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- Coyle, Daisy H, et al.
(författare)
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Estimating the potential impact of the Australian government's reformulation targets on household sugar purchases.
- 2021
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Ingår i: International Journal of Behavioral Nutrition and Physical Activity. - : Springer Nature. - 1479-5868. ; 18:1
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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- Coyle, Daisy H, et al.
(författare)
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Socio-economic difference in purchases of ultra-processed foods in Australia : an analysis of a nationally representative household grocery purchasing panel.
- 2022
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Ingår i: International Journal of Behavioral Nutrition and Physical Activity. - : Springer Science and Business Media LLC. - 1479-5868. ; 19:1, s. 148-
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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- Coyle, Daisy H, et al.
(författare)
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The Contribution of Major Food Categories and Companies to Household Purchases of Added Sugar in Australia.
- 2022
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Ingår i: Journal of the Academy of Nutrition and Dietetics. - : Elsevier. - 2212-2672 .- 2212-2680. ; 122:2, s. 345-353.e3
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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- Davies, Tazman, et al.
(författare)
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An Innovative Machine Learning Approach to Predict the Dietary Fiber Content of Packaged Foods
- 2021
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Ingår i: Nutrients. - : MDPI. - 2072-6643. ; 13:9
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Tidskriftsartikel (refereegranskat)abstract
- 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 (R-2 = 0.84 vs. R-2 = 0.68). Our findings highlight the opportunity to use machine learning to efficiently predict the fiber content of packaged products on a large scale.
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