Michelle Crino

Comparison of food industry policies and commitments on marketing to children and product (re)formulation in Australia, New Zealand and Fiji

Unhealthy food environments are known to be major drivers of diet-related non-communicable diseases globally, and there is an imperative for major food companies to be publicly accountable for their actions to improve the healthiness of food environments. This paper examines the prevalence of publicly available policies and commitments of major packaged food and soft drink manufacturers, and fast-food restaurants in Australia, New Zealand and Fiji with respect to reducing food marketing to children and product (re)formulation. In each country, the most prominent companies in each sector were selected. Company policies, commitments and relevant industry initiatives were gleaned from company and industry association websites. In Australia and New Zealand, there are a higher proportion of companies with publicly available marketing and formulation policies than in Fiji. However, even in Australia, a large proportion of the most prominent food companies do not have publicly available policies. Where they exist, policies on food marketing to children generally focus on those aged less than 12, do not apply to all types of media, marketing channels and techniques, and do not provide transparency with respect to the products to which the policies apply. Product formulation policies, where they exist, focus mostly on salt reduction and changes to the make-up of overall product portfolios, and do not generally address saturated fat, added sugar and energy reduction. In the absence of strong policies and corresponding actions by the private sector, it is likely that government action (e.g. through co-regulation or legislation) will be needed to drive improved company performance.

Smart RCTs: Development of a Smartphone App for Fully Automated Nutrition-Labeling Intervention Trials.

Background There is substantial interest in the effects of nutrition labels on consumer food-purchasing behavior. However, conducting randomized controlled trials on the impact of nutrition labels in the real world presents a significant challenge. Objective The Food Label Trial (FLT) smartphone app was developed to enable conducting fully automated trials, delivering intervention remotely, and collecting individual-level data on food purchases for two nutrition-labeling randomized controlled trials (RCTs) in New Zealand and Australia. Methods Two versions of the smartphone app were developed: one for a 5-arm trial (Australian) and the other for a 3-arm trial (New Zealand). The RCT protocols guided requirements for app functionality, that is, obtaining informed consent, two-stage eligibility check, questionnaire administration, randomization, intervention delivery, and outcome assessment. Intervention delivery (nutrition labels) and outcome data collection (individual shopping data) used the smartphone camera technology, where a barcode scanner was used to identify a packaged food and link it with its corresponding match in a food composition database. Scanned products were either recorded in an electronic list (data collection mode) or allocated a nutrition label on screen if matched successfully with an existing product in the database (intervention delivery mode). All recorded data were transmitted to the RCT database hosted on a server. Results In total approximately 4000 users have downloaded the FLT app to date; 606 (Australia) and 1470 (New Zealand) users met the eligibility criteria and were randomized. Individual shopping data collected by participants currently comprise more than 96,000 (Australia) and 229,000 (New Zealand) packaged food and beverage products. Conclusions The FLT app is one of the first smartphone apps to enable conducting fully automated RCTs. Preliminary app usage statistics demonstrate large potential of such technology, both for intervention delivery and data collection. Trial Registration Australian New Zealand Clinical Trials Registry ACTRN12614000964617. New Zealand trial: Australian New Zealand Clinical Trials Registry ACTRN12614000644662.

Modelled Cost-Effectiveness of a Package Size Cap and a Kilojoule Reduction Intervention to Reduce Energy Intake from Sugar-Sweetened Beverages in Australia

Interventions targeting portion size and energy density of food and beverage products have been identified as a promising approach for obesity prevention. This study modelled the potential cost-effectiveness of: a package size cap on single-serve sugar sweetened beverages (SSBs) >375 mL (package size cap), and product reformulation to reduce energy content of packaged SSBs (energy reduction). The cost-effectiveness of each intervention was modelled for the 2010 Australia population using a multi-state life table Markov model with a lifetime time horizon. Long-term health outcomes were modelled from calculated changes in body mass index to their impact on Health-Adjusted Life Years (HALYs). Intervention costs were estimated from a limited societal perspective. Cost and health outcomes were discounted at 3%. Total intervention costs estimated in AUD 2010 were AUD 210 million. Both interventions resulted in reduced mean body weight (package size cap: 0.12 kg; energy reduction: 0.23 kg); and HALYs gained (package size cap: 73,883; energy reduction: 144,621). Cost offsets were estimated at AUD 750.8 million (package size cap) and AUD 1.4 billion (energy reduction). Cost-effectiveness analyses showed that both interventions were “dominant”, and likely to result in long term cost savings and health benefits. A package size cap and kJ reduction of SSBs are likely to offer excellent “value for money” as obesity prevention measures in Australia.

