The enormous burden of diet-related chronic diseases has prompted interest in healthy food prescription programs. Yet, the impact of such programs remains unclear. The aim of this study was to conduct a systematic review of healthy food prescription programs and evaluate their impact on dietary behavior and cardiometabolic parameters by meta-analysis. A systematic search was carried out in Medline, Embase, Scopus, and Cochrane Central Register of Controlled Trials databases since their inception to 3 January, 2020 without language restriction. A systematic search of interventional studies investigating the effect of healthy food prescription on diet quality and/or cardiometabolic risk factors including BMI, systolic (SBP) and diastolic blood pressure (DBP), glycated hemoglobin (HbA1c), or blood lipids was carried out. Thirteen studies were identified for inclusion, most of which were quasi-experimental (pre/post) interventions without a control group (n = 9). Pooled estimates revealed a 22% (95% CI: 12, 32; n = 5 studies, n = 1039 participants; I² = 97%) increase in fruit and vegetable consumption, corresponding to 0.8 higher daily servings (95% CI: 0.2, 1.4; I² = 96%). BMI decreased by 0.6 kg/m² (95% CI: 0.2, 1.1; I² = 6.4%) and HbA1c by 0.8% (95% CI: 0.1, 1.6; I² = 92%). No significant change was observed in other cardiometabolic parameters. These findings should be interpreted with caution in light of considerable heterogeneity, methodological limitations of the included studies, and moderate to very low certainty of evidence. Our results support the need for well-designed, large, randomized controlled trials in various settings to further establish the efficacy of healthy food prescription programs on diet quality and cardiometabolic health.

Underconsumption of dietary fiber is prevalent worldwide and is associated with multiple adverse health conditions. Despite the importance of fiber, the labeling of fiber content on packaged foods and beverages is voluntary in most countries, making it challenging for consumers and policy makers to monitor fiber consumption. Here, we developed a machine learning approach for automated and systematic prediction of fiber content using nutrient information commonly available on packaged products. An Australian packaged food dataset with known fiber content information was divided into training (n = 8986) and test datasets (n = 2455). Utilization of a k-nearest neighbors machine learning algorithm explained a greater proportion of variance in fiber content than an existing manual fiber prediction approach (R² = 0.84 vs. R² = 0.68). Our findings highlight the opportunity to use machine learning to efficiently predict the fiber content of packaged products on a large scale.

To inform the interpretation of dietary data in the context of sex differences in diet–disease relations, it is important to understand whether there are any sex differences in accuracy of dietary reporting. To quantify sex differences in self-reported total energy intake (TEI) compared with a reference measure of total energy expenditure (TEE). Six electronic databases were systematically searched for published original research articles between 1980 and April 2020. Studies were included if they were conducted in adult populations with measures for both females and males of self-reported TEI and TEE from doubly labeled water (DLW). Studies were screened and quality assessed independently by 2 authors. Random-effects meta-analyses were conducted to pool the mean differences between TEI and TEE for, and between, females and males, by method of dietary assessment. From 1313 identified studies, 31 met the inclusion criteria. The studies collectively included information on 4518 individuals (54% females). Dietary assessment methods included 24-h recalls (n = 12, 2 with supplemental photos of food items consumed), estimated food records (EFRs; n = 11), FFQs (n = 10), weighed food records (WFRs, n = 5), and diet histories (n = 2). Meta-analyses identified underestimation of TEI by females and males, ranging from −1318 kJ/d (95% CI: −1967, −669) for FFQ to −2650 kJ/d (95% CI: −3492, −1807) for 24-h recalls for females, and from −1764 kJ/d (95% CI: −2285, −1242) for FFQ to −3438 kJ/d (95% CI: −5382, −1494) for WFR for males. There was no difference in the level of underestimation by sex, except when using EFR, for which males underestimated energy intake more than females (by 590 kJ/d, 95% CI: 35, 1,146). Substantial underestimation of TEI across a range of dietary assessment methods was identified, similar by sex. These underestimations should be considered when assessing TEI and interpreting diet–disease relations.

