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§ Some discrete Aboriginal and Torres Strait Islander communities with a small number of Aboriginal and Torres Strait Islander households; and
§ Some SA1s, or CDs in the NT, in remote areas with a small number of Aboriginal and Torres Strait Islander households.
14 These coverage exclusions result in an estimated undercoverage of approximately 4% of Aboriginal and Torres Strait Islander persons in Australia. Although these areas were not enumerated, the final sample was weighted to population benchmarks to account for these exclusions. Further information on undercoverage is provided in paragraphs 44 to 48 and more information on the scope and coverage of the survey is provided in the Users' Guide.
15 The estimated resident Aboriginal and Torres Strait Islander population aged 2 years and over living in private and non-private dwellings at 30 June 2011 was 636,945. Excluding persons in non-private dwellings, there were 606,915 Aboriginal and Torres Strait Islander people aged 2 years and over.
16 Population benchmarks, which align with the survey scope, are based on the most recently released Estimated Resident Aboriginal and Torres Strait Islander Population (ERP), which in this case are for 30 June 2011. The ERP data are based on the 2011 Census of Population and Housing, adjusted by the 2011 Post-Enumeration Survey (PES). More information about the Estimated Resident Aboriginal and Torres Strait Islander Population can be found in Estimates of Aboriginal and Torres Strait Islander Australians, June 2011 (cat. no. 3238.0.55.001).
17 After sample loss the NATSINPAS approached 3,661 households. Of these, 2,900 (79%) were fully or adequately responding, yielding a total sample for the survey of 4,109 persons (aged 2 years and over).
FINAL PERSONS IN SAMPLE
19 More information on response rates is available in the Users' Guide.
20 Trained ABS interviewers conducted personal interviews with selected Aboriginal and Torres Strait Islander residents in sampled private dwellings. Selected persons aged 18 years and over in each dwelling were 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 for selected children (aged 2 years and over) in the household. An adult, nominated by the household, was also asked to provide information about the household, such as the combined income of 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 Users' Guide.
21 The majority (61%) of Aboriginal and Torres Strait Islander children aged 15-17 years could were personally interviewed with consent from a parent or guardian. For the remaining 39% of children is this age group, proxy interviews were conducted with a parent or guardian.
24 Of the 4,109 people in the final sample, 99.5% provided the first (Day 1), with the missing 0.5% of Day 1 dietary recalls being imputed. The second 24-hour dietary recall (Day 2) which was only offered to those in non-remote areas had 771 participants (43% of the total in non-remote areas). The Day 2 24-hour dietary recall participation was slightly lower among female children than other respondents.
25 To take account of possible seasonal effects on health and nutrition characteristics, the NATSINPAS sample was surveyed across a 12-month enumeration period.
26 More information on data collection and a copy of the survey questionnaire are provided in the Users' Guide.
WEIGHTING, BENCHMARKING AND ESTIMATION
27 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 person in the sample. The weight is a value which indicates how many population units are represented by the sample unit.
28 The first step in calculating weights for each person was to assign an initial weight, which is 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 would represent 600 others).
29 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.
30 The NATSINPAS was benchmarked to the estimated resident population living in private dwellings at 30 June 2011. As people in non-private dwellings (e.g. hotels) are excluded from the scope of the survey, they have also been excluded from the survey benchmarks. Therefore, the NATSINPAS estimates do not (and are not intended to) match estimates for the total resident Aboriginal and Torres Strait Islander population obtained from other sources.
31 Estimates of counts of persons are obtained by summing person weights of persons with the characteristic of interest. The estimates presented in this release are based on benchmarked person weights.
32 More information on weighting, benchmarking and estimation is provided in the Users' Guide.
RELIABILITY OF ESTIMATES
33 All sample surveys are subject to error which can be broadly categorised as either sampling error or non-sampling error.
34 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 Margin of Error (MoE).
35 In this publication, estimates with a RSE in the range 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 a RSE greater than 50% are annotated with a double asterisk (e.g. **0.6) and are generally considered to be too unreliable for most purposes. These estimates can be aggregated with other estimates to reduce the overall sampling error. Another factor that may explain certain high RSE's in the NATSINPAS are some of the food groupings that make up the Food Classification. That is, a relatively high variance would be expected where foods with very different amounts of consumption are combined. For example, within sub-major level food group of Herbs, spices, seasonings and stock cubes there are foods with relatively small gram amounts of consumption (such as herbs and spices) that have been grouped with foods that are consumed in substantially greater amounts such as liquid stock. For more information on the Food classification see Food Intake in the AHS: Users' Guide, 2011-13.
36 Margin of Error (MoE) calculation at the 95% confidence level, are provided for all proportions to assist users in assessing the reliability of these data. Users may find this measure is more convenient to use, rather than the RSE, in particular for small and large proportions. 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.
37 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.
38 Of particular importance to nutrition surveys is a widely observed tendency for people to under-report their food intake. This can include:
· actual changes in foods eaten because people know they will be participating in the survey
· the misrepresentation of foods and beverages consumed (deliberate, unconscious or accidental), e.g. to make their diets appear more ‘healthy’ or be quicker to report.
