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Poor diet is now associated with ill health in most countries. Better food choices can be supported by quality labelling. The FoodSwitch app was designed to show consumers at-a-glance nutritional information while supporting the collation of data to drive industry improvements and government policy. By scanning the barcode using a smartphone's camera, FoodSwitch presents an overall nutritional profile of foods to the consumer, and suggests similar healthier alternative products. A crowd-sourcing function was built into FoodSwitch to allow users to send in photos of missing items. FoodSwitch has >700,000 downloads to date, and has been launched in Australia, the United Kingdom (UK), New Zealand, South Africa, China and India, with the United States (US) planned for 2017. Crowd-sourcing has resulted in 1 million photos of new food items contributed by users, equating to >100,000 new products being added to the database. A version for hypertension (SaltSwitch), and celiac disease (GlutenSwitch) and filters for sugar, saturated fat and energy have been launched. International interest in technologies supporting FoodSwitch has identified the system as a new method for tracking the global food supply. The huge volume of data crowd-sourced is enabling improvements to the broader food environment by holding industry and government to account for their performance.

In Australia, manufacturers can use two government-endorsed approaches to advertise product healthiness: the Health Star Rating (HSR) front-of-pack nutrition labelling system, and health claims. Related, but different, algorithms determine the star rating of a product (the HSR algorithm) and eligibility to display claims (the Nutrient Profiling Scoring Criterion (NPSC) algorithm). The objective of this study was to examine the agreement between the HSR and NPSC algorithms. Food composition information for 41,297 packaged products was extracted from The George Institute's FoodSwitch database. HSR and the NPSC scores were calculated, and the proportion of products in each HSR category that were eligible to display a health claim under the NPSC was examined. The highest agreement between the HSR scoring algorithm and the NPSC threshold to determine eligibility to display a health claim was at the HSR cut-off of 3.5 stars (k = 0.83). Overall, 97.3% (n = 40,167) of products with star ratings of 3.5 or higher were also eligible to display a health claim, and 94.3% (n = 38,939) of products with star ratings less than 3.5 were ineligible to display a health claim. The food group with greatest divergence was "edible oils", with 45% products (n = 342) with HSR >3.5, but 64% (n = 495) eligible to display a claim. Categories with large absolute numbers of products with HSR <3.5, but eligible to display a claim, were "yoghurts and yoghurt drinks" (335 products, 25.4%) and "soft drinks" (299 products, 29.7%). Categories with a large number of products with HSR ≥3.5, but ineligible to display a claim, were "milk" (260 products, 21.2%) and "nuts and seeds" (173 products, 19.7%). We conclude that there is good agreement between the HSR and the NPSC systems overall, but divergence in some food groups is likely to result in confusion for consumers, particularly where foods with low HSRs are eligible to display a health claim. The alignment of the NPSC and HSR scoring algorithms should be improved.

The decisions made by food companies are a potent factor shaping the nutritional quality of the food supply. A number of non-governmental organizations (NGOs) advocate for corporate action to reduce salt levels in foods, but few data define the effectiveness of advocacy. This present report describes the process evaluation of an advocacy intervention delivered by one Australian NGO directly to food companies to reduce the salt content of processed foods. Food companies were randomly assigned to intervention (n = 22) or control (n = 23) groups. Intervention group companies were exposed to pre-planned and opportunistic communications, and control companies to background activities. Seven pre-defined interim outcome measures provided an indication of the effect of the intervention and were assessed using intention-to-treat analysis. These were supplemented by qualitative data from nine semi-structured interviews. The mean number of public communications supporting healthy food made by intervention companies was 1.5 versus 1.8 for control companies (p = 0.63). Other outcomes, including the mean number of news articles, comments and reports (1.2 vs. 1.4; p = 0.72), a published nutrition policy (23% vs. 44%; p = 0.21), public commitment to the Australian government's Food and Health Dialogue (FHD) (41% vs. 61%; p = 0.24), evidence of a salt reduction plan (23% vs. 30%; p = 0.56), and mean number of communications with the NGO (15 vs. 11; p = 0.28) were also not significantly different. Qualitative data indicated the advocacy trial had little effect. The absence of detectable effects of the advocacy intervention on the interim markers indicates there may be no impact of the NGO advocacy trial on the primary outcome of salt reduction in processed foods.