Background: Consumption of ultra-processed foods is associated with increased risk of obesity and non-communicable diseases. Little is known about current patterns of ultra-processed foods intake in Australia. The aim of this study was to examine the amount and type of ultra-processed foods purchased by Australian households in 2019 and determine whether purchases differed by socio-economic status (SES). We also assessed whether purchases of ultra-processed foods changed between 2015 and 2019. Methods: We used grocery purchase data from a nationally representative consumer panel in Australia to assess packaged and unpackaged grocery purchases that were brought home between 2015 to 2019. Ultra-processed foods were identified according to the NOVA system, which classifies foods according to the nature, extent and purpose of industrial food processing. Purchases of ultra-processed foods were calculated per capita, using two outcomes: grams/day and percent of total energy. The top food categories contributing to purchases of ultra-processed foods in 2019 were identified, and differences in ultra-processed food purchases by SES (Index of Relative Social Advantage and Disadvantage) were assessed using survey-weighted linear regression. Changes in purchases of ultra-processed foods between 2015 to 2019 were examined overall and by SES using mixed linear models. Results: In 2019, the mean ± SD total grocery purchases made by Australian households was 881.1 ± 511.9 g/d per capita. Of this, 424.2 ± 319.0 g/d per capita was attributable to purchases of ultra-processed foods, which represented 56.4% of total energy purchased. The largest food categories contributing to total energy purchased included mass-produced, packaged breads (8.2% of total energy purchased), chocolate and sweets (5.7%), biscuits and crackers (5.7%) and ice-cream and edible ices (4.3%). In 2019, purchases of ultra-processed foods were significantly higher for the lowest SES households compared to all other SES quintiles (P < 0.001). There were no major changes in purchases of ultra-processed foods overall or by SES over the five-year period. Conclusions: Between 2015 and 2019, ultra-processed foods have consistently made up the majority of groceries purchased by Australians, particularly for the lowest SES households. Policies that reduce ultra-processed food consumption may reduce diet-related health inequalities.

The consumption of dairy products is encouraged at all life stages as a nutrient-rich component of the diet. However, many milk and yogurt products, particularly flavored varieties, may contain large amounts of free sugar. The aim of this paper was to evaluate the availability and sugar content of flavored milks and yogurts in supermarkets across 3 countries: Australia, England, and South. Nutrition information for flavored milks and yogurts was collected by trained researchers and supplemented by crowd-sourced data from a smartphone application. Data were extracted in April 2018 and 3724 milk and yogurt products were available for analysis. Mean sugar concentrations were compared across countries with the use of ANOVA followed by Tukey's post-hoc pairwise comparisons. Sugar concentrations were compared with the UK's “green” traffic-light classifications. Approximately 74% (n = 2753) of all products were flavored. Flavored products contained nearly twice the average total sugar content of unflavored products, with substantial variability: mean total sugar was 9.1 g/100 mL (range: 4.3–15.0 g/100 mL) and 11.5 g/100 g (range: 0.1–22.6 g/100 g) for flavored milks and yogurts, respectively. Free sugars contributed an estimated 41% and 42% of total sugar in milks and yogurts, respectively. Flavored milks in England had ∼0.7 g/100 mL higher total sugar on average compared with Australia and South Africa (P ≤ 0.04), whereas flavored yogurts in South Africa had the lowest average total sugar (∼2 g/100 g lower than England and Australia; P < 0.001). Less than 4% of flavored products would receive a “green” rating under the UK traffic-light labeling scheme. In Australia, England, and South Africa, flavored milks and yogurts are highly prevalent in the food supply and contain significantly higher concentrations of total and added sugars than unflavored products.

Background: The Australian Government recently established sodium targets for packaged foods to encourage voluntary reformulation to reduce population sodium consumption and related diseases. We modeled the health impact of Australia’s sodium reformulation targets and additional likely health gains if more ambitious, yet feasible sodium targets had been adopted instead. Methods and findings: Using comparative risk assessment models, we estimated the averted deaths, incidence, and disability-adjusted life years (DALYs) from cardiovascular disease (CVD), chronic kidney disease (CKD) and stomach cancer after implementation of (a) Australia’s sodium targets (overall and by individual companies); (b) United Kingdom’s targets (that covers more product categories); and (c) an optimistic scenario (sales-weighted 25th percentile sodium content for each food category included in the UK program). We used nationally representative data to estimate pre- and post-intervention sodium intake, and other key data sources from the Global Burden of Disease study. Full compliance with the Australian government’s sodium targets could prevent approximately 510 deaths/year (95% UI, 335 to 757), corresponding to about 1% of CVD, CKD, and stomach cancer deaths, and prevent some 1,920 (1,274 to 2,600) new cases and 7,240 (5,138 to 10,008) DALYs/year attributable to these diseases. Over half (59%) of deaths prevented is attributed to reformulation by 5 market-dominant companies. Compliance with the UK and optimistic scenario could avert approximately an additional 660 (207 to 1,227) and 1,070 (511 to 1,856) deaths/year, respectively, compared to Australia’s targets. The main limitation of this study (like other modeling studies) is that it does not prove that sodium reformulation programs will prevent deaths and disease events; rather, it provides the best quantitative estimates and the corresponding uncertainty of the potential effect of the different programs to guide the design of policies. Conclusions: There is significant potential to strengthen Australia’s sodium reformulation targets to improve its health impact. Promoting compliance by market-dominant food companies will be critical to achieving the potential health gains.