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Summary: Background: What we eat is fundamental to human and planetary health, with the current global dietary transition towards increased red meat intakes and ultra-processed foods likely detrimental. Methods: We modelled five red and processed meat replacement scenarios to consider health, equity, greenhouse gas emissions (GHGe), and cost outcomes using an established multistate life table model using data from New Zealand as a case study of a developed, westernised country. Current red and processed meat intakes were replaced with: minimally or ultra-processed plant based meat alternatives, cellular meat, or diets in line with EAT-Lancet or Heart Foundation recommendations on red meat intake. We then conducted a systematic review of literature from database inception to 14 November 2022 to identify implemented population-level meat replacement strategies which could inform evidence-based recommendations to achieve any benefits observed in modelling. PROSPERO CRD42020200023. Findings: When compared with current red and processed meat intakes, all red and processed meat replacement scenarios were nutritionally adequate and improved overall Quality Adjusted Life Years (159–297 per 1000 people over life course for the five scenarios modelled). Age standardised per capita health gain for Māori was 1.6–2.3 times that of non-Māori. Health system cost savings were $2530–$5096 per adult, and GHGe reduced 19–35%. Finally, grocery cost varied (↓7%–↑2%) per modelled scenario when compared with baseline costs. The greatest benefits for all outcomes were achieved by meat replacement with minimally-processed plant-based foods, such as legumes. The systematic review identified only two implemented population-level strategies to reduce meat intakes within the academic literature. Interpretation: All meat replacement scenarios considered indicated appreciable health gains and GHGe reductions. Replacement with minimally-processed plant-based foods appeared consistently superior than other scenarios. Evidence of real-world population strategies to achieve these benefits however is currently lacking. Funding: Healthier Lives National Science Challenge (Grant UOOX1902).

Abstract Objective: The Health Star Rating (HSR) is a voluntary front‐of‐pack nutrition labelling system that rates products from ½ to 5 stars (five being healthiest). The Chilean Warning Label system displays warnings on foods high in sugar, saturated fat, sodium, or energy. We aimed to evaluate alignment between the systems. Methods: New Zealand packaged products (n=13,868) were classified according to the two systems. Alignment was assessed by cross‐checking the number of products meeting the criteria for warnings against star ratings. Products with no warnings but an HSR 1 warning but an HSR of ≥3.5 were considered outliers. Results: Two‐thirds of products met the criteria for at least one warning. There was a significant positive relationship between the number of warnings and mean HSR: 0 warnings = HSR 3.77±.0166 (p1 warning = HSR 2.00±.0160 (p

Abstract Objective: The Health Star Rating (HSR) is a front‐of‐pack nutrition labelling system, implemented voluntarily in Australia and New Zealand since 2014. Our aim was to evaluate HSR's performance. Method: We used data from peer‐reviewed publications and government‐commissioned monitoring and evaluation, websites and communiqués to evaluate HSR's performance between June 2014 and October 2018 using the RE‐AIM (Reach, Efficacy, Adoption, Implementation and Maintenance) framework. Results: Thirty‐three peer‐reviewed publications, 21 government and three independent reports informed the assessment. Awareness and trust in HSR was increasing, though campaign reach remained low. Consumers liked, could understand and use the HSR logo, though effects on purchasing were largely unknown. The algorithm was the focus of a formal review. HSR was present on 20–28% of products but biased to those that scored better (HSR≥3.0). Necessary stakeholders were mostly engaged. Conclusions: A substantial body of work supports continuation and strengthening of HSR. Reasonable refinements to HSR's star graphic and algorithm, action to initiate mandatory implementation, and strengthened HSR governance present the clearest opportunities for improving public health impact. Implications for public health: Development and implementation of government‐led front‐of‐pack nutrition labelling systems have the potential to improve public health, while engaging a diverse set of stakeholders.