In Australia, manufacturers can use two government-endorsed approaches to advertise product healthiness: the Health Star Rating (HSR) front-of-pack nutrition labelling system, and health claims. Related, but different, algorithms determine the star rating of a product (the HSR algorithm) and eligibility to display claims (the Nutrient Profiling Scoring Criterion (NPSC) algorithm). The objective of this study was to examine the agreement between the HSR and NPSC algorithms. Food composition information for 41,297 packaged products was extracted from The George Institute's FoodSwitch database. HSR and the NPSC scores were calculated, and the proportion of products in each HSR category that were eligible to display a health claim under the NPSC was examined. The highest agreement between the HSR scoring algorithm and the NPSC threshold to determine eligibility to display a health claim was at the HSR cut-off of 3.5 stars (k = 0.83). Overall, 97.3% (n = 40,167) of products with star ratings of 3.5 or higher were also eligible to display a health claim, and 94.3% (n = 38,939) of products with star ratings less than 3.5 were ineligible to display a health claim. The food group with greatest divergence was "edible oils", with 45% products (n = 342) with HSR >3.5, but 64% (n = 495) eligible to display a claim. Categories with large absolute numbers of products with HSR <3.5, but eligible to display a claim, were "yoghurts and yoghurt drinks" (335 products, 25.4%) and "soft drinks" (299 products, 29.7%). Categories with a large number of products with HSR ≥3.5, but ineligible to display a claim, were "milk" (260 products, 21.2%) and "nuts and seeds" (173 products, 19.7%). We conclude that there is good agreement between the HSR and the NPSC systems overall, but divergence in some food groups is likely to result in confusion for consumers, particularly where foods with low HSRs are eligible to display a health claim. The alignment of the NPSC and HSR scoring algorithms should be improved.

Background In June 2014, the Australian government agreed to the voluntary implementation of an interpretive 'Health Star Rating' (HSR) front-of-pack labelling system for packaged foods. The aim of the system is to make it easier for consumers to compare the healthiness of products based on number of stars. With many Australians consuming fast food there is a strong rationale for extending the HSR system to include fast food items. Objective To examine the performance of the HSR system when applied to fast foods. Design Nutrient content data for fast food menu items were collected from the websites of 13 large Australian fast-food chains. The HSR was calculated for each menu item. Statistics describing HSR values for fast foods were calculated and compared to results for comparable packaged foods. Results Data for 1529 fast food products were compared to data for 3810 packaged food products across 16 of 17 fast food product categories. The mean HSR for the fast foods was 2.5 and ranged from 0.5 to 5.0 and corresponding values for the comparator packaged foods were 2.6 and 0.5 to 5.0. Visual inspection of the data showed broadly comparable distributions of HSR values across the fast food and the packaged food categories, although statistically significant differences were apparent for seven categories (all p < 0.04). In some cases these differences reflected the large sample size and the power to detect small variations across fast foods and packaged food, and in others it appeared to reflect primarily differences in the mix of product types within a category. Conclusions These data support the idea that the HSR system could be extended to Australian fast foods. There are likely to be significant benefits to the community from the use of a single standardised signposting system for healthiness across all fresh, packaged and restaurant foods.

Background: Countries around the world are putting in place sugar reformulation targets for packaged foods to reduce excess sugar consumption. The Australian government released its voluntary sugar reformulation targets for nine food categories in 2020. We estimated the potential impact of these targets on household sugar purchases and examined differences by income. For comparison, we also modelled the potential impact of the UK sugar reduction targets on per capita sugar purchases as the UK has one of the most comprehensive sugar reduction strategies in the world. Methods: Grocery purchase data from a nationally representative consumer panel (n=7,188) in Australia was linked with a large database (FoodSwitch) with product-specific sugar content information for packaged foods (n=25,261); both datasets were collected in 2018. Potential reductions in per capita sugar purchases were calculated overall and by food category. Differences in sugar reduction across income level were assessed by analysis of variance. Results: In 2018, the total sugar acquired from packaged food and beverage purchases consumed at-home was 56.1 g/day per capita. Australia's voluntary reformulation targets for sugar covered 2,471/25,261 (9.8%) unique products in the FoodSwitch dataset. Under the scenario that all food companies adhered to the voluntary targets, sugar purchases were estimated to be reduced by 0.9 g/day per capita, which represents a 1.5% reduction in sugar purchased from packaged foods. However, if Australia adopted the UK targets, over twice as many products would be covered (n=4,667), and this would result in a more than four times greater reduction in sugar purchases (4.1 g/day per capita). It was also estimated that if all food companies complied with Australia's voluntary sugar targets, reductions to sugar would be slightly greater in low-income households compared with high-income households by 0.3 g/day (95%CI 0.2 - 0.4 g/day, p<0.001). Conclusions: Sugar-reduction policies have the potential to substantially reduce population sugar consumption and may help to reduce health inequalities related to excess sugar consumption. However, the current reformulation targets in Australia are estimated to achieve only a small reduction to sugar intakes, particularly in comparison to the UK's sugar reduction program.