Incorporating Added Sugar Improves the Performance of the Health Star Rating Front-of-Pack Labelling System in Australia.

Background: The Health Star Rating (HSR) is an interpretive front-of-pack labelling system that rates the overall nutritional profile of packaged foods. The algorithm underpinning the HSR includes total sugar content as one of the components. This has been criticised because intrinsic sugars naturally present in dairy, fruits, and vegetables are treated the same as sugars added during food processing. We assessed whether the HSR could better discriminate between core and discretionary foods by including added sugar in the underlying algorithm. Methods: Nutrition information was extracted for 34,135 packaged foods available in The George Institute’s Australian FoodSwitch database. Added sugar levels were imputed from food composition databases. Products were classified as ‘core’ or ‘discretionary’ based on the Australian Dietary Guidelines. The ability of each of the nutrients included in the HSR algorithm, as well as added sugar, to discriminate between core and discretionary foods was estimated using the area under the curve (AUC). Results: 15,965 core and 18,350 discretionary foods were included. Of these, 8230 (52%) core foods and 15,947 (87%) discretionary foods contained added sugar. Median (Q1, Q3) HSRs were 4.0 (3.0, 4.5) for core foods and 2.0 (1.0, 3.0) for discretionary foods. Median added sugar contents (g/100 g) were 3.3 (1.5, 5.5) for core foods and 14.6 (1.8, 37.2) for discretionary foods. Of all the nutrients used in the current HSR algorithm, total sugar had the greatest individual capacity to discriminate between core and discretionary foods; AUC 0.692 (0.686; 0.697). Added sugar alone achieved an AUC of 0.777 (0.772; 0.782). A model with all nutrients in the current HSR algorithm had an AUC of 0.817 (0.812; 0.821), which increased to 0.871 (0.867; 0.874) with inclusion of added sugar. Conclusion: The HSR nutrients discriminate well between core and discretionary packaged foods. However, discrimination was improved when added sugar was also included. These data argue for inclusion of added sugar in an updated HSR algorithm and declaration of added sugar as part of mandatory nutrient declarations.

Effects of Different Types of Front-of-Pack Labelling Information on the Healthiness of Food Purchases—A Randomised Controlled Trial

Background: Front-of-pack nutrition labelling may support healthier packaged food purchases. Australia has adopted a novel Health Star Rating (HSR) system, but the legitimacy of this choice is unknown. Objective: To define the effects of different formats of front-of-pack labelling on the healthiness of food purchases and consumer perceptions. Design: Individuals were assigned at random to access one of four different formats of nutrition labelling—HSR, multiple traffic light labels (MTL), daily intake guides (DIG), recommendations/warnings (WARN)—or control (the nutrition information panel, NIP). Participants accessed nutrition information by using a smartphone application to scan the bar-codes of packaged foods, while shopping. The primary outcome was healthiness defined by the mean transformed nutrient profile score of packaged foods that were purchased over four weeks. Results: The 1578 participants, mean age 38 years, 84% female recorded purchases of 148,727 evaluable food items. The mean healthiness of the purchases in the HSR group was non-inferior to MTL, DIG, or WARN (all p < 0.001 at 2% non-inferiority margin). When compared to the NIP control, there was no difference in the mean healthiness of purchases for HSR, MTL, or DIG (all p > 0.07), but WARN resulted in healthier packaged food purchases (mean difference 0.87; 95% confidence interval 0.03 to 1.72; p = 0.04). HSR was perceived by participants as more useful than DIG, and easier to understand than MTL or DIG (all p < 0.05). Participants also reported the HSR to be easier to understand, and the HSR and MTL to be more useful, than NIP (all p < 0.03). Conclusions: These real-world data align with experimental findings and provide support for the policy choice of HSR. Recommendation/warning labels warrant further exploration, as they may be a stronger driver of healthy food purchases.

