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10 Trained ABS interviewers conducted personal interviews with selected residents in sampled dwellings. One person aged 18 years and over in each dwelling was selected and interviewed about their own health characteristics including a 24-hour dietary recall and a physical activity module. An adult, nominated by the household, was interviewed about one child (aged 2 years and over) in the household. Selected children aged 15-17 years may have been personally interviewed with parental consent. An adult, nominated by the household, was also asked to provide information about the household, such as the combined income of other household members. Children aged 6-14 years were encouraged to be involved in the survey, particularly for the 24-hour dietary recall and physical activity module. For further information, see Data Collection in the AHS: Users' Guide, 2011-13 (cat. no. 4363.0.55.001).
11 All selected persons were required to have a follow-up phone interview at least 8 days after the face to face interview to collect a further 24-hour dietary recall. For those who participated, pedometer data was reported during this telephone interview.
12 Dwellings were selected at random using a multistage area sample of private dwellings for the NNPAS.
The initial sample selected for the survey consisted of approximately 14,400 dwellings. This was reduced to approximately 12,400 dwellings after sample loss (for example, households selected in the survey which had no residents in scope of the survey, vacant or derelict buildings, buildings under construction). Of those remaining dwellings, 9,519 (or 77.0%) were fully or adequately responding, yielding a total sample for the survey of 12,153 persons (aged 2 years and over).
NNPAS, APPROACHED SAMPLE, FINAL SAMPLE AND RESPONSE RATES
13 The physical measures module of the NNPAS was voluntary. In 2011-12, 83.7% of respondents aged 2 years and over had their height and weight measured. As a proportion of the Australian population, 84.9% of persons aged 2 years and over have a height and weight measurement. BMI data from the NNPAS presented in this publication relates to the measured population only. Analysis of the characteristics of people who agreed to be measured compared to those who declined across the AHS suite of surveys indicated that age and sex were factors in non-response. Females were more likely to decline, and non-response increased with age.
14 Of the 12,153 people in the final sample, 98% provided the first (Day 1), with the missing 2% of Day 1 dietary recalls being imputed. The second 24-hour dietary recall (Day 2) had 7,735 participants (64% of the total). The Day 2 24-hour dietary recall participation was slightly higher among older respondents, and sex did not appear as a factor in participation.
15 More information on response rates and imputation is provided in the AHS: Users' Guide, 2011-13 (cat. no. 4363.0.55.001).
16 To take account of possible seasonal effects on health and nutrition characteristics, the NNPAS sample was spread randomly across a 12-month enumeration period. Between August and September 2011, survey enumeration was suspended due to field work associated with the 2011 Census of Population and Housing.
WEIGHTING, BENCHMARKING AND ESTIMATION
17 Weighting is a process of adjusting results from a sample survey to infer results for the in-scope total population. To do this, a weight is allocated to each sample unit; for example, a household or a person. The weight is a value which indicates how many population units are represented by the sample unit.
18 The first step in calculating weights for each person was to assign an initial weight, which was equal to the inverse of the probability of being selected in the survey. For example, if the probability of a person being selected in the survey was 1 in 600, then the person would have an initial weight of 600 (that is, they represent 600 others). An adjustment was then made to these initial weights to account for the time period in which a person was assigned to be enumerated.
19 The weights are calibrated to align with independent estimates of the population of interest, referred to as 'benchmarks', in designated categories of sex by age by area of usual residence. Weights calibrated against population benchmarks compensate for over or under-enumeration of particular categories of persons and ensure that the survey estimates conform to the independently estimated distribution of the population by age, sex and area of usual residence, rather than to the distribution within the sample itself.
