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This document was added or updated on 29/06/2020.
ii. Indirectly estimated measures of the food quantities available for consumption that are not captured by the major retailers in the SD which are based on household expenditure data.
5 The primary data source for this publication are the aggregated scanner data (SD) provided to the ABS from major supermarkets. The major supermarkets provide aggregated data based on information compiled from barcode scanning at the point of sale. The aggregate information comprises the Stock Keeping Unit (SKU) of the sales item, a description of the item, the geographical region and weekly aggregate amounts of the quantity and value of the sales. The major supermarkets that provide data to the ABS account for an estimated 82% of the Food Retail sector.
2015–16 Household Expenditure Survey
6 The 2015–16 Household Expenditure Survey (HES) is used to help estimate and impute the value of product sales which are not captured by the major supermarkets in the SD (that is, the remaining 18% of foodstuffs available for consumption in the Food Retail sector). For example, food purchases made at convenience stores, butchers, fish shops, bakeries, delis and vegetable markets. Expenditure data from the HES are used to estimate the ratio of expenditure in stores not represented by SD to expenditure from the supermarkets in scope of SD (for a given product category in a given geographical area). No adjustment is made to the 2015–16 HES data to align to the 2018–19 ACSF reference period.
7 The scope for this publication is all food and non-alcoholic beverages purchased for consumption in Australia from the Food Retail sector. This includes all food and non-alcoholic products assigned to the food and non-alcoholic beverage expenditure classes (ECs) used to compile the CPI with the exception of Restaurant meals and Take away and fast foods. See CPI's 17th Series Expenditure Weights for the complete list of ECs.
8 The 2015–16 HES collects expenditure from households representing about 97% of people in Australia. In expenditure terms, the 2015–16 HES estimated an average weekly expenditure of $237 per household on Food and non-alcoholic beverages. Of this, 34% ($80) was estimated to be on Meals out and fast foods, leaving 66% ($157 in 2015–16) of total food expenditure as in-scope in the ACSF.
9 All in-scope foods are coded to the AUSNUT 2011–13 database which was developed for the 2011–13 Australian Health Survey. Assignment of AUSNUT codes provides the ACSF with an established food classification and ability to group AUSNUT codes into useful food groups for analysis. In addition, AUSNUT 2011–13 contains comprehensive nutrient data for each food and non-alcoholic beverage product and links directly to other established metadata including information about the amounts (serves or grams) of Australian Dietary Guidelines (ADG) Food Groups. This alignment means that conceptually the data can be used for similar food and nutrient analyses as a national nutrition survey. There are however important differences which should be considered due to the differing nature of foods as purchased (scanner data) and food as eaten (nutrition surveys).
10 It should be noted that estimates of 'apparent consumption' of food and non-alcoholic beverage products are derived using information related to sales (that is, food available for consumption from purchases made from the Food Retail sector). Therefore, they are at best a partial representation of the apparent consumption due to scope limitations (see ‘Out of scope’ below). Furthermore, ‘apparent consumption’ methodologies typically don’t adjust for waste (see below) or changes in household inventories i.e. all food and non-alcoholic beverages available for apparent consumption (purchased) in a particular year is assumed to have been consumed in that year.
Out of scope
11 While SD (when used with the HES) can represent the majority of the food available for consumption in Australia, there are clear gaps where either SD sales or 2015–16 HES do not adequately capture representative food and non-alcoholic beverage amounts. These are:
12 The exclusions in the HES sample comprise of:
13 Comprehensive alcohol data are not currently available from scanner data. Although the annual Apparent Consumption of Alcohol (ACA) collection provides national annual estimates for beer, wine and spirits, the ACA data are not incorporated in the ACSF because of differences in scope and coverage. The alcohol estimates from the ACA represent all alcohol available for consumption in Australia, while the ACSF is limited to just the foods available for consumption in the Food Retail sector (less Restaurant meals and Take away and fast foods). Including ACA estimates in the ACSF would present an over-estimate of the quantity of alcoholic beverages available as well as the relative contribution of alcohol to macronutrient energy. A further misalignment of the data sources results from the different reference periods with the latest ACA data for 2017–18, 12 months prior to the ACSF reference period.