Background: Obesity and diet-related noncommunicable diseases (NCDs) account for the largest proportion of disease burden worldwide, and an unhealthy food environment is a key driver. Food retailers play an important role in food environments through the availability and purchases of healthy food products at various stores. Objectives: To assess whether the healthiness of food and non-alcoholic drink product purchases vary according to retail store type. Methods: We undertook a cross-sectional analysis of Nielsen New Zealand Homescan® panel data, which is a nationally representative sample of 2500 households in terms of certain key household demographic and socioeconomic characteristics. Panel members were asked to record all food and beverage products that were purchased and brought back to the home between October 2018 and October 2019. Household food and non-alcoholic drink purchases were linked with two food composition databases (Nutritrack, a New Zealand packaged food composition database, and the FOODfiles New Zealand Food Composition Database) to extract data on the nutrient profile of products purchased. We developed a store classification tool, and classified stores as supermarkets, grocery stores, convenience stores, fruit and vegetable stores, meat and fish stores, or bakeries. We estimated the Health Star Rating (HSR) for all products and defined a product with HSR ≥ 3.5 as ‘healthy’. We computed estimated mean HSR and conducted multivariate regression analyses. Results: In total, 3,940,458 product purchases were included in the analyses, consisting of 20,491 unique products purchased at different stores over the one-year period by 1800 panellist households. Supermarket products made up the majority of household food and drink purchases (3,545,141 of 3,940,458; 90%). Overall, the estimated mean HSR was 3.5 stars. In comparison to the reference group of supermarkets, the odds ratio for healthy products purchased at fruit and vegetable stores was 4.62, at grocery stores it was 2.36, and at meat and fish stores it was 1.99. In contrast, the odds ratios from convenience stores and bakeries were 0.58 and 0.03. Except for convenience stores, these differences were statistically significant (p

Summary: Background: One possible policy response to the burden of diet-related disease is food taxes and subsidies, but the net health gains of these approaches are uncertain because of substitution effects between foods. We estimated the health and cost impacts of various food taxes and subsidies in one high-income country, New Zealand. Methods: In this modelling study, we compared the effects in New Zealand of a 20% fruit and vegetable subsidy, of saturated fat, sugar and salt taxes (each set at a level that increased the total food price by the same magnitude of decrease from the fruit and vegetable subsidy), and of an 8% so-called junk food tax (on non-essential, energy-dense food). We modelled the effect of price changes on food purchases, the consequent changes in fruit and vegetable and sugar-sweetened beverage purchasing, nutrient risk factors, and body-mass index, and how these changes affect health status and health expenditure. The pre-intervention intake for 340 food groups was taken from the New Zealand National Nutrition Survey and the post-intervention intake was estimated using price and expenditure elasticities. The resultant changes in dietary risk factors were then propagated through a proportional multistate lifetable (with 17 diet-related diseases) to estimate the changes in health-adjusted life years (HALYs) and health system expenditure over the 2011 New Zealand population's remaining lifespan. Findings: Health gains (expressed in HALYs per 1000 people) ranged from 127 (95% uncertainty interval 96–167; undiscounted) for the 8% junk food tax and 212 (102–297) for the fruit and vegetable subsidy, up to 361 (275–474) for the saturated fat tax, 375 (272–508) for the salt tax, and 581 (429–792) for the sugar tax. Health expenditure savings across the remaining lifespan per capita (at a 3% discount rate) ranged from US$492 (334–694) for the junk food tax to $2164 (1472–3122) for the sugar tax. Interpretation: The large magnitude of the health gains and cost savings of these modelled taxes and subsidies suggests that their use warrants serious policy consideration. Funding: Health Research Council of New Zealand.