20 The NNPAS was benchmarked to the estimated resident population living in private dwellings in non-Very Remote areas of Australia at 31 October 2011. Excluded from these benchmarks were persons living in discrete Aboriginal and Torres Strait Islander communities, as well as a small number of persons living within Collection Districts that include discrete Aboriginal and Torres Strait Islander communities. The benchmarks, and hence the estimates from the survey, do not (and are not intended to) match estimates of the total Australian resident population (which include persons living in Very Remote areas or in non-private dwellings, such as hotels) obtained from other sources. For the NNPAS, a seasonal adjustment was also incorporated into the person weights.
21 Survey estimates of counts of persons are obtained by summing the weights of persons with the characteristic of interest. Estimates of non-person counts (for example, number of organised physical activities) are obtained by multiplying the characteristic of interest with the weight of the reporting person and aggregating.
RELIABILITY OF ESTIMATES
22 All sample surveys are subject to sampling and non-sampling error.
23 Sampling error is the difference between estimates, derived from a sample of persons, and the value that would have been produced if all persons in scope of the survey had been included. For more information refer to the Technical note. Indications of the level of sampling error are given by the Relative Standard Error (RSE) and 95% Margin of Error (MoE).
24 In this publication, estimates with an RSE of 25% to 50% are preceded by an asterisk (e.g. *3.4) to indicate that the estimate has a high level of sampling error relative to the size of the estimate, and should be used with caution. Estimates with an RSE over 50% are indicated by a double asterisk (e.g. **0.6) and are generally considered too unreliable for most purposes. These estimates can be used to aggregate with other estimates to reduce the overall sampling error. Another factor, particular to this survey, that may explain certain high RSE's are some of the food groupings that make up the Food Classification. That is, a relatively high variance would be expected where foods are combined that have very different amounts of consumption. For example, within sub-major level food group of Herbs, spices, seasonings and stock cubes there are foods that have relatively small gram amounts of consumption (such as herbs and spices) grouped with foods that are consumed substantially greater amounts such as liquid stock. For more information on the Food classification see Food Intake in the AHS: Users' Guide, 2011-13.
25 The MoEs are provided for all proportion and average estimates to assist users in assessing the reliability of these types of estimates. Users may find this measure is more convenient to use, rather than the RSE, in particular for small and large proportion estimates. The estimate combined with the MoE defines a range which is expected to include the true population value with a given level of confidence. This is known as the confidence interval. This range should be considered by users to inform decisions based on the estimate.
26 Non-sampling error may occur in any data collection, whether it is based on a sample or a full count such as a census. Non-sampling errors occur when survey processes work less effectively than intended. Sources of non-sampling error include non-response, errors in reporting by respondents or in recording of answers by interviewers, and occasional errors in coding and processing data.
27 Of particular importance to nutrition surveys is a widely observed tendency for people to under-report their food intake. This can include:
28 Another factor affecting the accuracy of the 24-hour dietary recall data is that most young children are unable to recall their intakes. Similarly, parents/carers of school-aged children may not be aware of a child’s total food intake, which can lead to systematic under-reporting. Young children were encouraged to assist in answering the dietary recall questions. See the Interviews section of Data collection for more information on proxy use in the 24-hour dietary recall module.
29 Another non-sampling error specific to Nutrition surveys is the accuracy of the nutrient and measures database containing thousands of foods used to derive the nutrient estimates. The databases used for the 2011-12 NNPAS were developed by Food Standards Australia New Zealand specifically for the survey. A complete nutrient profile of 44 nutrients was created based on FSANZ’s latest available data, however, not all data was based on directly analysed foods. Some data was borrowed from overseas food composition tables, food label information, imputed data from similar foods or data calculated using a recipe approach. See AUSNUT 2011-13 for more information.
30 Non-response occurs when people cannot or will not cooperate, or cannot be contacted. Non-response can affect the reliability of results and can introduce a bias. The magnitude of any bias depends on the rate of non-response and the extent of the difference between the characteristics of those people who responded to the survey and those who did not.