Analysis of the results of the 2012-13 NATSINPAS suggests that, like other nutrition surveys (including the 2011-12 NNPAS), there has been some under-reporting of food intake by participants in these surveys. It is difficult, from the available data, to accurately estimate the amount of under-reporting that has occurred and therefore how much energy and nutrients might be missing from the intakes reported by respondents. One method is to estimate the mean amount of energy required for the population to achieve an EI:BMR ratio of 1.55 (i.e. the conservative minimum energy requirement for a normally active but sedentary population). Using this method, it is estimated that the average energy intakes for Aboriginal and Torres Strait Islander people may be understated by as much as 24% for males and 31% for females. The factor most closely associated with under-reporting was BMI, where people who were overweight or obese were most likely to have lower than expected energy intakes. For more information see Under-reporting in the National Aboriginal and Torres Strait Islander Nutrition and Physical Activity Survey in the AATSIHS Users' Guide, 2011-13.
39 A further 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 use of proxies in the 24-hour dietary recall module.
40 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 2012-13 NATSINPAS were developed by Food Standards Australia New Zealand specifically for the survey. A complete nutrient profile of 44 nutrients was created based on the latest available data, however, not all data was based on directly analysed foods. Some data was obtained 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.
41 Non-response occurs when people cannot or will not cooperate, or cannot be contacted by interviewers. 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.
42 The following methods were adopted to reduce the level and impact of non-response:
§ face-to-face interviews with respondents
§ follow-up of respondents if there was initially no response
§ weighting to population benchmarks to reduce non-response bias.
43 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 a different methodology.
44 Under-coverage is the shortfall between the population represented by the achieved sample and the in-scope population. Weighting, as described in paragraphs 27 to 32 adjusts for under-coverage, reducing the under-coverage bias in estimates.
45 Under-coverage rates can be estimated by calculating the difference between the sum of the initial weights of the sample and the population count. If a survey has no under-coverage, then the sum of the initial weights of the sample will equal the population count (ignoring small variations due to sampling error).
46 It is usual for ABS Aboriginal and Torres Strait Islander surveys to have large levels of under-coverage. The NATSINPAS under-coverage rate was 63% of the in-scope population at the national level. However, 6% of this was due to planned frame exclusions and overlap with the Monthly Population Survey where analysis has shown that the impact of any bias is minimal. For comparison, the estimated under-coverage in the 2004–05 NATSIHS and the 2008 NATSISS was 42% and 53% respectively.
47 The NATSINPAS rate varies across states and territories, with Victoria (78%), the Northern Territory (72%) and New South Wales (68%) recording the highest rates of under-coverage. The lowest under-coverage rates were in Tasmania (6%) and the Australian Capital Territory (44%).
48 Under-coverage may occur due to a number of factors, including:
§ frame exclusions (areas being removed from the sampling frame);
§ non-identification of people as being of Aboriginal and/or Torres Strait Islander origin; and
§ issues arising in the field
For more details on these, refer to the Users' Guide.
49 The AATSIHS food classification was produced by Food Standards Australia New Zealand (FSANZ). 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.
50 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.
51 Techniques used to guard against identification or disclosure of confidential information in statistical tables includes: the suppression of sensitive cells, random adjustments to cells with very small values, and aggregation of data. To ensure confidentiality within this publication, some cell values may have been suppressed and are not available for publication but are included in totals where applicable. As a result, components may not always add exactly to totals.
52 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 shown as 0.0 with a high RSE or MoE have a true value of less than 0.05 but greater than 0.0.
53 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.
54 Proportions presented in this publication are based on unrounded figures. Calculations using rounded figures may differ from those published.
55 The success of the NATSINPAS was dependent on the high level of cooperation received from Aboriginal and Torres Strait Islander peoples and their communities. Without their continued cooperation, the wide range of Aboriginal and Torres Strait Islander 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.
56 The ABS gratefully acknowledges and thanks the Agricultural Research Service of the United States Department of Agriculture (USDA) for giving permission to adapt and use their Dietary Intake Data System, including the Automated Multiple-Pass Method (AMPM) for collecting dietary intake information, as well as other processing systems and associated materials.
57 Food Standards Australia New Zealand (FSANZ) was contracted to provide advice throughout the survey development, processing and collection phases of the 2012-13 NATSINPAS, 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 support, advice and expertise for the 2012-13 NATSINPAS.
PRODUCTS AND SERVICES
58 Summary results from this survey are available in spreadsheet form from the 'Downloads' tab in this release.
59 For users who wish to undertake more detailed analysis of the survey data, Survey Table Builder will also be made available in 2015. 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 website.
60 Special tabulations are available on request. Subject to confidentiality and sampling variability constraints, customised 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 Users' Guide.
61 Information from the NATSINPAS will be returned to Aboriginal and Torres Strait Islander people through the ABS State and Territory Statistical Services (STSS) Program and collaborations with other organisations.
63 Other ABS publications which may be of interest are shown under the 'Related Information' tab of this release.
64 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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