Microdata and TableBuilder: National Aboriginal and Torres Strait Islander Health Survey, Australia

The National Aboriginal and Torres Strait Islander Health Survey (NATSIHS) collects information on the health and wellbeing of Aboriginal and Torres Strait Islander people. It provides data such as the prevalence of chronic and long-term health conditions, self-reported health status and health risk factors such as smoking, alcohol consumption, fruit and vegetable consumption, and physical activity.

The 2022–23 NATSIHS is the fifth health survey of Aboriginal and Torres Strait Islander people conducted by the ABS. See National Aboriginal and Torres Strait Islander Health Survey, 2022–23 for summary results, methodology and other information.

This product provides information about the microdata releases from the most recent NATSIHS cycles, 2022–23 and 2018–19, including details about the data files and how to use the different microdata products. Data Item Lists, information about the survey methodology, and links to microdata for previous NATSIHS releases are also provided.

Most data in the 2022–23 NATSIHS are considered comparable with the 2018–19 NATSIHS. The Data Item List contains information on the availability and comparability of data items between surveys.

• TableBuilder – an online tool for creating tables and graphs. This product is available for the 2018–19 NATSIHS and the 2012–13 NATSIHS. 
• Detailed microdata – approved users can access DataLab for in-depth and interactive data analysis using a range of statistical software packages. This product is available for all iterations of the NATSIHS. 
• Basic microdata – approved users can download and analyse unit record data in their own environment. This product is available for the 2012–13 NATSIHS Nutrition and Physical activity data only.

Compare access options to see what's right for you or Apply for access.

Datasets from the NATSIHS are hierarchical in nature. A hierarchical data file is a way of presenting information which describes one-to-many, or many-to-many, relationships. For example, a person may report multiple days on which alcohol was consumed and also multiple types of alcoholic beverages consumed on each of these days.

Most data are contained as individual characteristics on person records. Estimates are also available at the household level. The data items and related output categories are described in Excel spreadsheets from the Data Item Lists section.

For the 2018–19 NATSIHS and the 2022–23 NATSIHS there are two main weight variables on the file:

• Household weight (FINHHWT) – Household level – benchmarked to produce Aboriginal and Torres Strait Islander household estimates. 
• Person weight (FINPERWT) – Selected person level – benchmarked to the total Aboriginal and Torres Strait Islander population. 

For the 2018–19 NATSIHS, there is an additional weight variable on the file for the Hearing level:

• Hearing test weight (FINHEAWT) – must be used whenever an item is used from the hearing level. The weighted counts for the 2018–19 selected person level and the hearing level are the same. This is because a maximum of one hearing test was counted for each person. However, the hearing weight must be used whenever an item is used from the hearing level.

There is no weight associated with the other levels for the NATSIHS. This is because the records are repeated for each person. If, for example, FINPERWT is merged onto the Conditions level, it will be attached to each condition record and therefore be repeated for each person where they have more than one condition. This should be considered when producing tables.

The NATSIHS is a sample survey, so to produce estimates for the in-scope population you must use weight fields in your calculations. When analysing a Household level item at the household level, you will need to use the household weight. For example, if you wanted to know the number of households in a state, rather than the number of persons living in that state.

Caution should be used when applying the Household weight to items from other levels. For example, if the household weight is applied to a selected person level demographic item, such as ‘Sex’, your table will show the number of households with one or more selected persons of that sex. Since up to four people can be selected in the NATSIHS, this will result in some households being counted up to four times, twice for the selected adult and twice for the selected child, if they are all the same sex.

Data items for the 2022–23 NATSIHS include:

• demographics – age, sex, language, social marital status
• household details – type, size, household composition, tenure, Socio-Economic Indexes for Areas (SEIFA), geography
• food security and financial stress
• cultural identification
• labour force status
• educational attainment
• personal and household income
• personal use of the internet
• self-assessed health status 
• self-reported height and weight
• long-term health conditions such as arthritis, asthma, cancer, diabetes, hypertension, kidney disease, mental and behavioural conditions
• health risk factors such as smoking, alcohol consumption, fruit and vegetable consumption, physical activity
• social and emotional wellbeing
• medications
• health actions such as use of health services, barriers to accessing health services, and cultural safety when using some of these services
• physical measurements – blood pressure, height, weight, and waist circumference.

The Data Item Lists section is the definitive source of available data items and categories. The detailed index within the 2022–23 NATSIHS Data Item List contains information on the comparability of data items to the 2018–19 NATSIHS. Additionally, each data item within the Data Item List has information about any changes between NATSIHS cycles.

Every record on each level of the file is uniquely identified. See Data Item Lists for details on which ID equates to which level.

Each household has a unique random identifier, ABSHIDD. This identifier appears on the household level and is repeated on each level on each record pertaining to that household. A combination of identifiers for a particular level and all levels above in the hierarchical structure uniquely identifies a record at a particular level. For example, each record on the conditions level is uniquely identified by a combination of the Household, Person and Conditions level identifiers. 

The Household record identifier, ABSHIDD, assists with linking people from the same household, and with household characteristics such as geography (located on the household level) to the Person records. When merging data with a level above, only those identifiers relevant to the level above are required.