31 The following methods were adopted to reduce the level and impact of non-response:
32 By careful design and testing of the questionnaire, training of interviewers, and extensive editing and quality control procedures at all stages of data collection and processing, other non-sampling error has been minimised. However, the information recorded in the survey is essentially 'as reported' by respondents, and hence may differ from information collected using different methodology. For example:
33 The AHS food classification was produced by Food Standards Australia New Zealand. It is formed by grouping the 8-digit food codes into broader food groups comprising major, sub-major and minor groups, along with dietary supplements. The AHS food classification is available as an Excel spreadsheet from the Downloads tab of this publication.
COMPARISONS WITH 1995 NNS
34 The NNPAS has not been collected in its current form before. However, the ABS has previously conducted nutrition surveys, the most recent being the 1995 National Nutrition Survey (1995 NNS). Published results from the 1995 NNS include:
35 While the 1995 NNS collected similar food and nutrition data to the NNPAS, some important changes in the food classification and methodology mean that care needs to be taken in making direct comparisons between surveys. See Comparisons with 1995 NNS for more details.
36 The Census and Statistics Act, 1905 provides the authority for the ABS to collect statistical information, and requires that statistical output shall not be published or disseminated in a manner that is likely to enable the identification of a particular person or organisation. This requirement means that the ABS must take care and make assurances that any statistical information about individual respondents cannot be derived from published data.
37 Some techniques used to guard against identification or disclosure of confidential information in statistical tables are suppression of sensitive cells, random adjustments to cells with very small values, and aggregation of data. To protect confidentiality within this publication, some cell values may have been suppressed and are not available for publication but included in totals where applicable. As a result, sums of components may not add exactly to totals due to the confidentialisation of individual cells.
38 Estimates presented in this publication have been rounded. As a result, sums of components may not add exactly to totals. Also note that due to rounding to one decimal place, estimates showing as 0.0 with a high RSE or MoE have a true figure being less than 0.05 but greater than 0.0.
39 For pedometer and other physical activity data, minutes and number of steps are reported as whole numbers. All other units in the data are reported to one decimal place.
40 Proportions presented in this publication are based on unrounded figures. Calculations using rounded figures may differ from those published.
41 ABS publications draw extensively on information provided freely by individuals, businesses, governments and other organisations. Their continued cooperation is very much appreciated; without it, the wide range of statistics published by the ABS would not be available. Information received by the ABS is treated in strict confidence as required by the Census and Statistics Act, 1905.
42 The ABS gratefully acknowledges and thanks the Agricultural Research Service of the USDA for giving permission to adapt and use their Dietary Intake Data System including the AMPM for collecting dietary intake information as well as other processing systems and associated materials.
43 Food Standards Australia New Zealand (FSANZ) was contracted to provide advice throughout the survey development, processing and collection phases of the 2011-12 NNPAS, and to provide a nutrient database for the coding of foods and supplements consumed. The ABS would like to acknowledge and thank FSANZ for providing their support, advice and expertise to 2011-12 NNPAS.
PRODUCTS AND SERVICES
44 Summary results from this survey are available in spreadsheet form from the 'Downloads' tab in this release.
45 For users who wish to undertake more detailed analysis of the survey data, Survey Table Builder will also be made available in 2014. Survey Table Builder is an online tool for creating tables from ABS survey data, where variables can be selected for cross-tabulation. It has been developed to complement the existing suite of ABS microdata products and services including Census TableBuilder and CURFs. Further information about ABS microdata, including conditions of use, is available via the Microdata section on the ABS web site.
46 Special tabulations are available on request. Subject to confidentiality and sampling variability constraints, tabulations can be produced from the survey incorporating data items, populations and geographic areas selected to meet individual requirements. A list of currently available data items is available from the AHS: Users' Guide, 2011-13 (cat. no. 4363.0.55.001).
47 Other ABS publications which may be of interest are shown under the 'Related Information' tab of this release.
48 Current publications and other products released by the ABS are listed on the ABS website www.abs.gov.au. The ABS also issues a daily Release Advice on the website which details products to be released in the week ahead.
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