We compared the healthiness of packaged foods and beverages between selected countries using the Health Star Rating (HSR) nutrient profiling system. Packaged food and beverage data collected 2013–2018 were obtained for Australia, Canada, Chile, China, India, Hong Kong, Mexico, New Zealand, Slovenia, South Africa, the UK, and USA. Each product was assigned to a food or beverage category and mean HSR was calculated overall by category and by country. Median energy density (kJ/100 g), saturated fat (g/100 g), total sugars (g/100 g) and sodium (mg/100 g) contents were calculated. Countries were ranked by mean HSR and median nutrient levels. Mean HSR for all products (n = 394,815) was 2.73 (SD 1.38) out of 5.0 (healthiest profile). The UK, USA, Australia and Canada ranked highest for overall nutrient profile (HSR 2.74–2.83) and India, Hong Kong, China and Chile ranked lowest (HSR 2.27–2.44). Countries with higher overall HSR generally ranked better with respect to nutrient levels. India ranked consistently in the least healthy third for all measures. There is considerable variability in the healthiness of packaged foods and beverages in different countries. The finding that packaged foods and beverages are less healthy in middle-income countries such as China and India suggests that nutrient profiling is an important tool to enable policymakers and industry actors to reformulate products available in the marketplace to reduce the risk of obesity and NCDs among populations.

Background: The Australian Government will soon be releasing a series of sugar reformulation targets for packaged foods. Objective: To estimate the amount of added sugar purchased from packaged food and beverages and the relative contribution that food categories and food companies made to these purchases in 2018. The secondary objective was to examine differences in purchases of added sugar across income levels. Design: Cross-sectional study. Participants/setting: We used 1 year of grocery purchase data from a nationally representative panel of Australian households (the NielsenIQ Homescan panel), combined with a packaged food and beverage database (FoodSwitch). Main outcome measures: Added sugar purchases (grams per day per capita), purchase-weighted added sugar content (grams per 100 g) and total weight of products (with added sugar) purchased (grams per day per capita). Statistical analyses performed: Food categories and food companies were ranked according to their contribution to added sugar purchases. Differences in added sugar purchases by income levels were assessed by 1-factor analysis of variance. Results: Added sugar information was available from 7188 households and across 26,291 unique foods and beverages. On average, the amount of added sugar acquired from packaged foods and beverages was (mean ± SE) 35.9 ± 0.01 g/d per capita. Low-income households purchased 11.0 g/d (95% CI: 10.9-11.0 g/d, P < .001) more added sugar from packaged products than high-income households per capita. The top 10 food categories accounted for 82.2% of added sugar purchased, largely due to purchases of chocolate and sweets, soft drinks, and ice cream and edible ices. Out of 994 food companies, the top 10 companies contributed to 62.1% of added sugar purchases. Conclusions: The Australian Government can strengthen their proposed sugar reduction program by adding further category-specific targets, prioritizing engagement with key food companies and considering a broader range of policies to reduce added sugar intakes across the Australian population.

BACKGROUND: The Australian Government will soon be releasing a series of sugar reformulation targets for packaged foods. OBJECTIVE: To estimate the amount of added sugar purchased from packaged food and beverages and the relative contribution that food categories and food companies made to these purchases in 2018. The secondary objective was to examine differences in purchases of added sugar across income levels. DESIGN: Cross-sectional study. PARTICIPANTS/SETTING: We used 1 year of grocery purchase data from a nationally representative panel of Australian households (the NielsenIQ Homescan panel), combined with a packaged food and beverage database (FoodSwitch). MAIN OUTCOME MEASURES: Added sugar purchases (grams per day per capita), purchase-weighted added sugar content (grams per 100 g) and total weight of products (with added sugar) purchased (grams per day per capita). STATISTICAL ANALYSES PERFORMED: Food categories and food companies were ranked according to their contribution to added sugar purchases. Differences in added sugar purchases by income levels were assessed by 1-factor analysis of variance. RESULTS: Added sugar information was available from 7188 households and across 26,291 unique foods and beverages. On average, the amount of added sugar acquired from packaged foods and beverages was (mean ± SE) 35.9 ± 0.01 g/d per capita. Low-income households purchased 11.0 g/d (95% CI: 10.9-11.0 g/d, P < .001) more added sugar from packaged products than high-income households per capita. The top 10 food categories accounted for 82.2% of added sugar purchased, largely due to purchases of chocolate and sweets, soft drinks, and ice cream and edible ices. Out of 994 food companies, the top 10 companies contributed to 62.1% of added sugar purchases. CONCLUSIONS: The Australian Government can strengthen their proposed sugar reduction program by adding further category-specific targets, prioritizing engagement with key food companies and considering a broader range of policies to reduce added sugar intakes across the Australian population.