Several questions in the survey allowed respondents to provide one or more responses. Each response category for these multi-response data items is treated as a separate data item. In the detailed microdata, these data items share the same identifier (SAS name) prefix but are each separately suffixed with a letter – A for the first response, B for the second response, C for the third response and so on.

For example, the multi-response data item 'All types of physical activity undertaken in last week' (PATYPEW) has seven response categories. There are seven data items named PATYPEWA, PATYPEWB, PATYPEWC....PATYPEWG. Each data item in the series will have either a positive response code or a null response code, with the exception of the first item in the series, PATYPEWA. 

PATYPEWA has four potential response codes: 

• code 1 – 'Walking for exercise, recreation and sport' – positive response 
• code 0 – null response
• code 8 – 'No physical activity in last week'
• code 9 – 'Not applicable'. 

The remaining items PATYPEWB, PATYPEWC....PATYPEWG have just two response codes each. The Data Item List identifies all multi-response items and lists the corresponding codes with the corresponding response categories.

Note that the sum of individual multi-response categories will be greater than the population applicable to a particular data item as respondents can select more than one response.

Some continuous data items are allocated special codes for certain responses (e.g. 9999 = 'Not applicable'). When creating ranges for such continuous items for use in the TableBuilder, these special codes will NOT be included in these ranges. Any special codes for continuous (summation) data items are listed in the Data Item List and will be found in the categorical version of the continuous item. However, note that labelling of '0's in the Data Item List does not necessarily mean they are excluded from the ranges (for example – identifying 0 as 'Did not visit' or 'Did not do') as they may still be important in some calculations. Reference should be made to the categorical version of the item to identify which codes are specifically excluded. Therefore the total shown only represents 'valid responses' of that continuous data item rather than all responses (including special codes).

As the survey was conducted on a sample of private households in Australia, it is important to take account of the method of sample selection when deriving estimates from the detailed microdata. A screening process is used in non-community areas to identify in-scope households. For both the community and non-community sample, a person’s chance of selection varied depending on the state or territory in which the person lived. For more information on sampling see the Methodology section. If the chances of selection are not accounted for by use of appropriate weights, the results could be biased.

Each person or household record has a main weight (FINHHWT or FINPERWT). This weight indicates how many population units are represented by the sample unit. When producing estimates of sub-populations from the detailed microdata, it is essential that they are calculated by adding the weights of persons or households in each category and not just by counting the sample number in each category. If each person or household’s weight were to be ignored when analysing the data to draw inferences about the population, then no account would be taken of a person or household's chance of selection or of different response rates across population groups, with the result that the estimates produced could be biased. The application of weights ensures that estimates will conform to an independently estimated distribution of the population by age, by sex, etc. rather than to the distributions within the sample itself.

It is also important to calculate a measure of sampling error for each estimate. Sampling error occurs because only part of the population is surveyed to represent the whole population. Sampling error should be considered when interpreting estimates as this gives an indication of accuracy and reflects the importance that can be placed on interpretations using the estimate. Measures of sampling error include standard error (SE), relative standard error (RSE) and margin of errors (MoE). These measures of sampling error can be estimated using the replicate weights. The replicate weight variables provided on the microdata are labelled WPM1XXX (person) and WHM1XXX (household), where XXX represents the number of the given replicate group. The exact number of replicates will vary depending on the survey. The NATSIHS generally uses 250 replicate groups for both household and person weights, so you will find, for example, 250 person replicate weight variables labelled WPM1001 to WPM1250.

ABS household surveys employ complex sample designs and weighting which require special methods for estimating the variance of survey statistics. Variance estimators for a simple random sample are not appropriate for this survey microdata.

A class of techniques called 'replication methods' provide a general process for estimating variance for the types of complex sample designs and weighting procedures employed in ABS household surveys. The ABS uses a method called the Group Jackknife Replication Method. 

A basic idea behind the replication approach is to split the sample into G replicate groups. One replicate group is then dropped from the file and a new set of weights is produced for the remaining sample. This is repeated for all G replicate groups to provide G sets of replicate weights. For each set of replicate weights, the statistic of interest is recalculated and the variance of the full sample statistic is estimated using the variability among the replicate statistics.

The statistics calculated from these replicates are called replicate estimates. Replicate weights provided on the microdata file enable variance of survey statistics, such as means and medians, to be calculated relatively simply. Further technical explanation can be found in Section 4 of Research Paper: Weighting and Standard Error Estimation for ABS Household Surveys (Methodology Advisory Committee.

A confidentiality process called perturbation is applied to the data in TableBuilder to avoid releasing information that may lead to the identification of individuals, families, households, dwellings or businesses. See Confidentiality in the TableBuilder user guide.

See National Aboriginal and Torres Strait Islander Health Survey methodology, 2022–23 for further information about the 2022–23 NATSIHS.

See National Aboriginal and Torres Strait Islander Health Survey methodology, 2018–19 for further information about the 2018–19 NATSIHS.

See Australian Aboriginal and Torres Strait Islander Health Survey: Users' Guide, 2012–13 for further information about the 2012–13 NATSIHS.