14 The estimates presented in this publication are not adjusted for possible product wastage. The most recent estimate of total organic food wastage in Australia was around 3.1 million tonnes for 2016–17. This is the equivalent of around 350 gram per person per day (or 2.4 kg per week). While it may be expected that all perishable goods will be susceptible to a degree of waste, the waste estimates cannot provide further breakdowns, for instance how much is vegetable and fruits and how much is baked goods, dairy produce, various meat products etc. Given most discretionary foods are packaged (and have a longer shelf life) they may be less likely to be disposed of than fresh produce. Such an imbalance in waste suggests greater care should be taken in interpreting the apparent consumption of non-discretionary foods and therefore the relative balance between discretionary and non-discretionary foods. In addition different fresh foods may have different edible portions (e.g. grapes have a higher edible portion than pineapple) and no adjustment has been made to subtract the inedible portion which may lead to further over-estimation of certain fresh products.
15 The SD provides aggregate data representing all regions of Australia serviced by major supermarket chains. Estimates in this publication are presented at the national level. While state and sub-state estimates are feasible, they are not included in this release.
16 The first release is for the financial year 2018–19. Because this is the first release of the new ACSF series covering just one year, trend information will only become available with subsequent releases.
17 The process of AUSNUT coding involves assigning each food item SKU to an AUSNUT code and a size (weight or volume) based on the product description. AUSNUT coding has been performed on around 95% of the total value of SD foods in the 2018–19 period, equivalent to over 100k unique SKUs.
18 As noted above, the AUSNUT 2011–13 classification was designed to code foods as consumed, whereas many foods in the SD require preparation. Although the great variety and detail of AUSNUT focuses on foods in their prepared forms, AUSNUT also includes codes and nutrient data for the unprepared varieties (e.g. uncooked pasta, flour, jelly crystals, uncooked cuts of meat) and these were applied as appropriate. The general limitation of coding is the availability of specific and accurate codes to handle the variety of unprepared foods and the changing nature of the food supply. For example, where manufacturers have reformulated their products to reduce sugar or sodium content, the available AUSNUT code used may not reflect the most up-to-date nutrient profile.
Coding weighting adjustment
19 Weighting has been applied to adjust for the food items (value and quantity) that were unable to be coded. The weight (or coding ratio) is a value which indicates the number of products in the Food Retail sector represented by the coded SD product. Each coded SD food item is assigned a weight, that is applied at the EC, week, Greater Capital City Statistical Area (GCCSA) and supermarket chain level. This level is used so reliable denominators can be calculated. This means that each food item within an EC/week/GCCSA/retailer will have the same weight, even though at a finer product category level there may be differences in coding coverage.
20 Therefore, the two principles and one assumption in this adjustment are:
2. Adjustments are relatively minor because coding rates are relatively high (~95%) and therefore minimising introduction of new bias when removing target bias
3. The coding ratio of each EC is representative of the foods within that EC.
21 Around 3% of the value of foods in the SD did not have an assigned EC. These products were grouped in their own EC and the same coding weighting adjustment was applied as described above. This group of ECs is more heterogenous, than the identified EC groupings, so the coding ratio of this group may not be fully representative of the different foods within it.
22 As previously mentioned, there are two components to the estimates of apparent consumption of foodstuffs presented in this publication. The components are:
ii. Indirectly estimated component. This uses 2015–16 HES data to derive an expenditure ratio of food items from the non-major supermarkets to the major supermarkets to estimate the food quantities available for consumption that are not captured in the SD. This expenditure ratio is then applied to a grams per dollar value to derive the estimated quantity (weight or volume) of that food and non-alcoholic beverages available for consumption from the non-major supermarkets.
23 The adjustment for the indirectly estimated component may be summarised as a four-step process:
1. Map 2015–16 HES food and non-alcoholic beverage expenditure codes to AUSNUT 2011–13
2. Estimate the Food Retail sector expenditure ratio from non-major supermarkets to the major supermarkets by each food group by region from the data reported in the 2015–16 HES. This step provides the expenditure ratio for a defined food group for each GCCSA
This is expressed below where i = a given food group in a given GCCSA geographical area, MS represents major supermarkets and NMS represents the food retailers from non-major supermarkets.