Background: The Australian federal government will soon release voluntary sodium reduction targets for 30 packaged food categories through the Healthy Food Partnership. Previous assessments of voluntary targets show variable industry engagement, and little is known about the extent that major food companies and their products contribute to dietary sodium purchases among Australian households. Methods: The aim of this cross-sectional study was to identify the relative contribution that food companies and their products made to Australian household sodium purchases in 2018, and to examine differences in sodium purchases by household income level. We used 1 year of grocery purchase data from a nationally representative consumer panel of Australian households who reported their grocery purchases (the Nielsen Homescan panel), combined with database that contains product-specific sodium content for packaged foods and beverages (FoodSwitch). The top food companies and food categories were ranked according to their contribution to household sodium purchases. Differences in per capita sodium purchases by income levels were assessed by 1-factor ANOVA. All analyses were modelled to the Australian population in 2018 using sample weights. Results: Sodium data were available from 7188 households who purchased 26,728 unique products and purchased just under 7.5 million food product units. Out of 1329 food companies, the top 10 accounted for 35% of unique products and contributed to 58% of all sodium purchased from packaged foods and beverages. The top three companies were grocery food retailers each contributing 12-15% of sodium purchases from sales of their private label products, particularly processed meat, cheese and bread. Out of the 67 food categories, the top 10 accounted for 73% of sodium purchased, particularly driven by purchases of processed meat (14%), bread (12%) and sauces (11%). Low-income Australian households purchased significantly more sodium from packaged products than high-income households per capita (452 mg/d, 95%CI: 363-540 mg/d, P < 0.001). Conclusions: A small number of food companies and food categories account for most of the dietary sodium purchased by Australian households. Prioritizing government engagement with these groups could deliver a large reduction in population sodium intake.

BACKGROUND: The Australian federal government will soon release voluntary sodium reduction targets for 30 packaged food categories through the Healthy Food Partnership. Previous assessments of voluntary targets show variable industry engagement, and little is known about the extent that major food companies and their products contribute to dietary sodium purchases among Australian households. METHODS: The aim of this cross-sectional study was to identify the relative contribution that food companies and their products made to Australian household sodium purchases in 2018, and to examine differences in sodium purchases by household income level. We used 1 year of grocery purchase data from a nationally representative consumer panel of Australian households who reported their grocery purchases (the Nielsen Homescan panel), combined with database that contains product-specific sodium content for packaged foods and beverages (FoodSwitch). The top food companies and food categories were ranked according to their contribution to household sodium purchases. Differences in per capita sodium purchases by income levels were assessed by 1-factor ANOVA. All analyses were modelled to the Australian population in 2018 using sample weights. RESULTS: Sodium data were available from 7188 households who purchased 26,728 unique products and purchased just under 7.5 million food product units. Out of 1329 food companies, the top 10 accounted for 35% of unique products and contributed to 58% of all sodium purchased from packaged foods and beverages. The top three companies were grocery food retailers each contributing 12-15% of sodium purchases from sales of their private label products, particularly processed meat, cheese and bread. Out of the 67 food categories, the top 10 accounted for 73% of sodium purchased, particularly driven by purchases of processed meat (14%), bread (12%) and sauces (11%). Low-income Australian households purchased significantly more sodium from packaged products than high-income households per capita (452 mg/d, 95%CI: 363-540 mg/d, P < 0.001). CONCLUSIONS: A small number of food companies and food categories account for most of the dietary sodium purchased by Australian households. Prioritizing government engagement with these groups could deliver a large reduction in population sodium intake.