24HR the following day [Automated Self-Administered Dietary Assessment Tool-Australia (ASA24); Intake24-Australia; mobile Food Record-Trained Analyst (mFR-TA); or Image-Assisted Interviewer-Administered 24-hour recall (IA-24HR)]. When assigned to IA-24HR, participants referred to images captured of their meals using the mobile Food Record (mFR) app. True and estimated energy and nutrient intakes were compared, and differences among methods were assessed using linear mixed models. RESULTS: The mean difference between true and estimated energy intake as a percentage of true intake was 5.4% (95% CI: 0.6, 10.2%) using ASA24, 1.7% (95% CI: -2.9, 6.3%) using Intake24, 1.3% (95% CI: -1.1, 3.8%) using mFR-TA, and 15.0% (95% CI: 11.6, 18.3%) using IA-24HR. The variances of estimated and true energy intakes were statistically significantly different for all methods (P < 0.01) except Intake24 (P = 0.1). Differential accuracy in nutrient estimation was present among the methods. CONCLUSIONS: Under controlled conditions, Intake24, ASA24, and mFR-TA estimated average energy and nutrient intakes with reasonable validity, but intake distributions were estimated accurately by Intake24 only (energy and protein). This study may inform considerations regarding instruments of choice in future population surveillance. This trial was registered at Australian New Zealand Clinical Trials Registry as ACTRN12621000209897.

Vitamin D Fortification of Milk Would Increase Vitamin D Intakes in the Australian Population, but a More Comprehensive Strategy Is Required.

Low vitamin D status (serum 25-hydroxyvitamin D (25(OH)D) concentration < 50 nmol/L) is prevalent in Australia, ranging between 15% and 32% in the adolescent and adult populations. Vitamin D intakes are also low across the population and were recently estimated at 1.8−3.2 µg/day on average, assuming equal bioactivity of the D vitamers. In combination, these findings strongly suggest that data-driven nutrition policy is needed to increase vitamin D intake and improve status in the Australian population. Food fortification is a potential strategy. We used up-to-date vitamin D food composition data for vitamin D3, 25(OH)D3, vitamin D2, and 25(OH)D2, and nationally representative food and supplement consumption data from the 2011−2013 Australian Health Survey, to model a fortification scenario of 0.8 µg/100 mL vitamin D for fluid dairy milks and alternatives. Under the modelled fortification scenario, the mean vitamin D intake increased by ~2 µg/day from baseline to 4.9 µg/day from food only (7.2 µg/day including supplements). Almost all individual intakes remained substantially below 10 µg/day, which is the Estimated Average Requirement in North America. In conclusion, this modelling showed that fortification of fluid milks/alternatives with vitamin D at the current permitted level would produce a meaningful increase in vitamin D intake, which could be of potential benefit to those with a low vitamin D status. However, this initial step would be insufficient to ensure that most of the population achieves the North American EAR for vitamin D intake. This approach could be included as an effective component of a more comprehensive strategy that includes vitamin D fortification of a range of foods.

Vitamin D composition of Australian foods.

Australia needs accurate vitamin D food composition data to support public health initiatives. Previously, limitations in analytical methodology have precluded development of a comprehensive database. We used liquid chromatography with triple quadrupole mass spectrometry (LC-QQQ) to analyse 149 composite samples representing 98 foods (primary samples n = 896) in duplicate for vitamin D3, 25-hydroxyvitamin D3 (25(OH)D3), vitamin D2, 25(OH)D2. The greatest concentrations of vitamin D3 were found in canned salmon and a malted chocolate drink powder (fortified); chicken eggs and chicken leg meat contained the most 25(OH)D3. Margarine (fortified) and chocolate contained the greatest concentrations of vitamin D2, with smaller amounts found in various meat products. 25(OH)D2 was detected in various foods, including meats, and was quantitated in lamb liver. These data advance knowledge of dietary vitamin D in Australia and highlight the importance of analysis of these four forms of vitamin D to accurately represent the vitamin D content of food.

Accuracy and Cost-effectiveness of Technology-Assisted Dietary Assessment Comparing the Automated Self-administered Dietary Assessment Tool, Intake24, and an Image-Assisted Mobile Food Record 24-Hour Recall Relative to Observed Intake: Protocol for a Randomized Crossover Feeding Study.

BACKGROUND: The assessment of dietary intake underpins population nutrition surveillance and nutritional epidemiology and is essential to inform effective public health policies and programs. Technological advances in dietary assessment that use images and automated methods have the potential to improve accuracy, respondent burden, and cost; however, they need to be evaluated to inform large-scale use. OBJECTIVE: The aim of this study is to compare the accuracy, acceptability, and cost-effectiveness of 3 technology-assisted 24-hour dietary recall (24HR) methods relative to observed intake across 3 meals. METHODS: Using a controlled feeding study design, 24HR data collected using 3 methods will be obtained for comparison with observed intake. A total of 150 healthy adults, aged 18 to 70 years, will be recruited and will complete web-based demographic and psychosocial questionnaires and cognitive tests. Participants will attend a university study center on 3 separate days to consume breakfast, lunch, and dinner, with unobtrusive documentation of the foods and beverages consumed and their amounts. Following each feeding day, participants will complete a 24HR process using 1 of 3 methods: the Automated Self-Administered Dietary Assessment Tool, Intake24, or the Image-Assisted mobile Food Record 24-Hour Recall. The sequence of the 3 methods will be randomized, with each participant exposed to each method approximately 1 week apart. Acceptability and the preferred 24HR method will be assessed using a questionnaire. Estimates of energy, nutrient, and food group intake and portion sizes from each 24HR method will be compared with the observed intake for each day. Linear mixed models will be used, with 24HR method and method order as fixed effects, to assess differences in the 24HR methods. Reporting bias will be assessed by examining the ratios of reported 24HR intake to observed intake. Food and beverage omission and intrusion rates will be calculated, and differences by 24HR method will be assessed using chi-square tests. Psychosocial, demographic, and cognitive factors associated with energy misestimation will be evaluated using chi-square tests and multivariable logistic regression. The financial costs, time costs, and cost-effectiveness of each 24HR method will be assessed and compared using repeated measures analysis of variance tests. RESULTS: Participant recruitment commenced in March 2021 and is planned to be completed by the end of 2021. CONCLUSIONS: This protocol outlines the methodology of a study that will evaluate the accuracy, acceptability, and cost-effectiveness of 3 technology-enabled dietary assessment methods. This will inform the selection of dietary assessment methods in future studies on nutrition surveillance and epidemiology. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12621000209897; https://tinyurl.com/2p9fpf2s. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/32891.