Summary: Background: The OL@-OR@ mobile health programme was co-designed with Māori and Pasifika communities in New Zealand, to support healthy lifestyle behaviours. We aimed to determine whether use of the programme improved adherence to health-related guidelines among Māori and Pasifika communities in New Zealand compared with a control group on a waiting list for the programme. Methods: The OL@-OR@ trial was a 12-week, two-arm, cluster-randomised controlled trial. A cluster was defined as any distinct location or setting in New Zealand where people with shared interests or contexts congregated, such as churches, sports clubs, and community groups. Members of a cluster were eligible to participate if they were aged 18 years or older, had regular access to a mobile device or computer, and had regular internet access. Clusters of Māori and of Pasifika (separately) were randomly assigned (1:1) to either the intervention or control condition. The intervention group received the OL@-OR@ mHealth programme (smartphone app and website). The control group received a control version of the app that only collected baseline and outcome data. The primary outcome was self-reported adherence to health-related guidelines, which were measured with a composite health behaviour score (of physical activity, smoking, alcohol intake, and fruit and vegetable intake) at 12 weeks. The secondary outcomes were self-reported adherence to health-related behaviour guidelines at 4 weeks; self-reported bodyweight at 12 weeks; and holistic health and wellbeing status at 12 weeks, in all enrolled individuals in eligible clusters; and user engagement with the app, in individuals allocated to the intervention. Adverse events were not collected. This study is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12617001484336. Findings: Between Jan 24 and Aug 14, 2018, we enrolled 337 Māori participants from 19 clusters and 389 Pasifika participants from 18 clusters (n=726 participants) in the intervention group and 320 Māori participants from 15 clusters and 405 Pasifika participants from 17 clusters (n=725 participants) in the control group. Of these participants, 227 (67%) Māori participants and 347 (89%) Pasifika participants (n=574 participants) in the intervention group and 281 (88%) Māori participants and 369 (91%) Pasifika participants (n=650 participants) in the control group completed the 12-week follow-up and were included in the final analysis. Relative to baseline, adherence to health-related behaviour guidelines increased at 12 weeks in both groups (315 [43%] of 726 participants at baseline to 329 [57%] of 574 participants in the intervention group; 331 [46%] of 725 participants to 369 [57%] of 650 participants in the control group); however, there was no significant difference between intervention and control groups in adherence at 12 weeks (odds ratio [OR] 1·13; 95% CI 0·84–1·52; p=0·42). Furthermore, the proportion of participants adhering to guidelines on physical activity (351 [61%] of 574 intervention group participants vs 407 [63%] of 650 control group participants; OR 1·03, 95% CI 0·73–1·45; p=0·88), smoking (434 [76%] participants vs 501 [77%] participants; 1·12, 0·67–1·87; p=0·66), alcohol consumption (518 [90%] participants vs 596 [92%] participants; 0·73, 0·37–1·44; p=0·36), and fruit and vegetable intake (194 [34%] participants vs 196 [30%] participants; 1·08, 0·79–1·49; p=0·64) did not differ between groups. We found no significant differences between the intervention and control groups in any secondary outcome. 147 (26%) intervention group participants engaged with the OL@-OR@ programme (ie, set at least one behaviour change goal online). Interpretation: The OL@-OR@ mobile health programme did not improve adherence to health-related behaviour guidelines amongst Māori and Pasifika individuals. Funding: Healthier Lives He Oranga Hauora National Science Challenge.

Summary: Background: Most evidence on health-related food taxes and subsidies relies on observational data and effects on single nutrients or foods instead of total diet. The aim of this study was to measure the effect of randomly assigned food price variations on consumer purchasing, where sets of prices emulated commonly discussed food tax and subsidy policies, including a subsidy on fruit and vegetables, a sweetened beverage tax, and taxes on foods according to sugar, sodium, and saturated fat content. Methods: In this study, adult participants (≥18 years) in New Zealand completed up to five weekly shops in a virtual supermarket. Each shopping occasion was randomly allocated to control (no change in prices) or one or more pricing options simulating the following: a fruit and vegetable subsidy (20%), a sweetened beverage tax (20% or 40%), a saturated fat tax (NZ$2 per 100 g or $4 per 100 g saturated fat), a salt tax ($0·02 per 100 mg or $0·04 per 100 mg sodium), or sugar tax ($0·40 per 100 g or $0·80 per 100 g sugar). The primary outcome was the healthiness of the total shopping basket for each weekly shop (% of total unit food items defined as healthy). Low and high price change options were combined in analyses (eg, results for a saturated fat tax are an average of $2 per 100 g or $4 per 100 g). Findings: Between Feb 1, and Dec 1, 2016, we randomly assigned 1132 shoppers, of whom 1038 (91·7%) completed at least one shop and 743 (71·6%) completed all five shops. Overall, data from 4258 shops were included in the analysis, including 645 control shops, 2545 shops where one policy was activated, and 1068 shops with two (or more) policies activated. In the control condition, 67·90% (SD 13·01) of food purchases were classified as healthy. Three of the five policies increased this proportion by a small, but significant amount (saturated fat tax mean absolute difference 1·77%, 95% CI 1·03 to 2·52, p