3. Derive amounts (grams or mLs) per dollar for each product based on the known prices per product size in the SD
4. Apply the amounts per dollar (from step 3) to the expenditure ratios (from step 2) and multiply by the observed sales values in the SD to derive the imputed amounts in grams or mLs of food and non-alcoholic beverages available for consumption from non-major supermarkets.
Limitations of method
24 Three main limitations have been identified which will impact the validity of the expenditure ratios. These are:
2. Representation of identified stores in the subset of HES data available to estimate proportion of expenditure from the major and non-major supermarkets (and that it represents a period 3 years prior to the ACSF reference period)
3. Estimation of the amount (grams or mLs) per dollar - of food represented in the SD.
Mapping of HES expenditure codes to AUSNUT codes
25 The aim of this step is to define common groups of products that represent broad types of food and non-alcoholic beverages groups in terms of their propensity to be bought from the major supermarkets or other retail outlets.
26 Alignment of 2015–16 HES food expenditures with the SD was performed by creating a correspondence which maps food and non-alcoholic beverages from both the HES (124 relevant codes) and AUSNUT (5,740 codes) to common food groups. After collapsing a number of HES codes which were not similarly split in AUSNUT, each classification was mapped to a common set of 63 defined food groups (see the 2011–13 AUSNUT to 2015–16 HEC correspondence table available from the Downloads tab).
Representation of identified stores in the subset of HES
27 The HES was used to provide estimates of the expenditure ratios (by food group and geography) of the in-scope supermarkets. However, the receipt information identifying the store at which food purchases were made in the 2015–16 HES was not an essential survey input data item, and only captured incidentally (where the respondent retained the receipt to assist in recording expenditures). While almost three-quarters (74%) of relevant food and non-alcoholic beverages expenditure had a store receipt which identified the store, this subset is likely to over represent the transactions where the respondent purchased multiple grocery items. This led to a moderate correlation between a food item having a receipt and that food being purchased at one of the major supermarkets in-scope of the SD. With no further adjustment made, this association may indicate some bias favouring expenditures from the major supermarkets. Such a bias may tend to underestimate the expenditure ratios for foods coming from from specialty shops (e.g. butchers, bakeries, vegetable markets or seafood shops).
Deriving a gram or mL amount of food or beverage per dollar
28 Translating the expenditure ratios to amounts (grams or mLs) of food per dollar was performed by:
2. Applying that gram/mL per dollar amount to the expenditure ratio to estimate the imputed amount of each food. In practice, foods being imputed are aggregates of AUSNUT coded foods, so the imputation uses weighted aggregates of the applicable AUSNUT code medians.
29 The major limitation of this method is the assumption that amounts per dollar from major supermarkets are likely to represent the amounts for other retailers (e.g. convenience stores and specialty stores). It is more likely that for many products the major supermarkets provide greater amounts per dollar on equivalent items.
30 By using the median, it assumes the midpoint value is representative of the aggregate amount (g or mLs) per dollar across all products in the defined food group. Analysis of the SD shows some amounts (g or mLs) per dollar distributions are bi-modal, with product items concentrated around small sized products (lower g or mL per dollar) and bulk products (higher g or mL per dollar). Therefore, if the expenditure in non-major supermarkets includes less of the bulk packages then the imputed amount (gram or mLs) per dollar may be an overestimate.
Per capita estimates
31 Daily per capita estimates are derived by dividing the annual total by 365 days and dividing by the Estimated Resident Population (ERP). Data representing the 2018–19 period uses the ERP for December 2018. No adjustments have been made to the ERP for food and non-alcoholic beverages that are typically consumed by selected age groups, for example baby food.
Measuring Australian Dietary Guidelines food groups
32 Because many food products are mixtures of food groups, measuring the total amounts of each of the food groups utilises an Australian Dietary Guidelines (ADG) database which measures the amounts of each food group in each AUSNUT food. The ADG database used was originally developed by FSANZ for the 2011–12 NNPAS. For more information, see Assessing the 2011–13 AHS against the Australian Dietary Guidelines - Classification System and Database Development Explanatory notes, available from https//www.foodstandards.gov.au/science/monitoringnutrients/australianhealthsurveyandaustraliandietaryguidelines/Pages/default/aspx.
Reliability of estimates
33 The estimates presented in this publication are subject to error which can be broadly categorised as either sampling error or non-sampling error. For more information refer to the Technical Note.
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