Cost-Effectiveness of Product Reformulation in Response to the Health Star Rating Food Labelling System in Australia

The Health Star Rating (HSR) system is a voluntary front-of-pack labelling (FoPL) initiative endorsed by the Australian government in 2014. This study examines the impact of the HSR system on pre-packaged food reformulation measured by changes in energy density between products with and without HSR. The cost-effectiveness of the HSR system was modelled using a proportional multi-state life table Markov model for the 2010 Australian population. We evaluated scenarios in which the HSR system was implemented on a voluntary and mandatory basis (i.e., HSR uptake across 6.7% and 100% of applicable products, respectively). The main outcomes were health-adjusted life years (HALYs), net costs, and incremental cost-effectiveness ratios (ICERs). These were calculated with accompanying 95% uncertainty intervals (95% UI). The model predicted that HSR-attributable reformulation leads to small reductions in mean population energy intake (voluntary: 0.98 kJ/day [95% UI: −1.08 to 2.86]; mandatory: 11.81 kJ/day [95% UI: −11.24 to 36.13]). These are likely to result in reductions in mean body weight (voluntary: 0.01 kg [95% UI: −0.01 to 0.03]; mandatory: 0.11 kg [95% UI: −0.12 to 0.32], and HALYs (voluntary: 4207 HALYs [95% UI: 2438 to 6081]; mandatory: 49,949 HALYs [95% UI: 29,291 to 72,153]). The HSR system evaluated via changes in reformulation could be considered cost-effective relative to a willingness-to-pay threshold of A

Use of Added Sugars Instead of Total Sugars May Improve the Capacity of the Health Star Rating System to Discriminate between Core and Discretionary Foods

50,000 per HALY (voluntary: A

Reliable quantification of the potential for equations based on spot urine samples to estimate population salt intake: protocol for a systematic review and meta-analysis.

1728 per HALY [95% UI: dominant to 10,445] and mandatory: A

Measuring the Healthiness of the Packaged Food Supply in Australia

4752 per HALY [95% UI: dominant to 16,236]).

Evaluation of Alignment between the Health Claims Nutrient Profiling Scoring Criterion (NPSC) and the Health Star Rating (HSR) Nutrient Profiling Models

BACKGROUND The Australian Government has introduced a voluntary front-of-package labeling system that includes total sugar in the calculation. OBJECTIVE Our aim was to determine the effect of substituting added sugars for total sugars when calculating Health Star Ratings (HSR) and identify whether use of added sugars improves the capacity to distinguish between core and discretionary food products. DESIGN This study included packaged food and beverage products available in Australian supermarkets (n=3,610). The product categories included in the analyses were breakfast cereals (n=513), fruit (n=571), milk (n=309), non-alcoholic beverages (n=1,040), vegetables (n=787), and yogurt (n=390). Added sugar values were estimated for each product using a validated method. HSRs were then estimated for every product according to the established method using total sugar, and then by substituting added sugar for total sugar. The scoring system was not modified when added sugar was used in place of total sugar in the HSR calculation. Products were classified as core or discretionary based on the Australian Dietary Guidelines. To investigate whether use of added sugar in the HSR algorithm improved the distinction between core and discretionary products as defined by the Australian Dietary Guidelines, the proportion of core products that received an HSR of ≥3.5 stars and the proportion of discretionary products that received an HSR of <3.5 stars, for algorithms based upon total vs added sugars were determined. RESULTS There were 2,263 core and 1,347 discretionary foods; 1,684 of 3,610 (47%) products contained added sugar (median 8.4 g/100 g, interquartile range=5.0 to 12.2 g). When the HSR was calculated with added sugar instead of total sugar, an additional 166 (7.3%) core products received an HSR of ≥3.5 stars and 103 (7.6%) discretionary products received a rating of ≥3.5 stars. The odds of correctly identifying a product as core vs discretionary were increased by 61% (odds ratio 1.61, 95% CI 1.26 to 2.06; P<0.001) when the algorithm was based on added compared to total sugars. CONCLUSIONS In the six product categories examined, substitution of added sugars for total sugars better aligned the HSR with the Australian Dietary Guidelines. Future work is required to investigate the impact in other product categories.