Abstract Objective: To assess the application of Multiple Traffic Light (MTL) nutrition labels and the Food Standards Australia New Zealand (FSANZ) Nutrient Profiling Standard Calculator (NPSC) to foods and their agreement in classification of foods. Methods: MTL and NPSC criteria were applied to selected packaged food products available in New Zealand (NZ) supermarkets in 2011: 157 breakfast cereals; 128 cereal bars; 40 pizzas; 69 sausages and hotdogs; and 13 burgers. A points‐based system combined MTL criteria into an overall score to enable comparison of food classification by the two systems. Results: Based on NPSC criteria, 156 (38%) NZ packaged food products were eligible to carry a health claim and thus classified as ‘healthy’; 251 were ‘less healthy’. Based on MTL criteria most ‘healthy’ products (61%) would also receive one red light. Using the MTL score, only 14% of products were classified as ‘healthy’. Percentage agreement between the two systems was 73%. Conclusions: More than half of NZ packaged foods are ‘less healthy’ according to criteria underpinning two front‐of‐pack labelling systems. A number of foods eligible to carry health claims would display at least one red traffic light if MTL were introduced.

Abstract Objective: To identify key opportunities for reformulation of processed foods that could best decrease population sodium intakes in New Zealand (NZ). Method: Relevant national literature and reports were used to identify major food groups contributing to population sodium intakes in NZ. Sodium content data for these food groups were collected from the Nutrition Information Panels of processed foods in one large supermarket. Key opportunities for reformulation were identified by comparing mean sodium content with 2012 targets from the United Kingdom (UK) Food Standards Agency (FSA) and mean sodium values from Australia and the UK. Results: Major contributors to NZ sodium intakes are: bread (26%), processed meats (10%), and sauces (6%). Mean (SD) sodium contents of these processed foods were: 447 (125) mg/100 g, 1,169 (444) mg/100 g, and 1,046 (1,235) mg/100 g, respectively. Food categories with the lowest percentage of products meeting corresponding FSA targets were: sausages/hot dogs and sliced meat (0%); salami/cured meat (2%); liquid meal‐based sauces (4%); and multigrain bread (14%). Mean sodium contents of NZ products were higher than for similar products in the UK. Key opportunities identified for sodium reduction were: white bread, sausages and hot dogs, and salami/cured meats. Conclusion: There is substantial scope to reduce the sodium content of NZ processed foods. Implications: This paper identifies three key opportunities for reformulation of processed foods that could produce substantial decreases in sodium intakes in NZ, and benefits to population health.

Background: Evidence is mounting that price discounts can be effective in improving diet. This study examined the effectiveness of a 20% price discount on food and drink purchases with and without consumer education in remote Indigenous Australia. Methods: A 20% discount on fruit, vegetables, water, and artificially sweetened soft drinks was applied for 24 weeks in 20 communities in remote Indigenous Australia where the community store was managed by the Arnhem Land Progress Aboriginal Corporation (ALPA) or Outback Stores (OBS) in a stepped-wedge randomised trial. Communities were randomly allocated to a fixed framework of five sets of four stratified by store association; ten stores (two in each set) were randomly assigned to receive consumer education. A store from each of the ALPA and OBS store groups (contained in separate opaque envelopes) was selected, and stores in turn continued to be consecutively allocated to the fixed store set framework, starting with the first store slot in the first store set, until all stores had been allocated. The effect of the discount on the weight of fruit and vegetables purchased (the primary endpoint) was assessed using weekly store sales data and mixed models per protocol. We did sensitivity analyses by repeating the analyses with the outliers included and repeating the analyses for the primary outcome measure removing each store one at a time. This trial was registered with Australian New Zealand Clinical Trials Registry, number ACTRN12613000694718. Findings: Weekly store sales data on all food and drink products sold in 20 stores were collected from July 1, 2012, to Dec 28, 2014. Price discount alone was associated with a 12·7% (95% CI 4·1–22·1) increase in purchases in grams of fruit and vegetables combined (primary outcome), and a 19·8% (6·2–35·1) increase post discount (after vs before); an effect of 12 g and 18 g per capita per day. Sensitivity analyses did not modify the results for the primary outcome measure. Interpretation: A 20% discount can only increase fruit and vegetable purchases to help protect against obesity and diet related disease to a certain extent. Large discounts might have a greater impact than small discounts. Creative merchandising approaches to consumer education could also be considered alongside fiscal interventions to achieve marked improvements in diet. Funding: Australian National Health and Medical Research Council.