2020
DOI: 10.1186/s12966-020-00982-z
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Contribution of major food companies and their products to household dietary sodium purchases in Australia

Abstract: 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 … Show more

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Cited by 11 publications
(23 citation statements)
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“…The datasets used are described in detail in Table 1 , and the detailed modeling steps are in Text A in S1 File . In brief, the modeling approach involved 4 stages ( Fig 1 ) , (1) estimating pre-reformulation sodium intakes from each packaged food group with a sodium target using nationally representative 24-hour dietary recall data of the Australian population (National Nutrition and Physical Activity Survey (NNPAS) and Food Standards Australia New Zealand Food Composition Database) for each age–sex group [ 15 ], adjusted for underreporting using sex-specific 24-hour urinary sodium excretion estimates of nondiscretionary sodium intake (Table A in S1 File ) [ 10 ]; (2) using a nationally representative household grocery shopping panel data (NielsenIQ Homescan [ 16 ]) and a brand-specific food composition database (FoodSwitch) [ 17 ] to estimate the pre- and post-reformulation sales-weighted average sodium content, and the expected percentage reduction in sales-weighted average sodium content for each targeted food group; (3) estimating the reduction in sodium intake from each individual targeted food group or company based on steps (1) and (2), i.e., multiply prereformulation sodium intake in each food group by the estimated percentage reduction in sales-weighted average sodium content following reformulation (reductions are then summed across food groups to estimate overall reductions in sodium intake); and (4) estimating the effect of reduced sodium intake on CVD, CKD, and stomach cancer outcomes via comparative risk assessment analysis for each age–sex group. Table B in S1 File outlines the model assumptions and restrictions.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The datasets used are described in detail in Table 1 , and the detailed modeling steps are in Text A in S1 File . In brief, the modeling approach involved 4 stages ( Fig 1 ) , (1) estimating pre-reformulation sodium intakes from each packaged food group with a sodium target using nationally representative 24-hour dietary recall data of the Australian population (National Nutrition and Physical Activity Survey (NNPAS) and Food Standards Australia New Zealand Food Composition Database) for each age–sex group [ 15 ], adjusted for underreporting using sex-specific 24-hour urinary sodium excretion estimates of nondiscretionary sodium intake (Table A in S1 File ) [ 10 ]; (2) using a nationally representative household grocery shopping panel data (NielsenIQ Homescan [ 16 ]) and a brand-specific food composition database (FoodSwitch) [ 17 ] to estimate the pre- and post-reformulation sales-weighted average sodium content, and the expected percentage reduction in sales-weighted average sodium content for each targeted food group; (3) estimating the reduction in sodium intake from each individual targeted food group or company based on steps (1) and (2), i.e., multiply prereformulation sodium intake in each food group by the estimated percentage reduction in sales-weighted average sodium content following reformulation (reductions are then summed across food groups to estimate overall reductions in sodium intake); and (4) estimating the effect of reduced sodium intake on CVD, CKD, and stomach cancer outcomes via comparative risk assessment analysis for each age–sex group. Table B in S1 File outlines the model assumptions and restrictions.…”
Section: Methodsmentioning
confidence: 99%
“…Based on data usage agreements with NielsenIQ, for the purposes of this analysis, food companies were deidentified; however, we classified whether a company was a “retailer” or a “manufacturer.” Manufacturers were classified as food companies that manufacture and distribute items (also known as “branded products”) for general trade. Retailers were defined as supermarkets that sell their own “private-label” products in their own stores [ 16 ], and the estimates for retailers were based on reformulation of their “private-label” products.…”
Section: Methodsmentioning
confidence: 99%
“…Using the categorisation system developed by the Global Food Monitoring Group, foods and beverages in FoodSwitch were classified into a hierarchical category tree to allow for comparison of nutritionally similar foods. [27][28][29] *Food categories and subcategories listed are those that are targeted as part of the Australian sodium reformulation programme. †Results are sales-weighted and projected to the Australian population using sample weights provided by Nielsen.…”
Section: Nutrition Informationmentioning
confidence: 99%
“…The 2019 FoodSwitch Annual Database, the dataset used in this study, was generated from in-store surveys carried out in Australia at five large supermarkets (IGA, ALDI, Woolworths, Coles and Harris Farm) between August and November 2019 [18]. The dataset represents the majority of supermarket food and beverage products purchased by Australian households [19,20].…”
Section: Food and Beverage Product Databasementioning
confidence: 99%
“…The median (interquartile range (IQR)) number of ingredients across all products was 8 (3-14) (Table 3). 'Convenience foods' had the greatest median number of ingredients per product (20 (14-27)), followed by 'Foods for specific dietary use' (12 (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)) and 'Bread and bakery products' (13 (9-19)) (Table 3).…”
Section: Number Of Ingredients Per Product Across Product Categoriesmentioning
confidence: 99%